diff --git a/CHANGELOG.md b/CHANGELOG.md
index b5d74636..7ac68632 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,19 +6,26 @@ Added new observing capabilities for limb sounding, ground-based radar, fixed su
Added:
- Analysis method `collect_limb_observations` (with enumeration `ScanDirection`) for limb sounding observations from periodic scans at a constant angular rate.
- - Analysis method `collect_space_observations` for the periods when satellites can observe other satellites (as by a sensor or a radio frequency link), subject to slant range limits and Earth occlusion (a minimum WGS 84 geodetic altitude of the line of sight). Each observation records the lines of sight at its start and end as its geometry.
+ - Analysis method `collect_space_observations` for the periods when satellites can observe other satellites (as by a sensor or a radio frequency link), subject to slant range limits and Earth occlusion (a minimum WGS 84 geodetic altitude of the line of sight). Each observation records the lines of sight at its start and end as its geometry, and the satellites' closest approach during the period (`closest_time` and `closest_range`).
- Object schemas `RadarStation` (with `RadarBand` and `RadarStation.from_band`) and `TerrainMask`, and analysis methods `collect_radar_track` and `compute_radar_track`, for ground-based radar coverage. `AllSurfaceObjects` includes `RadarStation`.
- Object schema `SurfacePlatform` (included in `AllSurfaceObjects`) for instruments on platforms fixed above the Earth's surface, such as towers or tethered balloons, with a `heading` that orients their views, and analysis methods `collect_platform_track` and `compute_platform_track` for the static footprints of their `Instrument` and `PointedInstrument` views at a target elevation, either below the instruments (looking down) or above them (looking up, as at clouds), with views clipped to the horizon of the ground below (argument `ground_elevation` of the footprint and pixel methods) and to an optional `terrain_mask` (a `TerrainMask`, as for a `RadarStation`), with method `SurfacePlatform.compute_visible_region` for the region of a target above the instruments visible over them. Utility method `compute_static_track` gives a fixed position the form of an orbit track, from which instruments project their views as from a satellite.
- Module `preprocess` to derive inputs from external datasets, including terrain masks from Copernicus DEM data (`compute_terrain_mask`, `compute_terrain_mask_for_station`, `compute_terrain_mask_for_platform`, `get_copernicus_dem_tile_urls`, and `sample_dem_elevation`, with an `effective_earth_radius_factor` for refraction, also an argument of `compute_terrain_elevation_angle`), and gridded weights from GeoTIFF rasters (`read_raster_weights`).
- Generation method `generate_points_random` for random points, uniform by area or weighted by a raster (for example, of population counts or densities), optionally within a mask, with a seed for reproducibility.
- Analysis methods `collect_region_observations` and `collect_multi_region_observations` to collect the periods (start and end) when an instrument observes any part of a region (shapely `Polygon` or `MultiPolygon`), using the footprints of pointed and conical instruments and the field of regard of other instruments. Each observation records its swath (the part of the region swept by the footprint) as its geometry and identifies the region by the hash of its geometry (`target_hash`). Methods `aggregate_observations` and `reduce_observations` combine the swaths of a region into the part of it observed.
- - Analysis methods `find_coincident_observations`, the periods when two or more satellites observe the same point at once (or within a maximum time difference), and `find_coincident_ground_track`, the overlapping footprints of pairs of satellites at the same time (or within a maximum time difference), with example notebook `FindCoincidentObservations` (Metop-SG-B ICI and NASA FALCON).
+ - Analysis methods `find_coincident_observations`, the periods when two or more satellites observe the same point at once (or within a maximum time difference), comparing their observation periods or, with `instantaneous`, their epochs (as the times scanning instruments sample the point), and `find_coincident_ground_track`, the overlapping footprints of pairs of satellites at the same time (or within a maximum time difference), with example notebook `FindCoincidentObservations` (Metop-SG-B ICI and NASA FALCON).
+ - Analysis method `collect_feature_observations` for the observations of features: regions that exist for a period of time (`start` to `end`), such as the storms of a nature run in an observing system simulation experiment (OSSE), sampled by the footprints of the ground track. Each observation is a satellite's view of the part of a feature inside its swath, with the fraction and area viewed and the viewing geometry (elevation angle, azimuth, slant range, and satellite height) at the viewed part.
+ - Analysis method `compute_sample_sizes` for the sizes of the samples of point or feature observations: the extent on the ground of a `PointedInstrument`'s pixel at each target, along and across track, and the spacing between pixels (which bound the detail an observation can resolve), with the satellite's slant range and the target's scan angle. Methods `PointedInstrument.compute_sample_size`, `compute_sample_spacing`, `get_cross_track_sample_spacing`, and `get_along_track_sample_spacing`, and utility method `compute_ground_sample_size` (the size on the ground of an angular extent centered on the line of sight to a target). Field `PointedInstrument.scan_period` for scanning instruments, whose scans are spaced along track by the distance they advance on the ground in one period (as for microwave sounders with one beam per scan position), and utility method `compute_ground_advance`.
+ - Fields `ConicalInstrument.beam_width` and `scan_sample_spacing`, and methods `ConicalInstrument.compute_sample_size` (the footprint of the beam, longer in the oblique direction of the view) and `compute_sample_spacing` (the distance the scanned arc advances per scan, and between samples along the arc), for the sample sizes of conical scanners.
- Analysis methods `compute_access_periods`, the periods when a satellite is in view of a point above a minimum elevation angle, and `compute_region_access_periods`, a conservative superset of the periods when an instrument's field of regard may observe any part of a region (for example, to cull analysis times).
- Support for shapely `Point` objects (longitude, latitude, and optional elevation) wherever a TAT-C `Point` is accepted (`collect_observations`, `collect_multi_observations`, `compute_dop`, and `GeneralPerturbationsOrbit.get_observation_events`), in anticipation of replacing TAT-C points.
- Object schema `ConicalInstrument` (included in `AllInstruments`) for conically scanning instruments, observed when a point crosses the scanned cone, and utility method `compute_cone_and_azimuth`.
+ - Object schemas `Attitude` and `AttitudeSchedule` (with `AttitudeSegment`), and field `attitude` of satellites and constellations, for the attitude of a spacecraft's body: its velocity frame (yaw steering) and yaw, roll, and pitch angles (constant or by argument of latitude), or a schedule of attitudes over time (for example, the yaw flips of a satellite that alternately flies forward and backward, or alternating observing modes). Instruments point relative to the body (their roll and pitch angles, scan azimuths, and pixels are measured from its axes), while their fields of regard remain measured from nadir. Method `get_instrument` of satellites and constellations gets an instrument bound to the attitude (see `Instrument.bind_attitude` and `Instrument.platform_attitude`), and projection utilities accept an `attitude`.
+ - Object schema `TargetAttitude` for a spacecraft that points its body at ground targets (as an agile satellite imaging sites, or a target mode): at each time, at the target closest to nadir within a maximum off-nadir angle, and otherwise along nadir, so that its instruments' views follow the targets. Schedules may switch to and from target pointing.
+ - Sun glint: utility methods `compute_specular_point` (the specular point, or center of the sun glint, on the WGS 84 ellipsoid seen from a satellite) and `compute_glint_angle` (the angle between the directions toward the satellite and of the Sun's specular reflection at a target); fields `Instrument.min_target_glint_angle` and `max_target_glint_angle` to avoid or require sun glint (for example, for ocean color or glint observations); a `glint_angle` column of observations and ground tracks with solar angles; and object schema `GlintAttitude` for a spacecraft that points its body at the specular point, or short of it toward nadir by an offset angle (constant or a function of the specular point's angle from nadir), as for a glint observing mode, which schedules may switch to and from.
- Instrument pointing options: fields `PointedInstrument.velocity_frame` (enumeration `VelocityFrame`, for spacecraft without yaw steering), `Instrument.nadir_reference` (enumeration `NadirReference`, for a geocentric nadir), `PointedInstrument.view_geometry` (enumeration `ViewGeometry`, for cross-track scanners), and `PointedInstrument.roll_angle_profile` and `pitch_angle_profile` (with methods `get_roll_angle` and `get_pitch_angle`) to vary pointing around the orbit. Projection functions accept matching arguments, including `tilt_angle`.
- Fields `Instrument.min_target_solar_elevation` and `Instrument.max_target_solar_elevation` to require a range of solar elevation angles at the target.
- Fields `GeneralPerturbationsOrbit.remove_drag`, to propagate without drag, and `GeneralPerturbationsOrbit.repeat_cycle`, to propagate the elements directly (`None`, the default), maintained on a repeat ground track with a repeat cycle found from the elements (`"auto"`), or with a declared repeat cycle (a duration), also accepted by `from_tle`, `from_omm_csv`, and `from_omm_json`. Related methods `GeneralPerturbationsOrbit.get_repeat_element` and `GeneralPerturbationsElements.get_repeat_element`, `refine_repeat_cycle`, `without_drag`, `is_sun_synchronous`, and property `has_drag`.
+ - Field `GeneralPerturbationsOrbit.element_switch_times` (with method `get_element_index`) to switch between elements at given times rather than midway between their epochs, and utility method `align_element_switches` to switch satellites whose elements are published together (as for a formation or a constellation) at common times, so that their relative positions do not jump while their elements switch at slightly different times.
- Field `inclination` of `MolniyaOrbit` and `TundraOrbit` (by default, the critical inclination), and methods `from_apogee_longitude` and `get_apogee_longitude` to place an orbit by the longitude of its apogee.
- Field `WalkerConstellation.seam_spacing` (and method `get_seam_spacing`) to set the seam of a star configuration.
- Argument `during_contact` of `compute_latencies` to choose when an observation that ends during a downlink is downlinked (`"end"`, `"next"`, or `"immediate"`).
@@ -40,9 +47,11 @@ Changed:
- `get_observation_events` completes passes whose rise or set Skyfield misses (grazing passes, which barely exceed the minimum elevation angle), finding the missing event from the culmination and dropping culminations below the minimum. `compute_access_periods` and `collect_observations` no longer treated such a pass as visible from the start (or to the end) of the analysis period, which made a conical instrument observe points through the Earth.
- `collect_observations` refines access periods to the instrument's field of regard, uses the time the view sweeps over the point as the epoch of a `PointedInstrument` observation, and evaluates orbits with a repeat cycle consistently.
- `collect_observations` and `collect_region_observations` accept one or more points or regions and one or more satellites, and `instrument_index=None` for every instrument of each satellite, computing all of their observations together (concatenated and sorted by start). `collect_multi_observations` and `collect_multi_region_observations` are kept as wrappers for backwards compatibility.
+ - `collect_observations` computes the observations of all points by each satellite and instrument together, vectorized across the points: the orbit is propagated once to screen the windows in which each point may be observed, and the periods, epochs, and validity of the observations are refined in all windows at once (in blocks that bound memory). Many points are about 50 times faster to collect (for example, 649 points, 24 satellites, and one day, from about 220 to 4.5 seconds), and a point's observations do not depend on the other points (to within a microsecond). Observation times are always in nanoseconds (`datetime64[ns, UTC]`), as are those of `collect_downlinks`.
- `collect_orbit_track`, `collect_ground_track`, `collect_ground_pixels`, `collect_limb_observations`, and `collect_downlinks` accept one or more satellites (and the track methods `instrument_index=None` for every instrument), and `collect_ro_observations` one or more receivers, with the results of several concatenated and sorted by time (or start). Downlinks and RO observations of several satellites are computed together.
- `PointedInstrument` roll and pitch angles rotate the view rigidly (roll, then pitch about the rolled cross-track axis), and `compute_view_tangents` accepts them. Documented the pointing conventions (positive roll looks left, positive pitch looks forward, and cross-track pixel indices run from right to left).
- `compute_latencies` downlinks an observation that ends during a downlink at the end of that downlink by default (`during_contact="end"`; formerly `"next"`).
+ - `PointedInstrument.cross_track_oversampling` and `along_track_oversampling` may be negative, for gaps between pixels (as of an undersampling sounder).
- `collect_ro_observations` uses the point of minimum WGS 84 geodetic altitude on the receiver-transmitter line as the tangent point.
- `SOCConstellation` uses the polar streets-of-coverage pattern for near-polar orbits (new field `polar` and methods `is_polar`, `get_footprint_angle`, `get_polar_design`, `get_satellites_per_plane`, and `get_number_planes`; `generate_walker` raises a `ValueError` for polar designs) and a continuously covering hexagonal lattice for inclined orbits, which requires more satellites at a given packing distance.
- `collect_ground_track` and `collect_ground_pixels` cull times with a mask using the periods when the instrument's field of regard may observe it, which no longer misses footprints near the poles or across the anti-meridian. Masks are split along the anti-meridian and poles before use, and results are sorted by time.
@@ -55,10 +64,18 @@ Changed:
- Fixed `TrainConstellation` for long intervals between members, and `MolniyaOrbit` and `TundraOrbit` periods to repeat the ground track as propagated.
- Fixed rectangular footprints of wide, elongated views.
- Fixed `PointedInstrument.compute_footprint_pixel_array` for a single-time (scalar) orbit track.
+ - Fixed `collect_observations` missing short observations (and misplacing the ends of long ones) when a field of regard extends to the horizon of a long access period, as near the perigee of a Molniya orbit, which were refined from too few samples of the access period.
+ - Fixed the pixel positions of a `PointedInstrument` with oversampling (`compute_projected_pixel_position`, `compute_footprint_pixel_array`, and `collect_ground_pixels`), which spaced the pixels by a fraction of the field of view rather than across all of it.
+ - Fixed `split_polygon` for polygons around a pole whose boundary is not monotonic in longitude (such as Antarctica's coastline), which it reconstructed by sorting their vertices by longitude, removing and adding strips of the polygon: they are now reconstructed in their vertex order, unrolled across the anti-meridian.
+ - Fixed `grid_observations` for cells whose points each have a single sample (and so no revisit), which raised an error, and to average the revisit of a cell over only the points with a revisit.
+ - Fixed `compute_ground_track` to identify orbits with the period of the element that propagates the orbit (as selected by `element_switch_times`), rather than the element whose epoch is closest.
+ - Fixed `collect_ro_observations` labeling some occultations of transmitters nearly abeam of the receiver (yaw angles near 90 degrees) as setting rather than rising: `is_rising` follows the direction of the tangent point through `sample_elevation`. `collect_ro_observations` also splits a grazing occultation, whose tangent point descends and rises again, into a setting and a rising observation where the tangent point turns, so that each observation crosses `sample_elevation` at most once.
+ - Fixed `compute_latencies` for observations and downlinks whose times have different units (for example, microseconds and nanoseconds).
- Fixed `split_polygon` for longitudes beyond 180 degrees, for polygons spanning -180 to 180 degrees of longitude, and for holes across the anti-meridian or around a pole (holes are now split like exteriors), and to drop degenerate (zero-area) parts left by repairing invalid polygons.
- Fixed cached orbit computations for copies with changed fields, and orbits and satellites to be picklable after propagation.
- Fixed numba-compiled utility methods (in `tatc.utils.orbital`, `observation`, `radar`, `atmosphere`, and `surface`) returning `nan` for integer inputs, which overflowed in compiled integer arithmetic (for example, `semimajor_axis_to_mean_motion(7000000)`): inputs are now converted to floats.
- Improved the performance of footprints (rays projected together from one view frame, vectorized ray and limb intersections, polygon assembly, splitting along the anti-meridian, and `split_polygon`), masked ground tracks, multi-element orbit propagation, `collect_ro_observations` (observation periods of all transmitters searched and sampled together, with sample times and profiles built at once), `collect_limb_observations` (all scans sampled together, with sample times built at once), radar footprints (terrain masks interpolated at all azimuths at once, and a closed-form local projection instead of PROJ transformers for each station), `collect_observations` (linear-time pairing of rise and set events, and validity checks of all epochs at once), `collect_region_observations` (validity checks and swaths of all periods at once), `collect_orbit_track` (masks tested and points built for all times at once), observations of several points, regions, or satellites (Earth orientation computed together at each step of their searches), rise and set times (each bracketed near Skyfield's estimate and bisected only until it is narrower than a millisecond), orbit propagation (IAU 2000A nutation angles interpolated from a cached table, to within about a microarcsecond, rather than computed for every time), instrument validity checks (without iterating over Skyfield times), and the searches that refine observation periods (times built from offsets without `datetime` objects). `to_datetime64_ns` and `GeneralPerturbationsOrbit.get_closest_element_index` accept Skyfield `Time` objects.
+ - `find_coincident_observations` and `aggregate_observations` compute all targets together rather than one at a time, with the same results: for a day of observations of about 100,000 to 250,000 points, in seconds rather than 5 to 13 minutes.
- `collect_region_observations` is about 3 times faster for detailed regions (for example, 30 days of observations of four regions of 2000 vertices each, from about 13 to 4 seconds), computing the distances to a region's boundary with a numba-compiled function.
- Numba-compiled functions cache their machine code (`cache=True`) in `__pycache__` directories next to the source files (or in the directory set by `NUMBA_CACHE_DIR`), so that new processes (for example, the workers of a multiprocessing pool) load rather than recompile them. After editing a constant or compiled function in an editable install, clear the cache (see the README).
- Unit tests run numba-compiled functions compiled rather than as Python (set `NUMBA_DISABLE_JIT=1` to include them in a coverage report).
diff --git a/docs/api_reference/analysis/coverage.rst b/docs/api_reference/analysis/coverage.rst
index 66e4e985..45bb8d75 100644
--- a/docs/api_reference/analysis/coverage.rst
+++ b/docs/api_reference/analysis/coverage.rst
@@ -25,6 +25,10 @@ Compute Region Access Periods
-----------------------------
.. autofunction:: tatc.analysis.compute_region_access_periods
+Compute Sample Sizes
+--------------------
+.. autofunction:: tatc.analysis.compute_sample_sizes
+
Aggregate Observations
----------------------
.. autofunction:: tatc.analysis.aggregate_observations
diff --git a/docs/api_reference/analysis/feature_coverage.rst b/docs/api_reference/analysis/feature_coverage.rst
new file mode 100644
index 00000000..e65e9a61
--- /dev/null
+++ b/docs/api_reference/analysis/feature_coverage.rst
@@ -0,0 +1,6 @@
+Feature Observations
+====================
+
+Collect Feature Observations
+----------------------------
+.. autofunction:: tatc.analysis.collect_feature_observations
diff --git a/docs/api_reference/analysis/index.rst b/docs/api_reference/analysis/index.rst
index 6657fe07..83bc1532 100644
--- a/docs/api_reference/analysis/index.rst
+++ b/docs/api_reference/analysis/index.rst
@@ -10,6 +10,7 @@ Analysis functions simulate mission operation to compute key performance metrics
coverage
track
coincidence
+ feature_coverage
latency
dop
ro_coverage
diff --git a/docs/api_reference/schemas/space.rst b/docs/api_reference/schemas/space.rst
index d3ca16a2..c8b13c3e 100644
--- a/docs/api_reference/schemas/space.rst
+++ b/docs/api_reference/schemas/space.rst
@@ -19,6 +19,33 @@ Conical Instrument
.. autopydantic_model:: tatc.schemas.ConicalInstrument
:members:
+Attitude
+--------
+
+.. autopydantic_model:: tatc.schemas.Attitude
+ :members:
+
+Target Attitude
+---------------
+
+.. autopydantic_model:: tatc.schemas.TargetAttitude
+ :members:
+
+Glint Attitude
+--------------
+
+.. autopydantic_model:: tatc.schemas.GlintAttitude
+ :members:
+
+Attitude Schedule
+-----------------
+
+.. autopydantic_model:: tatc.schemas.AttitudeSchedule
+ :members:
+
+.. autopydantic_model:: tatc.schemas.AttitudeSegment
+ :members:
+
Satellite
---------
diff --git a/docs/api_reference/utils/index.rst b/docs/api_reference/utils/index.rst
index d9ad5133..25391e88 100644
--- a/docs/api_reference/utils/index.rst
+++ b/docs/api_reference/utils/index.rst
@@ -32,6 +32,14 @@ Utility Functions
.. autofunction:: tatc.utils.compute_cone_and_azimuth
+.. autofunction:: tatc.utils.compute_ground_sample_size
+
+.. autofunction:: tatc.utils.compute_ground_advance
+
+.. autofunction:: tatc.utils.compute_specular_point
+
+.. autofunction:: tatc.utils.compute_glint_angle
+
.. autoclass:: tatc.utils.VelocityFrame
:members:
@@ -71,6 +79,8 @@ Utility Functions
.. autofunction:: tatc.utils.compute_j2_mean_motion_rate
+.. autofunction:: tatc.utils.align_element_switches
+
.. autofunction:: tatc.utils.compute_max_access_time
.. autofunction:: tatc.utils.compute_max_transit_time
diff --git a/docs/examples/CollectLimb.ipynb b/docs/examples/CollectLimb.ipynb
index 7777a3b5..517a49b6 100644
--- a/docs/examples/CollectLimb.ipynb
+++ b/docs/examples/CollectLimb.ipynb
@@ -12514,7 +12514,9 @@
"# embed frames as JPEG images, which are much smaller than PNG\n",
"plt.rcParams[\"animation.frame_format\"] = \"jpeg\"\n",
"\n",
- "fig, ax = plt.subplots(figsize=(8, 4), dpi=72, subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, ax = plt.subplots(\n",
+ " figsize=(8, 4), dpi=72, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"\n",
"frame_duration = timedelta(hours=1)\n",
"num_frames = int(duration / frame_duration)\n",
@@ -12525,7 +12527,9 @@
" time = times[0] + frame * frame_duration\n",
" orbit_track.loc[\n",
" (orbit_track.time >= time) & (orbit_track.time < time + frame_duration)\n",
- " ].plot(ax=ax, marker=\".\", color=\"k\", markersize=1, lw=0, transform=ccrs.PlateCarree())\n",
+ " ].plot(\n",
+ " ax=ax, marker=\".\", color=\"k\", markersize=1, lw=0, transform=ccrs.PlateCarree()\n",
+ " )\n",
" frame_obs = limb_obs.loc[\n",
" (limb_obs.start >= time) & (limb_obs.start < time + frame_duration)\n",
" ]\n",
diff --git a/docs/examples/CollectOrbitGroundTrack.ipynb b/docs/examples/CollectOrbitGroundTrack.ipynb
index 139e73db..049d5a02 100644
--- a/docs/examples/CollectOrbitGroundTrack.ipynb
+++ b/docs/examples/CollectOrbitGroundTrack.ipynb
@@ -340,11 +340,7 @@
"fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
"\n",
"results.plot(\n",
- " column=\"valid\",\n",
- " legend=True,\n",
- " cmap=\"inferno\",\n",
- " ax=ax,\n",
- " transform=ccrs.PlateCarree()\n",
+ " column=\"valid\", legend=True, cmap=\"inferno\", ax=ax, transform=ccrs.PlateCarree()\n",
")\n",
"ax.stock_img()\n",
"ax.set_global()\n",
@@ -558,13 +554,13 @@
"fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
"\n",
"results.plot(\n",
- " column=\"valid\", \n",
- " edgecolor=\"none\", \n",
- " alpha=0.4, \n",
- " legend=True, \n",
- " cmap=\"inferno\", \n",
+ " column=\"valid\",\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.4,\n",
+ " legend=True,\n",
+ " cmap=\"inferno\",\n",
" ax=ax,\n",
- " transform=ccrs.PlateCarree()\n",
+ " transform=ccrs.PlateCarree(),\n",
")\n",
"ax.stock_img()\n",
"ax.set_global()\n",
@@ -604,7 +600,14 @@
"\n",
"results = compute_ground_track(noaa20, times)\n",
"fig, ax = plt.subplots(figsize=(8, 5), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
- "ax = results.plot(facecolor=\"r\", edgecolor=\"none\", alpha=0.4, zorder=1, ax=ax, transform=ccrs.PlateCarree())\n",
+ "ax = results.plot(\n",
+ " facecolor=\"r\",\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.4,\n",
+ " zorder=1,\n",
+ " ax=ax,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
"ax.stock_img()\n",
"ax.set_global()\n",
"plt.show()"
diff --git a/docs/examples/CollectPlatformTrack.ipynb b/docs/examples/CollectPlatformTrack.ipynb
index c9362f9c..76dd667a 100644
--- a/docs/examples/CollectPlatformTrack.ipynb
+++ b/docs/examples/CollectPlatformTrack.ipynb
@@ -241,7 +241,9 @@
"def mean_radius(track, platform):\n",
" \"\"\"Mean distance (m) from a platform to the boundary of each footprint.\"\"\"\n",
" local = track.to_crs(local_crs(platform))\n",
- " return local.geometry.apply(lambda g: np.hypot(*np.array(g.exterior.coords)[:, :2].T).mean())\n",
+ " return local.geometry.apply(\n",
+ " lambda g: np.hypot(*np.array(g.exterior.coords)[:, :2].T).mean()\n",
+ " )\n",
"\n",
"\n",
"summary = results[[\"platform\", \"instrument\", \"elevation\"]].copy()\n",
@@ -303,7 +305,12 @@
" facecolor=\"tab:orange\", edgecolor=\"k\", alpha=0.5, ax=ax\n",
")\n",
"ax.plot(0, 0, \"k^\")\n",
- "ax.set(xlabel=\"East (m)\", ylabel=\"North (m)\", title=\"Camera Footprint from 1,000 m\", aspect=\"equal\")\n",
+ "ax.set(\n",
+ " xlabel=\"East (m)\",\n",
+ " ylabel=\"North (m)\",\n",
+ " title=\"Camera Footprint from 1,000 m\",\n",
+ " aspect=\"equal\",\n",
+ ")\n",
"ax.grid(True, alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
@@ -408,7 +415,10 @@
],
"source": [
"heights = [100, 250, 500, 1000, 2000]\n",
- "balloons = [balloon.model_copy(update={\"name\": f\"{height} m\", \"height\": height}) for height in heights]\n",
+ "balloons = [\n",
+ " balloon.model_copy(update={\"name\": f\"{height} m\", \"height\": height})\n",
+ " for height in heights\n",
+ "]\n",
"by_height = collect_platform_track(balloons, instrument_index=None)\n",
"by_height[\"radius (km)\"] = mean_radius(by_height, balloon) / 1e3\n",
"display(\n",
@@ -593,7 +603,12 @@
" column=\"instrument\", cmap=\"tab10\", edgecolor=\"k\", alpha=0.5, legend=True, ax=ax\n",
")\n",
"ax.plot(0, 0, \"k^\")\n",
- "ax.set(xlabel=\"East (m)\", ylabel=\"North (m)\", title=\"Tower Footprints (heading north)\", aspect=\"equal\")\n",
+ "ax.set(\n",
+ " xlabel=\"East (m)\",\n",
+ " ylabel=\"North (m)\",\n",
+ " title=\"Tower Footprints (heading north)\",\n",
+ " aspect=\"equal\",\n",
+ ")\n",
"ax.grid(True, alpha=0.3)\n",
"plt.show()"
]
@@ -643,8 +658,12 @@
"source": [
"import geopandas as gpd\n",
"\n",
- "pixel_camera = tower.instruments[0].model_copy(update={\"cross_track_pixels\": 12, \"along_track_pixels\": 8})\n",
- "pixels = pixel_camera.compute_footprint_pixel_array(tower.get_track(), elevation=tower.elevation)[0]\n",
+ "pixel_camera = tower.instruments[0].model_copy(\n",
+ " update={\"cross_track_pixels\": 12, \"along_track_pixels\": 8}\n",
+ ")\n",
+ "pixels = pixel_camera.compute_footprint_pixel_array(\n",
+ " tower.get_track(), elevation=tower.elevation\n",
+ ")[0]\n",
"pixels_local = gpd.GeoSeries([pixels], crs=\"EPSG:4326\").to_crs(local_crs(tower)).iloc[0]\n",
"\n",
"# pixel centers by cross-track (right to left) and along-track (fore to aft) index\n",
@@ -658,9 +677,19 @@
"tower_results[tower_results.instrument == \"North camera\"].to_crs(local_crs(tower)).plot(\n",
" facecolor=\"none\", edgecolor=\"k\", ax=ax\n",
")\n",
- "ax.plot([p.x for p in pixels_local.geoms], [p.y for p in pixels_local.geoms], \".\", color=\"tab:blue\")\n",
+ "ax.plot(\n",
+ " [p.x for p in pixels_local.geoms],\n",
+ " [p.y for p in pixels_local.geoms],\n",
+ " \".\",\n",
+ " color=\"tab:blue\",\n",
+ ")\n",
"ax.plot(0, 0, \"k^\")\n",
- "ax.set(xlabel=\"East (m)\", ylabel=\"North (m)\", title=\"North Camera Pixel Centers\", aspect=\"equal\")\n",
+ "ax.set(\n",
+ " xlabel=\"East (m)\",\n",
+ " ylabel=\"North (m)\",\n",
+ " title=\"North Camera Pixel Centers\",\n",
+ " aspect=\"equal\",\n",
+ ")\n",
"ax.grid(True, alpha=0.3)\n",
"plt.show()"
]
@@ -713,7 +742,10 @@
"\n",
"import pandas as pd\n",
"\n",
- "times = [datetime(2026, 10, 16, 7, tzinfo=timezone.utc) + timedelta(minutes=10 * i) for i in range(144)]\n",
+ "times = [\n",
+ " datetime(2026, 10, 16, 7, tzinfo=timezone.utc) + timedelta(minutes=10 * i)\n",
+ " for i in range(144)\n",
+ "]\n",
"camera = tower.instruments[0].model_copy(update={\"min_target_solar_elevation\": 10})\n",
"track = tower.get_track(times)\n",
"center = camera.compute_footprint_center(track, elevation=tower.elevation)\n",
@@ -727,7 +759,12 @@
"\n",
"fig, ax = plt.subplots(figsize=(10, 2.5))\n",
"ax.step(valid.index.tz_localize(None), valid.astype(int), where=\"post\")\n",
- "ax.set(yticks=[0, 1], yticklabels=[\"invalid\", \"valid\"], xlabel=\"Local time (MST)\", title=\"North Camera Valid Observations\")\n",
+ "ax.set(\n",
+ " yticks=[0, 1],\n",
+ " yticklabels=[\"invalid\", \"valid\"],\n",
+ " xlabel=\"Local time (MST)\",\n",
+ " title=\"North Camera Valid Observations\",\n",
+ ")\n",
"plt.show()"
]
},
@@ -800,16 +837,33 @@
"\n",
"composite = compute_platform_track(network)\n",
"composite_masked = compute_platform_track(network, mask=mask)\n",
- "print(f\"composite area: {composite.to_crs(local_crs(network[0])).area.iloc[0] / 1e6:.0f} km2\")\n",
- "print(f\"masked composite area: {composite_masked.to_crs(local_crs(network[0])).area.iloc[0] / 1e6:.0f} km2\")\n",
+ "print(\n",
+ " f\"composite area: {composite.to_crs(local_crs(network[0])).area.iloc[0] / 1e6:.0f} km2\"\n",
+ ")\n",
+ "print(\n",
+ " f\"masked composite area: {composite_masked.to_crs(local_crs(network[0])).area.iloc[0] / 1e6:.0f} km2\"\n",
+ ")\n",
"\n",
"fig, ax = plt.subplots(figsize=(8, 7), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
- "composite.plot(facecolor=\"none\", edgecolor=\"tab:red\", ls=\"--\", ax=ax, transform=ccrs.PlateCarree())\n",
- "composite_masked.plot(facecolor=\"tab:red\", edgecolor=\"none\", alpha=0.4, ax=ax, transform=ccrs.PlateCarree())\n",
+ "composite.plot(\n",
+ " facecolor=\"none\", edgecolor=\"tab:red\", ls=\"--\", ax=ax, transform=ccrs.PlateCarree()\n",
+ ")\n",
+ "composite_masked.plot(\n",
+ " facecolor=\"tab:red\",\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.4,\n",
+ " ax=ax,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
"ax.plot(*mask.exterior.xy, color=\"k\", lw=1, transform=ccrs.PlateCarree())\n",
"for platform in network:\n",
" ax.plot(platform.longitude, platform.latitude, \"k^\", transform=ccrs.PlateCarree())\n",
- " ax.text(platform.longitude + 0.01, platform.latitude + 0.01, platform.name, transform=ccrs.PlateCarree())\n",
+ " ax.text(\n",
+ " platform.longitude + 0.01,\n",
+ " platform.latitude + 0.01,\n",
+ " platform.name,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
"ax.add_feature(cfeature.STATES, edgecolor=\"gray\")\n",
"ax.set_extent([-112.45, -111.6, 33.1, 33.7], crs=ccrs.PlateCarree())\n",
"ax.set_title(\"Composite Balloon Network Coverage (shaded: masked)\")\n",
@@ -918,18 +972,24 @@
" }\n",
")\n",
"cloud_base = tower.elevation + 2000\n",
- "sky_results = collect_platform_track(sky_tower, instrument_index=None, elevation=cloud_base)\n",
+ "sky_results = collect_platform_track(\n",
+ " sky_tower, instrument_index=None, elevation=cloud_base\n",
+ ")\n",
"\n",
"\n",
"def distance_range(track, platform):\n",
" \"\"\"Minimum and maximum distances (m) from a platform to the boundary of each footprint.\"\"\"\n",
" local = track.to_crs(local_crs(platform))\n",
- " distances = local.geometry.apply(lambda g: np.hypot(*np.array(g.exterior.coords)[:, :2].T))\n",
+ " distances = local.geometry.apply(\n",
+ " lambda g: np.hypot(*np.array(g.exterior.coords)[:, :2].T)\n",
+ " )\n",
" return distances.apply(np.min), distances.apply(np.max)\n",
"\n",
"\n",
"sky_summary = sky_results[[\"platform\", \"instrument\", \"elevation\"]].copy()\n",
- "sky_summary[\"near (km)\"], sky_summary[\"far (km)\"] = (d / 1e3 for d in distance_range(sky_results, sky_tower))\n",
+ "sky_summary[\"near (km)\"], sky_summary[\"far (km)\"] = (\n",
+ " d / 1e3 for d in distance_range(sky_results, sky_tower)\n",
+ ")\n",
"sky_summary[\"area (km2)\"] = sky_results.to_crs(local_crs(sky_tower)).area / 1e6\n",
"display(sky_summary.round(3))"
]
@@ -963,7 +1023,11 @@
"fig = plt.figure(figsize=(13, 6))\n",
"ax = fig.add_subplot(1, 2, 1, projection=ccrs.PlateCarree())\n",
"sky_results[sky_results.instrument == \"West sky camera\"].plot(\n",
- " facecolor=\"tab:purple\", edgecolor=\"k\", alpha=0.4, ax=ax, transform=ccrs.PlateCarree()\n",
+ " facecolor=\"tab:purple\",\n",
+ " edgecolor=\"k\",\n",
+ " alpha=0.4,\n",
+ " ax=ax,\n",
+ " transform=ccrs.PlateCarree(),\n",
")\n",
"ax.plot(sky_tower.longitude, sky_tower.latitude, \"k^\", transform=ccrs.PlateCarree())\n",
"ax.stock_img()\n",
@@ -976,7 +1040,12 @@
" facecolor=\"tab:cyan\", edgecolor=\"k\", alpha=0.5, ax=ax\n",
")\n",
"ax.plot(0, 0, \"k^\")\n",
- "ax.set(xlabel=\"East (m)\", ylabel=\"North (m)\", title=\"Sky Camera Footprint at 2,000 m\", aspect=\"equal\")\n",
+ "ax.set(\n",
+ " xlabel=\"East (m)\",\n",
+ " ylabel=\"North (m)\",\n",
+ " title=\"Sky Camera Footprint at 2,000 m\",\n",
+ " aspect=\"equal\",\n",
+ ")\n",
"ax.grid(True, alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
@@ -1006,11 +1075,15 @@
}
],
"source": [
- "pixel_sky_camera = sky_tower.instruments[0].model_copy(update={\"cross_track_pixels\": 9, \"along_track_pixels\": 9})\n",
+ "pixel_sky_camera = sky_tower.instruments[0].model_copy(\n",
+ " update={\"cross_track_pixels\": 9, \"along_track_pixels\": 9}\n",
+ ")\n",
"sky_pixels = pixel_sky_camera.compute_footprint_pixel_array(\n",
" sky_tower.get_track(), elevation=cloud_base, ground_elevation=sky_tower.elevation\n",
")[0]\n",
- "sky_pixels_local = gpd.GeoSeries([sky_pixels], crs=\"EPSG:4326\").to_crs(local_crs(sky_tower)).iloc[0]\n",
+ "sky_pixels_local = (\n",
+ " gpd.GeoSeries([sky_pixels], crs=\"EPSG:4326\").to_crs(local_crs(sky_tower)).iloc[0]\n",
+ ")\n",
"\n",
"# pixel centers by cross-track and along-track index\n",
"sky_centers = np.array([(p.x, p.y) for p in sky_pixels_local.geoms]).reshape(9, 9, 2)\n",
@@ -1107,10 +1180,17 @@
"REGENERATE_TERRAIN_MASK = False # set True to re-derive from the DEM over the network\n",
"\n",
"if REGENERATE_TERRAIN_MASK:\n",
- " from tatc.preprocess import compute_terrain_mask_for_platform, get_copernicus_dem_tile_urls\n",
+ " from tatc.preprocess import (\n",
+ " compute_terrain_mask_for_platform,\n",
+ " get_copernicus_dem_tile_urls,\n",
+ " )\n",
"\n",
- " dem_urls = get_copernicus_dem_tile_urls(sky_tower.longitude, sky_tower.latitude, radius=100000)\n",
- " sky_tower.terrain_mask = compute_terrain_mask_for_platform(dem_urls, sky_tower, search_radius=100000)\n",
+ " dem_urls = get_copernicus_dem_tile_urls(\n",
+ " sky_tower.longitude, sky_tower.latitude, radius=100000\n",
+ " )\n",
+ " sky_tower.terrain_mask = compute_terrain_mask_for_platform(\n",
+ " dem_urls, sky_tower, search_radius=100000\n",
+ " )\n",
" with open(TERRAIN_MASK_PATH, \"w\", encoding=\"utf-8\") as f:\n",
" json.dump({sky_tower.name: sky_tower.terrain_mask.model_dump()}, f, indent=2)\n",
"else:\n",
@@ -1119,18 +1199,30 @@
"\n",
"open_sky = sky_tower.model_copy(update={\"terrain_mask\": None})\n",
"visible = gpd.GeoSeries(\n",
- " [open_sky.compute_visible_region(cloud_base), sky_tower.compute_visible_region(cloud_base)],\n",
+ " [\n",
+ " open_sky.compute_visible_region(cloud_base),\n",
+ " sky_tower.compute_visible_region(cloud_base),\n",
+ " ],\n",
" index=[\"ground horizon\", \"terrain mask\"],\n",
" crs=\"EPSG:4326\",\n",
")\n",
- "masked_results = collect_platform_track(sky_tower, instrument_index=None, elevation=cloud_base)\n",
+ "masked_results = collect_platform_track(\n",
+ " sky_tower, instrument_index=None, elevation=cloud_base\n",
+ ")\n",
"\n",
"angles = sky_tower.terrain_mask.min_elevation_angle\n",
"print(f\"terrain mask: {min(angles):.2f} to {max(angles):.2f} degrees\")\n",
- "print(\"visible cloud base (km2):\", (visible.to_crs(local_crs(sky_tower)).area / 1e6).round(0).to_dict())\n",
+ "print(\n",
+ " \"visible cloud base (km2):\",\n",
+ " (visible.to_crs(local_crs(sky_tower)).area / 1e6).round(0).to_dict(),\n",
+ ")\n",
"masked_summary = masked_results[[\"instrument\"]].copy()\n",
- "masked_summary[\"area without mask (km2)\"] = sky_results.to_crs(local_crs(sky_tower)).area / 1e6\n",
- "masked_summary[\"area with mask (km2)\"] = masked_results.to_crs(local_crs(sky_tower)).area / 1e6\n",
+ "masked_summary[\"area without mask (km2)\"] = (\n",
+ " sky_results.to_crs(local_crs(sky_tower)).area / 1e6\n",
+ ")\n",
+ "masked_summary[\"area with mask (km2)\"] = (\n",
+ " masked_results.to_crs(local_crs(sky_tower)).area / 1e6\n",
+ ")\n",
"display(masked_summary.round(1))"
]
},
@@ -1161,15 +1253,25 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
- "visible[[\"ground horizon\"]].plot(facecolor=\"none\", edgecolor=\"k\", ls=\"--\", ax=ax, transform=ccrs.PlateCarree())\n",
- "visible[[\"terrain mask\"]].plot(facecolor=\"tab:green\", edgecolor=\"k\", alpha=0.2, ax=ax, transform=ccrs.PlateCarree())\n",
+ "visible[[\"ground horizon\"]].plot(\n",
+ " facecolor=\"none\", edgecolor=\"k\", ls=\"--\", ax=ax, transform=ccrs.PlateCarree()\n",
+ ")\n",
+ "visible[[\"terrain mask\"]].plot(\n",
+ " facecolor=\"tab:green\", edgecolor=\"k\", alpha=0.2, ax=ax, transform=ccrs.PlateCarree()\n",
+ ")\n",
"masked_results[masked_results.instrument == \"West sky camera\"].plot(\n",
- " facecolor=\"tab:purple\", edgecolor=\"k\", alpha=0.5, ax=ax, transform=ccrs.PlateCarree()\n",
+ " facecolor=\"tab:purple\",\n",
+ " edgecolor=\"k\",\n",
+ " alpha=0.5,\n",
+ " ax=ax,\n",
+ " transform=ccrs.PlateCarree(),\n",
")\n",
"ax.plot(sky_tower.longitude, sky_tower.latitude, \"k^\", transform=ccrs.PlateCarree())\n",
"ax.add_feature(cfeature.STATES, edgecolor=\"gray\")\n",
"ax.set_extent([-114.1, -109.8, 31.8, 35.1], crs=ccrs.PlateCarree())\n",
- "ax.set_title(\"Cloud Base at 2,000 m Visible over the Horizon (dashed) and Terrain (green)\")\n",
+ "ax.set_title(\n",
+ " \"Cloud Base at 2,000 m Visible over the Horizon (dashed) and Terrain (green)\"\n",
+ ")\n",
"plt.show()"
]
},
diff --git a/docs/examples/CollectRO.ipynb b/docs/examples/CollectRO.ipynb
index df5fcdea..bafffda2 100644
--- a/docs/examples/CollectRO.ipynb
+++ b/docs/examples/CollectRO.ipynb
@@ -178,7 +178,7 @@
},
"outputs": [],
"source": [
- "from tatc.schemas import GeneralPerturbationsOrbit, Satellite\n",
+ "from tatc.schemas import GeneralPerturbationsOrbit, Satellite\n",
"\n",
"txs = []\n",
"\n",
@@ -188,7 +188,8 @@
" for i in range(0, len(lines), 3):\n",
" txs.append(\n",
" Satellite(\n",
- " name=lines[i], orbit=GeneralPerturbationsOrbit.from_tle(lines[i + 1 : i + 3])\n",
+ " name=lines[i],\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(lines[i + 1 : i + 3]),\n",
" )\n",
" )\n",
"\n",
@@ -212,7 +213,7 @@
"All reported positions refer to the geodetic tangent point (longitude, latitude, and elevation above the WGS 84 reference ellipsoid). The outputs report each RO observation including the following fields:\n",
" * `receiver`: the receiver satellite name\n",
" * `transmitter`: the transmitter satellite name\n",
- " * `is_rising`: true, if the transmitter is ahead of the receiver (a rising occultation); false indicates it is behind (a setting occultation)\n",
+ " * `is_rising`: true, if the tangent point rises through the sample elevation (a rising occultation); false indicates it descends (a setting occultation)\n",
" * `geometry`: a multipoint geometry that describes the tangent point observation arc of points separated by (at most) `time_step`\n",
" * `position`: a point geometry that describes the tangent point interpolated at the `sample_elevation` value\n",
" * `rx_tx_pitch`: the transmitter pitch angle as viewed by the receiver\n",
@@ -581,7 +582,7 @@
" color=ro_obs.apply(lambda r: \"m\" if r.is_rising else \"g\", axis=1),\n",
" scale=50,\n",
" scale_units=\"width\",\n",
- " transform=ccrs.PlateCarree()\n",
+ " transform=ccrs.PlateCarree(),\n",
")\n",
"ax.stock_img()\n",
"ax.set_global()\n",
@@ -15036,7 +15037,9 @@
"# embed frames as JPEG images, which are much smaller than PNG\n",
"plt.rcParams[\"animation.frame_format\"] = \"jpeg\"\n",
"\n",
- "fig, ax = plt.subplots(figsize=(8, 4), dpi=72, subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, ax = plt.subplots(\n",
+ " figsize=(8, 4), dpi=72, subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"\n",
"frame_duration = timedelta(minutes=1)\n",
"animation_duration = timedelta(minutes=30)\n",
@@ -15059,7 +15062,7 @@
" [point.y for point in r.geometry.geoms],\n",
" \".m\" if r.is_rising else \".g\",\n",
" markersize=1,\n",
- " transform=ccrs.PlateCarree()\n",
+ " transform=ccrs.PlateCarree(),\n",
" ),\n",
" axis=1,\n",
" )\n",
@@ -15075,7 +15078,7 @@
" color=ro_obs_filtered.apply(lambda r: \"m\" if r.is_rising else \"g\", axis=1),\n",
" scale=50,\n",
" scale_units=\"width\",\n",
- " transform=ccrs.PlateCarree()\n",
+ " transform=ccrs.PlateCarree(),\n",
" )\n",
" ax.set_global()\n",
" ax.coastlines()\n",
diff --git a/docs/examples/CollectRadarTrack.ipynb b/docs/examples/CollectRadarTrack.ipynb
index 1d576853..58449bc0 100644
--- a/docs/examples/CollectRadarTrack.ipynb
+++ b/docs/examples/CollectRadarTrack.ipynb
@@ -34,12 +34,35 @@
"# all four are real NEXRAD WSR-88D stations, which are S-band; coordinates and\n",
"# antenna elevations (1426, 7514, 239, and 5319 ft) are from the NCEI station table\n",
"stations = [\n",
- " RadarStation.from_band(RadarBand.S, name=\"KIWA\", latitude=33.289233, longitude=-111.66991, elevation=434.6),\n",
" RadarStation.from_band(\n",
- " RadarBand.S, name=\"KFSX\", latitude=34.574333, longitude=-111.19844, elevation=2290.3, min_elevation_angle=-0.2\n",
+ " RadarBand.S,\n",
+ " name=\"KIWA\",\n",
+ " latitude=33.289233,\n",
+ " longitude=-111.66991,\n",
+ " elevation=434.6,\n",
+ " ),\n",
+ " RadarStation.from_band(\n",
+ " RadarBand.S,\n",
+ " name=\"KFSX\",\n",
+ " latitude=34.574333,\n",
+ " longitude=-111.19844,\n",
+ " elevation=2290.3,\n",
+ " min_elevation_angle=-0.2,\n",
+ " ),\n",
+ " RadarStation.from_band(\n",
+ " RadarBand.S,\n",
+ " name=\"KYUX\",\n",
+ " latitude=32.495281,\n",
+ " longitude=-114.65671,\n",
+ " elevation=72.8,\n",
+ " ),\n",
+ " RadarStation.from_band(\n",
+ " RadarBand.S,\n",
+ " name=\"KEMX\",\n",
+ " latitude=31.89365,\n",
+ " longitude=-110.63025,\n",
+ " elevation=1621.2,\n",
" ),\n",
- " RadarStation.from_band(RadarBand.S, name=\"KYUX\", latitude=32.495281, longitude=-114.65671, elevation=72.8),\n",
- " RadarStation.from_band(RadarBand.S, name=\"KEMX\", latitude=31.89365, longitude=-110.63025, elevation=1621.2),\n",
"]"
]
},
@@ -377,7 +400,12 @@
"\n",
"fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
"composite.plot(\n",
- " facecolor=\"r\", edgecolor=\"none\", alpha=0.4, zorder=1, ax=ax, transform=ccrs.PlateCarree()\n",
+ " facecolor=\"r\",\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.4,\n",
+ " zorder=1,\n",
+ " ax=ax,\n",
+ " transform=ccrs.PlateCarree(),\n",
")\n",
"ax.stock_img()\n",
"ax.add_feature(cfeature.STATES, edgecolor=\"gray\")\n",
@@ -425,12 +453,19 @@
"REGENERATE_TERRAIN_MASKS = False # set True to re-derive from the DEM over the network\n",
"\n",
"if REGENERATE_TERRAIN_MASKS:\n",
- " from tatc.preprocess import get_copernicus_dem_tile_urls, compute_terrain_mask_for_station\n",
+ " from tatc.preprocess import (\n",
+ " get_copernicus_dem_tile_urls,\n",
+ " compute_terrain_mask_for_station,\n",
+ " )\n",
"\n",
" terrain_masks = {}\n",
" for station in stations:\n",
- " dem_urls = get_copernicus_dem_tile_urls(station.longitude, station.latitude, radius=100000)\n",
- " station.terrain_mask = compute_terrain_mask_for_station(dem_urls, station, search_radius=100000)\n",
+ " dem_urls = get_copernicus_dem_tile_urls(\n",
+ " station.longitude, station.latitude, radius=100000\n",
+ " )\n",
+ " station.terrain_mask = compute_terrain_mask_for_station(\n",
+ " dem_urls, station, search_radius=100000\n",
+ " )\n",
" terrain_masks[station.name] = station.terrain_mask.model_dump()\n",
" # persist for reuse, so subsequent runs do not need to repeat the DEM fetch\n",
" with open(TERRAIN_MASK_PATH, \"w\", encoding=\"utf-8\") as f:\n",
@@ -511,7 +546,9 @@
"source": [
"heights = [2438, 3048, 4572, 6096]\n",
"\n",
- "fig, axes = plt.subplots(2, 2, figsize=(12, 12), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, axes = plt.subplots(\n",
+ " 2, 2, figsize=(12, 12), subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"for ax, height in zip(axes.flat, heights):\n",
" results_h = collect_radar_track(stations, elevation=height)\n",
" results_h.plot(\n",
@@ -560,7 +597,12 @@
"\n",
"fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
"composite_masked.plot(\n",
- " facecolor=\"r\", edgecolor=\"none\", alpha=0.4, zorder=1, ax=ax, transform=ccrs.PlateCarree()\n",
+ " facecolor=\"r\",\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.4,\n",
+ " zorder=1,\n",
+ " ax=ax,\n",
+ " transform=ccrs.PlateCarree(),\n",
")\n",
"ax.stock_img()\n",
"ax.add_feature(cfeature.STATES, edgecolor=\"gray\")\n",
@@ -658,9 +700,27 @@
}
],
"source": [
- "s_band = RadarStation.from_band(RadarBand.S, name=\"S-band\", latitude=stations[0].latitude, longitude=stations[0].longitude, elevation=stations[0].elevation)\n",
- "c_band = RadarStation.from_band(RadarBand.C, name=\"C-band\", latitude=stations[0].latitude, longitude=stations[0].longitude, elevation=stations[0].elevation)\n",
- "x_band = RadarStation.from_band(RadarBand.X, name=\"X-band\", latitude=stations[0].latitude, longitude=stations[0].longitude, elevation=stations[0].elevation)\n",
+ "s_band = RadarStation.from_band(\n",
+ " RadarBand.S,\n",
+ " name=\"S-band\",\n",
+ " latitude=stations[0].latitude,\n",
+ " longitude=stations[0].longitude,\n",
+ " elevation=stations[0].elevation,\n",
+ ")\n",
+ "c_band = RadarStation.from_band(\n",
+ " RadarBand.C,\n",
+ " name=\"C-band\",\n",
+ " latitude=stations[0].latitude,\n",
+ " longitude=stations[0].longitude,\n",
+ " elevation=stations[0].elevation,\n",
+ ")\n",
+ "x_band = RadarStation.from_band(\n",
+ " RadarBand.X,\n",
+ " name=\"X-band\",\n",
+ " latitude=stations[0].latitude,\n",
+ " longitude=stations[0].longitude,\n",
+ " elevation=stations[0].elevation,\n",
+ ")\n",
"\n",
"band_results = collect_radar_track([s_band, c_band, x_band], elevation=6096)\n",
"display(band_results[[\"station\", \"band\", \"outer_ground_range\"]])\n",
diff --git a/docs/examples/CollectSpace.ipynb b/docs/examples/CollectSpace.ipynb
index 9e839bca..332abda2 100644
--- a/docs/examples/CollectSpace.ipynb
+++ b/docs/examples/CollectSpace.ipynb
@@ -90,10 +90,10 @@
"id": "57460b14",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T21:26:00.616319Z",
- "iopub.status.busy": "2026-10-08T21:26:00.616319Z",
- "iopub.status.idle": "2026-10-08T21:26:01.195234Z",
- "shell.execute_reply": "2026-10-08T21:26:01.194234Z"
+ "iopub.execute_input": "2026-10-26T00:14:27.234786Z",
+ "iopub.status.busy": "2026-10-26T00:14:27.234565Z",
+ "iopub.status.idle": "2026-10-26T00:14:27.588033Z",
+ "shell.execute_reply": "2026-10-26T00:14:27.587297Z"
}
},
"outputs": [],
@@ -122,10 +122,10 @@
"id": "540af7d4",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T21:26:01.197234Z",
- "iopub.status.busy": "2026-10-08T21:26:01.197234Z",
- "iopub.status.idle": "2026-10-08T21:26:02.769096Z",
- "shell.execute_reply": "2026-10-08T21:26:02.768388Z"
+ "iopub.execute_input": "2026-10-26T00:14:27.590175Z",
+ "iopub.status.busy": "2026-10-26T00:14:27.589905Z",
+ "iopub.status.idle": "2026-10-26T00:14:28.506469Z",
+ "shell.execute_reply": "2026-10-26T00:14:28.505653Z"
}
},
"outputs": [],
@@ -175,10 +175,10 @@
"id": "9f26de47",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T21:26:02.771611Z",
- "iopub.status.busy": "2026-10-08T21:26:02.771611Z",
- "iopub.status.idle": "2026-10-08T21:26:02.956951Z",
- "shell.execute_reply": "2026-10-08T21:26:02.956951Z"
+ "iopub.execute_input": "2026-10-26T00:14:28.508479Z",
+ "iopub.status.busy": "2026-10-26T00:14:28.508185Z",
+ "iopub.status.idle": "2026-10-26T00:14:28.619028Z",
+ "shell.execute_reply": "2026-10-26T00:14:28.618267Z"
}
},
"outputs": [
@@ -209,152 +209,186 @@
"
array(['2006-08-25T00:15:00.000000000', '2006-08-25T00:45:00.000000000',\n",
+ " extra_das_attribute: This is an example of metadata added using a supple...
Mon Sep 28 12:21:13 EDT 2026 : imported by GrADS Data Server 2.0
extra_das_attribute :
This is an example of metadata added using a supplementary DAS file. See testdata/extra.das for the source.
"
],
"text/plain": [
@@ -939,11 +941,18 @@
" \"\"\"One chunk of the Nature Run over the study region, retried if the server returns a field of zeros.\"\"\"\n",
" minx, miny, maxx, maxy = domain.bounds\n",
" for attempt in range(4):\n",
- " chunk = remote[VARIABLES].sel(\n",
- " time=slice(naive(chunk_start), naive(chunk_start + CHUNK - timedelta(seconds=1))),\n",
- " lon=slice(minx, maxx),\n",
- " lat=slice(miny, maxy),\n",
- " ).load()\n",
+ " chunk = (\n",
+ " remote[VARIABLES]\n",
+ " .sel(\n",
+ " time=slice(\n",
+ " naive(chunk_start),\n",
+ " naive(chunk_start + CHUNK - timedelta(seconds=1)),\n",
+ " ),\n",
+ " lon=slice(minx, maxx),\n",
+ " lat=slice(miny, maxy),\n",
+ " )\n",
+ " .load()\n",
+ " )\n",
" if float((chunk[\"lwtup\"] == 0).mean()) < 0.5:\n",
" return chunk\n",
" raise RuntimeError(f\"the server returned a field of zeros for {chunk_start}\")\n",
@@ -952,13 +961,18 @@
"nature_file = DATA_DIR / f\"g5nr_hurricane_{nature_start:%Y%m%d}_{duration.days}d.nc\"\n",
"if not nature_file.exists():\n",
" with xr.open_dataset(NATURE_URL) as remote:\n",
- " chunks = [fetch_chunk(remote, nature_start + k * CHUNK) for k in range(int(duration / CHUNK))]\n",
+ " chunks = [\n",
+ " fetch_chunk(remote, nature_start + k * CHUNK)\n",
+ " for k in range(int(duration / CHUNK))\n",
+ " ]\n",
" xr.concat(chunks, \"time\").to_netcdf(nature_file)\n",
"nature = xr.load_dataset(nature_file)\n",
"assert nature.sizes[\"time\"] == n_frames\n",
"assert float((nature[\"lwtup\"] == 0).mean()) == 0\n",
"# each time stamp is the middle of its 30-minute averaging interval\n",
- "assert (nature.time.values[0] - np.datetime64(naive(nature_start))) == np.timedelta64(15, \"m\")\n",
+ "assert (nature.time.values[0] - np.datetime64(naive(nature_start))) == np.timedelta64(\n",
+ " 15, \"m\"\n",
+ ")\n",
"nature"
]
},
@@ -985,10 +999,10 @@
"id": "37e47761",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:47:35.649871Z",
- "iopub.status.busy": "2026-10-24T21:47:35.649871Z",
- "iopub.status.idle": "2026-10-24T21:47:47.712725Z",
- "shell.execute_reply": "2026-10-24T21:47:47.712725Z"
+ "iopub.execute_input": "2026-10-25T04:52:42.397499Z",
+ "iopub.status.busy": "2026-10-25T04:52:42.397499Z",
+ "iopub.status.idle": "2026-10-25T04:52:54.316832Z",
+ "shell.execute_reply": "2026-10-25T04:52:54.316832Z"
}
},
"outputs": [
@@ -1089,9 +1103,13 @@
"\n",
"from tatc.constants import EARTH_MEAN_RADIUS\n",
"\n",
- "THRESHOLD = 5.67037e-8 * 220**4 # W m-2, the upwelling longwave flux of a 220 K blackbody\n",
+ "THRESHOLD = (\n",
+ " 5.67037e-8 * 220**4\n",
+ ") # W m-2, the upwelling longwave flux of a 220 K blackbody\n",
"PRECIP_THRESHOLD = 10 / 3600 # kg m-2 s-1 (10 mm/h)\n",
- "EFFECTIVE_RESOLUTION_KM = 4 * 7 # km, several grid spacings (Gelaro et al., 2015); smaller features are not resolved\n",
+ "EFFECTIVE_RESOLUTION_KM = (\n",
+ " 4 * 7\n",
+ ") # km, several grid spacings (Gelaro et al., 2015); smaller features are not resolved\n",
"STORM_RADIUS_KM = 500 # km, features within this distance of the storm center are attributed to the storm\n",
"KINDS = {\n",
" \"cold cloud top\": lambda frame: frame[\"lwtup\"].values < THRESHOLD,\n",
@@ -1103,13 +1121,17 @@
" \"\"\"The features of a kind (contiguous patches at least EFFECTIVE_RESOLUTION_KM in size) in each frame.\"\"\"\n",
" lon, lat = nature.lon.values, nature.lat.values\n",
" lon2d, lat2d = np.meshgrid(lon, lat)\n",
- " cell_area = (EARTH_MEAN_RADIUS * np.radians(GRID_SIZE)) ** 2 * np.cos(np.radians(lat2d)) # m^2\n",
+ " cell_area = (EARTH_MEAN_RADIUS * np.radians(GRID_SIZE)) ** 2 * np.cos(\n",
+ " np.radians(lat2d)\n",
+ " ) # m^2\n",
"\n",
" records, polygons = [], []\n",
" for frame in range(nature.sizes[\"time\"]):\n",
" fields = nature.isel(time=frame)\n",
" lwtup, prectot = fields[\"lwtup\"].values, fields[\"prectot\"].values\n",
- " labels, n_labels = ndi.label(KINDS[kind](fields)) # contiguous cells of the kind\n",
+ " labels, n_labels = ndi.label(\n",
+ " KINDS[kind](fields)\n",
+ " ) # contiguous cells of the kind\n",
" if n_labels == 0:\n",
" continue\n",
" ids = np.arange(1, n_labels + 1)\n",
@@ -1120,7 +1142,10 @@
" order = np.argsort(labels[j, i], kind=\"stable\")\n",
" j, i = j[order], i[order]\n",
" cells = shapely.box(\n",
- " lon[i] - GRID_SIZE / 2, lat[j] - GRID_SIZE / 2, lon[i] + GRID_SIZE / 2, lat[j] + GRID_SIZE / 2\n",
+ " lon[i] - GRID_SIZE / 2,\n",
+ " lat[j] - GRID_SIZE / 2,\n",
+ " lon[i] + GRID_SIZE / 2,\n",
+ " lat[j] + GRID_SIZE / 2,\n",
" )\n",
" for k, cluster in enumerate(np.split(cells, np.cumsum(n_cells)[:-1])):\n",
" records.append(\n",
@@ -1139,7 +1164,9 @@
" polygons.append(shapely.union_all(cluster))\n",
"\n",
" features = gpd.GeoDataFrame(records, geometry=polygons, crs=\"EPSG:4326\")\n",
- " features[\"diameter_km\"] = np.sqrt(4 * features.area_km2 / np.pi) # of a circle of equal area\n",
+ " features[\"diameter_km\"] = np.sqrt(\n",
+ " 4 * features.area_km2 / np.pi\n",
+ " ) # of a circle of equal area\n",
" return features[features.diameter_km >= EFFECTIVE_RESOLUTION_KM]\n",
"\n",
"\n",
@@ -1147,16 +1174,28 @@
"features[\"time\"] = start + features.frame * frame_duration\n",
"\n",
"# distance (great circle) from the storm center at the middle of the frame, interpolated from the hourly track\n",
- "frame_middle = (nature.time.values[features.frame] - track.index.values[0]) / np.timedelta64(1, \"h\")\n",
+ "frame_middle = (\n",
+ " nature.time.values[features.frame] - track.index.values[0]\n",
+ ") / np.timedelta64(1, \"h\")\n",
"track_hours = (track.index.values - track.index.values[0]) / np.timedelta64(1, \"h\")\n",
"center_lat = np.radians(np.interp(frame_middle, track_hours, track.lat))\n",
"center_lon = np.radians(np.interp(frame_middle, track_hours, track.lon))\n",
"lat, lon = np.radians(features.lat), np.radians(features.lon)\n",
"features[\"storm_distance_km\"] = (\n",
- " EARTH_MEAN_RADIUS / 1e3\n",
- " * np.arccos(np.clip(np.sin(lat) * np.sin(center_lat) + np.cos(lat) * np.cos(center_lat) * np.cos(lon - center_lon), -1, 1))\n",
+ " EARTH_MEAN_RADIUS\n",
+ " / 1e3\n",
+ " * np.arccos(\n",
+ " np.clip(\n",
+ " np.sin(lat) * np.sin(center_lat)\n",
+ " + np.cos(lat) * np.cos(center_lat) * np.cos(lon - center_lon),\n",
+ " -1,\n",
+ " 1,\n",
+ " )\n",
+ " )\n",
+ ")\n",
+ "features[\"region\"] = np.where(\n",
+ " features.storm_distance_km < STORM_RADIUS_KM, \"storm\", \"environment\"\n",
")\n",
- "features[\"region\"] = np.where(features.storm_distance_km < STORM_RADIUS_KM, \"storm\", \"environment\")\n",
"features.groupby([\"kind\", \"region\"]).agg(\n",
" features=(\"frame\", \"size\"),\n",
" median_diameter_km=(\"diameter_km\", \"median\"),\n",
@@ -1181,10 +1220,10 @@
"id": "069ee49e",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:47:47.714730Z",
- "iopub.status.busy": "2026-10-24T21:47:47.714730Z",
- "iopub.status.idle": "2026-10-24T21:47:47.742987Z",
- "shell.execute_reply": "2026-10-24T21:47:47.742987Z"
+ "iopub.execute_input": "2026-10-25T04:52:54.318837Z",
+ "iopub.status.busy": "2026-10-25T04:52:54.318837Z",
+ "iopub.status.idle": "2026-10-25T04:52:54.347844Z",
+ "shell.execute_reply": "2026-10-25T04:52:54.347334Z"
}
},
"outputs": [
@@ -1302,7 +1341,9 @@
" field_of_regard=field_of_regard,\n",
" cross_track_field_of_view=field_of_regard,\n",
" along_track_field_of_view=compute_along_track_field_of_view(\n",
- " orbits[\"NOAA-20\"].get_mean_altitude(), TIME_STEP, orbits[\"NOAA-20\"].get_inclination()\n",
+ " orbits[\"NOAA-20\"].get_mean_altitude(),\n",
+ " TIME_STEP,\n",
+ " orbits[\"NOAA-20\"].get_inclination(),\n",
" ),\n",
" roll_angle=scan[\"roll\"],\n",
" is_rectangular=True,\n",
@@ -1311,7 +1352,11 @@
"\n",
"\n",
"satellites = [\n",
- " Satellite(name=name, orbit=orbit, instruments=[swath_instrument(\"ATMS\"), swath_instrument(\"CrIS\")])\n",
+ " Satellite(\n",
+ " name=name,\n",
+ " orbit=orbit,\n",
+ " instruments=[swath_instrument(\"ATMS\"), swath_instrument(\"CrIS\")],\n",
+ " )\n",
" for name, orbit in orbits.items()\n",
"]\n",
"\n",
@@ -1330,7 +1375,43 @@
").set_index(\"sensor\")\n",
"# sample spacing at nadir (km): that of the heritage sensor, scaled for the future sensors in proportion to their footprint\n",
"heritage_km = sensors.horizontal_km[sensors.swath].values\n",
- "sensors[\"sampling_km\"] = REFERENCE_RANGE_KM * np.radians(sensors.swath.map(SAMPLING_DEG)) * sensors.horizontal_km / heritage_km\n",
+ "sensors[\"sampling_km\"] = (\n",
+ " REFERENCE_RANGE_KM\n",
+ " * np.radians(sensors.swath.map(SAMPLING_DEG))\n",
+ " * sensors.horizontal_km\n",
+ " / heritage_km\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def sensor_instrument(sensor):\n",
+ " \"\"\"The swath instrument of a sensor with its pixels (for its sample sizes, see `compute_sample_sizes`): pixels\n",
+ " spaced by the angle that the sample spacing subtends at nadir from the reference range (enough to span the\n",
+ " swath), each with the instantaneous field of view that its horizontal resolution subtends, along and across\n",
+ " track.\"\"\"\n",
+ " row = sensors.loc[sensor]\n",
+ " swath = swath_instrument(row.swath)\n",
+ " ifov = np.degrees(2 * np.arctan(row.horizontal_km / 2 / REFERENCE_RANGE_KM))\n",
+ " spacing = np.degrees(row.sampling_km / REFERENCE_RANGE_KM)\n",
+ " pixels = round(swath.cross_track_field_of_view / spacing)\n",
+ " # the oversampling makes each pixel's field of view ifov (negative: a gap between pixels)\n",
+ " return PointedInstrument(\n",
+ " **swath.model_dump(\n",
+ " exclude={\n",
+ " \"cross_track_field_of_view\",\n",
+ " \"along_track_field_of_view\",\n",
+ " \"cross_track_pixels\",\n",
+ " \"cross_track_oversampling\",\n",
+ " \"along_track_oversampling\",\n",
+ " }\n",
+ " ),\n",
+ " cross_track_field_of_view=pixels * spacing,\n",
+ " along_track_field_of_view=spacing,\n",
+ " cross_track_pixels=pixels,\n",
+ " cross_track_oversampling=1 - spacing / ifov,\n",
+ " along_track_oversampling=1 - spacing / ifov,\n",
+ " )\n",
+ "\n",
+ "\n",
"sensors.round(1)"
]
},
@@ -1350,10 +1431,10 @@
"id": "7e009a6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:47:47.745212Z",
- "iopub.status.busy": "2026-10-24T21:47:47.745212Z",
- "iopub.status.idle": "2026-10-24T21:47:48.084161Z",
- "shell.execute_reply": "2026-10-24T21:47:48.084161Z"
+ "iopub.execute_input": "2026-10-25T04:52:54.349850Z",
+ "iopub.status.busy": "2026-10-25T04:52:54.348852Z",
+ "iopub.status.idle": "2026-10-25T04:52:54.659010Z",
+ "shell.execute_reply": "2026-10-25T04:52:54.659010Z"
}
},
"outputs": [
@@ -1467,7 +1548,10 @@
"def collect_tracks(satellites, duration):\n",
" \"\"\"The footprint of every instrument of the satellites at each time step over a duration from start, clipped to the\n",
" study region, with its frame.\"\"\"\n",
- " times = [start + timedelta(seconds=TIME_STEP * (i + 0.5)) for i in range(int(duration.total_seconds() // TIME_STEP))]\n",
+ " times = [\n",
+ " start + timedelta(seconds=TIME_STEP * (i + 0.5))\n",
+ " for i in range(int(duration.total_seconds() // TIME_STEP))\n",
+ " ]\n",
" tracks = collect_ground_track(satellites, times, instrument_index=None, mask=domain)\n",
" tracks = tracks[tracks.valid_obs].reset_index(drop=True)\n",
" tracks[\"geometry\"] = shapely.force_2d(tracks.geometry.values)\n",
@@ -1478,7 +1562,9 @@
"tracks = collect_tracks(satellites, duration)\n",
"# the area swept by each swath (ATMS or CrIS) in each frame\n",
"swaths = tracks.groupby([\"frame\", \"instrument\"]).geometry.agg(shapely.union_all)\n",
- "tracks.groupby([\"satellite\", \"instrument\"]).agg(footprints=(\"time\", \"size\"), first=(\"time\", \"min\"), last=(\"time\", \"max\"))"
+ "tracks.groupby([\"satellite\", \"instrument\"]).agg(\n",
+ " footprints=(\"time\", \"size\"), first=(\"time\", \"min\"), last=(\"time\", \"max\")\n",
+ ")"
]
},
{
@@ -1488,7 +1574,7 @@
"source": [
"## Compute Observability and Resolution\n",
"\n",
- "As in the monsoon OSSE, a feature is **observed** by a swath if the swath footprint of some time step in its frame overlaps it, a **look** is one satellite's view of the part of a feature inside its swath, and a feature is **resolved** if its viewed part spans at least `RESOLUTION_FACTOR` (two) resolution elements at its best look. The resolution element is the larger of a sensor's footprint and sample spacing at nadir, scaled by the slant range and (across track) the secant of the zenith angle, with its size measured as the diameter of a circle of the same area (`ELEMENT_AXIS`)."
+ "As in the monsoon OSSE, a feature is **observed** by a swath if the swath footprint of some time step in its frame overlaps it, a **look** is one satellite's view of the part of a feature inside its swath, and a feature is **resolved** if its viewed part spans at least `RESOLUTION_FACTOR` (two) resolution elements at its best look. TAT-C's `collect_feature_observations` collects the looks at features that exist for a period of time (here, each feature's frame), with the viewing geometry of each. The resolution element is the larger of a sensor's pixel and the spacing between its pixels, projected onto the ground at the look by TAT-C's `compute_sample_sizes` along and across track (see the monsoon OSSE), with its size measured as the diameter of a circle of the same area (`ELEMENT_AXIS`)."
]
},
{
@@ -1497,10 +1583,10 @@
"id": "a502a7dc",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:47:48.087167Z",
- "iopub.status.busy": "2026-10-24T21:47:48.087167Z",
- "iopub.status.idle": "2026-10-24T21:47:48.460042Z",
- "shell.execute_reply": "2026-10-24T21:47:48.460042Z"
+ "iopub.execute_input": "2026-10-25T04:52:54.661016Z",
+ "iopub.status.busy": "2026-10-25T04:52:54.661016Z",
+ "iopub.status.idle": "2026-10-25T04:52:55.377629Z",
+ "shell.execute_reply": "2026-10-25T04:52:55.377629Z"
}
},
"outputs": [
@@ -1604,102 +1690,81 @@
}
],
"source": [
- "from pyproj import Transformer\n",
- "from skyfield.framelib import itrs\n",
+ "from tatc.analysis import collect_feature_observations, compute_sample_sizes\n",
"\n",
"RESOLUTION_FACTOR = 2 # a feature is resolved if its viewed part spans at least this many resolution elements\n",
"ELEMENT_AXIS = \"equivalent\" # the size of a resolution element: \"equivalent\" (diameter of equal area) or \"cross-track\"\n",
"AXES = [\"equivalent\", \"cross-track\"]\n",
"\n",
- "TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
- "\n",
"\n",
- "def up(lon, lat):\n",
- " \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
- " lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
- "\n",
- "\n",
- "def element_size(slant_range, zenith, nadir_km, axis):\n",
- " \"\"\"Size (km) of a resolution element of size nadir_km at nadir: its angular size times the slant range (m) along\n",
- " track, and longer by the secant of the zenith angle (deg) cross track. The size is the diameter of a circle of the\n",
- " same area (axis=\"equivalent\", the geometric mean of the two) or the cross-track size (axis=\"cross-track\").\"\"\"\n",
- " along = slant_range / 1e3 * nadir_km / REFERENCE_RANGE_KM\n",
- " cross = along / np.cos(np.radians(zenith))\n",
- " return cross if axis == \"cross-track\" else np.sqrt(along * cross)\n",
- "\n",
- "\n",
- "def collect_looks(tracks, features, orbits):\n",
- " \"\"\"The looks at the features from the tracks: the part of each feature inside a satellite's swath in its frame, at\n",
- " the time of the footprint that covers most of it, with the slant range and zenith angle at its centroid.\"\"\"\n",
- " # pieces: the parts of each feature inside each footprint (of a satellite's instrument, at a time) in the same frame\n",
- " pieces = gpd.sjoin(\n",
- " features[[\"frame\", \"geometry\"]],\n",
- " tracks[[\"frame\", \"satellite\", \"instrument\", \"time\", \"geometry\"]].rename(columns={\"frame\": \"frame_track\"}),\n",
- " predicate=\"intersects\",\n",
- " )\n",
- " pieces = pieces[pieces.frame == pieces.frame_track].rename_axis(\"feature\").reset_index()\n",
- " pieces[\"geometry\"] = shapely.intersection(pieces.geometry.values, tracks.geometry.values[pieces.index_right])\n",
- " pieces[\"piece_area\"] = shapely.area(pieces.geometry.values)\n",
- "\n",
- " # looks: the part of each feature inside a satellite's swath, at the time of the footprint that covers most of it\n",
- " looks = (\n",
- " pieces.sort_values(\"piece_area\", ascending=False)\n",
- " .groupby([\"feature\", \"satellite\", \"instrument\"])\n",
- " .agg(time=(\"time\", \"first\"), geometry=(\"geometry\", shapely.union_all))\n",
- " .reset_index()\n",
+ "def collect_looks(satellites, duration, features):\n",
+ " \"\"\"The looks at the features (see `collect_feature_observations`): each satellite's view of the part of each\n",
+ " feature inside its swath in the feature's frame, at the time of the footprint that covers most of it, with the\n",
+ " size of the viewed part and the slant range and zenith angle at its centroid.\"\"\"\n",
+ " times = [\n",
+ " start + timedelta(seconds=TIME_STEP * (i + 0.5))\n",
+ " for i in range(int(duration.total_seconds() // TIME_STEP))\n",
+ " ]\n",
+ " looks = collect_feature_observations(\n",
+ " features.assign(start=features.time, end=features.time + frame_duration),\n",
+ " satellites,\n",
+ " times,\n",
" )\n",
- " looks[\"viewed_fraction\"] = np.minimum(\n",
- " 1, shapely.area(looks.geometry.values) / shapely.area(features.geometry.values[looks.feature])\n",
+ " looks[\"viewed_km\"] = np.sqrt(\n",
+ " 4 * looks.viewed_fraction * features.area_km2.values[looks.feature] / np.pi\n",
" )\n",
- " looks[\"viewed_km\"] = np.sqrt(4 * looks.viewed_fraction * features.area_km2.values[looks.feature] / np.pi)\n",
- " centroids = shapely.centroid(looks.geometry.values)\n",
- " looks[\"lon\"], looks[\"lat\"] = shapely.get_x(centroids), shapely.get_y(centroids)\n",
- "\n",
- " # the viewing geometry (slant range and zenith angle) from the satellite position at the time of the footprint\n",
- " looks[\"range_m\"], looks[\"zenith\"] = np.nan, np.nan\n",
- " for name, group in looks.groupby(\"satellite\"):\n",
- " sat_xyz = np.array(orbits[name].to_gp_orbit().get_orbit_track(list(group.time)).frame_xyz(itrs).m).T\n",
- " target = np.stack(TO_ECEF.transform(group.lon.values, group.lat.values, np.zeros(len(group))), axis=-1)\n",
- " line_of_sight = sat_xyz - target\n",
- " looks.loc[group.index, \"range_m\"] = np.linalg.norm(line_of_sight, axis=-1)\n",
- " looks.loc[group.index, \"zenith\"] = np.degrees(\n",
- " np.arccos(\n",
- " np.sum(line_of_sight * up(group.lon.values, group.lat.values), axis=-1)\n",
- " / np.linalg.norm(line_of_sight, axis=-1)\n",
- " )\n",
- " )\n",
+ " looks[\"range_m\"], looks[\"zenith\"] = looks.sat_range, 90 - looks.sat_alt\n",
" return looks\n",
"\n",
"\n",
- "def assess(looks, features):\n",
+ "def assess(looks, features, satellites):\n",
" \"\"\"Whether each sensor observes and resolves each feature, from its best look.\"\"\"\n",
" results = []\n",
" for sensor, row in sensors.iterrows():\n",
- " sensor_looks = looks[looks.instrument == row.swath].copy()\n",
- " # the resolution element at nadir is the larger of the footprint and the sample spacing\n",
- " nadir_km = max(row.horizontal_km, row.sampling_km)\n",
- " for axis in AXES:\n",
- " element_km = element_size(sensor_looks.range_m, sensor_looks.zenith, nadir_km, axis)\n",
+ " # the sizes of the sensor's samples at its looks, flown on the same satellites\n",
+ " sensor_satellites = [\n",
+ " satellite.model_copy(update={\"instruments\": [sensor_instrument(sensor)]})\n",
+ " for satellite in satellites\n",
+ " ]\n",
+ " sensor_looks = compute_sample_sizes(\n",
+ " looks[looks.instrument == row.swath], sensor_satellites\n",
+ " )\n",
+ " # the resolution element is the larger of a sample and the spacing between samples (km), along and across track\n",
+ " along = np.maximum(sensor_looks.sample_along, sensor_looks.spacing_along) / 1e3\n",
+ " cross = np.maximum(sensor_looks.sample_cross, sensor_looks.spacing_cross) / 1e3\n",
+ " for axis, element_km in {\n",
+ " \"equivalent\": np.sqrt(along * cross),\n",
+ " \"cross-track\": cross,\n",
+ " }.items():\n",
" sensor_looks[f\"elements ({axis})\"] = sensor_looks.viewed_km / element_km\n",
" if axis == ELEMENT_AXIS:\n",
" sensor_looks[\"element_km\"] = element_km\n",
" # the best look at each feature: the one whose viewed part spans the most resolution elements\n",
- " best = sensor_looks.loc[sensor_looks.groupby(\"feature\")[f\"elements ({ELEMENT_AXIS})\"].idxmax()].set_index(\"feature\")\n",
- " result = features[[\"frame\", \"kind\", \"region\", \"diameter_km\", \"area_km2\", \"tot_prectot\"]].copy()\n",
+ " best = sensor_looks.loc[\n",
+ " sensor_looks.groupby(\"feature\")[f\"elements ({ELEMENT_AXIS})\"].idxmax()\n",
+ " ].set_index(\"feature\")\n",
+ " result = features[\n",
+ " [\"frame\", \"kind\", \"region\", \"diameter_km\", \"area_km2\", \"tot_prectot\"]\n",
+ " ].copy()\n",
" result[\"sensor\"] = sensor\n",
- " result[\"looks\"] = sensor_looks.groupby(\"feature\").size().reindex(result.index, fill_value=0)\n",
+ " result[\"looks\"] = (\n",
+ " sensor_looks.groupby(\"feature\").size().reindex(result.index, fill_value=0)\n",
+ " )\n",
" result[\"observed\"] = result.looks > 0\n",
- " result = result.join(best[[\"satellite\", \"zenith\", \"viewed_fraction\", \"viewed_km\", \"element_km\"]])\n",
+ " result = result.join(\n",
+ " best[[\"satellite\", \"zenith\", \"viewed_fraction\", \"viewed_km\", \"element_km\"]]\n",
+ " )\n",
" for axis in AXES:\n",
- " result[f\"elements ({axis})\"] = sensor_looks.groupby(\"feature\")[f\"elements ({axis})\"].max()\n",
+ " result[f\"elements ({axis})\"] = sensor_looks.groupby(\"feature\")[\n",
+ " f\"elements ({axis})\"\n",
+ " ].max()\n",
" result[\"resolved\"] = result[f\"elements ({ELEMENT_AXIS})\"] >= RESOLUTION_FACTOR\n",
" results.append(result)\n",
" return pd.concat(results).reset_index().rename(columns={\"index\": \"feature\"})\n",
"\n",
"\n",
- "looks = collect_looks(tracks, features, orbits)\n",
- "results = assess(looks, features)\n",
+ "looks = collect_looks(satellites, duration, features)\n",
+ "results = assess(looks, features, satellites)\n",
"results.groupby([\"kind\", \"sensor\"])[[\"observed\", \"resolved\"]].sum().unstack(\"kind\")"
]
},
@@ -1719,10 +1784,10 @@
"id": "0e740b36",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:47:48.462047Z",
- "iopub.status.busy": "2026-10-24T21:47:48.462047Z",
- "iopub.status.idle": "2026-10-24T21:47:50.252601Z",
- "shell.execute_reply": "2026-10-24T21:47:50.252601Z"
+ "iopub.execute_input": "2026-10-25T04:52:55.379634Z",
+ "iopub.status.busy": "2026-10-25T04:52:55.379634Z",
+ "iopub.status.idle": "2026-10-25T04:52:57.573365Z",
+ "shell.execute_reply": "2026-10-25T04:52:57.573365Z"
}
},
"outputs": [
@@ -1745,35 +1810,85 @@
"# the frame with the most features of the kind whose status (not observed, observed, or resolved) differs between the sensors\n",
"of_kind = results[results.kind == KIND]\n",
"status = of_kind.observed.astype(int) + of_kind.resolved.astype(int)\n",
- "example = (status.groupby([of_kind.frame, of_kind.feature]).nunique() > 1).groupby(\"frame\").sum().idxmax()\n",
+ "example = (\n",
+ " (status.groupby([of_kind.frame, of_kind.feature]).nunique() > 1)\n",
+ " .groupby(\"frame\")\n",
+ " .sum()\n",
+ " .idxmax()\n",
+ ")\n",
"example_time = start + example * frame_duration\n",
- "example_center = track.iloc[np.abs(track.index - pd.Timestamp(nature.time.values[example])).argmin()]\n",
- "STATUS = {\"not observed\": \"#d9d8d3\", \"observed, not resolved\": \"#eb6834\", \"resolved\": \"#2a78d6\"}\n",
+ "example_center = track.iloc[\n",
+ " np.abs(track.index - pd.Timestamp(nature.time.values[example])).argmin()\n",
+ "]\n",
+ "STATUS = {\n",
+ " \"not observed\": \"#d9d8d3\",\n",
+ " \"observed, not resolved\": \"#eb6834\",\n",
+ " \"resolved\": \"#2a78d6\",\n",
+ "}\n",
"\n",
- "fig, axes = plt.subplots(2, 2, figsize=(11, 11), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, axes = plt.subplots(\n",
+ " 2, 2, figsize=(11, 11), subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"for ax, sensor in zip(axes.ravel(), sensors.index):\n",
" background = nature[\"lwtup\"].isel(time=example)\n",
- " ax.pcolormesh(background.lon, background.lat, background, cmap=\"Greys_r\", vmin=100, vmax=320, shading=\"auto\", transform=ccrs.PlateCarree())\n",
+ " ax.pcolormesh(\n",
+ " background.lon,\n",
+ " background.lat,\n",
+ " background,\n",
+ " cmap=\"Greys_r\",\n",
+ " vmin=100,\n",
+ " vmax=320,\n",
+ " shading=\"auto\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
" members = of_kind[(of_kind.sensor == sensor) & (of_kind.frame == example)]\n",
- " member_status = np.where(members.resolved, \"resolved\", np.where(members.observed, \"observed, not resolved\", \"not observed\"))\n",
+ " member_status = np.where(\n",
+ " members.resolved,\n",
+ " \"resolved\",\n",
+ " np.where(members.observed, \"observed, not resolved\", \"not observed\"),\n",
+ " )\n",
" for label, color in STATUS.items():\n",
" selected = features.loc[members.feature[member_status == label]]\n",
" if len(selected):\n",
- " selected.plot(ax=ax, facecolor=color, edgecolor=\"black\", linewidth=0.4, transform=ccrs.PlateCarree())\n",
+ " selected.plot(\n",
+ " ax=ax,\n",
+ " facecolor=color,\n",
+ " edgecolor=\"black\",\n",
+ " linewidth=0.4,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
" # fade the part of the study region outside the swath\n",
" swath = swaths.get((example, sensors.loc[sensor, \"swath\"]))\n",
" outside = domain if swath is None else domain.difference(swath)\n",
- " ax.add_geometries([outside], crs=ccrs.PlateCarree(), facecolor=\"white\", edgecolor=\"none\", alpha=0.6)\n",
+ " ax.add_geometries(\n",
+ " [outside],\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=\"white\",\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.6,\n",
+ " )\n",
" ax.add_feature(cfeature.COASTLINE, edgecolor=\"#8a8984\", linewidth=0.5)\n",
" ax.plot(track.lon, track.lat, color=\"#0b0b0b\", lw=1, transform=ccrs.PlateCarree())\n",
- " ax.plot(example_center.lon, example_center.lat, marker=\"x\", ms=8, mew=2, color=\"#0b0b0b\", transform=ccrs.PlateCarree())\n",
- " ax.set_extent([domain.bounds[0], domain.bounds[2], domain.bounds[1], domain.bounds[3]])\n",
+ " ax.plot(\n",
+ " example_center.lon,\n",
+ " example_center.lat,\n",
+ " marker=\"x\",\n",
+ " ms=8,\n",
+ " mew=2,\n",
+ " color=\"#0b0b0b\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
+ " ax.set_extent(\n",
+ " [domain.bounds[0], domain.bounds[2], domain.bounds[1], domain.bounds[3]]\n",
+ " )\n",
" ax.set_title(f\"{sensor} ({sensors.loc[sensor, 'horizontal_km']} km)\", fontsize=10)\n",
"for label, color in STATUS.items():\n",
" axes[0, 0].plot([], [], color=color, lw=6, label=label)\n",
"axes[0, 0].plot([], [], color=\"#ffffff\", lw=6, alpha=0.6, label=\"outside swath\")\n",
"axes[0, 0].legend(loc=\"lower left\", fontsize=8, frameon=True)\n",
- "fig.suptitle(f\"Heavy precipitation features, {example_time:%Y-%m-%d %H:%M}-{example_time + frame_duration:%H:%M} UTC (Nature Run {nature_start:%Y})\")\n",
+ "fig.suptitle(\n",
+ " f\"Heavy precipitation features, {example_time:%Y-%m-%d %H:%M}-{example_time + frame_duration:%H:%M} UTC (Nature Run {nature_start:%Y})\"\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()"
]
@@ -1794,10 +1909,10 @@
"id": "37988d01",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:47:50.257606Z",
- "iopub.status.busy": "2026-10-24T21:47:50.257606Z",
- "iopub.status.idle": "2026-10-24T21:47:50.281200Z",
- "shell.execute_reply": "2026-10-24T21:47:50.281200Z"
+ "iopub.execute_input": "2026-10-25T04:52:57.577886Z",
+ "iopub.status.busy": "2026-10-25T04:52:57.577886Z",
+ "iopub.status.idle": "2026-10-25T04:52:57.601832Z",
+ "shell.execute_reply": "2026-10-25T04:52:57.601832Z"
}
},
"outputs": [
@@ -2052,7 +2167,9 @@
" \"features\": len(r),\n",
" \"observed (%)\": 100 * r.observed.mean(),\n",
" \"resolved (%)\": 100 * r.resolved.mean(),\n",
- " \"resolved (% of precipitation)\": 100 * r.tot_prectot[r.resolved].sum() / r.tot_prectot.sum(),\n",
+ " \"resolved (% of precipitation)\": 100\n",
+ " * r.tot_prectot[r.resolved].sum()\n",
+ " / r.tot_prectot.sum(),\n",
" \"median element of observed (km)\": r.element_km[r.observed].median(),\n",
" }\n",
" )\n",
@@ -2060,8 +2177,12 @@
"\n",
"summary = pd.concat(\n",
" {\n",
- " \"all\": results.groupby([\"kind\", \"sensor\"]).apply(summarize, include_groups=False),\n",
- " \"storm\": results[results.region == \"storm\"].groupby([\"kind\", \"sensor\"]).apply(summarize, include_groups=False),\n",
+ " \"all\": results.groupby([\"kind\", \"sensor\"]).apply(\n",
+ " summarize, include_groups=False\n",
+ " ),\n",
+ " \"storm\": results[results.region == \"storm\"]\n",
+ " .groupby([\"kind\", \"sensor\"])\n",
+ " .apply(summarize, include_groups=False),\n",
" },\n",
" names=[\"features\"],\n",
")\n",
@@ -2086,10 +2207,10 @@
"id": "705ebfc2",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:47:50.283206Z",
- "iopub.status.busy": "2026-10-24T21:47:50.283206Z",
- "iopub.status.idle": "2026-10-24T21:47:50.458744Z",
- "shell.execute_reply": "2026-10-24T21:47:50.458744Z"
+ "iopub.execute_input": "2026-10-25T04:52:57.603840Z",
+ "iopub.status.busy": "2026-10-25T04:52:57.603840Z",
+ "iopub.status.idle": "2026-10-25T04:52:57.777760Z",
+ "shell.execute_reply": "2026-10-25T04:52:57.777760Z"
}
},
"outputs": [
@@ -2105,10 +2226,25 @@
}
],
"source": [
- "BLUE, ORANGE, AQUA, YELLOW, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#eda100\", \"#8a8984\", \"#0b0b0b\"\n",
+ "BLUE, ORANGE, AQUA, YELLOW, GRAY, INK = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#eda100\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ ")\n",
"SENSOR_COLORS = {\"ATMS\": BLUE, \"Future MW\": ORANGE, \"CrIS\": AQUA, \"Future IR\": YELLOW}\n",
"SENSOR_STYLES = {\"ATMS\": \"-\", \"Future MW\": \"--\", \"CrIS\": \"-\", \"Future IR\": \"--\"}\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"edges = np.array([EFFECTIVE_RESOLUTION_KM, 40, 56, 80, 112, np.inf])\n",
"labels = [f\"{EFFECTIVE_RESOLUTION_KM}-40\", \"40-56\", \"56-80\", \"80-112\", \">112\"]\n",
@@ -2118,12 +2254,33 @@
"for ax, kind in zip(axes, KINDS):\n",
" of_kind = results[results.kind == kind]\n",
" observed = of_kind[of_kind.observed]\n",
- " by_size = observed.groupby([\"sensor\", \"size_class\"], observed=False).resolved.mean().unstack().loc[sensors.index] * 100\n",
+ " by_size = (\n",
+ " observed.groupby([\"sensor\", \"size_class\"], observed=False)\n",
+ " .resolved.mean()\n",
+ " .unstack()\n",
+ " .loc[sensors.index]\n",
+ " * 100\n",
+ " )\n",
" for sensor in sensors.index:\n",
- " ax.plot(labels, by_size.loc[sensor], marker=\"o\", color=SENSOR_COLORS[sensor], ls=SENSOR_STYLES[sensor], label=sensor)\n",
- " counts = observed[observed.sensor == \"CrIS\"].groupby(\"size_class\", observed=False).size()\n",
- " ax.set_xticks(range(len(labels)), [f\"{label}\\n(n = {n})\" for label, n in zip(labels, counts)])\n",
- " ax.set(xlabel=\"Feature diameter (km), with the number observed in the CrIS swath\", title=f\"{kind.capitalize()} features\", ylim=(0, 105))\n",
+ " ax.plot(\n",
+ " labels,\n",
+ " by_size.loc[sensor],\n",
+ " marker=\"o\",\n",
+ " color=SENSOR_COLORS[sensor],\n",
+ " ls=SENSOR_STYLES[sensor],\n",
+ " label=sensor,\n",
+ " )\n",
+ " counts = (\n",
+ " observed[observed.sensor == \"CrIS\"].groupby(\"size_class\", observed=False).size()\n",
+ " )\n",
+ " ax.set_xticks(\n",
+ " range(len(labels)), [f\"{label}\\n(n = {n})\" for label, n in zip(labels, counts)]\n",
+ " )\n",
+ " ax.set(\n",
+ " xlabel=\"Feature diameter (km), with the number observed in the CrIS swath\",\n",
+ " title=f\"{kind.capitalize()} features\",\n",
+ " ylim=(0, 105),\n",
+ " )\n",
" ax.title.set_fontsize(10)\n",
"axes[0].set_ylabel(\"Observed features resolved (%)\")\n",
"axes[0].legend(frameon=False, fontsize=8, loc=\"lower right\")\n",
diff --git a/docs/examples/RunOSSEMonsoon.ipynb b/docs/examples/RunOSSEMonsoon.ipynb
index 78f90c32..7b6eb7b2 100644
--- a/docs/examples/RunOSSEMonsoon.ipynb
+++ b/docs/examples/RunOSSEMonsoon.ipynb
@@ -45,10 +45,10 @@
"id": "d4c76230",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:27.746711Z",
- "iopub.status.busy": "2026-10-24T21:16:27.745208Z",
- "iopub.status.idle": "2026-10-24T21:16:28.704832Z",
- "shell.execute_reply": "2026-10-24T21:16:28.704832Z"
+ "iopub.execute_input": "2026-10-25T04:53:08.862564Z",
+ "iopub.status.busy": "2026-10-25T04:53:08.862564Z",
+ "iopub.status.idle": "2026-10-25T04:53:09.846840Z",
+ "shell.execute_reply": "2026-10-25T04:53:09.846840Z"
}
},
"outputs": [],
@@ -92,10 +92,10 @@
"id": "8f96f040",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:28.707343Z",
- "iopub.status.busy": "2026-10-24T21:16:28.707343Z",
- "iopub.status.idle": "2026-10-24T21:16:30.273969Z",
- "shell.execute_reply": "2026-10-24T21:16:30.273464Z"
+ "iopub.execute_input": "2026-10-25T04:53:09.849358Z",
+ "iopub.status.busy": "2026-10-25T04:53:09.849358Z",
+ "iopub.status.idle": "2026-10-25T04:53:11.387609Z",
+ "shell.execute_reply": "2026-10-25T04:53:11.387609Z"
}
},
"outputs": [
@@ -486,7 +486,7 @@
" Conventions: COARDS\\nGrADS\n",
" dataType: Grid\n",
" history: Mon Sep 28 14:37:35 EDT 2026 : imported by GrADS Da...\n",
- " extra_das_attribute: This is an example of metadata added using a supple...
xarray.Dataset
time: 96
lat: 113
lon: 193
time
(time)
datetime64[ns]
2005-07-16T00:15:00 ... 2005-07-...
grads_dim :
t
grads_mapping :
linear
grads_size :
36576
grads_min :
21:15z15may2005
grads_step :
30mn
long_name :
time
minimum :
21:15z15may2005
maximum :
20:45z16jun2007
resolution :
0.020833334
array(['2005-07-16T00:15:00.000000000', '2005-07-16T00:45:00.000000000',\n",
+ " extra_das_attribute: This is an example of metadata added using a supple...
Mon Sep 28 14:37:35 EDT 2026 : imported by GrADS Data Server 2.0
extra_das_attribute :
This is an example of metadata added using a supplementary DAS file. See testdata/extra.das for the source.
"
],
"text/plain": [
@@ -759,14 +759,19 @@
" minx, miny, maxx, maxy = domain.bounds\n",
" with xr.open_dataset(G5NR_URL, decode_times=True) as remote:\n",
" remote[[\"lwtup\", \"preccon\"]].sel(\n",
- " time=slice(nature_start.replace(tzinfo=None), (nature_start + duration).replace(tzinfo=None)),\n",
+ " time=slice(\n",
+ " nature_start.replace(tzinfo=None),\n",
+ " (nature_start + duration).replace(tzinfo=None),\n",
+ " ),\n",
" lon=slice(minx, maxx),\n",
" lat=slice(miny, maxy),\n",
" ).load().to_netcdf(nature_file)\n",
" nature = xr.load_dataset(nature_file)\n",
" assert nature.sizes[\"time\"] == int(duration / frame_duration)\n",
" # each time stamp is the middle of its 30-minute averaging interval\n",
- " assert (nature.time.values[0] - np.datetime64(nature_start.replace(tzinfo=None))) == np.timedelta64(15, \"m\")\n",
+ " assert (\n",
+ " nature.time.values[0] - np.datetime64(nature_start.replace(tzinfo=None))\n",
+ " ) == np.timedelta64(15, \"m\")\n",
" return nature\n",
"\n",
"\n",
@@ -792,10 +797,10 @@
"id": "25deaf45",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:30.275971Z",
- "iopub.status.busy": "2026-10-24T21:16:30.275971Z",
- "iopub.status.idle": "2026-10-24T21:16:31.863266Z",
- "shell.execute_reply": "2026-10-24T21:16:31.862756Z"
+ "iopub.execute_input": "2026-10-25T04:53:11.390389Z",
+ "iopub.status.busy": "2026-10-25T04:53:11.389382Z",
+ "iopub.status.idle": "2026-10-25T04:53:12.999514Z",
+ "shell.execute_reply": "2026-10-25T04:53:12.999514Z"
}
},
"outputs": [
@@ -956,21 +961,29 @@
"\n",
"from tatc.constants import EARTH_MEAN_RADIUS, de421, timescale\n",
"\n",
- "THRESHOLD = 5.67037e-8 * 220**4 # W m-2, the upwelling longwave flux of a 220 K blackbody\n",
- "EFFECTIVE_RESOLUTION_KM = 4 * 7 # km, several grid spacings (Gelaro et al., 2015); smaller features are not resolved\n",
+ "THRESHOLD = (\n",
+ " 5.67037e-8 * 220**4\n",
+ ") # W m-2, the upwelling longwave flux of a 220 K blackbody\n",
+ "EFFECTIVE_RESOLUTION_KM = (\n",
+ " 4 * 7\n",
+ ") # km, several grid spacings (Gelaro et al., 2015); smaller features are not resolved\n",
"\n",
"\n",
"def find_features(nature):\n",
" \"\"\"The features (contiguous cold cloud tops at least EFFECTIVE_RESOLUTION_KM in size) in each frame of the nature run.\"\"\"\n",
" lon, lat = nature.lon.values, nature.lat.values\n",
" lon2d, lat2d = np.meshgrid(lon, lat)\n",
- " cell_area = (EARTH_MEAN_RADIUS * np.radians(GRID_SIZE)) ** 2 * np.cos(np.radians(lat2d)) # m^2\n",
+ " cell_area = (EARTH_MEAN_RADIUS * np.radians(GRID_SIZE)) ** 2 * np.cos(\n",
+ " np.radians(lat2d)\n",
+ " ) # m^2\n",
"\n",
" records, polygons = [], []\n",
" for frame in range(nature.sizes[\"time\"]):\n",
" lwtup = nature[\"lwtup\"].isel(time=frame).values\n",
" preccon = nature[\"preccon\"].isel(time=frame).values\n",
- " labels, n_labels = ndi.label(lwtup < THRESHOLD) # contiguous cells colder than the threshold\n",
+ " labels, n_labels = ndi.label(\n",
+ " lwtup < THRESHOLD\n",
+ " ) # contiguous cells colder than the threshold\n",
" if n_labels == 0:\n",
" continue\n",
" ids = np.arange(1, n_labels + 1)\n",
@@ -981,7 +994,10 @@
" order = np.argsort(labels[j, i], kind=\"stable\")\n",
" j, i = j[order], i[order]\n",
" cells = shapely.box(\n",
- " lon[i] - GRID_SIZE / 2, lat[j] - GRID_SIZE / 2, lon[i] + GRID_SIZE / 2, lat[j] + GRID_SIZE / 2\n",
+ " lon[i] - GRID_SIZE / 2,\n",
+ " lat[j] - GRID_SIZE / 2,\n",
+ " lon[i] + GRID_SIZE / 2,\n",
+ " lat[j] + GRID_SIZE / 2,\n",
" )\n",
" for k, cluster in enumerate(np.split(cells, np.cumsum(n_cells)[:-1])):\n",
" records.append(\n",
@@ -999,11 +1015,17 @@
" polygons.append(shapely.union_all(cluster))\n",
"\n",
" features = gpd.GeoDataFrame(records, geometry=polygons, crs=\"EPSG:4326\")\n",
- " features[\"diameter_km\"] = np.sqrt(4 * features.area_km2 / np.pi) # of a circle of equal area\n",
- " features = features[features.diameter_km >= EFFECTIVE_RESOLUTION_KM].reset_index(drop=True)\n",
+ " features[\"diameter_km\"] = np.sqrt(\n",
+ " 4 * features.area_km2 / np.pi\n",
+ " ) # of a circle of equal area\n",
+ " features = features[features.diameter_km >= EFFECTIVE_RESOLUTION_KM].reset_index(\n",
+ " drop=True\n",
+ " )\n",
" features[\"time\"] = start + features.frame * frame_duration\n",
" # solar hour at the feature (hour angle of the Sun plus 12 hours) at the middle of the frame, in the Nature Run's year\n",
- " solar_time = timescale.from_datetimes([nature_start + (frame + 0.5) * frame_duration for frame in features.frame])\n",
+ " solar_time = timescale.from_datetimes(\n",
+ " [nature_start + (frame + 0.5) * frame_duration for frame in features.frame]\n",
+ " )\n",
" features[\"solar_hour\"] = (\n",
" de421[\"earth\"] + wgs84.latlon(features.lat.values, features.lon.values)\n",
" ).at(solar_time).observe(de421[\"sun\"]).apparent().hadec()[0].hours + 12\n",
@@ -1011,7 +1033,19 @@
"\n",
"\n",
"features = find_features(nature)\n",
- "features[[\"time\", \"n_cells\", \"area_km2\", \"diameter_km\", \"lon\", \"lat\", \"min_lwtup\", \"max_preccon\", \"solar_hour\"]].describe().round(2)"
+ "features[\n",
+ " [\n",
+ " \"time\",\n",
+ " \"n_cells\",\n",
+ " \"area_km2\",\n",
+ " \"diameter_km\",\n",
+ " \"lon\",\n",
+ " \"lat\",\n",
+ " \"min_lwtup\",\n",
+ " \"max_preccon\",\n",
+ " \"solar_hour\",\n",
+ " ]\n",
+ "].describe().round(2)"
]
},
{
@@ -1043,10 +1077,10 @@
"id": "4c6a2204",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:31.865273Z",
- "iopub.status.busy": "2026-10-24T21:16:31.865273Z",
- "iopub.status.idle": "2026-10-24T21:16:31.892545Z",
- "shell.execute_reply": "2026-10-24T21:16:31.892545Z"
+ "iopub.execute_input": "2026-10-25T04:53:13.001519Z",
+ "iopub.status.busy": "2026-10-25T04:53:13.001519Z",
+ "iopub.status.idle": "2026-10-25T04:53:13.031499Z",
+ "shell.execute_reply": "2026-10-25T04:53:13.030995Z"
}
},
"outputs": [
@@ -1164,7 +1198,9 @@
" field_of_regard=field_of_regard,\n",
" cross_track_field_of_view=field_of_regard,\n",
" along_track_field_of_view=compute_along_track_field_of_view(\n",
- " orbits[\"NOAA-20\"].get_mean_altitude(), TIME_STEP, orbits[\"NOAA-20\"].get_inclination()\n",
+ " orbits[\"NOAA-20\"].get_mean_altitude(),\n",
+ " TIME_STEP,\n",
+ " orbits[\"NOAA-20\"].get_inclination(),\n",
" ),\n",
" roll_angle=scan[\"roll\"],\n",
" is_rectangular=True,\n",
@@ -1173,7 +1209,11 @@
"\n",
"\n",
"satellites = [\n",
- " Satellite(name=name, orbit=orbit, instruments=[swath_instrument(\"ATMS\"), swath_instrument(\"CrIS\")])\n",
+ " Satellite(\n",
+ " name=name,\n",
+ " orbit=orbit,\n",
+ " instruments=[swath_instrument(\"ATMS\"), swath_instrument(\"CrIS\")],\n",
+ " )\n",
" for name, orbit in orbits.items()\n",
"]\n",
"\n",
@@ -1192,7 +1232,43 @@
").set_index(\"sensor\")\n",
"# sample spacing at nadir (km): that of the heritage sensor, scaled for the future sensors in proportion to their footprint\n",
"heritage_km = sensors.horizontal_km[sensors.swath].values\n",
- "sensors[\"sampling_km\"] = REFERENCE_RANGE_KM * np.radians(sensors.swath.map(SAMPLING_DEG)) * sensors.horizontal_km / heritage_km\n",
+ "sensors[\"sampling_km\"] = (\n",
+ " REFERENCE_RANGE_KM\n",
+ " * np.radians(sensors.swath.map(SAMPLING_DEG))\n",
+ " * sensors.horizontal_km\n",
+ " / heritage_km\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def sensor_instrument(sensor):\n",
+ " \"\"\"The swath instrument of a sensor with its pixels (for its sample sizes, see `compute_sample_sizes`): pixels\n",
+ " spaced by the angle that the sample spacing subtends at nadir from the reference range (enough to span the\n",
+ " swath), each with the instantaneous field of view that its horizontal resolution subtends, along and across\n",
+ " track.\"\"\"\n",
+ " row = sensors.loc[sensor]\n",
+ " swath = swath_instrument(row.swath)\n",
+ " ifov = np.degrees(2 * np.arctan(row.horizontal_km / 2 / REFERENCE_RANGE_KM))\n",
+ " spacing = np.degrees(row.sampling_km / REFERENCE_RANGE_KM)\n",
+ " pixels = round(swath.cross_track_field_of_view / spacing)\n",
+ " # the oversampling makes each pixel's field of view ifov (negative: a gap between pixels)\n",
+ " return PointedInstrument(\n",
+ " **swath.model_dump(\n",
+ " exclude={\n",
+ " \"cross_track_field_of_view\",\n",
+ " \"along_track_field_of_view\",\n",
+ " \"cross_track_pixels\",\n",
+ " \"cross_track_oversampling\",\n",
+ " \"along_track_oversampling\",\n",
+ " }\n",
+ " ),\n",
+ " cross_track_field_of_view=pixels * spacing,\n",
+ " along_track_field_of_view=spacing,\n",
+ " cross_track_pixels=pixels,\n",
+ " cross_track_oversampling=1 - spacing / ifov,\n",
+ " along_track_oversampling=1 - spacing / ifov,\n",
+ " )\n",
+ "\n",
+ "\n",
"sensors.round(1)"
]
},
@@ -1212,10 +1288,10 @@
"id": "01285f14",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:31.894549Z",
- "iopub.status.busy": "2026-10-24T21:16:31.894549Z",
- "iopub.status.idle": "2026-10-24T21:16:32.183675Z",
- "shell.execute_reply": "2026-10-24T21:16:32.183675Z"
+ "iopub.execute_input": "2026-10-25T04:53:13.033388Z",
+ "iopub.status.busy": "2026-10-25T04:53:13.033388Z",
+ "iopub.status.idle": "2026-10-25T04:53:13.321446Z",
+ "shell.execute_reply": "2026-10-25T04:53:13.321446Z"
}
},
"outputs": [
@@ -1329,7 +1405,10 @@
"def collect_tracks(satellites, duration):\n",
" \"\"\"The footprint of every instrument of the satellites at each time step over a duration from start, clipped to the\n",
" study region, with its frame.\"\"\"\n",
- " times = [start + timedelta(seconds=TIME_STEP * (i + 0.5)) for i in range(int(duration.total_seconds() // TIME_STEP))]\n",
+ " times = [\n",
+ " start + timedelta(seconds=TIME_STEP * (i + 0.5))\n",
+ " for i in range(int(duration.total_seconds() // TIME_STEP))\n",
+ " ]\n",
" tracks = collect_ground_track(satellites, times, instrument_index=None, mask=domain)\n",
" tracks = tracks[tracks.valid_obs].reset_index(drop=True)\n",
" tracks[\"geometry\"] = shapely.force_2d(tracks.geometry.values)\n",
@@ -1340,7 +1419,9 @@
"tracks = collect_tracks(satellites, duration)\n",
"# the area swept by each swath (ATMS or CrIS) in each frame\n",
"swaths = tracks.groupby([\"frame\", \"instrument\"]).geometry.agg(shapely.union_all)\n",
- "tracks.groupby([\"satellite\", \"instrument\"]).agg(footprints=(\"time\", \"size\"), first=(\"time\", \"min\"), last=(\"time\", \"max\"))"
+ "tracks.groupby([\"satellite\", \"instrument\"]).agg(\n",
+ " footprints=(\"time\", \"size\"), first=(\"time\", \"min\"), last=(\"time\", \"max\")\n",
+ ")"
]
},
{
@@ -1352,11 +1433,13 @@
"\n",
"A feature is **observed** by a swath if the swath footprint of some time step in its frame overlaps it. A **look** is one satellite's view of a feature in its frame. It covers the part of the feature inside that satellite's swath, which is only part of it if the feature straddles the swath edge, and the size of this viewed part (the diameter of a circle of the same area) is what the sensor must resolve.\n",
"\n",
- "For each look, the satellite's slant range and zenith angle at the centroid of the viewed part follow from the satellite position at the time of the footprint that covers most of it (TAT-C's orbit propagation), and give the size of the sensor's resolution element there: the element's angular size (the larger of the footprint and the sample spacing at nadir) times the slant range along track, and longer by the secant of the zenith angle across track. The validation notebooks confirmed this geometry against the reported viewing geometry of real data. Two measures of the size of the element are offered (`ELEMENT_AXIS`):\n",
+ "For each look, the satellite's slant range and zenith angle at the centroid of the viewed part follow from the satellite position at the time of the footprint that covers most of it (TAT-C's orbit propagation), and give the size of the sensor's resolution element there. Each sensor is modeled as its swath's instrument with pixels (`sensor_instrument`), spaced and sized by the angles that its sample spacing and horizontal resolution subtend at nadir, and TAT-C's `compute_sample_sizes` projects a pixel and the spacing between pixels onto the ground at the look, along and across track, so that they grow with the slant range and, across track, with the obliquity of the view. The element is the larger of the two in each direction. The validation notebooks confirmed this geometry against the reported viewing geometry of real data. Two measures of the size of the element are offered (`ELEMENT_AXIS`):\n",
"\n",
"* `\"equivalent\"` (the default): the diameter of a circle of the same area, the geometric mean of the along-track and cross-track sizes; and\n",
"* `\"cross-track\"`: the cross-track size, a stricter measure, which is up to about 2.3 times the along-track size at the swath edges of ATMS and 1.9 times at those of CrIS.\n",
"\n",
+ "TAT-C's `collect_feature_observations` collects the looks at features that exist for a period of time (here, each feature's frame), with the viewing geometry of each.\n",
+ "\n",
"If a feature has several looks, the best (the one whose viewed part spans the most resolution elements) is used.\n",
"\n",
"A feature is **resolved** if its viewed part spans at least `RESOLUTION_FACTOR` resolution elements. The default of two is a heuristic (a feature must span at least a couple of independent samples to be distinguished from its surroundings), and the sensitivity to it and to the measure of the element is shown below."
@@ -1368,10 +1451,10 @@
"id": "d40d4f90",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:32.186673Z",
- "iopub.status.busy": "2026-10-24T21:16:32.186673Z",
- "iopub.status.idle": "2026-10-24T21:16:32.302264Z",
- "shell.execute_reply": "2026-10-24T21:16:32.302264Z"
+ "iopub.execute_input": "2026-10-25T04:53:13.324454Z",
+ "iopub.status.busy": "2026-10-25T04:53:13.324454Z",
+ "iopub.status.idle": "2026-10-25T04:53:13.698980Z",
+ "shell.execute_reply": "2026-10-25T04:53:13.698980Z"
}
},
"outputs": [
@@ -1463,103 +1546,84 @@
}
],
"source": [
- "from pyproj import Transformer\n",
- "from skyfield.framelib import itrs\n",
+ "from tatc.analysis import collect_feature_observations, compute_sample_sizes\n",
"\n",
"RESOLUTION_FACTOR = 2 # a feature is resolved if its viewed part spans at least this many resolution elements\n",
"ELEMENT_AXIS = \"equivalent\" # the size of a resolution element: \"equivalent\" (diameter of equal area) or \"cross-track\"\n",
"AXES = [\"equivalent\", \"cross-track\"]\n",
"\n",
- "TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
- "\n",
"\n",
- "def up(lon, lat):\n",
- " \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
- " lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
- "\n",
- "\n",
- "def element_size(slant_range, zenith, nadir_km, axis):\n",
- " \"\"\"Size (km) of a resolution element of size nadir_km at nadir: its angular size times the slant range (m) along\n",
- " track, and longer by the secant of the zenith angle (deg) cross track. The size is the diameter of a circle of the\n",
- " same area (axis=\"equivalent\", the geometric mean of the two) or the cross-track size (axis=\"cross-track\").\"\"\"\n",
- " along = slant_range / 1e3 * nadir_km / REFERENCE_RANGE_KM\n",
- " cross = along / np.cos(np.radians(zenith))\n",
- " return cross if axis == \"cross-track\" else np.sqrt(along * cross)\n",
- "\n",
- "\n",
- "def collect_looks(tracks, features, orbits):\n",
- " \"\"\"The looks at the features from the tracks: the part of each feature inside a satellite's swath in its frame, at\n",
- " the time of the footprint that covers most of it, with the slant range and zenith angle at its centroid.\"\"\"\n",
- " # pieces: the parts of each feature inside each footprint (of a satellite's instrument, at a time) in the same frame\n",
- " pieces = gpd.sjoin(\n",
- " features[[\"frame\", \"geometry\"]],\n",
- " tracks[[\"frame\", \"satellite\", \"instrument\", \"time\", \"geometry\"]].rename(columns={\"frame\": \"frame_track\"}),\n",
- " predicate=\"intersects\",\n",
- " )\n",
- " pieces = pieces[pieces.frame == pieces.frame_track].rename_axis(\"feature\").reset_index()\n",
- " pieces[\"geometry\"] = shapely.intersection(pieces.geometry.values, tracks.geometry.values[pieces.index_right])\n",
- " pieces[\"piece_area\"] = shapely.area(pieces.geometry.values)\n",
- "\n",
- " # looks: the part of each feature inside a satellite's swath, at the time of the footprint that covers most of it\n",
- " looks = (\n",
- " pieces.sort_values(\"piece_area\", ascending=False)\n",
- " .groupby([\"feature\", \"satellite\", \"instrument\"])\n",
- " .agg(time=(\"time\", \"first\"), geometry=(\"geometry\", shapely.union_all))\n",
- " .reset_index()\n",
+ "def collect_looks(satellites, duration, features):\n",
+ " \"\"\"The looks at the features (see `collect_feature_observations`): each satellite's view of the part of each\n",
+ " feature inside its swath in the feature's frame, at the time of the footprint that covers most of it, with the\n",
+ " size of the viewed part and the slant range and zenith angle at its centroid.\"\"\"\n",
+ " times = [\n",
+ " start + timedelta(seconds=TIME_STEP * (i + 0.5))\n",
+ " for i in range(int(duration.total_seconds() // TIME_STEP))\n",
+ " ]\n",
+ " looks = collect_feature_observations(\n",
+ " features.assign(start=features.time, end=features.time + frame_duration),\n",
+ " satellites,\n",
+ " times,\n",
" )\n",
- " looks[\"viewed_fraction\"] = np.minimum(\n",
- " 1, shapely.area(looks.geometry.values) / shapely.area(features.geometry.values[looks.feature])\n",
+ " looks[\"viewed_km\"] = np.sqrt(\n",
+ " 4 * looks.viewed_fraction * features.area_km2.values[looks.feature] / np.pi\n",
" )\n",
- " looks[\"viewed_km\"] = np.sqrt(4 * looks.viewed_fraction * features.area_km2.values[looks.feature] / np.pi)\n",
- " centroids = shapely.centroid(looks.geometry.values)\n",
- " looks[\"lon\"], looks[\"lat\"] = shapely.get_x(centroids), shapely.get_y(centroids)\n",
- "\n",
- " # the viewing geometry (slant range and zenith angle) from the satellite position at the time of the footprint\n",
- " looks[\"range_m\"], looks[\"zenith\"] = np.nan, np.nan\n",
- " for name, group in looks.groupby(\"satellite\"):\n",
- " sat_xyz = np.array(orbits[name].to_gp_orbit().get_orbit_track(list(group.time)).frame_xyz(itrs).m).T\n",
- " target = np.stack(TO_ECEF.transform(group.lon.values, group.lat.values, np.zeros(len(group))), axis=-1)\n",
- " line_of_sight = sat_xyz - target\n",
- " looks.loc[group.index, \"range_m\"] = np.linalg.norm(line_of_sight, axis=-1)\n",
- " looks.loc[group.index, \"zenith\"] = np.degrees(\n",
- " np.arccos(\n",
- " np.sum(line_of_sight * up(group.lon.values, group.lat.values), axis=-1)\n",
- " / np.linalg.norm(line_of_sight, axis=-1)\n",
- " )\n",
- " )\n",
+ " looks[\"range_m\"], looks[\"zenith\"] = looks.sat_range, 90 - looks.sat_alt\n",
" return looks\n",
"\n",
"\n",
- "def assess(looks, features):\n",
+ "def assess(looks, features, satellites):\n",
" \"\"\"Whether each sensor observes and resolves each feature, from its best look.\"\"\"\n",
" results = []\n",
" for sensor, row in sensors.iterrows():\n",
- " sensor_looks = looks[looks.instrument == row.swath].copy()\n",
- " # the resolution element at nadir is the larger of the footprint and the sample spacing\n",
- " nadir_km = max(row.horizontal_km, row.sampling_km)\n",
- " for axis in AXES:\n",
- " element_km = element_size(sensor_looks.range_m, sensor_looks.zenith, nadir_km, axis)\n",
+ " # the sizes of the sensor's samples at its looks, flown on the same satellites\n",
+ " sensor_satellites = [\n",
+ " satellite.model_copy(update={\"instruments\": [sensor_instrument(sensor)]})\n",
+ " for satellite in satellites\n",
+ " ]\n",
+ " sensor_looks = compute_sample_sizes(\n",
+ " looks[looks.instrument == row.swath], sensor_satellites\n",
+ " )\n",
+ " # the resolution element is the larger of a sample and the spacing between samples (km), along and across track\n",
+ " along = np.maximum(sensor_looks.sample_along, sensor_looks.spacing_along) / 1e3\n",
+ " cross = np.maximum(sensor_looks.sample_cross, sensor_looks.spacing_cross) / 1e3\n",
+ " for axis, element_km in {\n",
+ " \"equivalent\": np.sqrt(along * cross),\n",
+ " \"cross-track\": cross,\n",
+ " }.items():\n",
" sensor_looks[f\"elements ({axis})\"] = sensor_looks.viewed_km / element_km\n",
" if axis == ELEMENT_AXIS:\n",
" sensor_looks[\"element_km\"] = element_km\n",
" # the best look at each feature: the one whose viewed part spans the most resolution elements\n",
- " best = sensor_looks.loc[sensor_looks.groupby(\"feature\")[f\"elements ({ELEMENT_AXIS})\"].idxmax()].set_index(\"feature\")\n",
- " result = features[[\"frame\", \"diameter_km\", \"area_km2\", \"tot_preccon\", \"solar_hour\"]].copy()\n",
+ " best = sensor_looks.loc[\n",
+ " sensor_looks.groupby(\"feature\")[f\"elements ({ELEMENT_AXIS})\"].idxmax()\n",
+ " ].set_index(\"feature\")\n",
+ " result = features[\n",
+ " [\"frame\", \"diameter_km\", \"area_km2\", \"tot_preccon\", \"solar_hour\"]\n",
+ " ].copy()\n",
" result[\"sensor\"] = sensor\n",
- " result[\"looks\"] = sensor_looks.groupby(\"feature\").size().reindex(result.index, fill_value=0)\n",
+ " result[\"looks\"] = (\n",
+ " sensor_looks.groupby(\"feature\").size().reindex(result.index, fill_value=0)\n",
+ " )\n",
" result[\"observed\"] = result.looks > 0\n",
- " result = result.join(best[[\"satellite\", \"zenith\", \"viewed_fraction\", \"viewed_km\", \"element_km\"]])\n",
+ " result = result.join(\n",
+ " best[[\"satellite\", \"zenith\", \"viewed_fraction\", \"viewed_km\", \"element_km\"]]\n",
+ " )\n",
" for axis in AXES:\n",
- " result[f\"elements ({axis})\"] = sensor_looks.groupby(\"feature\")[f\"elements ({axis})\"].max()\n",
+ " result[f\"elements ({axis})\"] = sensor_looks.groupby(\"feature\")[\n",
+ " f\"elements ({axis})\"\n",
+ " ].max()\n",
" result[\"resolved\"] = result[f\"elements ({ELEMENT_AXIS})\"] >= RESOLUTION_FACTOR\n",
" results.append(result)\n",
" return pd.concat(results).reset_index().rename(columns={\"index\": \"feature\"})\n",
"\n",
"\n",
- "looks = collect_looks(tracks, features, orbits)\n",
- "results = assess(looks, features)\n",
- "results.groupby(\"sensor\")[[\"observed\", \"resolved\"]].sum().join(sensors).loc[sensors.index].round(1)"
+ "looks = collect_looks(satellites, duration, features)\n",
+ "results = assess(looks, features, satellites)\n",
+ "results.groupby(\"sensor\")[[\"observed\", \"resolved\"]].sum().join(sensors).loc[\n",
+ " sensors.index\n",
+ "].round(1)"
]
},
{
@@ -1578,10 +1642,10 @@
"id": "aa237b59",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:32.304268Z",
- "iopub.status.busy": "2026-10-24T21:16:32.304268Z",
- "iopub.status.idle": "2026-10-24T21:16:35.105621Z",
- "shell.execute_reply": "2026-10-24T21:16:35.105621Z"
+ "iopub.execute_input": "2026-10-25T04:53:13.701987Z",
+ "iopub.status.busy": "2026-10-25T04:53:13.700986Z",
+ "iopub.status.idle": "2026-10-25T04:53:16.495768Z",
+ "shell.execute_reply": "2026-10-25T04:53:16.495768Z"
}
},
"outputs": [
@@ -1602,32 +1666,72 @@
"\n",
"# the frame with the most features whose status (not observed, observed, or resolved) differs between the sensors\n",
"status = results.observed.astype(int) + results.resolved.astype(int)\n",
- "example = (status.groupby([results.frame, results.feature]).nunique() > 1).groupby(\"frame\").sum().idxmax()\n",
+ "example = (\n",
+ " (status.groupby([results.frame, results.feature]).nunique() > 1)\n",
+ " .groupby(\"frame\")\n",
+ " .sum()\n",
+ " .idxmax()\n",
+ ")\n",
"example_time = start + example * frame_duration\n",
- "STATUS = {\"not observed\": \"#d9d8d3\", \"observed, not resolved\": \"#eb6834\", \"resolved\": \"#2a78d6\"}\n",
+ "STATUS = {\n",
+ " \"not observed\": \"#d9d8d3\",\n",
+ " \"observed, not resolved\": \"#eb6834\",\n",
+ " \"resolved\": \"#2a78d6\",\n",
+ "}\n",
"\n",
- "fig, axes = plt.subplots(2, 2, figsize=(12, 8), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, axes = plt.subplots(\n",
+ " 2, 2, figsize=(12, 8), subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"for ax, sensor in zip(axes.ravel(), sensors.index):\n",
" background = nature[\"lwtup\"].isel(time=example)\n",
- " ax.pcolormesh(background.lon, background.lat, background, cmap=\"Greys_r\", vmin=100, vmax=450, shading=\"auto\", transform=ccrs.PlateCarree())\n",
+ " ax.pcolormesh(\n",
+ " background.lon,\n",
+ " background.lat,\n",
+ " background,\n",
+ " cmap=\"Greys_r\",\n",
+ " vmin=100,\n",
+ " vmax=450,\n",
+ " shading=\"auto\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
" members = results[(results.sensor == sensor) & (results.frame == example)]\n",
- " status = np.where(members.resolved, \"resolved\", np.where(members.observed, \"observed, not resolved\", \"not observed\"))\n",
+ " status = np.where(\n",
+ " members.resolved,\n",
+ " \"resolved\",\n",
+ " np.where(members.observed, \"observed, not resolved\", \"not observed\"),\n",
+ " )\n",
" for label, color in STATUS.items():\n",
" selected = features.loc[members.feature[status == label]]\n",
" if len(selected):\n",
- " selected.plot(ax=ax, facecolor=color, edgecolor=\"black\", linewidth=0.4, transform=ccrs.PlateCarree())\n",
+ " selected.plot(\n",
+ " ax=ax,\n",
+ " facecolor=color,\n",
+ " edgecolor=\"black\",\n",
+ " linewidth=0.4,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
" # fade the part of the study region outside the swath\n",
" swath = swaths.get((example, sensors.loc[sensor, \"swath\"]))\n",
" outside = domain if swath is None else domain.difference(swath)\n",
- " ax.add_geometries([outside], crs=ccrs.PlateCarree(), facecolor=\"white\", edgecolor=\"none\", alpha=0.6)\n",
+ " ax.add_geometries(\n",
+ " [outside],\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=\"white\",\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.6,\n",
+ " )\n",
" ax.add_feature(cfeature.STATES, edgecolor=\"white\", linewidth=0.5)\n",
- " ax.set_extent([domain.bounds[0], domain.bounds[2], domain.bounds[1], domain.bounds[3]])\n",
+ " ax.set_extent(\n",
+ " [domain.bounds[0], domain.bounds[2], domain.bounds[1], domain.bounds[3]]\n",
+ " )\n",
" ax.set_title(f\"{sensor} ({sensors.loc[sensor, 'horizontal_km']} km)\", fontsize=10)\n",
"for label, color in STATUS.items():\n",
" axes[0, 0].plot([], [], color=color, lw=6, label=label)\n",
"axes[0, 0].plot([], [], color=\"#ffffff\", lw=6, alpha=0.6, label=\"outside swath\")\n",
"axes[0, 0].legend(loc=\"lower left\", fontsize=8, frameon=True)\n",
- "fig.suptitle(f\"Cold cloud top features, {example_time:%Y-%m-%d %H:%M}-{example_time + frame_duration:%H:%M} UTC (Nature Run {nature_start:%Y})\")\n",
+ "fig.suptitle(\n",
+ " f\"Cold cloud top features, {example_time:%Y-%m-%d %H:%M}-{example_time + frame_duration:%H:%M} UTC (Nature Run {nature_start:%Y})\"\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()"
]
@@ -1648,10 +1752,10 @@
"id": "fd2fc711",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:35.109625Z",
- "iopub.status.busy": "2026-10-24T21:16:35.108625Z",
- "iopub.status.idle": "2026-10-24T21:16:35.121050Z",
- "shell.execute_reply": "2026-10-24T21:16:35.121050Z"
+ "iopub.execute_input": "2026-10-25T04:53:16.499289Z",
+ "iopub.status.busy": "2026-10-25T04:53:16.499289Z",
+ "iopub.status.idle": "2026-10-25T04:53:16.517435Z",
+ "shell.execute_reply": "2026-10-25T04:53:16.516429Z"
}
},
"outputs": [
@@ -1757,19 +1861,33 @@
}
],
"source": [
- "total = {\"features\": len(features), \"area\": features.area_km2.sum(), \"precipitation\": features.tot_preccon.sum()}\n",
- "summary = results.groupby(\"sensor\").apply(\n",
- " lambda r: pd.Series(\n",
- " {\n",
- " \"observed (% of features)\": 100 * r.observed.mean(),\n",
- " \"resolved (% of features)\": 100 * r.resolved.mean(),\n",
- " \"resolved (% of area)\": 100 * r.area_km2[r.resolved].sum() / total[\"area\"],\n",
- " \"resolved (% of precipitation)\": 100 * r.tot_preccon[r.resolved].sum() / total[\"precipitation\"],\n",
- " \"median resolution element of observed (km)\": r.element_km[r.observed].median(),\n",
- " }\n",
- " ),\n",
- " include_groups=False,\n",
- ").loc[sensors.index]\n",
+ "total = {\n",
+ " \"features\": len(features),\n",
+ " \"area\": features.area_km2.sum(),\n",
+ " \"precipitation\": features.tot_preccon.sum(),\n",
+ "}\n",
+ "summary = (\n",
+ " results.groupby(\"sensor\")\n",
+ " .apply(\n",
+ " lambda r: pd.Series(\n",
+ " {\n",
+ " \"observed (% of features)\": 100 * r.observed.mean(),\n",
+ " \"resolved (% of features)\": 100 * r.resolved.mean(),\n",
+ " \"resolved (% of area)\": 100\n",
+ " * r.area_km2[r.resolved].sum()\n",
+ " / total[\"area\"],\n",
+ " \"resolved (% of precipitation)\": 100\n",
+ " * r.tot_preccon[r.resolved].sum()\n",
+ " / total[\"precipitation\"],\n",
+ " \"median resolution element of observed (km)\": r.element_km[\n",
+ " r.observed\n",
+ " ].median(),\n",
+ " }\n",
+ " ),\n",
+ " include_groups=False,\n",
+ " )\n",
+ " .loc[sensors.index]\n",
+ ")\n",
"summary.round(1)"
]
},
@@ -1791,10 +1909,10 @@
"id": "985db512",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:35.123054Z",
- "iopub.status.busy": "2026-10-24T21:16:35.123054Z",
- "iopub.status.idle": "2026-10-24T21:16:35.272252Z",
- "shell.execute_reply": "2026-10-24T21:16:35.271247Z"
+ "iopub.execute_input": "2026-10-25T04:53:16.519435Z",
+ "iopub.status.busy": "2026-10-25T04:53:16.519435Z",
+ "iopub.status.idle": "2026-10-25T04:53:16.672362Z",
+ "shell.execute_reply": "2026-10-25T04:53:16.672362Z"
}
},
"outputs": [
@@ -1896,21 +2014,54 @@
}
],
"source": [
- "BLUE, ORANGE, AQUA, YELLOW, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#eda100\", \"#8a8984\", \"#0b0b0b\"\n",
+ "BLUE, ORANGE, AQUA, YELLOW, GRAY, INK = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#eda100\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ ")\n",
"SENSOR_COLORS = {\"ATMS\": BLUE, \"Future MW\": ORANGE, \"CrIS\": AQUA, \"Future IR\": YELLOW}\n",
"SENSOR_STYLES = {\"ATMS\": \"-\", \"Future MW\": \"--\", \"CrIS\": \"-\", \"Future IR\": \"--\"}\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"edges = np.array([EFFECTIVE_RESOLUTION_KM, 40, 56, 80, 112, np.inf])\n",
"labels = [f\"{EFFECTIVE_RESOLUTION_KM}-40\", \"40-56\", \"56-80\", \"80-112\", \">112\"]\n",
"results[\"size_class\"] = pd.cut(results.diameter_km, edges, labels=labels, right=False)\n",
- "by_size = results.groupby([\"sensor\", \"size_class\"], observed=False).resolved.mean().unstack().loc[sensors.index] * 100\n",
+ "by_size = (\n",
+ " results.groupby([\"sensor\", \"size_class\"], observed=False)\n",
+ " .resolved.mean()\n",
+ " .unstack()\n",
+ " .loc[sensors.index]\n",
+ " * 100\n",
+ ")\n",
"counts = results[results.sensor == \"ATMS\"].groupby(\"size_class\", observed=False).size()\n",
"\n",
"fig, ax = plt.subplots(figsize=(7, 3.8))\n",
"for sensor in sensors.index:\n",
- " ax.plot(labels, by_size.loc[sensor], marker=\"o\", color=SENSOR_COLORS[sensor], ls=SENSOR_STYLES[sensor], label=sensor)\n",
- "ax.set(xlabel=\"Feature diameter (km)\", ylabel=\"Features resolved (% of size class)\", title=\"Resolved features by size\", ylim=(0, 100))\n",
+ " ax.plot(\n",
+ " labels,\n",
+ " by_size.loc[sensor],\n",
+ " marker=\"o\",\n",
+ " color=SENSOR_COLORS[sensor],\n",
+ " ls=SENSOR_STYLES[sensor],\n",
+ " label=sensor,\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Feature diameter (km)\",\n",
+ " ylabel=\"Features resolved (% of size class)\",\n",
+ " title=\"Resolved features by size\",\n",
+ " ylim=(0, 100),\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"for x, n in zip(labels, counts):\n",
" ax.annotate(f\"n = {n}\", (x, 2), ha=\"center\", fontsize=8, color=GRAY)\n",
@@ -1933,10 +2084,10 @@
"id": "d03edac1",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:35.274251Z",
- "iopub.status.busy": "2026-10-24T21:16:35.273251Z",
- "iopub.status.idle": "2026-10-24T21:16:35.441615Z",
- "shell.execute_reply": "2026-10-24T21:16:35.441111Z"
+ "iopub.execute_input": "2026-10-25T04:53:16.675831Z",
+ "iopub.status.busy": "2026-10-25T04:53:16.674830Z",
+ "iopub.status.idle": "2026-10-25T04:53:16.823131Z",
+ "shell.execute_reply": "2026-10-25T04:53:16.823131Z"
}
},
"outputs": [
@@ -1955,16 +2106,37 @@
"fig, ax = plt.subplots(figsize=(9, 4))\n",
"bins = np.arange(0, 25, 1)\n",
"nature_hist, _ = np.histogram(features.solar_hour, bins)\n",
- "ax.bar(bins[:-1] + 0.5, nature_hist, width=1, color=\"#e6e5e0\", label=\"Nature Run (all features)\")\n",
+ "ax.bar(\n",
+ " bins[:-1] + 0.5,\n",
+ " nature_hist,\n",
+ " width=1,\n",
+ " color=\"#e6e5e0\",\n",
+ " label=\"Nature Run (all features)\",\n",
+ ")\n",
"for swath, style, color in [(\"ATMS\", \":\", BLUE), (\"CrIS\", \":\", AQUA)]:\n",
" r = results[results.sensor == swath]\n",
" hist, _ = np.histogram(r.solar_hour[r.observed], bins)\n",
- " ax.plot(bins[:-1] + 0.5, hist, ls=\":\", color=color, label=f\"observed ({swath} swath)\")\n",
+ " ax.plot(\n",
+ " bins[:-1] + 0.5, hist, ls=\":\", color=color, label=f\"observed ({swath} swath)\"\n",
+ " )\n",
"for sensor in sensors.index:\n",
" r = results[results.sensor == sensor]\n",
" hist, _ = np.histogram(r.solar_hour[r.resolved], bins)\n",
- " ax.plot(bins[:-1] + 0.5, hist, ls=\"-\", marker=\"o\", ms=3, color=SENSOR_COLORS[sensor], label=f\"resolved ({sensor})\")\n",
- "ax.set(xlabel=\"Solar hour at feature\", ylabel=\"Features\", title=\"Features by solar hour\", xlim=(0, 24))\n",
+ " ax.plot(\n",
+ " bins[:-1] + 0.5,\n",
+ " hist,\n",
+ " ls=\"-\",\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " color=SENSOR_COLORS[sensor],\n",
+ " label=f\"resolved ({sensor})\",\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Solar hour at feature\",\n",
+ " ylabel=\"Features\",\n",
+ " title=\"Features by solar hour\",\n",
+ " xlim=(0, 24),\n",
+ ")\n",
"ax.set_xticks(range(0, 25, 3))\n",
"ax.legend(frameon=False, fontsize=8, loc=\"upper left\")\n",
"fig.tight_layout()\n",
@@ -1985,10 +2157,10 @@
"id": "b480d8db",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:35.443623Z",
- "iopub.status.busy": "2026-10-24T21:16:35.443623Z",
- "iopub.status.idle": "2026-10-24T21:16:35.458511Z",
- "shell.execute_reply": "2026-10-24T21:16:35.457959Z"
+ "iopub.execute_input": "2026-10-25T04:53:16.825418Z",
+ "iopub.status.busy": "2026-10-25T04:53:16.825418Z",
+ "iopub.status.idle": "2026-10-25T04:53:16.839433Z",
+ "shell.execute_reply": "2026-10-25T04:53:16.839433Z"
}
},
"outputs": [
@@ -2100,7 +2272,9 @@
"# resolution elements, of either size\n",
"sensitivity = pd.DataFrame(\n",
" {\n",
- " (axis, f\"N = {n}\"): results.groupby(\"sensor\")[f\"elements ({axis})\"].apply(lambda r: 100 * (r >= n).mean())\n",
+ " (axis, f\"N = {n}\"): results.groupby(\"sensor\")[f\"elements ({axis})\"].apply(\n",
+ " lambda r: 100 * (r >= n).mean()\n",
+ " )\n",
" for axis in AXES\n",
" for n in (1, 2, 3)\n",
" }\n",
@@ -2136,10 +2310,10 @@
"id": "b02e9ec9",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:16:35.460523Z",
- "iopub.status.busy": "2026-10-24T21:16:35.460523Z",
- "iopub.status.idle": "2026-10-24T21:19:45.963475Z",
- "shell.execute_reply": "2026-10-24T21:19:45.962966Z"
+ "iopub.execute_input": "2026-10-25T04:53:16.841463Z",
+ "iopub.status.busy": "2026-10-25T04:53:16.841463Z",
+ "iopub.status.idle": "2026-10-25T04:56:45.448422Z",
+ "shell.execute_reply": "2026-10-25T04:56:45.447394Z"
}
},
"outputs": [
@@ -2198,7 +2372,7 @@
"
3.5
\n",
"
0.5
\n",
"
2.2
\n",
- "
3.4
\n",
+ "
3.5
\n",
"
3.4
\n",
" \n",
"
\n",
@@ -2207,7 +2381,7 @@
"
8.4
\n",
"
6.9
\n",
"
0.8
\n",
- "
4.1
\n",
+ "
4.2
\n",
"
6.9
\n",
"
6.9
\n",
"
\n",
@@ -2216,7 +2390,7 @@
"
10.0
\n",
"
8.0
\n",
"
1.1
\n",
- "
4.4
\n",
+ "
4.5
\n",
"
7.7
\n",
"
7.9
\n",
" \n",
@@ -2227,7 +2401,7 @@
"
10.5
\n",
"
1.2
\n",
"
6.1
\n",
- "
10.2
\n",
+ "
10.3
\n",
"
10.4
\n",
" \n",
"
\n",
@@ -2236,14 +2410,14 @@
"
11.3
\n",
"
1.6
\n",
"
6.8
\n",
- "
11.2
\n",
+ "
11.3
\n",
"
11.2
\n",
"
\n",
"
\n",
"
current (3 in the 13:30 plane)
\n",
"
11.6
\n",
"
9.8
\n",
- "
0.9
\n",
+ "
1.0
\n",
"
5.3
\n",
"
9.6
\n",
"
9.6
\n",
@@ -2254,8 +2428,8 @@
"
14.8
\n",
"
12.3
\n",
"
1.5
\n",
- "
7.3
\n",
- "
12.0
\n",
+ "
7.4
\n",
+ "
12.1
\n",
"
12.1
\n",
"
\n",
"
\n",
@@ -2263,8 +2437,8 @@
"
19.0
\n",
"
15.6
\n",
"
2.1
\n",
- "
8.8
\n",
- "
15.1
\n",
+ "
8.9
\n",
+ "
15.2
\n",
"
15.4
\n",
"
\n",
"
\n",
@@ -2273,7 +2447,7 @@
"
15.5
\n",
"
13.2
\n",
"
1.5
\n",
- "
7.8
\n",
+ "
7.9
\n",
"
12.9
\n",
"
13.1
\n",
"
\n",
@@ -2282,8 +2456,8 @@
"
23.8
\n",
"
19.4
\n",
"
2.6
\n",
- "
11.1
\n",
- "
19.0
\n",
+ "
11.3
\n",
+ "
19.1
\n",
"
19.2
\n",
" \n",
"
\n",
@@ -2292,8 +2466,8 @@
"
15.5
\n",
"
13.3
\n",
"
1.8
\n",
- "
8.2
\n",
- "
13.1
\n",
+ "
8.3
\n",
+ "
13.2
\n",
"
13.2
\n",
"
\n",
"
\n",
@@ -2301,17 +2475,17 @@
"
29.8
\n",
"
24.0
\n",
"
3.4
\n",
- "
14.1
\n",
- "
23.4
\n",
- "
23.7
\n",
+ "
14.2
\n",
+ "
23.6
\n",
+ "
23.8
\n",
"
\n",
"
\n",
"
6 plane(s) x 1
\n",
"
29.3
\n",
"
23.9
\n",
"
3.6
\n",
- "
14.4
\n",
- "
23.2
\n",
+ "
14.5
\n",
+ "
23.3
\n",
"
23.6
\n",
"
\n",
" \n",
@@ -2354,34 +2528,34 @@
"sensor resolved, ATMS resolved, CrIS \\\n",
"satellites design \n",
"1 1 plane(s) x 1 0.5 2.2 \n",
- "2 1 plane(s) x 2 0.8 4.1 \n",
- " 2 plane(s) x 1 1.1 4.4 \n",
+ "2 1 plane(s) x 2 0.8 4.2 \n",
+ " 2 plane(s) x 1 1.1 4.5 \n",
"3 1 plane(s) x 3 1.2 6.1 \n",
" 3 plane(s) x 1 1.6 6.8 \n",
- " current (3 in the 13:30 plane) 0.9 5.3 \n",
- "4 1 plane(s) x 4 1.5 7.3 \n",
- " 4 plane(s) x 1 2.1 8.8 \n",
- "5 1 plane(s) x 5 1.5 7.8 \n",
- " 5 plane(s) x 1 2.6 11.1 \n",
- "6 1 plane(s) x 6 1.8 8.2 \n",
- " 2 plane(s) x 3 3.4 14.1 \n",
- " 6 plane(s) x 1 3.6 14.4 \n",
+ " current (3 in the 13:30 plane) 1.0 5.3 \n",
+ "4 1 plane(s) x 4 1.5 7.4 \n",
+ " 4 plane(s) x 1 2.1 8.9 \n",
+ "5 1 plane(s) x 5 1.5 7.9 \n",
+ " 5 plane(s) x 1 2.6 11.3 \n",
+ "6 1 plane(s) x 6 1.8 8.3 \n",
+ " 2 plane(s) x 3 3.4 14.2 \n",
+ " 6 plane(s) x 1 3.6 14.5 \n",
"\n",
"sensor resolved, Future MW \\\n",
"satellites design \n",
- "1 1 plane(s) x 1 3.4 \n",
+ "1 1 plane(s) x 1 3.5 \n",
"2 1 plane(s) x 2 6.9 \n",
" 2 plane(s) x 1 7.7 \n",
- "3 1 plane(s) x 3 10.2 \n",
- " 3 plane(s) x 1 11.2 \n",
+ "3 1 plane(s) x 3 10.3 \n",
+ " 3 plane(s) x 1 11.3 \n",
" current (3 in the 13:30 plane) 9.6 \n",
- "4 1 plane(s) x 4 12.0 \n",
- " 4 plane(s) x 1 15.1 \n",
+ "4 1 plane(s) x 4 12.1 \n",
+ " 4 plane(s) x 1 15.2 \n",
"5 1 plane(s) x 5 12.9 \n",
- " 5 plane(s) x 1 19.0 \n",
- "6 1 plane(s) x 6 13.1 \n",
- " 2 plane(s) x 3 23.4 \n",
- " 6 plane(s) x 1 23.2 \n",
+ " 5 plane(s) x 1 19.1 \n",
+ "6 1 plane(s) x 6 13.2 \n",
+ " 2 plane(s) x 3 23.6 \n",
+ " 6 plane(s) x 1 23.3 \n",
"\n",
"sensor resolved, Future IR \n",
"satellites design \n",
@@ -2396,7 +2570,7 @@
"5 1 plane(s) x 5 13.1 \n",
" 5 plane(s) x 1 19.2 \n",
"6 1 plane(s) x 6 13.2 \n",
- " 2 plane(s) x 3 23.7 \n",
+ " 2 plane(s) x 3 23.8 \n",
" 6 plane(s) x 1 23.6 "
]
},
@@ -2418,7 +2592,8 @@
"\n",
"def constellation(n_planes, per_plane, rng):\n",
" \"\"\"Satellites in n_planes sun-synchronous planes at 830 km, whose ascending equator crossing times are spread evenly\n",
- " over 12 hours from 13:30, with per_plane satellites evenly spaced in each plane at a random phase.\"\"\"\n",
+ " over 12 hours from 13:30, with per_plane satellites evenly spaced in each plane at a random phase.\n",
+ " \"\"\"\n",
" members = []\n",
" for p in range(n_planes):\n",
" hours = (13.5 + 12 * p / n_planes) % 24\n",
@@ -2443,9 +2618,13 @@
"\n",
"def evaluate(members, features):\n",
" \"\"\"The share (%) of the features that each sensor observes and resolves, flown on the members.\"\"\"\n",
- " tracks = collect_tracks(members, TRADE_DURATION)\n",
- " looks = collect_looks(tracks, features, {member.name: member.orbit for member in members})\n",
- " return assess(looks, features).groupby(\"sensor\")[[\"observed\", \"resolved\"]].mean().mul(100)\n",
+ " looks = collect_looks(members, TRADE_DURATION, features)\n",
+ " return (\n",
+ " assess(looks, features, members)\n",
+ " .groupby(\"sensor\")[[\"observed\", \"resolved\"]]\n",
+ " .mean()\n",
+ " .mul(100)\n",
+ " )\n",
"\n",
"\n",
"trade_features = find_features(load_nature(TRADE_DURATION))\n",
@@ -2460,9 +2639,13 @@
" ]\n",
").reset_index()\n",
"trade[\"satellites\"] = trade.planes * trade.per_plane\n",
- "trade[\"design\"] = trade.planes.astype(str) + \" plane(s) x \" + trade.per_plane.astype(str)\n",
+ "trade[\"design\"] = (\n",
+ " trade.planes.astype(str) + \" plane(s) x \" + trade.per_plane.astype(str)\n",
+ ")\n",
"# the current constellation over the same two weeks, for reference\n",
- "current = evaluate(satellites, trade_features).assign(satellites=3, design=\"current (3 in the 13:30 plane)\")\n",
+ "current = evaluate(satellites, trade_features).assign(\n",
+ " satellites=3, design=\"current (3 in the 13:30 plane)\"\n",
+ ")\n",
"\n",
"# features observed (by each swath) and resolved (by each sensor), % of all features, averaged over the draws\n",
"trade_summary = pd.concat([trade, current.reset_index()]).pivot_table(\n",
@@ -2470,7 +2653,9 @@
")\n",
"trade_summary = pd.concat(\n",
" [\n",
- " trade_summary[\"observed\"][[\"ATMS\", \"CrIS\"]].add_prefix(\"observed, \").add_suffix(\" swath\"),\n",
+ " trade_summary[\"observed\"][[\"ATMS\", \"CrIS\"]]\n",
+ " .add_prefix(\"observed, \")\n",
+ " .add_suffix(\" swath\"),\n",
" trade_summary[\"resolved\"][sensors.index].add_prefix(\"resolved, \"),\n",
" ],\n",
" axis=1,\n",
@@ -2485,16 +2670,16 @@
"id": "d9b2cedf",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-24T21:19:45.965476Z",
- "iopub.status.busy": "2026-10-24T21:19:45.965476Z",
- "iopub.status.idle": "2026-10-24T21:19:46.211542Z",
- "shell.execute_reply": "2026-10-24T21:19:46.211542Z"
+ "iopub.execute_input": "2026-10-25T04:56:45.450431Z",
+ "iopub.status.busy": "2026-10-25T04:56:45.450431Z",
+ "iopub.status.idle": "2026-10-25T04:56:45.696234Z",
+ "shell.execute_reply": "2026-10-25T04:56:45.696234Z"
}
},
"outputs": [
{
"data": {
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",
+ "image/png": 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",
"text/plain": [
"
"
]
@@ -2508,36 +2693,93 @@
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(11, 4.2), sharey=True)\n",
"for ax, (title, pair) in zip(\n",
- " axes, {\"Microwave (ATMS swath)\": (\"ATMS\", \"Future MW\"), \"Infrared (CrIS swath)\": (\"CrIS\", \"Future IR\")}.items()\n",
+ " axes,\n",
+ " {\n",
+ " \"Microwave (ATMS swath)\": (\"ATMS\", \"Future MW\"),\n",
+ " \"Infrared (CrIS swath)\": (\"CrIS\", \"Future IR\"),\n",
+ " }.items(),\n",
"):\n",
" ends = []\n",
" for sensor in pair:\n",
" color = SENSOR_COLORS[sensor]\n",
" for strategy, (ls, marker) in STRATEGIES.items():\n",
- " selected = trade[(trade.sensor == sensor) & ((trade.planes == 1) if strategy == \"one plane\" else (trade.per_plane == 1))]\n",
+ " selected = trade[\n",
+ " (trade.sensor == sensor)\n",
+ " & (\n",
+ " (trade.planes == 1)\n",
+ " if strategy == \"one plane\"\n",
+ " else (trade.per_plane == 1)\n",
+ " )\n",
+ " ]\n",
" stats = selected.groupby(\"satellites\")[\"resolved\"].agg([\"mean\", \"std\"])\n",
- " ax.fill_between(stats.index, stats[\"mean\"] - stats[\"std\"], stats[\"mean\"] + stats[\"std\"], color=color, alpha=0.12, lw=0)\n",
- " ax.plot(stats.index, stats[\"mean\"], ls=ls, marker=marker, ms=6, lw=2, color=color, mec=\"white\", mew=1.5)\n",
+ " ax.fill_between(\n",
+ " stats.index,\n",
+ " stats[\"mean\"] - stats[\"std\"],\n",
+ " stats[\"mean\"] + stats[\"std\"],\n",
+ " color=color,\n",
+ " alpha=0.12,\n",
+ " lw=0,\n",
+ " )\n",
+ " ax.plot(\n",
+ " stats.index,\n",
+ " stats[\"mean\"],\n",
+ " ls=ls,\n",
+ " marker=marker,\n",
+ " ms=6,\n",
+ " lw=2,\n",
+ " color=color,\n",
+ " mec=\"white\",\n",
+ " mew=1.5,\n",
+ " )\n",
" ends.append([stats[\"mean\"].iloc[-1], f\"{sensor}, {strategy}\"])\n",
- " two_planes = trade[(trade.sensor == sensor) & (trade.planes == 2) & (trade.per_plane == 3)][\"resolved\"].mean()\n",
- " ax.plot(6, two_planes, marker=\"D\", ms=8, color=\"white\", mec=color, mew=2, zorder=3)\n",
+ " two_planes = trade[\n",
+ " (trade.sensor == sensor) & (trade.planes == 2) & (trade.per_plane == 3)\n",
+ " ][\"resolved\"].mean()\n",
+ " ax.plot(\n",
+ " 6, two_planes, marker=\"D\", ms=8, color=\"white\", mec=color, mew=2, zorder=3\n",
+ " )\n",
" # direct labels at the line ends, nudged apart so that they do not overlap\n",
" ends.sort()\n",
" for i in range(1, len(ends)):\n",
" ends[i][0] = max(ends[i][0], ends[i - 1][0] + 1.4)\n",
" for y, label in ends:\n",
- " ax.annotate(label, (6, y), xytext=(10, 0), textcoords=\"offset points\", va=\"center\", fontsize=8, color=INK)\n",
+ " ax.annotate(\n",
+ " label,\n",
+ " (6, y),\n",
+ " xytext=(10, 0),\n",
+ " textcoords=\"offset points\",\n",
+ " va=\"center\",\n",
+ " fontsize=8,\n",
+ " color=INK,\n",
+ " )\n",
" # the features observed at any resolution, with separate planes: the most that any sensor can resolve\n",
- " observed = trade[(trade.sensor == pair[0]) & (trade.per_plane == 1)].groupby(\"satellites\")[\"observed\"].mean()\n",
+ " observed = (\n",
+ " trade[(trade.sensor == pair[0]) & (trade.per_plane == 1)]\n",
+ " .groupby(\"satellites\")[\"observed\"]\n",
+ " .mean()\n",
+ " )\n",
" ax.plot(observed.index, observed, color=GRAY, lw=1, ls=\":\")\n",
" ax.set(title=title, xlabel=\"Satellites\", xticks=range(1, 7), xlim=(0.7, 8.9))\n",
" ax.title.set_fontsize(10)\n",
"axes[0].set(ylabel=\"Features resolved (% of all features)\", ylim=(0, None))\n",
"legend = [\n",
- " plt.Line2D([], [], color=GRAY, ls=\"-\", marker=\"o\", mec=\"white\", label=\"separate planes\"),\n",
+ " plt.Line2D(\n",
+ " [], [], color=GRAY, ls=\"-\", marker=\"o\", mec=\"white\", label=\"separate planes\"\n",
+ " ),\n",
" plt.Line2D([], [], color=GRAY, ls=\"--\", marker=\"s\", mec=\"white\", label=\"one plane\"),\n",
- " plt.Line2D([], [], color=GRAY, ls=\"none\", marker=\"D\", mfc=\"white\", mew=2, label=\"2 planes x 3 satellites\"),\n",
- " plt.Line2D([], [], color=GRAY, ls=\":\", label=\"observed at any resolution (separate planes)\"),\n",
+ " plt.Line2D(\n",
+ " [],\n",
+ " [],\n",
+ " color=GRAY,\n",
+ " ls=\"none\",\n",
+ " marker=\"D\",\n",
+ " mfc=\"white\",\n",
+ " mew=2,\n",
+ " label=\"2 planes x 3 satellites\",\n",
+ " ),\n",
+ " plt.Line2D(\n",
+ " [], [], color=GRAY, ls=\":\", label=\"observed at any resolution (separate planes)\"\n",
+ " ),\n",
"]\n",
"fig.legend(handles=legend, loc=\"lower center\", ncol=4, fontsize=8, frameon=False)\n",
"fig.suptitle(\n",
@@ -2557,7 +2799,7 @@
"\n",
"* **Satellites in one plane saturate.** Adding satellites to a single plane observes more features up to about four satellites, and then hardly any more (15 to 16% in the ATMS swath for four to six satellites), because they all observe at the same local times (near 13:30 and 01:30).\n",
"* **A second plane gives most of the benefit.** Six satellites in separate planes observe 29% of the features, almost twice as many as six in one plane. Six satellites in two planes (three in each) observe as many (30%): what matters is that the satellites do not duplicate each other's local times, not how many planes there are.\n",
- "* **Planes and resolution trade against each other.** With heritage sensors, six satellites in separate planes resolve 14% of the features with CrIS, more than six satellites with a future infrared sounder in one plane (13%). Finer resolution pays off fully only with separate planes: a future sounder on six satellites in separate (or two) planes resolves 23 to 24% of the features: nearly all of those observed in the CrIS swath, and four fifths of those in the wider ATMS swath.\n",
+ "* **Planes and resolution trade against each other.** With heritage sensors, six satellites in separate planes resolve 14.5% of the features with CrIS, more than six satellites with a future infrared sounder in one plane (13.2%). Finer resolution pays off fully only with separate planes: a future sounder on six satellites in separate (or two) planes resolves 23 to 24% of the features: nearly all of those observed in the CrIS swath, and four fifths of those in the wider ATMS swath.\n",
"* **ATMS is limited by resolution, not sampling.** More planes raise the share resolved by ATMS only from 2% to 4%, because its resolution element is larger than most features wherever they are observed.\n",
"\n",
"The spread between the random phases (the shaded bands) is small compared with these differences. The trade holds the sensors and the altitude fixed; their swaths, altitudes, and resolutions, and orbits other than sun-synchronous ones, are further dimensions of a full trade study."
diff --git a/docs/validation/ValidateCoincidenceGMIAMSR2.ipynb b/docs/validation/ValidateCoincidenceGMIAMSR2.ipynb
new file mode 100644
index 00000000..59a505f3
--- /dev/null
+++ b/docs/validation/ValidateCoincidenceGMIAMSR2.ipynb
@@ -0,0 +1,1029 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "59fcd068",
+ "metadata": {},
+ "source": [
+ "# Coincidence Validation: GMI and AMSR2\n",
+ "\n",
+ "This notebook validates TAT-C's coincidence analyses against the observations of two microwave radiometers on 26 September 2026: the GPM Microwave Imager (GMI), on the Global Precipitation Measurement (GPM) Core Observatory in a 65 deg inclined orbit, and the Advanced Microwave Scanning Radiometer 2 (AMSR2), on GCOM-W in a sun-synchronous orbit. GPM uses GMI as the reference to which it intercalibrates partner radiometers such as AMSR2, from their coincident observations: where the two observe the same place within a short time, here 15 minutes. Their different orbits cross many times a day, but within 15 minutes only where GPM's orbit turns, at high latitudes: on this day, between 55 and 70 deg north and south.\n",
+ "\n",
+ "TAT-C finds coincidences in two ways, both validated here against the instruments' recorded sample positions:\n",
+ "\n",
+ "1. **Passes.** Does `collect_observations` predict the passes of each instrument over a global grid of points, the basis of the point coincidences? (The scan geometry of both instruments is validated in the [AMSR2](ValidateConicalAMSR2.ipynb) and [GMI](ValidateConicalGMI.ipynb) conical scanner notebooks.)\n",
+ "2. **Coincident observations.** Does `find_coincident_observations` find the points observed by both instruments within 15 minutes, and the time difference between their observations?\n",
+ "3. **Coincident ground track.** Does `find_coincident_ground_track` find the areas observed by both instruments within 15 minutes?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f531c9b",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "The Level 1C products of both instruments (`GPM_1CGPMGMI` and `GPM_1CGCOMW1AMSR2`, version 8, from NASA's [GES DISC](https://disc.gsfc.nasa.gov/); free [NASA Earthdata Login](https://urs.earthdata.nasa.gov/) account required) record the time of each scan and the position of each sample: 221 per scan for GMI's low-frequency feed (S1, 10 to 89 GHz) every 1.875 s, and 243 per scan for AMSR2's low-resolution samples (S1) every 1.5 s. The day's orbit files (16 for GMI and 15 for AMSR2) are downloaded once, and the scan times and sample positions are cached in local `data/gmi` and `data/amsr2` directories (shared with the conical scanner notebooks). GMI recorded the whole day; AMSR2's record has a gap of 3.3 hours, during which coincidences are not compared. GPM flew in its forward orientation (`SCorientation` 0) all day, as in the GMI notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "11b30426",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:12:56.183047Z",
+ "iopub.status.busy": "2026-10-26T00:12:56.182864Z",
+ "iopub.status.idle": "2026-10-26T00:12:57.413194Z",
+ "shell.execute_reply": "2026-10-26T00:12:57.412436Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ " \n",
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+ "
AMSR2
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+ "
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+ ],
+ "text/plain": [
+ " orbit files scans samples per scan data gaps (UTC)\n",
+ "AMSR2 14 49695 243 08:38-11:55\n",
+ "GMI 16 46081 221 none"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import io\n",
+ "from datetime import datetime, timedelta, timezone\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import h5py\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import requests\n",
+ "\n",
+ "DAY = datetime(2026, 9, 26, tzinfo=timezone.utc)\n",
+ "PRODUCTS = {\n",
+ " # instrument: (GES DISC collection, local directory, cached file, swath groups)\n",
+ " \"AMSR2\": (\n",
+ " \"GPM_1CGCOMW1AMSR2\",\n",
+ " \"amsr2\",\n",
+ " f\"amsr2_l1c_s1_{DAY:%Y%m%d}.npz\",\n",
+ " {\"S1\": \"\"},\n",
+ " ),\n",
+ " \"GMI\": (\n",
+ " \"GPM_1CGPMGMI\",\n",
+ " \"gmi\",\n",
+ " f\"gmi_l1c_{DAY:%Y%m%d}.npz\",\n",
+ " {\"S1\": \"S1_\", \"S2\": \"S2_\"},\n",
+ " ),\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def download(short_name, cache_file, swaths):\n",
+ " \"\"\"Caches the scan times, spacecraft positions, and sample positions of a day of GPM Level 1C files.\"\"\"\n",
+ " import earthaccess\n",
+ "\n",
+ " earthaccess.login()\n",
+ " session = earthaccess.get_requests_https_session()\n",
+ " granules = requests.get(\n",
+ " \"https://cmr.earthdata.nasa.gov/search/granules.umm_json\",\n",
+ " params={\n",
+ " \"short_name\": short_name,\n",
+ " \"version\": \"08\",\n",
+ " \"temporal\": f\"{DAY:%Y-%m-%d}T00:00:00Z,{DAY:%Y-%m-%d}T23:59:59Z\",\n",
+ " \"page_size\": 100,\n",
+ " },\n",
+ " timeout=120,\n",
+ " ).json()[\"items\"]\n",
+ " urls = [\n",
+ " next(\n",
+ " u[\"URL\"]\n",
+ " for u in g[\"umm\"][\"RelatedUrls\"]\n",
+ " if u[\"URL\"].startswith(\"https://data.gesdisc\")\n",
+ " )\n",
+ " for g in granules\n",
+ " ]\n",
+ " parts = []\n",
+ " for url in urls:\n",
+ " with h5py.File(io.BytesIO(session.get(url, timeout=900).content)) as f:\n",
+ " s1 = f[\"S1\"]\n",
+ " seconds = (\n",
+ " (\n",
+ " pd.to_datetime(\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"year\": s1[\"ScanTime/Year\"][:],\n",
+ " \"month\": s1[\"ScanTime/Month\"][:],\n",
+ " \"day\": s1[\"ScanTime/DayOfMonth\"][:],\n",
+ " \"hour\": s1[\"ScanTime/Hour\"][:],\n",
+ " \"minute\": s1[\"ScanTime/Minute\"][:],\n",
+ " \"second\": s1[\"ScanTime/Second\"][:],\n",
+ " \"ms\": s1[\"ScanTime/MilliSecond\"][:],\n",
+ " }\n",
+ " ),\n",
+ " utc=True,\n",
+ " )\n",
+ " - pd.Timestamp(DAY)\n",
+ " )\n",
+ " .dt.total_seconds()\n",
+ " .to_numpy()\n",
+ " )\n",
+ " part = {\n",
+ " \"time\": seconds,\n",
+ " \"sc_lat\": s1[\"SCstatus/SClatitude\"][:],\n",
+ " \"sc_lon\": s1[\"SCstatus/SClongitude\"][:],\n",
+ " \"sc_alt\": s1[\"SCstatus/SCaltitude\"][:],\n",
+ " }\n",
+ " if \"SCorientation\" in s1[\"SCstatus\"]:\n",
+ " part[\"orientation\"] = s1[\"SCstatus/SCorientation\"][:]\n",
+ " for swath, prefix in swaths.items():\n",
+ " part[f\"{prefix}lat\"] = f[f\"{swath}/Latitude\"][:]\n",
+ " part[f\"{prefix}lon\"] = f[f\"{swath}/Longitude\"][:]\n",
+ " parts.append(part)\n",
+ " reduced = {k: np.concatenate([p[k] for p in parts]) for k in parts[0]}\n",
+ " # keep each scan once (orbit files overlap), in time order, within the day\n",
+ " _, first = np.unique(reduced[\"time\"], return_index=True)\n",
+ " keep = first[(reduced[\"time\"][first] >= 0) & (reduced[\"time\"][first] < 86400)]\n",
+ " np.savez_compressed(\n",
+ " cache_file, granules=len(urls), **{k: v[keep] for k, v in reduced.items()}\n",
+ " )\n",
+ "\n",
+ "\n",
+ "data = {}\n",
+ "for instrument, (short_name, directory, file_name, swaths) in PRODUCTS.items():\n",
+ " cache_file = Path(\"data\") / directory / file_name\n",
+ " cache_file.parent.mkdir(parents=True, exist_ok=True)\n",
+ " if not cache_file.exists():\n",
+ " download(short_name, cache_file, swaths)\n",
+ " data[instrument] = dict(np.load(cache_file))\n",
+ "data[\"GMI\"][\"lat\"], data[\"GMI\"][\"lon\"] = (\n",
+ " data[\"GMI\"][\"S1_lat\"],\n",
+ " data[\"GMI\"][\"S1_lon\"],\n",
+ ") # low-frequency feed\n",
+ "\n",
+ "\n",
+ "def data_gaps(seconds, threshold=10):\n",
+ " \"\"\"Gaps (start, end, s) of more than threshold (s) between scans, including before the first and after the last.\"\"\"\n",
+ " i = np.flatnonzero(np.diff(seconds) > threshold)\n",
+ " gaps = [(seconds[k], seconds[k + 1]) for k in i]\n",
+ " if seconds[0] > threshold:\n",
+ " gaps.insert(0, (-np.inf, seconds[0]))\n",
+ " if seconds[-1] < 86400 - threshold:\n",
+ " gaps.append((seconds[-1], np.inf))\n",
+ " return gaps\n",
+ "\n",
+ "\n",
+ "GAPS = {instrument: data_gaps(d[\"time\"]) for instrument, d in data.items()}\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " instrument: {\n",
+ " \"orbit files\": int(d[\"granules\"]),\n",
+ " \"scans\": len(d[\"time\"]),\n",
+ " \"samples per scan\": d[\"lat\"].shape[1],\n",
+ " \"data gaps (UTC)\": \", \".join(\n",
+ " f\"{DAY + timedelta(seconds=max(a, 0)):%H:%M}-{DAY + timedelta(seconds=min(b, 86400)):%H:%M}\"\n",
+ " for a, b in GAPS[instrument]\n",
+ " )\n",
+ " or \"none\",\n",
+ " }\n",
+ " for instrument, d in data.items()\n",
+ " }\n",
+ ").T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e532d274",
+ "metadata": {},
+ "source": [
+ "## Test 1: Passes\n",
+ "\n",
+ "The instruments are modeled with the `ConicalInstrument` parameters fitted in the conical scanner notebooks (cone angle, and the center and half-width of the scanned sector, relative to the inertial velocity, as neither spacecraft is yaw-steered), on orbits from published two-line element sets (TLEs). The TLE of GPM has an epoch 8 days after the analysis day (as in the GMI notebook), and is about 11 km ahead of GPM's recorded position along track, so GMI's predicted observations come about 1.5 s early; the AMSR2 TLE is within about 1 km.\n",
+ "\n",
+ "The points form a Fibonacci lattice about 50 km apart, between 75 deg south and north (GMI's swath reaches about 70 deg). A point is observed on a pass when a sample lies within 8 km of it (about half the spacing of the samples along and between scans), at the time of the nearest sample; TAT-C's passes are those of `collect_observations` (at their `epoch`, when the scan crosses the point). Its run of about five minutes is cached in a local `data/coincidence` directory. Observed and predicted passes are paired by point within 60 s, outside the instruments' data gaps."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c1e54db6",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:12:57.415070Z",
+ "iopub.status.busy": "2026-10-26T00:12:57.414837Z",
+ "iopub.status.idle": "2026-10-26T00:13:19.594854Z",
+ "shell.execute_reply": "2026-10-26T00:13:19.594096Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "250925 points; observations outside the instruments' data gaps:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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+ "text/plain": [
+ " observed passes predicted observed passes predicted (%) \\\n",
+ "AMSR2 370748.0 367886.0 99.23 \n",
+ "GMI 313805.0 309418.0 98.60 \n",
+ "\n",
+ " predicted passes observed (%) time offset, median (s) \\\n",
+ "AMSR2 100.0 0.01 \n",
+ "GMI 100.0 -1.51 \n",
+ "\n",
+ " 95% |time offset| (s) \n",
+ "AMSR2 1.57 \n",
+ "GMI 2.49 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pickle\n",
+ "import time\n",
+ "\n",
+ "from scipy.spatial import cKDTree\n",
+ "from shapely.geometry import box\n",
+ "\n",
+ "from tatc.analysis import collect_observations\n",
+ "from tatc.generation import generate_points_fibonacci_lattice\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " ConicalInstrument,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Satellite,\n",
+ ")\n",
+ "from tatc.utils import VelocityFrame\n",
+ "\n",
+ "WINDOW = 900 # s, maximum time difference of coincident observations\n",
+ "RADIUS = 8e3 # m, distance within which a sample observes a point\n",
+ "EARTH_RADIUS = 6371.0088e3 # m, mean\n",
+ "# not yaw-steered: the bodies (and the instruments' views) align with the inertial velocity\n",
+ "INERTIAL = Attitude(velocity_frame=VelocityFrame.INERTIAL)\n",
+ "satellites = {\n",
+ " \"AMSR2\": Satellite(\n",
+ " name=\"GCOM-W\",\n",
+ " attitude=INERTIAL,\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(\n",
+ " [\n",
+ " \"1 38337U 12025A 26277.29678146 .00000240 00000+0 63281-4 0 9990\",\n",
+ " \"2 38337 98.2100 216.2278 0001644 77.4488 41.8708 14.57111943764929\",\n",
+ " ]\n",
+ " ),\n",
+ " instruments=[\n",
+ " ConicalInstrument(\n",
+ " name=\"AMSR2\",\n",
+ " cone_angle=47.564,\n",
+ " scan_center_azimuth=0.498,\n",
+ " scan_half_width=75.332,\n",
+ " along_track_field_of_view=1,\n",
+ " )\n",
+ " ],\n",
+ " ),\n",
+ " \"GMI\": Satellite(\n",
+ " name=\"GPM\",\n",
+ " attitude=INERTIAL,\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(\n",
+ " [\n",
+ " \"1 39574U 14009C 26277.54227627 .00004528 00000+0 10004-3 0 9997\",\n",
+ " \"2 39574 64.9701 301.6724 0010793 268.6572 91.3332 15.45217995714704\",\n",
+ " ]\n",
+ " ),\n",
+ " instruments=[\n",
+ " ConicalInstrument(\n",
+ " name=\"GMI\",\n",
+ " cone_angle=48.460,\n",
+ " scan_center_azimuth=-0.285,\n",
+ " scan_half_width=76.031,\n",
+ " along_track_field_of_view=1,\n",
+ " )\n",
+ " ],\n",
+ " ),\n",
+ "}\n",
+ "points = generate_points_fibonacci_lattice(50e3, mask=box(-180, -75, 180, 75))\n",
+ "point_index = pd.Series(np.arange(len(points)), index=points.point_id)\n",
+ "\n",
+ "\n",
+ "def unit(lon, lat):\n",
+ " \"\"\"Unit vectors of geodetic coordinates (deg) on a sphere, stacked on the last axis.\"\"\"\n",
+ " lon, lat = np.radians(lon), np.radians(lat)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def in_gap(instrument, seconds):\n",
+ " \"\"\"Whether times (s) fall within 30 s of a data gap of an instrument.\"\"\"\n",
+ " seconds = np.asarray(seconds)\n",
+ " return np.any(\n",
+ " [(seconds > a - 30) & (seconds < b + 30) for a, b in GAPS[instrument]]\n",
+ " + [np.zeros(seconds.shape, bool)],\n",
+ " axis=0,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def true_passes(d):\n",
+ " \"\"\"Each pass of an instrument over each point (samples within RADIUS, separated by at least 10 minutes):\n",
+ " the time (s) of the sample nearest the point.\"\"\"\n",
+ " valid = np.all(np.abs(d[\"lat\"]) <= 90, axis=1)\n",
+ " seconds, lat, lon = (\n",
+ " d[\"time\"][valid],\n",
+ " d[\"lat\"][valid].astype(float),\n",
+ " d[\"lon\"][valid].astype(float),\n",
+ " )\n",
+ " n = lat.shape[1]\n",
+ " samples = unit(lon.ravel(), lat.ravel())\n",
+ " targets = unit(points.geometry.x.to_numpy(), points.geometry.y.to_numpy())\n",
+ " rows = []\n",
+ " for k, hits in enumerate(\n",
+ " cKDTree(samples).query_ball_point(targets, RADIUS / EARTH_RADIUS, workers=-1)\n",
+ " ):\n",
+ " if not hits:\n",
+ " continue\n",
+ " hits = np.array(hits)\n",
+ " t = seconds[hits // n]\n",
+ " distance = np.linalg.norm(samples[hits] - targets[k], axis=1)\n",
+ " order = np.argsort(t)\n",
+ " t, distance = t[order], distance[order]\n",
+ " bounds = np.r_[0, np.flatnonzero(np.diff(t) > 600) + 1, len(t)]\n",
+ " rows += [\n",
+ " (k, t[a + np.argmin(distance[a:b])])\n",
+ " for a, b in zip(bounds[:-1], bounds[1:])\n",
+ " ]\n",
+ " return pd.DataFrame(rows, columns=[\"point\", \"seconds\"])\n",
+ "\n",
+ "\n",
+ "truth = {instrument: true_passes(d) for instrument, d in data.items()}\n",
+ "cache = Path(\"data\") / \"coincidence\" / f\"observations_{DAY:%Y%m%d}.pkl\"\n",
+ "cache.parent.mkdir(parents=True, exist_ok=True)\n",
+ "if not cache.exists():\n",
+ " tic = time.perf_counter()\n",
+ " observations = collect_observations(\n",
+ " list(points.geometry), list(satellites.values()), DAY, DAY + timedelta(days=1)\n",
+ " )\n",
+ " print(\n",
+ " f\"collect_observations: {len(observations)} observations of {len(points)} points in {time.perf_counter() - tic:.0f} s\"\n",
+ " )\n",
+ " observations.to_pickle(cache)\n",
+ "observations = pd.read_pickle(cache)\n",
+ "observations[\"point\"] = point_index.loc[observations.target_hash].to_numpy()\n",
+ "observations[\"seconds\"] = (observations.epoch - pd.Timestamp(DAY)).dt.total_seconds()\n",
+ "\n",
+ "\n",
+ "def pair(left, right, tolerance):\n",
+ " \"\"\"Pairs each row of left with the nearest row of right at the same point within tolerance (s) of time.\"\"\"\n",
+ " return pd.merge_asof(\n",
+ " left.sort_values(\"seconds\"),\n",
+ " right.rename(columns={\"seconds\": \"paired\"}).sort_values(\"paired\"),\n",
+ " left_on=\"seconds\",\n",
+ " right_on=\"paired\",\n",
+ " by=\"point\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=tolerance,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "rows = {}\n",
+ "for instrument in data:\n",
+ " observed = truth[instrument][~in_gap(instrument, truth[instrument].seconds)]\n",
+ " predicted = observations[observations.instrument == instrument][\n",
+ " [\"point\", \"seconds\"]\n",
+ " ]\n",
+ " predicted = predicted[~in_gap(instrument, predicted.seconds)]\n",
+ " forward, backward = pair(observed, predicted, 60), pair(predicted, observed, 60)\n",
+ " offset = (forward.paired - forward.seconds).dropna()\n",
+ " rows[instrument] = {\n",
+ " \"observed passes\": len(observed),\n",
+ " \"predicted\": len(predicted),\n",
+ " \"observed passes predicted (%)\": 100 * forward.paired.notna().mean(),\n",
+ " \"predicted passes observed (%)\": 100 * backward.paired.notna().mean(),\n",
+ " \"time offset, median (s)\": offset.median(),\n",
+ " \"95% |time offset| (s)\": offset.abs().quantile(0.95),\n",
+ " }\n",
+ "print(f\"{len(points)} points; observations outside the instruments' data gaps:\")\n",
+ "pd.DataFrame(rows).T.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "774726c2",
+ "metadata": {},
+ "source": [
+ "Outside AMSR2's data gap, every pass TAT-C predicts was observed, and TAT-C predicts 99.2% of AMSR2's and 98.6% of GMI's observed passes; the others are at the edges of the swaths, where the outermost samples lie within 8 km of points just outside TAT-C's swath. GMI's predicted passes come 1.5 s early (median), as expected from its TLE, and AMSR2's within 0.01 s; 95% are within 2.5 s (GMI) and 1.6 s (AMSR2)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f28cc5c",
+ "metadata": {},
+ "source": [
+ "## Test 2: Coincident Observations\n",
+ "\n",
+ "The true coincidences are the pairs of a GMI pass and an AMSR2 pass over the same point within 15 minutes of each other. `find_coincident_observations` widens each observation period by half of the maximum time difference before and after, and reports each period during which the widened periods of both satellites overlap. Each true coincidence is paired with the nearest coincident observation of the same point within 60 s, from the epochs of the GMI and AMSR2 observations that contribute to it.\n",
+ "\n",
+ "The observations of a `ConicalInstrument` are not instantaneous, however: their period (`start` to `end`) is the time the point spends inside the circle the cone traces on the ground, while their `epoch` is the time the scan crosses it. `find_coincident_observations` is therefore run twice: comparing the observations' periods (the default), and with `instantaneous=True`, comparing their epochs, in which case each coincident observation spans the epochs of its contributors, from the first to the last (so that its duration is their time difference)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "001a84ce",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:13:19.596913Z",
+ "iopub.status.busy": "2026-10-26T00:13:19.596646Z",
+ "iopub.status.idle": "2026-10-26T00:13:23.865097Z",
+ "shell.execute_reply": "2026-10-26T00:13:23.862810Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "observation periods: median AMSR2 234 s, GMI 141 s; epochs a median 8 s after the start\n",
+ "instantaneous coincidences: duration minus time difference of their epochs, max |x| 7.12e-12 s\n"
+ ]
+ },
+ {
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+ "
unpaired found within 20 s of the limit
\n",
+ "
time (s)
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
observation periods (default)
\n",
+ "
6554.0
\n",
+ "
8525.0
\n",
+ "
97.77
\n",
+ "
75.17
\n",
+ "
2116.0
\n",
+ "
1169.04
\n",
+ "
-1.03
\n",
+ "
2.38
\n",
+ "
6.0
\n",
+ "
2117.0
\n",
+ "
2.00
\n",
+ "
\n",
+ "
\n",
+ "
instantaneous (epochs)
\n",
+ "
6554.0
\n",
+ "
6409.0
\n",
+ "
97.70
\n",
+ "
99.91
\n",
+ "
0.0
\n",
+ "
899.99
\n",
+ "
-1.03
\n",
+ "
2.38
\n",
+ "
11.0
\n",
+ "
6.0
\n",
+ "
1.99
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " true coincidences coincident observations \\\n",
+ "observation periods (default) 6554.0 8525.0 \n",
+ "instantaneous (epochs) 6554.0 6409.0 \n",
+ "\n",
+ " true coincidences found (%) \\\n",
+ "observation periods (default) 97.77 \n",
+ "instantaneous (epochs) 97.70 \n",
+ "\n",
+ " found coincidences true (%) \\\n",
+ "observation periods (default) 75.17 \n",
+ "instantaneous (epochs) 99.91 \n",
+ "\n",
+ " epochs more than 15 minutes apart \\\n",
+ "observation periods (default) 2116.0 \n",
+ "instantaneous (epochs) 0.0 \n",
+ "\n",
+ " epoch difference, max (s) \\\n",
+ "observation periods (default) 1169.04 \n",
+ "instantaneous (epochs) 899.99 \n",
+ "\n",
+ " time difference error, median (s) \\\n",
+ "observation periods (default) -1.03 \n",
+ "instantaneous (epochs) -1.03 \n",
+ "\n",
+ " 95% |error| (s) \\\n",
+ "observation periods (default) 2.38 \n",
+ "instantaneous (epochs) 2.38 \n",
+ "\n",
+ " unpaired true within 20 s of the limit \\\n",
+ "observation periods (default) 6.0 \n",
+ "instantaneous (epochs) 11.0 \n",
+ "\n",
+ " unpaired found within 20 s of the limit \\\n",
+ "observation periods (default) 2117.0 \n",
+ "instantaneous (epochs) 6.0 \n",
+ "\n",
+ " time (s) \n",
+ "observation periods (default) 2.00 \n",
+ "instantaneous (epochs) 1.99 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from tatc.analysis import find_coincident_observations\n",
+ "\n",
+ "# true coincidences: pairs of passes over a point within WINDOW of each other, outside the data gaps\n",
+ "true_pairs = truth[\"GMI\"].merge(truth[\"AMSR2\"], on=\"point\", suffixes=(\"_gmi\", \"_amsr2\"))\n",
+ "true_pairs[\"dt\"] = true_pairs.seconds_gmi - true_pairs.seconds_amsr2\n",
+ "true_pairs = true_pairs[\n",
+ " (true_pairs.dt.abs() <= WINDOW)\n",
+ " & ~in_gap(\"AMSR2\", true_pairs.seconds_amsr2)\n",
+ " & ~in_gap(\"GMI\", true_pairs.seconds_gmi)\n",
+ "]\n",
+ "true_pairs[\"seconds\"] = (true_pairs.seconds_gmi + true_pairs.seconds_amsr2) / 2\n",
+ "\n",
+ "\n",
+ "def coincidences(instantaneous):\n",
+ " \"\"\"Coincident observations of pairs of satellites within WINDOW (of their periods, or their epochs if\n",
+ " instantaneous), with the epochs (s) of the GMI and AMSR2 observations that contribute to each (whose widened\n",
+ " periods, or epochs, overlap the coincident period).\"\"\"\n",
+ " tic = time.perf_counter()\n",
+ " found = find_coincident_observations(\n",
+ " observations,\n",
+ " max_time_difference=timedelta(seconds=WINDOW),\n",
+ " instantaneous=instantaneous,\n",
+ " )\n",
+ " elapsed = time.perf_counter() - tic\n",
+ " frame = (\n",
+ " observations.assign(start=observations.epoch, end=observations.epoch)\n",
+ " if instantaneous\n",
+ " else observations\n",
+ " )\n",
+ " found[\"point\"] = point_index.loc[found.target_hash].to_numpy()\n",
+ " half = pd.Timedelta(seconds=WINDOW / 2)\n",
+ " joined = (\n",
+ " found[[\"point\", \"start\", \"end\"]]\n",
+ " .reset_index()\n",
+ " .merge(\n",
+ " frame[[\"point\", \"instrument\", \"start\", \"end\", \"seconds\"]],\n",
+ " on=\"point\",\n",
+ " suffixes=(\"\", \"_observation\"),\n",
+ " )\n",
+ " )\n",
+ " joined = joined[\n",
+ " (joined.start_observation - half <= joined.end)\n",
+ " & (joined.end_observation + half >= joined.start)\n",
+ " ]\n",
+ " epochs = joined.groupby([\"index\", \"instrument\"]).seconds.first().unstack()\n",
+ " found[\"seconds_gmi\"], found[\"seconds_amsr2\"] = epochs[\"GMI\"], epochs[\"AMSR2\"]\n",
+ " found[\"dt\"] = found.seconds_gmi - found.seconds_amsr2\n",
+ " found[\"seconds\"] = (found.seconds_gmi + found.seconds_amsr2) / 2\n",
+ " found = found[\n",
+ " ~in_gap(\"AMSR2\", found.seconds_amsr2) & ~in_gap(\"GMI\", found.seconds_gmi)\n",
+ " ]\n",
+ " return found, elapsed\n",
+ "\n",
+ "\n",
+ "rows = {}\n",
+ "for label, instantaneous in [\n",
+ " (\"observation periods (default)\", False),\n",
+ " (\"instantaneous (epochs)\", True),\n",
+ "]:\n",
+ " found, elapsed = coincidences(instantaneous)\n",
+ " forward = pair(\n",
+ " true_pairs[[\"point\", \"seconds\", \"dt\"]],\n",
+ " found[[\"point\", \"seconds\", \"dt\"]].rename(columns={\"dt\": \"dt_found\"}),\n",
+ " 60,\n",
+ " )\n",
+ " backward = pair(\n",
+ " found[[\"point\", \"seconds\", \"dt\"]],\n",
+ " true_pairs[[\"point\", \"seconds\", \"dt\"]].rename(columns={\"dt\": \"dt_true\"}),\n",
+ " 60,\n",
+ " )\n",
+ " error = (forward.dt_found - forward.dt).dropna()\n",
+ " rows[label] = {\n",
+ " \"true coincidences\": len(true_pairs),\n",
+ " \"coincident observations\": len(found),\n",
+ " \"true coincidences found (%)\": 100 * forward.paired.notna().mean(),\n",
+ " \"found coincidences true (%)\": 100 * backward.paired.notna().mean(),\n",
+ " \"epochs more than 15 minutes apart\": int((found.dt.abs() > WINDOW).sum()),\n",
+ " \"epoch difference, max (s)\": found.dt.abs().max(),\n",
+ " \"time difference error, median (s)\": error.median(),\n",
+ " \"95% |error| (s)\": error.abs().quantile(0.95),\n",
+ " \"unpaired true within 20 s of the limit\": int(\n",
+ " (forward.paired.isna() & (forward.dt.abs() > WINDOW - 20)).sum()\n",
+ " ),\n",
+ " \"unpaired found within 20 s of the limit\": int(\n",
+ " (backward.paired.isna() & (backward.dt.abs() > WINDOW - 20)).sum()\n",
+ " ),\n",
+ " \"time (s)\": elapsed,\n",
+ " }\n",
+ " if instantaneous:\n",
+ " # each instantaneous coincidence spans the two epochs: its duration is their time difference\n",
+ " span = (found.end - found.start).dt.total_seconds()\n",
+ " span_error = (span - found.dt.abs()).abs().max()\n",
+ "durations = (observations.end - observations.start).dt.total_seconds()\n",
+ "print(\n",
+ " \"observation periods: median \"\n",
+ " + \", \".join(\n",
+ " f\"{i} {durations[observations.instrument == i].median():.0f} s\" for i in data\n",
+ " )\n",
+ " + f\"; epochs a median {(observations.epoch - observations.start).dt.total_seconds().median():.0f} s after the start\"\n",
+ ")\n",
+ "print(\n",
+ " f\"instantaneous coincidences: duration minus time difference of their epochs, max |x| {span_error:.2e} s\"\n",
+ ")\n",
+ "pd.DataFrame(rows).T.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb938861",
+ "metadata": {},
+ "source": [
+ "Comparing the observations' periods (the default), `find_coincident_observations` finds 97.8% of the true coincidences, but a quarter of the coincidences it reports (2,116 of 8,525) pair observations whose epochs are more than 15 minutes apart, by up to 19.5 minutes. Their periods last a median of 234 s (AMSR2) and 141 s (GMI) and begin when the scan first reaches the point (a median of 8 s before the epoch), so the periods of two observations come within 15 minutes of each other while their scans of the point do not.\n",
+ "\n",
+ "With `instantaneous=True`, the coincidences match: 97.7% of the true coincidences are found, and 99.9% of those found are true (the other 6 are within 20 s of the 15 minute limit, as GMI's 1.5 s timing offset implies), with time differences within 1.0 s (median, the timing offset) and 2.4 s (95%) of the true ones. The true coincidences not found are at the edges of the swaths: 97.7% is the product of the shares of each instrument's passes predicted in Test 1. Each instantaneous coincidence spans exactly the two epochs, so its duration is their time difference. Each run takes about 2 s for the 736,000 observations."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b70485c",
+ "metadata": {},
+ "source": [
+ "## Test 3: Coincident Ground Track\n",
+ "\n",
+ "`collect_ground_track` computes each instrument's footprint (the scanned arc) every 10 s over the day, with an along-track field of view tailored to that time step so that consecutive footprints tile the swath, and `find_coincident_ground_track` finds the pairs of GMI and AMSR2 footprints within 15 minutes of each other that overlap. The union of their overlaps is compared with the points observed by both instruments within 15 minutes (Test 2)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b2df81c4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:13:23.870847Z",
+ "iopub.status.busy": "2026-10-26T00:13:23.870155Z",
+ "iopub.status.idle": "2026-10-26T00:13:26.693840Z",
+ "shell.execute_reply": "2026-10-26T00:13:26.693158Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "16087 footprints, 3338 coincident pairs in 1.8 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "points observed in common 5479.00\n",
+ "predicted 5429.00\n",
+ "both 5365.00\n",
+ "observed only 114.00\n",
+ "predicted only 64.00\n",
+ "observed points predicted (%) 97.92\n",
+ "predicted points observed (%) 98.82\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import shapely\n",
+ "\n",
+ "from tatc.analysis import collect_ground_track, find_coincident_ground_track\n",
+ "from tatc.constants import EARTH_MEAN_RADIUS\n",
+ "from tatc.utils import compute_along_track_field_of_view\n",
+ "\n",
+ "STEP = 10 # s, ground track time step\n",
+ "tracked = []\n",
+ "for instrument, satellite in satellites.items():\n",
+ " orbit = satellite.orbit.to_gp_orbit()\n",
+ " # footprints that tile the swath: the along-track field of view tailored to the time step\n",
+ " width = compute_along_track_field_of_view(\n",
+ " orbit.get_semimajor_axis() - EARTH_MEAN_RADIUS,\n",
+ " STEP,\n",
+ " orbit.get_inclination(),\n",
+ " eccentricity=orbit.get_eccentricity(),\n",
+ " )\n",
+ " tracked.append(\n",
+ " satellite.model_copy(\n",
+ " update={\n",
+ " \"instruments\": [\n",
+ " satellite.instruments[0].model_copy(\n",
+ " update={\"along_track_field_of_view\": width}\n",
+ " )\n",
+ " ]\n",
+ " }\n",
+ " )\n",
+ " )\n",
+ "times = [DAY + timedelta(seconds=STEP * (i + 0.5)) for i in range(86400 // STEP)]\n",
+ "track = collect_ground_track(tracked, times)\n",
+ "seconds = (track.time - pd.Timestamp(DAY)).dt.total_seconds()\n",
+ "track = track[\n",
+ " ~(\n",
+ " ((track.instrument == \"AMSR2\") & in_gap(\"AMSR2\", seconds))\n",
+ " | ((track.instrument == \"GMI\") & in_gap(\"GMI\", seconds))\n",
+ " )\n",
+ "]\n",
+ "tic = time.perf_counter()\n",
+ "footprint_pairs = find_coincident_ground_track(\n",
+ " track, max_time_difference=timedelta(seconds=WINDOW)\n",
+ ")\n",
+ "elapsed = time.perf_counter() - tic\n",
+ "common = shapely.union_all(footprint_pairs.geometry.to_numpy())\n",
+ "shapely.prepare(common)\n",
+ "predicted = shapely.contains_xy(\n",
+ " common, points.geometry.x.to_numpy(), points.geometry.y.to_numpy()\n",
+ ")\n",
+ "observed = np.zeros(len(points), dtype=bool)\n",
+ "observed[true_pairs.point.unique()] = True\n",
+ "print(\n",
+ " f\"{len(track)} footprints, {len(footprint_pairs)} coincident pairs in {elapsed:.1f} s\"\n",
+ ")\n",
+ "pd.Series(\n",
+ " {\n",
+ " \"points observed in common\": int(observed.sum()),\n",
+ " \"predicted\": int(predicted.sum()),\n",
+ " \"both\": int((observed & predicted).sum()),\n",
+ " \"observed only\": int((observed & ~predicted).sum()),\n",
+ " \"predicted only\": int((predicted & ~observed).sum()),\n",
+ " \"observed points predicted (%)\": 100\n",
+ " * (observed & predicted).sum()\n",
+ " / observed.sum(),\n",
+ " \"predicted points observed (%)\": 100\n",
+ " * (observed & predicted).sum()\n",
+ " / predicted.sum(),\n",
+ " }\n",
+ ").round(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b179279f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:13:26.695578Z",
+ "iopub.status.busy": "2026-10-26T00:13:26.695345Z",
+ "iopub.status.idle": "2026-10-26T00:13:29.094520Z",
+ "shell.execute_reply": "2026-10-26T00:13:29.093743Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import cartopy.crs as ccrs\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "fig = plt.figure(figsize=(12, 5.2))\n",
+ "ax = fig.add_subplot(projection=ccrs.PlateCarree())\n",
+ "ax.coastlines(linewidth=0.4, color=\"#888780\")\n",
+ "ax.set_global()\n",
+ "for geometry in footprint_pairs.geometry.sample(\n",
+ " min(len(footprint_pairs), 2000), random_state=0\n",
+ "):\n",
+ " for part in getattr(geometry, \"geoms\", [geometry]):\n",
+ " if part.geom_type == \"Polygon\":\n",
+ " ax.fill(\n",
+ " *part.exterior.xy,\n",
+ " color=\"#93c5fd\",\n",
+ " alpha=0.5,\n",
+ " lw=0,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
+ "xy = points.geometry\n",
+ "ax.scatter(\n",
+ " xy.x[observed & predicted],\n",
+ " xy.y[observed & predicted],\n",
+ " s=1,\n",
+ " color=\"#1f4e79\",\n",
+ " label=\"observed and predicted\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
+ "ax.scatter(\n",
+ " xy.x[observed & ~predicted],\n",
+ " xy.y[observed & ~predicted],\n",
+ " s=6,\n",
+ " color=\"#b91c1c\",\n",
+ " label=\"observed only\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
+ "ax.scatter(\n",
+ " xy.x[predicted & ~observed],\n",
+ " xy.y[predicted & ~observed],\n",
+ " s=6,\n",
+ " color=\"#d97706\",\n",
+ " label=\"predicted only\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
+ "ax.set_title(\n",
+ " \"GMI and AMSR2 observations within 15 minutes, 26 September 2026 (blue: coincident footprints)\"\n",
+ ")\n",
+ "ax.legend(loc=\"lower left\", markerscale=3, fontsize=8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e7a5d57",
+ "metadata": {},
+ "source": [
+ "`find_coincident_ground_track` finds 3,338 pairs of footprints that overlap within 15 minutes, in under 2 s. Their union covers 97.9% of the points observed by both instruments within 15 minutes, and 98.8% of the points it covers were observed by both. The differences lie along the edges of the coincident areas: at the edges of the swaths, as in Test 1, and where the time difference reaches 15 minutes, which footprints 10 s apart resolve only to their time step. A footprint's time is instantaneous, so the ground track coincidences need no adjustment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6771635",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| 1. Passes | Every predicted pass observed; 98.6% (GMI) and 99.2% (AMSR2) of the observed passes predicted, within 2.5 s and 1.6 s (95%) |\n",
+ "| 2. Coincident observations | Comparing periods (the default), a quarter of the coincident observations pair epochs more than 15 minutes apart (the periods of conical observations last minutes); with `instantaneous=True`, 97.7% of the true coincidences found and 99.9% of those found true, with time differences within 2.4 s (95%) |\n",
+ "| 3. Coincident ground track | 97.9% of the points observed by both instruments within 15 minutes covered by coincident footprints, and 98.8% of the points covered observed by both |\n",
+ "\n",
+ "**TAT-C's coincidence analyses are validated against GMI and AMSR2.** With the conical scanner models of the companion notebooks and published orbits, TAT-C predicts where and when GMI and AMSR2 observe the same places within 15 minutes, as points (`find_coincident_observations`) and as areas (`find_coincident_ground_track`), to within the edges of their swaths and a couple of seconds, for a day of observations of a quarter of a million points in seconds.\n",
+ "\n",
+ "**Finding: coincidences of observation periods.** `find_coincident_observations` compares the observations' periods (`start` to `end`), not their epochs. For a `ConicalInstrument`, as for a `PointedInstrument`, the period is the time the point spends within reach of the scan, which lasts minutes, while the epoch is the time the scan crosses the point. Coincidences of the samples themselves, as in intercalibration, require comparing the epochs: `find_coincident_observations` does so with `instantaneous=True`, and reports each coincidence from the first contributing epoch to the last, so that its duration is the time difference.\n",
+ "\n",
+ "**Next steps:**\n",
+ "\n",
+ "1. *Polar coincidences.* Compare coincident observations of two sun-synchronous satellites, such as NOAA-20 and Metop, whose simultaneous nadir overpasses occur near the poles.\n",
+ "2. *Aircraft underflights.* Coincidences of satellites with moving platforms, such as research aircraft, which TAT-C does not yet model.\n",
+ "\n",
+ "**Limitations.** This study covers one day and two instruments. GPM's element set is 8 days from the analysis day, which shifts GMI's predicted observations by about 1.5 s, and an instrument observes a point when a sample lies within 8 km of it, which extends the observed swaths slightly beyond TAT-C's."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateConicalAMSR2.ipynb b/docs/validation/ValidateConicalAMSR2.ipynb
index f12056b5..0a1d16ec 100644
--- a/docs/validation/ValidateConicalAMSR2.ipynb
+++ b/docs/validation/ValidateConicalAMSR2.ipynb
@@ -32,10 +32,10 @@
"id": "dae87e44",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:02.813744Z",
- "iopub.status.busy": "2026-10-08T20:03:02.813744Z",
- "iopub.status.idle": "2026-10-08T20:03:03.620490Z",
- "shell.execute_reply": "2026-10-08T20:03:03.620490Z"
+ "iopub.execute_input": "2026-10-25T14:01:00.169553Z",
+ "iopub.status.busy": "2026-10-25T14:01:00.169553Z",
+ "iopub.status.idle": "2026-10-25T14:01:01.123396Z",
+ "shell.execute_reply": "2026-10-25T14:01:01.123396Z"
}
},
"outputs": [
@@ -88,7 +88,11 @@
" timeout=120,\n",
" ).json()[\"items\"]\n",
" urls = [\n",
- " next(u[\"URL\"] for u in g[\"umm\"][\"RelatedUrls\"] if u[\"URL\"].startswith(\"https://data.gesdisc\"))\n",
+ " next(\n",
+ " u[\"URL\"]\n",
+ " for u in g[\"umm\"][\"RelatedUrls\"]\n",
+ " if u[\"URL\"].startswith(\"https://data.gesdisc\")\n",
+ " )\n",
" for g in granules\n",
" ]\n",
" parts = []\n",
@@ -96,22 +100,26 @@
" with h5py.File(io.BytesIO(session.get(url, timeout=900).content)) as f:\n",
" s1 = f[\"S1\"]\n",
" seconds = (\n",
- " pd.to_datetime(\n",
- " pd.DataFrame(\n",
- " {\n",
- " \"year\": s1[\"ScanTime/Year\"][:],\n",
- " \"month\": s1[\"ScanTime/Month\"][:],\n",
- " \"day\": s1[\"ScanTime/DayOfMonth\"][:],\n",
- " \"hour\": s1[\"ScanTime/Hour\"][:],\n",
- " \"minute\": s1[\"ScanTime/Minute\"][:],\n",
- " \"second\": s1[\"ScanTime/Second\"][:],\n",
- " \"ms\": s1[\"ScanTime/MilliSecond\"][:],\n",
- " }\n",
- " ),\n",
- " utc=True,\n",
+ " (\n",
+ " pd.to_datetime(\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"year\": s1[\"ScanTime/Year\"][:],\n",
+ " \"month\": s1[\"ScanTime/Month\"][:],\n",
+ " \"day\": s1[\"ScanTime/DayOfMonth\"][:],\n",
+ " \"hour\": s1[\"ScanTime/Hour\"][:],\n",
+ " \"minute\": s1[\"ScanTime/Minute\"][:],\n",
+ " \"second\": s1[\"ScanTime/Second\"][:],\n",
+ " \"ms\": s1[\"ScanTime/MilliSecond\"][:],\n",
+ " }\n",
+ " ),\n",
+ " utc=True,\n",
+ " )\n",
+ " - pd.Timestamp(DAY)\n",
" )\n",
- " - pd.Timestamp(DAY)\n",
- " ).dt.total_seconds().to_numpy()\n",
+ " .dt.total_seconds()\n",
+ " .to_numpy()\n",
+ " )\n",
" parts.append(\n",
" {\n",
" \"time\": seconds,\n",
@@ -126,10 +134,18 @@
" # keep each scan once (orbit files overlap), in time order, within the day\n",
" _, first = np.unique(reduced[\"time\"], return_index=True)\n",
" keep = first[(reduced[\"time\"][first] >= 0) & (reduced[\"time\"][first] < 86400)]\n",
- " np.savez_compressed(CACHE_FILE, granules=len(urls), **{k: v[keep] for k, v in reduced.items()})\n",
+ " np.savez_compressed(\n",
+ " CACHE_FILE, granules=len(urls), **{k: v[keep] for k, v in reduced.items()}\n",
+ " )\n",
"\n",
"amsr2 = dict(np.load(CACHE_FILE))\n",
- "pd.Series({\"orbit files\": int(amsr2[\"granules\"]), \"scans\": len(amsr2[\"time\"]), \"samples per scan\": amsr2[\"lat\"].shape[1]})"
+ "pd.Series(\n",
+ " {\n",
+ " \"orbit files\": int(amsr2[\"granules\"]),\n",
+ " \"scans\": len(amsr2[\"time\"]),\n",
+ " \"samples per scan\": amsr2[\"lat\"].shape[1],\n",
+ " }\n",
+ ")"
]
},
{
@@ -148,10 +164,10 @@
"id": "9d928c88",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:03.622499Z",
- "iopub.status.busy": "2026-10-08T20:03:03.622499Z",
- "iopub.status.idle": "2026-10-08T20:03:05.210042Z",
- "shell.execute_reply": "2026-10-08T20:03:05.210042Z"
+ "iopub.execute_input": "2026-10-25T14:01:01.125725Z",
+ "iopub.status.busy": "2026-10-25T14:01:01.125725Z",
+ "iopub.status.idle": "2026-10-25T14:01:03.183997Z",
+ "shell.execute_reply": "2026-10-25T14:01:03.183997Z"
}
},
"outputs": [
@@ -183,7 +199,15 @@
"from tatc.generation import generate_points_fibonacci_lattice\n",
"from tatc.constants import EARTH_ROTATION_RATE\n",
"from tatc.analysis import compute_ground_track\n",
- "from tatc.schemas import ConicalInstrument, GeneralPerturbationsOrbit, Instrument, Point, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " ConicalInstrument,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Instrument,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"from tatc.utils import (\n",
" VelocityFrame,\n",
" compute_projected_ray_position,\n",
@@ -194,7 +218,11 @@
"GEOD = Geod(ellps=\"WGS84\")\n",
"TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
"TO_GEODETIC = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
- "gcomw = Satellite(name=\"GCOM-W\", orbit=GeneralPerturbationsOrbit.from_tle(TLE))\n",
+ "# not yaw-steered: the body (and its instruments' views) aligned with the inertial velocity\n",
+ "INERTIAL = Attitude(velocity_frame=VelocityFrame.INERTIAL)\n",
+ "gcomw = Satellite(\n",
+ " name=\"GCOM-W\", orbit=GeneralPerturbationsOrbit.from_tle(TLE), attitude=INERTIAL\n",
+ ")\n",
"orbit = gcomw.orbit.to_gp_orbit()\n",
"N_SAMPLES = amsr2[\"lat\"].shape[1]\n",
"\n",
@@ -205,13 +233,17 @@
"\n",
"def ecef(lon, lat, height=0.0):\n",
" \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
- " return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+ " return np.stack(\n",
+ " TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1\n",
+ " )\n",
"\n",
"\n",
"def up(lon, lat):\n",
" \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
" lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -220,29 +252,49 @@
"\n",
"def inertial_velocity(position, velocity):\n",
" \"\"\"Inertial velocity expressed in Earth-fixed coordinates.\"\"\"\n",
- " return velocity + EARTH_ROTATION_RATE * np.stack([-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1)\n",
+ " return velocity + EARTH_ROTATION_RATE * np.stack(\n",
+ " [-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def summarize(x):\n",
" \"\"\"Summary statistics of a residual sample.\"\"\"\n",
" x = np.asarray(x, dtype=float)\n",
" x = x[np.isfinite(x)]\n",
- " return pd.Series({\"n\": x.size, \"median\": np.median(x), \"p95 |x|\": np.percentile(np.abs(x), 95), \"max |x|\": np.abs(x).max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": x.size,\n",
+ " \"median\": np.median(x),\n",
+ " \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+ " \"max |x|\": np.abs(x).max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"def binned(x, y, edges, q=(5, 50, 95)):\n",
" \"\"\"Percentiles of y in bins of x.\"\"\"\n",
" i = np.digitize(x, edges) - 1\n",
- " return np.array([np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q) for k in range(len(edges) - 1)])\n",
+ " return np.array(\n",
+ " [\n",
+ " np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q)\n",
+ " for k in range(len(edges) - 1)\n",
+ " ]\n",
+ " )\n",
"\n",
"\n",
"scan_time = amsr2[\"time\"]\n",
- "sc_pos = ecef(amsr2[\"sc_lon\"].astype(float), amsr2[\"sc_lat\"].astype(float), amsr2[\"sc_alt\"].astype(float) * 1e3)\n",
+ "sc_pos = ecef(\n",
+ " amsr2[\"sc_lon\"].astype(float),\n",
+ " amsr2[\"sc_lat\"].astype(float),\n",
+ " amsr2[\"sc_alt\"].astype(float) * 1e3,\n",
+ ")\n",
"sc_vel = np.gradient(sc_pos, scan_time, axis=0)\n",
"# scans whose neighbors are contiguous (no data gap), for velocities\n",
"contiguous = np.r_[np.diff(scan_time) < 2, False] & np.r_[False, np.diff(scan_time) < 2]\n",
"foot_lat, foot_lon = amsr2[\"lat\"].astype(float), amsr2[\"lon\"].astype(float)\n",
- "gaps = [(scan_time[i], scan_time[i + 1]) for i in np.nonzero(np.diff(scan_time) > 60)[0]]\n",
+ "gaps = [\n",
+ " (scan_time[i], scan_time[i + 1]) for i in np.nonzero(np.diff(scan_time) > 60)[0]\n",
+ "]\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
"plt.rcParams.update(\n",
@@ -258,7 +310,9 @@
" {\n",
" \"scans\": len(scan_time),\n",
" \"scan period (s)\": np.median(np.diff(scan_time)),\n",
- " \"data gaps (UTC)\": \", \".join(f\"{a / 3600:.2f} to {b / 3600:.2f} h\" for a, b in gaps),\n",
+ " \"data gaps (UTC)\": \", \".join(\n",
+ " f\"{a / 3600:.2f} to {b / 3600:.2f} h\" for a, b in gaps\n",
+ " ),\n",
" \"altitude range (km)\": f\"{amsr2['sc_alt'].min():.0f} to {amsr2['sc_alt'].max():.0f}\",\n",
" }\n",
")"
@@ -280,10 +334,10 @@
"id": "0bed05f7",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:05.212048Z",
- "iopub.status.busy": "2026-10-08T20:03:05.212048Z",
- "iopub.status.idle": "2026-10-08T20:03:05.242759Z",
- "shell.execute_reply": "2026-10-08T20:03:05.242759Z"
+ "iopub.execute_input": "2026-10-25T14:01:03.187003Z",
+ "iopub.status.busy": "2026-10-25T14:01:03.186002Z",
+ "iopub.status.idle": "2026-10-25T14:01:03.230395Z",
+ "shell.execute_reply": "2026-10-25T14:01:03.230395Z"
}
},
"outputs": [
@@ -354,14 +408,20 @@
],
"source": [
"i1 = np.nonzero(contiguous)[0][::50]\n",
- "p1 = np.array(orbit.get_orbit_track(to_utc(scan_time[i1])).frame_xyz_and_velocity(itrs)[0].m).T\n",
+ "p1 = np.array(\n",
+ " orbit.get_orbit_track(to_utc(scan_time[i1])).frame_xyz_and_velocity(itrs)[0].m\n",
+ ").T\n",
"offset1 = p1 - sc_pos[i1]\n",
"radial1 = normalize(sc_pos[i1])\n",
- "along1 = normalize(sc_vel[i1] - np.sum(sc_vel[i1] * radial1, axis=-1, keepdims=True) * radial1)\n",
+ "along1 = normalize(\n",
+ " sc_vel[i1] - np.sum(sc_vel[i1] * radial1, axis=-1, keepdims=True) * radial1\n",
+ ")\n",
"pd.DataFrame(\n",
" {\n",
" \"along-track (km)\": summarize(np.sum(offset1 * along1, axis=-1) / 1e3),\n",
- " \"cross-track, right (km)\": summarize(np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3),\n",
+ " \"cross-track, right (km)\": summarize(\n",
+ " np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3\n",
+ " ),\n",
" \"radial (km)\": summarize(np.sum(offset1 * radial1, axis=-1) / 1e3),\n",
" }\n",
").T.round(3)"
@@ -385,10 +445,10 @@
"id": "4670f7ae",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:05.244766Z",
- "iopub.status.busy": "2026-10-08T20:03:05.244766Z",
- "iopub.status.idle": "2026-10-08T20:03:05.478544Z",
- "shell.execute_reply": "2026-10-08T20:03:05.478544Z"
+ "iopub.execute_input": "2026-10-25T14:01:03.232927Z",
+ "iopub.status.busy": "2026-10-25T14:01:03.231423Z",
+ "iopub.status.idle": "2026-10-25T14:01:03.493998Z",
+ "shell.execute_reply": "2026-10-25T14:01:03.493489Z"
}
},
"outputs": [
@@ -423,19 +483,41 @@
"\n",
"def azimuth(velocity):\n",
" \"\"\"Scan azimuth (deg) of each line of sight from the horizontal direction of a velocity, positive to the left.\"\"\"\n",
- " forward = normalize(velocity - np.sum(velocity * nadir, axis=-1, keepdims=True) * nadir)\n",
+ " forward = normalize(\n",
+ " velocity - np.sum(velocity * nadir, axis=-1, keepdims=True) * nadir\n",
+ " )\n",
" left = np.cross(forward, nadir)\n",
- " horizontal = los - np.sum(los * nadir[:, None], axis=-1, keepdims=True) * nadir[:, None]\n",
- " return np.degrees(np.arctan2(np.sum(horizontal * left[:, None], axis=-1), np.sum(horizontal * forward[:, None], axis=-1)))\n",
+ " horizontal = (\n",
+ " los - np.sum(los * nadir[:, None], axis=-1, keepdims=True) * nadir[:, None]\n",
+ " )\n",
+ " return np.degrees(\n",
+ " np.arctan2(\n",
+ " np.sum(horizontal * left[:, None], axis=-1),\n",
+ " np.sum(horizontal * forward[:, None], axis=-1),\n",
+ " )\n",
+ " )\n",
"\n",
"\n",
"azimuth_earth = azimuth(sc_vel[i2])\n",
"azimuth_inertial = azimuth(inertial_velocity(sc_pos[i2], sc_vel[i2]))\n",
"CONE = float(np.median(cone))\n",
- "SECTOR = float(np.median(azimuth_inertial[:, -1] - azimuth_inertial[:, 0]) / 2) # half-width (deg)\n",
+ "SECTOR = float(\n",
+ " np.median(azimuth_inertial[:, -1] - azimuth_inertial[:, 0]) / 2\n",
+ ") # half-width (deg)\n",
"CENTER = float(np.median(azimuth_inertial[:, -1] + azimuth_inertial[:, 0]) / 2) # deg\n",
- "chord = GEOD.inv(foot_lon[i2, 0], foot_lat[i2, 0], foot_lon[i2, -1], foot_lat[i2, -1])[2] / 1e3\n",
- "ahead = GEOD.inv(np.asarray(sub_lon), np.asarray(sub_lat), foot_lon[i2, N_SAMPLES // 2], foot_lat[i2, N_SAMPLES // 2])[2] / 1e3\n",
+ "chord = (\n",
+ " GEOD.inv(foot_lon[i2, 0], foot_lat[i2, 0], foot_lon[i2, -1], foot_lat[i2, -1])[2]\n",
+ " / 1e3\n",
+ ")\n",
+ "ahead = (\n",
+ " GEOD.inv(\n",
+ " np.asarray(sub_lon),\n",
+ " np.asarray(sub_lat),\n",
+ " foot_lon[i2, N_SAMPLES // 2],\n",
+ " foot_lat[i2, N_SAMPLES // 2],\n",
+ " )[2]\n",
+ " / 1e3\n",
+ ")\n",
"center_earth = (azimuth_earth[:, 0] + azimuth_earth[:, -1]) / 2\n",
"center_inertial = (azimuth_inertial[:, 0] + azimuth_inertial[:, -1]) / 2\n",
"pd.Series(\n",
@@ -446,8 +528,12 @@
" \"scan sector center (deg)\": CENTER,\n",
" \"scan sector half-width (deg)\": SECTOR,\n",
" \"sample spacing (deg)\": np.median(np.diff(azimuth_inertial, axis=1)),\n",
- " \"arc center offset from Earth-fixed velocity, p95 |x| (deg)\": np.percentile(np.abs(center_earth), 95),\n",
- " \"arc center offset from inertial velocity, p95 |x| (deg)\": np.percentile(np.abs(center_inertial), 95),\n",
+ " \"arc center offset from Earth-fixed velocity, p95 |x| (deg)\": np.percentile(\n",
+ " np.abs(center_earth), 95\n",
+ " ),\n",
+ " \"arc center offset from inertial velocity, p95 |x| (deg)\": np.percentile(\n",
+ " np.abs(center_inertial), 95\n",
+ " ),\n",
" \"arc center distance ahead of nadir (km)\": np.median(ahead),\n",
" \"arc end-to-end distance (swath width, km)\": np.median(chord),\n",
" }\n",
@@ -460,10 +546,10 @@
"id": "86dccf0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:05.480579Z",
- "iopub.status.busy": "2026-10-08T20:03:05.480579Z",
- "iopub.status.idle": "2026-10-08T20:03:05.771311Z",
- "shell.execute_reply": "2026-10-08T20:03:05.771311Z"
+ "iopub.execute_input": "2026-10-25T14:01:03.496003Z",
+ "iopub.status.busy": "2026-10-25T14:01:03.496003Z",
+ "iopub.status.idle": "2026-10-25T14:01:03.836337Z",
+ "shell.execute_reply": "2026-10-25T14:01:03.836337Z"
}
},
"outputs": [
@@ -481,12 +567,30 @@
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(11, 3.6))\n",
"axes[0].plot(np.median(azimuth_inertial, axis=0), np.median(cone, axis=0), color=BLUE)\n",
- "axes[0].set(xlabel=\"Scan azimuth from inertial velocity (deg, positive left)\", ylabel=\"Cone angle (deg)\", title=\"AMSR2 scan: constant cone angle\", ylim=(CONE - 1, CONE + 1))\n",
+ "axes[0].set(\n",
+ " xlabel=\"Scan azimuth from inertial velocity (deg, positive left)\",\n",
+ " ylabel=\"Cone angle (deg)\",\n",
+ " title=\"AMSR2 scan: constant cone angle\",\n",
+ " ylim=(CONE - 1, CONE + 1),\n",
+ ")\n",
"asc = sc_vel[i2, 2] > 0\n",
"for mask, color, label in [(asc, BLUE, \"ascending\"), (~asc, AQUA, \"descending\")]:\n",
- " axes[1].scatter(sub_lat[mask], center_earth[mask], s=3, color=color, label=f\"vs. Earth-fixed velocity, {label}\")\n",
- "axes[1].scatter(sub_lat, center_inertial, s=3, color=ORANGE, label=\"vs. inertial velocity, both\")\n",
- "axes[1].set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Arc center azimuth (deg)\", title=\"Orientation of the scan arc\", ylim=(-5, 5))\n",
+ " axes[1].scatter(\n",
+ " sub_lat[mask],\n",
+ " center_earth[mask],\n",
+ " s=3,\n",
+ " color=color,\n",
+ " label=f\"vs. Earth-fixed velocity, {label}\",\n",
+ " )\n",
+ "axes[1].scatter(\n",
+ " sub_lat, center_inertial, s=3, color=ORANGE, label=\"vs. inertial velocity, both\"\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Sub-satellite latitude (deg)\",\n",
+ " ylabel=\"Arc center azimuth (deg)\",\n",
+ " title=\"Orientation of the scan arc\",\n",
+ " ylim=(-5, 5),\n",
+ ")\n",
"axes[1].legend(frameon=False, fontsize=8, markerscale=4, loc=\"center\")\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -518,10 +622,10 @@
"id": "29582e67",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:05.773318Z",
- "iopub.status.busy": "2026-10-08T20:03:05.773318Z",
- "iopub.status.idle": "2026-10-08T20:03:06.034297Z",
- "shell.execute_reply": "2026-10-08T20:03:06.034297Z"
+ "iopub.execute_input": "2026-10-25T14:01:03.839341Z",
+ "iopub.status.busy": "2026-10-25T14:01:03.839341Z",
+ "iopub.status.idle": "2026-10-25T14:01:04.216418Z",
+ "shell.execute_reply": "2026-10-25T14:01:04.216418Z"
}
},
"outputs": [
@@ -547,12 +651,18 @@
"altitude = float(np.median(amsr2[\"sc_alt\"])) * 1e3\n",
"swath = float(np.median(chord)) * 1e3\n",
"c, a = np.radians(CONE), np.radians(SECTOR)\n",
- "pointed_cross = np.degrees(2 * np.arctan(np.sin(c) * np.sin(a) / (np.cos(c) ** 2 + np.sin(c) ** 2 * np.cos(a))))\n",
+ "pointed_cross = np.degrees(\n",
+ " 2 * np.arctan(np.sin(c) * np.sin(a) / (np.cos(c) ** 2 + np.sin(c) ** 2 * np.cos(a)))\n",
+ ")\n",
"# angle from nadir of the pointed view's corners, which its field of regard must contain\n",
- "pointed_corner = np.degrees(np.arccos(np.cos(c) / np.sqrt(1 + np.tan(np.radians(pointed_cross / 2)) ** 2)))\n",
+ "pointed_corner = np.degrees(\n",
+ " np.arccos(np.cos(c) / np.sqrt(1 + np.tan(np.radians(pointed_cross / 2)) ** 2))\n",
+ ")\n",
"candidates = {\n",
" \"cone field of regard\": Instrument(name=\"AMSR2\", field_of_regard=2 * CONE),\n",
- " \"swath field of regard\": Instrument(name=\"AMSR2\", field_of_regard=swath_width_to_field_of_regard(altitude, swath)),\n",
+ " \"swath field of regard\": Instrument(\n",
+ " name=\"AMSR2\", field_of_regard=swath_width_to_field_of_regard(altitude, swath)\n",
+ " ),\n",
" \"pointed view\": PointedInstrument(\n",
" name=\"AMSR2\",\n",
" field_of_regard=2 * pointed_corner + 2,\n",
@@ -560,7 +670,6 @@
" along_track_field_of_view=1,\n",
" pitch_angle=CONE,\n",
" is_rectangular=True,\n",
- " velocity_frame=VelocityFrame.INERTIAL,\n",
" ),\n",
" \"conical instrument\": ConicalInstrument(\n",
" name=\"AMSR2\",\n",
@@ -568,7 +677,6 @@
" scan_center_azimuth=CENTER,\n",
" scan_half_width=SECTOR,\n",
" along_track_field_of_view=1,\n",
- " velocity_frame=VelocityFrame.INERTIAL,\n",
" ),\n",
"}\n",
"\n",
@@ -576,7 +684,9 @@
"def arc_rays(azimuth_deg, cone_deg=CONE):\n",
" \"\"\"Roll and pitch angles (deg) of rays on a cone at scan azimuths (positive left).\"\"\"\n",
" c, a = np.radians(cone_deg), np.radians(azimuth_deg)\n",
- " return np.degrees(np.arctan2(np.sin(c) * np.sin(a), np.cos(c))), np.degrees(np.arcsin(np.sin(c) * np.cos(a)))\n",
+ " return np.degrees(np.arctan2(np.sin(c) * np.sin(a), np.cos(c))), np.degrees(\n",
+ " np.arcsin(np.sin(c) * np.cos(a))\n",
+ " )\n",
"\n",
"\n",
"# trace the arc at the median scan azimuth of every sixth sample, at every hundredth scan\n",
@@ -587,25 +697,52 @@
"track3 = orbit.get_orbit_track(to_utc(scan_time[i3]))\n",
"for n, j in enumerate(samples3):\n",
" roll, pitch = arc_rays(median_azimuth[j])\n",
- " ray = compute_projected_ray_position(track3, 0, 0, roll, pitch, velocity_frame=VelocityFrame.INERTIAL)\n",
- " residual3[:, n] = GEOD.inv(ray.longitude.degrees, ray.latitude.degrees, foot_lon[i3, j], foot_lat[i3, j])[2] / 1e3\n",
+ " ray = compute_projected_ray_position(\n",
+ " track3, 0, 0, roll, pitch, velocity_frame=VelocityFrame.INERTIAL\n",
+ " )\n",
+ " residual3[:, n] = (\n",
+ " GEOD.inv(\n",
+ " ray.longitude.degrees,\n",
+ " ray.latitude.degrees,\n",
+ " foot_lon[i3, j],\n",
+ " foot_lat[i3, j],\n",
+ " )[2]\n",
+ " / 1e3\n",
+ " )\n",
"swath_width = np.vectorize(field_of_regard_to_swath_width)\n",
"widths = {\n",
" \"cone field of regard\": swath_width(amsr2[\"sc_alt\"][i2] * 1e3, 2 * CONE) / 1e3,\n",
- " \"swath field of regard\": swath_width(amsr2[\"sc_alt\"][i2] * 1e3, candidates[\"swath field of regard\"].field_of_regard) / 1e3,\n",
- " \"conical instrument\": np.vectorize(candidates[\"conical instrument\"].get_swath_width)(amsr2[\"sc_alt\"][i2] * 1e3) / 1e3,\n",
+ " \"swath field of regard\": swath_width(\n",
+ " amsr2[\"sc_alt\"][i2] * 1e3, candidates[\"swath field of regard\"].field_of_regard\n",
+ " )\n",
+ " / 1e3,\n",
+ " \"conical instrument\": np.vectorize(\n",
+ " candidates[\"conical instrument\"].get_swath_width\n",
+ " )(amsr2[\"sc_alt\"][i2] * 1e3)\n",
+ " / 1e3,\n",
"}\n",
"pd.concat(\n",
" [\n",
" pd.Series(\n",
" {\n",
- " \"arc traced with roll and pitch, distance to footprints, median (km)\": np.median(residual3),\n",
- " \"arc traced with roll and pitch, distance to footprints, p95 (km)\": np.percentile(residual3, 95),\n",
- " \"swath field of regard (deg)\": candidates[\"swath field of regard\"].field_of_regard,\n",
+ " \"arc traced with roll and pitch, distance to footprints, median (km)\": np.median(\n",
+ " residual3\n",
+ " ),\n",
+ " \"arc traced with roll and pitch, distance to footprints, p95 (km)\": np.percentile(\n",
+ " residual3, 95\n",
+ " ),\n",
+ " \"swath field of regard (deg)\": candidates[\n",
+ " \"swath field of regard\"\n",
+ " ].field_of_regard,\n",
" \"pointed view cross-track field of view (deg)\": pointed_cross,\n",
" }\n",
" ),\n",
- " pd.Series({f\"swath width minus observed, {k}, median (km)\": np.median(v - chord) for k, v in widths.items()}),\n",
+ " pd.Series(\n",
+ " {\n",
+ " f\"swath width minus observed, {k}, median (km)\": np.median(v - chord)\n",
+ " for k, v in widths.items()\n",
+ " }\n",
+ " ),\n",
" ]\n",
").round(2)"
]
@@ -616,10 +753,10 @@
"id": "6adfd16b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:06.036301Z",
- "iopub.status.busy": "2026-10-08T20:03:06.036301Z",
- "iopub.status.idle": "2026-10-08T20:03:07.016866Z",
- "shell.execute_reply": "2026-10-08T20:03:07.016362Z"
+ "iopub.execute_input": "2026-10-25T14:01:04.219422Z",
+ "iopub.status.busy": "2026-10-25T14:01:04.218422Z",
+ "iopub.status.idle": "2026-10-25T14:01:05.852411Z",
+ "shell.execute_reply": "2026-10-25T14:01:05.851907Z"
}
},
"outputs": [
@@ -637,20 +774,44 @@
"source": [
"k = i2[len(i2) // 3]\n",
"track = orbit.get_orbit_track(to_utc(scan_time[k]))\n",
- "projection = ccrs.AzimuthalEquidistant(central_longitude=float(amsr2[\"sc_lon\"][k]), central_latitude=float(amsr2[\"sc_lat\"][k]))\n",
+ "projection = ccrs.AzimuthalEquidistant(\n",
+ " central_longitude=float(amsr2[\"sc_lon\"][k]),\n",
+ " central_latitude=float(amsr2[\"sc_lat\"][k]),\n",
+ ")\n",
"fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={\"projection\": projection})\n",
"for (name, instrument), color in zip(candidates.items(), [BLUE, AQUA, ORANGE, INK]):\n",
- " footprint = instrument.compute_footprint(track)[0]\n",
+ " footprint = (\n",
+ " gcomw.model_copy(update={\"instruments\": [instrument]})\n",
+ " .get_instrument()\n",
+ " .compute_footprint(track)[0]\n",
+ " )\n",
" for part in getattr(footprint, \"geoms\", [footprint]):\n",
" ax.plot(*part.exterior.xy, color=color, transform=ccrs.Geodetic(), label=name)\n",
- "ax.plot(foot_lon[k], foot_lat[k], color=GRAY, lw=6, alpha=0.6, transform=ccrs.Geodetic(), label=\"AMSR2 scan (footprint centers)\")\n",
- "ax.plot(amsr2[\"sc_lon\"][k], amsr2[\"sc_lat\"][k], \"k^\", transform=ccrs.Geodetic(), label=\"sub-satellite point\")\n",
+ "ax.plot(\n",
+ " foot_lon[k],\n",
+ " foot_lat[k],\n",
+ " color=GRAY,\n",
+ " lw=6,\n",
+ " alpha=0.6,\n",
+ " transform=ccrs.Geodetic(),\n",
+ " label=\"AMSR2 scan (footprint centers)\",\n",
+ ")\n",
+ "ax.plot(\n",
+ " amsr2[\"sc_lon\"][k],\n",
+ " amsr2[\"sc_lat\"][k],\n",
+ " \"k^\",\n",
+ " transform=ccrs.Geodetic(),\n",
+ " label=\"sub-satellite point\",\n",
+ ")\n",
"ax.coastlines(color=GRAY, linewidth=0.5)\n",
"ax.set_extent([-1900e3, 1900e3, -1900e3, 1900e3], crs=projection)\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"unique = dict(zip(labels, handles))\n",
"ax.legend(unique.values(), unique.keys(), frameon=False, fontsize=8, loc=\"lower left\")\n",
- "ax.set_title(f\"One AMSR2 scan and candidate TAT-C footprints ({DAY + timedelta(seconds=float(scan_time[k])):%H:%M:%S} UTC)\", fontsize=9)\n",
+ "ax.set_title(\n",
+ " f\"One AMSR2 scan and candidate TAT-C footprints ({DAY + timedelta(seconds=float(scan_time[k])):%H:%M:%S} UTC)\",\n",
+ " fontsize=9,\n",
+ ")\n",
"plt.show()"
]
},
@@ -678,10 +839,10 @@
"id": "2bb7fe8c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T20:03:07.018871Z",
- "iopub.status.busy": "2026-10-08T20:03:07.018871Z",
- "iopub.status.idle": "2026-10-08T20:09:34.032427Z",
- "shell.execute_reply": "2026-10-08T20:09:34.032427Z"
+ "iopub.execute_input": "2026-10-25T14:01:05.854415Z",
+ "iopub.status.busy": "2026-10-25T14:01:05.854415Z",
+ "iopub.status.idle": "2026-10-25T14:07:06.252631Z",
+ "shell.execute_reply": "2026-10-25T14:07:06.252125Z"
}
},
"outputs": [
@@ -731,20 +892,20 @@
"
"
]
@@ -1029,10 +1250,24 @@
"edges4 = np.arange(-72, 73, 4)\n",
"for (feed, model), merged in matched4.items():\n",
" hit = merged.predicted.notna()\n",
- " q = binned(merged.azimuth[hit].to_numpy(), (merged.predicted - merged.time).dt.total_seconds()[hit].to_numpy(), edges4)\n",
- " ax.plot((edges4[:-1] + edges4[1:]) / 2, q[:, 1], color=FEED_COLORS[feed], ls=\"-\" if model == \"conical instrument\" else \":\", label=f\"{feed}, {model}\")\n",
+ " q = binned(\n",
+ " merged.azimuth[hit].to_numpy(),\n",
+ " (merged.predicted - merged.time).dt.total_seconds()[hit].to_numpy(),\n",
+ " edges4,\n",
+ " )\n",
+ " ax.plot(\n",
+ " (edges4[:-1] + edges4[1:]) / 2,\n",
+ " q[:, 1],\n",
+ " color=FEED_COLORS[feed],\n",
+ " ls=\"-\" if model == \"conical instrument\" else \":\",\n",
+ " label=f\"{feed}, {model}\",\n",
+ " )\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
- "ax.set(xlabel=\"Scan azimuth of the observation (deg, positive left)\", ylabel=\"TAT-C minus GMI (s)\", title=\"Observation time by position in the scan\")\n",
+ "ax.set(\n",
+ " xlabel=\"Scan azimuth of the observation (deg, positive left)\",\n",
+ " ylabel=\"TAT-C minus GMI (s)\",\n",
+ " title=\"Observation time by position in the scan\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1043,7 +1278,7 @@
"id": "9c236088",
"metadata": {},
"source": [
- "The conical instruments predict 99.7% of the passes observed by the low-frequency feed (3,084 of 3,093) and 99.8% of those observed by the high-frequency feed (2,724 of 2,730), with a single extra prediction. Their epochs follow the observation times by 0.56 s (median) and by no more than 0.69 s (95th percentile), uniformly across the scan. The offset is the orbit's: TAT-C's satellite trails GPM by 4.2 km (Test 1), or about 0.6 s, so the times are otherwise consistent to about 0.1 s.\n",
+ "The conical instruments predict all but one of the passes observed by the low-frequency feed (3,092 of 3,093) and every pass observed by the high-frequency feed (2,730), with a single extra prediction. Their epochs follow the observation times by 0.56 s (median) and by no more than 0.69 s (95th percentile), uniformly across the scan. The offset is the orbit's: TAT-C's satellite trails GPM by 4.2 km (Test 1), or about 0.6 s, so the times are otherwise consistent to about 0.1 s.\n",
"\n",
"This test exposed an error in the access periods within which `collect_observations` searches for cone crossings. Over a full day, Skyfield's `find_events` returned some rise and set times several seconds late, and passes whose cone crossing came within a few seconds of the start of the access period were missed (about 1.5% of the passes, mostly near GPM's northernmost latitudes). TAT-C now refines rise and set times to within a millisecond.\n",
"\n",
@@ -1060,10 +1295,10 @@
"id": "62567c06",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:44:20.930658Z",
- "iopub.status.busy": "2026-10-08T17:44:20.930658Z",
- "iopub.status.idle": "2026-10-08T18:04:41.697254Z",
- "shell.execute_reply": "2026-10-08T18:04:41.697254Z"
+ "iopub.execute_input": "2026-10-25T14:16:26.743621Z",
+ "iopub.status.busy": "2026-10-25T14:16:26.743621Z",
+ "iopub.status.idle": "2026-10-25T14:27:36.250782Z",
+ "shell.execute_reply": "2026-10-25T14:27:36.250782Z"
}
},
"outputs": [
@@ -1193,9 +1428,13 @@
"source": [
"orbit_start = 12 * 3600\n",
"orbit_period = 86400 / float(TLE[1][52:63])\n",
- "scans5 = np.nonzero((scan_time >= orbit_start) & (scan_time < orbit_start + orbit_period))[0]\n",
+ "scans5 = np.nonzero(\n",
+ " (scan_time >= orbit_start) & (scan_time < orbit_start + orbit_period)\n",
+ ")[0]\n",
"STEP5, MARGIN5 = 0.1, 60 # deg, s\n",
- "lon5, lat5 = np.meshgrid(np.arange(-180 + STEP5 / 2, 180, STEP5), np.arange(-90 + STEP5 / 2, 90, STEP5))\n",
+ "lon5, lat5 = np.meshgrid(\n",
+ " np.arange(-180 + STEP5 / 2, 180, STEP5), np.arange(-90 + STEP5 / 2, 90, STEP5)\n",
+ ")\n",
"cell_area5 = (np.cos(np.radians(lat5)) * (STEP5 * 111.32) ** 2).ravel()\n",
"grid5 = up(lon5.ravel(), lat5.ravel())\n",
"inclination5 = float(TLE[1][8:16])\n",
@@ -1208,7 +1447,11 @@
" distance5, nearest5 = tree5.query(grid5, workers=-1)\n",
" nearest_time5 = scan_time[scans5][nearest5 // N_SAMPLES] - scan_time[scans5[0]]\n",
" duration5 = scan_time[scans5[-1]] - scan_time[scans5[0]]\n",
- " compared5 = (distance5 < 2 * np.sin(300 / 6371 / 2)) & (nearest_time5 > MARGIN5) & (nearest_time5 < duration5 - MARGIN5)\n",
+ " compared5 = (\n",
+ " (distance5 < 2 * np.sin(300 / 6371 / 2))\n",
+ " & (nearest_time5 > MARGIN5)\n",
+ " & (nearest_time5 < duration5 - MARGIN5)\n",
+ " )\n",
" observed5 = np.zeros(len(grid5), dtype=bool)\n",
" _, nearest_eight = tree5.query(grid5[compared5], k=8, workers=-1)\n",
" for column in range(8):\n",
@@ -1221,17 +1464,33 @@
" observed5[np.flatnonzero(compared5)] |= inside\n",
" for time_step in [1.875, 10, 30]:\n",
" width = compute_along_track_field_of_view(\n",
- " gpm.orbit.get_semimajor_axis() - EARTH_MEAN_RADIUS, time_step, inclination5, eccentricity=gpm.orbit.get_eccentricity()\n",
+ " gpm.orbit.get_semimajor_axis() - EARTH_MEAN_RADIUS,\n",
+ " time_step,\n",
+ " inclination5,\n",
+ " eccentricity=gpm.orbit.get_eccentricity(),\n",
" )\n",
" first, last = scan_time[scans5[0]], scan_time[scans5[-1]]\n",
- " times = to_utc(first + time_step * (np.arange(int((last - first) // time_step)) + 0.5))\n",
- " instrument = instruments[feed].model_copy(update={\"along_track_field_of_view\": width})\n",
- " geometry5 = compute_ground_track(gpm.model_copy(update={\"instruments\": [instrument]}), times).geometry.iloc[0]\n",
+ " times = to_utc(\n",
+ " first + time_step * (np.arange(int((last - first) // time_step)) + 0.5)\n",
+ " )\n",
+ " instrument = instruments[feed].model_copy(\n",
+ " update={\"along_track_field_of_view\": width}\n",
+ " )\n",
+ " geometry5 = compute_ground_track(\n",
+ " gpm.model_copy(update={\"instruments\": [instrument]}), times\n",
+ " ).geometry.iloc[0]\n",
" prepare(geometry5)\n",
" predicted = np.zeros(len(grid5), dtype=bool)\n",
- " predicted[compared5] = contains_xy(geometry5, lon5.ravel()[compared5], lat5.ravel()[compared5])\n",
+ " predicted[compared5] = contains_xy(\n",
+ " geometry5, lon5.ravel()[compared5], lat5.ravel()[compared5]\n",
+ " )\n",
" area = lambda mask: np.sum(mask * cell_area5) / 1e6\n",
- " rows5[(f\"{feed} ({FEEDS[feed]})\", f\"{time_step:g} s ({width:.2f} deg along track)\")] = {\n",
+ " rows5[\n",
+ " (\n",
+ " f\"{feed} ({FEEDS[feed]})\",\n",
+ " f\"{time_step:g} s ({width:.2f} deg along track)\",\n",
+ " )\n",
+ " ] = {\n",
" \"observed (10^6 km^2)\": area(observed5),\n",
" \"TAT-C (10^6 km^2)\": area(predicted),\n",
" \"IoU\": area(observed5 & predicted) / area(observed5 | predicted),\n",
@@ -1255,7 +1514,7 @@
"| 1. Orbit (CelesTrak TLE vs. reported position) | 4.2 km behind along track (median), within 0.5 km across track and radially, a day or more before the TLE epoch |\n",
"| 2. Scan geometry | constant cone angles of 48.46 deg (low frequency) and 45.28 deg (high frequency); both scan 76.0 deg on either side of a center 0.3 deg right of the *inertial* velocity; arcs 522 and 462 km ahead of nadir; swaths 1,012 and 897 km |\n",
"| 3. Footprints | conical instrument footprints follow both arcs; swath width within 0.1 km (median) and 0.4 km (95th percentile) |\n",
- "| 4. Observations (`collect_observations`) | conical instruments: 99.7 and 99.8% of passes matched, one extra, `epoch` 0.56 s late (the orbit offset) and within 0.69 s (95th percentile); cone field of regard: all passes matched, 3.3% extra |\n",
+ "| 4. Observations (`collect_observations`) | conical instruments: 99.97 and 100% of passes matched, one extra, `epoch` 0.56 s late (the orbit offset) and within 0.69 s (95th percentile); cone field of regard: all passes matched, 3.3% extra |\n",
"| 5. Swath coverage (`compute_ground_track`) | IoU of 0.999 or better for both feeds at time steps from 1.875 to 30 s |\n",
"\n",
"**Generality of the conical instrument.** The `ConicalInstrument` was developed against AMSR2; GMI tests it on a different orbit (non-sun-synchronous, 65 deg inclination, 440 km altitude) and with two feeds. Two conical instruments on one satellite, each with its measured cone angle and the common scan sector, in the inertial velocity frame, reproduce the observed passes, their times to within the orbit error, the swath widths to within half a kilometer, and the scanned areas at any time step tested. No change to the model was needed.\n",
diff --git a/docs/validation/ValidateConstellationIridium.ipynb b/docs/validation/ValidateConstellationIridium.ipynb
index a2fd1857..f77f4550 100644
--- a/docs/validation/ValidateConstellationIridium.ipynb
+++ b/docs/validation/ValidateConstellationIridium.ipynb
@@ -156,7 +156,15 @@
"from tatc.analysis import aggregate_observations, collect_multi_observations\n",
"from tatc.constants import EARTH_MEAN_RADIUS, timescale\n",
"from tatc.generation import generate_points_fibonacci_lattice\n",
- "from tatc.schemas import CircularOrbit, GeneralPerturbationsOrbit, Instrument, Point, Satellite, SOCConstellation, WalkerConstellation\n",
+ "from tatc.schemas import (\n",
+ " CircularOrbit,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Instrument,\n",
+ " Point,\n",
+ " Satellite,\n",
+ " SOCConstellation,\n",
+ " WalkerConstellation,\n",
+ ")\n",
"from tatc.schemas.space.walker import WalkerConfiguration\n",
"from tatc.utils import compute_field_of_regard\n",
"\n",
@@ -166,7 +174,11 @@
"END = START + timedelta(hours=12)\n",
"path = ORBIT_DIR / \"iridium-NEXT.tle\"\n",
"if not path.exists():\n",
- " response = requests.get(\"https://celestrak.org/NORAD/elements/gp.php\", params={\"GROUP\": \"iridium-NEXT\", \"FORMAT\": \"tle\"}, timeout=120)\n",
+ " response = requests.get(\n",
+ " \"https://celestrak.org/NORAD/elements/gp.php\",\n",
+ " params={\"GROUP\": \"iridium-NEXT\", \"FORMAT\": \"tle\"},\n",
+ " timeout=120,\n",
+ " )\n",
" response.raise_for_status()\n",
" path.write_text(response.text)\n",
"lines = [line.rstrip() for line in path.read_text().splitlines() if line.strip()]\n",
@@ -186,15 +198,38 @@
"# planes: clusters of right ascension of ascending node\n",
"operational = operational.sort_values(\"raan\")\n",
"operational[\"plane\"] = (operational.raan.diff() > 5).cumsum()\n",
- "planes = operational.groupby(\"plane\").agg(raan=(\"raan\", \"mean\"), satellites=(\"name\", \"size\"), inclination=(\"inclination\", \"mean\"))\n",
+ "planes = operational.groupby(\"plane\").agg(\n",
+ " raan=(\"raan\", \"mean\"),\n",
+ " satellites=(\"name\", \"size\"),\n",
+ " inclination=(\"inclination\", \"mean\"),\n",
+ ")\n",
"spacing = np.diff(np.r_[planes.raan.to_numpy(), planes.raan.iloc[0] + 360])\n",
"planes[\"spacing to next plane (deg)\"] = spacing\n",
- "ALTITUDE = (398600.4418e9 / (2 * np.pi * operational.mean_motion.median() / 86400) ** 2) ** (1 / 3) - EARTH_MEAN_RADIUS\n",
+ "ALTITUDE = (\n",
+ " 398600.4418e9 / (2 * np.pi * operational.mean_motion.median() / 86400) ** 2\n",
+ ") ** (1 / 3) - EARTH_MEAN_RADIUS\n",
"INCLINATION = operational.inclination.mean()\n",
"MIN_ELEVATION = 8.2\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
- "print(f\"{len(tles)} Iridium NEXT TLEs, {len(operational)} operational; mean altitude {ALTITUDE / 1e3:.0f} km, inclination {INCLINATION:.2f} deg\")\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
+ "print(\n",
+ " f\"{len(tles)} Iridium NEXT TLEs, {len(operational)} operational; mean altitude {ALTITUDE / 1e3:.0f} km, inclination {INCLINATION:.2f} deg\"\n",
+ ")\n",
"planes.round(1)"
]
},
@@ -380,7 +415,13 @@
}
],
"source": [
- "eta = math.degrees(math.asin(EARTH_MEAN_RADIUS / (EARTH_MEAN_RADIUS + ALTITUDE) * math.cos(math.radians(MIN_ELEVATION))))\n",
+ "eta = math.degrees(\n",
+ " math.asin(\n",
+ " EARTH_MEAN_RADIUS\n",
+ " / (EARTH_MEAN_RADIUS + ALTITUDE)\n",
+ " * math.cos(math.radians(MIN_ELEVATION))\n",
+ " )\n",
+ ")\n",
"gamma = 90 - MIN_ELEVATION - eta\n",
"swath_width = 2 * math.radians(gamma) * EARTH_MEAN_RADIUS\n",
"orbit = CircularOrbit(altitude=ALTITUDE, inclination=INCLINATION, epoch=START)\n",
@@ -388,19 +429,51 @@
"\n",
"def polar_streets(gamma, satellites_per_plane):\n",
" \"\"\"Planes and spacings of a polar streets-of-coverage design (Adams and Rider, 1987).\"\"\"\n",
- " c = math.degrees(math.acos(math.cos(math.radians(gamma)) / math.cos(math.pi / satellites_per_plane)))\n",
+ " c = math.degrees(\n",
+ " math.acos(\n",
+ " math.cos(math.radians(gamma)) / math.cos(math.pi / satellites_per_plane)\n",
+ " )\n",
+ " )\n",
" planes = math.ceil((180 - 2 * c) / (gamma + c) + 1)\n",
" return planes, gamma + c, 2 * c\n",
"\n",
"\n",
- "rows1 = {\"Iridium (operational)\": {\"satellites per plane\": 11, \"planes\": len(planes), \"satellites\": 11 * len(planes), \"plane spacing (deg)\": float(np.median(spacing[spacing < 90])), \"seam spacing (deg)\": 180 - (len(planes) - 1) * float(np.median(spacing[spacing < 90])), \"planes span (deg)\": 180}}\n",
+ "rows1 = {\n",
+ " \"Iridium (operational)\": {\n",
+ " \"satellites per plane\": 11,\n",
+ " \"planes\": len(planes),\n",
+ " \"satellites\": 11 * len(planes),\n",
+ " \"plane spacing (deg)\": float(np.median(spacing[spacing < 90])),\n",
+ " \"seam spacing (deg)\": 180\n",
+ " - (len(planes) - 1) * float(np.median(spacing[spacing < 90])),\n",
+ " \"planes span (deg)\": 180,\n",
+ " }\n",
+ "}\n",
"for s in (10, 11, 12):\n",
" p, co, seam = polar_streets(gamma, s)\n",
- " rows1[f\"polar streets of coverage, {s} per plane\"] = {\"satellites per plane\": s, \"planes\": p, \"satellites\": s * p, \"plane spacing (deg)\": co, \"seam spacing (deg)\": seam, \"planes span (deg)\": 180}\n",
+ " rows1[f\"polar streets of coverage, {s} per plane\"] = {\n",
+ " \"satellites per plane\": s,\n",
+ " \"planes\": p,\n",
+ " \"satellites\": s * p,\n",
+ " \"plane spacing (deg)\": co,\n",
+ " \"seam spacing (deg)\": seam,\n",
+ " \"planes span (deg)\": 180,\n",
+ " }\n",
"for packing in (1.0, 0.9):\n",
- " s, p, co, seam = SOCConstellation(name=\"SOC\", orbit=orbit, swath_width=swath_width, packing_distance=packing).get_polar_design()\n",
- " rows1[f\"SOCConstellation (polar), packing distance {packing}\"] = {\"satellites per plane\": s, \"planes\": p, \"satellites\": s * p, \"plane spacing (deg)\": co, \"seam spacing (deg)\": seam, \"planes span (deg)\": 180}\n",
- "walker = SOCConstellation(name=\"SOC\", orbit=orbit, swath_width=swath_width, packing_distance=1.0, polar=False).generate_walker()\n",
+ " s, p, co, seam = SOCConstellation(\n",
+ " name=\"SOC\", orbit=orbit, swath_width=swath_width, packing_distance=packing\n",
+ " ).get_polar_design()\n",
+ " rows1[f\"SOCConstellation (polar), packing distance {packing}\"] = {\n",
+ " \"satellites per plane\": s,\n",
+ " \"planes\": p,\n",
+ " \"satellites\": s * p,\n",
+ " \"plane spacing (deg)\": co,\n",
+ " \"seam spacing (deg)\": seam,\n",
+ " \"planes span (deg)\": 180,\n",
+ " }\n",
+ "walker = SOCConstellation(\n",
+ " name=\"SOC\", orbit=orbit, swath_width=swath_width, packing_distance=1.0, polar=False\n",
+ ").generate_walker()\n",
"rows1[\"SOCConstellation (polar=False), packing distance 1.0\"] = {\n",
" \"satellites per plane\": walker.number_satellites // walker.number_planes,\n",
" \"planes\": walker.number_planes,\n",
@@ -409,7 +482,9 @@
" \"seam spacing (deg)\": np.nan,\n",
" \"planes span (deg)\": 360,\n",
"}\n",
- "print(f\"footprint radius {gamma:.2f} deg ({swath_width / 2e3:.0f} km), swath width {swath_width / 1e3:.0f} km\")\n",
+ "print(\n",
+ " f\"footprint radius {gamma:.2f} deg ({swath_width / 2e3:.0f} km), swath width {swath_width / 1e3:.0f} km\"\n",
+ ")\n",
"pd.DataFrame(rows1).T.round(1)"
]
},
@@ -593,47 +668,124 @@
],
"source": [
"lattice = generate_points_fibonacci_lattice(1000e3)\n",
- "points = [Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x) for i, r in enumerate(lattice.itertuples())]\n",
- "instrument = Instrument(name=\"antenna\", field_of_regard=compute_field_of_regard(ALTITUDE, MIN_ELEVATION))\n",
+ "points = [\n",
+ " Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x)\n",
+ " for i, r in enumerate(lattice.itertuples())\n",
+ "]\n",
+ "instrument = Instrument(\n",
+ " name=\"antenna\", field_of_regard=compute_field_of_regard(ALTITUDE, MIN_ELEVATION)\n",
+ ")\n",
"constellations = {\n",
- " \"Iridium (operational)\": [Satellite(name=r.name, orbit=GeneralPerturbationsOrbit.from_tle(r.tle), instruments=[instrument]) for r in operational.itertuples()],\n",
- " \"Walker star 66/6\": WalkerConstellation(name=\"star\", orbit=orbit, instruments=[instrument], number_satellites=66, number_planes=6, relative_spacing=3, configuration=WalkerConfiguration.STAR).generate_members(),\n",
- " \"Walker star 66/6, 22 deg seam\": WalkerConstellation(name=\"star\", orbit=orbit, instruments=[instrument], number_satellites=66, number_planes=6, relative_spacing=3, configuration=WalkerConfiguration.STAR, seam_spacing=22).generate_members(),\n",
- " \"SOCConstellation (polar)\": SOCConstellation(name=\"SOC\", orbit=orbit, instruments=[instrument], swath_width=swath_width, packing_distance=1.0).generate_members(),\n",
- " \"SOCConstellation (polar=False)\": SOCConstellation(name=\"SOC\", orbit=orbit, instruments=[instrument], swath_width=swath_width, packing_distance=1.0, polar=False).generate_members(),\n",
- " \"Iridium, 10 per plane\": [Satellite(name=r.name, orbit=GeneralPerturbationsOrbit.from_tle(r.tle), instruments=[instrument]) for r in operational.groupby(\"plane\").head(10).itertuples()],\n",
+ " \"Iridium (operational)\": [\n",
+ " Satellite(\n",
+ " name=r.name,\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(r.tle),\n",
+ " instruments=[instrument],\n",
+ " )\n",
+ " for r in operational.itertuples()\n",
+ " ],\n",
+ " \"Walker star 66/6\": WalkerConstellation(\n",
+ " name=\"star\",\n",
+ " orbit=orbit,\n",
+ " instruments=[instrument],\n",
+ " number_satellites=66,\n",
+ " number_planes=6,\n",
+ " relative_spacing=3,\n",
+ " configuration=WalkerConfiguration.STAR,\n",
+ " ).generate_members(),\n",
+ " \"Walker star 66/6, 22 deg seam\": WalkerConstellation(\n",
+ " name=\"star\",\n",
+ " orbit=orbit,\n",
+ " instruments=[instrument],\n",
+ " number_satellites=66,\n",
+ " number_planes=6,\n",
+ " relative_spacing=3,\n",
+ " configuration=WalkerConfiguration.STAR,\n",
+ " seam_spacing=22,\n",
+ " ).generate_members(),\n",
+ " \"SOCConstellation (polar)\": SOCConstellation(\n",
+ " name=\"SOC\",\n",
+ " orbit=orbit,\n",
+ " instruments=[instrument],\n",
+ " swath_width=swath_width,\n",
+ " packing_distance=1.0,\n",
+ " ).generate_members(),\n",
+ " \"SOCConstellation (polar=False)\": SOCConstellation(\n",
+ " name=\"SOC\",\n",
+ " orbit=orbit,\n",
+ " instruments=[instrument],\n",
+ " swath_width=swath_width,\n",
+ " packing_distance=1.0,\n",
+ " polar=False,\n",
+ " ).generate_members(),\n",
+ " \"Iridium, 10 per plane\": [\n",
+ " Satellite(\n",
+ " name=r.name,\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(r.tle),\n",
+ " instruments=[instrument],\n",
+ " )\n",
+ " for r in operational.groupby(\"plane\").head(10).itertuples()\n",
+ " ],\n",
"}\n",
"PERIOD = (END - START).total_seconds()\n",
"\n",
"\n",
"def coverage(satellites):\n",
" \"\"\"Coverage statistics of each point from TAT-C's aggregated observations.\"\"\"\n",
- " observations = Parallel(n_jobs=-1)(delayed(collect_multi_observations)(point, satellites, START, END) for point in points)\n",
+ " observations = Parallel(n_jobs=-1)(\n",
+ " delayed(collect_multi_observations)(point, satellites, START, END)\n",
+ " for point in points\n",
+ " )\n",
"\n",
" def point_coverage(point, obs):\n",
" if obs.empty:\n",
- " return {\"point_id\": point.id, \"latitude\": point.latitude, \"covered (%)\": 0.0, \"max gap (min)\": PERIOD / 60}\n",
+ " return {\n",
+ " \"point_id\": point.id,\n",
+ " \"latitude\": point.latitude,\n",
+ " \"covered (%)\": 0.0,\n",
+ " \"max gap (min)\": PERIOD / 60,\n",
+ " }\n",
" merged = aggregate_observations(obs)\n",
" covered = merged.access.dt.total_seconds().sum()\n",
" # gaps between periods, and before the first and after the last\n",
- " gaps = np.r_[(merged.start.iloc[0] - START).total_seconds(), merged.revisit.dt.total_seconds().dropna().to_numpy(), (END - merged.end.iloc[-1]).total_seconds()]\n",
- " return {\"point_id\": point.id, \"latitude\": point.latitude, \"covered (%)\": 100 * min(covered, PERIOD) / PERIOD, \"max gap (min)\": max(0.0, gaps.max()) / 60}\n",
+ " gaps = np.r_[\n",
+ " (merged.start.iloc[0] - START).total_seconds(),\n",
+ " merged.revisit.dt.total_seconds().dropna().to_numpy(),\n",
+ " (END - merged.end.iloc[-1]).total_seconds(),\n",
+ " ]\n",
+ " return {\n",
+ " \"point_id\": point.id,\n",
+ " \"latitude\": point.latitude,\n",
+ " \"covered (%)\": 100 * min(covered, PERIOD) / PERIOD,\n",
+ " \"max gap (min)\": max(0.0, gaps.max()) / 60,\n",
+ " }\n",
"\n",
- " return pd.DataFrame([point_coverage(point, obs) for point, obs in zip(points, observations)])\n",
+ " return pd.DataFrame(\n",
+ " [point_coverage(point, obs) for point, obs in zip(points, observations)]\n",
+ " )\n",
"\n",
"\n",
"results = {label: coverage(satellites) for label, satellites in constellations.items()}\n",
"\n",
"# independent check: sampled elevation angles of the operational satellites\n",
- "times = timescale.from_datetimes([START + timedelta(seconds=s) for s in range(0, int(PERIOD), 20)])\n",
- "positions = np.stack([s.orbit.to_gp_orbit().elements[0].to_skyfield().at(times).frame_xyz(itrs).m for s in constellations[\"Iridium (operational)\"]])\n",
+ "times = timescale.from_datetimes(\n",
+ " [START + timedelta(seconds=s) for s in range(0, int(PERIOD), 20)]\n",
+ ")\n",
+ "positions = np.stack(\n",
+ " [\n",
+ " s.orbit.to_gp_orbit().elements[0].to_skyfield().at(times).frame_xyz(itrs).m\n",
+ " for s in constellations[\"Iridium (operational)\"]\n",
+ " ]\n",
+ ")\n",
"sampled = []\n",
"for point in points:\n",
" site = wgs84.latlon(point.latitude, point.longitude)\n",
" up = np.array(site.itrs_xyz.m) / np.linalg.norm(site.itrs_xyz.m)\n",
" los = positions - np.array(site.itrs_xyz.m)[None, :, None]\n",
" sin_elevation = np.einsum(\"j,ijk->ik\", up, los) / np.linalg.norm(los, axis=1)\n",
- " sampled.append(100 * np.mean((sin_elevation >= np.sin(np.radians(MIN_ELEVATION))).any(axis=0)))\n",
+ " sampled.append(\n",
+ " 100 * np.mean((sin_elevation >= np.sin(np.radians(MIN_ELEVATION))).any(axis=0))\n",
+ " )\n",
"results[\"Iridium (operational)\"][\"sampled covered (%)\"] = sampled\n",
"\n",
"summary = {}\n",
@@ -676,9 +828,17 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 3.8))\n",
- "for (label, frame), color in zip(results.items(), [INK, BLUE, PURPLE, AQUA, ORANGE, GRAY]):\n",
- " ax.scatter(frame.latitude, frame[\"covered (%)\"], s=8, color=color, label=label, alpha=0.8)\n",
- "ax.set(xlabel=\"Point latitude (deg)\", ylabel=\"Time covered (%)\", title=f\"Coverage above {MIN_ELEVATION} deg elevation over 12 hours\")\n",
+ "for (label, frame), color in zip(\n",
+ " results.items(), [INK, BLUE, PURPLE, AQUA, ORANGE, GRAY]\n",
+ "):\n",
+ " ax.scatter(\n",
+ " frame.latitude, frame[\"covered (%)\"], s=8, color=color, label=label, alpha=0.8\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Point latitude (deg)\",\n",
+ " ylabel=\"Time covered (%)\",\n",
+ " title=f\"Coverage above {MIN_ELEVATION} deg elevation over 12 hours\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, loc=\"lower center\", ncol=3)\n",
"plt.show()"
]
diff --git a/docs/validation/ValidateDopGNSS.ipynb b/docs/validation/ValidateDopGNSS.ipynb
index e12121c0..732a91c8 100644
--- a/docs/validation/ValidateDopGNSS.ipynb
+++ b/docs/validation/ValidateDopGNSS.ipynb
@@ -75,28 +75,52 @@
"ORBIT_DIR.mkdir(parents=True, exist_ok=True)\n",
"DAY = datetime(2026, 10, 3, tzinfo=timezone.utc)\n",
"BKG = \"https://igs.bkg.bund.de/root_ftp/IGS\"\n",
- "STATIONS = [\"KIR000SWE\", \"WTZR00DEU\", \"GUAM00GUM\", \"NKLG00GAB\", \"HOB200AUS\", \"MCM400ATA\"]\n",
+ "STATIONS = [\n",
+ " \"KIR000SWE\",\n",
+ " \"WTZR00DEU\",\n",
+ " \"GUAM00GUM\",\n",
+ " \"NKLG00GAB\",\n",
+ " \"HOB200AUS\",\n",
+ " \"MCM400ATA\",\n",
+ "]\n",
"FILES = {\n",
" \"BRDC00IGS_R_20262760000_01D_MN.rnx.gz\": f\"{BKG}/BRDC/2026/276/\",\n",
" \"IGS0OPSRAP_20262760000_01D_15M_ORB.SP3.gz\": f\"{BKG}/products/2438/\",\n",
- " **{f\"{station}_R_20262760000_01D_30S_MO.crx.gz\": f\"{BKG}/obs/2026/276/\" for station in STATIONS},\n",
+ " **{\n",
+ " f\"{station}_R_20262760000_01D_30S_MO.crx.gz\": f\"{BKG}/obs/2026/276/\"\n",
+ " for station in STATIONS\n",
+ " },\n",
"}\n",
"for name, base in FILES.items():\n",
" if not (DATA_DIR / name).exists():\n",
" response = requests.get(base + name, timeout=600)\n",
" response.raise_for_status()\n",
" (DATA_DIR / name).write_bytes(response.content)\n",
- "SYSTEMS = {\"G\": (\"GPS\", \"gps-ops\"), \"E\": (\"Galileo\", \"galileo\"), \"C\": (\"BeiDou\", \"beidou\"), \"R\": (\"GLONASS\", \"glo-ops\")}\n",
+ "SYSTEMS = {\n",
+ " \"G\": (\"GPS\", \"gps-ops\"),\n",
+ " \"E\": (\"Galileo\", \"galileo\"),\n",
+ " \"C\": (\"BeiDou\", \"beidou\"),\n",
+ " \"R\": (\"GLONASS\", \"glo-ops\"),\n",
+ "}\n",
"TLES = {}\n",
"for system, (label, group) in SYSTEMS.items():\n",
" path = ORBIT_DIR / f\"{group}.tle\"\n",
" if not path.exists():\n",
- " response = requests.get(\"https://celestrak.org/NORAD/elements/gp.php\", params={\"GROUP\": group, \"FORMAT\": \"tle\"}, timeout=120)\n",
+ " response = requests.get(\n",
+ " \"https://celestrak.org/NORAD/elements/gp.php\",\n",
+ " params={\"GROUP\": group, \"FORMAT\": \"tle\"},\n",
+ " timeout=120,\n",
+ " )\n",
" response.raise_for_status()\n",
" path.write_text(response.text)\n",
" lines = [line.rstrip() for line in path.read_text().splitlines() if line.strip()]\n",
- " TLES[system] = {lines[i].strip(): lines[i + 1 : i + 3] for i in range(0, len(lines) - 2, 3)}\n",
- "pd.Series({label: len(TLES[system]) for system, (label, _) in SYSTEMS.items()}, name=\"satellites in TLE group\")"
+ " TLES[system] = {\n",
+ " lines[i].strip(): lines[i + 1 : i + 3] for i in range(0, len(lines) - 2, 3)\n",
+ " }\n",
+ "pd.Series(\n",
+ " {label: len(TLES[system]) for system, (label, _) in SYSTEMS.items()},\n",
+ " name=\"satellites in TLE group\",\n",
+ ")"
]
},
{
@@ -159,7 +183,15 @@
" if system not in \"GECR\":\n",
" continue\n",
" prn = int(line[1:3])\n",
- " epoch = datetime(int(line[4:8]), int(line[9:11]), int(line[12:14]), int(line[15:17]), int(line[18:20]), int(line[21:23]), tzinfo=timezone.utc)\n",
+ " epoch = datetime(\n",
+ " int(line[4:8]),\n",
+ " int(line[9:11]),\n",
+ " int(line[12:14]),\n",
+ " int(line[15:17]),\n",
+ " int(line[18:20]),\n",
+ " int(line[21:23]),\n",
+ " tzinfo=timezone.utc,\n",
+ " )\n",
" values = [_float(line[23 + 19 * j : 42 + 19 * j]) for j in range(3)]\n",
" for row in block[1:]:\n",
" values += [_float(row[4 + 19 * j : 23 + 19 * j]) for j in range(4)]\n",
@@ -187,7 +219,11 @@
" nu = np.arctan2(np.sqrt(1 - e**2) * np.sin(ecc_anomaly), np.cos(ecc_anomaly) - e)\n",
" phi = nu + w\n",
" u = phi + cus * np.sin(2 * phi) + cuc * np.cos(2 * phi)\n",
- " r = a * (1 - e * np.cos(ecc_anomaly)) + crs * np.sin(2 * phi) + crc * np.cos(2 * phi)\n",
+ " r = (\n",
+ " a * (1 - e * np.cos(ecc_anomaly))\n",
+ " + crs * np.sin(2 * phi)\n",
+ " + crc * np.cos(2 * phi)\n",
+ " )\n",
" i = i0 + cis * np.sin(2 * phi) + cic * np.cos(2 * phi) + idot * tk\n",
" x, y = r * np.cos(u), r * np.sin(u)\n",
" if system == \"C\" and prn in BEIDOU_GEO:\n",
@@ -196,11 +232,21 @@
" yg = x * np.sin(omega) + y * np.cos(i) * np.cos(omega)\n",
" zg = y * np.sin(i)\n",
" f, c = np.radians(-5), omega_e * tk\n",
- " rx = np.array([[1, 0, 0], [0, np.cos(f), np.sin(f)], [0, -np.sin(f), np.cos(f)]])\n",
- " rz = np.array([[np.cos(c), np.sin(c), 0], [-np.sin(c), np.cos(c), 0], [0, 0, 1]])\n",
+ " rx = np.array(\n",
+ " [[1, 0, 0], [0, np.cos(f), np.sin(f)], [0, -np.sin(f), np.cos(f)]]\n",
+ " )\n",
+ " rz = np.array(\n",
+ " [[np.cos(c), np.sin(c), 0], [-np.sin(c), np.cos(c), 0], [0, 0, 1]]\n",
+ " )\n",
" return rz @ rx @ np.array([xg, yg, zg])\n",
" omega = omega0 + (omega_dot - omega_e) * tk - omega_e * toe\n",
- " return np.array([x * np.cos(omega) - y * np.cos(i) * np.sin(omega), x * np.sin(omega) + y * np.cos(i) * np.cos(omega), y * np.sin(i)])\n",
+ " return np.array(\n",
+ " [\n",
+ " x * np.cos(omega) - y * np.cos(i) * np.sin(omega),\n",
+ " x * np.sin(omega) + y * np.cos(i) * np.cos(omega),\n",
+ " y * np.sin(i),\n",
+ " ]\n",
+ " )\n",
"\n",
"\n",
"def _glonass_position(v, dt):\n",
@@ -213,12 +259,24 @@
" x, y, z, vx, vy, vz = s\n",
" r = np.sqrt(x * x + y * y + z * z)\n",
" c = 1.5 * j2 * mu * ae**2 / r**5\n",
- " return np.array([\n",
- " vx, vy, vz,\n",
- " -mu * x / r**3 - c * x * (1 - 5 * z * z / r**2) + omega**2 * x + 2 * omega * vy + acc[0],\n",
- " -mu * y / r**3 - c * y * (1 - 5 * z * z / r**2) + omega**2 * y - 2 * omega * vx + acc[1],\n",
- " -mu * z / r**3 - c * z * (3 - 5 * z * z / r**2) + acc[2],\n",
- " ])\n",
+ " return np.array(\n",
+ " [\n",
+ " vx,\n",
+ " vy,\n",
+ " vz,\n",
+ " -mu * x / r**3\n",
+ " - c * x * (1 - 5 * z * z / r**2)\n",
+ " + omega**2 * x\n",
+ " + 2 * omega * vy\n",
+ " + acc[0],\n",
+ " -mu * y / r**3\n",
+ " - c * y * (1 - 5 * z * z / r**2)\n",
+ " + omega**2 * y\n",
+ " - 2 * omega * vx\n",
+ " + acc[1],\n",
+ " -mu * z / r**3 - c * z * (3 - 5 * z * z / r**2) + acc[2],\n",
+ " ]\n",
+ " )\n",
"\n",
" steps = max(1, int(np.ceil(abs(dt) / 60)))\n",
" h = dt / steps\n",
@@ -268,10 +326,16 @@
" continue\n",
" # GPS/Galileo/BeiDou epochs (time of clock) are in the system's own time scale\n",
" offset = GPS_UTC + (BDT_GPS if system == \"C\" else 0)\n",
- " j = int(np.argmin(np.abs(epochs - ((t - GPS_EPOCH).total_seconds() + offset))))\n",
+ " j = int(\n",
+ " np.argmin(np.abs(epochs - ((t - GPS_EPOCH).total_seconds() + offset)))\n",
+ " )\n",
" if abs(epochs[j] - ((t - GPS_EPOCH).total_seconds() + offset)) > validity:\n",
" continue\n",
- " t_system = t_gps if system in \"GE\" else (t - BDT_EPOCH).total_seconds() + GPS_UTC + BDT_GPS\n",
+ " t_system = (\n",
+ " t_gps\n",
+ " if system in \"GE\"\n",
+ " else (t - BDT_EPOCH).total_seconds() + GPS_UTC + BDT_GPS\n",
+ " )\n",
" out[k] = _kepler_position(system, recs[j][1], recs[j][3], t_system)\n",
" positions[name] = out\n",
" return positions\n",
@@ -284,11 +348,23 @@
" for line in f:\n",
" if line.startswith(\"*\"):\n",
" parts = line.split()\n",
- " gps = datetime(int(parts[1]), int(parts[2]), int(parts[3]), int(parts[4]), int(parts[5]), int(float(parts[6])), tzinfo=timezone.utc)\n",
+ " gps = datetime(\n",
+ " int(parts[1]),\n",
+ " int(parts[2]),\n",
+ " int(parts[3]),\n",
+ " int(parts[4]),\n",
+ " int(parts[5]),\n",
+ " int(float(parts[6])),\n",
+ " tzinfo=timezone.utc,\n",
+ " )\n",
" epochs.append(gps - timedelta(seconds=GPS_UTC))\n",
" elif line.startswith(\"P\") and epochs:\n",
" name = line[1:4].replace(\" \", \"0\")\n",
- " data.setdefault(name, {})[len(epochs) - 1] = [float(line[4:18]) * 1e3, float(line[18:32]) * 1e3, float(line[32:46]) * 1e3]\n",
+ " data.setdefault(name, {})[len(epochs) - 1] = [\n",
+ " float(line[4:18]) * 1e3,\n",
+ " float(line[18:32]) * 1e3,\n",
+ " float(line[32:46]) * 1e3,\n",
+ " ]\n",
" out = {}\n",
" for name, values in data.items():\n",
" array = np.full((len(epochs), 3), np.nan)\n",
@@ -312,7 +388,14 @@
" xs, ps = x[j : j + order], p[j : j + order]\n",
" if np.isnan(ps).any():\n",
" continue\n",
- " weights = np.array([np.prod([(s - xs[m]) / (xs[i] - xs[m]) for m in range(order) if m != i]) for i in range(order)])\n",
+ " weights = np.array(\n",
+ " [\n",
+ " np.prod(\n",
+ " [(s - xs[m]) / (xs[i] - xs[m]) for m in range(order) if m != i]\n",
+ " )\n",
+ " for i in range(order)\n",
+ " ]\n",
+ " )\n",
" result[k] = weights @ ps\n",
" out[name] = result\n",
" return out\n",
@@ -322,7 +405,9 @@
" \"\"\"Station position (m, from the header) and the satellites with a pseudorange observation at each of the given UTC times (GPS time in the file).\"\"\"\n",
" import hatanaka\n",
"\n",
- " text = hatanaka.decompress(open(path, \"rb\").read()).decode(\"ascii\", errors=\"replace\")\n",
+ " text = hatanaka.decompress(open(path, \"rb\").read()).decode(\n",
+ " \"ascii\", errors=\"replace\"\n",
+ " )\n",
" lines = text.splitlines()\n",
" position = None\n",
" types = {}\n",
@@ -332,7 +417,9 @@
" line = lines[k]\n",
" label = line[60:].strip()\n",
" if label == \"APPROX POSITION XYZ\":\n",
- " position = np.array([float(line[0:14]), float(line[14:28]), float(line[28:42])])\n",
+ " position = np.array(\n",
+ " [float(line[0:14]), float(line[14:28]), float(line[28:42])]\n",
+ " )\n",
" if label == \"SYS / # / OBS TYPES\":\n",
" if line[0] != \" \":\n",
" current = line[0]\n",
@@ -347,7 +434,15 @@
" line = lines[k]\n",
" if line.startswith(\">\"):\n",
" count = int(line[32:35])\n",
- " epoch = datetime(int(line[2:6]), int(line[7:9]), int(line[10:12]), int(line[13:15]), int(line[16:18]), int(float(line[19:29])), tzinfo=timezone.utc)\n",
+ " epoch = datetime(\n",
+ " int(line[2:6]),\n",
+ " int(line[7:9]),\n",
+ " int(line[10:12]),\n",
+ " int(line[13:15]),\n",
+ " int(line[16:18]),\n",
+ " int(float(line[19:29])),\n",
+ " tzinfo=timezone.utc,\n",
+ " )\n",
" key = round((epoch - GPS_EPOCH).total_seconds())\n",
" if key in wanted and int(line[31]) in (0, 1):\n",
" sats = set()\n",
@@ -473,9 +568,19 @@
"from pyproj import Transformer\n",
"from skyfield.framelib import itrs\n",
"\n",
- "from tatc.analysis import DopMethod, collect_multi_observations, compute_dop # noqa: F401\n",
+ "from tatc.analysis import (\n",
+ " DopMethod,\n",
+ " collect_multi_observations,\n",
+ " compute_dop,\n",
+ ") # noqa: F401\n",
"from tatc.constants import EARTH_MEAN_RADIUS, timescale\n",
- "from tatc.schemas import CircularOrbit, GeneralPerturbationsOrbit, Point, Satellite, WalkerConstellation\n",
+ "from tatc.schemas import (\n",
+ " CircularOrbit,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Point,\n",
+ " Satellite,\n",
+ " WalkerConstellation,\n",
+ ")\n",
"\n",
"TIMES = [DAY + timedelta(minutes=5 * k, seconds=-GPS_UTC) for k in range(1, 289)]\n",
"MASK = 10 # deg\n",
@@ -484,23 +589,39 @@
"to_geodetic = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
"stations = {}\n",
"for station in STATIONS:\n",
- " xyz, tracked = read_tracked(DATA_DIR / f\"{station}_R_20262760000_01D_30S_MO.crx.gz\", TIMES)\n",
+ " xyz, tracked = read_tracked(\n",
+ " DATA_DIR / f\"{station}_R_20262760000_01D_30S_MO.crx.gz\", TIMES\n",
+ " )\n",
" lon, lat, height = to_geodetic.transform(*xyz)\n",
- " stations[station[:4]] = {\"xyz\": xyz, \"lat\": lat, \"lon\": lon, \"height\": height, \"tracked\": tracked}\n",
+ " stations[station[:4]] = {\n",
+ " \"xyz\": xyz,\n",
+ " \"lat\": lat,\n",
+ " \"lon\": lon,\n",
+ " \"height\": height,\n",
+ " \"tracked\": tracked,\n",
+ " }\n",
"# TAT-C satellites from TLEs, their Earth-fixed positions, and their PRNs\n",
"satellites, tle_positions, prn = {}, {}, {}\n",
"sk_times = timescale.from_datetimes(TIMES)\n",
"for system in SYSTEMS:\n",
" for name, (line1, line2) in TLES[system].items():\n",
- " sat = Satellite(name=name, orbit=GeneralPerturbationsOrbit.from_tle([line1, line2]))\n",
- " position = np.array(sat.orbit.to_gp_orbit().get_orbit_track_at_time(sk_times).frame_xyz(itrs).m).T\n",
+ " sat = Satellite(\n",
+ " name=name, orbit=GeneralPerturbationsOrbit.from_tle([line1, line2])\n",
+ " )\n",
+ " position = np.array(\n",
+ " sat.orbit.to_gp_orbit().get_orbit_track_at_time(sk_times).frame_xyz(itrs).m\n",
+ " ).T\n",
" satellites[name], tle_positions[name] = sat, position\n",
" code = re.search(r\"\\(PRN (\\d+)\\)\", name) if system == \"G\" else None\n",
" if code is not None:\n",
" if f\"{system}{int(code.group(1)):02d}\" in broadcast:\n",
" prn[name] = f\"{system}{int(code.group(1)):02d}\"\n",
" continue\n",
- " candidates = {s: np.linalg.norm(p[144] - position[144]) for s, p in broadcast.items() if s[0] == system and np.isfinite(p[144]).all()}\n",
+ " candidates = {\n",
+ " s: np.linalg.norm(p[144] - position[144])\n",
+ " for s, p in broadcast.items()\n",
+ " if s[0] == system and np.isfinite(p[144]).all()\n",
+ " }\n",
" if candidates and min(candidates.values()) < 100e3:\n",
" prn[name] = min(candidates, key=candidates.get)\n",
"pd.DataFrame(\n",
@@ -508,7 +629,9 @@
" label: {\n",
" \"TLEs\": len(TLES[system]),\n",
" \"healthy broadcast satellites\": sum(s[0] == system for s in broadcast),\n",
- " \"TLEs matched to healthy satellites\": sum(p[0] == system for p in prn.values()),\n",
+ " \"TLEs matched to healthy satellites\": sum(\n",
+ " p[0] == system for p in prn.values()\n",
+ " ),\n",
" }\n",
" for system, (label, _) in SYSTEMS.items()\n",
" }\n",
@@ -529,18 +652,35 @@
},
"outputs": [],
"source": [
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
"SYSTEM_COLORS = {\"G\": BLUE, \"E\": ORANGE, \"C\": AQUA, \"R\": PURPLE}\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"\n",
"def enu_matrix(lat, lon):\n",
" lat, lon = np.radians(lat), np.radians(lon)\n",
- " return np.array([\n",
- " [-np.sin(lon), np.cos(lon), 0],\n",
- " [-np.sin(lat) * np.cos(lon), -np.sin(lat) * np.sin(lon), np.cos(lat)],\n",
- " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)],\n",
- " ])\n",
+ " return np.array(\n",
+ " [\n",
+ " [-np.sin(lon), np.cos(lon), 0],\n",
+ " [-np.sin(lat) * np.cos(lon), -np.sin(lat) * np.sin(lon), np.cos(lat)],\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)],\n",
+ " ]\n",
+ " )\n",
"\n",
"\n",
"def reference_dop(station, positions, mask=MASK, clocks=\"single\", subsets=None):\n",
@@ -552,7 +692,12 @@
" rotation = enu_matrix(s[\"lat\"], s[\"lon\"])\n",
" rows = []\n",
" for k in range(len(TIMES)):\n",
- " names = [n for n in positions if np.isfinite(positions[n][k]).all() and (subsets is None or n in subsets[k])]\n",
+ " names = [\n",
+ " n\n",
+ " for n in positions\n",
+ " if np.isfinite(positions[n][k]).all()\n",
+ " and (subsets is None or n in subsets[k])\n",
+ " ]\n",
" if not names:\n",
" rows.append({\"n\": 0})\n",
" continue\n",
@@ -562,7 +707,10 @@
" e = e[visible]\n",
" systems = sorted({n[0] for n, v in zip(names, visible) if v})\n",
" if clocks == \"system\":\n",
- " columns = [[1.0 if n[0] == system else 0.0 for n, v in zip(names, visible) if v] for system in systems]\n",
+ " columns = [\n",
+ " [1.0 if n[0] == system else 0.0 for n, v in zip(names, visible) if v]\n",
+ " for system in systems\n",
+ " ]\n",
" h = np.column_stack([e] + [np.array(c) for c in columns])\n",
" else:\n",
" h = np.column_stack([e, np.ones(len(e))])\n",
@@ -570,7 +718,15 @@
" rows.append({\"n\": len(e)})\n",
" continue\n",
" q = np.linalg.inv(h.T @ h)\n",
- " rows.append({\"n\": len(e), \"HDOP\": np.sqrt(q[0, 0] + q[1, 1]), \"VDOP\": np.sqrt(q[2, 2]), \"PDOP\": np.sqrt(np.trace(q[:3, :3])), \"TDOP\": np.sqrt(q[3, 3])})\n",
+ " rows.append(\n",
+ " {\n",
+ " \"n\": len(e),\n",
+ " \"HDOP\": np.sqrt(q[0, 0] + q[1, 1]),\n",
+ " \"VDOP\": np.sqrt(q[2, 2]),\n",
+ " \"PDOP\": np.sqrt(np.trace(q[:3, :3])),\n",
+ " \"TDOP\": np.sqrt(q[3, 3]),\n",
+ " }\n",
+ " )\n",
" return pd.DataFrame(rows, index=TIMES)\n",
"\n",
"\n",
@@ -579,7 +735,12 @@
" s = stations[station]\n",
" point = Point(id=0, latitude=s[\"lat\"], longitude=s[\"lon\"], elevation=s[\"height\"])\n",
" sats = [satellites[n] for n in names]\n",
- " out = {method.value.upper(): compute_dop(TIMES, point, sats, mask, method, systems=systems).dop.to_numpy() for method in [DopMethod.PDOP, DopMethod.HDOP, DopMethod.VDOP, DopMethod.TDOP]}\n",
+ " out = {\n",
+ " method.value.upper(): compute_dop(\n",
+ " TIMES, point, sats, mask, method, systems=systems\n",
+ " ).dop.to_numpy()\n",
+ " for method in [DopMethod.PDOP, DopMethod.HDOP, DopMethod.VDOP, DopMethod.TDOP]\n",
+ " }\n",
" return pd.DataFrame(out, index=TIMES)\n",
"\n",
"\n",
@@ -697,13 +858,40 @@
"source": [
"sp3_epochs, sp3 = read_sp3(DATA_DIR / \"IGS0OPSRAP_20262760000_01D_15M_ORB.SP3.gz\")\n",
"sp3_positions = interpolate_sp3(sp3_epochs, sp3, TIMES[::6])\n",
- "check = np.concatenate([np.linalg.norm(broadcast[s][::6] - sp3_positions[s], axis=1) for s in sp3_positions if s in broadcast])\n",
+ "check = np.concatenate(\n",
+ " [\n",
+ " np.linalg.norm(broadcast[s][::6] - sp3_positions[s], axis=1)\n",
+ " for s in sp3_positions\n",
+ " if s in broadcast\n",
+ " ]\n",
+ ")\n",
"check = check[np.isfinite(check)]\n",
- "rows1 = {\"GPS broadcast vs. IGS rapid (m)\": {\"satellites\": len([s for s in sp3_positions if s in broadcast]), \"median\": np.median(check), \"p95\": np.percentile(check, 95), \"max\": check.max()}}\n",
+ "rows1 = {\n",
+ " \"GPS broadcast vs. IGS rapid (m)\": {\n",
+ " \"satellites\": len([s for s in sp3_positions if s in broadcast]),\n",
+ " \"median\": np.median(check),\n",
+ " \"p95\": np.percentile(check, 95),\n",
+ " \"max\": check.max(),\n",
+ " }\n",
+ "}\n",
"for system, (label, _) in SYSTEMS.items():\n",
- " errors = np.concatenate([np.linalg.norm(tle_positions[n] - broadcast[p], axis=1) for n, p in prn.items() if p[0] == system]) / 1e3\n",
+ " errors = (\n",
+ " np.concatenate(\n",
+ " [\n",
+ " np.linalg.norm(tle_positions[n] - broadcast[p], axis=1)\n",
+ " for n, p in prn.items()\n",
+ " if p[0] == system\n",
+ " ]\n",
+ " )\n",
+ " / 1e3\n",
+ " )\n",
" errors = errors[np.isfinite(errors)]\n",
- " rows1[f\"{label} TLE vs. broadcast (km)\"] = {\"satellites\": sum(p[0] == system for p in prn.values()), \"median\": np.median(errors), \"p95\": np.percentile(errors, 95), \"max\": errors.max()}\n",
+ " rows1[f\"{label} TLE vs. broadcast (km)\"] = {\n",
+ " \"satellites\": sum(p[0] == system for p in prn.values()),\n",
+ " \"median\": np.median(errors),\n",
+ " \"p95\": np.percentile(errors, 95),\n",
+ " \"max\": errors.max(),\n",
+ " }\n",
"pd.DataFrame(rows1).T.round(2)"
]
},
@@ -857,7 +1045,13 @@
}
],
"source": [
- "COMBINATIONS = {\"GPS\": \"G\", \"Galileo\": \"E\", \"BeiDou\": \"C\", \"GLONASS\": \"R\", \"all four\": \"GECR\"}\n",
+ "COMBINATIONS = {\n",
+ " \"GPS\": \"G\",\n",
+ " \"Galileo\": \"E\",\n",
+ " \"BeiDou\": \"C\",\n",
+ " \"GLONASS\": \"R\",\n",
+ " \"all four\": \"GECR\",\n",
+ "}\n",
"rows2, series2 = {}, {}\n",
"for label, systems in COMBINATIONS.items():\n",
" matched = [n for n, p in prn.items() if p[0] in systems]\n",
@@ -873,14 +1067,23 @@
" pooled[\"same\"].append(relative(same.PDOP, reference_same.PDOP))\n",
" pooled[\"group\"].append(relative(tatc_group.PDOP, reference.PDOP))\n",
" series2[(label, station)] = (reference, same, tatc_group)\n",
- " same_all, group_all = np.concatenate(pooled[\"same\"]), np.concatenate(pooled[\"group\"])\n",
+ " same_all, group_all = np.concatenate(pooled[\"same\"]), np.concatenate(\n",
+ " pooled[\"group\"]\n",
+ " )\n",
" rows2[label] = {\n",
" \"TLEs / healthy satellites / matched\": f\"{len(group)} / {len(reference_positions)} / {len(matched)}\",\n",
- " \"PDOP difference, same satellites, median |x| (%)\": 100 * np.median(np.abs(same_all)),\n",
- " \"PDOP difference, same satellites, p95 |x| (%)\": 100 * np.percentile(np.abs(same_all), 95),\n",
+ " \"PDOP difference, same satellites, median |x| (%)\": 100\n",
+ " * np.median(np.abs(same_all)),\n",
+ " \"PDOP difference, same satellites, p95 |x| (%)\": 100\n",
+ " * np.percentile(np.abs(same_all), 95),\n",
" \"PDOP difference, TLE groups, median (%)\": 100 * np.median(group_all),\n",
- " \"PDOP difference, TLE groups, p95 |x| (%)\": 100 * np.percentile(np.abs(group_all), 95),\n",
- " \"median PDOP (reference)\": np.nanmedian(np.concatenate([series2[(label, st)][0].PDOP.to_numpy(dtype=float) for st in stations])),\n",
+ " \"PDOP difference, TLE groups, p95 |x| (%)\": 100\n",
+ " * np.percentile(np.abs(group_all), 95),\n",
+ " \"median PDOP (reference)\": np.nanmedian(\n",
+ " np.concatenate(\n",
+ " [series2[(label, st)][0].PDOP.to_numpy(dtype=float) for st in stations]\n",
+ " )\n",
+ " ),\n",
" }\n",
"pd.DataFrame(rows2).T.round(2)"
]
@@ -914,10 +1117,21 @@
"for ax, label in zip(axes, [\"GPS\", \"Galileo\"]):\n",
" reference, same, group = series2[(label, \"WTZR\")]\n",
" hours = [(t - DAY) / timedelta(hours=1) for t in TIMES]\n",
- " ax.plot(hours, reference.PDOP, color=INK, lw=2.5, alpha=0.4, label=\"reference (healthy broadcast satellites)\")\n",
+ " ax.plot(\n",
+ " hours,\n",
+ " reference.PDOP,\n",
+ " color=INK,\n",
+ " lw=2.5,\n",
+ " alpha=0.4,\n",
+ " label=\"reference (healthy broadcast satellites)\",\n",
+ " )\n",
" ax.plot(hours, same.PDOP, color=BLUE, lw=1, label=\"TAT-C, same satellites\")\n",
" ax.plot(hours, group.PDOP, color=ORANGE, lw=1, ls=\"--\", label=\"TAT-C, TLE group\")\n",
- " ax.set(xlabel=\"Hours (UTC), 3 October 2026\", title=f\"{label} PDOP at Wettzell (10 deg mask)\", xlim=(0, 24))\n",
+ " ax.set(\n",
+ " xlabel=\"Hours (UTC), 3 October 2026\",\n",
+ " title=f\"{label} PDOP at Wettzell (10 deg mask)\",\n",
+ " xlim=(0, 24),\n",
+ " )\n",
"axes[0].set_ylabel(\"PDOP\")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
@@ -1119,22 +1333,33 @@
" if not np.isfinite(p[k]).all():\n",
" continue\n",
" los = p[k] - s[\"xyz\"]\n",
- " elevation = np.degrees(np.arcsin((rotation @ (los / np.linalg.norm(los)))[2]))\n",
+ " elevation = np.degrees(\n",
+ " np.arcsin((rotation @ (los / np.linalg.norm(los)))[2])\n",
+ " )\n",
" if elevation < 0:\n",
" continue\n",
" b = np.searchsorted(BINS, elevation, side=\"right\") - 1\n",
" counts[name[0]][0, b] += 1\n",
" counts[name[0]][1, b] += name in tracked\n",
- " fraction3[station] = {system: c[1] / np.maximum(c[0], 1) for system, c in counts.items()}\n",
+ " fraction3[station] = {\n",
+ " system: c[1] / np.maximum(c[0], 1) for system, c in counts.items()\n",
+ " }\n",
" tracked_sets = [s[\"tracked\"].get(t, set()) for t in TIMES]\n",
" tracked_dop = reference_dop(station, broadcast, subsets=tracked_sets)\n",
" _, _, group = series2[(\"all four\", station)]\n",
" rows3[station] = {\n",
" \"latitude (deg)\": s[\"lat\"],\n",
- " **{f\"{SYSTEMS[sys][0]} tracked above 10 deg (%)\": 100 * c[1, 2:].sum() / max(c[0, 2:].sum(), 1) for sys, c in counts.items()},\n",
+ " **{\n",
+ " f\"{SYSTEMS[sys][0]} tracked above 10 deg (%)\": 100\n",
+ " * c[1, 2:].sum()\n",
+ " / max(c[0, 2:].sum(), 1)\n",
+ " for sys, c in counts.items()\n",
+ " },\n",
" \"satellites tracked above 10 deg (mean)\": tracked_dop.n.mean(),\n",
" \"PDOP, tracked (median)\": np.nanmedian(tracked_dop.PDOP.to_numpy(dtype=float)),\n",
- " \"PDOP, healthy (median)\": np.nanmedian(series2[(\"all four\", station)][0].PDOP.to_numpy(dtype=float)),\n",
+ " \"PDOP, healthy (median)\": np.nanmedian(\n",
+ " series2[(\"all four\", station)][0].PDOP.to_numpy(dtype=float)\n",
+ " ),\n",
" \"PDOP, TAT-C (median)\": np.nanmedian(group.PDOP.to_numpy(dtype=float)),\n",
" }\n",
"table3 = pd.DataFrame(rows3).T\n",
@@ -1170,8 +1395,19 @@
"centers = (BINS[:-1] + BINS[1:]) / 2\n",
"for system, (label, _) in SYSTEMS.items():\n",
" values = np.array([fraction3[station][system] for station in stations])\n",
- " ax.plot(centers, 100 * np.median(values, axis=0), \"o-\", color=SYSTEM_COLORS[system], label=label)\n",
- "ax.set(xlabel=\"Elevation (deg)\", ylabel=\"Satellites tracked (%, median of stations)\", title=\"Fraction of satellites above the horizon tracked\", ylim=(0, 105))\n",
+ " ax.plot(\n",
+ " centers,\n",
+ " 100 * np.median(values, axis=0),\n",
+ " \"o-\",\n",
+ " color=SYSTEM_COLORS[system],\n",
+ " label=label,\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Elevation (deg)\",\n",
+ " ylabel=\"Satellites tracked (%, median of stations)\",\n",
+ " title=\"Fraction of satellites above the horizon tracked\",\n",
+ " ylim=(0, 105),\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1324,29 +1560,71 @@
],
"source": [
"rows4 = {}\n",
- "for label, systems in {\"GPS + Galileo\": \"GE\", \"GPS + GLONASS\": \"GR\", \"all four\": \"GECR\"}.items():\n",
+ "for label, systems in {\n",
+ " \"GPS + Galileo\": \"GE\",\n",
+ " \"GPS + GLONASS\": \"GR\",\n",
+ " \"all four\": \"GECR\",\n",
+ "}.items():\n",
" positions = {p: broadcast[p] for p in broadcast if p[0] in systems}\n",
" names = [n for n, p in prn.items() if p[0] in systems]\n",
" matched_positions = {prn[n]: broadcast[prn[n]] for n in names}\n",
- " single, system_clocks, tatc_values, tatc_system_values, same_one, same_system = [], [], [], [], [], []\n",
+ " single, system_clocks, tatc_values, tatc_system_values, same_one, same_system = (\n",
+ " [],\n",
+ " [],\n",
+ " [],\n",
+ " [],\n",
+ " [],\n",
+ " [],\n",
+ " )\n",
" for station in stations:\n",
- " same_one.append(reference_dop(station, matched_positions).PDOP.to_numpy(dtype=float))\n",
- " same_system.append(reference_dop(station, matched_positions, clocks=\"system\").PDOP.to_numpy(dtype=float))\n",
+ " same_one.append(\n",
+ " reference_dop(station, matched_positions).PDOP.to_numpy(dtype=float)\n",
+ " )\n",
+ " same_system.append(\n",
+ " reference_dop(station, matched_positions, clocks=\"system\").PDOP.to_numpy(\n",
+ " dtype=float\n",
+ " )\n",
+ " )\n",
" single.append(reference_dop(station, positions).PDOP.to_numpy(dtype=float))\n",
- " system_clocks.append(reference_dop(station, positions, clocks=\"system\").PDOP.to_numpy(dtype=float))\n",
+ " system_clocks.append(\n",
+ " reference_dop(station, positions, clocks=\"system\").PDOP.to_numpy(\n",
+ " dtype=float\n",
+ " )\n",
+ " )\n",
" tatc_values.append(tatc_dop(station, names).PDOP.to_numpy(dtype=float))\n",
- " tatc_system_values.append(tatc_dop(station, names, systems=[prn[n][0] for n in names]).PDOP.to_numpy(dtype=float))\n",
- " single, system_clocks, tatc_values, tatc_system_values, same_one, same_system = (np.concatenate(v) for v in (single, system_clocks, tatc_values, tatc_system_values, same_one, same_system))\n",
+ " tatc_system_values.append(\n",
+ " tatc_dop(station, names, systems=[prn[n][0] for n in names]).PDOP.to_numpy(\n",
+ " dtype=float\n",
+ " )\n",
+ " )\n",
+ " single, system_clocks, tatc_values, tatc_system_values, same_one, same_system = (\n",
+ " np.concatenate(v)\n",
+ " for v in (\n",
+ " single,\n",
+ " system_clocks,\n",
+ " tatc_values,\n",
+ " tatc_system_values,\n",
+ " same_one,\n",
+ " same_system,\n",
+ " )\n",
+ " )\n",
" rows4[label] = {\n",
" \"PDOP, one clock (median)\": np.nanmedian(single),\n",
" \"PDOP, clock per system (median)\": np.nanmedian(system_clocks),\n",
- " \"clock per system minus one clock, median (%)\": 100 * np.median(relative(system_clocks, single)),\n",
- " \"clock per system minus one clock, p95 (%)\": 100 * np.percentile(relative(system_clocks, single), 95),\n",
- " \"TAT-C one clock minus clock per system, median (%)\": 100 * np.median(relative(tatc_values, system_clocks)),\n",
- " \"same satellites, one clock: TAT-C minus reference, median |x| (%)\": 100 * np.median(np.abs(relative(tatc_values, same_one))),\n",
- " \"same satellites, one clock: TAT-C minus reference, p95 |x| (%)\": 100 * np.percentile(np.abs(relative(tatc_values, same_one)), 95),\n",
- " \"same satellites, clock per system: TAT-C minus reference, median |x| (%)\": 100 * np.median(np.abs(relative(tatc_system_values, same_system))),\n",
- " \"same satellites, clock per system: TAT-C minus reference, p95 |x| (%)\": 100 * np.percentile(np.abs(relative(tatc_system_values, same_system)), 95),\n",
+ " \"clock per system minus one clock, median (%)\": 100\n",
+ " * np.median(relative(system_clocks, single)),\n",
+ " \"clock per system minus one clock, p95 (%)\": 100\n",
+ " * np.percentile(relative(system_clocks, single), 95),\n",
+ " \"TAT-C one clock minus clock per system, median (%)\": 100\n",
+ " * np.median(relative(tatc_values, system_clocks)),\n",
+ " \"same satellites, one clock: TAT-C minus reference, median |x| (%)\": 100\n",
+ " * np.median(np.abs(relative(tatc_values, same_one))),\n",
+ " \"same satellites, one clock: TAT-C minus reference, p95 |x| (%)\": 100\n",
+ " * np.percentile(np.abs(relative(tatc_values, same_one)), 95),\n",
+ " \"same satellites, clock per system: TAT-C minus reference, median |x| (%)\": 100\n",
+ " * np.median(np.abs(relative(tatc_system_values, same_system))),\n",
+ " \"same satellites, clock per system: TAT-C minus reference, p95 |x| (%)\": 100\n",
+ " * np.percentile(np.abs(relative(tatc_system_values, same_system)), 95),\n",
" }\n",
"pd.DataFrame(rows4).T.round(2)"
]
@@ -1457,27 +1735,65 @@
"source": [
"def fit_walker(names, total=24, planes=3):\n",
" \"\"\"Fit a Walker delta constellation to satellites (TAT-C names), as in the orbit generation notebook.\"\"\"\n",
- " track = [satellites[n].orbit.to_gp_orbit().get_orbit_track_at_time(sk_times[144]) for n in names]\n",
+ " track = [\n",
+ " satellites[n].orbit.to_gp_orbit().get_orbit_track_at_time(sk_times[144])\n",
+ " for n in names\n",
+ " ]\n",
" elements = []\n",
" for g in track:\n",
" r, v = g.position.m, g.velocity.m_per_s\n",
" h = np.cross(r, v)\n",
" raan = np.degrees(np.arctan2(h[0], -h[1])) % 360\n",
" node = np.array([np.cos(np.radians(raan)), np.sin(np.radians(raan)), 0])\n",
- " u = np.degrees(np.arctan2(np.dot(np.cross(node, r), h) / np.linalg.norm(h), np.dot(node, r))) % 360\n",
- " elements.append((np.degrees(np.arccos(h[2] / np.linalg.norm(h))), raan, u, np.linalg.norm(r)))\n",
+ " u = (\n",
+ " np.degrees(\n",
+ " np.arctan2(\n",
+ " np.dot(np.cross(node, r), h) / np.linalg.norm(h), np.dot(node, r)\n",
+ " )\n",
+ " )\n",
+ " % 360\n",
+ " )\n",
+ " elements.append(\n",
+ " (\n",
+ " np.degrees(np.arccos(h[2] / np.linalg.norm(h))),\n",
+ " raan,\n",
+ " u,\n",
+ " np.linalg.norm(r),\n",
+ " )\n",
+ " )\n",
" inc, raan, u, radius = (np.array(x) for x in zip(*elements))\n",
" wrap = lambda x: (x + 180) % 360 - 180\n",
" best = None\n",
" for spacing in range(planes):\n",
" offset_u = offset_raan = 0.0\n",
" for _ in range(4):\n",
- " lead = CircularOrbit(mean_altitude=float(np.median(radius)) - EARTH_MEAN_RADIUS, inclination=float(np.median(inc)), right_ascension_ascending_node=float(raan[0] + offset_raan) % 360, true_anomaly=float(u[0] + offset_u) % 360, epoch=TIMES[144])\n",
- " walker = WalkerConstellation(name=\"walker\", orbit=lead, number_satellites=total, number_planes=planes, relative_spacing=spacing)\n",
+ " lead = CircularOrbit(\n",
+ " mean_altitude=float(np.median(radius)) - EARTH_MEAN_RADIUS,\n",
+ " inclination=float(np.median(inc)),\n",
+ " right_ascension_ascending_node=float(raan[0] + offset_raan) % 360,\n",
+ " true_anomaly=float(u[0] + offset_u) % 360,\n",
+ " epoch=TIMES[144],\n",
+ " )\n",
+ " walker = WalkerConstellation(\n",
+ " name=\"walker\",\n",
+ " orbit=lead,\n",
+ " number_satellites=total,\n",
+ " number_planes=planes,\n",
+ " relative_spacing=spacing,\n",
+ " )\n",
" members = walker.generate_members()\n",
- " d_raan = wrap(raan[:, None] - np.array([m.orbit.get_right_ascension_ascending_node() for m in members])[None, :])\n",
- " d_u = wrap(u[:, None] - np.array([m.orbit.true_anomaly for m in members])[None, :])\n",
- " slot = np.argmin(np.hypot(d_raan * np.sin(np.radians(np.median(inc))), d_u), axis=1)\n",
+ " d_raan = wrap(\n",
+ " raan[:, None]\n",
+ " - np.array(\n",
+ " [m.orbit.get_right_ascension_ascending_node() for m in members]\n",
+ " )[None, :]\n",
+ " )\n",
+ " d_u = wrap(\n",
+ " u[:, None] - np.array([m.orbit.true_anomaly for m in members])[None, :]\n",
+ " )\n",
+ " slot = np.argmin(\n",
+ " np.hypot(d_raan * np.sin(np.radians(np.median(inc))), d_u), axis=1\n",
+ " )\n",
" du, dr = d_u[np.arange(len(slot)), slot], d_raan[np.arange(len(slot)), slot]\n",
" offset_u += float(np.median(du))\n",
" offset_raan += float(np.median(dr))\n",
@@ -1492,7 +1808,15 @@
"for label, system in [(\"Galileo\", \"E\"), (\"BeiDou-3 MEO\", \"C\")]:\n",
" names = [n for n, p in prn.items() if p[0] == system]\n",
" if system == \"C\":\n",
- " names = [n for n in names if abs(satellites[n].orbit.to_gp_orbit().elements[0].mean_motion * 86400 / 360 - 1.86) < 0.05]\n",
+ " names = [\n",
+ " n\n",
+ " for n in names\n",
+ " if abs(\n",
+ " satellites[n].orbit.to_gp_orbit().elements[0].mean_motion * 86400 / 360\n",
+ " - 1.86\n",
+ " )\n",
+ " < 0.05\n",
+ " ]\n",
" members, in_slots = fit_walker(names)\n",
" for m in members:\n",
" satellites[f\"{label} Walker {m.name}\"] = m\n",
@@ -1501,7 +1825,9 @@
" for station in stations:\n",
" walker_values.append(tatc_dop(station, walker_names).PDOP.to_numpy(dtype=float))\n",
" real_values.append(tatc_dop(station, in_slots).PDOP.to_numpy(dtype=float))\n",
- " walker_values, real_values = np.concatenate(walker_values), np.concatenate(real_values)\n",
+ " walker_values, real_values = np.concatenate(walker_values), np.concatenate(\n",
+ " real_values\n",
+ " )\n",
" cdf5[label] = (real_values, walker_values)\n",
" rows5[label] = {\n",
" \"operational satellites in slots\": len(in_slots),\n",
@@ -1509,8 +1835,10 @@
" \"PDOP, Walker (median)\": np.nanmedian(walker_values),\n",
" \"PDOP, operational (p95)\": np.nanpercentile(real_values, 95),\n",
" \"PDOP, Walker (p95)\": np.nanpercentile(walker_values, 95),\n",
- " \"times without a solution, operational (%)\": 100 * np.mean(~np.isfinite(real_values)),\n",
- " \"times without a solution, Walker (%)\": 100 * np.mean(~np.isfinite(walker_values)),\n",
+ " \"times without a solution, operational (%)\": 100\n",
+ " * np.mean(~np.isfinite(real_values)),\n",
+ " \"times without a solution, Walker (%)\": 100\n",
+ " * np.mean(~np.isfinite(walker_values)),\n",
" }\n",
"pd.DataFrame(rows5).T.round(2)"
]
@@ -1542,10 +1870,24 @@
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
"for (label, (real_values, walker_values)), color in zip(cdf5.items(), [ORANGE, AQUA]):\n",
- " for values, ls, kind in [(real_values, \"-\", \"operational\"), (walker_values, \"--\", \"Walker\")]:\n",
+ " for values, ls, kind in [\n",
+ " (real_values, \"-\", \"operational\"),\n",
+ " (walker_values, \"--\", \"Walker\"),\n",
+ " ]:\n",
" v = np.sort(values[np.isfinite(values)])\n",
- " ax.plot(v, np.arange(1, len(v) + 1) / len(values), color=color, ls=ls, label=f\"{label}, {kind}\")\n",
- "ax.set(xlabel=\"PDOP (10 deg mask)\", ylabel=\"Cumulative fraction of times\", xlim=(1, 6), title=\"PDOP of operational and Walker constellations (six stations)\")\n",
+ " ax.plot(\n",
+ " v,\n",
+ " np.arange(1, len(v) + 1) / len(values),\n",
+ " color=color,\n",
+ " ls=ls,\n",
+ " label=f\"{label}, {kind}\",\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"PDOP (10 deg mask)\",\n",
+ " ylabel=\"Cumulative fraction of times\",\n",
+ " xlim=(1, 6),\n",
+ " title=\"PDOP of operational and Walker constellations (six stations)\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
diff --git a/docs/validation/ValidateDriftMODIS.ipynb b/docs/validation/ValidateDriftMODIS.ipynb
index 2169a343..544c75f2 100644
--- a/docs/validation/ValidateDriftMODIS.ipynb
+++ b/docs/validation/ValidateDriftMODIS.ipynb
@@ -38,10 +38,10 @@
"id": "0aa9944d",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:36:20.429629Z",
- "iopub.status.busy": "2026-10-08T17:36:20.429629Z",
- "iopub.status.idle": "2026-10-08T17:36:31.008723Z",
- "shell.execute_reply": "2026-10-08T17:36:31.008723Z"
+ "iopub.execute_input": "2026-10-25T15:24:16.021642Z",
+ "iopub.status.busy": "2026-10-25T15:24:16.020644Z",
+ "iopub.status.idle": "2026-10-25T15:24:18.027292Z",
+ "shell.execute_reply": "2026-10-25T15:24:18.027292Z"
}
},
"outputs": [
@@ -102,13 +102,17 @@
" def __init__(self, url, block_size=2**20, prefetch=1):\n",
" self.block_size, self.blocks, self.position = block_size, {}, 0\n",
" response = self._get(url, 0, prefetch * block_size)\n",
- " self.url, self.size = response.url, int(response.headers[\"Content-Range\"].split(\"/\")[1])\n",
+ " self.url, self.size = response.url, int(\n",
+ " response.headers[\"Content-Range\"].split(\"/\")[1]\n",
+ " )\n",
" self._store(0, response.content)\n",
"\n",
" def _get(self, url, start, end):\n",
" for attempt in range(5):\n",
" try:\n",
- " response = https_session().get(url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300)\n",
+ " response = https_session().get(\n",
+ " url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300\n",
+ " )\n",
" response.raise_for_status()\n",
" return response\n",
" except requests.RequestException:\n",
@@ -129,18 +133,30 @@
" return self.position\n",
"\n",
" def seek(self, offset, whence=io.SEEK_SET):\n",
- " self.position = {io.SEEK_SET: 0, io.SEEK_CUR: self.position, io.SEEK_END: self.size}[whence] + offset\n",
+ " self.position = {\n",
+ " io.SEEK_SET: 0,\n",
+ " io.SEEK_CUR: self.position,\n",
+ " io.SEEK_END: self.size,\n",
+ " }[whence] + offset\n",
" return self.position\n",
"\n",
" def readinto(self, buffer):\n",
" n = min(len(buffer), self.size - self.position)\n",
" if n <= 0:\n",
" return 0\n",
- " first, last = self.position // self.block_size, (self.position + n - 1) // self.block_size\n",
- " missing = [block for block in range(first, last + 1) if block not in self.blocks]\n",
+ " first, last = (\n",
+ " self.position // self.block_size,\n",
+ " (self.position + n - 1) // self.block_size,\n",
+ " )\n",
+ " missing = [\n",
+ " block for block in range(first, last + 1) if block not in self.blocks\n",
+ " ]\n",
" if missing:\n",
" end = min((missing[-1] + 1) * self.block_size, self.size)\n",
- " self._store(missing[0], self._get(self.url, missing[0] * self.block_size, end).content)\n",
+ " self._store(\n",
+ " missing[0],\n",
+ " self._get(self.url, missing[0] * self.block_size, end).content,\n",
+ " )\n",
" data = b\"\".join(self.blocks[block] for block in range(first, last + 1))\n",
" offset = self.position - first * self.block_size\n",
" buffer[:n] = data[offset : offset + n]\n",
@@ -172,7 +188,9 @@
"\n",
" np.savez_compressed(\n",
" path,\n",
- " start=attribute(f, \"RangeBeginningDate\") + \"T\" + attribute(f, \"RangeBeginningTime\"),\n",
+ " start=attribute(f, \"RangeBeginningDate\")\n",
+ " + \"T\"\n",
+ " + attribute(f, \"RangeBeginningTime\"),\n",
" ev_center_time=f[\"EV center time\"][:],\n",
" orb_pos=f[\"orb_pos\"][:],\n",
" orb_vel=f[\"orb_vel\"][:],\n",
@@ -202,7 +220,11 @@
"\n",
"\n",
"def url_of(result):\n",
- " return next(link[\"URL\"] for link in result[\"umm\"][\"RelatedUrls\"] if link[\"URL\"].startswith(\"https://data.laads\"))\n",
+ " return next(\n",
+ " link[\"URL\"]\n",
+ " for link in result[\"umm\"][\"RelatedUrls\"]\n",
+ " if link[\"URL\"].startswith(\"https://data.laads\")\n",
+ " )\n",
"\n",
"\n",
"crossings_file = DATA_DIR / \"equator_crossings.csv\"\n",
@@ -211,7 +233,15 @@
"\n",
" urls, day = [], datetime(2018, 1, 1, tzinfo=timezone.utc)\n",
" while day < DAY:\n",
- " found = earthaccess.search_data(short_name=\"MOD03\", version=\"7\", temporal=(day.strftime(\"%Y-%m-%dT%H:%M:%SZ\"), (day + timedelta(minutes=4)).strftime(\"%Y-%m-%dT%H:%M:%SZ\")), count=1)\n",
+ " found = earthaccess.search_data(\n",
+ " short_name=\"MOD03\",\n",
+ " version=\"7\",\n",
+ " temporal=(\n",
+ " day.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " (day + timedelta(minutes=4)).strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " ),\n",
+ " count=1,\n",
+ " )\n",
" urls += [url_of(found[0])] if found else []\n",
" day += timedelta(days=15)\n",
" with concurrent.futures.ThreadPoolExecutor(6) as pool:\n",
@@ -219,19 +249,35 @@
"if not any(DATA_DIR.glob(\"MOD03.*.npz\")):\n",
" import earthaccess\n",
"\n",
- " found = earthaccess.search_data(short_name=\"MOD03\", version=\"7\", temporal=(DAY.strftime(\"%Y-%m-%dT%H:%M:%SZ\"), (DAY + timedelta(days=1) - timedelta(seconds=1)).strftime(\"%Y-%m-%dT%H:%M:%SZ\")))\n",
+ " found = earthaccess.search_data(\n",
+ " short_name=\"MOD03\",\n",
+ " version=\"7\",\n",
+ " temporal=(\n",
+ " DAY.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " (DAY + timedelta(days=1) - timedelta(seconds=1)).strftime(\n",
+ " \"%Y-%m-%dT%H:%M:%SZ\"\n",
+ " ),\n",
+ " ),\n",
+ " )\n",
" with concurrent.futures.ThreadPoolExecutor(6) as pool:\n",
" list(pool.map(reduce_granule, sorted(url_of(r) for r in found)))\n",
"\n",
"crossings = pd.read_csv(crossings_file)\n",
- "crossings[\"time\"] = pd.to_datetime(crossings.date + \" \" + crossings.time.str[:15]).dt.tz_localize(\"UTC\")\n",
- "crossings[\"local time (h)\"] = ((crossings.time - crossings.time.dt.normalize()).dt.total_seconds() / 3600 + crossings.longitude / 15) % 24\n",
+ "crossings[\"time\"] = pd.to_datetime(\n",
+ " crossings.date + \" \" + crossings.time.str[:15]\n",
+ ").dt.tz_localize(\"UTC\")\n",
+ "crossings[\"local time (h)\"] = (\n",
+ " (crossings.time - crossings.time.dt.normalize()).dt.total_seconds() / 3600\n",
+ " + crossings.longitude / 15\n",
+ ") % 24\n",
"granules = []\n",
"for path in sorted(DATA_DIR.glob(\"MOD03.*.npz\")):\n",
" with np.load(path) as d:\n",
" granule = {key: d[key] for key in d.files}\n",
" granule[\"name\"] = path.stem\n",
- " valid = np.all(np.isfinite(granule[\"orb_pos\"]), axis=1) & (np.linalg.norm(granule[\"orb_pos\"], axis=1) > 6e6)\n",
+ " valid = np.all(np.isfinite(granule[\"orb_pos\"]), axis=1) & (\n",
+ " np.linalg.norm(granule[\"orb_pos\"], axis=1) > 6e6\n",
+ " )\n",
" granule[\"valid\"] = valid\n",
" granules.append(granule)\n",
"pd.Series(\n",
@@ -253,7 +299,7 @@
"source": [
"## Setup\n",
"\n",
- "MODIS scan times are given in seconds since 1 January 1993 in International Atomic Time (TAI), converted to UTC with Skyfield."
+ "MODIS scan times are given in seconds of International Atomic Time (TAI) since 1 January 1993 00:00:00 UTC, when TAI was 27 s ahead of UTC, converted to UTC with Skyfield."
]
},
{
@@ -262,10 +308,10 @@
"id": "8737d26b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:36:31.012329Z",
- "iopub.status.busy": "2026-10-08T17:36:31.012329Z",
- "iopub.status.idle": "2026-10-08T17:36:46.930512Z",
- "shell.execute_reply": "2026-10-08T17:36:46.930004Z"
+ "iopub.execute_input": "2026-10-25T15:24:18.029298Z",
+ "iopub.status.busy": "2026-10-25T15:24:18.029298Z",
+ "iopub.status.idle": "2026-10-25T15:24:19.754383Z",
+ "shell.execute_reply": "2026-10-25T15:24:19.754383Z"
}
},
"outputs": [],
@@ -281,7 +327,14 @@
"from tatc.analysis import collect_observations\n",
"from tatc.constants import timescale\n",
"from tatc.generation import generate_points_fibonacci_lattice\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Point, PointedInstrument, Satellite, SunSynchronousOrbit\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ " SunSynchronousOrbit,\n",
+ ")\n",
"from tatc.utils import (\n",
" VelocityFrame,\n",
" ViewGeometry,\n",
@@ -291,19 +344,39 @@
")\n",
"\n",
"orbit = GeneralPerturbationsOrbit.from_tle(TLE)\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"\n",
"def scan_times(granule, scans=slice(None)):\n",
- " \"\"\"Skyfield times of a granule's scans (EV center times, TAI seconds since 1993).\"\"\"\n",
- " return timescale.tai(1993, 1, 1, 0, 0, 26 + granule[\"ev_center_time\"][scans].astype(float))\n",
+ " \"\"\"Skyfield times of a granule's scans (EV center times, TAI seconds since 1993-01-01 00:00:00 UTC, when TAI - UTC was 27 s).\"\"\"\n",
+ " return timescale.tai(\n",
+ " 1993, 1, 1, 0, 0, 27 + granule[\"ev_center_time\"][scans].astype(float)\n",
+ " )\n",
"\n",
"\n",
"def ephemeris_track(granule, scans):\n",
" \"\"\"Terra's reported (Earth-fixed) position and velocity at scans, as an inertial orbit track.\"\"\"\n",
" return Geocentric.from_time_and_frame_vectors(\n",
- " scan_times(granule, scans), itrs, Distance(m=granule[\"orb_pos\"][scans].T.astype(float)), Velocity(km_per_s=granule[\"orb_vel\"][scans].T.astype(float) / 1e3)\n",
+ " scan_times(granule, scans),\n",
+ " itrs,\n",
+ " Distance(m=granule[\"orb_pos\"][scans].T.astype(float)),\n",
+ " Velocity(km_per_s=granule[\"orb_vel\"][scans].T.astype(float) / 1e3),\n",
" )"
]
},
@@ -323,10 +396,10 @@
"id": "908af07c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:36:46.934035Z",
- "iopub.status.busy": "2026-10-08T17:36:46.934035Z",
- "iopub.status.idle": "2026-10-08T17:36:48.194931Z",
- "shell.execute_reply": "2026-10-08T17:36:48.193921Z"
+ "iopub.execute_input": "2026-10-25T15:24:19.757706Z",
+ "iopub.status.busy": "2026-10-25T15:24:19.757706Z",
+ "iopub.status.idle": "2026-10-25T15:24:20.230009Z",
+ "shell.execute_reply": "2026-10-25T15:24:20.230009Z"
}
},
"outputs": [
@@ -360,31 +433,31 @@
" \n",
"
\n",
"
mean
\n",
- "
-7.610
\n",
+ "
-0.024
\n",
"
0.002
\n",
- "
-0.003
\n",
- "
7.613
\n",
+ "
0.001
\n",
+ "
0.583
\n",
"
\n",
"
\n",
"
50%
\n",
- "
-7.634
\n",
+ "
-0.047
\n",
"
-0.005
\n",
- "
-0.001
\n",
- "
7.637
\n",
+ "
0.003
\n",
+ "
0.620
\n",
"
\n",
"
\n",
"
95%
\n",
- "
-6.622
\n",
+ "
0.960
\n",
"
0.233
\n",
- "
0.240
\n",
- "
8.466
\n",
+ "
0.237
\n",
+ "
0.994
\n",
"
\n",
"
\n",
"
max
\n",
- "
-6.491
\n",
+ "
1.080
\n",
"
0.325
\n",
- "
0.341
\n",
- "
8.629
\n",
+ "
0.336
\n",
+ "
1.091
\n",
"
\n",
" \n",
"\n",
@@ -392,10 +465,10 @@
],
"text/plain": [
" along-track (km) cross-track (km) radial (km) total (km)\n",
- "mean -7.610 0.002 -0.003 7.613\n",
- "50% -7.634 -0.005 -0.001 7.637\n",
- "95% -6.622 0.233 0.240 8.466\n",
- "max -6.491 0.325 0.341 8.629"
+ "mean -0.024 0.002 0.001 0.583\n",
+ "50% -0.047 -0.005 0.003 0.620\n",
+ "95% 0.960 0.233 0.237 0.994\n",
+ "max 1.080 0.325 0.336 1.091"
]
},
"execution_count": 3,
@@ -411,13 +484,24 @@
" continue\n",
" t = scan_times(g, scans)\n",
" tle_xyz = orbit.get_orbit_track(list(t.utc_datetime())).frame_xyz(itrs).m\n",
- " reported, velocity = g[\"orb_pos\"][scans].T.astype(float), g[\"orb_vel\"][scans].T.astype(float)\n",
+ " reported, velocity = g[\"orb_pos\"][scans].T.astype(float), g[\"orb_vel\"][\n",
+ " scans\n",
+ " ].T.astype(float)\n",
" radial = reported / np.linalg.norm(reported, axis=0)\n",
" cross = np.cross(reported, velocity, axis=0)\n",
" cross /= np.linalg.norm(cross, axis=0)\n",
" along = np.cross(cross, radial, axis=0)\n",
" difference = (tle_xyz - reported) / 1e3\n",
- " records.append(pd.DataFrame({\"along-track (km)\": np.sum(difference * along, axis=0), \"cross-track (km)\": np.sum(difference * cross, axis=0), \"radial (km)\": np.sum(difference * radial, axis=0), \"total (km)\": np.linalg.norm(difference, axis=0)}))\n",
+ " records.append(\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"along-track (km)\": np.sum(difference * along, axis=0),\n",
+ " \"cross-track (km)\": np.sum(difference * cross, axis=0),\n",
+ " \"radial (km)\": np.sum(difference * radial, axis=0),\n",
+ " \"total (km)\": np.linalg.norm(difference, axis=0),\n",
+ " }\n",
+ " )\n",
+ " )\n",
"errors = pd.concat(records)\n",
"errors.describe(percentiles=[0.5, 0.95]).loc[[\"mean\", \"50%\", \"95%\", \"max\"]].round(3)"
]
@@ -427,7 +511,7 @@
"id": "422da008",
"metadata": {},
"source": [
- "The propagated TLE trails Terra's reported position by 7.6 km along track (about 1.1 s), consistently over the day, which is about two days before the TLE epoch; the cross-track and radial differences are within 0.35 km.\n",
+ "The propagated TLE matches Terra's reported position to within 1.0 km along track (95th percentile; 0.05 km median) over the day, which is about two days before the TLE epoch; the cross-track and radial differences are within 0.35 km.\n",
"\n",
"## Test 2: Local Time Drift\n",
"\n",
@@ -440,10 +524,10 @@
"id": "60287b5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:36:48.197931Z",
- "iopub.status.busy": "2026-10-08T17:36:48.196932Z",
- "iopub.status.idle": "2026-10-08T17:37:01.262997Z",
- "shell.execute_reply": "2026-10-08T17:37:01.262997Z"
+ "iopub.execute_input": "2026-10-25T15:24:20.232014Z",
+ "iopub.status.busy": "2026-10-25T15:24:20.232014Z",
+ "iopub.status.idle": "2026-10-25T15:24:21.681611Z",
+ "shell.execute_reply": "2026-10-25T15:24:21.681611Z"
}
},
"outputs": [
@@ -605,19 +689,39 @@
"def tle_crossing(time):\n",
" \"\"\"Time and local time of the TLE orbit's descending equator crossing nearest a time.\"\"\"\n",
" seconds = np.arange(-3000, 3001, 10)\n",
- " track = orbit.get_orbit_track([time.to_pydatetime() + timedelta(seconds=float(s)) for s in seconds])\n",
+ " track = orbit.get_orbit_track(\n",
+ " [time.to_pydatetime() + timedelta(seconds=float(s)) for s in seconds]\n",
+ " )\n",
" xyz = track.frame_xyz(itrs).m\n",
" k = np.nonzero((xyz[2, :-1] > 0) & (xyz[2, 1:] <= 0))[0]\n",
" k = k[np.argmin(np.abs(seconds[k]))]\n",
" fraction = xyz[2, k] / (xyz[2, k] - xyz[2, k + 1])\n",
" crossing_time = time + pd.Timedelta(seconds=float(seconds[k] + 10 * fraction))\n",
- " longitude = np.degrees(np.arctan2(*(xyz[1, k] + fraction * (xyz[1, k + 1] - xyz[1, k]), xyz[0, k] + fraction * (xyz[0, k + 1] - xyz[0, k]))))\n",
- " return crossing_time, ((crossing_time - crossing_time.normalize()).total_seconds() / 3600 + longitude / 15) % 24\n",
+ " longitude = np.degrees(\n",
+ " np.arctan2(\n",
+ " *(\n",
+ " xyz[1, k] + fraction * (xyz[1, k + 1] - xyz[1, k]),\n",
+ " xyz[0, k] + fraction * (xyz[0, k + 1] - xyz[0, k]),\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " return (\n",
+ " crossing_time,\n",
+ " (\n",
+ " (crossing_time - crossing_time.normalize()).total_seconds() / 3600\n",
+ " + longitude / 15\n",
+ " )\n",
+ " % 24,\n",
+ " )\n",
"\n",
"\n",
"crossings[\"TAT-C (TLE) local time (h)\"] = [tle_crossing(t)[1] for t in crossings.time]\n",
- "crossings[\"TAT-C minus reported (min)\"] = 60 * (crossings[\"TAT-C (TLE) local time (h)\"] - crossings[\"local time (h)\"])\n",
- "crossings[\"years from TLE epoch\"] = (crossings.time - pd.Timestamp(orbit.get_epoch())).dt.total_seconds() / (365.25 * 86400)\n",
+ "crossings[\"TAT-C minus reported (min)\"] = 60 * (\n",
+ " crossings[\"TAT-C (TLE) local time (h)\"] - crossings[\"local time (h)\"]\n",
+ ")\n",
+ "crossings[\"years from TLE epoch\"] = (\n",
+ " crossings.time - pd.Timestamp(orbit.get_epoch())\n",
+ ").dt.total_seconds() / (365.25 * 86400)\n",
"yearly = crossings.groupby(crossings.time.dt.year).agg(\n",
" **{\n",
" \"reported local time (h)\": (\"local time (h)\", \"mean\"),\n",
@@ -627,13 +731,23 @@
" }\n",
")\n",
"rates = crossings.set_index(\"time\")[\"local time (h)\"]\n",
- "yearly[\"reported drift (min/year)\"] = [60 * np.polyfit(((g.index - g.index[0]).total_seconds() / (365.25 * 86400)), g.to_numpy(), 1)[0] for _, g in rates.groupby(rates.index.year)]\n",
+ "yearly[\"reported drift (min/year)\"] = [\n",
+ " 60\n",
+ " * np.polyfit(\n",
+ " ((g.index - g.index[0]).total_seconds() / (365.25 * 86400)), g.to_numpy(), 1\n",
+ " )[0]\n",
+ " for _, g in rates.groupby(rates.index.year)\n",
+ "]\n",
"recent = crossings[crossings[\"years from TLE epoch\"] > -0.5]\n",
- "print(f\"drift over the last 6 months: reported {60 * np.polyfit(recent['years from TLE epoch'], recent['local time (h)'], 1)[0]:.1f} min/year, TAT-C (TLE) {60 * np.polyfit(recent['years from TLE epoch'], recent['TAT-C (TLE) local time (h)'], 1)[0]:.1f} min/year\")\n",
+ "print(\n",
+ " f\"drift over the last 6 months: reported {60 * np.polyfit(recent['years from TLE epoch'], recent['local time (h)'], 1)[0]:.1f} min/year, TAT-C (TLE) {60 * np.polyfit(recent['years from TLE epoch'], recent['TAT-C (TLE) local time (h)'], 1)[0]:.1f} min/year\"\n",
+ ")\n",
"for horizon in (0.1, 0.25, 0.5, 1, 2, 4):\n",
" near = crossings[(crossings[\"years from TLE epoch\"] + horizon).abs() < 0.05]\n",
" if len(near):\n",
- " print(f\"{horizon:4.2f} years before the TLE epoch: TAT-C minus reported {near['TAT-C minus reported (min)'].mean():6.1f} min\")\n",
+ " print(\n",
+ " f\"{horizon:4.2f} years before the TLE epoch: TAT-C minus reported {near['TAT-C minus reported (min)'].mean():6.1f} min\"\n",
+ " )\n",
"yearly.round(3)"
]
},
@@ -643,10 +757,10 @@
"id": "6de22a45",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:37:01.267538Z",
- "iopub.status.busy": "2026-10-08T17:37:01.267538Z",
- "iopub.status.idle": "2026-10-08T17:37:01.553061Z",
- "shell.execute_reply": "2026-10-08T17:37:01.552554Z"
+ "iopub.execute_input": "2026-10-25T15:24:21.683866Z",
+ "iopub.status.busy": "2026-10-25T15:24:21.683866Z",
+ "iopub.status.idle": "2026-10-25T15:24:21.931255Z",
+ "shell.execute_reply": "2026-10-25T15:24:21.931255Z"
}
},
"outputs": [
@@ -663,13 +777,33 @@
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.8))\n",
- "axes[0].plot(crossings.time, crossings[\"local time (h)\"], color=INK, label=\"reported (MOD03)\")\n",
- "axes[0].plot(crossings.time, crossings[\"TAT-C (TLE) local time (h)\"], color=BLUE, ls=\"--\", label=\"TAT-C (single TLE)\")\n",
+ "axes[0].plot(\n",
+ " crossings.time, crossings[\"local time (h)\"], color=INK, label=\"reported (MOD03)\"\n",
+ ")\n",
+ "axes[0].plot(\n",
+ " crossings.time,\n",
+ " crossings[\"TAT-C (TLE) local time (h)\"],\n",
+ " color=BLUE,\n",
+ " ls=\"--\",\n",
+ " label=\"TAT-C (single TLE)\",\n",
+ ")\n",
"axes[0].axhline(10.5, color=GRAY, ls=\":\", label=\"SunSynchronousOrbit (10:30)\")\n",
- "axes[0].set(ylabel=\"Descending node local time (h)\", title=\"Terra local time\", ylim=(8, 13))\n",
+ "axes[0].set(\n",
+ " ylabel=\"Descending node local time (h)\", title=\"Terra local time\", ylim=(8, 13)\n",
+ ")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
- "axes[1].plot(crossings[\"years from TLE epoch\"], crossings[\"TAT-C minus reported (min)\"], color=BLUE)\n",
- "axes[1].set(xlabel=\"Years from TLE epoch\", ylabel=\"TAT-C minus reported (min)\", title=\"Local time error of a single TLE\", xlim=(-2.5, 0.05), ylim=(-10, 120))\n",
+ "axes[1].plot(\n",
+ " crossings[\"years from TLE epoch\"],\n",
+ " crossings[\"TAT-C minus reported (min)\"],\n",
+ " color=BLUE,\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Years from TLE epoch\",\n",
+ " ylabel=\"TAT-C minus reported (min)\",\n",
+ " title=\"Local time error of a single TLE\",\n",
+ " xlim=(-2.5, 0.05),\n",
+ " ylim=(-10, 120),\n",
+ ")\n",
"plt.show()"
]
},
@@ -693,10 +827,10 @@
"id": "e49fe65c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:37:01.556070Z",
- "iopub.status.busy": "2026-10-08T17:37:01.555070Z",
- "iopub.status.idle": "2026-10-08T17:37:07.549160Z",
- "shell.execute_reply": "2026-10-08T17:37:07.549160Z"
+ "iopub.execute_input": "2026-10-25T15:24:21.933475Z",
+ "iopub.status.busy": "2026-10-25T15:24:21.933475Z",
+ "iopub.status.idle": "2026-10-25T15:24:29.276497Z",
+ "shell.execute_reply": "2026-10-25T15:24:29.276497Z"
}
},
"outputs": [
@@ -718,7 +852,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_7272\\4133548599.py:27: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_44296\\4133548599.py:27: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
" twist = angles.groupby(\"granule\").apply(lambda g: pd.Series({frame: np.polyfit(g[\"roll (deg)\"], g[f\"pitch, {frame} (deg)\"], 1)[0] for frame in (\"inertial\", \"Earth-fixed\")}))\n"
]
},
@@ -821,7 +955,19 @@
" for j in range(0, len(granule[\"frames\"]), 4):\n",
" values = {}\n",
" for frame in VelocityFrame:\n",
- " angles = [compute_view_angles(track, wgs84.latlon(*(float(granule[k][r, j]) for k in (\"latitude\", \"longitude\", \"height\"))), velocity_frame=frame) for r in (2 * scan, 2 * scan + 1)]\n",
+ " angles = [\n",
+ " compute_view_angles(\n",
+ " track,\n",
+ " wgs84.latlon(\n",
+ " *(\n",
+ " float(granule[k][r, j])\n",
+ " for k in (\"latitude\", \"longitude\", \"height\")\n",
+ " )\n",
+ " ),\n",
+ " velocity_frame=frame,\n",
+ " )\n",
+ " for r in (2 * scan, 2 * scan + 1)\n",
+ " ]\n",
" values[frame.value] = angles\n",
" (r1, a1), (r10, a10) = values[\"inertial\"]\n",
" rows.append(\n",
@@ -829,7 +975,9 @@
" \"frame\": int(granule[\"frames\"][j]),\n",
" \"roll (deg)\": float((r1 + r10) / 2),\n",
" \"pitch, inertial (deg)\": float((a1 + a10) / 2),\n",
- " \"pitch, Earth-fixed (deg)\": float(sum(a for _, a in values[\"earth_fixed\"]) / 2),\n",
+ " \"pitch, Earth-fixed (deg)\": float(\n",
+ " sum(a for _, a in values[\"earth_fixed\"]) / 2\n",
+ " ),\n",
" \"along-track extent, detectors 1 to 10 (deg)\": float(a10 - a1),\n",
" \"granule\": granule[\"name\"],\n",
" }\n",
@@ -837,16 +985,43 @@
" return rows\n",
"\n",
"\n",
- "angles = pd.DataFrame([row for rows in Parallel(n_jobs=-1)(delayed(view_angles)(g) for g in granules[::6]) for row in rows])\n",
+ "angles = pd.DataFrame(\n",
+ " [\n",
+ " row\n",
+ " for rows in Parallel(n_jobs=-1)(delayed(view_angles)(g) for g in granules[::6])\n",
+ " for row in rows\n",
+ " ]\n",
+ ")\n",
"slope, offset = np.polyfit(angles.frame - 676.5, angles[\"roll (deg)\"], 1)\n",
- "angles[\"scan angle residual (deg)\"] = angles[\"roll (deg)\"] - (slope * (angles.frame - 676.5) + offset)\n",
- "twist = angles.groupby(\"granule\").apply(lambda g: pd.Series({frame: np.polyfit(g[\"roll (deg)\"], g[f\"pitch, {frame} (deg)\"], 1)[0] for frame in (\"inertial\", \"Earth-fixed\")}))\n",
- "print(f\"roll = {slope:.5f} deg x (frame - 676.5) {offset:+.3f} deg; residual 95th percentile {angles['scan angle residual (deg)'].abs().quantile(0.95):.3f} deg\")\n",
+ "angles[\"scan angle residual (deg)\"] = angles[\"roll (deg)\"] - (\n",
+ " slope * (angles.frame - 676.5) + offset\n",
+ ")\n",
+ "twist = angles.groupby(\"granule\").apply(\n",
+ " lambda g: pd.Series(\n",
+ " {\n",
+ " frame: np.polyfit(g[\"roll (deg)\"], g[f\"pitch, {frame} (deg)\"], 1)[0]\n",
+ " for frame in (\"inertial\", \"Earth-fixed\")\n",
+ " }\n",
+ " )\n",
+ ")\n",
+ "print(\n",
+ " f\"roll = {slope:.5f} deg x (frame - 676.5) {offset:+.3f} deg; residual 95th percentile {angles['scan angle residual (deg)'].abs().quantile(0.95):.3f} deg\"\n",
+ ")\n",
"print(f\"scan angles {slope * -676.5 + offset:.2f} to {slope * 676.5 + offset:.2f} deg\")\n",
- "print(\"trend of the along-track angle across the scan (deg per deg of roll), by granule:\")\n",
+ "print(\n",
+ " \"trend of the along-track angle across the scan (deg per deg of roll), by granule:\"\n",
+ ")\n",
"print(twist.describe().loc[[\"mean\", \"std\", \"min\", \"max\"]].round(5).to_string())\n",
- "angles[\"scan angle bin (deg)\"] = pd.cut(angles[\"roll (deg)\"], [-56, -45, -30, -15, 0, 15, 30, 45, 56])\n",
- "angles.groupby(\"scan angle bin (deg)\", observed=True)[[\"pitch, inertial (deg)\", \"pitch, Earth-fixed (deg)\", \"along-track extent, detectors 1 to 10 (deg)\"]].median().round(4).T"
+ "angles[\"scan angle bin (deg)\"] = pd.cut(\n",
+ " angles[\"roll (deg)\"], [-56, -45, -30, -15, 0, 15, 30, 45, 56]\n",
+ ")\n",
+ "angles.groupby(\"scan angle bin (deg)\", observed=True)[\n",
+ " [\n",
+ " \"pitch, inertial (deg)\",\n",
+ " \"pitch, Earth-fixed (deg)\",\n",
+ " \"along-track extent, detectors 1 to 10 (deg)\",\n",
+ " ]\n",
+ "].median().round(4).T"
]
},
{
@@ -865,10 +1040,10 @@
"id": "acbef128",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:37:07.552167Z",
- "iopub.status.busy": "2026-10-08T17:37:07.552167Z",
- "iopub.status.idle": "2026-10-08T17:37:08.107881Z",
- "shell.execute_reply": "2026-10-08T17:37:08.107374Z"
+ "iopub.execute_input": "2026-10-25T15:24:29.278546Z",
+ "iopub.status.busy": "2026-10-25T15:24:29.278546Z",
+ "iopub.status.idle": "2026-10-25T15:24:30.057335Z",
+ "shell.execute_reply": "2026-10-25T15:24:30.057335Z"
}
},
"outputs": [
@@ -966,11 +1141,29 @@
}
],
"source": [
- "modis = PointedInstrument(\n",
- " name=\"MODIS\", field_of_regard=112, cross_track_field_of_view=1354 * 0.0812, along_track_field_of_view=10 * 0.0812,\n",
- " cross_track_pixels=1354, along_track_pixels=10, is_rectangular=True, view_geometry=ViewGeometry.SCAN, velocity_frame=VelocityFrame.INERTIAL,\n",
+ "# not yaw-steered: the body (and its instruments' views) aligned with the inertial velocity\n",
+ "INERTIAL = Attitude(velocity_frame=VelocityFrame.INERTIAL)\n",
+ "modis_scan = PointedInstrument(\n",
+ " name=\"MODIS\",\n",
+ " field_of_regard=112,\n",
+ " cross_track_field_of_view=1354 * 0.0812,\n",
+ " along_track_field_of_view=10 * 0.0812,\n",
+ " cross_track_pixels=1354,\n",
+ " along_track_pixels=10,\n",
+ " is_rectangular=True,\n",
+ " view_geometry=ViewGeometry.SCAN,\n",
")\n",
- "modis_frame = modis.model_copy(update={\"view_geometry\": ViewGeometry.FRAME})\n",
+ "terra = Satellite(\n",
+ " name=\"Terra\",\n",
+ " orbit=orbit,\n",
+ " instruments=[\n",
+ " modis_scan,\n",
+ " modis_scan.model_copy(update={\"view_geometry\": ViewGeometry.FRAME}),\n",
+ " ],\n",
+ " attitude=INERTIAL,\n",
+ ")\n",
+ "# the instruments with their views rotated from Terra's body\n",
+ "modis, modis_frame = terra.get_instrument(0), terra.get_instrument(1)\n",
"\n",
"\n",
"def pixel_errors(granule, step=20):\n",
@@ -982,28 +1175,89 @@
" j = sampled.index(frame)\n",
" # MODIS frames run right to left (frame 0 on the right); TAT-C's cross-track index 0 is also on the right\n",
" for detector, r in ((0, 2 * scan), (9, 2 * scan + 1)):\n",
- " lat, lon = float(granule[\"latitude\"][r, j]), float(granule[\"longitude\"][r, j])\n",
+ " lat, lon = float(granule[\"latitude\"][r, j]), float(\n",
+ " granule[\"longitude\"][r, j]\n",
+ " )\n",
" height = float(granule[\"height\"][r, j])\n",
" # TAT-C's along-track index 0 is the most forward detector (detector 10)\n",
- " pixel = modis.compute_projected_pixel_position(track, frame, 9 - detector, elevation=height)\n",
- " rows.append({\"frame\": frame, \"detector\": detector + 1, \"distance (km)\": geodesic_distance(lon, lat, pixel.longitude.degrees, pixel.latitude.degrees) / 1e3})\n",
+ " pixel = modis.compute_projected_pixel_position(\n",
+ " track, frame, 9 - detector, elevation=height\n",
+ " )\n",
+ " rows.append(\n",
+ " {\n",
+ " \"frame\": frame,\n",
+ " \"detector\": detector + 1,\n",
+ " \"distance (km)\": geodesic_distance(\n",
+ " lon, lat, pixel.longitude.degrees, pixel.latitude.degrees\n",
+ " )\n",
+ " / 1e3,\n",
+ " }\n",
+ " )\n",
" # scan footprint lengths along track at nadir and the edges\n",
" for frame in (0, 680, 1353):\n",
" j = sampled.index(frame)\n",
- " first, last = ((float(granule[\"latitude\"][r, j]), float(granule[\"longitude\"][r, j])) for r in (2 * scan, 2 * scan + 1))\n",
- " observed = geodesic_distance(first[1], first[0], last[1], last[0]) / 1e3 * 10 / 9\n",
+ " first, last = (\n",
+ " (float(granule[\"latitude\"][r, j]), float(granule[\"longitude\"][r, j]))\n",
+ " for r in (2 * scan, 2 * scan + 1)\n",
+ " )\n",
+ " observed = (\n",
+ " geodesic_distance(first[1], first[0], last[1], last[0]) / 1e3 * 10 / 9\n",
+ " )\n",
" lengths = {}\n",
- " for label, instrument in ((\"scan geometry\", modis), (\"frame geometry\", modis_frame)):\n",
- " ends = [instrument.compute_projected_pixel_position(track, frame, k) for k in (0, 9)]\n",
- " lengths[label] = geodesic_distance(ends[0].longitude.degrees, ends[0].latitude.degrees, ends[1].longitude.degrees, ends[1].latitude.degrees) / 1e3 * 10 / 9\n",
- " rows.append({\"frame\": frame, \"scan length (km)\": observed, \"TAT-C scan geometry length (km)\": lengths[\"scan geometry\"], \"TAT-C frame geometry length (km)\": lengths[\"frame geometry\"]})\n",
+ " for label, instrument in (\n",
+ " (\"scan geometry\", modis),\n",
+ " (\"frame geometry\", modis_frame),\n",
+ " ):\n",
+ " ends = [\n",
+ " instrument.compute_projected_pixel_position(track, frame, k)\n",
+ " for k in (0, 9)\n",
+ " ]\n",
+ " lengths[label] = (\n",
+ " geodesic_distance(\n",
+ " ends[0].longitude.degrees,\n",
+ " ends[0].latitude.degrees,\n",
+ " ends[1].longitude.degrees,\n",
+ " ends[1].latitude.degrees,\n",
+ " )\n",
+ " / 1e3\n",
+ " * 10\n",
+ " / 9\n",
+ " )\n",
+ " rows.append(\n",
+ " {\n",
+ " \"frame\": frame,\n",
+ " \"scan length (km)\": observed,\n",
+ " \"TAT-C scan geometry length (km)\": lengths[\"scan geometry\"],\n",
+ " \"TAT-C frame geometry length (km)\": lengths[\"frame geometry\"],\n",
+ " }\n",
+ " )\n",
" return rows\n",
"\n",
"\n",
- "pixels = pd.DataFrame([row for rows in Parallel(n_jobs=-1)(delayed(pixel_errors)(g) for g in granules[::4]) for row in rows])\n",
- "position = pixels.dropna(subset=[\"distance (km)\"]).groupby([\"frame\", \"detector\"])[\"distance (km)\"].describe(percentiles=[0.5, 0.95])[[\"count\", \"50%\", \"95%\", \"max\"]]\n",
+ "pixels = pd.DataFrame(\n",
+ " [\n",
+ " row\n",
+ " for rows in Parallel(n_jobs=-1)(delayed(pixel_errors)(g) for g in granules[::4])\n",
+ " for row in rows\n",
+ " ]\n",
+ ")\n",
+ "position = (\n",
+ " pixels.dropna(subset=[\"distance (km)\"])\n",
+ " .groupby([\"frame\", \"detector\"])[\"distance (km)\"]\n",
+ " .describe(percentiles=[0.5, 0.95])[[\"count\", \"50%\", \"95%\", \"max\"]]\n",
+ ")\n",
"print(position.round(2).to_string())\n",
- "lengths = pixels.dropna(subset=[\"scan length (km)\"]).groupby(\"frame\")[[\"scan length (km)\", \"TAT-C scan geometry length (km)\", \"TAT-C frame geometry length (km)\"]].median()\n",
+ "lengths = (\n",
+ " pixels.dropna(subset=[\"scan length (km)\"])\n",
+ " .groupby(\"frame\")[\n",
+ " [\n",
+ " \"scan length (km)\",\n",
+ " \"TAT-C scan geometry length (km)\",\n",
+ " \"TAT-C frame geometry length (km)\",\n",
+ " ]\n",
+ " ]\n",
+ " .median()\n",
+ ")\n",
"lengths[\"ground track advance per scan (km)\"] = 1.477 * 6.75\n",
"lengths.round(2)"
]
@@ -1017,7 +1271,7 @@
"\n",
"## Test 4: Daily Observations\n",
"\n",
- "The points of interest are those of a Fibonacci lattice with a typical spacing of 500 km. For each point and each MODIS pass over it, the observation time is the time of the scan through the point, interpolated between scans from the nearest sampled pixel (within 30 km, excluding the outermost sampled frames). `collect_observations` predicts the observations of the MODIS `PointedInstrument` above with the TLE orbit over the same day."
+ "The points of interest are those of a Fibonacci lattice with a typical spacing of 500 km. For each point and each MODIS pass over it, the observation time is the time of the scan through the point, interpolated between the centers of consecutive scans (midway between their first and last detectors, to which the scan times refer) at the nearest sampled pixel (within 30 km, excluding the outermost sampled frames). `collect_observations` predicts the observations of the MODIS `PointedInstrument` above with the TLE orbit over the same day."
]
},
{
@@ -1026,10 +1280,10 @@
"id": "97737e50",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:37:08.110888Z",
- "iopub.status.busy": "2026-10-08T17:37:08.109888Z",
- "iopub.status.idle": "2026-10-08T17:38:39.339165Z",
- "shell.execute_reply": "2026-10-08T17:38:39.338165Z"
+ "iopub.execute_input": "2026-10-25T15:24:30.059344Z",
+ "iopub.status.busy": "2026-10-25T15:24:30.059344Z",
+ "iopub.status.idle": "2026-10-25T15:25:08.420263Z",
+ "shell.execute_reply": "2026-10-25T15:25:08.420263Z"
}
},
"outputs": [
@@ -1041,9 +1295,9 @@
"points observed by TAT-C 2572.00\n",
"MODIS observations 6826.00\n",
"matched by TAT-C (%) 99.63\n",
- "time difference, median (s) 0.26\n",
- "time difference, 95th percentile (s) 0.70\n",
- "time difference, max (s) 1.19\n",
+ "time difference, median (s) 0.22\n",
+ "time difference, 95th percentile (s) 0.60\n",
+ "time difference, max (s) 0.94\n",
"TAT-C observations 6867.00\n",
"found in MODIS data (%) 99.04\n",
"dtype: float64"
@@ -1056,7 +1310,10 @@
],
"source": [
"lattice = generate_points_fibonacci_lattice(500e3)\n",
- "points = [Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x) for i, r in enumerate(lattice.itertuples())]\n",
+ "points = [\n",
+ " Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x)\n",
+ " for i, r in enumerate(lattice.itertuples())\n",
+ "]\n",
"pixel_xyz, pixel_index = [], []\n",
"for gi, g in enumerate(granules):\n",
" for r in range(0, len(g[\"rows\"]), 2):\n",
@@ -1064,9 +1321,19 @@
" if not g[\"valid\"][scan]:\n",
" continue\n",
" valid = np.isfinite(g[\"latitude\"][r])\n",
- " xyz = wgs84.latlon(g[\"latitude\"][r][valid], g[\"longitude\"][r][valid], g[\"height\"][r][valid]).itrs_xyz.m.T\n",
+ " xyz = wgs84.latlon(\n",
+ " g[\"latitude\"][r][valid], g[\"longitude\"][r][valid], g[\"height\"][r][valid]\n",
+ " ).itrs_xyz.m.T\n",
" pixel_xyz.append(xyz)\n",
- " pixel_index.append(np.column_stack([np.full(valid.sum(), gi), np.full(valid.sum(), scan), np.nonzero(valid)[0]]))\n",
+ " pixel_index.append(\n",
+ " np.column_stack(\n",
+ " [\n",
+ " np.full(valid.sum(), gi),\n",
+ " np.full(valid.sum(), scan),\n",
+ " np.nonzero(valid)[0],\n",
+ " ]\n",
+ " )\n",
+ " )\n",
"pixel_xyz, pixel_index = np.concatenate(pixel_xyz), np.concatenate(pixel_index)\n",
"tree = cKDTree(pixel_xyz)\n",
"reference = []\n",
@@ -1075,35 +1342,78 @@
" nearby = tree.query_ball_point(xyz, 30e3)\n",
" if not nearby:\n",
" continue\n",
- " candidates = pd.DataFrame(pixel_index[nearby], columns=[\"granule\", \"scan\", \"column\"]).assign(distance=np.linalg.norm(pixel_xyz[nearby] - xyz, axis=1))\n",
- " candidates[\"time\"] = [scan_times(granules[g], [s]).utc_datetime()[0] for g, s in zip(candidates.granule, candidates.scan)]\n",
+ " candidates = pd.DataFrame(\n",
+ " pixel_index[nearby], columns=[\"granule\", \"scan\", \"column\"]\n",
+ " ).assign(distance=np.linalg.norm(pixel_xyz[nearby] - xyz, axis=1))\n",
+ " candidates[\"time\"] = [\n",
+ " scan_times(granules[g], [s]).utc_datetime()[0]\n",
+ " for g, s in zip(candidates.granule, candidates.scan)\n",
+ " ]\n",
" candidates = candidates.sort_values(\"time\")\n",
- " for _, group in candidates.groupby((candidates.time.diff().dt.total_seconds() > 600).cumsum()):\n",
+ " for _, group in candidates.groupby(\n",
+ " (candidates.time.diff().dt.total_seconds() > 600).cumsum()\n",
+ " ):\n",
" best = group.loc[group.distance.idxmin()]\n",
" g, s, c = granules[int(best.granule)], int(best.scan), int(best.column)\n",
" if c in (0, len(g[\"frames\"]) - 1):\n",
" continue\n",
" s0, s1 = (s, s + 1) if s + 1 < len(g[\"ev_center_time\"]) else (s - 1, s)\n",
- " x0, x1 = (np.array(wgs84.latlon(g[\"latitude\"][2 * k, c], g[\"longitude\"][2 * k, c]).itrs_xyz.m) for k in (s0, s1))\n",
+ " # the scan centers (midway between the first and last detectors), to which the scan times refer\n",
+ " x0, x1 = (\n",
+ " np.mean(\n",
+ " [\n",
+ " wgs84.latlon(g[\"latitude\"][r, c], g[\"longitude\"][r, c]).itrs_xyz.m\n",
+ " for r in (2 * k, 2 * k + 1)\n",
+ " ],\n",
+ " axis=0,\n",
+ " )\n",
+ " for k in (s0, s1)\n",
+ " )\n",
" fraction = float(np.dot(xyz - x0, x1 - x0) / np.dot(x1 - x0, x1 - x0))\n",
" t0, t1 = (scan_times(g, [k]).utc_datetime()[0] for k in (s0, s1))\n",
- " reference.append({\"point_id\": point.id, \"latitude\": point.latitude, \"time\": pd.Timestamp(t0) + (pd.Timestamp(t1) - pd.Timestamp(t0)) * fraction, \"frame\": int(g[\"frames\"][c])})\n",
+ " reference.append(\n",
+ " {\n",
+ " \"point_id\": point.id,\n",
+ " \"latitude\": point.latitude,\n",
+ " \"time\": pd.Timestamp(t0)\n",
+ " + (pd.Timestamp(t1) - pd.Timestamp(t0)) * fraction,\n",
+ " \"frame\": int(g[\"frames\"][c]),\n",
+ " }\n",
+ " )\n",
"reference = pd.DataFrame(reference)\n",
"\n",
"\n",
"def observe(point):\n",
- " return collect_observations(point, Satellite(name=\"Terra\", orbit=orbit, instruments=[modis]), DAY, DAY + timedelta(days=1)).assign(point_id=point.id)\n",
+ " return collect_observations(point, terra, DAY, DAY + timedelta(days=1)).assign(\n",
+ " point_id=point.id\n",
+ " )\n",
"\n",
"\n",
- "predicted = pd.concat([f for f in Parallel(n_jobs=-1)(delayed(observe)(point) for point in points) if not f.empty])\n",
+ "predicted = pd.concat(\n",
+ " [\n",
+ " f\n",
+ " for f in Parallel(n_jobs=-1)(delayed(observe)(point) for point in points)\n",
+ " if not f.empty\n",
+ " ]\n",
+ ")\n",
"merged = pd.merge_asof(\n",
- " reference.sort_values(\"time\"), predicted[[\"point_id\", \"epoch\"]].sort_values(\"epoch\"),\n",
- " left_on=\"time\", right_on=\"epoch\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=3),\n",
+ " reference.sort_values(\"time\"),\n",
+ " predicted[[\"point_id\", \"epoch\"]].sort_values(\"epoch\"),\n",
+ " left_on=\"time\",\n",
+ " right_on=\"epoch\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=3),\n",
")\n",
"merged[\"difference (s)\"] = (merged.epoch - merged.time).dt.total_seconds()\n",
"found = pd.merge_asof(\n",
- " predicted.sort_values(\"epoch\"), reference[[\"point_id\", \"time\"]].sort_values(\"time\"),\n",
- " left_on=\"epoch\", right_on=\"time\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=3),\n",
+ " predicted.sort_values(\"epoch\"),\n",
+ " reference[[\"point_id\", \"time\"]].sort_values(\"time\"),\n",
+ " left_on=\"epoch\",\n",
+ " right_on=\"time\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=3),\n",
")\n",
"covered = reference.point_id.nunique()\n",
"pd.Series(\n",
@@ -1114,7 +1424,9 @@
" \"MODIS observations\": len(reference),\n",
" \"matched by TAT-C (%)\": 100 * merged.epoch.notna().mean(),\n",
" \"time difference, median (s)\": merged[\"difference (s)\"].abs().median(),\n",
- " \"time difference, 95th percentile (s)\": merged[\"difference (s)\"].abs().quantile(0.95),\n",
+ " \"time difference, 95th percentile (s)\": merged[\"difference (s)\"]\n",
+ " .abs()\n",
+ " .quantile(0.95),\n",
" \"time difference, max (s)\": merged[\"difference (s)\"].abs().max(),\n",
" \"TAT-C observations\": len(predicted),\n",
" \"found in MODIS data (%)\": 100 * found.time.notna().mean(),\n",
@@ -1127,16 +1439,16 @@
"id": "c6797454",
"metadata": {},
"source": [
- "`collect_observations` predicts MODIS's observations: 99.6% of the 6,826 passes over the points are matched by a predicted observation, within 0.26 s (median) and 1.2 s (maximum), and 99.0% of the predicted observations are found in the MODIS data. The remaining differences are at the swath edges, where the sampled pixels are sparse, and at the ends of the day. MODIS observes 99% of the points in a day (2,568 of 2,598); the others lie in the gaps between adjacent swaths near the equator, which TAT-C also predicts (2,572 points observed).\n",
+ "`collect_observations` predicts MODIS's observations: 99.6% of the 6,826 passes over the points are matched by a predicted observation, within 0.22 s (median) and 0.94 s (maximum), and 99.0% of the predicted observations are found in the MODIS data. The remaining differences are at the swath edges, where the sampled pixels are sparse, and at the ends of the day. MODIS observes 99% of the points in a day (2,568 of 2,598); the others lie in the gaps between adjacent swaths near the equator, which TAT-C also predicts (2,572 points observed).\n",
"\n",
"## Summary\n",
"\n",
"| Test | Result |\n",
"| --- | --- |\n",
- "| 1. Orbit (TLE) | 7.6 km along track (1.1 s) two days before the TLE epoch; within 0.35 km across track and radially |\n",
+ "| 1. Orbit (TLE) | within 1.0 km along track (95th percentile) two days before the TLE epoch; within 0.35 km across track and radially |\n",
"| 2. Local time drift | a single TLE reproduces the current drift (39.5 vs. 37.3 minutes per year) and local times within 0.3 minutes over three months, but departs by 4.4 minutes after one year and an hour after four years, as SGP4 keeps the inclination constant |\n",
- "| 3. Scan geometry (`ViewGeometry.SCAN`, `VelocityFrame.INERTIAL`) | scan angles 0.0812 deg per frame; scans perpendicular to the inertial velocity; constant along-track extent (0.731 deg between the first and last detectors); pixels within 1.5 km at nadir and 3 to 5 km at the edges; scan footprints 9.9 km long at nadir and 19.8 km at the edges (19.9 km observed) |\n",
- "| 4. Daily observations (`collect_observations`) | 99.6% of passes matched, within 0.26 s (median); 99.0% of predicted observations found |\n",
+ "| 3. Scan geometry (`ViewGeometry.SCAN`, inertial `Attitude`) | scan angles 0.0812 deg per frame; scans perpendicular to the inertial velocity; constant along-track extent (0.731 deg between the first and last detectors); pixels within 1.5 km at nadir and 3 to 5 km at the edges; scan footprints 9.9 km long at nadir and 19.8 km at the edges (19.9 km observed) |\n",
+ "| 4. Daily observations (`collect_observations`) | 99.6% of passes matched, within 0.22 s (median); 99.0% of predicted observations found |\n",
"\n",
"**Scan geometry.** A `PointedInstrument` in scan geometry with the inertial velocity frame reproduces MODIS's scans, including their growth along track toward the swath edges, confirming the VIIRS validation for a second scanner and a spacecraft without yaw steering.\n",
"\n",
diff --git a/docs/validation/ValidateFeaturesTCPRIMED.ipynb b/docs/validation/ValidateFeaturesTCPRIMED.ipynb
new file mode 100644
index 00000000..cb972541
--- /dev/null
+++ b/docs/validation/ValidateFeaturesTCPRIMED.ipynb
@@ -0,0 +1,2862 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "42c03d4d",
+ "metadata": {},
+ "source": [
+ "# Feature Observation Validation: Tropical Cyclone Overpasses (TC PRIMED)\n",
+ "\n",
+ "This notebook validates TAT-C's feature observations against an archive of real satellite observations of storms: the [Tropical Cyclone PRecipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)](https://rammb-data.cira.colostate.edu/tcprimed/), compiled by the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University.\n",
+ "\n",
+ "For every tropical cyclone, TC PRIMED collects the overpasses that cover enough of the storm from the passive microwave imagers and sounders of the Global Precipitation Measurement (GPM) constellation. Whether an overpass is kept is a geometric decision: it depends on how much of a circle around the storm's center the swath covers.\n",
+ "\n",
+ "In TAT-C, `collect_feature_observations` collects observations of *features*: regions that exist for a period of time, such as the storms of a nature run in an observing system simulation experiment (OSSE; see the [hurricane OSSE example](../examples/RunOSSEHurricane.ipynb)). Each observation is one satellite's view of the part of a feature inside its swath, together with the fraction of the feature's area viewed.\n",
+ "\n",
+ "Here, the features are TC PRIMED's circles around each storm, at its best-track position. TAT-C reproduces TC PRIMED's selection of overpasses for the 13 Atlantic tropical cyclones of 2025, by 10 satellites carrying five sensors:\n",
+ "* **Cross-track sounders:** ATMS on Suomi NPP, NOAA-20, and NOAA-21, and MHS on Metop-B and Metop-C.\n",
+ "* **Conical imagers:** AMSR2 on GCOM-W1, GMI on GPM, and SSMIS on DMSP F16, F17, and F18.\n",
+ "\n",
+ "The comparison asks four questions:\n",
+ "\n",
+ "1. **Swaths.** Which geometry (scan angles, cone angles, scan sectors, and velocity frames) reproduces the swath of each sensor in TC PRIMED's geolocation?\n",
+ "2. **Overpasses.** Does TAT-C predict the overpasses TC PRIMED contains, and which does it predict that TC PRIMED does not contain?\n",
+ "3. **Coverage.** Does the fraction of the storm's circle in TAT-C's swath match the coverage TC PRIMED reports?\n",
+ "4. **Sampling of storms.** How often does the constellation observe a storm, as predicted by TAT-C and as archived?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e91db82c",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "TC PRIMED is distributed through the [NOAA Open Data Dissemination](https://www.noaa.gov/nodd) program on AWS ([`noaa-nesdis-tcprimed-pds`](https://registry.opendata.aws/noaa-nesdis-tcprimed-pds/)) and requires no account. This notebook uses the final version (v01r01) of the 2025 North Atlantic season. Each storm has two kinds of file:\n",
+ "\n",
+ "* **One environmental file**, whose metadata groups give the storm's best track every 6 hours (`storm_metadata`), and the time, sensor, platform, and coverage of each of its overpasses (`overpass_metadata` and `overpass_storm_metadata`).\n",
+ "* **One overpass file per overpass**, whose `passive_microwave/S1` group gives the geolocation of the swath used to select the overpass.\n",
+ "\n",
+ "The environmental files also contain large reanalysis fields, so only their metadata groups are read, with HTTP range requests. Test 1 also reads the S1 geolocation of a few overpass files per platform, and of every GPM overpass.\n",
+ "\n",
+ "The selection rule is documented in the [TC PRIMED documentation](https://rammb-data.cira.colostate.edu/tcprimed/TCPRIMED_v01r01_documentation.pdf) (Section 2.2):\n",
+ "\n",
+ "* The storm's center is interpolated from the best track to the time of the observation with a cubic B-spline.\n",
+ "* An overpass is kept if its S1 swath (the region enclosed by the swath's sample centers) covers at least 47.9% of a circle of 750 km radius around that center, or otherwise at least 43.6% of a circle of 250 km radius. Both thresholds correspond to a straight swath edge 25 km beyond the storm's center.\n",
+ "* The coverage of the 750 km circle is reported for each overpass (`coverage_fraction`).\n",
+ "* The time of an overpass is the midpoint of the part of the satellite's orbit (granule) within 20 deg of latitude and longitude of the storm.\n",
+ "\n",
+ "**Element sets.** The satellites' orbits are modeled with the history of their two-line element sets from [Space-Track.org](https://www.space-track.org/), which requires a free account and does not allow the data to be redistributed. To set this up:\n",
+ "\n",
+ "1. Download the element sets of the 10 satellites (NORAD catalog numbers 37849, 43013, 54234, 38771, 43689, 38337, 39574, 28054, 29522, and 35951) with epochs from 15 June to 8 November 2025, in three-line format. For example, use the query `https://www.space-track.org/basicspacedata/query/class/gp_history/NORAD_CAT_ID/37849,43013,54234,38337,39574,38771,43689,28054,29522,35951/EPOCH/2025-06-15--2025-11-08/orderby/NORAD_CAT_ID,EPOCH/format/3le`.\n",
+ "2. Save them as `data/orbits/tcprimed_2025_gp_history.tle`.\n",
+ "\n",
+ "TAT-C propagates each time from the element set nearest to it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3139a3ae",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T15:58:18.146819Z",
+ "iopub.status.busy": "2026-10-25T15:58:18.145819Z",
+ "iopub.status.idle": "2026-10-25T15:58:18.783841Z",
+ "shell.execute_reply": "2026-10-25T15:58:18.783841Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ " storm platform TC PRIMED time TC PRIMED coverage (%) \\\n",
+ "160 AL042025 F17 2025-08-11 19:18:10+00:00 34.6 \n",
+ "203 AL042025 METOPB 2025-08-02 15:00:49+00:00 77.5 \n",
+ "357 AL052025 F17 2025-08-15 21:36:27+00:00 66.4 \n",
+ "597 AL052025 NPP 2025-08-12 16:27:54+00:00 44.4 \n",
+ "630 AL052025 NPP 2025-08-26 13:57:27+00:00 83.7 \n",
+ "982 AL092025 F18 2025-09-30 09:58:47+00:00 41.3 \n",
+ "\n",
+ " TAT-C coverage (%) \n",
+ "160 71.5 \n",
+ "203 69.2 \n",
+ "357 96.3 \n",
+ "597 60.2 \n",
+ "630 100.0 \n",
+ "982 73.0 "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "outliers = matched[matched[\"coverage difference\"].abs() > 5]\n",
+ "outliers[\n",
+ " [\n",
+ " \"storm\",\n",
+ " \"platform\",\n",
+ " \"TC PRIMED time\",\n",
+ " \"TC PRIMED coverage (%)\",\n",
+ " \"TAT-C coverage (%)\",\n",
+ " ]\n",
+ "].round(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a82225f",
+ "metadata": {},
+ "source": [
+ "TAT-C's coverage matches TC PRIMED's to within a fraction of a percentage point:\n",
+ "* the median difference is within 0.2 points for every sensor; and\n",
+ "* 90% of the overpasses (5th to 95th percentile) differ by -1.3 to +1.7 points, with the largest spread for GMI.\n",
+ "\n",
+ "Six overpasses differ by more than 5 points, and in all six TC PRIMED reports less coverage than TAT-C. TC PRIMED computes coverage from the swath it retains, which excludes scans and samples with missing geolocation (fill values; Section 2.2.2 of its documentation). An incomplete swath covers less of the circle than the full swath that TAT-C predicts. Three of these six overpasses are in TC PRIMED only through the 250 km criterion.\n",
+ "\n",
+ "### Observation Times\n",
+ "\n",
+ "The two sources define the time of an overpass differently:\n",
+ "* **TAT-C:** the time of the footprint that views most of the 750 km circle.\n",
+ "* **TC PRIMED:** the midpoint of the part of the granule within 20 deg of latitude and longitude of the storm.\n",
+ "\n",
+ "These definitions need not coincide, so the times are compared only as a check."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "4d5828ff",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T15:59:21.208130Z",
+ "iopub.status.busy": "2026-10-25T15:59:21.208130Z",
+ "iopub.status.idle": "2026-10-25T15:59:21.224151Z",
+ "shell.execute_reply": "2026-10-25T15:59:21.224151Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
n
\n",
+ "
median
\n",
+ "
5th percentile
\n",
+ "
95th percentile
\n",
+ "
max |x|
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
NPP
\n",
+ "
206.0
\n",
+ "
1.0
\n",
+ "
-24.0
\n",
+ "
30.8
\n",
+ "
94.0
\n",
+ "
\n",
+ "
\n",
+ "
NOAA20
\n",
+ "
213.0
\n",
+ "
3.0
\n",
+ "
-27.2
\n",
+ "
25.0
\n",
+ "
67.0
\n",
+ "
\n",
+ "
\n",
+ "
NOAA21
\n",
+ "
209.0
\n",
+ "
3.0
\n",
+ "
-23.0
\n",
+ "
24.6
\n",
+ "
64.0
\n",
+ "
\n",
+ "
\n",
+ "
METOPB
\n",
+ "
174.0
\n",
+ "
0.0
\n",
+ "
-30.0
\n",
+ "
27.0
\n",
+ "
43.0
\n",
+ "
\n",
+ "
\n",
+ "
METOPC
\n",
+ "
182.0
\n",
+ "
2.0
\n",
+ "
-29.0
\n",
+ "
27.9
\n",
+ "
64.0
\n",
+ "
\n",
+ "
\n",
+ "
GCOMW1
\n",
+ "
137.0
\n",
+ "
-30.0
\n",
+ "
-72.0
\n",
+ "
18.0
\n",
+ "
89.0
\n",
+ "
\n",
+ "
\n",
+ "
GPM
\n",
+ "
101.0
\n",
+ "
2.0
\n",
+ "
-78.0
\n",
+ "
68.0
\n",
+ "
126.0
\n",
+ "
\n",
+ "
\n",
+ "
F16
\n",
+ "
66.0
\n",
+ "
-31.0
\n",
+ "
-65.8
\n",
+ "
2.0
\n",
+ "
75.0
\n",
+ "
\n",
+ "
\n",
+ "
F17
\n",
+ "
76.0
\n",
+ "
-33.5
\n",
+ "
-89.5
\n",
+ "
0.8
\n",
+ "
220.0
\n",
+ "
\n",
+ "
\n",
+ "
F18
\n",
+ "
69.0
\n",
+ "
-27.0
\n",
+ "
-89.6
\n",
+ "
9.2
\n",
+ "
247.0
\n",
+ "
\n",
+ "
\n",
+ "
GPM, forward
\n",
+ "
52.0
\n",
+ "
-8.5
\n",
+ "
-57.7
\n",
+ "
89.3
\n",
+ "
126.0
\n",
+ "
\n",
+ "
\n",
+ "
GPM, backward
\n",
+ "
49.0
\n",
+ "
8.0
\n",
+ "
-78.6
\n",
+ "
56.8
\n",
+ "
85.0
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " n median 5th percentile 95th percentile max |x|\n",
+ "NPP 206.0 1.0 -24.0 30.8 94.0\n",
+ "NOAA20 213.0 3.0 -27.2 25.0 67.0\n",
+ "NOAA21 209.0 3.0 -23.0 24.6 64.0\n",
+ "METOPB 174.0 0.0 -30.0 27.0 43.0\n",
+ "METOPC 182.0 2.0 -29.0 27.9 64.0\n",
+ "GCOMW1 137.0 -30.0 -72.0 18.0 89.0\n",
+ "GPM 101.0 2.0 -78.0 68.0 126.0\n",
+ "F16 66.0 -31.0 -65.8 2.0 75.0\n",
+ "F17 76.0 -33.5 -89.5 0.8 220.0\n",
+ "F18 69.0 -27.0 -89.6 9.2 247.0\n",
+ "GPM, forward 52.0 -8.5 -57.7 89.3 126.0\n",
+ "GPM, backward 49.0 8.0 -78.6 56.8 85.0"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "matched[\"time difference (s)\"] = (\n",
+ " matched[\"TAT-C time\"] - matched[\"TC PRIMED time\"]\n",
+ ").dt.total_seconds()\n",
+ "times3 = {\n",
+ " platform: summarize(g[\"time difference (s)\"])\n",
+ " for platform, g in matched.groupby(\"platform\")\n",
+ "}\n",
+ "# GPM's overpasses by its orientation (from the attitude schedule)\n",
+ "gpm_overpasses = matched[matched.platform == \"GPM\"]\n",
+ "backward = np.array(\n",
+ " [\n",
+ " gpm_attitude.get_attitude(t.to_pydatetime()).yaw_angle == 180\n",
+ " for t in gpm_overpasses[\"TC PRIMED time\"]\n",
+ " ]\n",
+ ")\n",
+ "times3[\"GPM, forward\"] = summarize(gpm_overpasses[\"time difference (s)\"][~backward])\n",
+ "times3[\"GPM, backward\"] = summarize(gpm_overpasses[\"time difference (s)\"][backward])\n",
+ "pd.DataFrame(times3).T.loc[[*PLATFORMS.index, \"GPM, forward\", \"GPM, backward\"]].round(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "566bc64a",
+ "metadata": {},
+ "source": [
+ "**Cross-track sounders.** The two times agree within 3 s (median) and 31 s (95th percentile). TC PRIMED's times are given to the nearest second.\n",
+ "\n",
+ "**AMSR2 and SSMIS.** TAT-C's times are about 30 s earlier (median). Their arcs lie 850 to 900 km ahead of nadir at the center of the scan, but much less at its ends. The part of a granule within 20 deg of the storm ends when the arc's ends leave that region, so its midpoint is later than the time at which the arc's center sweeps across the storm.\n",
+ "\n",
+ "**GMI.** GMI's arc is closer to nadir (about 520 km). Its median offset is -8.5 s when GPM flies forward and +8 s when it flies backward, when the arc trails nadir. Modeled as always flying forward, GPM's backward passes would be predicted about 2 minutes early: the arc would be about 1,040 km (about 140 s of flight) from where it really is. With the attitude schedule, the median offset of all of GPM's passes is 2 s.\n",
+ "\n",
+ "The largest offsets (up to 4 minutes, for SSMIS) are again overpasses with incomplete swaths.\n",
+ "\n",
+ "## Test 4: Sampling of Storms\n",
+ "\n",
+ "An OSSE asks how often an observing system observes a feature. For each storm, the time between consecutive overpasses that meet TC PRIMED's criteria (by any of the 10 satellites) is compared three ways: as archived in TC PRIMED, as predicted by TAT-C, and as predicted without the three SSMIS. The overpasses of Hurricane Melissa (AL13), the strongest storm of the season, are shown as an example."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "4eba5e78",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T15:59:21.226322Z",
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+ "shell.execute_reply": "2026-10-25T15:59:21.404297Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ " gaps median (h) 5th percentile (h) \\\n",
+ "TC PRIMED (archived overpasses) 1420.0 0.84 0.13 \n",
+ "TAT-C (all satellites) 1678.0 0.83 0.12 \n",
+ "TAT-C (without SSMIS) 1224.0 0.82 0.10 \n",
+ "\n",
+ " 95th percentile (h) max |x| (h) \n",
+ "TC PRIMED (archived overpasses) 4.72 7.65 \n",
+ "TAT-C (all satellites) 3.85 5.67 \n",
+ "TAT-C (without SSMIS) 6.85 8.45 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "STORM = \"AL132025\"\n",
+ "storm_name = \"Melissa\"\n",
+ "fig, ax = plt.subplots(figsize=(11, 3.8))\n",
+ "for k, platform in enumerate(PLATFORMS.index):\n",
+ " tatc = included[(included.storm == STORM) & (included.platform == platform)]\n",
+ " archived = matched[(matched.storm == STORM) & (matched.platform == platform)]\n",
+ " ax.plot(\n",
+ " tatc[\"pass\"],\n",
+ " np.full(len(tatc), k + 0.15),\n",
+ " \"|\",\n",
+ " ms=9,\n",
+ " mew=2,\n",
+ " color=BLUE,\n",
+ " label=\"TAT-C\" if k == 0 else None,\n",
+ " )\n",
+ " ax.plot(\n",
+ " archived[\"TC PRIMED time\"],\n",
+ " np.full(len(archived), k - 0.15),\n",
+ " \"|\",\n",
+ " ms=9,\n",
+ " mew=2,\n",
+ " color=ORANGE,\n",
+ " label=\"TC PRIMED\" if k == 0 else None,\n",
+ " )\n",
+ "ax.set_yticks(\n",
+ " range(len(PLATFORMS)), [f\"{PLATFORMS.sensor[p]} {p}\" for p in PLATFORMS.index]\n",
+ ")\n",
+ "ax.set(\n",
+ " title=f\"Overpasses of {storm_name} ({STORM}) meeting the coverage criteria\",\n",
+ " xlabel=\"Time (UTC, 2025)\",\n",
+ ")\n",
+ "ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%b %d\"))\n",
+ "ax.legend(\n",
+ " frameon=False, fontsize=8, ncols=2, loc=\"upper right\", bbox_to_anchor=(1, 1.12)\n",
+ ")\n",
+ "fig.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "def gaps(times_by_storm):\n",
+ " \"\"\"Hours between consecutive overpasses of each storm (by any of the satellites).\"\"\"\n",
+ " return np.concatenate(\n",
+ " [\n",
+ " np.diff(np.sort(t.to_numpy())).astype(\"timedelta64[s]\").astype(float) / 3600\n",
+ " for t in times_by_storm\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " \"TC PRIMED (archived overpasses)\": summarize(\n",
+ " gaps([g[\"TC PRIMED time\"] for _, g in matched.groupby(\"storm\")])\n",
+ " ),\n",
+ " \"TAT-C (all satellites)\": summarize(\n",
+ " gaps([g[\"pass\"] for _, g in included.groupby(\"storm\")])\n",
+ " ),\n",
+ " \"TAT-C (without SSMIS)\": summarize(\n",
+ " gaps(\n",
+ " [\n",
+ " g[\"pass\"]\n",
+ " for _, g in included[\n",
+ " ~included.platform.str.startswith(\"F\")\n",
+ " ].groupby(\"storm\")\n",
+ " ]\n",
+ " )\n",
+ " ),\n",
+ " }\n",
+ ").T.rename(columns=lambda c: f\"{c} (h)\" if c != \"n\" else \"gaps\").round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c762448",
+ "metadata": {},
+ "source": [
+ "The constellation observes a storm about every 50 minutes (a median gap of 0.83 h), whether archived or predicted. The longest gaps differ:\n",
+ "\n",
+ "| Overpasses | 95th percentile of the gaps | Longest gap |\n",
+ "| --- | --- | --- |\n",
+ "| Predicted by TAT-C, all 10 satellites | 3.9 h | 5.7 h |\n",
+ "| Archived in TC PRIMED (many SSMIS overpasses missing) | 4.7 h | 7.7 h |\n",
+ "| Predicted by TAT-C, without SSMIS | 6.9 h | 8.5 h |\n",
+ "\n",
+ "The three DMSP satellites, in different orbit planes from the other seven, fill the longest gaps between their passes. The gaps in the archive therefore follow from the availability of data, not from the constellation's geometry.\n",
+ "\n",
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| 1. Swaths | ATMS +/-52.7 deg (inertial frame) and MHS +/-49.4 deg (Earth-fixed, as Metop is yaw steered); AMSR2, GMI, and SSMIS cones of 47.6, 48.5, and 45.3 to 45.7 deg over sectors of +/-75.0, +/-75.7, and +/-70.8 deg (inertial); GPM flies backward in two of four periods, modeled with an `AttitudeSchedule` |\n",
+ "| 2. Overpasses (`collect_feature_observations`) | all 1,433 TC PRIMED overpasses predicted; 94% to 100% of the predicted passes are in TC PRIMED for the non-SSMIS sensors (12 passes missing from TC PRIMED are within 0.05 of a threshold, a few others fall in data gaps), and 43% to 49% for SSMIS (data availability) |\n",
+ "| 3. Coverage (`viewed_fraction`) | median difference within 0.2 percentage points for every sensor; 5th to 95th percentile -1.3 to +1.7 points; larger differences only where TC PRIMED's swath is incomplete; observation times within 3 s (median) for cross-track sounders, and 30 s apart for conical scanners from the definitions of time |\n",
+ "| 4. Sampling of storms | median gap between overpasses 0.83 h predicted and 0.84 h archived; longest gap 5.7 h predicted and 7.7 h archived (missing SSMIS data) |\n",
+ "\n",
+ "**Feature observations.** With features that exist for a period of time (a storm's circle during a pass), `collect_feature_observations` reproduces a real archive's selection of overpasses one by one, and its coverage to a fraction of a percent. It does so for five sensors on 10 satellites over a season, from public element sets and swath geometry measured from the archive itself. TAT-C can therefore predict how constellations, including planned sensors, would observe storms in an OSSE.\n",
+ "\n",
+ "**Capability and archives.** Most passes that TAT-C predicts but the archive lacks fall in gaps in the data, notably for SSMIS. TAT-C predicts what a constellation can observe, which an archive undercounts wherever data are missing. Comparing the two identifies the gaps.\n",
+ "\n",
+ "**Attitude.** The swaths depend on each spacecraft's attitude: the inertial velocity frame for the satellites that are not yaw steered, the Earth-fixed frame for Metop, and GPM's yaw flips. The flips are recovered from the observations and modeled with an `AttitudeSchedule`, which moves GMI's observation times by about 2 minutes in the periods when GPM flies backward.\n",
+ "\n",
+ "**Limitations.** TAT-C predicts complete swaths; missing scans or samples reduce the coverage of a few archived overpasses. The element sets are a public history with about 3.6 element sets per day per satellite. A storm's circle is centered at its position at the closest approach, which is within a few kilometers of its position at TC PRIMED's time of observation."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateGeostationaryGOES.ipynb b/docs/validation/ValidateGeostationaryGOES.ipynb
index 6a2595ef..5fe262f5 100644
--- a/docs/validation/ValidateGeostationaryGOES.ipynb
+++ b/docs/validation/ValidateGeostationaryGOES.ipynb
@@ -135,8 +135,14 @@
"def download(product):\n",
" \"\"\"Download the first full-disk file of a product starting in the hour of TIME.\"\"\"\n",
" prefix = f\"{product}/{TIME:%Y}/{TIME.timetuple().tm_yday:03d}/{TIME:%H}/\"\n",
- " root = ET.fromstring(requests.get(BUCKET, params={\"list-type\": \"2\", \"prefix\": prefix}, timeout=120).content)\n",
- " key = root.find(\"{http://s3.amazonaws.com/doc/2006-03-01/}Contents/{http://s3.amazonaws.com/doc/2006-03-01/}Key\").text\n",
+ " root = ET.fromstring(\n",
+ " requests.get(\n",
+ " BUCKET, params={\"list-type\": \"2\", \"prefix\": prefix}, timeout=120\n",
+ " ).content\n",
+ " )\n",
+ " key = root.find(\n",
+ " \"{http://s3.amazonaws.com/doc/2006-03-01/}Contents/{http://s3.amazonaws.com/doc/2006-03-01/}Key\"\n",
+ " ).text\n",
" path = DATA_DIR / key.split(\"/\")[-1]\n",
" if not path.exists():\n",
" response = requests.get(BUCKET + key, timeout=600)\n",
@@ -145,8 +151,18 @@
" return path\n",
"\n",
"\n",
- "paths = {product: sorted(DATA_DIR.glob(f\"OR_{product}-M6_G19_s{TIME:%Y}{TIME.timetuple().tm_yday:03d}{TIME:%H}*.nc\")) for product in [\"ABI-L2-ACMF\", \"ABI-L2-SSTF\", \"ABI-L2-LSTF\"]}\n",
- "paths = {product: found[0] if found else download(product) for product, found in paths.items()}\n",
+ "paths = {\n",
+ " product: sorted(\n",
+ " DATA_DIR.glob(\n",
+ " f\"OR_{product}-M6_G19_s{TIME:%Y}{TIME.timetuple().tm_yday:03d}{TIME:%H}*.nc\"\n",
+ " )\n",
+ " )\n",
+ " for product in [\"ABI-L2-ACMF\", \"ABI-L2-SSTF\", \"ABI-L2-LSTF\"]\n",
+ "}\n",
+ "paths = {\n",
+ " product: found[0] if found else download(product)\n",
+ " for product, found in paths.items()\n",
+ "}\n",
"\n",
"\n",
"def fixed_grid(ds):\n",
@@ -156,20 +172,30 @@
" h = projection[\"perspective_point_height\"] + r_eq\n",
" lon_0 = np.radians(projection[\"longitude_of_projection_origin\"])\n",
" x, y = np.meshgrid(ds.x.values, ds.y.values)\n",
- " a = np.sin(x) ** 2 + np.cos(x) ** 2 * (np.cos(y) ** 2 + (r_eq / r_pol) ** 2 * np.sin(y) ** 2)\n",
+ " a = np.sin(x) ** 2 + np.cos(x) ** 2 * (\n",
+ " np.cos(y) ** 2 + (r_eq / r_pol) ** 2 * np.sin(y) ** 2\n",
+ " )\n",
" b = -2 * h * np.cos(x) * np.cos(y)\n",
" c = h**2 - r_eq**2\n",
" discriminant = b**2 - 4 * a * c\n",
" with np.errstate(invalid=\"ignore\"):\n",
" r_s = (-b - np.sqrt(discriminant)) / (2 * a)\n",
- " s_x, s_y, s_z = r_s * np.cos(x) * np.cos(y), -r_s * np.sin(x), r_s * np.cos(x) * np.sin(y)\n",
- " latitude = np.degrees(np.arctan((r_eq / r_pol) ** 2 * s_z / np.sqrt((h - s_x) ** 2 + s_y**2)))\n",
+ " s_x, s_y, s_z = (\n",
+ " r_s * np.cos(x) * np.cos(y),\n",
+ " -r_s * np.sin(x),\n",
+ " r_s * np.cos(x) * np.sin(y),\n",
+ " )\n",
+ " latitude = np.degrees(\n",
+ " np.arctan((r_eq / r_pol) ** 2 * s_z / np.sqrt((h - s_x) ** 2 + s_y**2))\n",
+ " )\n",
" longitude = np.degrees(lon_0 - np.arctan(s_y / (h - s_x)))\n",
" return latitude, longitude\n",
"\n",
"\n",
"goes19 = GeneralPerturbationsOrbit.from_tle(TLE)\n",
- "satellite_itrs = np.array(goes19.elements[0].to_skyfield().at(timescale.from_datetime(TIME)).frame_xyz(itrs).m)\n",
+ "satellite_itrs = np.array(\n",
+ " goes19.elements[0].to_skyfield().at(timescale.from_datetime(TIME)).frame_xyz(itrs).m\n",
+ ")\n",
"\n",
"\n",
"def elevation(latitude, longitude):\n",
@@ -178,21 +204,53 @@
" a, f = 6378137.0, 1 / 298.257223563\n",
" e2 = f * (2 - f)\n",
" n = a / np.sqrt(1 - e2 * np.sin(lat) ** 2)\n",
- " position = np.stack([n * np.cos(lat) * np.cos(lon), n * np.cos(lat) * np.sin(lon), n * (1 - e2) * np.sin(lat)], axis=-1)\n",
- " up = np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " position = np.stack(\n",
+ " [\n",
+ " n * np.cos(lat) * np.cos(lon),\n",
+ " n * np.cos(lat) * np.sin(lon),\n",
+ " n * (1 - e2) * np.sin(lat),\n",
+ " ],\n",
+ " axis=-1,\n",
+ " )\n",
+ " up = np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
" line_of_sight = satellite_itrs - position\n",
- " return np.degrees(np.arcsin(np.sum(up * line_of_sight, axis=-1) / np.linalg.norm(line_of_sight, axis=-1)))\n",
- "\n",
- "\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ " return np.degrees(\n",
+ " np.arcsin(\n",
+ " np.sum(up * line_of_sight, axis=-1) / np.linalg.norm(line_of_sight, axis=-1)\n",
+ " )\n",
+ " )\n",
+ "\n",
+ "\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"products = {product: xr.open_dataset(path) for product, path in paths.items()}\n",
"pd.DataFrame(\n",
" {\n",
" product.replace(\"ABI-L2-\", \"\").replace(\"F\", \"\"): {\n",
" \"pixels\": f\"{ds.sizes['x']} x {ds.sizes['y']}\",\n",
- " \"retrieval local zenith angles (deg)\": \"0 to {:g}\".format(float(ds.retrieval_local_zenith_angle_bounds[1])),\n",
- " \"good-quality local zenith angles (deg)\": \"0 to {:g}\".format(float(ds.quantitative_local_zenith_angle_bounds[1])),\n",
+ " \"retrieval local zenith angles (deg)\": \"0 to {:g}\".format(\n",
+ " float(ds.retrieval_local_zenith_angle_bounds[1])\n",
+ " ),\n",
+ " \"good-quality local zenith angles (deg)\": \"0 to {:g}\".format(\n",
+ " float(ds.quantitative_local_zenith_angle_bounds[1])\n",
+ " ),\n",
" }\n",
" for product, ds in products.items()\n",
" }\n",
@@ -246,7 +304,9 @@
"latitude, longitude = fixed_grid(acm)\n",
"on_grid = np.isfinite(latitude)\n",
"imaged = np.isfinite(acm.ACM.values)\n",
- "grid_elevation = np.where(on_grid, elevation(np.nan_to_num(latitude), np.nan_to_num(longitude)), np.nan)\n",
+ "grid_elevation = np.where(\n",
+ " on_grid, elevation(np.nan_to_num(latitude), np.nan_to_num(longitude)), np.nan\n",
+ ")\n",
"abi = Instrument(name=\"ABI\", field_of_regard=17.4)\n",
"satellite = Satellite(name=\"GOES-19\", orbit=goes19, instruments=[abi])\n",
"rng = np.random.default_rng(1)\n",
@@ -255,9 +315,17 @@
"\n",
"def observed(index):\n",
" \"\"\"TAT-C's observation of a pixel, and the satellite's elevation angle.\"\"\"\n",
- " point = Point(id=0, latitude=float(latitude.flat[index]), longitude=float(longitude.flat[index]))\n",
+ " point = Point(\n",
+ " id=0,\n",
+ " latitude=float(latitude.flat[index]),\n",
+ " longitude=float(longitude.flat[index]),\n",
+ " )\n",
" obs = collect_observations(point, satellite, TIME, TIME + timedelta(minutes=10))\n",
- " return {\"index\": index, \"observed\": not obs.empty, \"sat_alt\": obs.sat_alt.iloc[0] if not obs.empty else np.nan}\n",
+ " return {\n",
+ " \"index\": index,\n",
+ " \"observed\": not obs.empty,\n",
+ " \"sat_alt\": obs.sat_alt.iloc[0] if not obs.empty else np.nan,\n",
+ " }\n",
"\n",
"\n",
"with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
@@ -269,11 +337,16 @@
" \"grid pixels on the Earth\": int(on_grid.sum()),\n",
" \"pixels on the Earth imaged (%)\": 100 * imaged[on_grid].mean(),\n",
" \"imaged pixels off the Earth\": int((imaged & ~on_grid).sum()),\n",
- " \"minimum elevation angle of imaged pixels (deg)\": float(np.nanmin(grid_elevation[imaged])),\n",
+ " \"minimum elevation angle of imaged pixels (deg)\": float(\n",
+ " np.nanmin(grid_elevation[imaged])\n",
+ " ),\n",
" \"sampled pixels: imaged (%)\": 100 * sample1.imaged.mean(),\n",
" \"sampled pixels: observed by TAT-C (%)\": 100 * sample1.observed.mean(),\n",
- " \"sampled imaged pixels observed by TAT-C (%)\": 100 * sample1.observed[sample1.imaged].mean(),\n",
- " \"TAT-C sat_alt minus elevation, max |x| (deg)\": float((sample1.sat_alt - sample1.elevation).abs().max()),\n",
+ " \"sampled imaged pixels observed by TAT-C (%)\": 100\n",
+ " * sample1.observed[sample1.imaged].mean(),\n",
+ " \"TAT-C sat_alt minus elevation, max |x| (deg)\": float(\n",
+ " (sample1.sat_alt - sample1.elevation).abs().max()\n",
+ " ),\n",
" }\n",
").round(4)"
]
@@ -425,36 +498,69 @@
" ds = ds.isel(x=slice(0, None, step), y=slice(0, None, step))\n",
" lat, lon = fixed_grid(ds)\n",
" valid = np.isfinite(lat)\n",
- " zenith = np.where(valid, 90 - elevation(np.nan_to_num(lat), np.nan_to_num(lon)), np.nan)\n",
+ " zenith = np.where(\n",
+ " valid, 90 - elevation(np.nan_to_num(lat), np.nan_to_num(lon)), np.nan\n",
+ " )\n",
" retrieved = np.isfinite(ds[quality_name].values)\n",
" good = retrieved & (ds.DQF.values == 0)\n",
" good_bound = float(ds.quantitative_local_zenith_angle_bounds[1])\n",
" retrieval_bound = float(ds.retrieval_local_zenith_angle_bounds[1])\n",
- " zenith_by_product[quality_name] = (zenith, retrieved, good, good_bound, retrieval_bound)\n",
+ " zenith_by_product[quality_name] = (\n",
+ " zenith,\n",
+ " retrieved,\n",
+ " good,\n",
+ " good_bound,\n",
+ " retrieval_bound,\n",
+ " )\n",
" field_of_regard = compute_field_of_regard(35786e3, 90 - good_bound)\n",
- " product_satellite = Satellite(name=\"GOES-19\", orbit=goes19, instruments=[Instrument(name=quality_name, field_of_regard=field_of_regard)])\n",
+ " product_satellite = Satellite(\n",
+ " name=\"GOES-19\",\n",
+ " orbit=goes19,\n",
+ " instruments=[Instrument(name=quality_name, field_of_regard=field_of_regard)],\n",
+ " )\n",
" near_bound = retrieved & (np.abs(zenith - good_bound) <= 5)\n",
" picks = rng.choice(np.flatnonzero(near_bound), 2000, replace=False)\n",
"\n",
- " def observed_with_bound(index, lat=lat, lon=lon, product_satellite=product_satellite):\n",
- " point = Point(id=0, latitude=float(lat.flat[index]), longitude=float(lon.flat[index]))\n",
- " return not collect_observations(point, product_satellite, TIME, TIME + timedelta(minutes=10)).empty\n",
+ " def observed_with_bound(\n",
+ " index, lat=lat, lon=lon, product_satellite=product_satellite\n",
+ " ):\n",
+ " point = Point(\n",
+ " id=0, latitude=float(lat.flat[index]), longitude=float(lon.flat[index])\n",
+ " )\n",
+ " return not collect_observations(\n",
+ " point, product_satellite, TIME, TIME + timedelta(minutes=10)\n",
+ " ).empty\n",
"\n",
" with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
" tatc = np.array(list(pool.map(observed_with_bound, picks)))\n",
" picked_zenith, picked_good = zenith.flat[picks], good.flat[picks]\n",
" rows2[quality_name] = {\n",
" \"declared bounds, good / retrieved (deg)\": f\"{good_bound:g} / {retrieval_bound:g}\",\n",
- " \"max local zenith angle, retrieved pixels (deg)\": float(np.nanmax(zenith[retrieved])),\n",
- " \"max local zenith angle, good-quality pixels (deg)\": float(np.nanmax(zenith[good])),\n",
- " \"retrieved pixels beyond the retrieval bound\": int((retrieved & (zenith > retrieval_bound)).sum()),\n",
- " \"good-quality pixels beyond the good bound (%)\": 100 * (good & (zenith > good_bound)).sum() / good.sum(),\n",
+ " \"max local zenith angle, retrieved pixels (deg)\": float(\n",
+ " np.nanmax(zenith[retrieved])\n",
+ " ),\n",
+ " \"max local zenith angle, good-quality pixels (deg)\": float(\n",
+ " np.nanmax(zenith[good])\n",
+ " ),\n",
+ " \"retrieved pixels beyond the retrieval bound\": int(\n",
+ " (retrieved & (zenith > retrieval_bound)).sum()\n",
+ " ),\n",
+ " \"good-quality pixels beyond the good bound (%)\": 100\n",
+ " * (good & (zenith > good_bound)).sum()\n",
+ " / good.sum(),\n",
" \"TAT-C field of regard (deg)\": field_of_regard,\n",
- " \"TAT-C: max local zenith angle observed (deg)\": float(picked_zenith[tatc].max()),\n",
- " \"TAT-C: min local zenith angle not observed (deg)\": float(picked_zenith[~tatc].min()),\n",
- " \"sampled good-quality pixels observed by TAT-C (%)\": 100 * tatc[picked_good].mean(),\n",
- " \"sampled pixels observed by TAT-C that are good quality (%)\": 100 * picked_good[tatc].mean(),\n",
- " \"sampled pixels not observed by TAT-C that are good quality (%)\": 100 * picked_good[~tatc].mean(),\n",
+ " \"TAT-C: max local zenith angle observed (deg)\": float(\n",
+ " picked_zenith[tatc].max()\n",
+ " ),\n",
+ " \"TAT-C: min local zenith angle not observed (deg)\": float(\n",
+ " picked_zenith[~tatc].min()\n",
+ " ),\n",
+ " \"sampled good-quality pixels observed by TAT-C (%)\": 100\n",
+ " * tatc[picked_good].mean(),\n",
+ " \"sampled pixels observed by TAT-C that are good quality (%)\": 100\n",
+ " * picked_good[tatc].mean(),\n",
+ " \"sampled pixels not observed by TAT-C that are good quality (%)\": 100\n",
+ " * picked_good[~tatc].mean(),\n",
" }\n",
"pd.DataFrame(rows2).T.round(3)"
]
@@ -486,16 +592,33 @@
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.6), sharey=True)\n",
"bins = np.arange(0, 91, 1)\n",
- "for ax, (name, (zenith, retrieved, good, good_bound, retrieval_bound)) in zip(axes, zenith_by_product.items()):\n",
+ "for ax, (name, (zenith, retrieved, good, good_bound, retrieval_bound)) in zip(\n",
+ " axes, zenith_by_product.items()\n",
+ "):\n",
" counts_retrieved = np.histogram(zenith[retrieved], bins)[0]\n",
" counts_good = np.histogram(zenith[good], bins)[0]\n",
" total = np.histogram(zenith[np.isfinite(zenith)], bins)[0]\n",
" with np.errstate(invalid=\"ignore\", divide=\"ignore\"):\n",
- " ax.plot(bins[:-1] + 0.5, 100 * counts_retrieved / total, color=GRAY, lw=2, label=\"retrieved\")\n",
- " ax.plot(bins[:-1] + 0.5, 100 * counts_good / total, color=BLUE, lw=1.5, label=\"good quality\")\n",
+ " ax.plot(\n",
+ " bins[:-1] + 0.5,\n",
+ " 100 * counts_retrieved / total,\n",
+ " color=GRAY,\n",
+ " lw=2,\n",
+ " label=\"retrieved\",\n",
+ " )\n",
+ " ax.plot(\n",
+ " bins[:-1] + 0.5,\n",
+ " 100 * counts_good / total,\n",
+ " color=BLUE,\n",
+ " lw=1.5,\n",
+ " label=\"good quality\",\n",
+ " )\n",
" ax.axvline(good_bound, color=BLUE, ls=\"--\", lw=1)\n",
" ax.axvline(retrieval_bound, color=GRAY, ls=\"--\", lw=1)\n",
- " ax.set(xlabel=\"Local zenith angle (deg)\", title=f\"{name} pixels by local zenith angle (dashed: declared bounds)\")\n",
+ " ax.set(\n",
+ " xlabel=\"Local zenith angle (deg)\",\n",
+ " title=f\"{name} pixels by local zenith angle (dashed: declared bounds)\",\n",
+ " )\n",
"axes[0].set_ylabel(\"Pixels on the disk (%)\")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
diff --git a/docs/validation/ValidateGridSMAP.ipynb b/docs/validation/ValidateGridSMAP.ipynb
new file mode 100644
index 00000000..1864030f
--- /dev/null
+++ b/docs/validation/ValidateGridSMAP.ipynb
@@ -0,0 +1,771 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "3be993f6",
+ "metadata": {},
+ "source": [
+ "# Gridded Coverage Validation: SMAP\n",
+ "\n",
+ "This notebook validates TAT-C's coverage statistics and their gridding (`aggregate_observations`, `reduce_observations`, and `grid_observations`) against a day of observations by the radiometer of NASA's [Soil Moisture Active Passive](https://smap.jpl.nasa.gov/) (SMAP) mission on 24 October 2026. SMAP's 6 m antenna rotates at about 14.6 revolutions per minute, so its radiometer scans a full circle on the ground around the satellite, about 1,000 km wide, at an incidence angle of 40 deg, viewing each place twice on each pass: once looking forward and once looking aft, about two minutes later. A `ConicalInstrument` that scans the full circle (a half-width of 180 deg) models this geometry, which the AMSR2 and GMI notebooks (sector scanners) do not test.\n",
+ "\n",
+ "1. **Looks.** Does `collect_observations` predict each forward and aft look at each cell of SMAP's grid?\n",
+ "2. **Statistics by cell.** Do `aggregate_observations` and `reduce_observations` give each cell's observed number of passes, access, and revisit?\n",
+ "3. **Gridded statistics.** Does `grid_observations` combine them correctly into larger cells?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2338c4d9",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "SMAP's Level-1C brightness temperatures (`SPL1CTB`, version 6, from the [NSIDC DAAC](https://nsidc.org/data/smap); free [NASA Earthdata Login](https://urs.earthdata.nasa.gov/) account required) are resampled to the 36 km global Equal-Area Scalable Earth grid (EASE-Grid 2.0), one file per half orbit, and record for each cell the time of the forward and aft looks (the weighted mean time of the footprints centered in it). The day's half orbits are downloaded once (keeping the latest version of each), and the look times are cached in a local `data/smap` directory.\n",
+ "\n",
+ "The look times are converted to UTC from the seconds since J2000 (noon TT on 1 January 2000). The products' own UTC strings (`cell_tb_time_utc_fore` and `_aft`) add these seconds to noon UTC, and are 69.184 s late (the difference between TT and UTC). The archive lacks a few half orbits late in the day, whose times are excluded from the comparisons."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ff67d07e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:47:30.878683Z",
+ "iopub.status.busy": "2026-10-25T23:47:30.878459Z",
+ "iopub.status.idle": "2026-10-25T23:47:32.365206Z",
+ "shell.execute_reply": "2026-10-25T23:47:32.364410Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "32 half orbits, 890452 looks within the day of 273726 cells\n",
+ "data gaps (UTC): 21:01-22:35, 23:29-00:00\n"
+ ]
+ }
+ ],
+ "source": [
+ "import re\n",
+ "from datetime import datetime, timedelta, timezone\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import h5py\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from tatc.constants import timescale\n",
+ "\n",
+ "DATA_DIR = Path(\"data\") / \"smap\"\n",
+ "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "DAY = datetime(2026, 10, 24, tzinfo=timezone.utc)\n",
+ "CACHE_FILE = DATA_DIR / f\"smap_l1c_tb_{DAY:%Y%m%d}.npz\"\n",
+ "\n",
+ "if not CACHE_FILE.exists():\n",
+ " import earthaccess\n",
+ "\n",
+ " earthaccess.login()\n",
+ " granules = earthaccess.search_data(\n",
+ " short_name=\"SPL1CTB\",\n",
+ " temporal=(\n",
+ " f\"{DAY - timedelta(hours=2):%Y-%m-%dT%H:%M:%S}\",\n",
+ " f\"{DAY + timedelta(hours=26):%Y-%m-%dT%H:%M:%S}\",\n",
+ " ),\n",
+ " )\n",
+ " # the latest version of each half orbit (orbit number and direction)\n",
+ " latest = {}\n",
+ " for g in sorted(granules, key=lambda g: g[\"umm\"][\"GranuleUR\"]):\n",
+ " name = g[\"umm\"][\"GranuleUR\"]\n",
+ " latest[re.search(r\"_TB_(\\d+_[AD])_\", name).group(1)] = g\n",
+ " files = earthaccess.download(list(latest.values()), str(DATA_DIR / \"raw\"))\n",
+ " parts = []\n",
+ " for k, path in enumerate(sorted(files)):\n",
+ " with h5py.File(path) as f:\n",
+ " cells = f[\"Global_Projection\"]\n",
+ " part = {\n",
+ " \"row\": cells[\"cell_row\"][:],\n",
+ " \"col\": cells[\"cell_column\"][:],\n",
+ " \"incidence\": cells[\"cell_boresight_incidence_fore\"][:],\n",
+ " }\n",
+ " for look in (\"fore\", \"aft\"):\n",
+ " # seconds since J2000 (noon TT on 1 January 2000), converted to UTC\n",
+ " seconds = cells[f\"cell_tb_time_seconds_{look}\"][:]\n",
+ " valid = seconds > -9999\n",
+ " utc = pd.to_datetime(\n",
+ " timescale.tt_jd(2451545.0 + seconds[valid] / 86400).utc_datetime(),\n",
+ " utc=True,\n",
+ " )\n",
+ " part[look] = np.full(len(seconds), np.nan)\n",
+ " part[look][valid] = (utc - pd.Timestamp(DAY)).total_seconds()\n",
+ " part[\"half_orbit\"] = np.full(len(part[\"row\"]), k)\n",
+ " parts.append(part)\n",
+ " np.savez_compressed(\n",
+ " CACHE_FILE, **{key: np.concatenate([p[key] for p in parts]) for key in parts[0]}\n",
+ " )\n",
+ " for path in files:\n",
+ " Path(\n",
+ " path\n",
+ " ).unlink() # keep only the cached looks (each half orbit is about 5 MB)\n",
+ "\n",
+ "smap = dict(np.load(CACHE_FILE))\n",
+ "# looks (forward and aft) within the day, by cell\n",
+ "looks = pd.concat(\n",
+ " [\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"row\": smap[\"row\"],\n",
+ " \"col\": smap[\"col\"],\n",
+ " \"half_orbit\": smap[\"half_orbit\"],\n",
+ " \"seconds\": smap[look],\n",
+ " \"look\": look,\n",
+ " }\n",
+ " )\n",
+ " for look in (\"fore\", \"aft\")\n",
+ " ]\n",
+ ").dropna(subset=[\"seconds\"])\n",
+ "looks = looks[(looks.seconds >= 0) & (looks.seconds < 86400)]\n",
+ "# data gaps: periods of more than a minute without a look\n",
+ "covered = looks.groupby(\"half_orbit\").seconds.agg([\"min\", \"max\"]).sort_values(\"min\")\n",
+ "GAPS = [\n",
+ " (a, b)\n",
+ " for a, b in zip(\n",
+ " covered[\"max\"].cummax().to_numpy()[:-1], covered[\"min\"].to_numpy()[1:]\n",
+ " )\n",
+ " if b - a > 60\n",
+ "]\n",
+ "if covered[\"min\"].iloc[0] > 60:\n",
+ " GAPS.insert(0, (0, covered[\"min\"].iloc[0]))\n",
+ "if covered[\"max\"].max() < 86400 - 60:\n",
+ " GAPS.append((covered[\"max\"].max(), 86400))\n",
+ "print(\n",
+ " f\"{smap['half_orbit'].max() + 1} half orbits, {len(looks)} looks within the day of {looks.groupby(['row', 'col']).ngroups} cells\"\n",
+ ")\n",
+ "print(\n",
+ " \"data gaps (UTC): \"\n",
+ " + \", \".join(\n",
+ " f\"{DAY + timedelta(seconds=a):%H:%M}-{DAY + timedelta(seconds=b):%H:%M}\"\n",
+ " for a, b in GAPS\n",
+ " )\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3c8efe70",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "SMAP is modeled as a `ConicalInstrument` scanning the full circle, with the cone angle of the median incidence angle of its looks at its mean altitude (on a spherical Earth), with an along-track field of view of 0.01 deg (so that each observation period spans the forward to the aft crossing of the circle), on an orbit from a published two-line element set (TLE) with an epoch about a day and a half after the analysis day. The points are the centers of every other cell of every other row of the EASE-Grid 2.0 (about 72 km apart), between about 81 deg south and 84 deg north. `collect_observations` reports each pass over a point as two observations, at the forward and aft crossings of the circle (`epoch`), which share the period during which the point lies inside the circle; its run of about half a minute is cached in `data/smap`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e5761e9f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:47:32.367160Z",
+ "iopub.status.busy": "2026-10-25T23:47:32.366817Z",
+ "iopub.status.idle": "2026-10-25T23:48:11.701901Z",
+ "shell.execute_reply": "2026-10-25T23:48:11.701210Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "collect_observations: 230357 observations of 97846 points in 35 s\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "median incidence angle 39.97 deg, mean altitude 689.4 km: cone angle 35.431 deg\n",
+ "97846 points, latitudes -81.5 to 83.6 deg; 230357 observations\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pickle\n",
+ "import time\n",
+ "\n",
+ "from pyproj import Transformer\n",
+ "from shapely.geometry import Point\n",
+ "\n",
+ "from tatc.analysis import (\n",
+ " aggregate_observations,\n",
+ " collect_observations,\n",
+ " grid_observations,\n",
+ " reduce_observations,\n",
+ ")\n",
+ "from tatc.schemas import ConicalInstrument, GeneralPerturbationsOrbit, Satellite\n",
+ "\n",
+ "TLE = [\n",
+ " \"1 40376U 15003A 26298.67804942 .00000247 00000+0 56844-4 0 9999\",\n",
+ " \"2 40376 98.1310 303.9506 0001331 85.8091 274.3263 14.63397245626701\",\n",
+ "]\n",
+ "EARTH_RADIUS = 6371.0088e3 # m, mean\n",
+ "# EASE-Grid 2.0 global, 36 km: cell size (m) and the projected coordinates of the upper left corner\n",
+ "CELL, X0, Y0 = 36032.220840584, -17367530.44516138, 7314540.835948884\n",
+ "\n",
+ "orbit = GeneralPerturbationsOrbit.from_tle(TLE)\n",
+ "incidence = float(np.nanmedian(smap[\"incidence\"]))\n",
+ "# the cone angle (from nadir) of a boresight at the median incidence angle, at the mean altitude (spherical Earth)\n",
+ "cone = float(\n",
+ " np.degrees(\n",
+ " np.arcsin(\n",
+ " EARTH_RADIUS\n",
+ " * np.sin(np.radians(incidence))\n",
+ " / (EARTH_RADIUS + orbit.get_mean_altitude())\n",
+ " )\n",
+ " )\n",
+ ")\n",
+ "smap_sat = Satellite(\n",
+ " name=\"SMAP\",\n",
+ " orbit=orbit,\n",
+ " instruments=[\n",
+ " ConicalInstrument(\n",
+ " name=\"radiometer\",\n",
+ " cone_angle=cone,\n",
+ " scan_center_azimuth=0,\n",
+ " scan_half_width=180,\n",
+ " along_track_field_of_view=0.01,\n",
+ " )\n",
+ " ],\n",
+ ")\n",
+ "# points at the centers of every other cell of every other row\n",
+ "rows, cols = np.meshgrid(np.arange(0, 406, 2), np.arange(0, 964, 2), indexing=\"ij\")\n",
+ "lon, lat = Transformer.from_crs(\"EPSG:6933\", \"EPSG:4326\", always_xy=True).transform(\n",
+ " X0 + (cols.ravel() + 0.5) * CELL, Y0 - (rows.ravel() + 0.5) * CELL\n",
+ ")\n",
+ "points = pd.DataFrame(\n",
+ " {\"row\": rows.ravel(), \"col\": cols.ravel(), \"lon\": lon, \"lat\": lat}\n",
+ ")\n",
+ "point_index = pd.Series(\n",
+ " np.arange(len(points)), index=pd.MultiIndex.from_arrays([points.row, points.col])\n",
+ ")\n",
+ "looks[\"point\"] = point_index.reindex(\n",
+ " pd.MultiIndex.from_arrays([looks.row, looks.col])\n",
+ ").to_numpy()\n",
+ "looks = looks.dropna(subset=[\"point\"]).astype({\"point\": int})\n",
+ "\n",
+ "cache = DATA_DIR / f\"observations_{DAY:%Y%m%d}.pkl\"\n",
+ "if not cache.exists():\n",
+ " tic = time.perf_counter()\n",
+ " found = collect_observations(\n",
+ " [Point(x, y) for x, y in zip(lon, lat)], smap_sat, DAY, DAY + timedelta(days=1)\n",
+ " )\n",
+ " print(\n",
+ " f\"collect_observations: {len(found)} observations of {len(points)} points in {time.perf_counter() - tic:.0f} s\"\n",
+ " )\n",
+ " found.to_pickle(cache)\n",
+ "observations = pd.read_pickle(cache)\n",
+ "hash_index = pd.Series(\n",
+ " np.arange(len(points)), index=[Point(x, y).wkb for x, y in zip(lon, lat)]\n",
+ ")\n",
+ "observations[\"point\"] = observations.geometry.apply(\n",
+ " lambda g: hash_index[Point(g.x, g.y).wkb]\n",
+ ").to_numpy()\n",
+ "observations[\"seconds\"] = (observations.epoch - pd.Timestamp(DAY)).dt.total_seconds()\n",
+ "\n",
+ "\n",
+ "def in_gap(seconds, margin=180):\n",
+ " \"\"\"Whether times (s) fall within a margin (s) of a data gap (a pass's looks span about two minutes).\"\"\"\n",
+ " seconds = np.asarray(seconds)\n",
+ " return np.any(\n",
+ " [(seconds > a - margin) & (seconds < b + margin) for a, b in GAPS]\n",
+ " + [np.zeros(seconds.shape, bool)],\n",
+ " axis=0,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "print(\n",
+ " f\"median incidence angle {incidence:.2f} deg, mean altitude {orbit.get_mean_altitude() / 1e3:.1f} km: cone angle {cone:.3f} deg\"\n",
+ ")\n",
+ "print(\n",
+ " f\"{len(points)} points, latitudes {lat.min():.1f} to {lat.max():.1f} deg; {len(observations)} observations\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3ba858f2",
+ "metadata": {},
+ "source": [
+ "## Test 1: Looks\n",
+ "\n",
+ "Each observed look (forward or aft) at a point is paired with the nearest predicted observation of the same point within 60 s, outside the data gaps."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b20ad7d0",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:48:11.703932Z",
+ "iopub.status.busy": "2026-10-25T23:48:11.703699Z",
+ "iopub.status.idle": "2026-10-25T23:48:11.925706Z",
+ "shell.execute_reply": "2026-10-25T23:48:11.924929Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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observed looks
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+ "
predicted (%)
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+ "
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+ " \n",
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fore
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110535.0
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aft
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110559.0
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all predicted looks
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+ ],
+ "text/plain": [
+ " observed looks predicted (%) time offset, median (s) \\\n",
+ "fore 110535.0 95.28 -0.19 \n",
+ "aft 110559.0 95.28 0.11 \n",
+ "all predicted looks 208936.0 96.63 NaN \n",
+ "\n",
+ " 95% |offset| (s) \n",
+ "fore 1.60 \n",
+ "aft 1.54 \n",
+ "all predicted looks NaN "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def pair(left, right, tolerance=60):\n",
+ " \"\"\"Pairs each row of left with the nearest row of right at the same point within tolerance (s) of time.\"\"\"\n",
+ " return pd.merge_asof(\n",
+ " left.sort_values(\"seconds\"),\n",
+ " right.rename(columns={\"seconds\": \"paired\"}).sort_values(\"paired\"),\n",
+ " left_on=\"seconds\",\n",
+ " right_on=\"paired\",\n",
+ " by=\"point\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=tolerance,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "observed = looks[~in_gap(looks.seconds)]\n",
+ "predicted = observations[~in_gap(observations.seconds)]\n",
+ "forward = pair(observed[[\"point\", \"seconds\", \"look\"]], predicted[[\"point\", \"seconds\"]])\n",
+ "backward = pair(predicted[[\"point\", \"seconds\"]], observed[[\"point\", \"seconds\"]])\n",
+ "offset = (forward.paired - forward.seconds).dropna()\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " look: {\n",
+ " \"observed looks\": int((forward.look == look).sum()),\n",
+ " \"predicted (%)\": 100 * forward.paired[forward.look == look].notna().mean(),\n",
+ " \"time offset, median (s)\": offset[forward.look == look].median(),\n",
+ " \"95% |offset| (s)\": offset[forward.look == look].abs().quantile(0.95),\n",
+ " }\n",
+ " for look in (\"fore\", \"aft\")\n",
+ " }\n",
+ " | {\n",
+ " \"all predicted looks\": {\n",
+ " \"observed looks\": len(predicted),\n",
+ " \"predicted (%)\": 100 * backward.paired.notna().mean(),\n",
+ " }\n",
+ " }\n",
+ ").T.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "60c7477d",
+ "metadata": {},
+ "source": [
+ "A full-circle `ConicalInstrument` reproduces SMAP's two looks per pass: `collect_observations` predicts 95.3% of the forward and the aft looks alike, within 0.2 s (median) and 1.6 s (95%) of their times, and 96.6% of its predicted looks were observed. Every look not predicted belongs to a pass over a point that TAT-C does not predict at all, not to a misplaced one: these points lie just outside TAT-C's swath, where SMAP records a look at a cell whenever a footprint is centered anywhere within it (up to half a cell, 18 km, from its center), and are more common at high latitudes (a median of 64 deg, against 47 deg for all looks), where the edges of successive swaths cross more cells."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ea62178",
+ "metadata": {},
+ "source": [
+ "## Test 2: Statistics by Cell\n",
+ "\n",
+ "`aggregate_observations` merges each point's overlapping observations into periods (here, a pass's forward and aft observations, which share a period), and `reduce_observations` gives each point's number of samples (passes), mean access (the duration of the periods), and mean revisit (the time between them). The observed statistics are computed the same way from each pass's looks: a period from the first look to the last (forward to aft). Observations within the data gaps are excluded from both."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f2d4e5f6",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:48:11.927530Z",
+ "iopub.status.busy": "2026-10-25T23:48:11.927306Z",
+ "iopub.status.idle": "2026-10-25T23:48:17.133079Z",
+ "shell.execute_reply": "2026-10-25T23:48:17.132382Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "aggregate_observations and reduce_observations: 230357 observations in 4.1 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "points observed 68435.00\n",
+ "predicted 68485.00\n",
+ "same number of passes (%) 89.82\n",
+ "one more predicted (%) 3.86\n",
+ "one fewer predicted (%) 5.63\n",
+ "other (%) 0.70\n",
+ "access difference, median (s) 7.50\n",
+ "95% |access difference| (s) 29.12\n",
+ "revisit difference (same passes), median (s) -7.78\n",
+ "95% |revisit difference| (s) 16.13\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def statistics(periods):\n",
+ " \"\"\"Samples, mean access (s), and mean revisit (s) of each point, from its observation periods (point, start,\n",
+ " end, in s), merged where they overlap (as by aggregate_observations).\"\"\"\n",
+ " periods = periods.sort_values([\"point\", \"start\"])\n",
+ " first = periods.point.ne(periods.point.shift())\n",
+ " reach = periods.groupby(\"point\").end.cummax()\n",
+ " periods = periods.assign(\n",
+ " group=(first | (periods.start > reach.groupby(periods.point).shift())).cumsum()\n",
+ " )\n",
+ " merged = periods.groupby(\"group\").agg(\n",
+ " point=(\"point\", \"first\"), start=(\"start\", \"min\"), end=(\"end\", \"max\")\n",
+ " )\n",
+ " merged[\"access\"] = merged.end - merged.start\n",
+ " merged[\"revisit\"] = merged.start - merged.end.groupby(merged.point).shift()\n",
+ " return merged.groupby(\"point\").agg(\n",
+ " samples=(\"start\", \"size\"),\n",
+ " access=(\"access\", \"mean\"),\n",
+ " revisit=(\"revisit\", \"mean\"),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "# observed: each half orbit's pass over a point, from its first to its last look\n",
+ "passes = (\n",
+ " looks[~in_gap(looks.seconds)]\n",
+ " .groupby([\"point\", \"half_orbit\"])\n",
+ " .seconds.agg(start=\"min\", end=\"max\")\n",
+ " .reset_index()\n",
+ ")\n",
+ "observed = statistics(passes)\n",
+ "# predicted: aggregate_observations and reduce_observations, outside the data gaps\n",
+ "tic = time.perf_counter()\n",
+ "aggregated = aggregate_observations(\n",
+ " observations[~in_gap(observations.seconds)].drop(columns=[\"point\", \"seconds\"])\n",
+ ")\n",
+ "reduced = reduce_observations(aggregated)\n",
+ "elapsed = time.perf_counter() - tic\n",
+ "reduced[\"point\"] = reduced.geometry.apply(\n",
+ " lambda g: hash_index[Point(g.x, g.y).wkb]\n",
+ ").to_numpy()\n",
+ "predicted = reduced.set_index(\"point\")[[\"samples\", \"access\", \"revisit\"]].assign(\n",
+ " access=lambda f: f.access.dt.total_seconds(),\n",
+ " revisit=lambda f: f.revisit.dt.total_seconds(),\n",
+ ")\n",
+ "both = observed.add_suffix(\"_observed\").join(\n",
+ " predicted.add_suffix(\"_predicted\"), how=\"outer\"\n",
+ ")\n",
+ "both[[\"samples_observed\", \"samples_predicted\"]] = both[\n",
+ " [\"samples_observed\", \"samples_predicted\"]\n",
+ "].fillna(0)\n",
+ "difference = both.samples_predicted - both.samples_observed\n",
+ "same = (difference == 0) & both.revisit_observed.notna()\n",
+ "print(\n",
+ " f\"aggregate_observations and reduce_observations: {len(observations)} observations in {elapsed:.1f} s\"\n",
+ ")\n",
+ "pd.Series(\n",
+ " {\n",
+ " \"points observed\": int((both.samples_observed > 0).sum()),\n",
+ " \"predicted\": int((both.samples_predicted > 0).sum()),\n",
+ " \"same number of passes (%)\": 100 * (difference == 0).mean(),\n",
+ " \"one more predicted (%)\": 100 * (difference == 1).mean(),\n",
+ " \"one fewer predicted (%)\": 100 * (difference == -1).mean(),\n",
+ " \"other (%)\": 100 * (difference.abs() > 1).mean(),\n",
+ " \"access difference, median (s)\": (\n",
+ " both.access_predicted - both.access_observed\n",
+ " ).median(),\n",
+ " \"95% |access difference| (s)\": (both.access_predicted - both.access_observed)\n",
+ " .abs()\n",
+ " .quantile(0.95),\n",
+ " \"revisit difference (same passes), median (s)\": (\n",
+ " both.revisit_predicted - both.revisit_observed\n",
+ " )[same].median(),\n",
+ " \"95% |revisit difference| (s)\": (\n",
+ " both.revisit_predicted - both.revisit_observed\n",
+ " )[same]\n",
+ " .abs()\n",
+ " .quantile(0.95),\n",
+ " }\n",
+ ").round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5893eeaa",
+ "metadata": {},
+ "source": [
+ "`aggregate_observations` and `reduce_observations` give the same number of passes for 90% of the points observed or predicted (68,435 and 68,485), one more for 3.9%, and one fewer for 5.6%, at the edges of the swaths as in Test 1, in about 4 s for the 230,000 observations of the day. Their access periods are 7.5 s longer (median) than the observed ones, from the forward to the aft look, and their revisits correspondingly shorter: by default, a `ConicalInstrument`'s field of regard, whose access defines each observation's period, extends 1 deg beyond its cone (about 3.4 s on either side of the circle); the epochs themselves match (Test 1)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81a94f43",
+ "metadata": {},
+ "source": [
+ "## Test 3: Gridded Statistics\n",
+ "\n",
+ "`grid_observations` combines the statistics of the points within each cell of a coarser grid, here TAT-C's cells about 1,000 km wide (`generate_cells_uniform_spacing`): the total number of samples, the sample-weighted mean access, and the sample-weighted harmonic mean revisit (the mean of the revisit rates). The observed statistics of Test 2 are gridded the same way into the same cells."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "6718731d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:48:17.134858Z",
+ "iopub.status.busy": "2026-10-25T23:48:17.134621Z",
+ "iopub.status.idle": "2026-10-25T23:48:18.307669Z",
+ "shell.execute_reply": "2026-10-25T23:48:18.307009Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "800 cells, 780 with observations, gridded in 1.1 s; 780 compared\n"
+ ]
+ },
+ {
+ "data": {
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+ " 5% 50% 95%\n",
+ "samples (predicted / observed) 0.841 0.976 1.086\n",
+ "access (predicted / observed) 1.063 1.120 1.500\n",
+ "revisit (predicted / observed) 0.947 1.000 1.098"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import geopandas as gpd\n",
+ "\n",
+ "from tatc.generation import generate_cells_uniform_spacing\n",
+ "\n",
+ "cells = generate_cells_uniform_spacing(1000e3)\n",
+ "tic = time.perf_counter()\n",
+ "gridded = grid_observations(reduced, cells)\n",
+ "elapsed = time.perf_counter() - tic\n",
+ "# the same statistics gridded from the observed points: total samples, sample-weighted mean access, and\n",
+ "# sample-weighted harmonic mean revisit\n",
+ "located = gpd.GeoDataFrame(\n",
+ " observed.join(points[[\"lon\", \"lat\"]]),\n",
+ " geometry=gpd.points_from_xy(points.lon, points.lat)[observed.index],\n",
+ " crs=\"EPSG:4326\",\n",
+ ").sjoin(cells[[\"cell_id\", \"geometry\"]], predicate=\"within\")\n",
+ "located[\"revisit_samples\"] = located.samples.where(located.revisit.notna(), 0)\n",
+ "located[\"rate\"] = (located.samples / located.revisit).fillna(0)\n",
+ "located[\"access_x_samples\"] = located.access * located.samples\n",
+ "truth = located.groupby(\"cell_id\")[\n",
+ " [\"samples\", \"access_x_samples\", \"revisit_samples\", \"rate\"]\n",
+ "].sum()\n",
+ "truth[\"access\"] = truth.access_x_samples / truth.samples\n",
+ "truth[\"revisit\"] = (truth.revisit_samples / truth.rate).where(truth.rate > 0)\n",
+ "compared = (\n",
+ " gridded.set_index(\"cell_id\")[[\"samples\", \"access\", \"revisit\"]]\n",
+ " .assign(\n",
+ " access=lambda f: pd.to_timedelta(f.access).dt.total_seconds(),\n",
+ " revisit=lambda f: pd.to_timedelta(f.revisit).dt.total_seconds(),\n",
+ " )\n",
+ " .add_suffix(\"_predicted\")\n",
+ " .join(truth[[\"samples\", \"access\", \"revisit\"]].add_suffix(\"_observed\"), how=\"inner\")\n",
+ ")\n",
+ "ratio = compared.samples_predicted / compared.samples_observed\n",
+ "print(\n",
+ " f\"{len(cells)} cells, {len(gridded)} with observations, gridded in {elapsed:.1f} s; {len(compared)} compared\"\n",
+ ")\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " \"samples (predicted / observed)\": ratio.quantile([0.05, 0.5, 0.95]),\n",
+ " \"access (predicted / observed)\": (\n",
+ " compared.access_predicted / compared.access_observed\n",
+ " ).quantile([0.05, 0.5, 0.95]),\n",
+ " \"revisit (predicted / observed)\": (\n",
+ " compared.revisit_predicted / compared.revisit_observed\n",
+ " ).quantile([0.05, 0.5, 0.95]),\n",
+ " }\n",
+ ").T.rename(columns=lambda q: f\"{q:.0%}\").round(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6528aff",
+ "metadata": {},
+ "source": [
+ "Gridded into 800 cells about 1,000 km wide, the statistics agree with the observed ones gridded the same way: the median cell has 97.6% of the observed samples (84 to 109% for 90% of the cells, from the swath edges of Test 2), the same revisit (a ratio of 1.00, and within 5 to 10% for 90% of the cells), and an access longer by the field of regard's margin (12%, median), in about a second. In cells whose points each have a single sample, the revisit is undefined."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e060c041",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| 1. Looks | 95.3% of SMAP's forward and aft looks predicted, within 0.2 s (median) and 1.6 s (95%); 96.6% of predicted looks observed; the others are whole passes at the swath edges |\n",
+ "| 2. Statistics by cell | Same number of passes for 90% of the points; access 7.5 s longer (median) by the field of regard's default margin |\n",
+ "| 3. Gridded statistics | Samples within 2.4% (median) and revisit equal (median) in 1,000 km cells; access longer by the margin |\n",
+ "\n",
+ "**TAT-C's coverage statistics are validated against SMAP.** A full-circle `ConicalInstrument` predicts SMAP's forward and aft looks to a fraction of a second, and `aggregate_observations`, `reduce_observations`, and `grid_observations` reproduce its passes, revisits, and their gridded means, for a day of observations of nearly 100,000 points in seconds.\n",
+ "\n",
+ "**Findings.** The validation exposed two problems, both corrected: `grid_observations` failed (with an infinite revisit) for a cell whose points each had a single sample, and included those points' samples in the weights of the cell's mean revisit; and `aggregate_observations` took about five minutes for a day of observations of 100,000 points, which it now computes for all points together in a few seconds, with the same results. The reference data needed care too: the products' UTC strings are 69.184 s late (they add seconds since J2000, a TT epoch, to noon UTC), and the archive lacked a few half orbits.\n",
+ "\n",
+ "**Next steps:**\n",
+ "\n",
+ "1. *Polar grids.* SMAP's products also include polar EASE grids, which would extend the comparison over the poles.\n",
+ "2. *Longer periods.* Over SMAP's 8-day repeat cycle, the revisit statistics would compare the variation between days.\n",
+ "\n",
+ "**Limitations.** This study covers one day. The points are the centers of every other cell, where SMAP observes a cell from any footprint centered within it, so passes differ at the swath edges, and the cone angle comes from a spherical Earth."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateImagerATMS.ipynb b/docs/validation/ValidateImagerATMS.ipynb
index 2449b609..bf4cceb4 100644
--- a/docs/validation/ValidateImagerATMS.ipynb
+++ b/docs/validation/ValidateImagerATMS.ipynb
@@ -47,10 +47,10 @@
"id": "6a685a06",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:19.249243Z",
- "iopub.status.busy": "2026-10-08T17:41:19.248243Z",
- "iopub.status.idle": "2026-10-08T17:41:21.310192Z",
- "shell.execute_reply": "2026-10-08T17:41:21.309658Z"
+ "iopub.execute_input": "2026-10-25T13:48:32.121350Z",
+ "iopub.status.busy": "2026-10-25T13:48:32.121350Z",
+ "iopub.status.idle": "2026-10-25T13:48:32.867917Z",
+ "shell.execute_reply": "2026-10-25T13:48:32.867917Z"
}
},
"outputs": [
@@ -103,7 +103,9 @@
" params[\"continuation-token\"] = token\n",
" listing = requests.get(BUCKET, params=params, timeout=60).text\n",
" keys += re.findall(r\"(.*?)\", listing)\n",
- " match = re.search(r\"(.*?)\", listing)\n",
+ " match = re.search(\n",
+ " r\"(.*?)\", listing\n",
+ " )\n",
" if match is None:\n",
" return keys\n",
" token = match.group(1)\n",
@@ -124,30 +126,57 @@
"TLE_FILE = DATA_DIR / f\"n20_tle_{DAY:%Y%m%d}.txt\"\n",
"if not TLE_FILE.exists():\n",
" # the element set with an epoch at the start of the day\n",
- " key = next(k for k in list_keys(f\"TLE-AUX/{DAY:%Y/%m/%d}/\") if f\"Z_{DAY:%Y%m%d}000000Z_\" in k)\n",
+ " key = next(\n",
+ " k\n",
+ " for k in list_keys(f\"TLE-AUX/{DAY:%Y/%m/%d}/\")\n",
+ " if f\"Z_{DAY:%Y%m%d}000000Z_\" in k\n",
+ " )\n",
" with h5py.File(io.BytesIO(fetch(key))) as f:\n",
- " TLE_FILE.write_text(bytes(f[\"All_Data/TLE-AUX_All/Dataset_Array_0\"][:]).decode().strip() + \"\\n\")\n",
+ " TLE_FILE.write_text(\n",
+ " bytes(f[\"All_Data/TLE-AUX_All/Dataset_Array_0\"][:]).decode().strip() + \"\\n\"\n",
+ " )\n",
"tle = TLE_FILE.read_text().splitlines()\n",
"\n",
"ATMS_FILE = DATA_DIR / f\"atms_gatmo_{DAY:%Y%m%d}.npz\"\n",
"ATMS_FIELDS = [\n",
- " \"StartTime\", \"MidTime\", \"SCPosition\", \"SCVelocity\", \"Latitude\", \"Longitude\", \"Height\",\n",
- " \"SatelliteZenithAngle\", \"QF1_ATMSSDRGEO\",\n",
+ " \"StartTime\",\n",
+ " \"MidTime\",\n",
+ " \"SCPosition\",\n",
+ " \"SCVelocity\",\n",
+ " \"Latitude\",\n",
+ " \"Longitude\",\n",
+ " \"Height\",\n",
+ " \"SatelliteZenithAngle\",\n",
+ " \"QF1_ATMSSDRGEO\",\n",
"]\n",
"if not ATMS_FILE.exists():\n",
"\n",
" def read_granule(key):\n",
" with h5py.File(io.BytesIO(fetch(key))) as f:\n",
- " return {field: f[\"All_Data/ATMS-SDR-GEO_All/\" + field][:] for field in ATMS_FIELDS}\n",
+ " return {\n",
+ " field: f[\"All_Data/ATMS-SDR-GEO_All/\" + field][:]\n",
+ " for field in ATMS_FIELDS\n",
+ " }\n",
"\n",
" keys = list_keys(f\"ATMS-SDR-GEO/{DAY:%Y/%m/%d}/\")\n",
" with concurrent.futures.ThreadPoolExecutor(16) as pool:\n",
" granules = list(pool.map(read_granule, keys))\n",
- " np.savez_compressed(ATMS_FILE, **{field: np.concatenate([g[field] for g in granules]) for field in ATMS_FIELDS})\n",
+ " np.savez_compressed(\n",
+ " ATMS_FILE,\n",
+ " **{\n",
+ " field: np.concatenate([g[field] for g in granules]) for field in ATMS_FIELDS\n",
+ " },\n",
+ " )\n",
"\n",
"atms = dict(np.load(ATMS_FILE))\n",
"print(tle[0], tle[1], sep=\"\\n\")\n",
- "pd.Series({\"granules\": len(atms[\"MidTime\"]) // 12, \"scans\": len(atms[\"MidTime\"]), \"beams per scan\": atms[\"Latitude\"].shape[1]})"
+ "pd.Series(\n",
+ " {\n",
+ " \"granules\": len(atms[\"MidTime\"]) // 12,\n",
+ " \"scans\": len(atms[\"MidTime\"]),\n",
+ " \"beams per scan\": atms[\"Latitude\"].shape[1],\n",
+ " }\n",
+ ")"
]
},
{
@@ -170,10 +199,10 @@
"id": "8c4300e8",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:21.313920Z",
- "iopub.status.busy": "2026-10-08T17:41:21.313389Z",
- "iopub.status.idle": "2026-10-08T17:41:25.250560Z",
- "shell.execute_reply": "2026-10-08T17:41:25.250055Z"
+ "iopub.execute_input": "2026-10-25T13:48:32.870149Z",
+ "iopub.status.busy": "2026-10-25T13:48:32.869150Z",
+ "iopub.status.idle": "2026-10-25T13:48:34.665427Z",
+ "shell.execute_reply": "2026-10-25T13:48:34.665427Z"
}
},
"outputs": [
@@ -204,10 +233,21 @@
"from skyfield.api import wgs84\n",
"from skyfield.framelib import itrs\n",
"\n",
- "from tatc.analysis import collect_ground_pixels, collect_observations, compute_ground_track\n",
+ "from tatc.analysis import (\n",
+ " collect_ground_pixels,\n",
+ " collect_observations,\n",
+ " compute_ground_track,\n",
+ ")\n",
"from tatc.constants import EARTH_MEAN_RADIUS, EARTH_ROTATION_RATE\n",
"from tatc.generation import generate_points_fibonacci_lattice\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, Point, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Instrument,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"from tatc.utils import (\n",
" VelocityFrame,\n",
" compute_apoapsis_radius,\n",
@@ -223,18 +263,33 @@
"\n",
"\n",
"def iet_to_utc(iet):\n",
- " return IET_EPOCH + pd.to_timedelta(np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\")\n",
+ " return IET_EPOCH + pd.to_timedelta(\n",
+ " np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\"\n",
+ " )\n",
"\n",
"\n",
"good = (atms[\"QF1_ATMSSDRGEO\"] == 0) & np.all(np.abs(atms[\"Latitude\"]) <= 90, axis=1)\n",
"t_start, t_mid = iet_to_utc(atms[\"StartTime\"][good]), iet_to_utc(atms[\"MidTime\"][good])\n",
- "sc_pos, sc_vel = atms[\"SCPosition\"][good].astype(float), atms[\"SCVelocity\"][good].astype(float)\n",
- "beam_lat, beam_lon = atms[\"Latitude\"][good].astype(float), atms[\"Longitude\"][good].astype(float)\n",
+ "sc_pos, sc_vel = atms[\"SCPosition\"][good].astype(float), atms[\"SCVelocity\"][\n",
+ " good\n",
+ "].astype(float)\n",
+ "beam_lat, beam_lon = atms[\"Latitude\"][good].astype(float), atms[\"Longitude\"][\n",
+ " good\n",
+ "].astype(float)\n",
"beam_height = atms[\"Height\"][good].astype(float)\n",
"beam_zenith = atms[\"SatelliteZenithAngle\"][good].astype(float)\n",
"\n",
"noaa20 = Satellite(name=\"NOAA-20\", orbit=GeneralPerturbationsOrbit.from_tle(tle))\n",
"orbit = noaa20.orbit.to_gp_orbit()\n",
+ "# NOAA-20 is not yaw-steered: its body (and its instruments' views) aligned with the inertial velocity\n",
+ "INERTIAL = Attitude(velocity_frame=VelocityFrame.INERTIAL)\n",
+ "\n",
+ "\n",
+ "def on_noaa20(instrument, attitude=None):\n",
+ " \"\"\"NOAA-20 carrying an instrument, with an attitude.\"\"\"\n",
+ " return noaa20.model_copy(update={\"instruments\": [instrument], \"attitude\": attitude})\n",
+ "\n",
+ "\n",
"atms_pixels = PointedInstrument(\n",
" name=\"ATMS\",\n",
" field_of_regard=2 * EDGE,\n",
@@ -250,13 +305,17 @@
"\n",
"def ecef(lon, lat, height=0.0):\n",
" \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
- " return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+ " return np.stack(\n",
+ " TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1\n",
+ " )\n",
"\n",
"\n",
"def up(lon, lat):\n",
" \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
" lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -295,7 +354,12 @@
"def binned(x, y, edges, q=(5, 50, 95)):\n",
" \"\"\"Percentiles of y in bins of x.\"\"\"\n",
" i = np.digitize(x, edges) - 1\n",
- " return np.array([np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q) for k in range(len(edges) - 1)])\n",
+ " return np.array(\n",
+ " [\n",
+ " np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q)\n",
+ " for k in range(len(edges) - 1)\n",
+ " ]\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -337,10 +401,10 @@
"id": "0472cd7e",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:25.252567Z",
- "iopub.status.busy": "2026-10-08T17:41:25.252567Z",
- "iopub.status.idle": "2026-10-08T17:41:25.312605Z",
- "shell.execute_reply": "2026-10-08T17:41:25.312236Z"
+ "iopub.execute_input": "2026-10-25T13:48:34.667433Z",
+ "iopub.status.busy": "2026-10-25T13:48:34.667433Z",
+ "iopub.status.idle": "2026-10-25T13:48:34.707300Z",
+ "shell.execute_reply": "2026-10-25T13:48:34.707300Z"
}
},
"outputs": [
@@ -422,13 +486,19 @@
"p1, v1 = orbit.get_orbit_track(list(t_mid[i1])).frame_xyz_and_velocity(itrs)\n",
"offset1 = np.array(p1.m).T - sc_pos[i1]\n",
"radial1 = normalize(sc_pos[i1])\n",
- "along1 = normalize(sc_vel[i1] - np.sum(sc_vel[i1] * radial1, axis=-1, keepdims=True) * radial1)\n",
+ "along1 = normalize(\n",
+ " sc_vel[i1] - np.sum(sc_vel[i1] * radial1, axis=-1, keepdims=True) * radial1\n",
+ ")\n",
"test1 = pd.DataFrame(\n",
" {\n",
" \"along-track (km)\": summarize(np.sum(offset1 * along1, axis=-1) / 1e3),\n",
- " \"cross-track, right (km)\": summarize(np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3),\n",
+ " \"cross-track, right (km)\": summarize(\n",
+ " np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3\n",
+ " ),\n",
" \"radial (km)\": summarize(np.sum(offset1 * radial1, axis=-1) / 1e3),\n",
- " \"velocity (m/s)\": summarize(np.linalg.norm(np.array(v1.m_per_s).T - sc_vel[i1], axis=-1)),\n",
+ " \"velocity (m/s)\": summarize(\n",
+ " np.linalg.norm(np.array(v1.m_per_s).T - sc_vel[i1], axis=-1)\n",
+ " ),\n",
" }\n",
").T\n",
"test1.round(3)"
@@ -458,10 +528,10 @@
"id": "48ce833f",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:25.314347Z",
- "iopub.status.busy": "2026-10-08T17:41:25.314347Z",
- "iopub.status.idle": "2026-10-08T17:41:26.799146Z",
- "shell.execute_reply": "2026-10-08T17:41:26.798140Z"
+ "iopub.execute_input": "2026-10-25T13:48:34.709821Z",
+ "iopub.status.busy": "2026-10-25T13:48:34.708816Z",
+ "iopub.status.idle": "2026-10-25T13:48:35.839181Z",
+ "shell.execute_reply": "2026-10-25T13:48:35.838675Z"
}
},
"outputs": [
@@ -487,10 +557,14 @@
],
"source": [
"dt_beam = 2 * np.median(atms[\"MidTime\"][good] - atms[\"StartTime\"][good]) / 1e6 / N_BEAMS\n",
- "beam_offset = (np.arange(N_BEAMS) - (N_BEAMS - 1) / 2) * dt_beam # s from the middle time\n",
+ "beam_offset = (\n",
+ " np.arange(N_BEAMS) - (N_BEAMS - 1) / 2\n",
+ ") * dt_beam # s from the middle time\n",
"beam_xyz = ecef(beam_lon, beam_lat, beam_height)\n",
"beam_sc = sc_pos[:, np.newaxis] + sc_vel[:, np.newaxis] * beam_offset[:, np.newaxis]\n",
- "sc_lon, sc_lat, _ = TO_GEODETIC.transform(beam_sc[..., 0], beam_sc[..., 1], beam_sc[..., 2])\n",
+ "sc_lon, sc_lat, _ = TO_GEODETIC.transform(\n",
+ " beam_sc[..., 0], beam_sc[..., 1], beam_sc[..., 2]\n",
+ ")\n",
"los = normalize(beam_xyz - beam_sc)\n",
"side = np.where(np.arange(N_BEAMS) < N_BEAMS / 2, 1, -1) # beams 0-47 are to the left\n",
"scan_angle = side * np.degrees(np.arccos(np.sum(los * -up(sc_lon, sc_lat), axis=-1)))\n",
@@ -526,10 +600,10 @@
"id": "2a2744ef",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:26.801146Z",
- "iopub.status.busy": "2026-10-08T17:41:26.800146Z",
- "iopub.status.idle": "2026-10-08T17:41:26.946457Z",
- "shell.execute_reply": "2026-10-08T17:41:26.945449Z"
+ "iopub.execute_input": "2026-10-25T13:48:35.841184Z",
+ "iopub.status.busy": "2026-10-25T13:48:35.840183Z",
+ "iopub.status.idle": "2026-10-25T13:48:35.957752Z",
+ "shell.execute_reply": "2026-10-25T13:48:35.957752Z"
}
},
"outputs": [
@@ -546,10 +620,27 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.4))\n",
- "ax.plot(np.arange(N_BEAMS), measured - nominal, color=BLUE, marker=\"o\", ms=3, lw=0, label=\"measured (median)\")\n",
- "ax.plot(np.arange(N_BEAMS), model(fit) - nominal, color=ORANGE, label=f\"fit: spacing {fitted_spacing:.4f} deg, roll {fitted_roll:+.3f} deg\")\n",
+ "ax.plot(\n",
+ " np.arange(N_BEAMS),\n",
+ " measured - nominal,\n",
+ " color=BLUE,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " lw=0,\n",
+ " label=\"measured (median)\",\n",
+ ")\n",
+ "ax.plot(\n",
+ " np.arange(N_BEAMS),\n",
+ " model(fit) - nominal,\n",
+ " color=ORANGE,\n",
+ " label=f\"fit: spacing {fitted_spacing:.4f} deg, roll {fitted_roll:+.3f} deg\",\n",
+ ")\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
- "ax.set(xlabel=\"ATMS beam index (0 = left edge)\", ylabel=\"Scan angle minus nominal (deg)\", title=\"ATMS scan angles\")\n",
+ "ax.set(\n",
+ " xlabel=\"ATMS beam index (0 = left edge)\",\n",
+ " ylabel=\"Scan angle minus nominal (deg)\",\n",
+ " title=\"ATMS scan angles\",\n",
+ ")\n",
"ax.legend(frameon=False)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -575,10 +666,10 @@
"id": "de8f33a7",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:26.948456Z",
- "iopub.status.busy": "2026-10-08T17:41:26.948456Z",
- "iopub.status.idle": "2026-10-08T17:41:29.004153Z",
- "shell.execute_reply": "2026-10-08T17:41:29.004153Z"
+ "iopub.execute_input": "2026-10-25T13:48:35.959756Z",
+ "iopub.status.busy": "2026-10-25T13:48:35.959756Z",
+ "iopub.status.idle": "2026-10-25T13:48:37.531840Z",
+ "shell.execute_reply": "2026-10-25T13:48:37.531840Z"
}
},
"outputs": [
@@ -669,7 +760,9 @@
" \"\"\"Signed angle (deg) from the horizontal projection of a reference direction to that of a direction.\"\"\"\n",
" a = normalize(reference - np.sum(reference * u2, axis=-1, keepdims=True) * u2)\n",
" b = normalize(direction - np.sum(direction * u2, axis=-1, keepdims=True) * u2)\n",
- " return np.degrees(np.arctan2(np.sum(np.cross(a, b) * u2, axis=-1), np.sum(a * b, axis=-1)))\n",
+ " return np.degrees(\n",
+ " np.arctan2(np.sum(np.cross(a, b) * u2, axis=-1), np.sum(a * b, axis=-1))\n",
+ " )\n",
"\n",
"\n",
"yaw_earth_fixed = yaw(normal, sc_vel[i2])\n",
@@ -678,11 +771,27 @@
"out_of_plane = np.degrees(np.abs(np.arcsin(np.einsum(\"kjc,kc->kj\", los[i2], normal))))\n",
"\n",
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
- "for mask, color, label in [(ascending, BLUE, \"ascending\"), (~ascending, AQUA, \"descending\")]:\n",
- " ax.scatter(sub_lat[mask], yaw_earth_fixed[mask], s=3, color=color, label=f\"vs. Earth-fixed velocity, {label}\")\n",
- "ax.scatter(sub_lat, yaw_inertial, s=3, color=ORANGE, label=\"vs. inertial velocity, both\")\n",
+ "for mask, color, label in [\n",
+ " (ascending, BLUE, \"ascending\"),\n",
+ " (~ascending, AQUA, \"descending\"),\n",
+ "]:\n",
+ " ax.scatter(\n",
+ " sub_lat[mask],\n",
+ " yaw_earth_fixed[mask],\n",
+ " s=3,\n",
+ " color=color,\n",
+ " label=f\"vs. Earth-fixed velocity, {label}\",\n",
+ " )\n",
+ "ax.scatter(\n",
+ " sub_lat, yaw_inertial, s=3, color=ORANGE, label=\"vs. inertial velocity, both\"\n",
+ ")\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
- "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Scan plane yaw (deg)\", title=\"Orientation of the ATMS scan plane\", ylim=(-5, 5))\n",
+ "ax.set(\n",
+ " xlabel=\"Sub-satellite latitude (deg)\",\n",
+ " ylabel=\"Scan plane yaw (deg)\",\n",
+ " title=\"Orientation of the ATMS scan plane\",\n",
+ " ylim=(-5, 5),\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, markerscale=4, loc=\"center\")\n",
"fig.tight_layout()\n",
"plt.show()\n",
@@ -700,14 +809,14 @@
"id": "10096da4",
"metadata": {},
"source": [
- "The lines of sight of each scan lie in a plane (to within about 0.03 deg) that faces the inertial velocity at all latitudes, to within a constant 0.06 deg, whereas the Earth-fixed velocity is rotated by up to 4 deg, with opposite signs on ascending and descending passes. A `PointedInstrument` with `velocity_frame=VelocityFrame.INERTIAL` reproduces this orientation; the Earth-fixed default would rotate the scan line about nadir, displacing the swath edges (about 1,280 km from the ground track) by up to about 85 km along track.\n",
+ "The lines of sight of each scan lie in a plane (to within about 0.03 deg) that faces the inertial velocity at all latitudes, to within a constant 0.06 deg, whereas the Earth-fixed velocity is rotated by up to 4 deg, with opposite signs on ascending and descending passes. A satellite with an inertial velocity frame (`Attitude(velocity_frame=VelocityFrame.INERTIAL)`), whose instruments' views are rotated from its body, reproduces this orientation; the Earth-fixed default would rotate the scan line about nadir, displacing the swath edges (about 1,280 km from the ground track) by up to about 85 km along track.\n",
"\n",
"## Test 3: Beam Geolocation\n",
"\n",
"TAT-C pixel positions from `collect_ground_pixels`, at the middle time of every hundredth scan, are compared with the ATMS beam centers. Four configurations isolate the contributions of the along-track reference direction, the scan angles, and scan timing:\n",
"\n",
"* **Nominal, Earth-fixed:** 1.11 deg spacing with TAT-C's default velocity frame.\n",
- "* **Nominal, inertial:** the same with `velocity_frame=VelocityFrame.INERTIAL`.\n",
+ "* **Nominal, inertial:** the same on a satellite with `Attitude(velocity_frame=VelocityFrame.INERTIAL)`.\n",
"* **Fitted, inertial:** the spacing and roll angle fitted in Test 2.\n",
"* **Fitted, inertial, sample times:** the same, but with each pixel projected at its own beam's sample time rather than all at the scan's middle time. TAT-C does not do this; it is computed here (with `PointedInstrument.compute_projected_pixel_position`) to quantify the effect of scan timing.\n",
"\n",
@@ -720,10 +829,10 @@
"id": "876c016d",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:29.007159Z",
- "iopub.status.busy": "2026-10-08T17:41:29.006159Z",
- "iopub.status.idle": "2026-10-08T17:41:43.834942Z",
- "shell.execute_reply": "2026-10-08T17:41:43.834942Z"
+ "iopub.execute_input": "2026-10-25T13:48:37.533891Z",
+ "iopub.status.busy": "2026-10-25T13:48:37.533891Z",
+ "iopub.status.idle": "2026-10-25T13:48:46.533410Z",
+ "shell.execute_reply": "2026-10-25T13:48:46.533410Z"
}
},
"outputs": [
@@ -860,19 +969,21 @@
"i3 = np.arange(0, len(t_mid), 100)\n",
"times3 = list(t_mid[i3])\n",
"configs = {\n",
- " \"nominal, Earth-fixed\": atms_pixels,\n",
- " \"nominal, inertial\": atms_pixels.model_copy(update={\"velocity_frame\": VelocityFrame.INERTIAL}),\n",
- " \"fitted, inertial\": atms_pixels.model_copy(\n",
- " update={\n",
- " \"velocity_frame\": VelocityFrame.INERTIAL,\n",
- " \"cross_track_field_of_view\": N_BEAMS * fitted_spacing,\n",
- " \"roll_angle\": fitted_roll,\n",
- " }\n",
+ " \"nominal, Earth-fixed\": on_noaa20(atms_pixels),\n",
+ " \"nominal, inertial\": on_noaa20(atms_pixels, INERTIAL),\n",
+ " \"fitted, inertial\": on_noaa20(\n",
+ " atms_pixels.model_copy(\n",
+ " update={\n",
+ " \"cross_track_field_of_view\": N_BEAMS * fitted_spacing,\n",
+ " \"roll_angle\": fitted_roll,\n",
+ " }\n",
+ " ),\n",
+ " INERTIAL,\n",
" ),\n",
"}\n",
"pixels = {}\n",
- "for name, instrument in configs.items():\n",
- " track = collect_ground_pixels(noaa20.model_copy(update={\"instruments\": [instrument]}), times3)\n",
+ "for name, satellite in configs.items():\n",
+ " track = collect_ground_pixels(satellite, times3)\n",
" # one row per time and pixel (TAT-C pixel 0 on the right); reverse to ATMS beam order\n",
" pixels[name] = (\n",
" track.geometry.x.to_numpy().reshape(len(i3), N_BEAMS)[:, ::-1],\n",
@@ -880,21 +991,38 @@
" )\n",
"lon3, lat3 = np.zeros((len(i3), N_BEAMS)), np.zeros((len(i3), N_BEAMS))\n",
"for j in range(N_BEAMS):\n",
- " position = configs[\"fitted, inertial\"].compute_projected_pixel_position(\n",
- " orbit.get_orbit_track([t + timedelta(seconds=beam_offset[j]) for t in times3]), N_BEAMS - 1 - j, 0\n",
+ " position = (\n",
+ " configs[\"fitted, inertial\"]\n",
+ " .get_instrument()\n",
+ " .compute_projected_pixel_position(\n",
+ " orbit.get_orbit_track(\n",
+ " [t + timedelta(seconds=beam_offset[j]) for t in times3]\n",
+ " ),\n",
+ " N_BEAMS - 1 - j,\n",
+ " 0,\n",
+ " )\n",
" )\n",
" lon3[:, j], lat3[:, j] = position.longitude.degrees, position.latitude.degrees\n",
"pixels[\"fitted, inertial, sample times\"] = (lon3, lat3)\n",
"\n",
"residuals3 = {\n",
- " name: along_right(beam_lon[i3], beam_lat[i3], ecef(lon, lat) - ecef(beam_lon[i3], beam_lat[i3]), sc_vel[i3, np.newaxis])\n",
+ " name: along_right(\n",
+ " beam_lon[i3],\n",
+ " beam_lat[i3],\n",
+ " ecef(lon, lat) - ecef(beam_lon[i3], beam_lat[i3]),\n",
+ " sc_vel[i3, np.newaxis],\n",
+ " )\n",
" for name, (lon, lat) in pixels.items()\n",
"}\n",
"pd.DataFrame(\n",
" {\n",
" (name, component): summarize(values).loc[[\"median\", \"p95 |x|\", \"max |x|\"]]\n",
" for name, (along, right) in residuals3.items()\n",
- " for component, values in [(\"along\", along), (\"right\", right), (\"horizontal\", np.hypot(along, right))]\n",
+ " for component, values in [\n",
+ " (\"along\", along),\n",
+ " (\"right\", right),\n",
+ " (\"horizontal\", np.hypot(along, right)),\n",
+ " ]\n",
" }\n",
").T.round(2)"
]
@@ -905,10 +1033,10 @@
"id": "e6f60117",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:43.837955Z",
- "iopub.status.busy": "2026-10-08T17:41:43.836949Z",
- "iopub.status.idle": "2026-10-08T17:41:44.487297Z",
- "shell.execute_reply": "2026-10-08T17:41:44.486792Z"
+ "iopub.execute_input": "2026-10-25T13:48:46.536217Z",
+ "iopub.status.busy": "2026-10-25T13:48:46.536217Z",
+ "iopub.status.idle": "2026-10-25T13:48:46.977211Z",
+ "shell.execute_reply": "2026-10-25T13:48:46.976696Z"
}
},
"outputs": [
@@ -985,10 +1113,10 @@
"id": "07443446",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:44.490303Z",
- "iopub.status.busy": "2026-10-08T17:41:44.490303Z",
- "iopub.status.idle": "2026-10-08T17:41:45.045639Z",
- "shell.execute_reply": "2026-10-08T17:41:45.045639Z"
+ "iopub.execute_input": "2026-10-25T13:48:46.979217Z",
+ "iopub.status.busy": "2026-10-25T13:48:46.979217Z",
+ "iopub.status.idle": "2026-10-25T13:48:47.347754Z",
+ "shell.execute_reply": "2026-10-25T13:48:47.347754Z"
}
},
"outputs": [
@@ -1074,32 +1202,63 @@
],
"source": [
"i4 = np.arange(0, len(t_mid), 5)\n",
- "observed_width = np.sum(\n",
- " np.linalg.norm(np.diff(beam_xyz[i4], axis=1), axis=-1), axis=1\n",
- ") / 1e3\n",
+ "observed_width = (\n",
+ " np.sum(np.linalg.norm(np.diff(beam_xyz[i4], axis=1), axis=-1), axis=1) / 1e3\n",
+ ")\n",
"sub4 = wgs84.geographic_position_of(orbit.get_orbit_track(list(t_mid[i4])))\n",
"# as in collect_observations\n",
- "apogee_altitude = compute_apoapsis_radius(noaa20.orbit.get_semimajor_axis(), noaa20.orbit.get_eccentricity()) - EARTH_MEAN_RADIUS\n",
+ "apogee_altitude = (\n",
+ " compute_apoapsis_radius(\n",
+ " noaa20.orbit.get_semimajor_axis(), noaa20.orbit.get_eccentricity()\n",
+ " )\n",
+ " - EARTH_MEAN_RADIUS\n",
+ ")\n",
"swath_width = np.vectorize(field_of_regard_to_swath_width)\n",
"# the field of regard at the geodetic altitude equivalent to the minimum elevation angle in collect_observations\n",
"min_elevation_angle = compute_min_elevation_angle(apogee_altitude, 2 * EDGE)\n",
- "equivalent_field_of_regard = np.vectorize(compute_field_of_regard)(sub4.elevation.m, min_elevation_angle)\n",
+ "equivalent_field_of_regard = np.vectorize(compute_field_of_regard)(\n",
+ " sub4.elevation.m, min_elevation_angle\n",
+ ")\n",
"widths = {\n",
" \"nominal field of regard\": swath_width(sub4.elevation.m, 2 * EDGE) / 1e3,\n",
- " \"nominal field of regard, collect_observations\": swath_width(sub4.elevation.m, equivalent_field_of_regard) / 1e3,\n",
- " \"measured field of regard\": swath_width(sub4.elevation.m, measured[0] - measured[-1]) / 1e3,\n",
+ " \"nominal field of regard, collect_observations\": swath_width(\n",
+ " sub4.elevation.m, equivalent_field_of_regard\n",
+ " )\n",
+ " / 1e3,\n",
+ " \"measured field of regard\": swath_width(\n",
+ " sub4.elevation.m, measured[0] - measured[-1]\n",
+ " )\n",
+ " / 1e3,\n",
"}\n",
"edges4 = np.arange(-90, 91, 5)\n",
"centers4 = (edges4[:-1] + edges4[1:]) / 2\n",
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
- "ax.plot(centers4, binned(sub4.latitude.degrees, observed_width, edges4)[:, 1], color=INK, marker=\"o\", ms=3, label=\"observed (ATMS)\")\n",
+ "ax.plot(\n",
+ " centers4,\n",
+ " binned(sub4.latitude.degrees, observed_width, edges4)[:, 1],\n",
+ " color=INK,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " label=\"observed (ATMS)\",\n",
+ ")\n",
"for (name, width), color in zip(widths.items(), [BLUE, ORANGE, AQUA]):\n",
- " ax.plot(centers4, binned(sub4.latitude.degrees, width, edges4)[:, 1], color=color, label=f\"TAT-C: {name}\")\n",
- "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Swath width (km)\", title=\"ATMS swath width\")\n",
+ " ax.plot(\n",
+ " centers4,\n",
+ " binned(sub4.latitude.degrees, width, edges4)[:, 1],\n",
+ " color=color,\n",
+ " label=f\"TAT-C: {name}\",\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Sub-satellite latitude (deg)\",\n",
+ " ylabel=\"Swath width (km)\",\n",
+ " title=\"ATMS swath width\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()\n",
- "pd.DataFrame({name: summarize(width - observed_width) for name, width in widths.items()}).T.round(1)"
+ "pd.DataFrame(\n",
+ " {name: summarize(width - observed_width) for name, width in widths.items()}\n",
+ ").T.round(1)"
]
},
{
@@ -1125,10 +1284,10 @@
"id": "5f00a5ef",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:45.049167Z",
- "iopub.status.busy": "2026-10-08T17:41:45.048161Z",
- "iopub.status.idle": "2026-10-08T17:42:44.857993Z",
- "shell.execute_reply": "2026-10-08T17:42:44.857461Z"
+ "iopub.execute_input": "2026-10-25T13:48:47.350168Z",
+ "iopub.status.busy": "2026-10-25T13:48:47.350168Z",
+ "iopub.status.idle": "2026-10-25T13:49:33.623537Z",
+ "shell.execute_reply": "2026-10-25T13:49:33.623030Z"
}
},
"outputs": [
@@ -1246,17 +1405,33 @@
" for dk in (-1, 0):\n",
" for dj in (-1, 0):\n",
" kk, jj = k + dk, j + dj\n",
- " valid = (kk >= 0) & (kk < len(cells) - 1) & (jj >= 0) & (jj < N_BEAMS - 1)\n",
+ " valid = (\n",
+ " (kk >= 0) & (kk < len(cells) - 1) & (jj >= 0) & (jj < N_BEAMS - 1)\n",
+ " )\n",
" kk, jj = np.clip(kk, 0, len(cells) - 2), np.clip(jj, 0, N_BEAMS - 2)\n",
" valid &= contiguous[k0 + kk]\n",
- " corners = [cells[kk, jj], cells[kk, jj + 1], cells[kk + 1, jj + 1], cells[kk + 1, jj]]\n",
- " signs = np.stack([np.sum(np.cross(a, b) * points, axis=-1) for a, b in zip(corners, corners[1:] + corners[:1])])\n",
- " inside |= valid & (np.all(signs >= 0, axis=0) | np.all(signs <= 0, axis=0))\n",
+ " corners = [\n",
+ " cells[kk, jj],\n",
+ " cells[kk, jj + 1],\n",
+ " cells[kk + 1, jj + 1],\n",
+ " cells[kk + 1, jj],\n",
+ " ]\n",
+ " signs = np.stack(\n",
+ " [\n",
+ " np.sum(np.cross(a, b) * points, axis=-1)\n",
+ " for a, b in zip(corners, corners[1:] + corners[:1])\n",
+ " ]\n",
+ " )\n",
+ " inside |= valid & (\n",
+ " np.all(signs >= 0, axis=0) | np.all(signs <= 0, axis=0)\n",
+ " )\n",
" return inside\n",
"\n",
"\n",
"STEP4, MARGIN4 = 0.1, 128 # deg, s\n",
- "lon_grid, lat_grid = np.meshgrid(np.arange(-180 + STEP4 / 2, 180, STEP4), np.arange(-90 + STEP4 / 2, 90, STEP4))\n",
+ "lon_grid, lat_grid = np.meshgrid(\n",
+ " np.arange(-180 + STEP4 / 2, 180, STEP4), np.arange(-90 + STEP4 / 2, 90, STEP4)\n",
+ ")\n",
"cell_area = np.cos(np.radians(lat_grid)) * (STEP4 * 111.32) ** 2 # km^2\n",
"grid_unit = unit_vector(lat_grid.ravel(), lon_grid.ravel())\n",
"orbit_start = datetime(2026, 10, 2, 12, tzinfo=timezone.utc)\n",
@@ -1264,22 +1439,36 @@
"scans4 = np.nonzero((t_mid >= orbit_start) & (t_mid < orbit_start + orbit_period))[0]\n",
"first, last = t_mid[scans4[0]].to_pydatetime(), t_mid[scans4[-1]].to_pydatetime()\n",
"# grid cells near beams sampled away from the ends of the orbit\n",
- "distance, nearest = cKDTree(beam_unit[scans4].reshape(-1, 3)).query(grid_unit, workers=-1)\n",
- "nearest_time = (t_mid[scans4] - t_mid[scans4[0]]).total_seconds().to_numpy()[nearest // N_BEAMS]\n",
- "compared = (distance < 2 * np.sin(300 / 6371 / 2)) & (nearest_time > MARGIN4) & (nearest_time < (last - first).total_seconds() - MARGIN4)\n",
+ "distance, nearest = cKDTree(beam_unit[scans4].reshape(-1, 3)).query(\n",
+ " grid_unit, workers=-1\n",
+ ")\n",
+ "nearest_time = (\n",
+ " (t_mid[scans4] - t_mid[scans4[0]]).total_seconds().to_numpy()[nearest // N_BEAMS]\n",
+ ")\n",
+ "compared = (\n",
+ " (distance < 2 * np.sin(300 / 6371 / 2))\n",
+ " & (nearest_time > MARGIN4)\n",
+ " & (nearest_time < (last - first).total_seconds() - MARGIN4)\n",
+ ")\n",
"observed4 = np.zeros(len(grid_unit), dtype=bool)\n",
"observed4[compared] = observed_cells(grid_unit[compared], scans4)\n",
- "ground_speed = 2 * np.pi * 6371.0088e3 / orbit_period.total_seconds() # m/s, approximate\n",
+ "ground_speed = (\n",
+ " 2 * np.pi * 6371.0088e3 / orbit_period.total_seconds()\n",
+ ") # m/s, approximate\n",
"altitude = float(np.median(sub4.elevation.m))\n",
"\n",
"\n",
- "def swath_area_row(instrument, time_step):\n",
+ "def swath_area_row(instrument, time_step, attitude=None):\n",
" n = int((last - first).total_seconds() // time_step)\n",
" times = [first + timedelta(seconds=time_step * (i + 0.5)) for i in range(n)]\n",
- " geometry = compute_ground_track(noaa20.model_copy(update={\"instruments\": [instrument]}), times).geometry.iloc[0]\n",
+ " geometry = compute_ground_track(\n",
+ " on_noaa20(instrument, attitude), times\n",
+ " ).geometry.iloc[0]\n",
" prepare(geometry)\n",
" predicted = np.zeros(len(grid_unit), dtype=bool)\n",
- " predicted[compared] = contains_xy(geometry, lon_grid.ravel()[compared], lat_grid.ravel()[compared])\n",
+ " predicted[compared] = contains_xy(\n",
+ " geometry, lon_grid.ravel()[compared], lat_grid.ravel()[compared]\n",
+ " )\n",
" area = lambda mask: np.sum(mask * cell_area.ravel()) / 1e6\n",
" return predicted.reshape(lat_grid.shape), {\n",
" \"observed (10^6 km^2)\": area(observed4),\n",
@@ -1291,8 +1480,8 @@
"\n",
"\n",
"swath_rows, swath_maps = {}, {}\n",
- "swath_maps[\"field of regard, 60 s\"], swath_rows[\"field of regard, 60 s\"] = swath_area_row(\n",
- " Instrument(name=\"ATMS\", field_of_regard=2 * EDGE), 60\n",
+ "swath_maps[\"field of regard, 60 s\"], swath_rows[\"field of regard, 60 s\"] = (\n",
+ " swath_area_row(Instrument(name=\"ATMS\", field_of_regard=2 * EDGE), 60)\n",
")\n",
"for time_step in [SCAN_PERIOD, 32, 128]:\n",
" along_fov = np.degrees(2 * np.arctan(ground_speed * time_step / 2 / altitude))\n",
@@ -1304,9 +1493,9 @@
" cross_track_field_of_view=2 * EDGE,\n",
" along_track_field_of_view=along_fov,\n",
" is_rectangular=True,\n",
- " velocity_frame=VelocityFrame.INERTIAL,\n",
" ),\n",
" time_step,\n",
+ " INERTIAL,\n",
" )\n",
"observed4 = observed4.reshape(lat_grid.shape)\n",
"pd.DataFrame(swath_rows).T.round(3)"
@@ -1318,10 +1507,10 @@
"id": "bfe341fe",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:42:44.860636Z",
- "iopub.status.busy": "2026-10-08T17:42:44.860636Z",
- "iopub.status.idle": "2026-10-08T17:42:55.467909Z",
- "shell.execute_reply": "2026-10-08T17:42:55.467909Z"
+ "iopub.execute_input": "2026-10-25T13:49:33.625539Z",
+ "iopub.status.busy": "2026-10-25T13:49:33.625539Z",
+ "iopub.status.idle": "2026-10-25T13:49:40.785662Z",
+ "shell.execute_reply": "2026-10-25T13:49:40.785662Z"
}
},
"outputs": [
@@ -1337,15 +1526,24 @@
}
],
"source": [
- "fig, axes = plt.subplots(1, 2, figsize=(12, 4.2), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, axes = plt.subplots(\n",
+ " 1, 2, figsize=(12, 4.2), subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"for ax, name in zip(axes, [list(swath_maps)[0], list(swath_maps)[-1]]):\n",
" predicted = swath_maps[name]\n",
- " category = np.where(observed4 & predicted, 1, np.where(observed4, 2, np.where(predicted, 3, 0))).astype(float)\n",
+ " category = np.where(\n",
+ " observed4 & predicted, 1, np.where(observed4, 2, np.where(predicted, 3, 0))\n",
+ " ).astype(float)\n",
" category[~compared.reshape(lat_grid.shape)] = 0\n",
" ax.pcolormesh(\n",
- " lon_grid, lat_grid, np.ma.masked_equal(category, 0),\n",
- " cmap=plt.matplotlib.colors.ListedColormap([\"#d9d8d3\", ORANGE, BLUE]), vmin=0.5, vmax=3.5,\n",
- " transform=ccrs.PlateCarree(), shading=\"auto\",\n",
+ " lon_grid,\n",
+ " lat_grid,\n",
+ " np.ma.masked_equal(category, 0),\n",
+ " cmap=plt.matplotlib.colors.ListedColormap([\"#d9d8d3\", ORANGE, BLUE]),\n",
+ " vmin=0.5,\n",
+ " vmax=3.5,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " shading=\"auto\",\n",
" )\n",
" ax.coastlines(color=GRAY, linewidth=0.5)\n",
" ax.set_global()\n",
@@ -1382,10 +1580,10 @@
"id": "b8a9a1f6",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:42:55.471435Z",
- "iopub.status.busy": "2026-10-08T17:42:55.471435Z",
- "iopub.status.idle": "2026-10-08T17:51:57.899165Z",
- "shell.execute_reply": "2026-10-08T17:51:57.898159Z"
+ "iopub.execute_input": "2026-10-25T13:49:40.788669Z",
+ "iopub.status.busy": "2026-10-25T13:49:40.788669Z",
+ "iopub.status.idle": "2026-10-25T13:53:04.410446Z",
+ "shell.execute_reply": "2026-10-25T13:53:04.410446Z"
}
},
"outputs": [
@@ -1449,9 +1647,9 @@
"
\n",
"
PointedInstrument, inertial
\n",
"
7402.0
\n",
+ "
7372.0
\n",
"
7371.0
\n",
- "
7370.0
\n",
- "
32.0
\n",
+ "
31.0
\n",
"
1.0
\n",
"
0.05
\n",
"
0.26
\n",
@@ -1466,7 +1664,7 @@
" observed predicted matched observed only \\\n",
"nadir Instrument 7402.0 7407.0 7380.0 22.0 \n",
"PointedInstrument, Earth-fixed 7402.0 7382.0 7380.0 22.0 \n",
- "PointedInstrument, inertial 7402.0 7371.0 7370.0 32.0 \n",
+ "PointedInstrument, inertial 7402.0 7372.0 7371.0 31.0 \n",
"\n",
" predicted only time, median (s) \\\n",
"nadir Instrument 27.0 0.06 \n",
@@ -1496,17 +1694,40 @@
"mid_s = (t_mid - t_mid[0]).total_seconds().to_numpy()\n",
"beam_s = mid_s[:, np.newaxis] + beam_offset[np.newaxis, :]\n",
"observed_rows = []\n",
- "for p, hits in enumerate(tree5.query_ball_point(point_unit, r=2 * np.sin(60 / 6371 / 2), workers=-1)):\n",
+ "for p, hits in enumerate(\n",
+ " tree5.query_ball_point(point_unit, r=2 * np.sin(60 / 6371 / 2), workers=-1)\n",
+ "):\n",
" if not hits:\n",
" continue\n",
" hits = np.array(hits)\n",
" hits = hits[np.argsort(beam_s.ravel()[hits])]\n",
- " for group in np.split(hits, np.nonzero(np.diff(beam_s.ravel()[hits]) > 1200)[0] + 1):\n",
- " k, j = np.divmod(group[np.argmin(np.linalg.norm(beam_unit.reshape(-1, 3)[group] - point_unit[p], axis=-1))], N_BEAMS)\n",
- " if not observed_cells(point_unit[[p]], np.arange(max(k - 1, 0), min(k + 2, len(t_mid))))[0]:\n",
+ " for group in np.split(\n",
+ " hits, np.nonzero(np.diff(beam_s.ravel()[hits]) > 1200)[0] + 1\n",
+ " ):\n",
+ " k, j = np.divmod(\n",
+ " group[\n",
+ " np.argmin(\n",
+ " np.linalg.norm(\n",
+ " beam_unit.reshape(-1, 3)[group] - point_unit[p], axis=-1\n",
+ " )\n",
+ " )\n",
+ " ],\n",
+ " N_BEAMS,\n",
+ " )\n",
+ " if not observed_cells(\n",
+ " point_unit[[p]], np.arange(max(k - 1, 0), min(k + 2, len(t_mid)))\n",
+ " )[0]:\n",
" continue\n",
" # interpolate between the scans before and after the point\n",
- " k0 = k - 1 if np.sum((point_unit[p] - beam_unit[k, j]) * (beam_unit[k, j] - beam_unit[k - 1, j])) < 0 else k\n",
+ " k0 = (\n",
+ " k - 1\n",
+ " if np.sum(\n",
+ " (point_unit[p] - beam_unit[k, j])\n",
+ " * (beam_unit[k, j] - beam_unit[k - 1, j])\n",
+ " )\n",
+ " < 0\n",
+ " else k\n",
+ " )\n",
" k0 = int(np.clip(k0, 0, len(t_mid) - 2))\n",
" a, b = beam_unit[k0, j], beam_unit[k0 + 1, j]\n",
" f = np.clip(np.sum((point_unit[p] - a) * (b - a)) / np.sum((b - a) ** 2), 0, 1)\n",
@@ -1519,25 +1740,28 @@
" \"point_id\": p,\n",
" \"time\": t_mid[0] + timedelta(seconds=float(seconds)),\n",
" \"scan_angle\": scan_angle[k0, j],\n",
- " \"elevation\": np.degrees(np.arcsin(np.sum(normalize(position - target) * u))),\n",
+ " \"elevation\": np.degrees(\n",
+ " np.arcsin(np.sum(normalize(position - target) * u))\n",
+ " ),\n",
" }\n",
" )\n",
"observed5 = pd.DataFrame(observed_rows)\n",
- "start5, end5 = t_start[0].to_pydatetime(), t_start[-1].to_pydatetime() + timedelta(seconds=SCAN_PERIOD)\n",
+ "start5, end5 = t_start[0].to_pydatetime(), t_start[-1].to_pydatetime() + timedelta(\n",
+ " seconds=SCAN_PERIOD\n",
+ ")\n",
"along_fov5 = np.degrees(2 * np.arctan(ground_speed * SCAN_PERIOD / 2 / altitude))\n",
- "instruments5 = {\n",
- " \"nadir Instrument\": Instrument(name=\"ATMS\", field_of_regard=2 * EDGE),\n",
- " \"PointedInstrument, Earth-fixed\": PointedInstrument(\n",
- " name=\"ATMS\",\n",
- " field_of_regard=2 * EDGE,\n",
- " cross_track_field_of_view=2 * EDGE,\n",
- " along_track_field_of_view=along_fov5,\n",
- " is_rectangular=True,\n",
- " ),\n",
- "}\n",
- "instruments5[\"PointedInstrument, inertial\"] = instruments5[\"PointedInstrument, Earth-fixed\"].model_copy(\n",
- " update={\"velocity_frame\": VelocityFrame.INERTIAL}\n",
+ "pointed5 = PointedInstrument(\n",
+ " name=\"ATMS\",\n",
+ " field_of_regard=2 * EDGE,\n",
+ " cross_track_field_of_view=2 * EDGE,\n",
+ " along_track_field_of_view=along_fov5,\n",
+ " is_rectangular=True,\n",
")\n",
+ "satellites5 = {\n",
+ " \"nadir Instrument\": on_noaa20(Instrument(name=\"ATMS\", field_of_regard=2 * EDGE)),\n",
+ " \"PointedInstrument, Earth-fixed\": on_noaa20(pointed5),\n",
+ " \"PointedInstrument, inertial\": on_noaa20(pointed5, INERTIAL),\n",
+ "}\n",
"\n",
"\n",
"def match(predicted):\n",
@@ -1545,18 +1769,22 @@
" merged = pd.merge_asof(\n",
" observed5.sort_values(\"time\"),\n",
" predicted[[\"point_id\", \"epoch\", \"sat_alt\"]].sort_values(\"epoch\"),\n",
- " left_on=\"time\", right_on=\"epoch\", by=\"point_id\",\n",
- " direction=\"nearest\", tolerance=pd.Timedelta(minutes=10),\n",
+ " left_on=\"time\",\n",
+ " right_on=\"epoch\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=10),\n",
" )\n",
" return merged\n",
"\n",
"\n",
"predicted5, matched5, rows5 = {}, {}, {}\n",
- "for name, instrument in instruments5.items():\n",
- " satellite = noaa20.model_copy(update={\"instruments\": [instrument]})\n",
+ "for name, satellite in satellites5.items():\n",
" predicted5[name] = pd.concat(\n",
" [\n",
- " collect_observations(Point(latitude=g.y, longitude=g.x), satellite, start5, end5).assign(point_id=p)\n",
+ " collect_observations(\n",
+ " Point(latitude=g.y, longitude=g.x), satellite, start5, end5\n",
+ " ).assign(point_id=p)\n",
" for p, g in enumerate(points5.geometry)\n",
" ]\n",
" ).reset_index(drop=True)\n",
@@ -1571,8 +1799,12 @@
" \"predicted only\": len(predicted5[name]) - int(hit.sum()),\n",
" \"time, median (s)\": np.median(dt),\n",
" \"time, p95 |x| (s)\": np.percentile(np.abs(dt), 95),\n",
- " \"elevation, median (deg)\": np.median((matched5[name].sat_alt - matched5[name].elevation)[hit]),\n",
- " \"elevation, p95 |x| (deg)\": np.percentile(np.abs(matched5[name].sat_alt - matched5[name].elevation)[hit], 95),\n",
+ " \"elevation, median (deg)\": np.median(\n",
+ " (matched5[name].sat_alt - matched5[name].elevation)[hit]\n",
+ " ),\n",
+ " \"elevation, p95 |x| (deg)\": np.percentile(\n",
+ " np.abs(matched5[name].sat_alt - matched5[name].elevation)[hit], 95\n",
+ " ),\n",
" }\n",
"pd.DataFrame(rows5).T.round(2)"
]
@@ -1583,16 +1815,16 @@
"id": "bcefd495",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:51:57.903706Z",
- "iopub.status.busy": "2026-10-08T17:51:57.903192Z",
- "iopub.status.idle": "2026-10-08T17:51:58.329050Z",
- "shell.execute_reply": "2026-10-08T17:51:58.329050Z"
+ "iopub.execute_input": "2026-10-25T13:53:04.412662Z",
+ "iopub.status.busy": "2026-10-25T13:53:04.412662Z",
+ "iopub.status.idle": "2026-10-25T13:53:04.692141Z",
+ "shell.execute_reply": "2026-10-25T13:53:04.692141Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1608,18 +1840,42 @@
" hit = matched.epoch.notna()\n",
" dt = (matched.epoch - matched.time).dt.total_seconds()\n",
" q = binned(matched.scan_angle[hit].to_numpy(), dt[hit].to_numpy(), edges5)\n",
- " axes[0].fill_between((edges5[:-1] + edges5[1:]) / 2, q[:, 0], q[:, 2], color=color, alpha=0.15, lw=0)\n",
+ " axes[0].fill_between(\n",
+ " (edges5[:-1] + edges5[1:]) / 2, q[:, 0], q[:, 2], color=color, alpha=0.15, lw=0\n",
+ " )\n",
" axes[0].plot((edges5[:-1] + edges5[1:]) / 2, q[:, 1], color=color, label=name)\n",
"axes[0].axhline(0, color=GRAY, lw=0.8)\n",
- "axes[0].set(xlabel=\"ATMS scan angle (deg, positive left)\", ylabel=\"TAT-C minus ATMS (s)\", title=\"Observation time\")\n",
+ "axes[0].set(\n",
+ " xlabel=\"ATMS scan angle (deg, positive left)\",\n",
+ " ylabel=\"TAT-C minus ATMS (s)\",\n",
+ " title=\"Observation time\",\n",
+ ")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
"lat_edges = np.arange(-90, 91, 10)\n",
"point_lat = points5.geometry.y.to_numpy()\n",
"n_points = np.histogram(point_lat, lat_edges)[0]\n",
- "axes[1].plot((lat_edges[:-1] + lat_edges[1:]) / 2, np.histogram(point_lat[observed5.point_id], lat_edges)[0] / n_points, color=INK, marker=\"o\", ms=3, label=\"observed (ATMS)\")\n",
+ "axes[1].plot(\n",
+ " (lat_edges[:-1] + lat_edges[1:]) / 2,\n",
+ " np.histogram(point_lat[observed5.point_id], lat_edges)[0] / n_points,\n",
+ " color=INK,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " label=\"observed (ATMS)\",\n",
+ ")\n",
"for (name, predicted), color in zip(predicted5.items(), [BLUE, ORANGE, AQUA]):\n",
- " axes[1].plot((lat_edges[:-1] + lat_edges[1:]) / 2, np.histogram(point_lat[predicted.point_id.astype(int)], lat_edges)[0] / n_points, color=color, ls=\"--\", label=name)\n",
- "axes[1].set(xlabel=\"Point latitude (deg)\", ylabel=\"Observations per point per day\", title=\"Daily observations\")\n",
+ " axes[1].plot(\n",
+ " (lat_edges[:-1] + lat_edges[1:]) / 2,\n",
+ " np.histogram(point_lat[predicted.point_id.astype(int)], lat_edges)[0]\n",
+ " / n_points,\n",
+ " color=color,\n",
+ " ls=\"--\",\n",
+ " label=name,\n",
+ " )\n",
+ "axes[1].set(\n",
+ " xlabel=\"Point latitude (deg)\",\n",
+ " ylabel=\"Observations per point per day\",\n",
+ " title=\"Daily observations\",\n",
+ ")\n",
"axes[1].legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1653,10 +1909,10 @@
"id": "a7c1e602",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:50:58.824854Z",
- "iopub.status.busy": "2026-10-15T18:50:58.824854Z",
- "iopub.status.idle": "2026-10-15T18:51:15.626878Z",
- "shell.execute_reply": "2026-10-15T18:51:15.626878Z"
+ "iopub.execute_input": "2026-10-25T13:53:04.695147Z",
+ "iopub.status.busy": "2026-10-25T13:53:04.694150Z",
+ "iopub.status.idle": "2026-10-25T13:53:28.200136Z",
+ "shell.execute_reply": "2026-10-25T13:53:28.200136Z"
}
},
"outputs": [
@@ -1732,9 +1988,7 @@
],
"source": [
"# TAT-C's satellite zenith angle (from the satellite's elevation as seen from each pixel) at the fitted configuration, in ATMS beam order\n",
- "track6 = collect_ground_pixels(\n",
- " noaa20.model_copy(update={\"instruments\": [configs[\"fitted, inertial\"]]}), times3, sat_altaz=True\n",
- ")\n",
+ "track6 = collect_ground_pixels(configs[\"fitted, inertial\"], times3, sat_altaz=True)\n",
"zenith_model = (90 - track6[\"sat_alt\"].to_numpy().reshape(len(i3), N_BEAMS))[:, ::-1]\n",
"zenith_reported = beam_zenith[i3]\n",
"range_reported = np.linalg.norm(beam_xyz[i3] - beam_sc[i3], axis=-1)\n",
@@ -1758,8 +2012,12 @@
"range_model = slant_range(altitude6[:, np.newaxis], zenith_model)\n",
"pd.DataFrame(\n",
" {\n",
- " \"zenith angle, TAT-C minus reported (deg)\": summarize(zenith_model - zenith_reported),\n",
- " \"slant range, TAT-C minus reported (km)\": summarize((range_model - range_reported) / 1e3),\n",
+ " \"zenith angle, TAT-C minus reported (deg)\": summarize(\n",
+ " zenith_model - zenith_reported\n",
+ " ),\n",
+ " \"slant range, TAT-C minus reported (km)\": summarize(\n",
+ " (range_model - range_reported) / 1e3\n",
+ " ),\n",
" \"equivalent diameter, TAT-C minus reported (km)\": summarize(\n",
" footprint_size(range_model, zenith_model, BEAMWIDTH)[2]\n",
" - footprint_size(range_reported, zenith_reported, BEAMWIDTH)[2]\n",
@@ -1774,10 +2032,10 @@
"id": "a7c1e603",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:51:15.626878Z",
- "iopub.status.busy": "2026-10-15T18:51:15.626878Z",
- "iopub.status.idle": "2026-10-15T18:51:15.784263Z",
- "shell.execute_reply": "2026-10-15T18:51:15.784263Z"
+ "iopub.execute_input": "2026-10-25T13:53:28.202143Z",
+ "iopub.status.busy": "2026-10-25T13:53:28.202143Z",
+ "iopub.status.idle": "2026-10-25T13:53:28.340788Z",
+ "shell.execute_reply": "2026-10-25T13:53:28.340267Z"
}
},
"outputs": [
@@ -1958,10 +2216,17 @@
],
"source": [
"# measured spacing (km) between adjacent beam centers within a scan and between the same beam on consecutive scans\n",
- "spacing_cross = np.median(np.linalg.norm(np.diff(beam_xyz[i4], axis=1), axis=-1), axis=0) / 1e3\n",
+ "spacing_cross = (\n",
+ " np.median(np.linalg.norm(np.diff(beam_xyz[i4], axis=1), axis=-1), axis=0) / 1e3\n",
+ ")\n",
"spacing_cross = np.append(spacing_cross, spacing_cross[-1])\n",
- "spacing_along = np.median(np.linalg.norm(np.diff(beam_xyz, axis=0), axis=-1)[contiguous], axis=0) / 1e3\n",
- "along_r, cross_r, equivalent_r = footprint_size(range_reported, zenith_reported, BEAMWIDTH)\n",
+ "spacing_along = (\n",
+ " np.median(np.linalg.norm(np.diff(beam_xyz, axis=0), axis=-1)[contiguous], axis=0)\n",
+ " / 1e3\n",
+ ")\n",
+ "along_r, cross_r, equivalent_r = footprint_size(\n",
+ " range_reported, zenith_reported, BEAMWIDTH\n",
+ ")\n",
"rows = [0, 12, 24, 47, 48, 71, 83, 95]\n",
"table6 = pd.DataFrame(\n",
" {\n",
@@ -1985,16 +2250,16 @@
"id": "a7c1e604",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:51:15.784263Z",
- "iopub.status.busy": "2026-10-15T18:51:15.784263Z",
- "iopub.status.idle": "2026-10-15T18:51:15.879292Z",
- "shell.execute_reply": "2026-10-15T18:51:15.879292Z"
+ "iopub.execute_input": "2026-10-25T13:53:28.342794Z",
+ "iopub.status.busy": "2026-10-25T13:53:28.342794Z",
+ "iopub.status.idle": "2026-10-25T13:53:28.477430Z",
+ "shell.execute_reply": "2026-10-25T13:53:28.477430Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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tyBHAtQOJUeA7LwjuhFhkBcEFgECFUC3MGgK/R/jU4UWNwV8lBf6LN/JhhPDAywz7hI8mfOzwgsULFN2dsOpiEFhxgaUR1lEMHMMLGeVAzM2FCxeyRU+xnmE53B8gQrFfuDBAsMASjH3bW1gxAA3iFH6EsFrC8mbfPQ2Rh0EwEA+w1qGLtlu3bk6xOt2obPbALSApKYl/R0xU+Gxi0BQEkL1V2h5Y5+GO8sQTT1wzaAsDBtF9jXOK/U+cOJHPOc4frOGwsEJwwRUBjZvCQOY3CDxY0h966CHbctQH3B7geoDzm19AKpnllHVRZ2hAwXoLsG90raMbG8eHXgJ7UHYcQ2Hgb+EugOsTIhiNBlwvsKzC/1QBIhZ1AJcHDFSE8Md9BD9xDBpErGSlYQWxiyx2WIbsYOhmx284fntxez1g9X744Ydp586dXH5kKUNPgr0VFVZo3O9otCJGLa4TDPSDYEaMWNQTekvQYEMDAccI/3b4zsICjmvJ/hhLCwyMQwY/nHtBcCvKOmyCIAgWjqeK0EOIMVkYDzzwgEWr1VoSEhKKHX7reuQPv4V4kr/++ivHbEVIIYT1QWxPhBDC/nJzc0sUfgsgtBFCVSFEFI4FsT+xTftQVkoZEO+0du3avB7iYyJsELI/5Q+jhGxKgYGBfAxKuCslTNb69estjz32GMcDDQgIsIwZM+aa2LjXy8hUWPitnTt33jA0VmFlK4g5c+ZYBgwYwOGrECYN8T8ff/xxWxitgvah1FtBU/59ISRUt27dOOwTJsS3xXVx/Phxy41A/FKcO/tQUMp5KGzC7/nLjfIqXK/s1wsxZg9i2+I4cd9UrVqVw1r99ttvBd4P2N7AgQM55BbWr1u3Lt9PCHdnz6xZs/jc4JpHmKtFixZx5jssuxHKdbRy5UqO6Ypt4O9mz55d4H2A6xnxi1HfiOvbpUsXzq5lHy4Ox4N7RNkWzqty7m50n+McYDnur/znAsvzl6tjx47XZHATBHfAC/+VtZgWBEEoTRBhAVY1WMbg1yuUHIR8gnUVUS5gbSxvwCoOK64nZ7zCQENYrBGdAccrCO6E+MgKgiAIhYJwTejKh09rYSlgPQH40Cr+qQpIB7x///4bphd2dxBxQhk0KAjuhlhkBUHwOMQiKxQX+KYiksC9997Lg78wQA1+yBDyiJsMf2BBEFwPGewlCIIglHuQ1AIDryZPnszRDDCADAOuYK0UESsIrotYZAVBEARBEAS3RHxkBUEQBEEQBLdEhKwgCIIgCILgloiQvZLhBhlYxMtCEARBEATBfRAhS8QZlTAyFZ/OIiE+xmn7EpyD1KlnIvXqeUideiZSr+UTEbJlhMXiufEYyytSp56J1KvnIXXqmUi9lk9EyAqCIAiCIAhuiQhZQRAEQRAEwS0RISsIgiAIgiC4JSJkBUEQBEEQBLdEhKwgCIIgCILgloiQFQRBEARBENwSEbKCIAiCIAiCWyJCVhAEQRAEQXBLRMgKguDxnD17lkJCQpy6z//973+Uk5NT6tv18vKilJSUEv3trl27aNSoUaVeJkEQhLJChKyTyd2zhlK/f45o54qyLoogCA7knXfeKVTIGo1GKgvatWtHf//9d5nsWxAEweOE7IYNG2jo0KFUpUoVtjIsWLCg0HWfeOIJXufrr7/OszwpKYnGjBlDQUFBbHF5+OGHKSMjg1wVU8JFMhzaRHR6b1kXRRBKDYvFQll6s0Mn7ONG4FkAsdaiRQsaPHgwxcQUnHt95cqV1KZNG16vZ8+edOTIEV6+bt06atasGY0bN45atmxJTZs2pX379tv+7ueff6YGDRrw37733nv8TCrseQW6d+9OrVq1ori4OHrggQfooYceoh49evA+blTepUuXUvv27bkc2Mb27duvOeevvPIK3XbbbZSVlZXnt+zsbLa8NmnShP9+wIABtuPDtsDkyZN5XpnUajWtX7+ef5s2bRp17NiRjxPl3b//6jkQBKH8kTHjY8qcP4lMybHkamjKcueZmZn8kMXD/Y477ih0vfnz59O2bdtY8OYHL4Lo6GhatWoVGQwGevDBB+mxxx6jGTNmkCuirdvSOnPhGL+ICnsRCoI7kW2wUPu3jzt0HzvfaUh+uuvfL2joRkRE8PzHH3/M3fs//fRTnnUgKu+55x4Wdc2bN6e//vqLRo4cSYcPH+bfjx07Rr/99hv98MMP/LcffvQJ9R8wiA4dOsTb27t3L1WqVInefvvtQsuBv4Po3bhxYx6Xht27d9OmTZsoMDDwuuU9ceIEP8vQ2G/UqBE/2+zFam5uLt19990UHh7Oz0eIUHtWrFjB7geKQEeDPz+PPPIIT+CDDz6gihUrUteuXWnz5s00c+ZM3re3tzcfA86Xcn4EQShfWPQ5lLNlIZHJSD7dbidXo0yF7KBBg3i6HpcuXaJnnnmGLSiwWNhz9OhRfmDv3LmTrRpg0qRJdOutt9Lnn39eoPAtazQ1GhFptOSVkULmhEukjqhW1kUSBI8BDVhYE9Glj6lChQrXrAPLJgQsJqUx/NRTT/GzBtSrV4+tkaBz5870ySef8PyaNWvolltuYRELHn30UXr33XeLVb4777zTJmKvV140zLEviFig1WopODjY9nd4Fg4bNozefPPNAvcDAwGej7Asw+KMZ2JhYP9z585la6xGo6GFCxeyBVY5B4oQhpXX19e3WMcrCIL7Yzx3hEWsV1A4qSpUJVejTIXsjTCbzXTffffRSy+9xF18+dm6dStbOxQRC/r160cqlYpfVrff7notBy+tN4tZY9RBMpzeL0JW8Ah8tV5sMXX0Pq4HLJ3ffvstPxciIyNp0aJF9NZbbxV7Pz4+PrZ5WDpNpoL9We17U6ZOnUpffvklz48fP56tqQUREBBQKuXt06cPi13sC25V+alTpw5bYyG+//vvP3r55Zdp375916yH3yHGYX1VBDZ6iu6//3768MMPi1QWQRA8G8Pp/bYeZVfsRXbpwV6whMBC8Oyzzxb4O/zJ8AKwB+uHhYUV6hundMulpaXlmZyJto7VvcAYdcCp+xUER4GHm59O5dDpRg/Q5ORkFmPobtfr9dy1XxCdOnWigwcPsqsAmDVrFlWtWpWn69G7d2/uGYJrAoD7gcLYsWNZKGJSRCzKkpqaWqLyDhw4kPcFNwcA1wL7bb322mvsjoWGe2Ji4jXbvnjxIp8v+M+idwri9MKFC3nWwTmAWxfGJlSuXNm2HH8zffp0On/+vM2ggGgHgiCUTwynD+R1jXQxXNYiC1+yb775hvbs2VPqLYCPPvqIRxTnJyE+mnJznDBQrEI1whFlH99N2XGXHb8/wSkYjQaKl/osM9q2aUG1atZg14Cw0FDq0aMbnTt3luskMSGWxZxSPz98/y3dc8/dZDSaKCQkmH7+6Xu+/1OSE/LUY1JinO3vKlUMp/Hjn6ZOnTqyZbVP715sDS2szp944jHq06c3d8f/8/dMysnJooz0VNv61ytvcJAfff3V5zR69CgWsbAMf/bpx9SmTWv+W5R1zD13kdlkoB49utPff8+ginaN+k0b19H7H3zEZTeZTDTijuFUuVIFijp9wnZ8H374PmVkpNOoUXfZ/u6rLz+nVq1a0huvT6ShQ4dwdAXsv1+/vlSzhuu5apUUuVc9E6lXB2A2E53ex5olI7QKZTjx/EZEFu2Z42UpylBgJwCxikELw4cPtw2CeP7559lNQAEPZHyvXr06x4X8/fff6YUXXmDLhgIevOganD17dqGuBbDIYlKARRbbhMWjoG660saclkhJr9yCg6awz1eTyu+qz5zgvuABWtQbT3DPek1PT7d1waOhDR/95cuXl3EJheIi96pnIvVa+hijoyjl3VFEWm8K/2odealdz/7peiW6Anxj0W1mD7rbsFzpusNADIzMhfW2bdu2Np8vdIXZD1TID0biYiorVEHhZAmrRF5JMWQ8c5B0TbuUWVkEQSg6r776Ko/qh5USg0kLc18QBEHwBIxX/GM1tZq6pIgFZVoqxHs9deqU7fuZM2fYxww+rjVq1GDfMXswchcjhhs2tA4qady4MY/sxehhhKzBy+Xpp5+m0aNHu2TEgjxUb0yUFMNO1CJkBcE9+P7778u6CIIgCGUy0MtVKdPBXhhA0Lp1a54AXAkwX5yRxogBiRA1ffv25RAz3bp1o19++YVcnhqN+cN4xYlaEARBEATBlTC4+ECvMrfI9urVq0jZehTgF5sfWG9dNfnBdalujQ9pOHuILIjP5qIme0EQBEEQyh/mtEQyx1ujnWhqW+NuuyIuHX7Lo4moTl6+gUT6HDJdPFnWpREEQRAEQbBhuBIiVF25Dqn8HT8QvqSIkC0rVCrS1Gme52IRBEEQBEFwpYFeWhd2KwAiZMsQbZ0WeZypBUEQBEEQXMk/ViNCVigM5eIwRomQFQRPAH78iKBS2qxbt45atWpV4r9HmT777LNSLZMgCJ6LRZ9DxvNHeV4sskKhaGs1JVKpyZwcR6akwlPqCoJQeiBpSlkJWUfu+3o88cQT9NJLL5XJvgVBcD+M544SYSB6UBipKlw/fXdZI0K2DPHy9iVNtQY8L2G4BHcG0UcsudmOnYoQ4WTr1q0cgq9ly5bUokULWrhwIS+vVasWvfLKK9ShQwe6//77OYb1Qw89RM2aNePJPmX1+++/zzGqYQHFdOHCRcrOzqZRo0ZRkyZNeNsDBgwoVDAeP36c/+62224rcN8xMTHUu3dvTuLStGlTjn2NJC4Kn3zyCTVv3pz306lTJ8rKysqzD2QixP7ffffda/Z/8uRJ6tq1K/8ttvHGG2/w8v/97380YcIEWxmVY2vQoIEtBTjicCPhA8qJ3+666648WRMFQSg/GK70FGvrtLQ9I1wViflUxmjqtmDzPS4a7/YFvxwFweXR51DihB4O3UX41xuIvH0L/T0pKYlTXM+ZM4e6d+/O4hCZ/xQSExNp+/bt/FCGsESa6gMHDrBIhfhFPGoIxM8//5yio6PJ19eXRWRiQgynosW2jhw5YttXQcAaC8GIxC722O87JyeHFi9eTAEBAZx2e9iwYfTPP/9wIpcpU6bQ3LlzadOmTRQcHMxC0j4L4YULF/gYx48fT2PHjr1m/9999x0NGTKEJk6cWGg5FYsxrMNDhw7lbIkArgf+/v60Y8cO/v7ee++xEJYkEIJQ/jC6QfxYBRGyZQwukpy1f9ucqgVBKBmwxiLrH0QsUKlUHGda4YEHHrBZFv777z/64osveB2IN4jCVatW0ciRI6l+/fp07733sqgdPHgw+fj4sIXz6NGjNG7cOOrZsycnXykO9vuGwIaQhliFlTkuLo6twhCyS5YsYYspRCwIDQ21bSM2NpZ69OhBkydP5gQwBYHf4UIAizPKmT/Ntz1PPvkkVa1ald58803+vmDBAkpNTWUhDfR6PVuTBUEoX1gsFls0JVcf6AVEyJYxmiuRC0yXTpIlJ4u8fPzKukiCUHx0PlaLqYP3cTPAAloYishUq9W0bds22rJlCw+wQtf+jz9MoqG33c7W2DVr1rAIfvnll9nq+vbbb9OGDdbjnjZtWpH2/eWXX7J4hYUWIhkZDWGlvREhISFUr149Frt9+vQpsLtvxIgR1KVLFxblsM5+/fXXtGzZsmvWg7X14sWLbBm2f3lNmjSpULcJQRDKB6bYc2TJTCXSepOmekNydcRHtoxRh1YkVVglIrOJs3wJgjsCUQWfb4dON/DTgoCDj+jGjRttls/CXABgqfztt99YvGVmZrIIhYBLT09nyyesurBUwuXg4MFDLPqwf/i9wvUAf4du/m+//ZYFLSb4pAYFBbFV83rAXaBSpUosYuEvO3v2bNtv2D66/pVtwJ0B7gcALgbz5s2jy5cv06OPPprHr1YBx1+xYkW2MH/66acsyvMD9wVYX7FfjeaqLQMuC1999ZXNJxefhw8fvu6xCILgufFjNbWakJdGS66OCFkXssoaJTGCIJQYdMPPnz+fByxhoFebNm1o8+bNBa4LkarVall8duzYkQUkBjdBQN5xxx28HNvAAKhRo+6kgwcP2gZRtW7dmv1K8Xt+sAwDuOAqoAz2yg/8W2GNxXrYjn33P74rVlXsCy4M8OVVQJmRkhvidsyYMddEQYB/MMqOMmJwWkERFDDwCwIfIl0Z9AXg7tC+fXs+HzgOWKPz+/oKguD5GJRECHVc360AeFmKMhTYw8EoYPik4SUGi4oziI+7TBGRVXg+e90/lPn3Z6Rt0omCn5nklP0Ljq1TwXOQevU8pE49E6nX0iH57RFkijtPQeO+Il3zbuTqiEXWBVBGBRqjDpKlgO5CQRAEQRAER2NOT2YRCzR1mpM7IELWBVBXqcthhSw5mWSKjirr4giCIAiCUA4xXHFxVFeuTSp/a/QUV0eErAvgpdaQtlYznjecEp80QRAEQRDKcKBXXffwjwUiZF0Ebf3W/Gk4sausiyIIgiAIQjnE4GYDvYAIWRdB27gTfxqO7iCLqWzysQuCIAiCUD4xZ2eQ8aw15J62QRtyF0TIuggcr80viCzZ6WQ8Z02DKQiCIAiC4AwMx3dyTHt1ZA1Sh7tP9AcRsi6Cl0pN2kYdeF5/5Nog5oIglA1333MfHT9+vMR/jwxhSqzWkoLECB9//HGR1z906JAtvSwSKChpe50BYs/OmjXLafsTBKF0MBzeyp/aJp3JnRAh60LomlxxLxAhKwguw8wZ06hhw7JN01hcIWtPlSpVbNnOXF3I5k/wIAiCc7BYLDYjmq6pCFmhhGgbd+RP+KiYM9PKujiC4Hbs3LmT+vTpQ+3atePsVvbpX3/++WeqX78+L3/vvffypLzFPMSiQoUKFejs2bM837ZdRxZnyBKGrFn29OrVixYuXMgCbODAgbxfZOy65557OPVtQaxcuZKzarVt25Y6dOhAa9eutVlukRFs3LhxnNUL29m1yzr484knnuD0ubDsYh8FgYxdOD5s115I4jhCQkJs35ERDNtA9q7Bgwdzmlz79ZD1DFnRsC0c83PPPcf7Rdlg6VVAWl9kAcO6PXr0oP3791NcXBy99dZbfEz4G5T7evWi7BNZxbCd7777rkj1LAhC6WKKPUfmpGgijZa09d3HP5ZBZq/yTmpqKrKb8aeziIu9VODypP/daYl/op0lZ/d/TiuL4Ng6LQ+YzWZLRkaGQyfs43okJydbWrVqZbl8+TJ/j4+Pt1SvXt1y8eJFy8GDBy0VK1a0/TZx4kS+5xUwj79XCA8Pt5w5c4bnq1evZtm7dy/P169f37Jz506eP336tKVSpUoWg8HAZUtISLCdiyeeeMLy0Ucf8fe1a9daWrZsafubTp062Z41J0+e5G3k5OTwemq12rJt2zb+7ccff7QMGDCA51GW4ODgQo99yZIlliZNmvB2sf8xY8ZYatasWeDfxsXF2eZRxscff9y2Hs7D/Pnz+fvkyZMt/v7+ljVr1vD3Tz/91DJy5Eie37Rpk2XQoEFcbrBhwwbeP/jjjz8sw4YNK1K9KPucMmWKxZmU53vVk5F6LTlZq2ey9kj56kmLu6EpayEt5AVpak0xZ0h/ZCt5t+lb1sURhCKRlZVFFStWdOg+YmNjyd/fv9Dft2zZQlFRUTRo0KA8y+HfCksilleuXJmXPfnkk/TRRx8VuwwPPvgg/fHHH2xZnDJlCls3NRoNmc1m+uqrr2jp0qVsnUW66y5dulzz9ytWrKBTp06xBVNBpVLR+fPWTDr16tVjKyfo3Lkzff7550Uq1+rVq+muu+6ypdh+/PHHadOmTQWuO2PGDLam5uTk8ATrs4KPjw8NHz6c53GMAQEB1Lt3b/4O6/Fff/3F87BCwwKrlBUkJSVRdnZ2seqlTp06pNVq6d577y3ScQqC4Bj0R674x7qZWwEQIeuCfrI5a2aynywMRfbdn4IgFA7uF3THQzjlx75LHOS/r9RqNZlMJtt3CLyCuP/++7nbHwJz6tSptGTJEps4XLNmDa1fv57F5LfffsvfCypj//79ef38XLp0iYWkfZlK6jNa2HMD4hZl27p1K0VGRtKiRYvYFUDB29s7z/4LKw+OA+fiww8/vKl6gWuBn58fi3lBEMoGiyGXDCd287zOzQZ6AXl6uBjsm6LRkTk5lkwxVh89QXB1IEZgMXXkhH1cD1hAz5w5Q//9959tGXxb9Xo9+2fCGqr4g/700095/haW0O3bt/P8vHnzCvVvxcCp9u3bs98ohCAEGkhOTmbLJkQsfFn//PPPAv8efrQo34ED1jSQYMeOHTc8v9gurJ04loLo168f+51i3xCOv/zyS4HroZyBgYEUHh7O24LfcEm47bbbaPr06TZLMizSij8vygqLdFHqRRCEsseAjKKGXFIFR5C6Sl1yN0TIuhheOp+rWb6OSvQCwT2ABRDd/o6cbtQ7ERoayl37sBLCatqkSRN69dVXWWRhoBIGQyEMFQYb2VseAdwCxo8fzwOO9u7dy0Lveu4FEID4VBg7diy7VyC6AbrQCwt3BcEMayy6/lHGxo0b09dff33D8xsWFsb7wACtggZ73XrrrTRy5EguP36vUaNGgdu55ZZbuIyYUMaShgXD33766ad0++232wamKQPM+vbtS7m5uVxWDPa6Xr0IglD2GBS3giYd3bIX2AuOslTOSUtLo+DgYLYiKD5mjiY+7jJFRBYccDhr1TTKmvctaZt2oeCnv3FKeQTH1qngWmRkZLBlsiiPP6lXz0Pq1DORei0Zye+NJtPl0xT48Afk3W4AuRtlapHdsGEDDR06lLvr0ApYsGCB7TeDwcAhWRDuBtYYrAOLBIJ724MBBhhwAQGKMC4PP/wwv6TcGcVHBT4r8F0RBEEQBEEobUwpcSxiycvLlpTJ3ShTIQs/NHQ1ff/999f8hm66PXv2cExDfMJvDaNc4ZtlD0Ts4cOHadWqVTzwAuL4scceI3cGPirwVYHPCvuuCIJQqmA0vnRGCYJQ3jFcSYKgqdmEVAFX4027E2UatQC+ZPlDsiigqx/i1B4Ey0YIGAwwgA/Y0aNHeQAHgm0rfmOTJk1ifzGMKoYV1x2BdRq+Krlbl/BFpruSKEEQBEEQBKHUw241cb9oBW452As+rBB5SpYahJDBvP3gB4zeRSgXZQSyu6JrbL2o9Efd+zgEQRAEQXA9LGYTGY7usIX+dFfcJo4s4jrCZ/buu++2DchCKB2EwLEHwckxwlcJs1MQGFGLyX6wl6uhbdyBfVZMl06SOTWBVMFXg5YLgiAIgiDcDMZzR8mSlUZevgGkqWUNJeiOuIWQxcAvZK2BT9uPP/5409tDRp933nnnmuUJ8dGUm+OcgWJGo4FHWF6XynXJ6/IpSty+gqhVH6eUS3BwnQpuh9Sr5yF16plIvRaTHf8Sgm2Za7eghMQ4cjWKGoFC4y4i9ty5c5wpxz48VqVKlSguLu/JR+YZRDLAb4UxceJEev755/NYZKtXr04VIiq7RPgthcwWPSj78inyuXCMAgdICkdXR0K/eCZSr56H1KlnIvVaPFLOHyLk6gts3Zt83Pi8qdxBxJ48eZKzwuQPUo5c5CkpKbR7tzW1GoDYRaBt+xzg+UEwdAhW+8kVUXxW9Me2k0WChwuCy4OUq/mzhpUG69atK3HyAkeAY/zss8+ctr9t27ZxKEYks1i5cmWe35BFbfjw4U4riyB4AubMNDKeOczzWjf2jy1ziyzivZ46dcr2HWkMkboQPq6VK1fmTDUIvYWwWsiDrvi94nedTsdZcZCp5tFHH+UHK4Tv008/TaNHj3bbiAX2aOo0Jy8ff7JkpJDxwjHS1mxS1kUSBLcHvTbwpXekkEVGK2fv25kUdnyOYsqUKXTPPfdwb5ogCDeP4fhOjPYidaXapA4rvAfbHShTiyxyc6OFjQmgux/zb731Fl26dIkWLVpEFy9eZEsEhK0ybdmyxbaNv/76ixo1asRpERF2q1u3boXmGXc3vNQa0jZqz/P6AxvLujiCcEP0en2hE0RcUddFo7QkIJIJngGIT40UqQsXLuTltWrV4sGiCN93//33cyP6oYce4tS1mOx95t9//31uJOO5g+nChYuUnZ1No0aN4vSq2PaAAQMKFXiId42/U2Je5983GuS9e/emtm3bcmpXNL7t07V+8sknbH3Efjp16sQxte2BKxT2/+67716zf/Rede3alf8W23jjjTd4OdLzjhgxgvr06cPPSySiSUxM5N9Wr17NvVt49qI8v/32W55IMY888gifI2wT50zZ3oQJE2wWUUSLwUBc7BNRZKKiomzbePvttzk1b/v27bk8OB8FATexO+64g7eB/SENMPj444/p77//5vCLOK/ohcsPzgnON+qnR48e3KBQQChGnHuk74XhA25qNzruBx54gOOR47hq167Nx71jxw7q1asX1alTJ49rWv7rRdm+ILgy+sNXwm41dd+wWwplahrAQ+F6QcmLErAc1lnkLvdUdK16k37fOtLvXU3+Qx8v6+IIwnX55uvCu5vr1KlLI0aOtn3/4fuvCxWs1avXoNF331esfcM3Hl3Mc+bMoe7du7M4tBc9EG4Iy4cQfhCWiFxy4MABFqkQvxB4EIgQPtHR0eTr68siMjEhhuNVY1tHjhyx7asgYI2FwEPPkj32+0YElsWLF3NSBvQ0DRs2jP755x/uSYLlce7cubRp0yaOpZ2cnMyuUAoXLlzgYxw/fjxnOswPxN6QIUNslkv7cm7cuJGPF+MHxo0bx+ug0Q+Bh/2p1WpeH8Ju4MCBVK1aNT4WnAf8HcIaxsfHF3jciOWNY4boe/XVV1mMQ4guXbqUj2fv3r18vIoQLohnnnmGGjZsyMlvIGoh9CGesb1jx46xSFTEc342b97M+4eg/PTTT1mE/vvvv/xuQMMCDRwc37Rp0/jY//zj1+seNzh48CCtXbuWjxsCGXWB2OZoaEHMIoskev7yXy9YXxBcPeyW/sAGntc17ULujtxxLo6uRQ8itYZM0WfIGH3VyiEIQl4gViCEIGIBBAUauvZWNghJAJ97uCRhHaTAhiiESIG/fP369enee+9lIQaB4+Pjw4IKCVgggmAd1Gq1xSqb/b4hsCGksU2IJ/RMKcIXblSw6kLEgtDQUBZaIDY2lq2NEGoFiViA33/99Vd6/fXXWcgpMbfB4MGDbYNgIfRwDhSRfeedd7IVFBZbfD906JCtPC+++KJNnEVERBS4X1g2IWKV+dOnT9usnth2YGAgHz/EX2GgPI8/bm2sI6wirLNKGW9Ely5dWMQqxwafYjQSkPYc24AohhDGuUNCnRsdN0ADA3UPNzZYiSFyUe+4XiBsYf0u7HoRBFfGcHIvWTKSycs/mLQN2pK74/7OWh6OyjeAtI07keHQJtLvXUOaynXKukiCUCjjJ7xU6G/5LVXjnirYugYU0VeawCJ4o/1BNGJgEdyXIIbQtf/jD5No6G23szUWg0khjF5++WUWn+g2R1psAGtfUfb95ZdfssURFlqIHnRTw0p7IyBK0UUPcQnhVdA5gvsARB1EOayzX3/9NS1btuy6xwzhDLcsWE6xDJbKopTHHnvxhnOY340k/z6LQmlcA+jVg+U5f9pyjG6/0XHnP6aCjrGg62XmzJm2xpQguCL6Pav5U9eyF7swujtikXUDvNtYY8jm7llT1kURhOsC61VhU/5BTtdbt7gWTwABBysZutAVy2dhLgDwfYRPJIROZmYmi1C4FaSnp7PlE0LkzTffZJeDgwcPsa8+xA78MNGVjL9DN/+3337LghYTrHaw0MGv9HqgixqWUQgj+MvOnj3b9hu2D/cEZRtwZ4BlEcDFAN3uly9fZmuyvV+tAo6/YsWKbLGF9REiSwGCFscGJk+ezOdAKU/NmjX5+CDK9+/fn6c8OF5lX4W5FhQGBDeEInyScc5+//33QtdFeWBNVvaDY+3fv3+RrfFwP1CODT7IEJlww8D5VK4DuLLAzeFGx11UCrpelO0Lgqu6FeTuW5tHW7g7xZLi6FqbNWsWvyjg0A5/IHQ1Kb5FsAbY+3MJpeheoFJzli9T7DlSV6xZ1kUSBJcD3fDz58+nF154gQUGLMDvvfceD2zKD0THs88+y+IToIsZof4gWBEtBeIWAgfdxqNG3cn+krDsQYzBEnfffffxYLL8YBkGDqG7Gn6UGLCaH/i3Yh9YDz6WiqAE2C6EKkQ5hD+6se271yHw4feJAVhjxoxhAW7fQIB/8PTp07kxAPFpHwoMYgsj/zGQFseFQVrKYCq4TOBcofvdPnThV199Rc899xyfJ+wbA7YUsVkU4K8LyzO2C4tyz54987g72INGwZNPPsn7wnmGe8T1wijag/MFdw1EwUGYxqlTp/JynCO4DEDYAtQd/HTH3nf3dY+7qKDBkf96wYA+QXBVjKf3kyUtkbx8A0nb0DqY3N3xshRhRBVCYKErDY7xGBGLEaB4AMO5HS1d+BVB3GLkKNaDQ747CVqUGz5peCi5UkIEe1InPUOGI9vIb9g48rvlQYeWTSgZEozbM/GEekWUAVh34WrgbNCogI8sXjVoZGBwXWlkaCzvdSpci9Trjcn4+3PKWfc3eXceQoFj36ZyY5GFpfWll17i1n5hrWmle+ebb76hL774gl577bXSLGe5x7t1XxaycC8QISsIgrsANweEw4L/KazQjkgYIQjCjUFiJYy1UTSFp1AkIXvixIki+axhtCqmksaAFApH16oX0cyPyXThGJniL5I6whoiRhAEoSgW2bIC7h6CIJQ9xjMHyZwaz4mWtI06kKdQpMFexR14UZKBGsL1UQWEkLZ+G57P3Wt11BYEQRAEQSgKubZoBT3JS6sjT6FEcRcQ/BqBohFCJv/IWYSWERyDrk1fTiuH5Ah+A4oXLF4QBEEQhPLsVrCa53WtPSNaQYmF7IcffshpBhF4HGFe7GP9OSL2o3AV71a9KHPWJ2Q8e5hMidGkDq9c1kUSBEEQBMHFMZ49TObkOHYr0DXpROVayGIwF2IBIlON4FxUQeGkqdeajCf3kH7fWvLte09ZF0kQBEEQBBdHr1hjm3cjL637RJVySEIExGZECC6hbPBu0zePr4sgCIIgCEJhIPSdklAJLoqeRrGFLIJjf//9944pjXBDdK2uBPaOOkCmlLiyLo4guAUI/3S90IGOihRQ3FSvRQEuXIgJ6wrs2rWLRo0a5bT9IW45DClIYPDBBx84bb+C4M4Yzx0hc1I0kbcv6Zp0Jk+j2K4FL774Ig0ePJjq1q1LTZo0uSZCAdIKCo5DHRJBmrotOTuHfu9a8u3tvJeIIAhF55133uHkMEhFmx9kmMqfstcdadeuHf39999O29+qVasoICCANm/e7LR9CoK7o78SO1bXrBt56a59HpU7iyzSOiJiQYMGDTgVIDJi2U+C4/G+MuIw94rPiyC4Cnq9nif7hIEmk4mXQbyVdN2igpSkEFdIFYsGd0xMTIHrrVy5ktq0acPrIW3qkSNHePm6des4vSxSl7Zs2ZID+O/bt9/2dz///DM/+/C3SG1a2ADXJ554wpYWFtZDRHjBuAKkR+3Rowfv40blXbp0KaeERTmwDaR6tQfnDWlZb7vtNk4Xbg+yZ8FSCmMD/n7AgAF5jg9JCvDZtm1b2rdvH/+GfSOVK5bhuJ9++uk8UWk++eQTTh+L7XXq1In3ie2hbPZW77fffpu3Ua9ePVq2bJnt7xcuXEiNGzfmv0e5K1SowH+TH1wDSMCD8mF65pln+JpAql4s37ZtG+/TPnUviI+P5+NEGXE+H3zwweuUPfu6x4v0vUgdfPfdd/PfoY6ioqL4t5MnT7JVGNvCbxj8LAgu7Vaw+788rokeh6WYBAQEWJYsWWLxJFJTU/Em5U9nERd7qcR/a0yMtsQ/0c4S/2R7iyklvlTLJZRNnXoKn37yPk+ZmRm2ZVu2bORlK5bnfW589eUnvDwlJdm2bOfO7bxs8aL5Jdp/XFycbf6jjz6yPP744zx/5swZS3BwMM/HxsZawsLCLAcOHODv06dPtzRu3NhiNpsta9eutajVasu2bdv4tx9//NHSq1dPnj948KClUqVKlujoaP7+1ltv8XOjMPBbcvLVY7v//vstLVq0sKSlpd2wvMePH7dERERYjh49yt/1er0lJSXFtt2YmBjLqFGjLOPGjbMYjcZr9j1v3jzLgAEDbN8TExP5E8eHv//vv//4+99//21p2LAhH3t2drYlPT2dl2ObgwcPtsycOZO///nnn5b27dvbypCUlMTrYHstW7a0nWNse86cOfx9+fLllgYNGuQ558rx/P7777wu/iY/P/zwg6Vnz56WnJwci8FgsAwaNMjy8ccf829//PGHZdiwYQWe7y+//NLy2GOPXXPMBZU9+vL56x4v9hMUFGSJiori76+88opt288++6zlww8/vGY/Qtkjz+BrMZw7atULz3azmHOzLZ5IsS2yYWFh7FYglB3qsEqkqdMcr1DK2bmirIsjCC7DjBkz2HoGS97kyZNt1kZ7YNmEJQ2TYhW9fPkyXbp0ib/DktixY0eeR6bCs2fP8fyaNWvolltuoUqVKvH3Rx99tNjlu/POOykwMPCG5UUXOvbVqFEj/g4XLvseL1hvYUXEeAW1Wn3NfmAtPHr0KFuW0fVv7wJWq1Yt6tvXapm566672DJ54cIFtkbCUoq/bd26Nfu/KuVZsmQJW5mVMoSGhha4X7hR3HHHHbZzd/r0aZ6HFRVWUuV47r//ftLpCg7IDksrrNfe3t7sfoHzjPNxI2BpXb58Ob3wwgts/fX3979u2a93vEr5a9eufc2xwKL+66+/0uuvv07//vuv032vBaE45O623ju6Zl090q0AqEoygAFdR/m7sgTn4tNpCH/mblmUp2tWEMqS8RNe4snX18+2rEOHzrysb7+BedYd99QEXh4UdFWgtW7dlpfdMsh6fReHTZs20bfffsvd2YcOHeLkLCUZbGXv0wrBYzIV7OZg71YwdepU7u7G9McffxS6bfh3lkZ5+/Tpw+IuLS2twN/r1KnD7hIQw/AnhVBOTk4u9DgwYf9wgYDQP3DgAN1zzz3FPn8Qn8p5sZ47U7H+vrDyFQWITQhRNEIwVgNuGdfb/42ON/91oLi7jBgxgs8pYql/9913NGRI8a9VQXAGFpORcrdb3Xu821rdizyRYgtZPHjR6kUyBFg04CtmPwnOQdduAJHWm0zRZzjQsSC4ArCyYbIXHxABWJZ/cFNx1i0KEGqwdsJ3Hz6V8GctzHJ38OBBFo9g1qxZVLVqVZ6uB/wp4VsL8QN+++0322/wOYWIwqT4ZqIsqampJSrvwIEDeV/Hjh3j7waDIc+2XnvtNbZ8wo8zMTHxmm1fvHiRzyv8Zz///HNu7MLqCuCXinEOYM6cOfwsr1atGpcH1mYIOFhpZ8+ebdsetvPTTz/ZyoCoCcURqTjnEIvHjx/n79OnT+djLggcExoGiq80LNWKj+/1OHPmDDcUYGWeNGkSnThxgjIyMgot+/WO93rARxbnDHX+6aefsrVZEFwRw5GtZE5NIK+AUNK16E6eSrHfFsOHD3dMSYRiofINIO82/Sh3+1LK2bKQtLWtg0cEobwC6yMEEixlEIcQRIq7gD0RERH0119/sRCBUEJXM0TMjSx/ysAeDPSBAMX+rjfAFV3c/fv3Jz8/P+6CLk554d4Ay+69997LIhYCH2KsQ4cOtr9HRAR0n8M6C9GruDwACPWJEyeygMUx3nfffdy1j8FZcEnAYCYM3EWjYebMmXzs48ePp5EjR/LvVapU4fIo4O/hftGlSxduZGC/+QdbXY/IyEgWpHh/wGqL8wLRWVC3/GOPPcbd+IphpFevXnysNwLHBiurYj397LPPuH4KKvvMGdOue7zXA+If9YZzB/cE1IsguCI5mxfxp3fHQeSlyRthypPwgqNsaW0Mm3LHNLXonsMDDy32oKAgp+wzPu4yRURWualtGE7sptSvnuCUc2EfLycvb99SK59QNnUquHa9pqen23xckeVwxYoV3EPlLkDsQRQW5DvsaOzP3YIFC1how4+3LJB71TORer2KOS2RkiYOJjKbKOTNWaSp4rljm4rtWoBWbkGgqwY+RoLz0NRvQ6qIamTJyaTcK3HiBEFwHK+++ir7wcKKh0FEkhym6KC7HwOr4K+LLnlYxQVBcAw525axiNXUaubRIrZErgUQsohc8PDDD+cRsaNHj7b5nAnOAdZvn85DKWvRj+xe4NNpcFkXSRA8GncXruimLwtrrOLXi0kQBCfEjt2ykOd9ut5Gnk6xLbII0o3sXvATAvBFQkiZw4cP2wYQCM7DG+LVS0XGk3vJFHe+rIsjCIIgCEIZYkQK+9hzRDof0rXtT55OsYUsQprMnTuXM9QsWrSIQ5FgJCpErP1gA8E5qEMrkrZJJ57P2bqkrIsjCIIgCEIZkrPZao31btuPB4Z7OsUWsgCjZBEeBSIWIU/Wr1/P4UiEssGni7XrIHfrErKYbz5uoyAIgiAI7ocZY2b2WCOK+HQZRuWBIvnIKplaCgpjg/ApCJeigEDUgnPRNe9OXv7BZE6NJ8ORbZzBQxAEQRCE8oV+939EudmkrliTNHVbUnmgSBZZhKYqaELQbqSrtV9WHDZs2EBDhw7lGH4YuISQLPkdlt966y2qXLky+fr6cpw/BKO2JykpiVNMImwWRDUGoSEIdnnCS6sj7w6DeD5nizVunCAIgiAI5YucK4O8vLvc5pbhUB1mkb1eysWbITMzk8OxwN+2IKsvQrQgk9iUKVM45/Wbb77J4hmpF5X0gRCx0dHRnK4RgcORVQcWYuQwL0/4dB1GOWtnkf7ABjKnJ5MqMLSsiyQIgiAIgpMwItNn1EEilZp8Ot5K5YXi54EsRQYNGsRTQcAa+/XXX3MmnWHDrH4e8MuFLy4stwj3hWDaCEi+c+dOateunS1W4a233sppGWHpLS9oqtYjTc3GZDx3lHJ3LCffvhLTVxAEQRDKC7lXrLG65t1IFVyBygtFci1AKsWi5JNG5pZPPvmkVGItYhAZ8l/bpw2E60LHjh1p69at/B2fcCdQRCzA+iqVirZv307lDe/Ot9lGLJZiwjZBEARBEFwYi9FgTYJwxa2gPFEkiyzixCJCAYQkfFohHGHtRPd+cnIyd/Vv2rSJli1bRoMHDy40+1dxgIgF+aMh4LvyGz6RwzvPAWk0nLBBWacgcnNzebJPUesJeLcfSJlzvyZTdBQZT+8nbb1WZV0kQRAEQRAcjP7ARrJkJLMlVte0C5UniiRkMYDq3nvvpdmzZ9Pff/9Nv/zyC6WmpvJvcCZu0qQJ+66ii79x48bk6nz00Uf0zjvvXLM8IT6acnOcM1DMaDRwXuhSp3kP8tqzilKW/UY0emLpb19wfp0KZYrUq+chdeqZlOt6/XcKYWiXqUUvSkiMI08gIrJK6frIent7s5jFBCBks7OzKTw8nLRaLZU2SnKF2NhYjlqggO/Ida6sExeXt8KQaQyRDK6XnGHixIn0/PPP57HIVq9enSpEVOboB84AN1tRK6k4GIc8Qil7VpHXse0U6mUmdUS1Ut+H4Nw6FcoWqVfPQ+rUMymv9Wo4e5hSzx0hUmso7NaHSB0SQeWJEiVEAHAzgFh0hIgFiFKA7a9evTqP4ITva+fOnfk7PlNSUmj37t22ddasWUNms5l9aa8nyiFY7SdPQVO5DmnRrWCxUPaamWVdHEEQBEEQHEj2amuUJu92A8qdiL0pIVsaIN7rvn37eFIGeGH+/Pnz7LIwYcIEev/99zkV7sGDB2ns2LHsmzt8+HBeH24MGIj26KOP0o4dO2jz5s309NNPc0SD8hSxID9KxALElDVneob/ryAIgiAIeTElxZB+j9Xg59tvDJVHyjT81q5du6h3796270p3//33309//vknvfzyyxxrFnFhYXnt1q0bh9tSYsiCv/76i8Vr3759OVoBBqUh9mx5RtuoA6mr1ifTpZOUs2ke+Q18oKyLJAiCIAhCKZONnlezibQN25OmWgMqj3hZJE4TuyzAVQJ+v+7uI6uQs3UJZUx9h1TBERT6/kLy0jjGBUS4Snn1z/J0pF49D6lTz6S81as5O4OSXxtClpxMCnrq63Kbnr5MXQsExwFfGa+gcDKnxlPu7lVlXRxBEARBEEqRXMSMz8kkdaXapG1iHTtUHhEh66F4aXXk22sUz2f/95ckSBAEQRAED8FiMlL22lm2cTFeqvIr54p95KGhoZxwIP+EMFxVq1alnj170h9//OGY0grFwqf77URabzJdPEGGE1cjOwiCIAiC4L7o96whc1IMeQWEknfHQVSeKbaQfeutt3hQFTJ4IakAJsxj2VNPPUUNGjSgJ598kn799VfHlFgoMqqAEPLpPMRmlRUEQRAEwb1BD2v26uk879vzTvLSelN5pthRC5CKFiGxnnjiiTzLf/75Z/r3339p7ty51KJFC44cgLBYwrXo9XpO3OAMfPvcTTkb55Hh0CYyxpwlTaVaTtmvIAiCIAilj/HUPjKeO8o9rj49R1J5p9gW2ZUrV1K/fv2uWY7wV/gN3HrrrRQVFVU6JfQwkOIXQn/+/IVO2Z+6Yk3SNe/O8zmSIEEQBEEQ3Jrs1dYeVp+Ot5IqMNQp+5w9ezZt2bKFPELIwh928eLF1yzHMvwGEPs1MDCwdEroYSDF7sWLF+n7H350nlVWSZCwbSmZUuKdsk9BEARBEEoXU+w50h/YwPM+V97tzghRijj/AwYM4Oypbu9a8Oabb7IP7Nq1a6lDhw68bOfOnbRs2TL66aef+PuqVat40JdwLQ8//DB98cUXdO7cefrnn3/onnscfyFq6rchTZ3mZIw6SNkrp1DAqBcdvk9BEARBEEqXrOW/cwp6bbNuTnMV/Pnnnyk5OZnHQLmitiu2RRZ+r+vXryd/f3+aN28eT35+frwMIg288MIL3IUuXAvO2/jx43n+008/JZPJ5PB9It2v31CrTzMyfSGlnSAIgiAI7oMx+gzl7ljB835DnDMGKT09nSZNmsTzr7zyCqnVanI1ShR4rGvXrjRz5kzas2cPT5jv0qVL6ZfOQ0HK3dDQEDp16hQPjnMGnL6ufmsio4GyV0h4NEEQBEFwJ7KW/kJkMZOuZU/S1mzilH3+8ssvlJSURPXq1aMRI0aQK1IiIWs2m+nEiRMcwWDDhg15JuHGBAQE0MMPPcjzH3/8sdOssv6KVXbzQjIlXHL4PgVBEARBuHmMF0+Sfvd/PO835HGn7DMjI4MjUIGXX36ZNJpie6O6ppDdtm0bK/PGjRtTjx49qFevXrapd+/ejimlBzJ27H0UEhLCDYL58+c7ZZ/a+m1I26gDkdlk9bMRBEEQBMHlyVryM3/q2vYjTbX6Ttnn5MmTKTExkerWrUt33XUXuSrFFrKIH9uuXTs6dOgQm5vhAKxM+C4UjcDAAE4gAT755BO2cjsDxVc2FxEM4i44ZZ+CIAiCIJQMw9nDpN+/nshLRX6DH3PKPjMzM+nrr7/m+ZdeesllrbElErInT56kDz/8kC2ysCgGBwfnmYSig+gPOGdHjx6lRYsWOWWf2jrNSdusq9Uqu1SyrwmCIAiCK5O12GqN9e4wiDSVaztln7/99hslJCRQ7dq1afTo0eTKFFvIduzYkQcpCTcPGgIQs4qvrLOssv5X/Gtyd67gUZCCIAiCILgehtP7yXBkK5FKTX6DH3HKPrOysuirr75yC2tsiYTsM888w+G1/vzzT9q9ezcdOHAgzyQUj3HjxnHyCLhqLF261Cn71NRsTLqWvTgWnVhlBUEQBME1yVpkjc/v3WUoqSOqOWWff/zxB8XHx1PNmjXp7rvvJlen2EIW4RfQFf7QQw9R+/btqVWrVtS6dWvbp1A8kA3t8ccft1llLRaLU/brN8TqZ6PfvYqMl8TCLgiCIAiuhP7YTjKc2EWk0ZLfIGucfkeTnZ1NX375Jc+/+OKLpNVqyeOE7JkzZ66ZoqKibJ9C8Xn66ac5UcL+/fs5Q5ozwKhHXZt+efxvBEEQBEEoe2DUylpstcb6dLud1GGVnGaNjY2NperVq9OYMWPIHSi2kIWp+XqTUHwqVKjA0SDAG2+8QQaDwXlWWS8V6fevI/3xXU7ZpyAIgiAI10d/YAMZow4Qab3J7xZr3HlHk5aWRp9//rnNGqvT6cgdKJIHL0bUDxo0iE3MNxpdf9ttt5VW2coVzz//PE2ZMoWjQiCThhKay5Fg9KNP9zsoZ8Mcypz9BWknTiMvtWs7dQuCIAiCJ2Mx5FLmbGv3vm+fu0kVXMEp+/3ss88oLi6OcwXcd9995C54WYrglKlSqSgmJoYiIyN5vtCNeXk5JUuVI1ohCIOVmppKQUFBTtlnfNxlioisco1JH4PpUJZ9+/ZRRESEw8thzkih5P+NJEtmKvmPeol8e7lu0GNXp6A6FdwfqVfPQ+rUM/GUes1a/gdlLfqBVMERFPq/OeTl4+fwfZ46dYrHPaFHeM6cOXTLLbeQu1Ak1wKEhYKIVeYLm9xRxLoSY8eOpebNm7Ogfv/9952yT1VACPkNfdzmKwthKwiCIAiC8zElx1LWCmvmTb87nnWKiAWvv/46i9j+/fvTwIEDyZ0oto+s4DjUajV9+umnNussQnI5A3Ykr1qPLFlpMvBLEARBEMqIrHmTiPQ5pKnbkrzbO0dQrl69msN/Il7sRx99xL3rHi1kn332Wfr222+vWf7dd9/RhAkTSqtc5Zbu3bvT8OHD2cL9yiuvOCUcF/xi/e96kedzNs4j48UTDt+nIAiCIAhXMZzcS7m7VsJPkwLuetEpgtJgMLDWAAgF2qhRI3I3ii1k586dS127dr1meZcuXdivQrh5PvjgA/L29qb169fTkiVLnLJPXYO21nBcFjNl/POF0+LZCoIgCEJ5x2I2UcY/1ogBPl2Hk6aGcwTl5MmT6dixYxQeHk6vvvoquSPFFrKJiYk8GCk/GCSFvLzCzYMwZhj0BV577TXKzc11yn7973iWQ30YT+4h/Z7VTtmnIAiCIJR3cjYvJNPFE+TlG0h+t1lT1zuahIQENpyBt956i0JDQ6lcCFmEZVixYsU1y5cvX0516tQprXKVexDDrVKlSpxo4vvvv3fKPtXhlcl3wFiez5z7NVn0OU7ZryAIgiCUV8yZqZS18Aeex+BrVaBzBOUHH3xAKSkp1KxZM3rggQfIXVGVJN7pyy+/TG+//TZ3fWOCkodJ+rnnnnNMKcshAQEB9O677/I8BoAh/Jkz8BswllRhlciMkZMrpzhln4IgCIJQXsla8guHwFRXrkM+PUY4ZZ+HDh2i3377zaYxMNi83AjZhx56iL744gs+Ab179+Zp+vTp9OOPP9Kjjz5aqoVDOK8333yTateuTb6+vlS3bl1677338vhvYh5CunLlyrxOv379OKmAJzB69Ghq164dZWRkcAPCKQO/dD7kP8I6aC975Z9kvHTK4fsUBEEQhPKI4dwRytkwl+f973rBKUmJzGYzawp8YnB5jx49yJ0pUfitJ598ki5evMj5eJFMICoqimOgljaffPIJC2RERDh69Ch/R8th0qRJtnXwHVEUfvrpJ9q+fTv5+/tzDLScHPfvFkfyCRwbQmIgo9q8efOcsl9d6z6ka96dyGSkjGnvksVkdMp+BUEQBKG8YDHoKWPqu0RmE+na9iddow5O2e/PP/9MW7Zs4Z7fDz/8kNydm4oji8xTOBGOAid62LBhNHjwYKpVqxaNHDmSBgwYQDt27ODfYaH8+uuv6Y033uD1WrRoQVOnTqXLly/TggULyBPAMcFfFqAFFR8f7/B9IuSH/z0Tycs3gIznjlL2f385fJ+CIAiCUJ5A4gPT5dPkFRBKAaNecso+o6Ki2DUUIPFSjRo1qFwKWYTZuuuuu6hTp07Upk2bPFNpgpBeCNR74oQ1run+/ftp06ZNNGjQIP6OgVDwHYU7gQIiKnTs2JG2bt1a6HYRBQCWZPvJlYFPctOmTTlixAsvvOCUfapDIsh/5HM2/x1jzFmn7FcQBEEQPB3j+WOUveJPng+4+xWnDPAym800btw4ysrKYncCuIp6AsV2xkBXN1KZYYTbwoUL6cEHH6TTp0/Tzp076amnnirVwmEAGUQmAvTCERk+sxhlN2bMGP5dGQBVsWLFPH+H79cbHIXMFe+88841yxPioyk3J4OcgdFo4LzQReWjD9+j2++4k90L+vXtTYMGOSHjR922RPVak9epvZT8+xtED31EpHJfh3BXq1PBPZB69TykTj0Tt6lXo4Ho9zfJy2wiS9OulFatMZETyj116nQ2Bvr5+dH7771NiQnOGUReUiIiqxRtRUsxadiwoWXGjBk8HxAQYDl9+jTPv/nmm5annnrKUprMnDnTUq1aNf48cOCAZerUqZawsDDLn3/+yb9v3rwZo58sly9fzvN3d955p+Wuu+4qdLs5OTmW1NRU23ThwgXeDuadRVzspWL/zdtvv23x9/e31KpVyxIfH29xBsbEaEvChJ6W+CfaWbL++8sp+3RXSlKngusj9ep5SJ16Ju5Sr5mLf+F3asKL/Sym1ESn7DMqKsoSGRnJGuKnn36yeBLFdi04f/48d/kDRAlIT0/n+fvuu49mzpxJpclLL73EVlmM3m/evDnvAyG+YFEFiLMKMOjMHnxXfisIZM1CAgf7yR2YOHEiW6fhJwt3A2egDqtkTZSA2LILfyBT3AWn7FcQBEEQPA2kgM9abg17Bb9YVVCY01wKMjMzqVu3bqUeYaqsKbaQhUBMSkrieTgJb9u2zeavWtrhoeDHgZH79sDFAJUCEJYL5YEfrQJcERC9oHPnzuRpQIBjtCHOyT///OO09LXe3W4nbcN2RIZcSp/+HlmunH9BEARBEIoGIgClT3nHGqWgVW+OVOAMfvvtN9q4cSO7FPzwww/X6Cp3p9hH06dPHw4FBeAfCwtp//79adSoUXT77beXauGGDh3KPrFLly6ls2fP0vz58+nLL7+07Qej6ydMmMAj71CmgwcPchiwKlWqcGw0T6Rt27Z8zGD8+PE8AMzR4DwH3PsGkbcvGU/upZz1sx2+T0EQBEHwJLJXTrGmofUP5gFeeLc6mnPnznFkJ4CxQZ6YgdUL/gXF+QNYQzEhtimYNWsWh8mqX78+Pf7446TT6UqtcHBbQEIECNi4uDgWqHfffTcnQFD2g+IjlMQvv/zCqdZgNkeLo0GDBkXeD6y4iHaQmprqNDcDOKQX2ZE5H4iRC/cORHNAzNzZs2c7pYWVve4fyvz7MyKNjkJe/p001Rs6fJ/uxM3UqeC6SL16HlKnnokr16vhzCFK/fwRtsYGPPge+XS4xfH7NBg4yhN6zqEZVqxY4XHW2BIJWU/E3YQsgPUZWdUgapHKFjFmHQ0ulbQfnyfDwU2kjqxBIROnkZePn8P36y648kNUKDlSr56H1Kln4qr1as5Mo5QP7yVzUjTp2vSjwEc+dIo19vXXX6dvvvmG9Q2iFcAd0xMpkTRPTk6mzz//nB5++GGekLJW8ZsVnAMGv3322We27gJYxR0NbrzAsW+TKjSSTHHnKWPmx05JmysIgiAI7gjekRnT32MRq6pQlQLufd0pInbJkiUsYgEypHqqiC2RkN2wYQOfEMSThaDFhHksw2+C80AsX/gmI74u5p2R9UsVEEKBD33A8WRzdyyn3G3OGXAmCIIgCO4GxpTo960jUmvYEqvydVw2VAWMKXriiSd4/umnn6bbbruNPJliC1kkPUBWL0QpQHB+TEh5hhBZpZ0QQbg+aNWhxQX/ZKTlRUgNJaKDI9HWa0V+Qx7j+YxZn5Ix+ozD9ykIgiAI7pa9K3Pu1zzvf8d40tZs4vB95ubm8qB3jBnq0KEDux56OsUWsqdOneI0qQiDpYB5+GjiN8G5BAQE0LRp0zim73///cduHs7Ad+D9pG3UgUifQ+mTJ5JFn+OU/QqCIAiCq2POzqC0yRM5i5euZS/y6T3KKft97bXXaM+ePRQWFkZTpkwp1QH4HiNk27RpQ0ePHr1mOZa1bNmytMolFINmzZrZBOx7773H8eIcjZdKTYEPvENegWFkunyaMmd/6fB9CoIgCIJb+MX+9SGZ4y+SKqwyBdz3plP8YufOncux5gEiOVWvXp3KA9YYWsXg2Wef5filsL526tSJlyG0w/fff08ff/wxHThwwLZuixYtSre0QqEg6xlGJc6YMYPj+8JfGeHKHIkquAIFPvgupU16hnI2zSdN/TZOCSkiCIIgCK4K3of63at4LEngwx+Qyt/x0ZBOnDjB/rAAvea33FJ+3sXFDr91oxhkaHVgk/jEICR3wB3DbxUE0s/16tXLZh1fuXIlux44msyFP1L2it+JtN4U/PzPpK3VlMojrhr6Rbg5pF49D6lTz8QV6tVwej+lfv0kuxT43fEs+fW/z+H7TExM5GRVp0+f5nixy5Yts8X6Lw8U+0gxyEtwTfz9/Tk5AsTs/v37OTQaLLT2/syOwG/oY2S8dILjy6b/9CIFvzqF1CGRDt2nIAiCILgSpsTLlPbTS1a/2NZ9yLfvGKcM7rr77rtZxNaoUYPHzJQnEVsiH9maNWsWeRKcT61atejvv/8mb29vTu2LgMhO8Zd98D1SV65D5tQESv/xRRn8JQiCIJQbzDmZlPbD82TJSCZ19YYUeP//yMvBWbTQ+/3UU09xHHn0Js+ZM4cqVqxI5Y1in2WMgoNAUnj55ZcpJCSEzdnI6SuUPR07drQ5fH/33Xf066+/OnyfiI0XNO5L8goIIeP5o5Q+9R1JliAIgiB4PBazidJ/f4MHPnsFhVPQk1+Ql7evw/eLcUmzZs3iXtfp06dTkyaOD+/lEUL2ww8/5FBPYOvWrSyUPv30U6pQoQI999xzjiijUAJGjhxJb731Fs+/+OKLHJrL0agrVKWgxz7hwM/63f9R9tLJDt+nIAiCIJQlWQu+Z9c6jBOBiFWHOt4q+s8//9AHH3zA81999RX7yJZXii1kL1y4QPXq1eP5BQsWsGB67LHH6KOPPnJK2Ceh6Lz00ks0ZswYHnSHqAaHDx92+D619dtQwN2v8nzW0l8od7fjBbQgCIIglAU5WxdT9qppPB9431tOGewMVwIlc9eECRPooYceovJMsYUsRsFjhBz4999/qX///jzv4+ND2dnZpV9CocQgcsSkSZOoW7dulJ6eTiNGjKDz5887fL8+XYeRT997eD59yv/IcNbxAloQBEEQnInh5F6OFwt8b32YvNsPcEqYLWRS1ev1nHr23XKQuavUhSyE6yOPPMITTuitt97Ky2Htw0AjwbVAVg9ELmjQoAFdvHiRhg4dSjExMQ7fr/8dz5K2aRciQy6lfTeejNFRDt+nIAiCIDgD46VTlPbjC0QmI+na9CW/wda07Y7k7NmzNGTIEEpKSqK2bdvS5MmTbxgStTxQ7DOAxAedO3em+Ph4ziIRHh7Oy3fv3s0hIATXA6nqFi9ezJEkEKIDYjYhIcHhkQyCHvmINLWakiUzldK+fYZMidEO3acgCIIgOBpT/EVK/fZpsmSnk6ZOc6dEKLh06RKL2MuXL1OjRo04QoGfn59D9+mxCRE8EWcnREhIiKcTx49Ql649yZkgBvDAgQP5RmjVqhUtWbKEI044EnNGCqV++RiZos+QKrIGhbzwK6mCwsgTcYVg3ELpI/XqeUideibOqFeEmEz5/BEyJ1widdV6FPzczw7P3BUbG8uZuk6ePEl169blZEeVKlVyyL7+XbmMDhzYl8dF8YUXJ5IrIzZpJ5OYmEB//P4Lbd26hQW0M6lduzZbZhFhYt++fTxQLyMjw6H7VAWEUNCz33G+aXPceUr97lkyZzt2n4IgCIJQ2pgz0yh10jMsYlUVqlLwM5McLmIxJgm+sBCx1atXZwOUo0RsbEw07d+/l0Nn2k+ujghZJxMeXoFq1KhJZrOZtm/b7PT9N2zYkMUsLLHbtm2jUaNGOXyQHrJ8BY//jrwCw8h04Til/fi8JEwQBEEQ3AZLbjal/fAcmS6d4lixwTDQBFdw6D7RSzx8+HAegwTxChELMesotmzdxJ+NGjWhJ8eN5+mJJ58lV0eEbBnQpUt3/oT53tlWWdC8eXOaP38+R6BYv3493XPPPY4Xs5E1KPiZb8nLx5+MJ/dS+uTXyGIyOnSfgiAIgnCzWIwGSvvlFTJGHSAv30AWseqIag7dJyINodd07969PBYJBii4FTiKuLhYOnXyBM936dqd9YEyeZSQhYkZ4ZtycsSadjNUr1GTW1dlZZUF7du3p9mzZ3Nyi1WrVnGrz9GiWlO9IWf/QtBo/cGNlP7rRH5ACIIgCIIrgndU+m+vk+HIVmvCg6e+Ik1Vayx9R4HB2IMHD+akU+g9hYht3LixQ/fp5+tHrVq1oabNWnDPsTtRbCGLZAhIiiDcHK1atS5Tqyzo3r07J7XAALfNmzfzjePoaAZImMDZvzQ60u9fR2k/v0wWQ65D9ykIgiAIxQXvprRfXib9vrVEGi0FPf4paeu2dOg+MRgbA7v27NljizjUokULcjQBgYHUf8AgGjRoCLkbxRKyiFdWv359W0IEoeTAIluWvrIKXbt2pWXLlnHXBbowlKgGjkTXrKvNMms4tInSfnxRfGYFQRAElwHvJLyb7FPP6hAb3YFERUVxrP5jx45RlSpVOOlU69ZWo5ez8PLyInej2D6yH3/8Mac+PXTokGNKVM58ZdG1HxJatuGoEIoLNwxunOPHj/ONhBvKkegad6Sgp74m0vmQ4eg2dqKHM70gCIIglPnAru+f43cT3lFB474iXZPODt0nBnTh3Xvu3Dn2hYXLH+LFOiMc6Pz5szliQbmJIxsaGkpZWVlkNBo5axSEmD3IOOFuODuOrH28O4PBQFqtllwB3EAI84GkCRUrVqRFixZR06aOzRttOLWPM39ZcrNIU681+x+pfPzJHZHYlJ6J1KvnIXXqmZRGvSI8JESs8fQ+HpwMg4u2XityJDt37qQ77riDkpOTqVmzZrRw4UJ+BzuDxYvm07FjR6h+g4Y0fPhIckc0xf2Dr7/+2jElKae4iogFyPwFyyzErNI6nDZtGvXt29dh+8QDAnFm0yY9Q8ZTe/kTrV+Vf7DD9ikIgiAI+TFnpVPapGfJePYQefkGUNAzk0hbu5lD94mQWg8//DBlZmZSx44dOWMXDIbOimt/7NiRPNGUyoWQvf/++x1TknIMjOKnTp3g1liHDp3KtCxoBS5fvpxGjx5NW7Zs4Vbi559/To8++qjD9qmt05yCJvxAad88Tcaog5T6+SMU9PQ3pA4Xi4kgCILgeExJMdw7aIqOIi//YE52oKnZ2KHv/W+++YbefPNNnofB6K+//nJquKutW63jc+rVb0CRkc6xALtMHFmTyURz586l999/nyfEJMUyoWjkGsx5vkdHX6YF8+fQxg1ryyyCgT3KSMkxY8ZwvT733HP08ssvO7SOtTWbUPALv5AqNJJMMWcp5dOHyHjhuMP2JwiCIAjAePEEpX76EItYVXAEBU/40aEiVq/X01NPPUVvvPEGi9hHHnmELbHOFLFJSYl07Ohhnu/SuRu5M8UWsqdOneJ4ZmPHjqV58+bxdO+997IvJXwrS5tLly7x9jGqHv64COa/a9cu2++4CN566y2qXLky/96vXz9O5eaqnIzJoYGfnaaNp67GT61SpSpVr26NYLBp4zpyBby9vemnn36id955h7//8MMPdNdddzlUaCM2X/BLv3P+aktaIqV+8RjpEbtPEARBEByA/uh2fteYU+NJXbkOBb/8O2mq1Xd4ytmpU6dyJKjPPvuMvvrqK6e7GW7ZvJH1U9269alipcrXXRfrzdmZTG/Ni3bJlLXFFrLPPvssj6hDLFnEOcOEJAm1a9fm30oTdLUjPBQqGN3dR44coS+++CKP/8inn35K3377LYuu7du3k7+/P4eQctWkDX9sTKL4dCO9uTiLPlsWSwaT9aLo2as3fx4+fJAuXnSNOL0Iw/HCCy/Q9OnTuZGwcuVKbihgUJijUIdWpOAXfiVtw3Y8AAxO9zlbFztsf4IgCEL5JGf7Mutg45xM0jZoS8EvTiZ1WCWH7e/EiRPUp08f2rRpEwUGBrIV9sknn3R6yKuLFy/Q0SvW2K5dr+8bm6U302uzo+nteTE0d2cKbTieQW4ftQBCcdu2bWwZtWf//v0sOjMySu8gX331VQ7Uv3HjxgJ/R9ERMgpi68UXX+RliDwAP88///yT/TxdLWoBhOtXK+JoyiZrdIfWNX3p87urUqVgLa1csZQTJERERNLY+x/m1pqrsHv3brbIxsbGsuvB5MmTacCAAQ7NppIx7V3K3bGCv/sNeYx8b33EpWPcyUhoz0Tq1fOQOi3f9QrtkL3iT8pa9AN/17UbQIFj3yYvrc5hZYO73hNPPME6o0aNGpxZ09FRgQoDvb979+6mjIx06tmzT6HrnY7Lpef+ukin4/SkVhE9OyCCHuoeTiqVa72HVSXpckYO4PxAwCIcV2mC8E/t2rWjO++8kyIjIzkw8K+//mr7/cyZMxQTE8NWQgUIUoz8Q2q3wsjNzWXxaj85C63ai14eXJHeG+pHAd4q2nsum0ZOOkNbTmZQ9x69ycfHl+Lj4/gicyXatm1L69ev50+EWMMgsHfffddhfrNeGi0F3P8O+Q60Di7MWvILpU+eSJacLIfsTxAEQSgfiQ4y/nzbJmJ9B4ylwAffc5iIRYjN1157je6++24WsZ07d6Z169aVmYgFMJK1bdv+uiJ2yb5UGvX9GRaxEYEa+v2RmvRIzwouJ2JLZJGFbyzcCX777Tfq0KEDL0OXPka1Q+TAElpa+Pj48Ofzzz/PYhax1saPH89uBIiegFH1sAIjExV8ZBVgOYTl7u+//y5wu//73/9svp/2nD51jM39zsBoNFBshor+tzSLTsaZCZfG2E7e1Cn0LG3ftpW0Wh2NHDmSGw6uRG6unj748COaNu0v/t61S2f6+usvqUKFcMftdNe/RMt+Ji+TkSwVaxKNfo3Igd0/N1OnGo3rhFMTSgepV89D6rSc1mtKHNGsj8grOoos6PG85RGijoMdVh4Y2p55ZgLt2r2Hvz/yyEP08ksvlFnYzaysLDY4ajSFB6zKNVrou3U5tPignr+3qa6mNwb5UZi/83uIi9prUmwhm5KSwiISZnKlMpAcAc7LELGwiJYWOOGwyEKwKsAPF4IWFteSCllYZDEpwCJbvXr1MkmIgAgGHy2Jpdk7Unh5m5reNKTCXurQtjXVrl2HXJV//vmHnn76ab4x4N4xZcoUbmk6CsPp/Zzz2pKWRF5+QRT4yIecHcyVkO5Kz0Tq1fOQOi1/9ao/vsvaq5eRQl4BIRT4yEeka9jOYWVZu3YtPfjgg5SQkMC64scff6Rhw4ZRWWGxWGj2PzMpNTWFhgwdRpUrV71mnWPROTTxn8t0IiaX4MX3eO8KNK5vBVK7oBXWnmJL7JCQEM46AadlOCpjQlpThOAqTRELIE6bNGmSZxkiJmBwGahUyWqVg9+mPfiu/FYQsHLiwrKfygpvrYr+d3tl+viuKuSnU9Gec7n02YGmtD021CVHB9o3FuBq0KBBA25IDBo0iOPNOsrVQFu3JYVMnEaaWk3JkpXGQauz/pvu0udIEARBKFvYH3b1DEr79mkWserqjSjk1akOE7Ew7H3wwQds3IOIbdGiBY/zKUsRC44dPULnzp2h9PQ08vHxI3uMJgv9vDaBXQkgYkP91fTTA9Xpmf4RLi9iQYltxfXq1aOhQ4fyhHlHAGsrRLI9ENDIQAUQKQGCdfXq1Xmsq3B1cKR10BEMbR1MCybUpvZ1/CjbYKH3FsbQY39coDPRyeyY7YqgUQExCxcI3Lxw2bjlllvYd9kRqEMiKfj5n8m78xAii5my5n5D6b+/QeacTIfsTxAEQXBfLLnZ7A+bOecrIrOJvDsMopAXfyV1+PXDTZUUhP7EmJ2PPvqIBfQDDzzA+gSRnsqSnJwcWrN2Fc937twtT+SnqLhcGvPTWfr233gymoj6NgmghRPqULcGzotpe7O4zrD4AkAgfkRI+PDDDzl+7YwZM+iXX37hQMIA7gMTJkzgpAwYGHbw4EH24UVX9/Dhw8ndqBqqo98frkGvDqlI3hovij93iGZO/YWmLdrkspZH+BT/8ccf7LeMebh8oBGBGHmOKLOX1psC7nuL/Ee9RKRSk37Xv5Ty4b1kOGdNsycIgiAISKiT8tF9lLtjOb8r/O98ngIeeIe8dNaxN6UJ3nUYiN6lSxeOc4/e6d9//52+++47Dl1Z1mzcsJayMjMpLCyc2l/JHmo2W2jKpkQebH7oYg4F+qi4Z/ibe6tReECxk76WKcX2kXU2yEM8ceJEbunAAouBX/bpUlH8t99+mwUu/He7devGwfvR5V1UnBl+q6g+WmglfTljPdXU7yS9RUMXw4fRxNvrUI1wx4UHuVnOnj3LdaNEjBgyZAjH+EXECUf5zbJFNimGSK0h/+FPk0+fu8mrjMKWid+dZyL16nlInXpuvVaIqEw5a/+mzPnfEhkNnKkr8KH3OE6sI4iOjqZx48bRqlVWi2evXr3YsFOtWjVyBS5fvkR/TbcOwh81+l6qUaMm+8K+tyCG9p3P5uVd6/vTuyMqcxhQd8TlhawzcEUhC/QGE036+TcyZ8XTJUMkrc/pRo/1qkAP9wwnncY1jenwkUX+6Pfee4/DjkRERHASi9tvv90hMWDNmWmU8dcHpN+7hr9rm3bheICqoDByNvJy9EykXj0PqVPPJP7MMfJe/gvpD1pjz+uad6eAsW+RKiCk1PfF2a7mzOE49ghJiShLiIaEBAeuEgPeYDDQ1Cm/cTrapk2bU89+g+n7/xJo+pYkMpmJx+W8NDiS7mwf4tIx2m+Ea5xtoUB0WjXdP/p2Uqk1VFUbR/XUJ+m7/xLo9m/O0LbTrukXqlar2WoO31n40MbHx7O7x6hRo+jixYulvj+VfxAFPvox+d/9KpFGR4bDWyj5g3s47aAgCIJQPkBUAvpxvFXEarTkf9eLFPjkFw4RsRhwjrEhiEoAEduqVSse0AW3R1cRsQBjV0JDw8jfP4Cockca+mUUJ2OCiB3QLJAWP1+H7uoQ6tYiFpTojKPC7r33XvaFvHTpEi+bNm0ap10TSpcKFSKoTx9rwoeO/oepTmAanU3Q08OTz9PLsy5RTKqBXBFlpOYrr7zCYdqWLVvGodQQgqS0IxvgJvTtMYJCXp1C6kq1yZKWyCNUM2Z8LAPBBEEQPD3BwT9fUNo348grPYnUFWtSyCt/km/vUaUu0PDu+v7776l9+/acsh0hQpHsYM2aNWy4cTV8fX2pbc9hdMRnAL00O4li04xUPUzLEQm+GlPNbV0JblrIzp07lwYOHMgnaO/evbZ4rOiWx6AsofRp1aoN1avfgCwWMw2tsJvu6RjAMd6W7k+jwV+cpkmr4ikz1/UiG6Cr5c033+Q0w8i2huxvL730Eo/qPHToUKnvT1O1HoVMnEo+PUby95yNcynl/XusLXVBEATBozCc3EvJ799NOWtnoa+fLG36W8M0Viv6GJmigsHkffr0YeNMZmYmG/IQyx5CtrSzmpaG4E7JMtHHS2Lptq+jaO0pIo0acWHDacGEOtS9oftEJHCIjyzSxCKaALqLMUp9//79VKdOHRa1iCWKTBbuhqv6yNqTnZ3Fvi5NmjanLl2607EYPX2yJJZ2n7U6a1cIVNOz/SNpeNtgl4z7hhBikydP5oF5SHGMzCKPP/44D+RDbOLSRn9sJ2VMe4/MSdH8HeLW//ZnyCtf/LzSRPzuPBOpV89D6tT9rbCZC76nnHV/s4BVhUZSwJjXKTWiVqnXK3TBJ598wpZYCERoBIwBgVuBK7kRKCDJ0s9TZtHJeBNtTGtJBtJStwb+9MrgilQn0rUyhZaZkPXz86MjR45QrVq18gjZqKgoTl6AeGXuhjsIWaDX6/O0/FB1/x1Opy+Wx9GFJKuLQYNK3vTioEjqUt/fJf1ekDwBPrSIRgEqVKjA8Wfvu+8+9q8tTeBWkDXvW8rZOI+/q8KrUMC9r5OukTW1cmkjL0fPROrV85A6dV8MJ/dQOowU8dYxF95dbiP/kc+RyjegVOsVohUuk3g/IbEBQFIDJP6xzyTqKlgsFlp1OJ1mLttGTWgnmSxedFA3kMYNbkhd3SgmbEkodnMCCQgQ0zU/8I+FoBUch72IxU2WlZVJ/ZsF0aLn6tLLgyMpyEfFWTmQSOGBX8/TzijX8w9FjN9Zs2bRggULqH79+vyAQKrbnj172sJ2lRYqH38KuGciBT37HalCK5I58TKlffOUNWRXqvXBJAiCILg+5vRkSp/6LqV++TiLWFhhg57+hgLve5NFbGkClwG8k/BuwjsK76p58+bRX3/95XIi1mKx0IZjGTTq+7P05swTVN+yl5dXqNuRpo5v4/EitkRCFnFCx48fz9mzYPGDhQ2V++KLL3LYCcHxJCcnc1y4+fPnsKDVabzo/m7htPzFunRf1zD+vutMFovZhyafoz1ns8jVgJ/sjh076OOPP2Yr+L59+6h///700EMPcTza0kTXuCOFvDmLfHreiZFhlLtzJSX/byRlr/uHLGbHpNQVBEEQbh6L2cy9anhm525dzMu8uw6jkDf/Jl3TLqUejQAuAwMGDOB3Et5NeEfhXYVlriZgN5/IoHt+PEtPTrlARy5lUa+AXaT1MlLlKtXpoTt6u6SboUu4FmB1DOpCCrasLKtA8vb2ZiELvxF3xF1cCxRSU1Noyp+TeaBd23YdqE+f/nl+RySDX9cl0pydyZxyTgl4/FS/CGpZo+yzjOQnLi6O4+8p2cAQ5eCRRx6hl19+mePQlibIAJY542Mynj/K3zU1GpP/3a+QtlbTm962dFd6JlKvnofUqftk50L0GeNZ6+BgdbX6FHD3q6St06JU6xVW188++4yzc8GFD0a6+++/n9566y2HJfQpKXhHbo/Kou9WxdPec9YxMj5aLxpd+xRR/H7S6bzpgQcfoeDg0h974nEJEVDZcDHASHT4xgYEuK/52t2ELDhx4hgtXDCX54cMHU6NG18rxC4nG+jntQm0YHcKGa8ENWhfx48e6RnOwtbVfGjRAkaUg7Vr1/J3XFPPPvssPfPMM+yPXVrACosWftbCH8iSncFWWvazGvoEqYIrlHi78nL0TKRePQ+pU9fGnJFCWUt+oZwNc/HAJi8ff/Ib+jj3qnmpNaVWr9AvSCOLJD4YhAzgUvDBBx9wbFhXAill1x7NoMnrE+jABetYJPS+juoYSn2qxtH6VYt42ZAhw6lxk5s3zHi0kIXVDDHU8sdMwyCvf/75h6MZuBvuKGTB+vVraMf2rWzBHHPvAxQRUXDL8UKSnn5ek0CL96baBG2jyt70SK8KHBTZ1bofIGTREkYkDGVAGKyz6PIpzbzV8JPNnPcN5e5YYV3g7Ut+A+4n335jSpSPW16OnonUq+chdeqaWAx6yl77N2Wv+N1qZIBYazeA/EdMIHVIRKnVK3oz//jjD45GgKQ9AML13Xffpd69e7uUkUdvtNDSfan0+4ZEiorX2wTsiHYh9FjvcIoM0tKlSxdpwYI5nL2rV6++VN4otpBFuAl/f3/6888/acSIEbblsbGxPJCntIPdOwN3FbIIaTVn9iw6d+4MZ++4b+yD5O1duACLTjHQ1E1JNHtnMmXrrdWO4Mj3dwuj29qEkL+364QSwbHNnz+fHyynT5+2DTRE6DcIWkTPKC0Mp/dT5pyvyHj2MH/HIAK/YU+Rd/tbyKsY4VXk5eiZSL16HlKnrgVkiH7Pf5Q5/zselAvU1RqwgNU1al9q9Zqdnc3a5auvvuLxPaBu3brcE3jHHXe4VDit9BwTzd2Zwu9sJDIAgT4qGt0plMZ0CaOIwLyW6YyMdPLz83epY3BpIYvwE2+88QZbyRCaAoiQLZsHKfyUp039jY8BrbFbB992w79JyTTSjG3J9NeWZA6arNwgd7QLobs7h1L1MNcJ7oxc0QiBAv+lCxcu8DL4zU6YMIH9aNGoKq0BBfrdqyhzwXdkTrLGQtbUbMyCVtuoQ5Fa6PJy9EykXj0PqVPXgQ0J874hY9RB/q4KjiC/YU+Sd8dbyUulLpV6xXvy999/ZwELrQKgV5Cg54EHHuBeTVchKi6XZmxNpgV7UmwGJ4jWsd3C6K4OIRTgo7aln8V4mfDwkrvDlWshi6QHiBt7++23U9euXVloQEiJkC2bB2lM9GVavWYVDRkyrFgO3ll6M83flcKC9lyitcsCeq1XowC6t0sYdazr5zJdLPDJnjFjBgvac+fO8bLw8HD2n3344YcpNDS01AJtZ6+dRdkr/iTLlfS2mvptyH/YONLWbXndv5WXo2ci9ep5SJ2WPRh4m7XoJzIcuRJ2UedDfgPGkm+/e8nL27dU6hXvdAjYb7/91uZCUL16dXrhhRc4djkGqruK/+umE5k0fUsSbT55NWxmvYredF/XULqtdTDpNFctrZBtK1YsoePHjtLgIcOofv2GVJ4ptpBF0Pro6GgeyYdQFbfddhuLnZ9++om6dOkiQraMHqSoxpKKTtxEuHmmbc57E9WO0NHI9iE0rE0whfoX7mDvbAst4tBC0KIxBWCVxQjTp556imrWrFkq+zGnJVLWij+syRSM1mQT2iadyf+2J9lSWxDycvRMpF49D6nTssN48SQP5NLvX2ddoFKTd5eh5Df4sSL5wRalXi9evEg//PAD+8Eqg7iQxAnRle655x6XSSmbkG5ky+ucHSm2pEZ4jfduHMDuAx3rFGxM2r1rB61Zs4p/GzFyFNWuXZfKMyW2yCohKWCyHzNmDK1evZrzD4uQLfsH6ZHDh6hCRARFRlYslW4NrdqL+jUNpDs7hFCHQm4sZ4NuldmzZ9PXX39Nhw8ftjWy0EuAOMdIpVwamJJiKGv5b5S7ZTHRlZizupa9yG/QQ9cIWnk5eiZSr56H1KnzMUZHUdbSyezCxXipyLvDIPIb/AipI6qVyj7Wrf2Ppk2fSXPnzuV3BGjYsCGPrRg1apRLuBDAcLTlVCbN3pFC646m2wZgw71vRPsQ9oG9nnvfyZPHOWIRpFuv3v2offuOVN4ptpBFvE/4leQfbPP222/Thg0bbKGT3AlPErK4yBfMn8Oj++8aNaZEYhZk5Jho2YE0bikevnQ17XDNcB0NbxtMQ1sHU+WQsn8o4PJds2YNC1r7aw8uL48//jgNHTq0VB5eprgLlLVsMuXuWM65vYG2cSfyu+UBdj2AuJeXo2ci9ep5SJ06D8OZQ5S98k/S719vW6Zr248tsJrKtW96+xCsy5Yto59//pnWr7+6jx49erBRA4l2XGEA1MUkPS3am0rzd6XS5RSr9RW0rO5LIzuE0C0tgshPd/1ynj59kt/vGAzdrFkLumXQEJcwLLltHFlPwpOELMKgzf5nBsXERJOvrx+NGj2m0LBcReXIpWyaszOFluxLo8xca/MR9w6ss/DdQZpcV4h4cODAAY4HOGeONeMZQDpB+NAi0kHFiiUT9fmtCvCfzd31r81Cq6nTggVtamQdiqhY9ab3IbgWIno8D6lTxwJZYTi2g5+VhhO7rAu9vKy9WYMfIU21BqWSSAcRCOADC1cCpVcO0QcQf7y0euVuNvLAyoNptGhPKu0+a01eAJBO/rY2wWyBbVCpaKEez5w5TfPnzeZ3W8OGjTl+vCsIdLcRsosWLaJBgwaxZQvzhW7My4stYO6GJwlZR4lZABH7L27Kvam0I+pq2ltfrReL2VtbBlGnev7silCWXLp0iR9umBQHf1y7w4cPZ1ELa+3NtmJN8Rcpe9V0ykHKRKN1oJylYk0KHDCWvNsPJC+tawwiEG4eET2eh9SpY7CYjKTfs4ay/5tuy57IPrAdbyXfAWNJU6nWzWe12r6dM3DNmzePx0woA38RfeD24UOpVet2VNZxX7eczGDDz5oj6ZRrtEosvHI61/VnAdu/WSD5aIsnQpctW0yHDx2g+g0a0tCht7NoF4ohZO39Yq/XAoA4EB9Z13iQ5uRk0z9/z6DY2Bh2Axk1+l6qUKH00r0ia9jifam0cHeqLeIBCPZVU79mgTSoRRC1r+1HmjIUtQh6vXDhQu5ywsNPoX79+py4A07/N2ulRVKF7NUzOAONJdcq7r0Cw8inxwjy7TGCVEHhN30cQtkiosfzkDot/UxcOZvmU876OWROibMu1PmQT9fhnGBGHVbppq2vGOQ7ZcoUOn78uG05kjM99thjPDbCx8enzOrVYLLQ9tOZtOJAGq0+nE5pOVccXxGnNlLH4nVIq2CqFFxyNze4E+zds4tatW4rIjYf4lrgoUL2WjHrT/c/8DAFBJReqleAy+fghRy20v57KI0SM642ZMID1DxIbECzIGpb269MLbXIEjZ58mR2O8CgRICHAXoa0JLv168faTQlj8xgzkyjxJVTSb1rOZmTrzzINVq2zvr2Hk2a6uU7PIo7I6LH85A6LR2Ml09Tztq/KWf7MiJDLi/zCgrnRjzSyaoCih4OMj8wimEQOcTr0qVLbYO3MP4DyZggYNu0aVNm9QrxujMqk/49lE6rDqXbYrIrcV8HNg9kAdukik+JewCTEhMpNCxM/GBvgAhZDxaySiaTf/7+i2rVqkM9ejo29Z7JbKGdZ7K4VfrvwXRKzb56Ywf5qqhXo0AWtl3q+5PvDZzaHQVCsaBLCg/HHTt22JYjycKdd95Jd999N6cqLMl5Qp1WCI8k/d41lL1mFhnPWAN8A02tZuTT/XbybjegROlvhbJDRI/nIXV6c2lkc/euoZxN88h40ppGHKirNyTfvveQd5t+5KUtWXgryJFDhw6x9RUp7xHqU6Ft27bckzZy5Eh+X5dFvWbrzbT5ZAb9dyid1h/LyGN5DfNXs8sAeiPb1PK76dTvF86fo7lz/6GGjRrRwIGDxR+2NITs1q1bKTExkYYMGWJbNnXqVI5WAAsX/A8nTZrkMgGGi4MnC1klmQB8RJ3ZqkNrddupTFp1KI3WHM2g5MyrotZH60XdGgRQz0YB1L1hwDWp9pzFkSNH+BqeOXMmX9sKDRo0oNGjR3O4luLEpc1fp4aog5xcAcKWTFZrgpdvAHl3HMyiVlOlfMf+cxdE9HgeUqfFxxR7zuo+sHUJWTJTrQu9VKRr2YN8+9xDmnolMwAADNaCcIWAxXNZAYlu8CxGnPBmzZqVSb0i1uvG4xm09mg6x1nPMVjyiNfeTUrfle70qZO0aNE8tkLXqFGL7hhxl0uEDnN7IYsu2F69etErr7zC3w8ePMhmfXTLNm7cmAPUI9yRkrLWnfB0IWsPbozFi+ZTm7btqWbNm3O8L46ldu+5bPrvcDr7D9mHHgHNqvmwqEVGscY30Q1TUjBgAF1YELTowsJgOYUOHTrwKFg01KpVq1aiOjWnJfGgMCRXUPKIK9EOfDoPIV3b/qTyDSjloxJKCxE9nofUadGw5Gaz9TV365Kr0QcwbiY0kny6DCfvrreROrRk4www7mbBggU0f/582rJlC1tjAZIVQG9AwA4YMKBYxrHSqFfEeT0ancMWV0yHLl59H4CqoVrq28Tau9iqpu9NW14LigO/bNkiPh/16tWnobfdcVNub+WBIgtZhDFavHgxtWtnHRH4+uuvc8y2TZs28XcEp4d11r415S6UJyG7ZfNG2rx5A/uHDhs+gurWre/U/eNyOxadS2uPpNO6Yxl5YtSCCoFq6lIvgLo28KfO9fwpPEDj9GsBkTlgGcD1bX97dOrUiQcVYEI65uLWqcVs5pA0ORvnkv7ARlv4LtJ6k65lT/LpNJi0jTsWO7+44FhE9HgeUqd0/efUqb2Uu3Uxi1jKvRI2ysuLtE27cm+SrmkX8lJrSk28gm7durF4HTZsWIlTjpe0XpMyjLT1VCYnKth8IpPi0609aApNq1qNLX2aBFKjyt4OM7bs2bOLVv+3kuebNG1OgwYNEZeC0hSyGBF48uRJzlOsXHRoNUHQgrNnz1Lz5s1t6eDcifJnkZ1Hp06d5BsEeZobNWpCZUV8moE2nsjklu+Wk5mUpb/qcwTgKK+I2lY1fMm7mCFLbgbloQufWrjW2N8qaNDBzWbw4MHUqFGjYidEQLSDnO3LKXfbEjJFW1PtAlVwBA8Qgy+tuoZ1u0LZIqLH85A6LThGdu7OlZS7fTmZk676pqoiqnEj27vTkBJFH4BuQE8Xpm3btuV5jqLHC4YB9Hgp2sIZ9ao3mmn/+Wx2FcB758jlHCXPDYPEBBjLwe5vDfwpIsjx3frbt2+hDeutSX3atGlHffoOkOd/aQtZ+ApOmzaNs2XA5zIkJIQttH379rW5GvTs2ZOSkpLI3ShPQlYZDbp82WI6evQw3yj9Bwyili3LPng0Hi57zmXzg2XziQy23NrjrfGi1jV9qWNdf57QSnZWeK/Lly/bLAkQtfbUq1ePBW3XLh1pwMBbi9UNhNsP8RZzty3ll4jN9wwvkMga5N2uP4taTeU6pXo8QtER0eN5SJ1ejYedu2sV5e7+l0yXTtmWe/n4k65df/LpNIRdoIojqBAmateuXSxclyxZkidclhIyS3HXKg3xWpR6NZosdORSDm2PyqTtp7No77msPL6uoGFlb+paP4C61venNrV8SadxriU06vQpmj9/NnXs1IW6du0hItYRQvbJJ5+k/fv30yeffMIvdIz6xssd/izgr7/+4jShO3fuJHejvAlZ5WGz6t/ldODAPv4On9levfq6VHw6dO9shag9mcEPn/zdPcgm1raWL4f2alvLj5pWxcPHyymWWqRExEN63bp13LBTQJcYGncDBw7kkF6IhlCc0cD6w5s5axi7HlwJZwPUVeuRd5u+pGvVm9SV68hDzomU9b0qlD7luU5ZvO5bS/rdq8h47krSAqDWkK5JZ/JufwsP4CpOdBUYsDDO4N9//6VVq1ZRQkKC7Tc07Lt3786NfUylLV4LqlfDFeG660wW7T6TRbvOZtmyUtqHh0RPH8Rr5/r+ZTLoGPLL/lmelJRIYWESe9xhQhYXJlpR8IkNCAhgIYsuAQW8vOFD+MEHH5Cj+Pjjj2nixImcPxmiGWBgzgsvvMA+jQiADwHxww8/FCvQfXkUsgBVD5/ZLVs2cmy+sfc/4rTjL0lZz8Tradtpa4t6R1QmpWXnfTAhGkKL6r7UrrYfhz9pXs2HAnwcK8zhSoMHN0TtypUr+RpSwAMKLgjI9d2nTx+eL6q11pKTRbkHNpAeovbIVlvUA6COrEG6Vr1I17oPaWo0Ji/xofL4e1UoXcpTneLZabp4gnL3rSP9/nV5LK+IOqBt1J57fZA+VuUfVORePRi2FPGKhDMwjijgPYLnHoQrBmyhB9eRQKQevJhNGw/F0tF4NR04n03Z+SyuSAvboa4/p1ZHjx4SFZSlQSA5KYkHdQ26daiIV2fHkcWLGkI2v+UOLTIsVyy0pQ0svXfddRffIL1797YJWViK0YWBnMsQo08//TT7fm7evLnI2y6vQlbh5Mnj5K3zphpOimJQGmBk6fGYXG5xc6v7bFaeEF8Ag0nrVfRmd4RWNfx4hGn1MMeFIYu+fJ7OnrvEghYP9wMHDuT5HdcWXHNw/aLhV7du3SKVBckW8AKCFcVwdDuR8WrUB1VIJOmadydd866kbdheYtR6+L0qlA6eXqfo3cGALf3BTaQ/sCFPtBSkjNXWb0O61r3Ju3VfUgWFFWmbZ86coTVr1tDatWtpw4YN17gRInoRDEmYYNRyVLgoSJZLyQbady6b9p3HlEXHo3PJnE/JhPipuccORo32dfyoUWWfUo8wUFLOnDlNixctoNzcHH7vjho1pqyL5Na4RUKEjIwMDvUFS+v777/PAeshZCE80XU7Y8YMDpIMjh07xjcU/BhxMxWF8i5kC4phh+7yxk2akruAyzgqXs/dSBC1eMhdTM4b5guE+qvZUtu8ui81r+bLn3jgOaJO4XoDQQurBVwQkpOT86yPcF7ocsMEgQs/9BsJW3NOJhkOb6HcvWvJcGizLS0uo/UmbcN2pGvWlXTNupE6vHKpHFd5x5XvVaFkeGKdYgCp/tBm0h/aRIajO655NsBtgHtymncjlX/BCQXyx3bduHEjTxCuGNBdUMMcLlSwutaoUcMRh8WJdQ5fzKaDF3PowIVsOnghO08GSYXKIRpqUtGLujYOZ1ezOhE6UrmIcLV/T+3auZ3Wr1/D81WqVKVhw0eyEVDwcCGLYMhhYWH01VdfcSxbRciidQjLFgSCfdcFBMGECRPoueeeK3B7cEHAZC9k4bcjQhYW9xT684/JpNfnUtu27alHzz5uG8MOERGsLfZsFrYI9QXfqfzUCNeyf22Tqj7WqYoPBfmqS7VO0RW3b98+vmYxYfQu4tfag2sQorZr167UuXNnql+//nWFrcWQS4bju6xWl0ObyJwUk+d3dcWapG3ciXRNOpK2flvy8vEr9jEJrnuvCuW7Ti36HDKc3s+9NPqj29l9wB6kiuVGbfNupGvciby8fQvflsXCQhUGILgPQrzCAmsP3gMYqAU3KUzItFXa74aMHBMdvZzDz2p8Iobr2YSrYxBsZVFbI9ooPW0ta/hSpWCtS9crNMeqVSvo6JFD/L1585bUr/8tbvt+dSVcXsjC9xV+t3AtQAgweyELS+yDDz6YR5QqIT3QfYuBaQWBpA3vvPPONctPnzpGgYGB5AyMRgNpNK6XqQN+Tnv37qWDB63d4mgg9OjRkxsS7o7eaKFT8SY6FmOiIzHWz4spef1sFaoEq6hhRTXVi1BRvUh8qincX1VqdZqVlUV79uylbdt30PZt22nf/gO2XOIKYWGh1LZtG/atbde2DTVt2pS8vQtx3cFtHHee6MROohO7iS4cIy/L1WOzIOZjtYZEdVsR1W5OVKUekQtef66Iq96rQjmrU8SdjjlLFLXfOp07Ql7GqyLPgkYv7usG7axTpTpEhfjPoxF99Ogx2rVrN+3avYc/4+Pj86wDF73mzZtRp44dqWPH9tS+fbtStRwmZ5npdLyJTsWb6WSciU7EmehCcuHP48aV1LYJz2REsXGXekWv8ooVy/kTxglolEaNGsug3RtQ1EaJSwvZCxcu8Escg2latGjBy0pDyIpF9sacPn2SVixfSllZmewP3b17L2rXvqPH3XgpWSY6dDGbR7jCCoDP/JnH7Ee5Nqzsw2FaGlT0ofqVvHnAgBKm5WbqFGmed+zYwUkYYBXZvXt3ngxjAP7nLVu2ZKsI7gtc54W5I8CvFpl42FpzZFteHznemA9p67YibYM2pG3QljQ1m5QowHl5wNXvVcEz69RiNpHp4kkynNhtnU7tJUt2Rp514COvbdyBtI06kq5Rh0L9XS9dusTPF4TFglEIxors7CuJDq4An9bWrVtTly5d2GUArnml8T6EAeFcQi6diLFOx6NzOLRi/ig09i4C6CFDlkeEWIQrWIi/xq3rFQaiWTOn8eBgxG6vVs1xkRvKIy4tZBHmC5ER7AeWoYsWL260FjGoBv45xXUtyI/4yBYurv5duZSTJwDkfB5552iXCtHlCFIyjRwgG8IWgwjw4D2ToM8TMFtBrSKqWUFHDSp6U5UAPbWoU4HqRnpTjXDdTcW4hY8yXBEgapUpMTHxmvXgI44uPjTu4EeOqVKlSgWG3IGgRWYxw8k9eeLV2oRt7WakqduStHVbkqZ2c0mb60b3quD+dcquAmcPkxHuAqf3kzHqwDXCFfFdNfVbs2hFFkB1pdrXNGRhWcWzA2J1z5493CiOjr6a4EAB78yOHTuyGxNEK54jiF5zM6nILyYZ6HRcLp2KzaWTmGJy6UxCLhmvdWlFojCqGa7jTFkwEChuXWE3kc3RleoVugQ9vIrrQHp6GhsjvL1lQG65ErJovZw7dy7PMlhgkUnplVdeYSsqXuQzZ86kESNG8O8IvozfZbBX6YDLA7Fm166BVbwVZxspjyDj2KmYXDoWYxW3J2Ny+CGdllNwVxh8uGpX8OaoCRh0UDvSm2pV0PHkq1OVLPzYmTNsUYFlBVYVREXI72erpJOGZQW9GLDg4hMDMZQXHlJQIpuYzdJTkLD1UpG6al3S1mlBmlrNSFO7GYf9Ko+hvtzlXhXcp05xP6OXxHj2sE28Gs8fu5q22l641mvNPSe6Bu1IXb2BLYU1toEBpXgOICGRIl7Rk5kfGB/gmqT05OCzQYMGJUp/mmsw07lEPYdDxAThignzsL4WRIC3iupV8qb6Fb05egDEa/1KPhwL3JPq1fbO3L+X1q79j1q0bE19+vQv0/KUB1xayBaEvWuBEn4LwekRfgsi9JlnnuHlyONcVETI3pjkZIRXC7SFVElLS+WWpp+fP5VXcOvEphlZ0KLL7PC5ZLqYpqaouNxrYhjm7zqrHWEVtrBIwKKLqUqItlhWXLgeIJYjXl7KhKgd9vEcFXB9I4U0RC1eaJjQ4IPPGwvbmDNWKxCsQaf2XeuKgIeFbwC7ILCwrdWENDUacdemp7mbuPu9KrhenZrTk8l44ZhVuJ45zJ+WjLxRTADuJ03dFqStY+0ZUVerzy4/cAOAkebIkSN06NAhFq+YCsukiYGiaMzaT/7+/sWyrsakGuhsvJ5F67kEPQ+6wvdLKYYCe6cA/FbrROq4VwqN+AYsXn34meeM50RZ36vQECtXLqVzZ8+Uq17MssbthaySEAFWWfuECAV1rxaGCNkS+PvMmk7JSYmc3rZBg0ZlXSSXQKlTxLiNSTVy99optlRYrRVRcXoOJVMYcLWtFqbjKArVw3RUPVzHcW/xWS1US95aVZHcQfCCsw7YO8jzePkVZLkFtWvXZlGLkHWYGjZsyC9Bb32mVdSeOcgvXbYW2WUaU/AKDGNBq6neiDQ18dmQVGGVPUrcuvO9Kji3TtnSmppApgvHrcL1vHUyJ8deu7JaQ5pqDUhTqymngYVwNfiH0unTp1m0olF6+PBhvn+xrKAGKgQSLKtooGKCYEUvDN5nNwLW08vJerqQZKDzidbPi0lW4Xoh0VBghBf75ALcGI+wilaMFagL96oQbZnGai2re1Wxwq5bt5rdwmDk6YZxJe06eNSz0FVxOyHrCETIFo+MjHSaPXsWJcTH8fcmTZpR334DyMen5P5VnkBR6jQ508jxbiFuYeE4n4AXh/VFUli3HMCzMDJQQ1VCtSxqq4ZZxW2VK1PFIG2h6XnxYD1x4gRbbyFu8XLEFBcXV8i+vKhWrVosajHVq1eP6tWtQ3X81RSaEUOmc4c5taUp+gz8FK79ex9/UletT5qq9diipKlan9RV6pDKxz2t9+58rwqOq1OEv8M9YLx0kowXT5IJn5dOkSUjpcD1VZE1SFuzMfdopAZXpTPZRKfPnqOTJ0+ycMUUFRXF40AKApFjmjRpwg1PRbii8YloPgVhNFkoLt1Il5MNdClZT5cgVJMN/P1isp5iU43XJBGwR6v24oZ0jSs9RxCtdSK8qXaEjsL81S4p0MriXoXv6/JlS+jcOasVtkrVajRo0BDJ1uVERMiKkC0RCBWF1LY7tm/l1ii6qOE/C+usKz7gXL1O2YqbZuQuvAtXrCMXkq7O588TXpjQrQxhG2KdKgZrOLZi5RAtVQrWcOIH+7rBoBBYeyBqjx49arMCFdZdCXB/sLCtV49q16hOtUL8qIa3iaqb0ygk+RyZER7ILp2uParQiqSuXIfUlWuTpkpd63zFmqTyc07Iu/J6rwo3V6cYhGWKOUvG6CgWrvAvN0afIXPCpQIbcvAvV1WsQenhtemiOpjOGTR0Li2Xzpy/SKdOneLpRveY0oiEaFXEK9Ku2/zcLRZKzTZTbKqBXQDQAxSTYuCIK9FXPuPSjGS6/mODfLVeeXp+uCcoTMuuTnhuuEomLFcXsn/8/gs3QBDdp03b9iXyPRZKjghZEbI3xeXLl2jZ0kXsQwuqV69Btw27o1z6zjqyuxLpd+2tKfbWFby4cq9jzbX3X4O4hfU2MkhDkfbzQRqKCNRQhQA1paUk2kQtLLl48cJqhIGX13tcoDFTq1ZNqlExgqqH+FN1Xy+qqsqmqvokqkpZ5F+Ia4RXUBgLWmXSQNxGVCd1eBXy0jom5XV5vFeFwuvUYjJyQhFT3HkyxWI6Z53izhfsFgC3NqOZLpl96LJ3OF20+NHFbAtdSM2m83EJdPbceUpJKdgya5/ZD248SFUN0QqfdUxhFSpSYqaJEtKMFJtmFaTKhO+wpELAXs8P337QaeVgLbssKb059r06uN89yfDgjHvVmjziDNWqdTViRNTpUxQSGipW2DJChKwI2ZsG/pewzO7YsZVv5PvGPlQuW6Rl6Z+FlI2KNYYtMskGHoiGeVhsCkrpeD3/twpXhC2L20ANhQdoKEhrpNyUC5QWe5YSY85RzMWzdOZMFHeHIp3ljR4lIUGBVK1CKFUN8qUqPl5UWZVLlVS5VNlfS5X8NFTRV0s6+8FuCLMHK26FaqSKrMafSLurqlCV1PDDDQwtFwNIhJsH16YlK53MSdFkSrxMaWePkW92KpniL5Ep4SKZE6PzRAwwmi0Un22k6CwjxWQZKNqgoWjyoegcL7qYnk2XElMpIfn6QlWJIFKnTh32Ra9crRZFVK1DwZG1yCe8BmUYtZSQbqLEDCPHVE1It36i0VpUkHIbvS0Vg9HrYu2JwcAq7pUJ1VKFAI3LpWl153sV7gPr162h2NgYGjFiFNWpW89h+xKKjghZEbKlBiIZYPBdZGRFm8A9eHA/h+0qD2n4XLlO9UYzC1vFmmO17hg5jS8+8R0v0ev56eYHGjLYV01hAWoK8TaTJjuazOmXyJh6mbKSL1Fq/EVKir1AsZfPU3pavvBehRAR6EeV/LUUqSOK1HlRpJ+GIn2tU0U/DVXw0VC4j5q8EcBX681WW1V4ZVKHVmTRq0zqsIrWiAo6H4+uV8GKxWggc2o8mZNiyZQcy1ZUc0qc1cqaGM1ROCw5mSxQk3KMFJ9jorhsI8VlGfkzNttAcTkWijMQxWQaKS49i8xFeDX6+wdQxao1KLxidQqqUJX8w6uRLrgKqQKrksG3MqUbdJSUaRWn1/NHLciSChHKPSbcc4Jek6u9JxCt6F3xKcIA0PKEo+7VuLhYWr9+DZ09E8XfEQ8WYbWat2hV6vsSio8IWRGyDmPL5o20efMG7nLu2KkLtWjR2qMFrbvXKR4FiIsbn3bVOoTPhAwjJfKn1XqEZclZpkJD8BSEWZ9BxvRY8sqKIV1OHFF2LJnSY8iQHkfZqbGUkRxDJsO1OdULI1inYlFbwRfiVk3hPhoK88bnlXkfNYV5ayg0JJjCIiuRT3hFUgVXIFVwhPUzKIxUgWGcj14VFM6hxQqz7rp7vbozlpwsMqcnkTkt8cpnEkcFYNGamkD6pFhKjovlCCoQqIk5RkrKMVFSronnE3NMlJBjpIRs62dyromKetmq1BryD4kkv5CKpAusSOrASmTxq0hGn0gy+1bi7yrvwGL1CqDhVyFQzT0cmKy9HWpb70fElZ4QrFeeLKmlRWnfq6mpKbRp43o6cuQQf0dPY6tWbahzl27l0n3OVfFcVSGUOSEhoRQYGMTO8Kv/+5e2b9t6RdCWDwutu4EXMl6gmBAD8kZxJpHeNwkiN9PEFqfEKwIXkRn4t0wTpWTi00ipWQGk0gUQhde1CQnYkryvTMEIW5STSqaMWDJmxJEpM4FMWQnWT57iyZyVSMasJCKzkVL1ZkrV6+l0WtHEr79GRSHeagr1VlOITk3B3ioK0qopyFvNojjYx5uCgwMpKCiYgsPCKDg0nILDIygYvQtaHeVWqkFe/kHk5RdEKr8g67yPv6T1LSKIVQyLqCUrjSyZaWTGZ1Y6mTJTKSspgVITYiktMZ5SkxIpLTmJ0lJTKSU1hdKycilVb+IpjevcRCm5JkrRm1iUYlmxQbIP3xBS+VUgtb910uBT+R5QkTQBkaTyCyMvr6sWT+W6VWocOjPYT83d+6H8qeF5DKoM89dwTwXEKpaFY7m/hiMBCO7TsJ8/fzbFX4nu0qhREw6pFRoaWtZFE/IhFlmxyDo8ugHcC7Zv28yZ2gASK6BFi5atJ1Fe6rQk4DGD7GgQuDxB5GaZKC3bxFbgVGU+28SjsdNzTJR+5TMj12yz/nKcztw0MmUlkjkriUw8JZIpO4XM2clkyk4mc3YKf1pyksmUm1HwyPJiAOmBgWoQw/affhoV+Wg15OutI18fHw4/5+frS94+vuTj60c+fr7k6xtAvv4B5BsYcGWZP/kEYJk/+QUGko9fAK+n8wsgna8f6Xz9SePjSyqtN8cZddZAHH4NwEfUaCBjbjblZmaSISeLDDnZlJuVQZnpGZSdkUHZmemUk5lJ2VmZvE5WRjplZ2ZSTtaVZTnZlJ2dxVNOTi7l5OZQTm4uZesNlG00U4bBTJlGM2XafV4nXGmRUen8SeUbSmrfUFJBpGLeJ8T63S+M1H7hpObPMFL5hNiyYwFklwr0waSmQF8VBV1pzOEzyFdla9xBtGIKuTIhW5VYTT3rGYxY9IjNqxhaTp48Tnv27KKePftQpUqVS7GkQmki5gTBoeCB0Lp1W2revCUdPLiPtm3dwnFoL1284HFCVigcCDJ/bzVPVUOLH5osU2/OI2wz8Jlj5nksQ3iyrFwzf2LKUL7nGCk9PZXSU5MpMy2FsjNSyJSTSubcdBbE+KScFFLnJJOXPo0suelk0meRQZ9Fen0O+0lCZ0GAYaJscgpwfVR5eZHGy4tDIKkx8M3LugyT15Xv+OR/Begp1ocQ/tYZ/uSGgG0iMlksPOEcQ1AazNbjdTYovk7nTVqdH6l1flbrvXcQmXxCyeIdyl34PPngM4h8AkIoICiUp8CgEArw1bEg9fNW8af9BMEZ4KOmAJ+r8/qMBKpZrTJ/L042PcFzjVl7du+k/fv3UtduPTiRAahXrwHVr9+wrIsn3AARsoITBW07at68FR04sI9q1Khp+y0hIZ4OHthP7dp3YFcEQbAHVi+2lvnAimZNkVxSINgQtijHYKZsvZlyDFZLMeZzDfjN+qn8np6RRbGx0TxIKDMjgzIyMigLlsmMVDJlpZIxK5XMGP2enc7WX1NOJpkMOVemXDIZ9WTkyUAmk5GMJhMZTCbrp9lMBlPBFkloZojP3DKRlQWITLWKtJhUatKoVWy10qo1pNZoSaPRkUarI7XGm9RaH55U3v6kRmIMnwBS+wZZJ78Q8gkKIT//APL3D6SAAH/yDwig4KAgCgoMIF+dmny0XjyAyVvrRb5aFfnqYPX24k9f5VOnuun4pvFxKraqCuWb2Jho2rlzOx0/ftSWOe3E8aPUtm17ayPRg0KTeTIiZAWnC9o2bdrlWbZz5zY6dPAA7dmzkxo3bkqtWrelypWryENEcIgo9vfGVNTR3uEUH6cuNZcRCGmj2RreCX7GyL5kMJopV2+gHL2ecrNzKDcbXfsZZDToeQCcIdcqiPU5OWQ2Gtnf1Gw2kRmxT00mMhkRrulawQvrKwanqNRq8lJBfGpwAvg7BKhWpyO1zipCtTof/tT5ws3Bz+oGodOSzltHOg3Eq5d1UiGVspd0qQtuzelTJ1nAXrhwzrYMxpV27TpySC1597gXImSFMgdO9KkpKXThwnk6fBgpVA9ShYhIatmyFTVp0rzQFIyC4G5AAOpURDq2c9qDxA8yCloQnMHRY0dYxKKh17BRE2rfrgNVFB9Yt0WErFDm1K5dlydkCdu3bw8dP3aEEuLjONLBnt276OFHnpAWsiAIglBs4LqGAVvoCaxQIYKXtWvbnoICg7j3z1kDvAXHIUJWcBmqVKnKU58+/Thu3/79+6hB/YY2EQsfpoMH9lGjxk3I21ustIIgCMK1IBnPiRPH6MD+vXTx4gXb8gEDBvFnpcpVeBI8AxGygsuBMEZt2rTnwWGKAz44ffok/fvvclq79j92R2jZqg2HRBFrrSAIgpCcnEwHDx5i9zRkmQR4PyD6QJPGTcu6eIKDECEruCx4AGF0tP338PAKlJiYwLFpMcGXtlGjxixsQ0PDyrS8giAIQtkAo8e//66k7GxrjDy4DCCbZLPmLSQajocjQlZwG9Cqrlu3Pl26dJF9aREmBb60mzBtXE+PPvYUhYSElHUxBUEQBAeCbJEnjh+js2ej6PY77rJG51CpqEGDBpSZlU0tW7amWrXq8DLB8xEhK7gVsMpWq1adp+y+AzjzyrFjRygnOzuPiN24cR370TZs2JiztgmCIAjuCzJvnjxxjH1fYcxQOH/+LItW0KpVa4qsWLUMSymUBSJkBbfF19eXWrRoxZPJhFiaVvR6Pe3auZ3T465ft5pj0jZo2JgaNmxEwcFisRUEQXAXEJZx3dr/KCYmOs/yqlWrsaEiIiLStkzGS5RPRMgKHoG9Ly3o1bsvHT92jC5ePE/R0Zd5gqiNjKzIWVuaNW9ZZmUVBEEQrgUGicuXLpLO25sqVqzEy3Q6HYtYpTeuQYNGVL9BQ/F7FWyIkBU8Djz4EPEAU2ZmBp04cZz9adGyj4uLpawrgwEABgZcunSBalSvyQ9PQRAEwXngGX327BmKOn2Kzpw5Tbm5udSkaXMaPPg2/h3Gh0G3DuVY4/7+kjREuBYRsoJH4+8fQK1bt+UpKyuToqJOU/XqNfOE9Fq+bDG39hFXEGkKMVWtWp20Wm2Zll0QBMETQfrkNav/pXPnznIUmvwuY7522RzxbG7WrEUZlFJwF0TICuUGPz//ax6IyFsPv9nU1BSKvnyJp+3btvBo18pVqnIAbSUbjCAIglAyd4Hk5CRq0bK1TZwiUYEiYmF1rVO3HtWpU4/HNEi0AaE4iJAVyjXNW7TiCSNiL5w/S+fPn+MJ4V0uXbxAfn5+tnWPHz9KGRkZVLNmLY5nKwMLBEEQriUtLY3dBM5EnaZz587wAFyMY2jUuCm7foHOXbqRxUJUo0YN8vW9+pwVhOIiQlYQiDhEV3DzljwIDN1eKSkpFBcbw1Zchb17dtOFC+d4Xqfz5qxilSpXZgsCJvydIAhCeUF55imN+p07t9Oundu4wW8PhGqt2nVIn5trE7IYtCUIpYEIWUHIBx7KoaGhPNlTr1597vLC4DC9PpfjF2ICPj4+NGrUaNu6SUmJPKpW/GwFQfAUDAYDRxCAq8Dly5gu0ei77+MeKgWIWDxD0bivXacuD9KSVOKCIxEhKwhFpF37jjwhFWJCQjzFXAnrhQd7QEBgngf17H9mUkZGOvt+ValSlSrCelupMoWFhYv/lyAIbkNsbAwdOrifRSuivuD5l+f3mBibkEVcVwhYPPcUy6sgOBoRsoJQTCBE8aDGpAxeQBdbQrw1YHdubg4ZTUZ+4EPk2gfyhoW2cZNmNHDgrbZlSNyg0citKAhC2YBnVkJCAsXHx7FLVZMmzaha9Rr8G8YL7NmzK08kmKrVqlGVKpiq2uK9gqCgIJ4EwZm49Nvzo48+onnz5tGxY8c4JEeXLl3ok08+oYYNG9rWycnJoRdeeIFmzZrF8ecGDhxIP/zwA1WsWLFMyy6UL+ytsUiNO27ceB5Ahu63mOhoio3FFMNdc/YWWQyCmPTtFxw5oUJEBEVUiORPREoICQm9JtGDIAjCzYJn0/59e1i4JiTE8eAse/wDAmxCtlKlKpxEpnLlqlSlajUWquImILgSLi1k169fT0899RS1b9+erVavvfYaDRgwgI4cOWILjPzcc8/R0qVLafbs2Txg5+mnn6Y77riDNm/eXNbFF8oxeNCHhITwBOsGgIUWIWhUqqviNCkxwbYc08kTx22/QfB26NiZunfvZQtjA9/b0NAwseAKglAoeKakpaVSSkoyJSYmUlxcDNWsUZuaNLU+i4xGA23fviXP38A9KgKN6YiKVLNmbbvlAdSn7wCnH4MgFBWXfhuuWLEiz/c///yTIiMjaffu3dSjRw9uVf722280Y8YM6tOnD6/zxx9/UOPGjWnbtm3UqVOnMiq5IFwLhKn9oAgA39knx41nn9uE+DiKT4ijxIQE7uYzGPTk4301MDhiLk75czLPB4eEUHhYOIViCg2lkJAwvjfQ7ScIQvkQq+jh8b6SkRAJX5YuWcTiFSI2vy+r2WS2CVk0hlu1bksVKlTgXqDwChHc6ykI7ohLC9n8QLiCsLAw/oSgxY3cr18/2zqNGjXiuHRbt24tVMjCBQGTQv5uFUFwpuUWFg9MtWpdtYLA5xbXpVZ79RbNysrilxau3dSUFJ4o6rTt9x49e1PHjl14HuHDEAZHEbqhoeHcYyEDzQTBfYAYRQM2LTWVUtNSKC01jYUqembw2bhJUxo0aKgtJODZs1G2v4VbElyW8L6ElbV6DaurAMBzoH//W8rkmASh3ApZ3NATJkygrl27UrNm1lZlTEwMj4xE96098I/Fb9fzvX3nnXeuWY7BOrk5eePfOQp07cTHXXbKvgRy2zrV5xJlZlgbcP5+3jR69N3sF45GHbKR4TM9PZ0njVpl2/+5c+do797debaFlxcEM9xymjZtRtWqVePlEMYImYPfcD+J/1te5F71PFyhTtFYVe69zMwMysjI5EgnCNvXpEkTmzvRtGlTC91GfFxcnuPo0aMnJ3EJDAzgGNj57+WyPubyUK9C6RERWcWzhCx8ZQ8dOkSbNm266W1NnDiRnn/+edt3WL6qV69OFSIqO23EJW62olaS4B64Up1aSE0dOmZbfW+Tkth6Az9zXOuY2rXvZCvryZPHafHiRTwPIRsUHELBQcFswcVLtV69BhQWHm5rUOLlWJ7ErivVq+AedYr7BD0oEKiYNBot1ahR0/bbn3/8yt3/6FHMT/XqNahnr6u9jBj4CetqUFAwBSFxC6ysoWHc24L3lX0vS3m/TuVeLZ+4hZDFAK4lS5bQhg0bbFYkUKlSJR71jW5Ue6tsbGws/1YY6J5V/IoEwRNRwoPZW38QRgf3SkZ6OofOUTAZTWy9gY8d7if46mJSgD+uImRPnjxBixfN4/UV6y78cpVPBECH/52yz/IkeAXPBtczEqHAgooeEIhLiE6FuXNmUXpGOmVlZrKItc/0h/Vq1LiP5yE80auiiFjcOyxSrwjVyIi8EXcefOgxpx2jILgjLi1k8SB45plnaP78+bRu3TqqXfuqDyFo27Ytx+VcvXo1jRgxgpcdP36czp8/T507dy6jUpePVIRKtxeW48GsWAWUAQhYS2fXWEBqQpPZRFqtzjbiXrFaYJtKFAqQnZ1FBoORfLy9bdvAvvDywO5hkVDIzMzkbfv4+toGKyhRAEDejDPp/ALBesqgKCW5AYiIiLQdH8ReZlbmlW46q5Uex4oQWkgQHlmxku2YIRCx7eysDIqIvHrOoqMvcS5xCErlmLHd1LRU8vP1o9Arvt4AwcZRFsRkVLKBYd3klGQuL6wyCpcuXeTzUaliJdv5wcsVERBwHuwFLNZFdxu2q7wscc4QdicrM4MqVa5CjRo34en8+XOUkpzEvnY5OdnstoD9oz6wnapVq7F1CedBsTTlp2NmV6pduw6/uE+cOEbLly3mcGRanY4C2Rc4kMuI84GyIEFEzZq1uA5zcnPYWoyYlhUqRLJFmK8dvZ4uXjhPKrWKatWqY9sXgrPjHEFkI1QZwLWHNMJe5MWiWgF1nJ6WRiHsL2w97ziH589ZM7MhfaZS90mJieyPaPUvtAp4HPPZs2d4HuVV6h7XGVw8cI3YX2uKr2K1ajVsdY/1kpOTuQFgX5847xaLmRsXSt3jPCQnJ3KDAdelwsWLF7jcCDqvBJzHtYcy5697XFNGg4EqVqrEdaDcL/C5REPePv4nknvg3EVERpKPj/U+Qr0nxMfzNWa/Lu4B1AkGCiH1KMB9hcaPRqvlxB8KuM5yc3LYeqjc46jruPg4FoI4Dvs6wnbg063cn9gP6lmtVnH4JwX4iGZnZ/M1gmsKoMcBZUM9Ir6pAs45GmmwXir3Ms7hhQvnuWwINZWZkUEZmRlkMhl5P9gm1l+4YC6LU1xn2Ab+TgFhqfr1H8BlxbUMA0r+e8KPG3ho9AVy8hQ8T3Bd3X7HnZwNEM8/tUpFFSIibdcJtoHY06iHvAaaGL4OcZ0p1wnKhOva28fHdg8o5x3PE1y/yro4X7AC4/62z1qI6wHnDveFck2hHrAu/la5X/hcJiWRwWjgY1CMQahPPNM0ak2eZxp6gXBN4Twq1x/qE/dB/nXxrMHgVn4++FjXxd9iXbVKnWddPG+xHZxX5VpFvWBd3Df2FlmcS73ewM9QZbs4L9Z3iVeeHlhr40JP3rqr7x2cb+s7yppmV3lGoGy4VtRqje38Yl2lcYJlyrrYn9KolzEK5VDIwp0AEQkWLlxIgYGBNr9XPLxwYeLz4YcfZjcBOLTjooTwhYh15YgFUVGnaPeu7VS7Tn1q166DbfniRfP5YTNg4K22B9ipUydo29bNVLVaderd+2p30+zZM/kBNmTocNvLC+uuWf0vvxCxXGHWzGn8sLpt2AibBQFlwP7wMB41eoxt3Rl/TeEH7vDhI6luvfq87Ny5M/TP3zP45WtvHZgzeya/hLGvxo2b8jI8gP+a/ic/6B57/CnbukuWLKDTp0/RwFsGU4sWrXgZyoQuNrysn3p6gm3d/1atpGPHjnDIF8QvBHhATf71BxbCE557ybbuxg1r6eDB/dSjR2/q2Mk60AkPnt9/+5nnX3r5ddu627Ztob17dlHnzl2p25WQVnjRK5EAnnv+FdvLZPeenbRj+1Zq264D9enT37aNaVN/58+nn3nO9hLH/jdv2kANGjSkOnWvxjieOWMaP2Aff+JpftGBo8eO0Lq1/3FIrsFDhtnWnTvnbxaPDz38uE0QoT5XrVpB9es3pOG3j8xzneBhft/Yh2yiAXW0bOkiFmR33nm3bd0Vy5fwSx9pJJW6R4pdvKBxTd1zz1jbuigXXpYjRo5iYQvOnDlNc2bP4mvs/gceoZYtW1P9+g1o3tx/WGC0bNWG700Wx3FxtH3bZjpx/Cg98uiT/BLBg115uCcnJVJ+8LdPPPksRUWdpsWL59uW4yWJc4YXFQTsubNn+EX0zLMvsFUZqYEPHzrAwq51m3bUunVbXteg1/O5hEh6/oVXbdtDQHfEzezatQd16dqdl+FlOGfOLJ5/4cWJthfP/v17adeu7dShQ2fq2csaDQX1iOsdPDv+RdtL/PChg7R16yYuQ79+A237wznDy+vJcc/ahBau6Q3r11Kz5i1sA3TA/Hn/cFkeeXScTWCcOnmcVq/+lzMl3TbsDtu6ixbO5XONulDu+zNRp2nFiqVUt249umPEqDz3HAYE3jPmfm6EgAvnz/F5Rjf3qNH32tZdvmIpC9G7Rt1jC72Exsv8ebNZbN5734O2dXFNRl++RLfffifVq9+Al+G6+efvv655RqxZvYrrasiQ4TwwCSQkJtDMGVOveUbg3Jw+fTLPMwLiBOvmf0bgfrN/RuCZhUgfK5Yv5rrv2q0H5eZYG0d4JsXGRFP3Hr2oU6eufL9DZOGc5weiFNet8ozAOcgvTiHK0BhBA3H6tD9tz4gBAwexUMH1B8Y9Nd4myLds2UjTp/3BCVSQDEUR8Hj+QUTaPyOOHDlc4DMCz+D8z4gTJ47Tqn+XX/OMQBnyPyPwzOdnRK06dOddV58RC+bPueYZgTor6BmBa0d5RtSpU896nVy+mOcZYbumli3m+xPvnYYNG/EyJFuYOXMai2M8I2zX1L/L6MyZKBp061Bq1qwFL0PDfOrU323PCAXcFwhTiMFqiLwAUJ945kOIj59g3RdYt3Y1HTlyiHr16kvtO3SyNfp/+fm7a54RG9av4Xvf/hmB8/3D919f84zYuHEd7d6145pnxDdff3bNM2LL5o3WZ0TrttTPboDdF59/xCLX/hmxY8c22rxpPTVt2px1gMJPP07i63Xs/Q/ZGiwHD+yjjRvX8z04YMAg27rTp/9J2VlZdMeIu2zXCdzHcM+gfvvaPacWLZzHDeH+AwbZGsxo4CE0G+oT71WF9evX8P3jir3ZLi1kf/zxR/7s1csqOhQQYuuBBx7g+a+++oofHrDI2idEcGVSkpPp7Nmz5O3tS2QnZGFJYgsjRvhcARckHtKKaFJgS1BKSh4fK6PByC1be4slbyMnh8WdvTUBNxBuDLRA7cFynix5Q7cUzBXr7NUetBuvW9Av+X5CfeIhY78cDxC0cu1H8QMIT1gYVHaJA7AuRE/+bm085GARgdXIfuewmuQvHVrleFnlv2kVi4798SBEFhpVPj5510U9wAqt8rraCoeVGQ8i7NMeCJicHN88MWZhZYFFJSAwMN+6VsuJfSxZlBMiQnkZ2q+Lc6NYDaznwZsfWvYWHGVd1Dt+t18XDzTFMol6wTlQLHQNGjSyRVvAC27liqW2MjRv3orzrOMFERcXR3Xq1qOgwCB+OSSnpFBiQrzNIgIrD+pdCRmEexlWJQWUVdkuRBT2o4DGCSYFrAcrm/JQ3rRxPT+scSxoHEAc4XzhvOC4lPOIewT7RJ3x8XpZX5BY7uuXbbPY219X/gH+bMUMvPIiUsC6Sm+FAqzwWH71GrKC0Ee4j+2TX8C6ivoMzOezHxZmtYLComWrex+fK+teW/dYz/460Xnr+OWW/zoJCQ4hi9nMvpy2dbU6Pg/514X/NCyZsLIr4PrCuooVHfWI5w2sZsr1ivOB8wurP84BrF54weLYlR4brIt7D+suWjSPLaUoP55TECr4zM3V8/2Na0K5P/+eNd32LMR+IYoVIBLgHmNvlbYXsRBKEJy433HdYX/eVyx9/foPZKEJYenlpWKhhmcI2L59K+3ft5t8r6wLX3KUWzlf9vWJfeNaV9a9uu8g3r79NYVzheX5w2GhfHj+2T9PcN5xfPnXxXnPf/3huLCN/OvieHDc9uXFdYNt5C8vrj0st18XFlM8z/K/o1BHaIBo7NbFPYf94fq2B+cn/3XtpVJxWZW6sJ0frY4brWq76xrnD9eC/XNOOWYsy/t+sC7Pn2xGsZh6qQp+VxXoJlUkz6krL8h8f381PNrV5Wazia+H/KHTFGuxnbcK6Q0GbmTl5uTkWRfPN7i32G8DBjLce/nvZTTysL69jkDvAxrHcDmzJ+r0KepwpTHganhZ7B15yimw9uEBDBHojMFesGQdO3qQqteozS96hePHj3F3RZ06dW1dJujeiYuN5Remfdcady8bjXm6DdEViBcvXtj23ZzoDkLKVAgrpesID3LrIARNnhcrluEGwP6VhwJeDOjuxU2ulEvZBi4f+4cCviuC2f4FqtxUnjxQSAYa3BzcNafXU1Z2NotdTHgA47NOnfo2kQTr866dO/j6Q6MvF1NODj/sASxTsFABWO1gwS6MQYOGULPmLXke1kBYmgsDlow2bdrxPCxNC+bPvnLta7hxpL1yH6Ax0qp1G7amKvcfLDjsgoPf8cJUq7lLGS9r3O+KywRE1oEDe6/eI1eezpYrM+htgWsDgPiD+wb2z/ed2cTPBBNEJD8bKtvWxf27c8c261byP/K9vNhqp5wzrLt69SrurYDV0Wi0uhBZXxUWqlmrNnXu3M1aBr2eeymwHtyBrOsbba5I9hZlLPv8sw8LPb84DyNGjrZ9/+rLT3hbBZHfUogy4PjRqMTzECIKnxA3cCeBhcvejSI1NYmfv/bdxYL748hnsL3Pf0HuAviuXPv2rgUFuSEAq5uWtSGhbMMaGjSHG5RKg0kxXFnMFm6QKe9aNLDRQPf29s5jvML1jWcAGs3K+x7WefSAomGC54ICrOC4h9FDozRw0ON18cI525gHhcOHD/L97IrJeFyvROUAWLi8yHTNDad0v9ij+DXmR+kqtAcP5fytYmDvX6SAC1ynu3Z5QQH1ceOgZV3QNvKDm7egC118g4QbgWsHVjqrf1reXgV70PVtn3lIAS8X5SWgAOEHIYUXDMQdPvFiYfGbm8vWUAX8HSybWIfXN1k/lZeTvWUJ1hGI7MJQ3HIAfK2PHz9a6Lp4gVwVshlsQS4MuM8o4hSid/nyJYWu27Zth6uiV6+nrVsLz3aIrnxFyOJY4bZRGAF2DV88G9AtXRg4h/b1i0az8oxQGsCw7MIKa+9bqzQcIPrZr16rIa1GS1qdlhvq9g1qgC70ogK/cFRlQc80QSgM+wZPQe8zpdcwP9aeRG2R3rWFDUS391NWgNC1F7v213d+cN/l7wkCGM+QHyUjZX7sG4OuhghZQRA8Arxc8jfk0MNS1F4WiL6CRojDyhNeIW8UlKpVq/O6/2/vPqCjqNowAF9KCp0AEkroIL3X0JEmBg5NDiC9l9CVIlIEpBepSpGiEqrCkUTRQ0cwlNARCBGi9E4IEGpy//N+uvvPbgobwSSzeZ9zls3MTmbuzh2y39x7v7uWQPfvxwvpjkNAnd3T06bbvn79Rn93t8vQnUj5liUM48CzcQYJtCQiqLTvKLN8iBqTo9C1ihYTHBOvIyg0PnLkzGlz04lxvNIjYtiv5SjG5CgEjrXrvCMtzGhpllZmdGen+LscxhtrvNauXUfZ7u/gFM84fmoJ/I3dv9C330DlKMs4WSKiuHBoQSIMLQB2Qzsf1qlzYr06H9apc2K9Jk/s7yUiIiIiU2IgS0RERESmxECWiIiIiEyJgSwRERERmRIDWSIiIiIyJQayRERERGRKDGSJiIiIyJQYyBIRERGRKTGQJSIiIiJT4lfU/vPd4pZv+EooDx8+VG7uCXc8+u+xTp0T69X5sE6dE+vV+WTIkMH6Fd2xYSD7z8UPefLkSeyiEBEREZFS6sGDBypjxoxxbpNCW5ojk7GoqCh17do1hyL/NwEtvwiaL1++/MoKInNgnTon1qvzYZ06J9arc2KLrINSpkypvLy8Evy4+M/G/3DOhXXqnFivzod16pxYr8kPk72IiIiIyJQYyBIRERGRKTGQTQRubm5q/Pjx8kzOgXXqnFivzod16pxYr8kXk72IiIiIyJTYIktEREREpsRAloiIiIhMiYEsEREREZkSA9kEtmjRIpU/f37l7u6uqlatqg4dOpTYRSIHTZ06VVWuXFkmaM6ePbtq0aKFCg4Ottnm6dOnytfXV2XNmlWlT59etW7dWt28eTPRykzxN23aNJmAe8iQIdZ1rFdzunr1qurYsaPUW5o0aVTp0qVVUFCQ9XWkiIwbN07lzJlTXm/QoIEKCQlJ1DJT7CIjI9XYsWNVgQIFpL4KFSqkJk2aZP2aeWCdJj8MZBPQ+vXr1bBhwySz8ujRo6ps2bKqcePG6tatW4ldNHLAnj17JJg5cOCA2rZtm3rx4oVq1KiRevz4sXWboUOHKn9/f7Vx40bZHt8Y16pVq0QtNznu8OHDasmSJapMmTI261mv5nP//n1Vo0YN5eLiorZu3arOnDmjZs+erTw8PKzbzJgxQ82fP18tXrxYHTx4UKVLl07+JuPGhZKe6dOnqy+//FItXLhQnT17VpZRhwsWLLBuwzpNhjBrASWMKlWqaF9fX+tyZGSkzpUrl546dWqilov+nVu3bqEZQO/Zs0eWw8LCtIuLi964caN1m7Nnz8o2gYGBiVhScsTDhw91kSJF9LZt23SdOnX04MGDZT3r1ZxGjhypa9asGevrUVFROkeOHHrmzJnWdahrNzc3vXbt2gQqJcWHj4+P7t69u826Vq1a6Q4dOsjPrNPkiS2yCeT58+fqyJEj0s1h/GpcLAcGBiZq2ejfefDggTxnyZJFnlG/aKU11nGxYsVU3rx5WccmgNZ2Hx8fm/oD1qs5bdmyRVWqVEm1adNGhgKVL19eLVu2zPp6aGiounHjhk29ZsqUSYZ8sV6TpurVq6sdO3ao8+fPy/KJEyfUvn37VJMmTWSZdZo8pU7sAiQXd+7ckfE9np6eNuuxfO7cuUQrF/07UVFRMoYSXZelSpWSdfgD6urqqjJnzhytjvEaJV3r1q2T4T4YWmCP9WpOFy9elG5oDOcaPXq01O2gQYOkLrt06WKtu5j+JrNek6ZRo0ap8PBwuZFMlSqVfKZOnjxZdejQQV5nnSZPDGSJ/mXr3enTp6U1gMzt8uXLavDgwTLuGUmY5Dw3m2iRnTJliiyjRRb/ZzF2EoEsmc+GDRuUn5+fWrNmjSpZsqQ6fvy4NCjkypWLdZqMcWhBAsmWLZvcQdpnOmM5R44ciVYuir8BAwaogIAAtWvXLuXl5WVdj3rEEJKwsDCb7VnHSRuGDiDhskKFCip16tTyQEIXEkbwM1pzWK/mg6z1EiVK2KwrXry4unTpkvxsqTv+TTaP4cOHS6tsu3btZAaKTp06SSImZpQB1mnyxEA2gaA7q2LFijK+x9higGVvb+9ELRs5BtO6IIjdvHmz2rlzp0wBY4T6RYa0sY4xPRc+OFnHSVf9+vXVqVOnpHXH8kBLHrorLT+zXs0Hw37sp8fD2Mp8+fLJz/j/i+DGWK/otkamO+s1aYqIiJDcEiM0EOGzFFinyVRiZ5slJ+vWrZPsyVWrVukzZ87o3r1768yZM+sbN24kdtHIAf369dOZMmXSu3fv1tevX7c+IiIirNv07dtX582bV+/cuVMHBQVpb29veZC5GGctANar+Rw6dEinTp1aT548WYeEhGg/Pz+dNm1avXr1aus206ZNk7/BP/zwgz558qRu3ry5LlCggH7y5Emilp1i1qVLF507d24dEBCgQ0ND9aZNm3S2bNn0iBEjrNuwTpMfBrIJbMGCBfKB6OrqKtNxHThwILGLRA7CfV9Mj5UrV1q3wR/L/v37aw8PD/nQbNmypQS7ZO5AlvVqTv7+/rpUqVLSgFCsWDG9dOlSm9cxXdPYsWO1p6enbFO/fn0dHBycaOWluIWHh8v/S3yGuru764IFC+pPPvlEP3v2zLoN6zT5SYF/ErtVmIiIiIgovjhGloiIiIhMiYEsEREREZkSA1kiIiIiMiUGskRERERkSgxkiYiIiMiUGMgSERERkSkxkCUiIiIiU2IgS0RERESmxECWiEylbt26asiQIcos8ufPr+bOnfta+9i9e7dKkSKFCgsLU/+12rVrqzVr1iiznbOlS5eqPHnyqJQpU772+X6VVatWqcyZM7/Rfd65c0dlz55dXbly5Y3ul8jZMZAlIhuBgYEqVapUysfHx7qua9euEkjF9kDgYQkysTxt2rRo+8X+8Nqnn35qXRcaGqo++OADlStXLuXu7q68vLxU8+bN1blz55SzOHz4sOrdu7cygy1btqibN2+qdu3aWdc9ffpU+fr6qqxZs6r06dOr1q1byzZJSXh4uBowYIAaOXKkunr1qpxvs93wZMuWTXXu3FmNHz8+sYtCZCoMZInIxvLly9XAgQPV3r171bVr12TdvHnz1PXr160PWLlypXUZwZoFWsXQYmWE4GLHjh0qZ86c1nUvXrxQDRs2VA8ePFCbNm1SwcHBav369ap06dIJ0vKYUN566y2VNm1aZQbz589X3bp1k1ZNi6FDhyp/f3+1ceNGtWfPHrkmWrVqpZKSS5cuyfWEmyVcY2Y53/Zw7v38/NS9e/cSuyhEpsFAloisHj16JMFkv379JCiwBKSZMmVSOXLksD4AXauWZQRrFk2bNpVu0v3791vXff3116pRo0bSdWrx+++/qwsXLqgvvvhCVatWTeXLl0/VqFFDffbZZ7Icl5cvX0oLHMqFlqyxY8cqrbX19WfPnqmPPvpI5c6dW6VLl05VrVpVuuct7t69q9q3by+vI+hB8Lx27VqbY6BFDwE9WvU8PDyUp6enWrZsmXr8+LEEHBkyZFCFCxdWW7dujVc3OVqlv/rqK9WyZUs5dpEiRaQl1Oinn35Sb7/9tkqTJo2qV6+e+vPPP6Ptd9++fapWrVqyDW4eBg0aJGWDb775RlpPQ0JCrNv3799fFStWTEVERMRYztu3b6udO3eqZs2aWdfhJgM3NnPmzFHvvPOOqlixotzA/Pbbb+rAgQMqoeDGpmfPnnKdZcyYUcpy4sQJeQ3XKOoPChYsKOcXPQgIunEDZuk1iOkcOnKtWI6RN29eqS/UG64fe7hucX3jukBZR40apcqVK2ezDeq9ePHi0vuAusC1b1SyZEnpndi8efNrnzOiZEMTEf1j+fLlulKlSvKzv7+/LlSokI6Kioq2Hf50bN68Odr6OnXq6MGDB+tBgwbpHj16WNcXKVJEti9btqweP368rLty5YpOmTKlnjVrln758qXDZcQx0qdPL8c5d+6cXr16tU6bNq1eunSpdZuePXvq6tWr67179+o//vhDz5w5U7u5uenz589bj411x44d0xcuXNDz58/XqVKl0gcPHrQ5ToYMGfSkSZPk9/CMbZo0aSLHwrp+/frprFmz6sePH8da3nz58unPP//c5tx5eXnpNWvW6JCQEDlXeD93796V1y9duiRlHTZsmPX9eXp6yu/dv39ftsF7SpcunewX5di/f78uX7687tq1q/U4bdq00ZUrV9YvXrzQAQEB2sXFRQcFBcVazk2bNsk+IyMjret27Nhhc1yLvHnz6jlz5sS6rz59+si+4nrExf6cNWjQQDdr1kwfPnxY3u+HH34o5x3nLCIiQm/fvl3KeejQIX39+nUdFhamvb29da9evWQZj9iusVddKwcOHJDrdPr06To4OFjPmzdPZ86cWWfKlMm6D9SRu7u7XrFihWwzYcIEnTFjRrnejdvkzJlTf//99/rixYvynCVLFr1q1Sqb8rRt21Z36dIlzvNDRP/HQJaIrPCBPnfuXPkZAVC2bNn0rl274h3IHj9+XILAR48e6T179ujs2bPL/oyBLCxcuFCCUGxbr149PXHiRAks44JjFC9e3CbAHjlypKyDv/76SwLOq1ev2vxe/fr19ccffxzrfn18fCRAMh6nZs2a1mUEQgjAOnXqZF2HAAnnIjAwMF6B7JgxY6zLOEdYt3XrVllGGUuUKGGzD7w/Y0CJm4TevXvbbPPrr79KwPXkyRNZvnfvngTMCLYRCE+ePFnHBWUsWLCgzTo/Pz/t6uoabVsEyCNGjIh1Xzdv3pQgPa5HXIznDO8LQeHTp09ttsFN1pIlS+Rn3JDg/ISGhka7FuPiyLXSvn17/d5770ULNo2BbNWqVbWvr6/NNjVq1LAJZFFe3LwY4eYIAbfR0KFDdd26deMsNxH9X+rEbhEmoqQBY1QPHTpk7dZMnTq1atu2rXQto5s9PsqWLStd5t99953atWuX6tSpk+zPHpKIkOCCrlx0VWMc5pQpU6SrHeNnY4OhB+gutvD29lazZ89WkZGR6tSpU/KMrnn7LmQkLAFex3E2bNgg43efP38ur9uPrSxTpoz1ZyTA4fct3diA4QZw69ateJ0f437RnY3ucss+zp49K93bRnh/RuhWP3nypIyntECMHBUVJQl06L7GcAjUXePGjVX16tWlqzsuT548kS7vNwFd7MZhJK8D7xVDXix1Zywvhqa8DkeuFdQHhhPY18fPP/9s838HQzeMqlSpIkM1AEM+UNYePXqoXr162QyRwfAYIwwViW34BxFFx0CWiASCHnywYoyeMThyc3NTCxcujPaB+yrdu3dXixYtUmfOnJEAOTYYU4hxmXhgnCECLzzHFcjGBUEPgs4jR47IsxHGjcLMmTNl/CTGriIwRTCJsbAIaI1cXFxslhE8G9dZgmkEkPER037jsw+8xz59+si4WHsYy2mBhD2cAyTkIZjCuY4Nxhrfv3/fZh3GP+OcYIyqcbopzFpgGSsdk759+6rVq1e/8j04Atshgct+3Cq87hRYjlwrb4LlvWKMtf1Niv1xkehlHHNORHFjIEtEEsAiQQitmkjKMmrRooUkQiE4iQ9Mq4UkGrTOlihRwqHfQUCHJBgkE8Xl4MGDNstozUULMIKC8uXLSysbWjiRDBUTJKJhmq+OHTvKMoLI8+fPO1zO/xJaU+2Tv+wTqypUqCA3CEg2iw3O4fTp02XGAUxLheQ4JN3FBuftxo0bEsyiNReQ3IWgGzNOYNotS+sjZgmwbyU2mjhxotT9m4D3inKhRd8yzZsjXF1d5TqIiyPXCuojpuvNqGjRojJzB3oXLIwzeaDlHjeIFy9eVB06dIizTKdPn453DwhRcsZAlohUQECABDDo+rRveUUAg9ba+AayCIbQEmjf+mhx/PhxmTMTww4QQCLwQKb5ihUrJPCKCwKpYcOGSavk0aNH1YIFCyQIB3QTI1hAUIF1CFaQkY9gDF36mI3BMuwBwR7Kiax8tDImhUAW5xnlHj58uGS/o7XQfjoznB8Mr0Bwim3QoozAdtu2bdJ6/vDhQzmvaLFt0qSJzM9buXJlafV+//33YzwuzhNaZRHkY+YJwLWAawLnOkuWLDIEAjM5IIiNa2aJNzm0oEGDBnI83FDNmDFD6hdTgP3444/S5V+pUqUYfw9BLwJQzFaA1lWU3zitmKPXCs4hZtOYNWuW3Pz88ssvNsMKAOcEQwZQFgzjwMwfGPqBWRQsJkyYIPvCOX333Xdl+EJQUJD8v8P5BQwpQH1j2AsROcgwXpaIkqmmTZtGS2ixQCY//lScOHHC4WSv2BiTvW7fvi0Z+6VKlZKsfSR8lS5dWmYxMGbOx3SM/v376759+0oSkIeHhx49erRN8tfz58/1uHHjdP78+SVbH9niLVu21CdPnpTXke3evHlzOS4S0ZB81blzZ1kX13uxT9yK61zE9jsxbY/EoZUrV1qXMWNE4cKFJXu+Vq1akg1vP3sAMvQbNmwo7wFJaGXKlLEmdHXr1k3OpTFBavbs2ZIljxkbYoMErnbt2tmsQ/IYzjfOMxLzcB6R5PZfsj9n4eHheuDAgTpXrlxSn3ny5NEdOnSQGR5iS/bC7AHVqlXTadKkifaa0auuFctsHkicw74wewKuUWOyFyBREcmRqI/u3bvLtY3j2yfPlStXThLocD5r164ts0VYIBmsaNGib+AMEiUfKfCPo0EvERE5L3ThYy5TtHJjXl/69zDGG+OIv/32W4d/B63caLXFsBwicgyHFhARkUDghWEkGLrBQNZxGBKwePFiSVTEOG2MKd++fbsM9XAUvkQE35iGL+ogIsexRZaIiOg1YCowjD8+duyYevr0qSR/jRkzJsl9lS+RM2IgS0RERESmZJvCSURERERkEgxkiYiIiMiUGMgSERERkSkxkCUiIiIiU2IgS0RERESmxECWiIiIiEyJgSwRERERmRIDWSIiIiIyJQayRERERKTM6H8vynXTICL8eAAAAABJRU5ErkJggg==",
+ "image/png": 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",
"text/plain": [
""
]
@@ -2006,11 +2271,34 @@
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
"ax.plot(np.arange(N_BEAMS), along_r.mean(axis=0), color=BLUE, label=\"along-track size\")\n",
- "ax.plot(np.arange(N_BEAMS), cross_r.mean(axis=0), color=ORANGE, label=\"cross-track size\")\n",
- "ax.plot(np.arange(N_BEAMS), equivalent_r.mean(axis=0), color=INK, label=\"equivalent diameter\")\n",
- "ax.plot(np.arange(N_BEAMS), spacing_cross, color=GRAY, ls=\"--\", label=\"cross-track spacing of beams\")\n",
- "ax.plot(np.arange(N_BEAMS), spacing_along, color=GRAY, ls=\":\", label=\"along-track spacing of scans\")\n",
- "ax.set(xlabel=\"ATMS beam index (0 = left edge)\", ylabel=\"Size or spacing (km)\", title=\"ATMS footprint size (2.2 deg beam)\")\n",
+ "ax.plot(\n",
+ " np.arange(N_BEAMS), cross_r.mean(axis=0), color=ORANGE, label=\"cross-track size\"\n",
+ ")\n",
+ "ax.plot(\n",
+ " np.arange(N_BEAMS),\n",
+ " equivalent_r.mean(axis=0),\n",
+ " color=INK,\n",
+ " label=\"equivalent diameter\",\n",
+ ")\n",
+ "ax.plot(\n",
+ " np.arange(N_BEAMS),\n",
+ " spacing_cross,\n",
+ " color=GRAY,\n",
+ " ls=\"--\",\n",
+ " label=\"cross-track spacing of beams\",\n",
+ ")\n",
+ "ax.plot(\n",
+ " np.arange(N_BEAMS),\n",
+ " spacing_along,\n",
+ " color=GRAY,\n",
+ " ls=\":\",\n",
+ " label=\"along-track spacing of scans\",\n",
+ ")\n",
+ "ax.set(\n",
+ " xlabel=\"ATMS beam index (0 = left edge)\",\n",
+ " ylabel=\"Size or spacing (km)\",\n",
+ " title=\"ATMS footprint size (2.2 deg beam)\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -2042,7 +2330,7 @@
"| 5. Daily observations (`collect_observations`) | more than 99% of passes matched for all instruments; epochs within 0.26 s (95th percentile) for an inertial-frame `PointedInstrument`, vs. 11 s for a nadir `Instrument` |\n",
"| 6. Footprint size | zenith angles within 0.03 deg and slant ranges within 1 km (95th percentile) of reported; 32.2 km footprint at nadir for the 2.2 deg beam, growing to an equivalent diameter of 93 km at the swath edge |\n",
"\n",
- "**Along-track reference direction.** The largest difference between TAT-C's default imager geometry and ATMS is the orientation of the scan. Like most Earth observation spacecraft without yaw steering, NOAA-20 keeps its attitude aligned with the orbit plane, so the scan is perpendicular to the inertial velocity, while TAT-C's default view is perpendicular to the ground track (the Earth-fixed velocity, as for a yaw-steered spacecraft). Setting `velocity_frame=VelocityFrame.INERTIAL` on a `PointedInstrument` removes along-track errors of up to 90 km at the swath edges and observation time errors of about 10 s.\n",
+ "**Along-track reference direction.** The largest difference between TAT-C's default imager geometry and ATMS is the orientation of the scan. Like most Earth observation spacecraft without yaw steering, NOAA-20 keeps its attitude aligned with the orbit plane, so the scan is perpendicular to the inertial velocity, while TAT-C's default view is perpendicular to the ground track (the Earth-fixed velocity, as for a yaw-steered spacecraft). Setting the satellite's attitude to the inertial velocity frame (`Attitude(velocity_frame=VelocityFrame.INERTIAL)`, or a `PointedInstrument`'s `velocity_frame` on a satellite without an attitude) removes along-track errors of up to 90 km at the swath edges and observation time errors of about 10 s.\n",
"\n",
"**Scan timing.** TAT-C observes all pixels of a view at one instant, whereas ATMS sweeps its 96 beams over about 1.7 s. The resulting along-track error, about 4.5 km at the swath edges, is a fraction of an ATMS footprint and of the 17.6 km between scans. For coverage, the along-track field of view stands in for the scan: tailored to the time step, consecutive views tile the swath at time steps from one scan to two minutes.\n",
"\n",
@@ -2050,8 +2338,7 @@
"\n",
"**Changes to TAT-C.** This validation led to the `velocity_frame` option and exposed two problems with the rectangular `PointedInstrument`, which have been fixed: rectangular footprint polygons for wide, elongated views were inaccurate (misplaced corners and too few points along the long sides), and `collect_observations` checked a pointed instrument's view only at the midpoint of each access period, missing up to 77% of observations for a view tailored to a scan period. It now checks the view at the time the view sweeps over the point and reports that time as the epoch.\n",
"\n",
- "**Footprint size.** TAT-C's viewing geometry gives the zenith angle and slant range of every pixel, from which the footprint of a beam of known width follows. ATMS's 2.2 deg beam has a 32 km footprint at nadir (confirming the nominal value), which grows to about 93 km (equivalent diameter) at the swath edge. Resolution therefore depends on where in the swath a feature is observed, which the [monsoon OSSE example](../examples/RunOSSEMonsoon.ipynb) accounts for.\n",
- ""
+ "**Footprint size.** TAT-C's viewing geometry gives the zenith angle and slant range of every pixel, from which the footprint of a beam of known width follows. ATMS's 2.2 deg beam has a 32 km footprint at nadir (confirming the nominal value), which grows to about 93 km (equivalent diameter) at the swath edge. Resolution therefore depends on where in the swath a feature is observed, which the [monsoon OSSE example](../examples/RunOSSEMonsoon.ipynb) accounts for.\n"
]
}
],
diff --git a/docs/validation/ValidateImagerCrIS.ipynb b/docs/validation/ValidateImagerCrIS.ipynb
index 3b1c4a49..63544621 100644
--- a/docs/validation/ValidateImagerCrIS.ipynb
+++ b/docs/validation/ValidateImagerCrIS.ipynb
@@ -39,10 +39,10 @@
"id": "aa358a3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:36.836904Z",
- "iopub.status.busy": "2026-10-15T18:43:36.836904Z",
- "iopub.status.idle": "2026-10-15T18:43:37.505734Z",
- "shell.execute_reply": "2026-10-15T18:43:37.505734Z"
+ "iopub.execute_input": "2026-10-25T13:54:34.265025Z",
+ "iopub.status.busy": "2026-10-25T13:54:34.265025Z",
+ "iopub.status.idle": "2026-10-25T13:54:42.323375Z",
+ "shell.execute_reply": "2026-10-25T13:54:42.323375Z"
}
},
"outputs": [
@@ -96,7 +96,9 @@
" params[\"continuation-token\"] = token\n",
" listing = requests.get(BUCKET, params=params, timeout=60).content.decode()\n",
" keys += re.findall(r\"(.*?)\", listing)\n",
- " match = re.search(r\"(.*?)\", listing)\n",
+ " match = re.search(\n",
+ " r\"(.*?)\", listing\n",
+ " )\n",
" if match is None:\n",
" return keys\n",
" token = match.group(1)\n",
@@ -117,21 +119,39 @@
"TLE_FILE = DATA_DIR / f\"n20_tle_{DAY:%Y%m%d}.txt\"\n",
"if not TLE_FILE.exists():\n",
" # the element set with an epoch at the start of the day\n",
- " key = next(k for k in list_keys(f\"TLE-AUX/{DAY:%Y/%m/%d}/\") if f\"Z_{DAY:%Y%m%d}000000Z_\" in k)\n",
+ " key = next(\n",
+ " k\n",
+ " for k in list_keys(f\"TLE-AUX/{DAY:%Y/%m/%d}/\")\n",
+ " if f\"Z_{DAY:%Y%m%d}000000Z_\" in k\n",
+ " )\n",
" with h5py.File(io.BytesIO(fetch(key))) as f:\n",
- " TLE_FILE.write_text(bytes(f[\"All_Data/TLE-AUX_All/Dataset_Array_0\"][:]).decode().strip() + \"\\n\")\n",
+ " TLE_FILE.write_text(\n",
+ " bytes(f[\"All_Data/TLE-AUX_All/Dataset_Array_0\"][:]).decode().strip() + \"\\n\"\n",
+ " )\n",
"tle = TLE_FILE.read_text().splitlines()\n",
"\n",
"CRIS_FILE = DATA_DIR / f\"cris_gcrso_{DAY:%Y%m%d}_{HOURS[0]:02d}-{HOURS[1]:02d}h.npz\"\n",
"CRIS_FIELDS = [\n",
- " \"StartTime\", \"MidTime\", \"FORTime\", \"SCPosition\", \"SCVelocity\", \"Latitude\", \"Longitude\", \"Height\",\n",
- " \"SatelliteZenithAngle\", \"SatelliteRange\", \"QF1_CRISSDRGEO\",\n",
+ " \"StartTime\",\n",
+ " \"MidTime\",\n",
+ " \"FORTime\",\n",
+ " \"SCPosition\",\n",
+ " \"SCVelocity\",\n",
+ " \"Latitude\",\n",
+ " \"Longitude\",\n",
+ " \"Height\",\n",
+ " \"SatelliteZenithAngle\",\n",
+ " \"SatelliteRange\",\n",
+ " \"QF1_CRISSDRGEO\",\n",
"]\n",
"if not CRIS_FILE.exists():\n",
"\n",
" def read_granule(key):\n",
" with h5py.File(io.BytesIO(fetch(key))) as f:\n",
- " return {field: f[\"All_Data/CrIS-SDR-GEO_All/\" + field][:] for field in CRIS_FIELDS}\n",
+ " return {\n",
+ " field: f[\"All_Data/CrIS-SDR-GEO_All/\" + field][:]\n",
+ " for field in CRIS_FIELDS\n",
+ " }\n",
"\n",
" keys = [\n",
" k\n",
@@ -140,11 +160,22 @@
" ]\n",
" with concurrent.futures.ThreadPoolExecutor(16) as pool:\n",
" granules = list(pool.map(read_granule, keys))\n",
- " np.savez_compressed(CRIS_FILE, **{field: np.concatenate([g[field] for g in granules]) for field in CRIS_FIELDS})\n",
+ " np.savez_compressed(\n",
+ " CRIS_FILE,\n",
+ " **{\n",
+ " field: np.concatenate([g[field] for g in granules]) for field in CRIS_FIELDS\n",
+ " },\n",
+ " )\n",
"\n",
"cris = dict(np.load(CRIS_FILE))\n",
"print(tle[0], tle[1], sep=\"\\n\")\n",
- "pd.Series({\"scans\": len(cris[\"MidTime\"]), \"FORs per scan\": cris[\"Latitude\"].shape[1], \"FOVs per FOR\": cris[\"Latitude\"].shape[2]})"
+ "pd.Series(\n",
+ " {\n",
+ " \"scans\": len(cris[\"MidTime\"]),\n",
+ " \"FORs per scan\": cris[\"Latitude\"].shape[1],\n",
+ " \"FOVs per FOR\": cris[\"Latitude\"].shape[2],\n",
+ " }\n",
+ ")"
]
},
{
@@ -165,10 +196,10 @@
"id": "ef1bc172",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:37.507277Z",
- "iopub.status.busy": "2026-10-15T18:43:37.507277Z",
- "iopub.status.idle": "2026-10-15T18:43:39.306511Z",
- "shell.execute_reply": "2026-10-15T18:43:39.305583Z"
+ "iopub.execute_input": "2026-10-25T13:54:42.325885Z",
+ "iopub.status.busy": "2026-10-25T13:54:42.325885Z",
+ "iopub.status.idle": "2026-10-25T13:54:44.182940Z",
+ "shell.execute_reply": "2026-10-25T13:54:44.181933Z"
}
},
"outputs": [
@@ -201,35 +232,66 @@
"\n",
"from tatc.analysis import collect_ground_pixels, compute_ground_track\n",
"from tatc.constants import EARTH_MEAN_RADIUS, EARTH_ROTATION_RATE\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Instrument,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"from tatc.utils import VelocityFrame, field_of_regard_to_swath_width\n",
"\n",
"IET_EPOCH = pd.Timestamp(\"1958-01-01\", tz=\"UTC\")\n",
"LEAP_SECONDS = 37\n",
- "N_FOR, N_FOV = 30, 9 # fields of regard per scan, fields of view per field of regard (3 x 3)\n",
+ "N_FOR, N_FOV = (\n",
+ " 30,\n",
+ " 9,\n",
+ ") # fields of regard per scan, fields of view per field of regard (3 x 3)\n",
"EDGE = 48.33 # deg, nominal scan angle of the outermost field of regard\n",
"FOR_SPACING = 2 * EDGE / (N_FOR - 1) # deg\n",
- "FOV_SPACING = FOR_SPACING / 3 # deg, spacing of the 3 x 3 array within a field of regard\n",
+ "FOV_SPACING = (\n",
+ " FOR_SPACING / 3\n",
+ ") # deg, spacing of the 3 x 3 array within a field of regard\n",
"FOV_DIAMETER = 0.963 # deg, nominal instantaneous field of view (diameter)\n",
"SCAN_PERIOD = 8 # s\n",
"\n",
"\n",
"def iet_to_utc(iet):\n",
- " return IET_EPOCH + pd.to_timedelta(np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\")\n",
+ " return IET_EPOCH + pd.to_timedelta(\n",
+ " np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\"\n",
+ " )\n",
"\n",
"\n",
- "good = (cris[\"QF1_CRISSDRGEO\"] == 0) & np.all(np.abs(cris[\"Latitude\"]) <= 90, axis=(1, 2))\n",
+ "good = (cris[\"QF1_CRISSDRGEO\"] == 0) & np.all(\n",
+ " np.abs(cris[\"Latitude\"]) <= 90, axis=(1, 2)\n",
+ ")\n",
"t_mid = iet_to_utc(cris[\"MidTime\"][good])\n",
"t_for = iet_to_utc(cris[\"FORTime\"][good].ravel()).to_numpy().reshape(-1, N_FOR)\n",
- "dt_for = (cris[\"FORTime\"][good] - cris[\"MidTime\"][good][:, np.newaxis]) / 1e6 # s from the middle time\n",
- "sc_pos, sc_vel = cris[\"SCPosition\"][good].astype(float), cris[\"SCVelocity\"][good].astype(float)\n",
- "fov_lat, fov_lon = cris[\"Latitude\"][good].astype(float), cris[\"Longitude\"][good].astype(float)\n",
- "fov_height = cris[\"Height\"][good].astype(float) if \"Height\" in cris else np.zeros_like(fov_lat)\n",
+ "dt_for = (\n",
+ " cris[\"FORTime\"][good] - cris[\"MidTime\"][good][:, np.newaxis]\n",
+ ") / 1e6 # s from the middle time\n",
+ "sc_pos, sc_vel = cris[\"SCPosition\"][good].astype(float), cris[\"SCVelocity\"][\n",
+ " good\n",
+ "].astype(float)\n",
+ "fov_lat, fov_lon = cris[\"Latitude\"][good].astype(float), cris[\"Longitude\"][good].astype(\n",
+ " float\n",
+ ")\n",
+ "fov_height = (\n",
+ " cris[\"Height\"][good].astype(float) if \"Height\" in cris else np.zeros_like(fov_lat)\n",
+ ")\n",
"fov_zenith = cris[\"SatelliteZenithAngle\"][good].astype(float)\n",
"fov_range = cris[\"SatelliteRange\"][good].astype(float)\n",
"\n",
"noaa20 = Satellite(name=\"NOAA-20\", orbit=GeneralPerturbationsOrbit.from_tle(tle))\n",
"orbit = noaa20.orbit.to_gp_orbit()\n",
+ "# NOAA-20 is not yaw-steered: its body (and its instruments' views) aligned with the inertial velocity\n",
+ "INERTIAL = Attitude(velocity_frame=VelocityFrame.INERTIAL)\n",
+ "\n",
+ "\n",
+ "def on_noaa20(instrument, attitude=None):\n",
+ " \"\"\"NOAA-20 carrying an instrument, with an attitude.\"\"\"\n",
+ " return noaa20.model_copy(update={\"instruments\": [instrument], \"attitude\": attitude})\n",
+ "\n",
"\n",
"TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
"TO_GEODETIC = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
@@ -237,13 +299,17 @@
"\n",
"def ecef(lon, lat, height=0.0):\n",
" \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
- " return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+ " return np.stack(\n",
+ " TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1\n",
+ " )\n",
"\n",
"\n",
"def up(lon, lat):\n",
" \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
" lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -282,7 +348,12 @@
"def binned(x, y, edges, q=(5, 50, 95)):\n",
" \"\"\"Percentiles of y in bins of x.\"\"\"\n",
" i = np.digitize(x, edges) - 1\n",
- " return np.array([np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q) for k in range(len(edges) - 1)])\n",
+ " return np.array(\n",
+ " [\n",
+ " np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q)\n",
+ " for k in range(len(edges) - 1)\n",
+ " ]\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -320,10 +391,10 @@
"id": "19b2daba",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:39.307489Z",
- "iopub.status.busy": "2026-10-15T18:43:39.307489Z",
- "iopub.status.idle": "2026-10-15T18:43:39.353939Z",
- "shell.execute_reply": "2026-10-15T18:43:39.352945Z"
+ "iopub.execute_input": "2026-10-25T13:54:44.185442Z",
+ "iopub.status.busy": "2026-10-25T13:54:44.183938Z",
+ "iopub.status.idle": "2026-10-25T13:54:44.217605Z",
+ "shell.execute_reply": "2026-10-25T13:54:44.217605Z"
}
},
"outputs": [
@@ -358,16 +429,16 @@
"
\n",
"
along-track (km)
\n",
"
180.0
\n",
- "
0.499
\n",
- "
0.688
\n",
- "
0.693
\n",
+ "
0.495
\n",
+ "
0.685
\n",
+ "
0.690
\n",
"
\n",
"
\n",
"
cross-track, right (km)
\n",
"
180.0
\n",
- "
0.023
\n",
- "
0.282
\n",
- "
0.287
\n",
+ "
0.026
\n",
+ "
0.303
\n",
+ "
0.308
\n",
"
\n",
"
\n",
"
radial (km)
\n",
@@ -379,8 +450,8 @@
"
\n",
"
velocity (m/s)
\n",
"
180.0
\n",
- "
0.590
\n",
- "
0.690
\n",
+ "
0.588
\n",
+ "
0.693
\n",
"
0.740
\n",
"
\n",
" \n",
@@ -389,10 +460,10 @@
],
"text/plain": [
" n median p95 |x| max |x|\n",
- "along-track (km) 180.0 0.499 0.688 0.693\n",
- "cross-track, right (km) 180.0 0.023 0.282 0.287\n",
+ "along-track (km) 180.0 0.495 0.685 0.690\n",
+ "cross-track, right (km) 180.0 0.026 0.303 0.308\n",
"radial (km) 180.0 0.042 0.132 0.133\n",
- "velocity (m/s) 180.0 0.590 0.690 0.740"
+ "velocity (m/s) 180.0 0.588 0.693 0.740"
]
},
"execution_count": 3,
@@ -405,13 +476,19 @@
"p1, v1 = orbit.get_orbit_track(list(t_mid[i1])).frame_xyz_and_velocity(itrs)\n",
"offset1 = np.array(p1.m).T - sc_pos[i1]\n",
"radial1 = normalize(sc_pos[i1])\n",
- "along1 = normalize(sc_vel[i1] - np.sum(sc_vel[i1] * radial1, axis=-1, keepdims=True) * radial1)\n",
+ "along1 = normalize(\n",
+ " sc_vel[i1] - np.sum(sc_vel[i1] * radial1, axis=-1, keepdims=True) * radial1\n",
+ ")\n",
"test1 = pd.DataFrame(\n",
" {\n",
" \"along-track (km)\": summarize(np.sum(offset1 * along1, axis=-1) / 1e3),\n",
- " \"cross-track, right (km)\": summarize(np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3),\n",
+ " \"cross-track, right (km)\": summarize(\n",
+ " np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3\n",
+ " ),\n",
" \"radial (km)\": summarize(np.sum(offset1 * radial1, axis=-1) / 1e3),\n",
- " \"velocity (m/s)\": summarize(np.linalg.norm(np.array(v1.m_per_s).T - sc_vel[i1], axis=-1)),\n",
+ " \"velocity (m/s)\": summarize(\n",
+ " np.linalg.norm(np.array(v1.m_per_s).T - sc_vel[i1], axis=-1)\n",
+ " ),\n",
" }\n",
").T\n",
"test1.round(3)"
@@ -441,10 +518,10 @@
"id": "a06ec367",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:39.355060Z",
- "iopub.status.busy": "2026-10-15T18:43:39.355060Z",
- "iopub.status.idle": "2026-10-15T18:43:39.437276Z",
- "shell.execute_reply": "2026-10-15T18:43:39.437276Z"
+ "iopub.execute_input": "2026-10-25T13:54:44.220120Z",
+ "iopub.status.busy": "2026-10-25T13:54:44.220120Z",
+ "iopub.status.idle": "2026-10-25T13:54:44.322414Z",
+ "shell.execute_reply": "2026-10-25T13:54:44.322414Z"
}
},
"outputs": [
@@ -471,9 +548,13 @@
],
"source": [
"# spacecraft position at each field of regard's time (extrapolated linearly from the middle time)\n",
- "sc_for = sc_pos[:, np.newaxis] + sc_vel[:, np.newaxis] * dt_for[..., np.newaxis] # (scan, FOR, xyz)\n",
+ "sc_for = (\n",
+ " sc_pos[:, np.newaxis] + sc_vel[:, np.newaxis] * dt_for[..., np.newaxis]\n",
+ ") # (scan, FOR, xyz)\n",
"fov_xyz = ecef(fov_lon, fov_lat, fov_height) # (scan, FOR, FOV, xyz)\n",
- "sc_lon, sc_lat, _ = TO_GEODETIC.transform(sc_for[..., 0], sc_for[..., 1], sc_for[..., 2])\n",
+ "sc_lon, sc_lat, _ = TO_GEODETIC.transform(\n",
+ " sc_for[..., 0], sc_for[..., 1], sc_for[..., 2]\n",
+ ")\n",
"nadir = -up(sc_lon, sc_lat) # (scan, FOR, xyz)\n",
"los = normalize(fov_xyz - sc_for[:, :, np.newaxis]) # (scan, FOR, FOV, xyz)\n",
"\n",
@@ -485,14 +566,22 @@
"# cross-track (scan) angle in the scan plane, positive to the left, and along-track angle out of it, positive forward\n",
"nadir_plane = normalize(nadir - np.sum(nadir * n_, axis=-1, keepdims=True) * n_)\n",
"left_plane = np.cross(n_, nadir_plane)\n",
- "los_plane = los - np.sum(los * n_[:, :, np.newaxis], axis=-1, keepdims=True) * n_[:, :, np.newaxis]\n",
+ "los_plane = (\n",
+ " los\n",
+ " - np.sum(los * n_[:, :, np.newaxis], axis=-1, keepdims=True) * n_[:, :, np.newaxis]\n",
+ ")\n",
"cross_angle = np.degrees(\n",
- " np.arctan2(np.sum(los_plane * left_plane[:, :, np.newaxis], axis=-1), np.sum(los_plane * nadir_plane[:, :, np.newaxis], axis=-1))\n",
+ " np.arctan2(\n",
+ " np.sum(los_plane * left_plane[:, :, np.newaxis], axis=-1),\n",
+ " np.sum(los_plane * nadir_plane[:, :, np.newaxis], axis=-1),\n",
+ " )\n",
")\n",
"along_angle = np.degrees(np.arcsin(np.einsum(\"kjic,kc->kji\", los, normal)))\n",
"for_angle = cross_angle.mean(axis=2) # scan angle of the center of each field of regard\n",
"measured = np.median(for_angle, axis=0)\n",
- "orientation = np.sign(measured[0] - measured[-1]) # +1 if field of regard 0 is on the left\n",
+ "orientation = np.sign(\n",
+ " measured[0] - measured[-1]\n",
+ ") # +1 if field of regard 0 is on the left\n",
"nominal = orientation * ((N_FOR - 1) / 2 - np.arange(N_FOR)) * FOR_SPACING\n",
"\n",
"\n",
@@ -506,7 +595,8 @@
"pd.Series(\n",
" {\n",
" \"field of regard 0 side\": \"left\" if orientation > 0 else \"right\",\n",
- " \"sample interval, field of regard (ms)\": np.median(np.diff(dt_for, axis=1)) * 1e3,\n",
+ " \"sample interval, field of regard (ms)\": np.median(np.diff(dt_for, axis=1))\n",
+ " * 1e3,\n",
" \"nominal spacing (deg)\": FOR_SPACING,\n",
" \"fitted spacing (deg)\": fitted_spacing,\n",
" \"fitted roll (deg)\": fitted_roll,\n",
@@ -525,16 +615,16 @@
"id": "d1b31837",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:39.439269Z",
- "iopub.status.busy": "2026-10-15T18:43:39.439269Z",
- "iopub.status.idle": "2026-10-15T18:43:39.553727Z",
- "shell.execute_reply": "2026-10-15T18:43:39.553727Z"
+ "iopub.execute_input": "2026-10-25T13:54:44.323928Z",
+ "iopub.status.busy": "2026-10-25T13:54:44.323928Z",
+ "iopub.status.idle": "2026-10-25T13:54:44.465710Z",
+ "shell.execute_reply": "2026-10-25T13:54:44.465710Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -545,10 +635,27 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.4))\n",
- "ax.plot(np.arange(N_FOR), measured - nominal, color=BLUE, marker=\"o\", ms=3, lw=0, label=\"measured (median)\")\n",
- "ax.plot(np.arange(N_FOR), model(fit) - nominal, color=ORANGE, label=f\"fit: spacing {fitted_spacing:.4f} deg, roll {fitted_roll:+.3f} deg\")\n",
+ "ax.plot(\n",
+ " np.arange(N_FOR),\n",
+ " measured - nominal,\n",
+ " color=BLUE,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " lw=0,\n",
+ " label=\"measured (median)\",\n",
+ ")\n",
+ "ax.plot(\n",
+ " np.arange(N_FOR),\n",
+ " model(fit) - nominal,\n",
+ " color=ORANGE,\n",
+ " label=f\"fit: spacing {fitted_spacing:.4f} deg, roll {fitted_roll:+.3f} deg\",\n",
+ ")\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
- "ax.set(xlabel=\"CrIS field of regard index (0 = first scanned)\", ylabel=\"Scan angle minus nominal (deg)\", title=\"CrIS scan angles\")\n",
+ "ax.set(\n",
+ " xlabel=\"CrIS field of regard index (0 = first scanned)\",\n",
+ " ylabel=\"Scan angle minus nominal (deg)\",\n",
+ " title=\"CrIS scan angles\",\n",
+ ")\n",
"ax.legend(frameon=False)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -572,10 +679,10 @@
"id": "3cd3ee82",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:39.555836Z",
- "iopub.status.busy": "2026-10-15T18:43:39.555836Z",
- "iopub.status.idle": "2026-10-15T18:43:39.569728Z",
- "shell.execute_reply": "2026-10-15T18:43:39.569728Z"
+ "iopub.execute_input": "2026-10-25T13:54:44.467909Z",
+ "iopub.status.busy": "2026-10-25T13:54:44.467909Z",
+ "iopub.status.idle": "2026-10-25T13:54:44.485368Z",
+ "shell.execute_reply": "2026-10-25T13:54:44.485368Z"
}
},
"outputs": [
@@ -723,22 +830,36 @@
"# offsets of each view from the center of its field of regard (deg): cross-track (positive left) and along-track (positive forward)\n",
"offset_cross = cross_angle - for_angle[..., np.newaxis]\n",
"offset_along = along_angle - along_angle.mean(axis=2, keepdims=True)\n",
- "fov_offsets = np.stack([np.median(offset_cross, axis=0), np.median(offset_along, axis=0)], axis=-1) # (FOR, FOV, 2)\n",
- "central = [N_FOR // 2 - 1, N_FOR // 2] # fields of regard nearest nadir (symmetric, so their rotations cancel)\n",
+ "fov_offsets = np.stack(\n",
+ " [np.median(offset_cross, axis=0), np.median(offset_along, axis=0)], axis=-1\n",
+ ") # (FOR, FOV, 2)\n",
+ "central = [\n",
+ " N_FOR // 2 - 1,\n",
+ " N_FOR // 2,\n",
+ "] # fields of regard nearest nadir (symmetric, so their rotations cancel)\n",
"reference = fov_offsets[central].mean(axis=0) # (FOV, 2)\n",
"layout = pd.DataFrame(\n",
- " {\"cross-track offset (deg)\": reference[:, 0], \"along-track offset (deg)\": reference[:, 1]},\n",
+ " {\n",
+ " \"cross-track offset (deg)\": reference[:, 0],\n",
+ " \"along-track offset (deg)\": reference[:, 1],\n",
+ " },\n",
" index=pd.RangeIndex(1, N_FOV + 1, name=\"FOV\"),\n",
")\n",
- "fov_pitch = np.sort(np.hypot(*reference.T))[1:5].mean() # distance to the four nearest neighbors of the central view\n",
+ "fov_pitch = np.sort(np.hypot(*reference.T))[\n",
+ " 1:5\n",
+ "].mean() # distance to the four nearest neighbors of the central view\n",
"# TAT-C pixel indices within a field of regard: cross-track 0 (right) to 2 (left), along-track 0 (fore) to 2 (aft)\n",
"pixel_cross = np.round(reference[:, 0] / fov_pitch).astype(int) + 1\n",
"pixel_along = 1 - np.round(reference[:, 1] / fov_pitch).astype(int)\n",
"assert len(set(zip(pixel_cross, pixel_along))) == N_FOV\n",
- "center = int(np.flatnonzero((pixel_cross == 1) & (pixel_along == 1))[0]) # the central view of the array\n",
+ "center = int(\n",
+ " np.flatnonzero((pixel_cross == 1) & (pixel_along == 1))[0]\n",
+ ") # the central view of the array\n",
"layout[\"TAT-C cross-track pixel\"] = pixel_cross\n",
"layout[\"TAT-C along-track pixel\"] = pixel_along\n",
- "print(f\"FOV pitch {fov_pitch:.4f} deg (nominal {FOV_SPACING:.4f}); central view: FOV {center + 1}\")\n",
+ "print(\n",
+ " f\"FOV pitch {fov_pitch:.4f} deg (nominal {FOV_SPACING:.4f}); central view: FOV {center + 1}\"\n",
+ ")\n",
"layout.round(3)"
]
},
@@ -756,16 +877,16 @@
"id": "b20313d0",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:39.571887Z",
- "iopub.status.busy": "2026-10-15T18:43:39.570728Z",
- "iopub.status.idle": "2026-10-15T18:43:39.656251Z",
- "shell.execute_reply": "2026-10-15T18:43:39.656251Z"
+ "iopub.execute_input": "2026-10-25T13:54:44.487879Z",
+ "iopub.status.busy": "2026-10-25T13:54:44.487879Z",
+ "iopub.status.idle": "2026-10-25T13:54:44.599034Z",
+ "shell.execute_reply": "2026-10-25T13:54:44.599034Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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VOaxWK23atOHChQuMHTuWatWq4eLigtVqpWHDhnbHNxgMDBgwgClTpjB58mRSU1OZM2cOI0eOzHQd9O1y8/W4m3v5LGo0GlavXs17773HpEmTGDVqFF5eXvTq1YsPP/wQNzc3Ll26BMBjjz2W5XFy8nreT7x5cZ4rV67g4OCAl5eXXb+sfgbc7tZn49byrcxcvXoVFxeXux5LFFySyIpCp1KlSrYLvEJDQ7FYLHz77bfMnTuXHj162H6wxsTEZNj3woULaLXau5bpWrNmDRcuXGDdunW2WVjgvurNQvoP/azigvRkLTc8qPPklI+PDxqNhqioqEyTlDslLjqdjpYtW7J06VLOnTt3x1/8t7vTOtHsaNq0KW3atGH+/PnExcXZ1oZm5oUXXmDy5Mn89ttvPP300yxYsIDhw4fbzar/162xGDNmDN27d8+0T4UKFYD0i9kaN27MqlWreOyxxwgICKBatWq2PwDWrVvH6tWrM9THzcxPP/3E888/z4QJE+zaL1++nGkpuqxex8za9+/fz19//cWsWbPo06ePrf348eOZHmPQoEF8/PHHfP/995hMJtLS0hg4cOBdn0Nuvh55pWTJkraLto4ePcqcOXMYP348KSkpzJw503YR5blz57Jc+53T1/Ne5cV5vL29SUtL4+rVq3bJbGxs7F33vfXZmD59epaVPrKTEIuCTZYWiEJv0qRJeHp68s4772C1WqlQoQLFihXjl19+sbtqOykpiXnz5tkqGUDWsxS3fjn/N7H6+uuvM5w/u7OkkP5V6MGDB9m1a5dd+60rtP9b9uletWzZ0paM//c8zs7O91T+KSfPMyudOnVCKcX58+epW7duhn/VqlW74/5jxoxBKUW/fv1ISUnJsD01NZWFCxfeU2wXL17MtMSWxWLh2LFjODs737XWcKVKlWjQoAHh4eH88ssvmM3mu5aEq1ChAuXKleOvv/7K9DWpW7cubm5utv6tWrVi586dzJs3z/Z1uYuLCw0bNmT69OlcuHAhW1+jazSaDO/vxYsXc/78+bvum51jQ/Y+P5A+2/vkk0/y1VdfMXPmTDp37kyJEiWyda7cej0MBsN9vbezo3z58rz99ttUq1bN9jOgTZs26HQ6ZsyYkeV+OX0971VenOfWRMDvv/9u1/7bb7/ddd+QkBA8PDw4ePBglp8NvV5/z7GJgkFmZEWh5+npyZgxY3j99df55Zdf6N27N5MmTaJXr1506tSJAQMGYDabmTx5MvHx8Xz88ce2fW8lTp999hl9+vTB0dGRChUq0KhRIzw9PRk4cCDjxo3D0dGRn3/+mb/++ivD+W8dY+LEibRv3x6dTkf16tUz/QE7YsQIfvjhBzp27Mh7771HyZIlWbx4MV999RWDBg3K9lrOuxk3bhyLFi0iNDSUd955By8vL37++WcWL17MpEmTcHd3z/Ex3dzcKFmyJH/++SctW7bEy8sLHx+fLG+gkJmQkBD69+/PCy+8wI4dO2jatCkuLi7ExMSwceNGqlWrxqBBg7Lc/9bV1IMHD6ZOnToMGjSIKlWqkJqayu7du/nmm2+oWrWqXWWJ7Prxxx/5+uuv6dmzJ/Xq1cPd3Z1z587x7bffcuDAAd55551s/dJ88cUXGTBgABcuXKBRo0a22dQ7+frrr2nfvj1t27YlLCyMYsWKcfXqVQ4dOsSuXbv4448/bH1btmyJxWJh9erVduXUWrVqxbhx49BoNLRo0eKu5+zUqROzZs2iYsWKVK9enZ07dzJ58uRsz3TfScWKFSlTpgyjR49GKYWXlxcLFy60rVvNzLBhw2jQoAGA3Trju8mt16NatWqsW7eOhQsXEhgYiJubW7bG7k727t3LkCFDePLJJylXrhx6vZ41a9awd+9eRo8eDaSXiHvzzTd5//33SU5OtpWyOnjwIJcvX+bdd9+9p9fzXuTFedq1a0dISAijRo0iMTGROnXqsGXLFn744QfgzmXtXF1dmT59On369OHq1av06NEDPz8/Ll26xF9//cWlS5fu+AeAKCTy7zozIXLXrStao6OjM2xLTk5WJUqUUOXKlbNd5T9//nzVoEEDZTQalYuLi2rZsqXatGlThn3HjBmjihYtqrRard3V5Js3b1bBwcHK2dlZ+fr6qr59+6pdu3YpQIWHh9v2N5vNqm/fvsrX11dpNBq7q27/W7VAKaXOnDmjevbsqby9vZWjo6OqUKGCmjx5sl2FhVtVCyZPnpwhXrKoHPBf+/btU507d1bu7u5Kr9erGjVq2MV9+/GyU7VAKaVWrVqlatWqpQwGgwJsz+3WFcyXLl2y6//fq5Bv+f7771WDBg2Ui4uLcnJyUmXKlFHPP/+82rFjR7bi2LNnj+rTp48qUaKE0uv1ysXFRdWqVUu98847Ki4uztYvs8oEWW07ePCgGjVqlKpbt67y9fVVDg4OytPTUzVr1kz9+OOP2YpLKaUSEhKUk5OTAtT//ve/DNszq1qglFJ//fWXeuqpp5Sfn59ydHRUAQEBqkWLFmrmzJl2/axWq/Lx8VGAOn/+vK1906ZNClC1a9fOVpzXrl1TL730kvLz81POzs6qcePGKioqSjVr1kw1a9YsQ7x//PFHhmP06dNHubi4ZHr8gwcPqtatWys3Nzfl6empnnzySXX27Nk7vn+DgoJUpUqVshX/LffyemRWtWDPnj0qJCREOTs7K8D2GmT1cyercbzdxYsXVVhYmKpYsaJycXFRrq6uqnr16urTTz+1q0ailFI//PCDqlevnjIajcrV1VXVqlXL7vOa3dczp5/F/8qL81y9elW98MILysPDQzk7O6vWrVurrVu3KkB99tlnd41x/fr1qmPHjsrLy0s5OjqqYsWKqY4dO2b6nhSFj0apO1TEFkIIIR4Ce/fupUaNGnz55ZcMHjw4v8MReeyXX36hV69ebNq0iUaNGuV3OOIhJomsEEKIh9aJEyc4c+YMb775JmfPnuX48eMP/S1uRc78+uuvnD9/nmrVqqHVatm6dSuTJ0+mVq1atvJcQmRF1sgKIYR4aL3//vv8+OOPVKpUiT/++EOS2ELIzc2N3377jQ8++ICkpCQCAwMJCwvjgw8+yO/QRAEgM7JCCCGEEKJAkvJbQgghhBCiQJJEVgghhBBCFEiSyP6HUorExERkxYUQQgghxMNNEtn/uH79Ou7u7ly/fj2/QykwLl+6+60ERcEgY1l4yFgWHjKWhYeMZe6TRFbcN6Uy3rZTFEwyloWHjGXhIWNZeMhY5j5JZIUQQgghRIEkiawQQgghhCiQJJEVQgghhBAFkiSyQgghhBCiQJJEVgghhBBCFEiSyAohhBBCiAJJElkhhBBCCFEgSSIrhBBCCCGyFJuQyrYTScQmpOZ3KBk45HcAQgghhBDi4TRv2xVW/rGUZinbGeDSj7Hdi/NEPY/8DstGElkhhBBCCGHHcjWWK2siqbwmkmbqGgDrU+oyPtKBkPIuBLg75nOE6SSRFUIIIYQQKKuF1ANbMEVFkLJ/E1plxReI17ixVN+EY7ogrArOXkmRRFYIIYQQQuQ/S/wlzJv/xLTpT6xXY23t1tK1+eBiQzY41CFVk564ajVQwlufX6FmIImsKPTWrVtHaGgo165dw8PDI7/DEUIIIfKdslpJPbwN04YIUvZFgdUCgMbFHUPDjhgbd8MhIIhm0fGsjYwBlZ7Eju8W+NDMxoIkskIIIYQQjwxr4hVMmxdg2jgf65ULtnaHMjUxNu2OoVYLNI4GW/sT9TwIKe/C2SsplPDWP1RJLEgim29iE1I5czmFkj4P35viYaSUwmKx4OAgb1khhBDibm7PM/zddKQeica0MZKUPev+nX11csPQsEP67GvRMlkeK8Dd8aHNVe6pjuzff/9NVFQUy5cvZ9euXZjN5tyOq1CbFx1P64nHefHbs7SeeJx50fF5er7mzZvzyiuvMHz4cDw9PfH39+ebb74hKSmJF154ATc3N8qUKcPSpUtt+xw8eJAOHTrg6uqKv78/zz33HJcvX7ZtX7ZsGY0bN8bDw4MKFavQqVMnTpw4YduekpLCkCFDCAwMxGg0EhQUxEcffQTA6dOn0Wg07Nmzx9Y/Pj4ejUbDunXrgPTlABqNhuXLl1O3bl0MBgNRUVEopZg0aRKlS5fGycmJGjVqMHfuXLvnu2TJEsqXL4+TkxOhoaGcPn06919UIYQQ4iF1K88Y8c1+vn33M/4e043Ez4eQsms1WC04lK6G6/Pj8Pp4Ca5PvXrHJPZhl+1E9syZM4wZM4agoCCCgoJo1qwZ7du3p27duri7u9O6dWv++OMPrFZrXsZb4MUmpDI+MgarSn9sVTA+MibPiwzPnj0bHx8ftm/fziuvvMKgQYN48sknadSoEbt27aJt27Y899xz3Lx5k5iYGJo1a0bNmjXZsWMHy5Yt4+LFizz11FO24yUlJTFy5Eiio6OZO/d3tFot3bp1s43/559/zoIFC5gzZw5Hjhzhp59+IigoKMdxv/7663z00UccOnSI6tWr8/bbbxMeHs6MGTM4cOAAI0aMoHfv3qxfvx5I/yOre/fudOjQgT179tC3b19Gjx6dK6+hEEII8bCLjU8hcs4a3r7xFXMThzMw+XecEy+gDC4Ym/bA461f8Hjte4zBndDojfkd7n3TKKXU3ToNGzaM8PBw2rRpQ5cuXahfvz7FihXDycmJq1evsn//fqKiovj1119xcHAgPDycevXqPYj4c11iYiLu7u4kJCRQpEiRXD/+thNJvPjt2Qzt4f1KUL+0S66fD9JnZC0WC1FRUQBYLBbc3d3p3r07P/zwAwCxsbEEBgayZcsWlixZwrZt21i+fLntGOfOnaN48eIcOXKE8uXL2x3/UtwF0Dji5+fHvn37qFq1KkOHDuXAgQOsWrUKjUZj1//06dOUKlWK3bt3U7NmTSB9RtbT05O1a9fSvHlz2wVa8+fPp2vXrkB68uzj48OaNWsIDg62Ha9v377cvHmTX375hTfffJP58+dz4MAB23lHjx7NxIkT5WKvbLgUdwFfv6L5HYbIBTKWhYeMZeGRl2NpvRGPeetirq2dh/7q37b2Q7rSLNCH8lT/HtSr6JMn585P2VpwqNfrOXHiBL6+vhm2+fn50aJFC1q0aMG4ceNYsmQJZ86cKbCJbF4r6aNHq8E2IwsPppRF9erVbf/X6XR4e3tTrVo1W5u/vz8AcXFx7Ny5k7Vr1+Lq6prhOCdOnKB8+fKcOHGCsWPHsnXrVi5dusStv4fOnj1L1apVCQsLo3Xr1lSoUIF27drRqVMn2rRpk+O469ata/v/wYMHMZlMtG7d2q5PSkoKtWrVAuDQoUM0bNjQLnm+PekVQgghCgulFGkn/sIUFYF512pIS0EP3MTISn0wC/WhHHMIQquBUYHu+R1unshWIjt58uRsH7BDhw73HMyjIMDdkfHdAm3LCx5UKQtHR/vjazQau7ZbiZ/VasVqtdK5c2cmTpyY4TiBgYEAdO7cmeLFi/O///0Po8EBD08fqlatSkpKCgC1a9fm1KlTLF26lFWrVvHUU0/RqlUr5s6di1abvqLl9i8DUlMzX1rh4vLvLPWtZQuLFy+mWLFidv0MBkOGYwohhBCFkTUpEfO2JZg2RmCJOWVr1xWvgFOT7mzUNWTaooQHmmfkF7kEPB887KUsateuzbx58wgKCsq0SsCVK1c4dOgQX3/9NU2aNOFS3AWOHD2ZoV+RIkV4+umnefrpp+nRowft2rXj6tWrtpn9mJgY20zq7Rd+ZaVy5coYDAbOnj1Ls2bNsuwzf/58u7atW7fe9dhCCCHEw0wpRdqpfemzrztXQeo/F9rrjRjqtsXYpBsOJSuj0Wh4HGhYxfehzTNyU44T2Vq1amVY8wjpM3pGo5GyZcsSFhZGaGhorgRYWD3MpSxefvll/ve///Hss8/y2muv4ePjw/Hjx/ntt9/43//+h6enJ97e3nzzzTcEBgay969dfDzxE7tjfPrppwQGBlKzZk20Wi1//PEHAQEBeHh4oNVqadiwIR9//DFBQUFcvnyZt99++65xubm58eqrrzJixAisViuNGzcmMTGRzZs34+rqSp8+fRg4cCBTpkxh5MiRDBgwgJ07dzJr1qw8eqWEEEKIvGVNvoF5+1JMUZFYzh+zteuKlcPYpBuG+u3ROmVcCvgw5xm5Kcflt9q1a8fJkydxcXEhNDSU5s2b4+rqyokTJ6hXrx4xMTG0atWKP//8My/iFQ9A0aJF2bRpExaLhbZt21K1alWGDRuGu7s7Wq0WrVbLb7/9xs6dO6latSpj3xmfYfmJq6srEydOpG7dutSrV4/Tp0+zZMkS27KC77//ntTUVOrWrcuwYcP44IMPshXb+++/zzvvvMNHH31EpUqVaNu2LQsXLqRUqVIAlChRgnnz5rFw4UJq1KjBzJkzmTBhQu6+QEIIIUQeiE1IZduJJGLjU0g9fYDrP37A1dHtSfptUnoS62jAENwJ99e+x+Otn3Fq9mSmSeyjJFtVC27Xr18/SpQowdixY+3aP/jgA86cOcP//vc/xo0bx+LFi9mxY0euBvsg5HXVgsJIrqgtPGQsCw8Zy8JDxrLwuNNYzouOZ2LESVqYt9AlZS3lLWds23SBpTA27o6hQQe0LpKb3C7HSwvmzJnDzp07M7Q/88wz1KlTx/aV9NSpU3MlQCGEEEKIwiz24H4SfvqBuSlbcMYEQAqOONRqgWeLHjiUqZHpsk5xD4ms0Whk8+bNlC1b1q598+bNGI3phXWtVqvtKnIhhBBCCGFPmZMx71iBKSoChzMH6fJP+xltIAv1oSzXh/BZqyr45VGN+cIix4nsK6+8wsCBA9m5cyf16tVDo9Gwfft2vv32W958800Ali9fbrsaPa989NFHvPnmmwwbNoxp06YB6Vf0vfvuu3zzzTdcu3aNBg0a8OWXX1KlSpU8jUUIIYQQIjvSzh1LrzywfSnKlASA0jmwRluXBfpQ9jhUBI3mgdSYLwxynMi+/fbblCpVii+++IIff/wRgAoVKvC///2Pnj17AjBw4EAGDRqUu5HeJjo6mm+++cauyD/ApEmTmDp1KrNmzaJ8+fJ88MEHtG7dmiNHjuDm5pZn8QghhBBCZEWlmDDvXAlrfif+3BFbu9a3OMbG3TAGd8LhsIa9kTHwCNR+zU05vtgrv924cYPatWvz1Vdf8cEHH1CzZk2mTZuGUoqiRYsyfPhw3njjDQDMZjP+/v5MnDiRAQMGZHo8s9mM2Wy2PU5MTKR48eJysVcOyIUIhYeMZeEhY1l4yFgWXGkxJ9NnX7cuQSVfT2/U6tDXDMXYpBuO5eui0f5bQCo2IfWRqP2am+4pkY2Pj2fu3LmcPHmSV199FS8vL3bt2oW/v3+GOy7ltj59+uDl5cWnn35K8+bNbYnsyZMnKVOmDLt27bJb1tC1a1c8PDyYPXt2pscbP3487777bob2E8cPyyxuNqWlpeLgIB+4wkDGsvCQsSw8ZCwLmNQUOLgZdixHc/agrVl5+mOt2RJtnTbg5pmPARYM2f3jLcdLC/bu3UurVq1wd3fn9OnT9O3bFy8vLyIjIzlz5gw//PBDjoPNrt9++41du3YRHR2dYVtsbCwA/v7+du3+/v6cOXMmQ/9bxowZw8iRI22Pb83I+vgGyoxsNslsQeEhY1l4yFgWHjKWD7fYhFTOXE6hpPUCrn8twrx1MSopIX2jVoe+ehOMTbrjWLEBly/HyljmshwnsiNHjiQsLIxJkybZzVi2b9/etkY2L/z9998MGzaMFStW2KojZOa/5SmUUncsWWEwGKTCghBCCCFyLGJrHOvnLqKTaS0OlsP/FM4Crac/xsbdMDTqgs7DN19jLOxynMhGR0fz9ddfZ2gvVqyYbVY0L+zcuZO4uDjq1Klja7NYLGzYsIEvvviCI0fSF0/HxsYSGBho6xMXF5dhllYIIYQQ4l5Z4v7myup5VI9aQFOVvvbVgoatjjVp2Ksn3vWaoNHq8jnKR0OOb1FrNBpJTEzM0H7kyBF8ffPur46WLVuyb98+9uzZY/tXt25devXqxZ49eyhdujQBAQGsXLnStk9KSgrr16+nUaNGeRZXQaCUon///nh5eaHRaNizZw/Nmzdn+PDh+R2aKGQ0Gg3z58/P7zAeGqdPn7Z95oQQBZtKS8W8cxUJ0wZzbVx3tBt+xkNdJ07jySzD4zxdZCpvuozgjG89SWIfoBwnsl27duW9994jNTUVSP/FdfbsWUaPHs0TTzyR6wHe4ubmRtWqVe3+ubi44O3tTdWqVdFoNAwfPpwJEyYQGRnJ/v37CQsLw9nZOU+XPBQEy5YtY9asWSxatIiYmBiqVq1KREQE77//vq1PUFCQrR5vXjpw4ABPPPEEQUFBaDSabJ9z3759NGvWDCcnJ4oVK8Z7773Hf69TXL9+PXXq1MFoNFK6dGlmzpxptz0iIoK6devi4eGBi4sLNWvWtJWQEwXX3cY9M2fPnqVz5864uLjg4+PD0KFDSUlJseujlOKTTz6hfPnyGAwGihcvzoQJE/LqaQghHlKWS+dImv8FV9/sxPVvx5B6JBo0GqwVgnnbZRjPFJlKuFN3Lmm9pfZrPsjx0oJPPvmEDh064OfnR3JyMs2aNSM2Npbg4GA+/PDDvIgx215//XWSk5MZPHiw7YYIK1aseOSrD5w4cYLAwEC7mWkvL688OdesWbOYNWsW69aty3T7zZs3KV26NE8++SQjRozI1jETExNp3bo1oaGhREdHc/ToUcLCwnBxcWHUqFEAnDp1ig4dOtCvXz9++uknNm3axODBg/H19bX9geXl5cVbb71FxYoV0ev1LFq0iBdeeAE/Pz/atm2bK89f5K5169YRFhbG6dOnM92enXH/L4vFQseOHfH19WXjxo1cuXKFPn36oJRi+vTptn631uR/8sknVKtWjYSEBC5fvpwXT1MI8ZBRljRS9kVh2hBB6uFt8M/EiaaIN8ZGXTE27orOuyito+PZJLVf85e6R6tXr1aTJ09WEydOVCtXrrzXwzx0EhISFKASEhLu2tdqtSqr6Wb+/LNas/V8+vTpowDbv5IlSyqllGrWrJkaNmyY7f+398np2yLu4nnb/8PDw1WzZs2ytV/JkiXVp59+etd+X331lXJ3d1cmk8nW9tFHH6miRYvaXofXX39dVaxY0W6/AQMGqIYNG97x2LVq1VJvv/12lttfeOEFVa1aNdu5U1JSVO3atVXPnj2z3OePP/5QVatWVUajUXl5eamWLVuqGzdu2LZ/9913qnLlykqv16uAgAD18ssv27ZNmTJFVa1aVTk7O6vHHntMDRo0SF2/ft22PTw8XLm7u6tly5apihUrKhcXF9W2bVt14cKFLONJS0tTL774ogoKClJGo1GVL19eTZs2za5Pnz59VNeuXdW4cWNVQECA8vLyUoMHD1YpKSm2PhcuXFAdOnRQRqNRBQUFqZ9//jnDGAIqMjLS9vjcuXPqqaeeUh4eHsrLy0t16dJFnTp1KstY/2vt2rW292xm7mXclyxZorRarTp//t/37a+//qoMBoPtc3/w4EHl4OCgDh8+nO1YlVJq27ZtqmbNmspgMKg6deqoiIgIBajdu3fb+hw4cEC1b99eubi4KD8/P9W7d2916dIl2/bExETVs2dP5ezsrAICAtTUqVPtPq/ZdfvnUhRsMpYPTtrlC+rGn1+pK2+0U5cG1rX9i/9siDLtWq2saakZ9omJT1HbTtxQMfEpmRzRnoxl7svxjOwtLVq0oEWLFvefSRdkKSauDG+aL6f2nrYBDE537ffZZ59RpkwZvvnmG6Kjo9HpMq7biYiIoEaNGvTv359+/frZbdNoNISHhxMWFpZboefYli1baNasmV11ibZt2zJmzBhOnz5NqVKl2LJlC23atLHbr23btnz33Xekpqbi6Gj/F7JSijVr1nDkyBEmTpyY5bk///xzatSowejRo/n0008ZO3Ysly9f5quvvsq0f0xMDM8++yyTJk2iW7duXL9+naioKNsyiBkzZjBy5Eg+/vhj2rdvT0JCAps2bbLtr9Vq+fzzzwkKCuLUqVMMHjyY119/3e58N2/e5JNPPuHHH39Eq9XSu3dvXn31VX7++edMY7JarTz22GPMmTMHHx8fNm/eTP/+/QkMDOSpp56y9Vu7di0eHkVYu3Ytx48f5+mnn6ZmzZq298Tzzz/P5cuXWbduHY6OjowcOZK4uLgsX7ubN28SGhpKkyZN2LBhAw4ODnzwwQe0a9eOvXv3otff/9dvOR33W/tUrVqVokWL2u1jNpvZuXMnoaGhLFy4kNKlS7No0SLatWuHUopWrVoxadKkLL/NSEpKolOnTrRo0YKffvqJU6dOMWzYMLs+MTExNGvWjH79+jF16lSSk5N54403eOqpp1izZg2QXhlm06ZNLFiwAH9/f9555x127dpFzZo17/PVEkLcYiuZ5aPH31VDyv5NmDZGknpg87+zr25eGIM7YWzcDZ3vY1keK8DdUWZh81G2EtnPP/882wccOnToPQcjcp+7uztubm7odDoCAgIy7ePl5YVOp8PNzS1DnwoVKuDu7v4gQs1SbGwsQUFBdm23KlHExsZSqlQpYmNjM60hnJaWxuXLl22VLBISEihWrBhmsxmdTsdXX31F69atszy3q6srP/30E82aNcPNzY0pU6awevXqLF+TmJgY0tLS6N69OyVLlgSgWrVqtu0ffPABo0aNsktw6tWrZ/v/7RfglSpVivfff59BgwbZJbKpqanMnDmTMmXKADBkyBDee++9LJ+Do6Oj3U0/SpUqxebNm5kzZ45dIuvp6cnHH31IQGBxKlasSMeOHVm9ejX9+vXj8OHDrFq1iujoaOrWrQvAt99+S7ly5bI872+//YZWq+Xbb7+1lcALDw/Hw8ODdevWZUhA70V2x/1u+3h6eqLX622VV06ePMmZM2f4448/+OGHH7BYLIwYMYIePXrYEs7/+vnnn7FYLHz//fc4OztTpUoVzp07Z3e77hkzZlC7dm27tbbff/89xYsX5+jRowQGBjJ79mx++eUXWrZsCaS/Zrcn3UKI+zMvOp7xkTF4W67SMWU9z+o2Yky6ZNvuWKEuxibd0ddojkZuRPHQy1Yi++mnn9o9vnTpEjdv3sTDwwNIv9OXs7Mzfn5+j1Yiqzemz4zm07kfhMOHD99x+9mzZ6lUqZItUUlLSyM1NRVXV1dbn969e2frApw7yaw+8H/bs9PHzc2NPXv2cOPGDVavXs3IkSMpXbo0zZs3z/LcwcHBvPrqq7z//vu88cYbNG2a9Sx8jRo1aNmyJdWqVaNt27a0adOGHj164OnpSVxcHBcuXLAlKJlZu3YtEyZM4ODBgyQmJpKWlobJZCIpKQkXFxcAnJ2dbUksQGBg4B1nRgFmzpzJt99+y5kzZ0hOTiYlJSXDDF+VKlXsZuwDAwPZt28fkF6VxMHBgdq1a9u2ly1bFk/PrO9Os3PnTo4fP55hjbrJZOLEiRNZ7nf7e8disWA2m+3amjRpwtKlS22PszPu/5XZNnVbzWmr1YrZbOaHH36gfPnyAHz33XfUqVOHI0eOUKFChQz7Hzp0iBo1auDs7GxrCw4Otuuzc+dO1q5da/d8bjlx4gTJycmkpqZSv359W7u7u3um5xNC5FzsNRNL5yzjfdMagtP2oCP954Vydse5UWeMjR9H518yn6MUOZGtRPbUqVO2///yyy989dVXfPfdd7YfrkeOHKFfv34MGDAgb6J8SGk0mmx9vV+YFS1alDVrVuDtnT7DFRERwbx58+y+5r7fO6QFBARkqFF8K3G7NbOWVR8HBwe8vb1tbVqtlrJlywJQs2ZNDh06xEcffXTHRNZqtbJp0yZ0Oh3Hjh27Y6w6nY6VK1eyefNmVqxYwfTp03nrrbfYtm0bPj4+d9z3zJkzdOjQgYEDB/L+++/j5eXFxo0beemll2xVQoAMX5drNJoMFRxuN2fOHEaMGMGUKVMIDg7Gzc2NyZMns23bNrt+mR3XarUCZHn8O53XarVSp06dTJc83KlU3+2lqrZt28Ybb7xhd/Ggk9O/n7nsjvvtAgICMjz3a9eukZqaans/BQYG4uDgYEtiASpVqgSk//GWWWJ5p9fiFqvVSufOnTNdzhIYGGh7f2WVnAsh7o014TKmTX+i1kfy8Y2LtvY9ugosMLSg94Bu1C8vt40tiHK8Rnbs2LHMnTvX7gd5hQoV+PTTT+nRowe9evXK1QDFg6HX67FYLDnez8HBgdKlStluuefn54eTk5MtWcwNwcHBvPnmm6SkpNjWVa5YsYKiRYvalhwEBwezcOFCu/1WrFhB3bp1M10neYtSCrPZfMfzT548mUOHDrF+/Xratm1LeHg4L7zwQpb9NRoNISEhhISE8M4771CyZEkiIyMZOXIkQUFBrF69mtDQ0Az77dixg7S0NKZMmYJWm14Zb86cOXeMLTuioqJo1KgRgwcPtrXdaUY0MxUrViQtLY3du3fbbkpy/Phx4uPjs9yndu3a/P777/j5+eXoj5nb3zvnzp3DwcEhy/fTvYz7rQorMTExtqUHK1aswGAw2J5bSEgIaWlpnDhxwjb7ffToUQDbkpH/qly5Mj/++CPJycm2ZHvr1q12fWrXrs28efMICgrCwSHjj98yZcrg6OjI9u3bKV68OJBetePYsWM0a9Ys0/MKITKnrFZSD2/HFBVByt4NYLXgCCRqXFimb8wifXPO6Iqh1cCb/hm/JREFQ47ryMbExNjNDt1isVi4ePFiJnuIgiAoKIgNGzZw/vx5uxJDFStWJDIyMtfOk5KSYruhRUpKCufPn2fPnj0cP37c1ueLL76w+/q9Z8+eGAwGwsLC2L9/P5GRkUyYMIGRI0faZq4GDhzImTNnGDlyJIcOHeL777/nu+++49VXX7Ud56OPPmLlypWcPHmSw4cPM3XqVH744Qd69+6dZbx79uzhnXfe4bvvviMkJITPPvuMYcOGcfLkyUz7b9u2jQkTJrBjxw7Onj1LREQEly5dss3mjR8/nilTpvD5559z7Ngxdu3aZSv5VKZMGdLS0pg+fTonT57kxx9/vO8lGZCeGO7YsYPly5dz9OhRxo4dS3R0dI6OUbFiRVq1akX//v3Zvn07u3fvpn///jg5OWX5FX6vXr3w8fGha9euREVFcerUKdavX8+wYcM4d+7cfT8vyN64R0ZGUrFiRdvjNm3aULlyZZ577jl2797N6tWrefXVV+nXr58t4W7VqhW1a9fmxRdfZPfu3ezcuZMBAwbQunVru1na2/Xs2ROtVstLL73EwYMHWbJkCZ988oldn5dffpmrV6/y7LPPsn37dk6ePMmKFSt48cUXsVgsuLm50adPH1577TXWrl3LgQMHePHFF9FqtXdcKiGE+Jc18Qo3l8/i2rjuJE5/hZQ9a8FqwaFMDVzD3uWv535nhnMvWxIrJbMKuJyWOejUqZOqXr26io6OtpU+io6OVjVr1lSdO3fOjUoK+Son5bcKik8//TRDCaP/lvPZsmWLql69ujIYDHbltwAVHh5+x+PnpPzWqVOnMpT6Auz2GTduXIZ49+7dq5o0aaIMBoMKCAhQ48ePz1CCbN26dapWrVpKr9eroKAgNWPGDLvtb731lipbtqwyGo3K09NTBQcHq99++y3LWJOTk1XlypVV//797dq7deumGjVqpNLS0jLsc/DgQdW2bVvl6+urDAaDKl++vJo+fbpdn5kzZ6oKFSooR0dHFRgYqF555RXbtqlTp6rAwEDl5OSk2rZtq3744QcFqGvXriml/i2/dbvIyMg7lkwzmUwqLCxMubu7Kw8PDzVo0CA1evRoVaNGDVufW+W3bh/LYcOG2Y3LhQsXVPv27ZXBYFAlS5ZUv/zyi/Lz81MzZ8609eE/5bdiYmLU888/r3x8fJTBYFClS5dW/fr1y/bn627lt5S6+7iHh4dneH3OnDmjOnbsqJycnJSXl5caMmSIXXk3pZQ6f/686t69u3J1dVX+/v4qLCxMXbly5Y6xbNmyRdWoUUPp9XpVs2ZNNW/evAzlt44ePaq6deumPDw8lJOTk6pYsaIaPny47f2cWfmt+vXrq9GjR9/l1bInZX4KDxnLu7NaLMp8aJtK+Ga0uvRyQ1vZrMsjmqvrv01SqeeO2fXPScms3CRjmfs0SuVs8dWlS5fo06cPy5Yts311l5aWRtu2bZk1axZ+fn65lWPni8TERNzd3UlISLjvtZ2PiktxF2xLC0TBlpOxPHfuHMWLF2fVqlV3vIBN3J+kpCSKFSvGlClTeOmll7K9n3wuCw8Zy6xZb8Rj2rIQ08b5WOPO2todgqpibNINQ902aB7QxdHZIWOZ+3K8RtbX15clS5Zw7NgxDh06hFKKSpUqZfl1mxCicFizZg03btygWrVqxMTE8PrrrxMUFHTHKg4i53bv3s3hw4epX78+CQkJttJqXbt2zefIhMg/dnVfiziQdmwXpqhIzHvWQFr6ckeN0QVD/XYYm3TH4THJSR4V93xDhHLlyt2xhqQQonBJTU3lzTff5OTJk7i5udGoUSN+/vnnO15MJ+7NJ598wpEjR9Dr9dSpU4eoqKi7Vr0QorC6VffVxXKD9qkbCdNH4ZLwt227Q4lK/8y+tkVjdL7DkURhlK1E9uOPP2bo0KF29RGzsm3bNi5fvkzHjh3vOzghxMOjbdu2tG3bNr/DKPRq1arFzp078zsMIR4KsfEpzP1jPaNNa2meuh0DqZAMSu+EsX5bnBp3x6FkpfwOU+SjbCWyBw8epESJEjz55JN06dKFunXr2upApqWlcfDgQTZu3MhPP/1ETEwMP/zwQ54GLYQQQojCy3rzOuZtS7Cunsv066dt7ce0JVhgaEH3fk9Sr1LBviZH5I5sJbI//PADe/fu5csvv6RXr14kJCSg0+kwGAzcvHkTSJ9F6N+/P3369MFgMORp0EIIIYQoXJRSpJ3aj2ljBOYdKyHVjB4woWeNvgEL9C04pCuNVqthWFG5eYFIl+01stWrV+frr79m5syZ7N27l9OnT5OcnIyPjw81a9aU9VtCCCGEyDFr8g3M25diiorEcv7fuyfqipbB2KQ7Gx0aMXnJdawKqfsqMsjxxV4ajYYaNWpQo0aNvIhHCCGEEI+A1DMHMUVFYI5eDimm9EZHA4barTA26YZD6epoNBoeBxpWS+XslRRKeOsliRV27rlqgRBCCCFETlhNSaRELyc5KhLL34dt7bqAIIyNu2Fo2BGti3uG/QLcHSWBFZmSRFYIIYQQue722q8+iSfTZ1+3L0OZ06+twcERQ60W6XVfy9aS2zCLeyKJrBBCCCFy1bzoeD6OOE1z81a6pKzFwXLStk3nVwJDk24YG3ZC6+qRf0GKQkESWSGEEELkmtgjh7n682z+MG/ClWQAUtGhq94cjxY9cCxfR2ZfRa7JcSI7a9YsnnrqqWzdHEEIIYQQhZ9KMWHeuQrTxggcTu6j2z/t57V+LNQ3Z6m+KZ+2rUr90i75GqcofHKcyI4ZM4ahQ4fy5JNP8tJLL9GoUaO8iEsIIYQQD7m0mFPpa1+3LkYlXwdAaXVs0NVmgT6UnQ6VURotWg2U8Nbnc7SiMMpxInvu3DkWL17MrFmzCA0NpVSpUrzwwgv06dOHgICAvIhRCCGEEA8JlWrGvHsNpqhI0o7vtrVrvQIxNu6GsVFnOOrArsgYlNR+FXlMo5RS97pzXFwcP/30E7NmzeLw4cO0a9eOl156ic6dO6PVanMzzgcmMTERd3d3EhISKFKkSH6HUyBciruAr1/R/A5D5AIZy8JDxrLweFjGMi32NKaNkemzr0kJ6Y1aHfpqjTE27oZj5YZotDpb/9gEqf36Xw/LWBYm93Wxl5+fHyEhIRw5coSjR4+yb98+wsLC8PDwIDw8nObNm+dSmEIIIYR40FRaKil71mKKiiT16A5bu9bTD2PI4xhCuqLz8Mt0X6n9Kh6Ee0pkL168yI8//kh4eDgnT57k8ccfZ9GiRbRq1Yrk5GTefvtt+vTpw5kzZ3I7XiGEEELkEVvtV00cbn8twrR5IerGtfSNGg2OVUJwatodxyqN7GZfhcgvOU5kO3fuzPLlyylfvjz9+vXj+eefx8vLy7bdycmJUaNG8emnn+ZqoEIIIYTIOxHbLrP6jyV0Mq+lTNr+fwpngdbdF0NIF4whj6PzkmthxMMlx4msn58f69evJzg4OMs+gYGBnDp16r4CE0IIIUTes1y5wJXV86iy7k+aqvS1r1Y0RDtUo+6zz+LdoDkanZSdFw+nHL8zv/vuu7v20Wg0lCxZ8p4CEkIIIUTeUpY0UvZtxBQVQeqhrWiVwhu4onFnqb4pC/XNidX5Eh5QAn9JYsVD7J7enUlJSaxfv56zZ8+SkpJit23o0KG5EpgQQgghcpflaiymTX9i3rwAa3ycrd1ath7vxjQgyqE2Fk16aiC1X0VBkONEdvfu3XTo0IGbN2+SlJSEl5cXly9fxtnZGT8/P0lkhRBCiIeIslpIPbCZ5KgIUvdvBmUFQOPqibFR5/S1r37FaREdz4bIGJDar6IAyXEiO2LECDp37syMGTPw8PBg69atODo60rt3b4YNG5YXMQohhBAihyzxcZg3/Ylp059Yr120tTuWr4uxSTf0NZqjcfx3xvWJeh6ElHeR2q+iQMlxIrtnzx6+/vprdDodOp0Os9lM6dKlmTRpEn369KF79+55EacQQgghMmErmeWjx99NR+qhrZiiIknZFwVWCwAaF3cMDTtibNwNh4CgLI8ltV9FQZPjRNbR0RGNRgOAv78/Z8+epVKlSri7u3P27NlcD1AIIYQQmZsXHc/4yBg8LPF0TNlAL4eNON2ItW13KFsLY5NuGGq1QONoyMdIhcgbOU5ka9WqxY4dOyhfvjyhoaG88847XL58mR9//JFq1arlRYxCCCGE+I/Ya2YWzlnJOPNaGqfuwoH02VdldMMpuCPGJt1wCCydz1EKkbdynMhOmDCB69evA/D+++/Tp08fBg0aRNmyZQkPD8/1AIUQQgjxL2viVUxbFqLWRfDJjQu29n26ciw0hPJs/yeoV8HrDkcQovDIUSKrlMLX15cqVaoA4Ovry5IlS/IksP/66KOPiIiI4PDhwzg5OdGoUSMmTpxIhQoV7OJ79913+eabb7h27RoNGjTgyy+/tMUrhBBCFERKKVKP7sQUFUHKnrVgScMRuIETK/QhLDSEclJXHK0GXg9wy+9whXhgtDnprJSiXLlynDt3Lq/iydL69et5+eWX2bp1KytXriQtLY02bdqQlJRk6zNp0iSmTp3KF198QXR0NAEBAbRu3do2gyyEEEIUJNYb8bBpPvHje5A4bRApO1eCJQ2HoCq4PjeW3c/9znSX521JrJTMEo+aHM3IarVaypUrx5UrVyhXrlxexZSpZcuW2T0ODw/Hz8+PnTt30rRpU5RSTJs2jbfeestWOWH27Nn4+/vzyy+/MGDAgEyPazabMZvNtseJiYl59ySEEEKIu1BKkXZ8D6aoeZh3r0GTlooF0BicMdRvn772tXj6t5HdgOAqPlIySzyyNEoplZMdFi9ezMcff8yMGTOoWrVqXsV1V8ePH6dcuXLs27ePqlWrcvLkScqUKcOuXbuoVauWrV/Xrl3x8PBg9uzZmR5n/PjxvPvuuxnaTxw/jJubfD2THWlpqTg4yA/PwkDGsvCQsSyAkm/AnrWwcxmaS/9+86kCSkO9dlCtCRic8zFAcb/kc5l9vn5Fs9Uvx4msp6cnN2/eJC0tDb1ej5OTk932q1ev5uRw90QpRdeuXbl27RpRUVEAbN68mZCQEM6fP0/Rov8++f79+3PmzBmWL1+e6bEym5EtXrw4CQkJFClSJG+fSCFxKe5Ctt9w4uEmY1l4yFg+3Gy1X70d8b5yCFNUJOZdqyD1n99HeiOGem0xNulOvJOHjGUhIZ/L3JfjqgXTpk3LgzByZsiQIezdu5eNGzdm2Harxu0tSqkMbbczGAwYDFJbTwghxIMxLzqeyREnaGneTJeUtThY/rZt0xUrl173tX57tE6u6Y1xF7I4khAiR4lsamoq69atY+zYsZQunT+16V555RUWLFjAhg0beOyxx2ztAQEBAMTGxhIYGGhrj4uLw9/f/4HHKYQQQtxOKcXFA3tJ+ukH/kjZihMpAJjQo6/TCs8WPXAoVfWOky9CCHs5qlrg6OhIZGRkXsVyR0ophgwZQkREBGvWrKFUqVJ220uVKkVAQAArV660taWkpLB+/XoaNWr0oMMVQgghALCakkjeMI/4Cb1x+LIvHVI24EQKp7TF+NypN08U+YzTLV7HsXQ1SWKFyKEcLy3o1q0b8+fPZ+TIkXkRT5ZefvllfvnlF/7880/c3NyIjU2/BZ+7uztOTk5oNBqGDx/OhAkTKFeuHOXKlWPChAk4OzvTs2fPBxqrEEIIkXbmEMkbIzBHLwdzMgDKQc9KbV0W6EPZpysPGg1aDZTw1udztEIUTDlOZMuWLcv777/P5s2bqVOnDi4uLnbbhw4dmmvB3W7GjBkANG/e3K49PDycsLAwAF5//XWSk5MZPHiw7YYIK1askOoDQgghHghluol5xwpMGyNIO3PI1q7zL4mxSXcMDTpgOAQHImNAIbVfhbhPOa5a8N+v9O0OptFw8uTJ+w4qPyUmJuLu7i5VC3JArsIsPGQsCw8Zywcr7dxRTFERmLcvQ5n+uVGPzgF9rRYYm3THsVxtu2UDsQmp2a79KmNZeMhY5r4cz8ieOnUqL+IQQgghChSVYkqffY2KJO30flu71rc4xsbdMAZ3Quvmmem+Ae6OMgsrRC7IcSIrhBBCPIpstV8t53DdvRDztsWo5BvpG7U69DVDMTbphmP5umi0ObqWWghxj+4pkT137hwLFizg7NmzpKSk2G2bOnVqrgQmhBBCPCwit1wkau5COpnX4GA5humfdq13UYxNumEM7oy2iHe+xijEoyjHiezq1avp0qULpUqV4siRI1StWpXTp0+jlKJ27dp5EaMQQgiRL9JiT3N11VxqbF5EE5W+9jUNLZsdaxHyXC+864TI7KsQ+SjHieyYMWMYNWoU7733Hm5ubsybNw8/Pz969epFu3bt8iJGIYQQ4oFRqSmYd69JrzxwbDdaoAgQq/FmkaE5S/RNuaL1JNy7BP6SxAqRr3KcyB46dIhff/01fWcHB5KTk3F1deW9996ja9euDBo0KNeDFEIIIfKaJe4spqhITFsXoW7EpzdqtFgrNuLNvxuwTVcNqyY9cZXar0I8HHKcyLq4uGA2mwEoWrQoJ06coEqVKgBcvnw5d6MTQggh8pBKSyXlr3WYoiJIPbLD1q718MMQ0hVjoy7ovAJoHx3PNqn9KsRDJ8eJbMOGDdm0aROVK1emY8eOjBo1in379hEREUHDhg3zIkYhhBAiV1kuncO0aT6mzQtR16+mN2o0OFZphLFxN/RVQ9Do/v0V+UQ9D0LKu2S79qsQ4sHIcSI7depUbtxILzcyfvx4bty4we+//07ZsmX59NNPcz1AIYQQ4l7YymX5pCeeypJGyt4NmKIiST201dZP6+6DoVEXjCGPo/MOzPJ4UvtViIdPjhPZ0qVL2/7v7OzMV199lasBCSGEEPdrXnQ84yNjsCoItF5mYqkdlDq9AmvCP0vgNBocKzXA2KQ7+mpN7GZfhRAFh3xyhRBCFCqxCam8F3GOhql/0cW8lgZpe9H+pbACmiJeGIO7YGz8ODqfYvkdqhDiPkkiK4QQotCwXLtI4sI/+DVhAX7qmq19h0Nlind4isqt26BxkOUBQhQWksgKIYQo0JTVQuqBLZiiIkjZvwkvZQUgXuPGUn0TFupDiXHwZ2WjspLEClHISCIrhBCiQLLEX8K8eQGmTfOxXo21tTuWr8NfRdsyfE8ZzDhKuSwhCrFsJ7LDhw+nb9++VK1aNS/jEUIIIbKkrFZSD29Ln33dGwVWCwAaF3cMDTtibNwNh4AgmgJL2qVKuSwhCrlsJ7LLli1j+vTp1KlTh759+/LMM89QpEiRvIxNCCGEAMCaeAXT5gWYNs7HeuWCrd2hTE2MTbphqN0SjaPBbh8plyVE4ZftRPbw4cNs2rSJ77//nldffZWRI0fSvXt3+vbtS9OmTfMyRiGEEI+I22u/+rvpSD26A1NUJCl71v47++rkiqFBR4xNuuFQtEw+RyyEyE85WiMbEhJCSEgI06dP5/fffyc8PJzmzZtTpkwZXnrpJZ5//nmKFi2aV7EKIYQoxG7VfnWzJNIhNYrnHaNwTrxt9rVUtfTZ1zqt0eiN+RipEOJhoVFKqfs5wIkTJ/j++++ZMWMGN27cICUlJbdiyxeJiYm4u7uTkJAgSyey6VLcBXz95A+YwkDGsvAoaGMZG5/Cqx8uorN5LU1Sd6AnDQBlcMapQYf02dfHyudzlPmjoI2lyJqMZe67r6oFSUlJrF+/nvXr1xMfH0+FChVyKy4hhBCPAOuNeMxbF2NdO49pN/62tR/WlWKBPpQn+/egXkXffIxQCPEwu6dEdsOGDYSHhzN37lwAnnzySSZOnEhISEiuBieEEKLwUUqRduIvTFERmHethrQU9EAyBlbpg1mgD+WoQym0GhgZ6JHf4QohHmLZTmTPnTvH7NmzmTVrFidOnKBBgwZ8+umnPPPMM7i6uuZljEIIIQoBa1Ii5m1LMG2MwBJzytaue6w8xibd2ahryNTFiVgVUvtVCJEt2U5kg4KC8Pb25rnnnuOll16iUqVKeRmXEEKIQkApRdqpfemzrztXQao5fYPeiKFuG4xNuuNQsjIajYbHgYZV/aT2qxAi27KdyM6ZM4cuXbrg4CA3AxNCCHFn1uQbmLcvxRQVieX8MVu7rlhZjI27YWjQAa1Txm/zpParECInsp2Vdu/eHYA//viDX3/9laNHj6LRaChXrhw9e/akR48eeRakEEKIh5et9qu3I97xxzBFRWLesRxSTOkdHA0YardKrzxQujoajSZ/AxZCFBrZTmStVivPPPMMc+fOpXz58lSsWBGlFAcOHODpp5/mySef5Ndff5UfUEII8QiZFx3PxIiTtDBvoUvKWhwsZ2zbdAGl0uu+NuiI1kXKGQohcl+2E9lp06axevVqFixYQKdOney2LViwgBdeeIHPPvuM4cOH53aMQgghHkKxB/eT8NMPzE3ZgjPps68pOOJQMxTPFj1wKFtTJjeEEHkq24nsrFmzmDx5coYkFqBLly5MmjSJadOmSSIrhBCFmDInY96xAlNUBA5nDtLln/az2gAW6kNZpm/MZ62r4FfaJV/jFEI8GrKdyB47doxWrVplub1Vq1YMGTIkV4ISQgjxcEk7dyy98sD2pShTEgBK58BabR3+1Ldgj0NF0GjQaqCEtz6foxVCPCqyncg6OTkRHx9PiRIlMt2emJiIk5NTrgUmhBAif6kUE+adqzBtjCDt5D5bu9b3MYyNu2Fs2AndES17I2NAar8KIfJBthPZ4OBgZsyYwYwZMzLd/uWXXxIcHJxrgQkhhMgfaTEn02dfty5BJV9Pb9Tq0NdsjrFxNxwr1EOj1QLwRD0IKe8itV+FEPki24nsW2+9RfPmzbly5QqvvvqqrWrBoUOHmDJlCn/++Sdr167Ny1iFEELkEZVqxrxrNaaoSNJO7LG1a72LYgx5HGOjzmjdfTLdV2q/CiHyS7YT2UaNGvH777/Tv39/5s2bZ7fN09OTX3/9lZCQkFwPUAghRO6z1X5VMbjuWYh562JUUkL6Rq0OfbUmGJt0w7FSQ9vsqxBCPGxydJuubt260bZtW5YvX86xY+l3ailfvjxt2rTB2dk5TwIUQgiRuyK2xrFu7mI6m9fikHbon8JZoPX0x9j4cQyNuqDz8MvXGIUQIjtyfL9ZZ2dnunXrlhexCCGEyEOWuL+5snoe1aMW0FSlr321oGGbYw0a9OyFd/0maLS6fI5SCCGyL9vfF5UoUYIrV67YHn/xxRckJibmSVD366uvvqJUqVIYjUbq1KlDVFRUfockhBD5QlnSMO9aTcK0wVwb1x3thp/xUNe5pPFkluFxni4ylTEuIznjV0+SWCFEgZPtGdlz585hsVhsj9988006dOhAkSIP120Hf//9d4YPH85XX31FSEgIX3/9Ne3bt+fgwYNZlg4TQohC59pFkjZHYtqyAJV4Nb1No8FaviHvnKvPZoeaWDTpiavUfhVCFFQ5Xlpwi1IqN+PINVOnTuWll16ib9++QPqtdZcvX86MGTP46KOPMvQ3m82YzWbb41uzzP9tF1lLSUmR16qQkLEs2JQljZSDmzFvXkjKkR2k/vNzWuPuh7F+BwzBHdF5BdJyVwLbF8aiVVa0GhjTOQBPo1XG/iEln8vCQ8Yy+wwGQ7b63XMi+zBKSUlh586djB492q69TZs2bN68OdN9PvroI959990M7TNnfI7RaMyTOIUQIm/5Q8mO9k3H4+H4z7aHz3r8u+l0FHwuK7CEEA+R115/K1v9cpTIfvvtt7i6ugKQlpbGrFmz8PGxrys4dOjQnBwyV12+fBmLxYK/v79du7+/P7GxsZnuM2bMGEaOHGl7nJiYSPHixRk4aOhDt2ziYXX5Ugw+voH5HYbIBTKWD7eLian8fSWF4t56/Fy1pBzainnzIlIPbYVbs6+uHhjrt+dmhQb4lK+ZvwGLXCGfy8JDxjL3ZTuRLVGiBP/73/9sjwMCAvjxxx/t+mg0mnxNZG+P43ZKqQxttxgMhkynr7NqFxnp9Xp5rQoJGcuH17zoeMZHxuBtuUrHlPU8q9uIMekSGkAPOJavi7FJN/Q1Q9E4OJIWd0HGspCQz2XhIWOZ+7KdyJ4+fToPw8gdPj4+6HS6DLOvcXFxGWZphRCioIi9ZmLJnGW8Z1pLo7Td6EiffVXO7jg36oSxcTd0/iXzOUohhHjwCtUaWb1eT506dVi5cqVdrduVK1fStWvXfIxMCCFyzppwGdPmBah1EUy8cdHWvkdXgYWGUHoP6Ea98l75GKEQQuSvQpXIAowcOZLnnnuOunXrEhwczDfffMPZs2cZOHBgfocmhBB3paxWUg9vxxQVQcreDWC14Ahc1zizzLExCw2hnNEVS6824O+W3+EKIUS+KnSJ7NNPP82VK1d47733iImJoWrVqixZsoSSJeVrNyHEw8uaeAXTloWYNs7Hevm8rd2hdHWMTboTpery1cKrWFV63dfx3QIJcHfMx4iFECL/FbpEFmDw4MEMHjw4v8MQQog7UkqRemQHpo0RpOxZB5Y0ADROrhgadMDYuBsOxcoC0A0IruzF2SsplPDWSxIrhBAU0kRWCCEeZtYb8f/OvsadtbU7BFXF2KQbhjqt0RicMuwX4O4oCawQQtzmnhLZEydOEB4ezokTJ/jss8/w8/Nj2bJlFC9enCpVquR2jEIIUWDFJqRy5nIKJb0d8Y7bhykqEvOeNZCWCoDG6IKhXjuMTbrhULxCPkcrhBAFS44T2fXr19O+fXtCQkLYsGEDH374IX5+fuzdu5dvv/2WuXPn5kWcQghR4MyLjmfKvGO0Nm+kS8paHKwxtm0OJSqlz77WbYvG6JyPUQohRMGV40R29OjRfPDBB4wcORI3t3+vmA0NDeWzzz7L1eCEEKIgUkpxce8uzD/9yB8p2zGQPvuajAFD/bZ4teiBQ8lK+RylEEIUfDlOZPft28cvv/ySod3X15crV67kSlBCCFEQWW9ex7xtCaaNkThcOEGbf9qPaUuw0BDKSn0jvmxWAb+SLvkapxBCFBY5TmQ9PDyIiYmhVKlSdu27d++mWLFiuRaYEEIUBEop0k4fwBQVgXnHCkg1p7c7Glimqc+f+hYc0pUGjQatBkp46/M5YiGEKDxynMj27NmTN954gz/++AONRoPVamXTpk28+uqrPP/883kRoxBCPHSsyTcwb1+GaWMElnPHbO26omUwNumOoX57nA9YOBIZA1L7VQgh8kSOE9kPP/yQsLAwihUrhlKKypUrY7FY6NmzJ2+//XZexCiEEA+NtDOHSN4YgTl6OZiT0xsd9BjqtMLYpDsOpauj0WgAeKIehJR3kdqvQgiRR3KcyDo6OvLzzz/z3nvvsXv3bqxWK7Vq1aJcuXJ5EZ8QQuQ7ZbqJecdyTFGRpJ09ZGvXBQRhbNwNQ8OOaF3cM91Xar8KIUTeuecbIpQpU4YyZcrkZixCCJHvbHVfffT4XD+VvvZ1+zKUKSm9g4Mjhlot0mdfy9ayzb4KIYR48HKcyFosFmbNmsXq1auJi4vDarXabV+zZk2uBSeEEA/SvOh4Poo4TXPztvS6r5YTtm1avxIYm3TD2LATWleP/AtSCCGETY4T2WHDhjFr1iw6duxI1apVZTZCCFEoxB45zJWfZ/OHeTNu3AQgFR266s3xCH0Cxwp15eedEEI8ZHKcyP7222/MmTOHDh065EU8QgjxwKgUE+ZdqzFFReBwci/d/2k/r/Vjob45S/VN+bRtVeqXlrqvQgjxMMpxIqvX6ylbtmxexCKEEA9EWswpTBsjMW9djLqZCIDS6tigq80CfSg7HSqjNFqp+yqEEA+5HCeyo0aN4rPPPuOLL76Qr9mEEAWGSk3BvHsNpo0RpB3bbWvXegVibNwNY6POcNSBXZExKKn7KoQQBUK2Etnu3bvbPV6zZg1Lly6lSpUqODra/5CPiIjIveiEEOI+WS6ewbQxEtOWRaikhPRGjRZ99SYYG3fDsXJDNFodIHVfhRCioMlWIuvubl8fsVu3bnkSjBBC3IvbS2YFuDui0lJJ2bMWU1QkqUd32PppPf0whjyOIaQrOg+/TI8ldV+FEKLgyFYiGx4entdxCCHEPZkXHc/4yBisCh6zXuSjkjsoeWoF6sa19A4aDY5VQnBq2h3HKo1ss69CCCEKvhyvkW3RogURERF4eHjYtScmJvL4449LHVkhxAMTm5DK+xF/0zhlN11S1lIvbT/sAwVo3X0whHTFGPI4Oq+A/A5VCCFEHshxIrtu3TpSUlIytJtMJqKionIlKCGEuBvLlQtcX/gHvycsxFulr321oiHaoSolOz1FlVat0Oju+eaFQgghCoBs/5Tfu3ev7f8HDx4kNjbW9thisbBs2TKKFSuWu9EJIcRtlCWNlP2bMG2YR+qhrXgqBcAVjTtL9U1ZqG9OnIMvKxuWlSRWCCEeAdn+SV+zZk00Gg0ajYYWLVpk2O7k5MT06dNzNTghhACwXI3FtOlPzJsXYI2Ps7U7VqzPnsC2DN9dihQcpGSWEEI8YrKdyJ46dQqlFKVLl2b79u34+vratun1evz8/NDp5CIKIUTuUFYLqQe2kBw1j9T9m0FZAdC4emJs1Dl97atfcZoBS9umSsksIYR4BGU7kS1ZsiQAVqs1z4IRQghL/CXMm//EtOlPrFf/XcLkWL4Oxibd0ddojsbR/m5bUjJLCCEeTbKITAiRL26v/ervpiP10FZMUZGk7IsCqwUAjYs7hoYdMTbuhkNAUP4GLIQQ4qEjiawQ4oG7VfvVwxJPh5QoejtE4XTj39lXh7K1MDbphqFWCzSOhnyMVAghxMNMElkhxAMVe83MgjkrGWdeS+PUXTiQPvuqjG44BXfE2KQbDoGl8zlKIYQQBUG2EtnPP/+c/v37YzQaOXv2LMWLF0ej0eR1bEKIQsR6/RqmLQtRa+cx5cYFW/t+XVkWGEJ5tn8P6lXwyscIhRBCFDTZSmRHjhzJM888g9FopFSpUsTExODnl/l9yoUQ4halFKlHd2KKiiBlz1qwpOEI3MCJFfoQFhpCOakrjlYDrwe45Xe4QgghCphsJbJFixZl3rx5dOjQAaUU586dw2QyZdq3RIkSuRqgEKLgsd6Ix7x1MaaoCCxxZ23tDkFVMDbpThT1mL4oHqtCar8KIYS4Z9lKZN9++21eeeUVhgwZgkajoV69ehn6KKXQaDRYLJZcD1II8fBTSpF2fA+mjRGYd62BtPRbWWsMzhjqt8PYpDsOxSsA0A0IruIjtV+FEELcl2wlsv379+fZZ5/lzJkzVK9enVWrVuHt7Z3XsQkhCgBrUiLmbYsxbYzEEnPK1q4rXhGnJt3Q12uL1uiSYT+p/SqEEOJ+ZbtqgZubG1WrViU8PJyQkBAMBimJI8Sjxlb71dsR7yuHMEVFYt61ClLN6R30Rgx122Js2h3HkpXzN1ghhBCFXo7Lb/Xp0weAnTt3cujQITQaDZUqVaJ27dq5HpwQ4uExLzqeyREnaGneTJeUtThY/rZt0xUrl173tX57tE6u+RilEEKIR0mOE9m4uDieeeYZ1q1bh4eHB0opEhISCA0N5bfffsPX1zcv4hRC5BOlFBcP7CXppx/4I2UrTqSvfTXjiGPt1ni27IFDqapSkk8IIcQDp83pDq+88gqJiYkcOHCAq1evcu3aNfbv309iYiJDhw7Nixg5ffo0L730EqVKlcLJyYkyZcowbtw4UlJS7PqdPXuWzp074+Ligo+PD0OHDs3QRwiRPVZTEskb5hE/oTcOX/alQ8oGnEjhlLYYnzv1pnuRzznd8nUcS1eTJFYIIUS+yPGM7LJly1i1ahWVKlWytVWuXJkvv/ySNm3a5Gpwtxw+fBir1crXX39N2bJl2b9/P/369SMpKYlPPvkEAIvFQseOHfH19WXjxo1cuXKFPn36oJRi+vTpeRKXEIVR2tnDmKIiMEcvR5lvAqAc9KzU1mWBPpR9uvKg0aDVQAlvfT5HK4QQ4lGW40TWarXi6JjxSmNHR0esVmuuBPVf7dq1o127drbHpUuX5siRI8yYMcOWyK5YsYKDBw/y999/U7RoUQCmTJlCWFgYH374IUWKFMmT2IQoDJTpJuYdK2Dt78RfOG5r1/mXxNikO4YGHTAcggORMSC1X4UQQjwkcpzItmjRgmHDhvHrr7/aEsbz588zYsQIWrZsmesBZiUhIQEvr39vZ7llyxaqVq1qiwmgbdu2mM1mdu7cSWhoaKbHMZvNmM1m2+PExMS8C1qIh0zauaPps6/bl6FMSWgAdA7oa7XA2KQ7juVq25YNPFEPQsq7SO1XIYQQD40cJ7JffPEFXbt2JSgoiOLFi6PRaDh79izVqlXjp59+yosYMzhx4gTTp09nypQptrbY2Fj8/f3t+nl6eqLX64mNjc3yWB999BHvvvtuhvbLl2Iwm27kXtCFWFpaKpfiLuR3GCK7UsxwYCPsWI7m3BFbs/IKxFqrFdo6rTG7uGMGuBRjt6sOKOUKmOFS3IMMWuSUfC4LDxnLwkPGMvt8/YrevRP3kMgWL16cXbt2sXLlSg4fPoxSisqVK9OqVascBzl+/PhMk8jbRUdHU7duXdvjCxcu0K5dO5588kn69u1r1zezC05u3XEsK2PGjGHkyJG2x4mJiRQvXhwf30BZjpBNl+IuZPsNJx48W+1Xyzlcdy/EvG0JKvl6+katDn3NUIxNuuFYvi6XL8fKWBYS8rksPGQsCw8Zy9yX40T2ltatW9O6dev7OvmQIUN45pln7tgnKCjI9v8LFy4QGhpKcHAw33zzjV2/gIAAtm3bZtd27do1UlNTM8zU3s5gMMjNHUShFbEljqi5C+hsXoOD5Rimf9q13kUxNn4cY3BntO4++RqjEEIIca/uOZHNDT4+Pvj4ZO+X6Pnz5wkNDaVOnTqEh4ej1dpXDgsODubDDz8kJiaGwMBAIP0CMIPBQJ06dXI9diEeZmmxp7m6ei41Ni2iqUpKb0PLZsdahDzXC+86IWi0Oa6+J4QQQjxU8jWRza4LFy7QvHlzSpQowSeffMKlS5ds2wICAgBo06YNlStX5rnnnmPy5MlcvXqVV199lX79+skSAfFIUKkppOxZS3JUBGnHdqEF3IGLGi8WGZqzWN+MK1pPwr1L4C9JrBBCiEKgQCSyK1as4Pjx4xw/fpzHHnvMbptSCgCdTsfixYsZPHgwISEhODk50bNnT1t5LiEKK0vcWUxRkZi2LkLdiE9v1GixVmzEW3/XZ6uuOlZNeuIqtV+FEEIUJgUikQ0LCyMsLOyu/UqUKMGiRYvyPiAh8plKSyXlr/WYoiJIPRJta9d6+GEI6YqxURd0XgG0i45nq9R+FUIIUUjdUyJrtVo5fvw4cXFxGW6C0LRp01wJTAiRkeXSOUyb5mPavBB1/Wp6o0aDY+VgjE26o68agkb378f6iXoeUvtVCCFEoZXjRHbr1q307NmTM2fO2L7Wv0Wj0WCxWHItOCEeVbaSWT56/F01pOzdgCkqktRDW219NEW8MTbqirFxV3TeWZdzCXB3lARWCCFEoZTjRHbgwIHUrVuXxYsXExgYeMcarUKInJsXHc/4yBh8LZfplLKOpzUbMSRftW13rNQQY5Nu6Ks3tZt9FUIIIR41Of4teOzYMebOnUvZsmXzIh4hHmmxV5NZ8ftiJpjX0iBtL1rSv/VQrp44h3TBGPI4Ot/H7nIUIYQQ4tGQ40S2QYMGHD9+XBJZIXKR5dpFTJv+hA3z+TDp3/JyOx0qs0Dfgj79u1K/nEf+BSiEEEI8hHKcyL7yyiuMGjWK2NhYqlWrhqOj/dq76tWr51pwQhRmymoh9eBWTFERpOzbCMqKAxCvcWOpvjEL9aGc1wWg1cBYP5f8DlcIIYR46OQ4kX3iiScAePHFF21tGo0GpZRc7CVENlgTLmPa9CemTfOxXo21tTuUq41Tk+5ssNTimwVXsErJLCGEEOKOcpzInjp1Ki/iEKJQU1YrqYe3pc++7o0Ca/offBrnIhiCO2Fs3A2HgCAAugONKnlKySwhhBDiLnKcyJYsWTIv4hCiULImXsG0ZSGmjfOxXj5va3coUwNjk+4YarVAozdm2E9KZgkhhBB3d0+1e06cOMG0adM4dOgQGo2GSpUqMWzYMMqUKZPb8QlRINjVfXXTkXp0B6aoSFL+WgeWNAA0Tq4YGnTE2KQbDkXlsyKEEELcrxwnssuXL6dLly7UrFmTkJAQlFJs3ryZKlWqsHDhQlq3bp0XcQrx0LpV99XNcp32qVH0cdyAc+IF23aHUtUwNumGoU7rTGdfhRBCCHFvcpzIjh49mhEjRvDxxx9naH/jjTckkRWPlNj4FCLnrOUt8xqapu5ATxokgzI449SgQ/rs62Pl8ztMIYQQolDKcSJ76NAh5syZk6H9xRdfZNq0abkRkxAPPWtSAuati7Gumce0G2dt7Yd1pfhT34Kn+vegXkWffIxQCCGEKPxynMj6+vqyZ88eypUrZ9e+Z88e/Pz8ci0wIR42SinSTvyFKSoC867VkJaCHriJkVX6YBboQznmEIRWA6MC3fM7XCGEEKLQy3Ei269fP/r378/Jkydp1KgRGo2GjRs3MnHiREaNGpUXMQqRr6xJiZi3LcG0MRJLzElbu654BYxNurNR15BPFyVI3VchhBDiActxIjt27Fjc3NyYMmUKY8aMAaBo0aKMHz+eoUOH5nqAQuQHpRRpp/Zj2hiBecdKSDWnb9AbMdRtg7FJdxxKVkaj0fA40LCKr9R9FUIIIR6wHCeyGo2GESNGMGLECK5fvw6Am5tbrgcmRH6wJt/AvH0ppqhILOeP2dp1xcpibNwNQ4MOaJ1cM+wndV+FEEKIB++e6sjeIgmsKMhstV+9HfGOP4YpKhLzjuWQYkrv4GjAULtVeuWB0tXRaDT5G7AQQggh7GQrka1duzarV6/G09OTWrVq3fEX+q5du3ItOCHyyrzoeCZGnKSFeStdUtbgYDlj26YLKJVe97VBR7QuRfIxSiGEEELcSbYS2a5du2IwGGz/l5kpUZDFHtxPwk8/MDdlC86kz76m4IBDzRZ4tuiBQ9ma8h4XQgghCoBsJbLjxo2z/X/8+PF5FYsQeUaZkzHvWIEpKgKHMwfp8k/731p/FuhbsFzfmGmtq+BX2iVf4xRCCCFE9uV4jWzp0qWJjo7G29vbrj0+Pp7atWtz8uTJLPYU4sFLO3csve7r9qUoUxIASufAWm0dFuhD2e1QCTQatBoo4a3P52iFEEIIkRM5TmRPnz6NxWLJ0G42mzl37lyuBCXE/VApJsw7V2HaGEHayX22dq3vYxgbd8PYsBO6I1r+iowBqf0qhBBCFFjZTmQXLFhg+//y5ctxd//3zkUWi4XVq1dTqlSp3I1OiBxIizmZPvu6dQkqOb00HFod+hrNMDbpjmOFemi0WgCeqAch5V2k9qsQQghRgGU7kX388ceB9Dqyffr0sdvm6OhIUFAQU6ZMydXghLgblWrGvGs1pqhI0k7ssbVrvYtiDHkcY6POaN19Mt1Xar8KIYQQBVu2E1mr1QpAqVKliI6Oxscn8+RAiLxkq/2qYnDdsxDz1sWopIT0jVod+mpNMDbphmOlhrbZVyGEEEIUTjleI3vq1Km8iEOIu4rYGse6uYvpbF6LQ9qhfwpngdbTH2PjxzE06oLOwy9fYxRCCCHEg3NPd/ZKSkpi/fr1nD17lpSUFLttQ4cOzZXAhLjFEvc3V1bPo3rUApqq9LWvFjRsc6xBg5698K7fBI1Wl89RCiGEEOJBy3Eiu3v3bjp06MDNmzdJSkrCy8uLy5cv4+zsjJ+fnySyIlcoSxopf63HFBVB6uHtaAEP4JLGk8X6ZiwyNOOS1ptwvxL4SxIrhBBCPJJynMiOGDGCzp07M2PGDDw8PNi6dSuOjo707t2bYcOG5UWM4hFiuXwe08b5mLYsQCVeTW/UaLCWb8g75+qz2aEmFk164iq1X4UQQohHW44T2T179vD111+j0+nQ6XSYzWZKly7NpEmT6NOnD927d8+LOEUhpixppOzbiClqHqmHtoFSAGiKeGNs1AVj48fReReldXQ8m6T2qxBCCCH+keNE1tHR0XYfen9/f86ePUulSpVwd3fn7NmzuR6gKLwsV2MxbZqPedMCrAmXbO2OlRpgbNwdfY2maHT/vkWfqOchtV+FEEIIYZPjRLZWrVrs2LGD8uXLExoayjvvvMPly5f58ccfqVatWl7EKAo4W8ksHz3+blpSD2wmOSqC1P2bQaWXddO4emJs1Blj427ofB/L8lhS+1UIIYQQt+Q4kZ0wYQLXr6dfOf7+++/Tp08fBg0aRNmyZfn+++9zPUBRsM2Ljmd8ZAxelqt0StnAs7oojEm3zb6Wr4uxSTf0NUPROEiCKoQQQojsy3EiW7duXdv/fX19WbJkSa4GJAqP2GsmlsxZxnumtQSn7cGB9NlX5eyOc6NO6bOv/iXzOUohhBBCFFQ5vvVRixYtiI+Pz9CemJhIixYtciOmOzKbzdSsWRONRsOePXvstp09e5bOnTvj4uKCj48PQ4cOzVDnVuQ9a8Jlbi79HvVxDybemEKTtF04YGWPrgLvOw/k9IA5uDwxXJJYIYQQQtyXHM/Irlu3LtPk0GQyERUVlStB3cnrr79O0aJF+euvv+zaLRYLHTt2xNfXl40bN3LlyhX69OmDUorp06fneVyPOmW1knokGlNUBCl/rQerBUfgusaZZY6NWWgI5YyuGFoNjPF3y+9whRBCCFEIZDuR3bt3r+3/Bw8eJDY21vbYYrGwbNkyihUrlrvR/cfSpUtZsWIF8+bNY+nSpXbbVqxYwcGDB/n7778pWrQoAFOmTCEsLIwPP/yQIkWK5Glsjypr4lWImse1v9ZgvXTO1u5QujrGJt2JUnX5auFVrFIySwghhBC5LNuJ7K2v8zUaTaZLCJycnPJ05vPixYv069eP+fPn4+zsnGH7li1bqFq1qi2JBWjbti1ms5mdO3cSGhqa6XHNZjNms9n2ODExMfeDL2SUUqQe2YFpYwQpe9ahsaRhBTROrhgadMDYuBsOxcoC0A0IruwlJbOEEEIIkeuyncieOnUKpRSlS5dm+/bt+Pr62rbp9Xr8/PzQ6fLmVqFKKcLCwhg4cCB169bl9OnTGfrExsbi7+9v1+bp6Yler7ebPf6vjz76iHfffTdD++VLMZhNN+479kIlKRH2rIadK9BcuWBrthYrh6ZeO1SVxiTrjSQDxP27XQeUcgXMcCnuQQctciItLZVLt42dKLhkLAsPGcvCQ8Yy+3z9it69EzlIZEuWTL8wx2q13ltEmRg/fnymSeTtoqOj2bx5M4mJiYwZM+aOfW/dqOF2SqlM228ZM2YMI0eOtD1OTEykePHi+PgGPtLLEWy1X70d8b60H1NUBObdayAtFQCN0QVDvXYYm3TjmsEt22848XC7FHdBxrKQkLEsPGQsCw8Zy9yX44u9AE6cOMG0adM4dOgQGo2GSpUqMWzYMMqUKZOj4wwZMoRnnnnmjn2CgoL44IMP2Lp1KwaDwW5b3bp16dWrF7NnzyYgIIBt27bZbb927RqpqakZZmpvZzAYMhz3UTcvOp4p847R2ryRzinrcLD++9ejQ4lKGJt0w1C3LRrjP0s85K9LIYQQQuSDHCeyy5cvp0uXLtSsWZOQkBCUUmzevJkqVaqwcOFCWrdune1j+fj44OPjc9d+n3/+OR988IHt8YULF2jbti2///47DRo0ACA4OJgPP/yQmJgYAgMDgfQLwAwGA3Xq1Mnhs3w0KaW4uHcX5p9+5I+U7RhIn31NxoChXhs8W/bAsWTlfI5SCCGEECJdjhPZ0aNHM2LECD7++OMM7W+88UaOEtnsKlGihN1jV1dXAMqUKcNjj6XfzrRNmzZUrlyZ5557jsmTJ3P16lVeffVV+vXr90gvEcgO683rmLcvxRQVgcOFE7T5p/2YtgQLDaGs1Dfiy+YV8Cvpkq9xCiGEEELcLseJ7KFDh5gzZ06G9hdffJFp06blRkz3RKfTsXjxYgYPHkxISAhOTk707NmTTz75JN9iepgppUg7fSB97euOFZCaXrlBORpYpqnPn/oWHNKVBo0GrQZKeOvzOWIhhBBCCHs5TmR9fX3Zs2cP5cqVs2vfs2cPfn5+uRbYnQQFBaGUytBeokQJFi1a9EBiKKisyTcwb1+GaWMElnPHbO26wNIYmz6BoX57nA9YOBIZA1L7VQghhBAPsRwnsv369aN///6cPHmSRo0aodFo2LhxIxMnTmTUqFF5EaPIBWlnDpG8MQJz9HIwJ6c3Ougx1GmFsUl3HEpXt1V3eKIehJR3kdqvQgghhHio5TiRHTt2LG5ubkyZMsVWDqto0aKMHz+eoUOH5nqA4t4p003MO5Zjiook7ewhW7suIAhj424YGnZE6+Ke6b4B7o6SwAohhBDioZbjRFaj0TBixAhGjBjB9evXAXBzcwPg/PnzeX6bWpE1W+3XlNO47l6IOXoZypSUvtHBEUPNFhibdsehbK071tYVQgghhCgI7qmO7C23EtjY2Fg+/PBDvv32W5KTk3MlMJEzkVti2Tz3TzqZ1+JgOYHpn3atXwmMjR/HGNwZratHfoYohBBCCJGrtNntGB8fT69evfD19aVo0aJ8/vnnWK1W3nnnHUqXLs3WrVv5/vvv8zJWkYm088eJ+2EitX58mjdufksVywlS0bHWsT6Wvp/jOX4uzq2fkyRWCCGEEIVOtmdk33zzTTZs2ECfPn1YtmwZI0aMYNmyZZhMJpYuXUqzZs3yMk5xG5ViwrxrNaaoCNJO7kULuAIXtL4s1Ddnqb4p17TuhHuWwF+WEAghhBCikMp2Irt48WLCw8Np1aoVgwcPpmzZspQvXz5fa8c+atJiTmHaGIl562LUzcT0Rq0Oa6UmvHGmHtG6KihN+iS71H4VQgghRGGX7UT2woULVK6cfnvS0qVLYzQa6du3b54FJtKp1BTMu9dg2hhB2rHdtnatV2D62tdGXdC6+9ApOp4dkTEoqf0qhBBCiEdEthNZq9WKo+O/iZFOp8PFRW5ZmlcsF89g2hiJacsiVFJCeqNGi75aY4xNuuNYuSEarc7W/4l6HlL7VQghhBCPlGwnskopwsLCMBgMAJhMJgYOHJghmY2IiMjdCB8hKi2VlL/WYdoQQerRHbZ2racfxkaPYwjpgs7TP8v9pfarEEIIIR4l2U5k+/TpY/e4d+/euR7Mo8ZW91UTh9tfi9JnX69fTd+o0eBYJQRjk27oqzRCo7uvSmlCCCGEEIVOtrOj8PDwvIzjkTMvOp4Vvy/mcfMqyqTt51b1Xa27D4aQrhhDHkfnFZCvMQohhBBCPMxkmi8fxCakMj4yhrdTtlAvbT9WNOxwqEqdZ3ri3bC5zL4KIYQQQmSDZEz54MzlFKwK5hnaEKP1Y6G+ObE6X8IDS+AvSawQQgghRLZI1pQPSvro0WrggEM5DjiUA6TuqxBCCCFETmX7FrUi9wS4OzK+WyDaf266JXVfhRBCCCFyTmZk84nUfRVCCCGEuD+SyOYjqfsqhBBCCHHvZGmBEEIIIYQokCSRFUIIIYQQBZIkskIIIYQQokCSRFYIIYQQQhRIksgKIYQQQogCSaoW/IdSCoDExMR8jqTguH79OgajvF6FgYxl4SFjWXjIWBYeMpY54+bmhkajuWMfSWT/4/r16wAUL148nyMRQgghhHh0JSQkUKRIkTv20ahbU5ACAKvVyoULF7L1V4BIn7kuXrw4f//9913fbOLhJmNZeMhYFh4yloWHjGXOyYzsPdBqtTz22GP5HUaBU6RIEflgFhIyloWHjGXhIWNZeMhY5i652EsIIYQQQhRIksgKIYQQQogCSRJZcV8MBgPjxo3DYDDkdyjiPslYFh4yloWHjGXhIWOZN+RiLyGEEEIIUSDJjKwQQgghhCiQJJEVQgghhBAFkiSyQgghhBCiQJJEVgghhBBCFEiSyIr7ZjabqVmzJhqNhj179thtO3v2LJ07d8bFxQUfHx+GDh1KSkpK/gQqMnX69GleeuklSpUqhZOTE2XKlGHcuHEZxknGsmD46quvKFWqFEajkTp16hAVFZXfIYm7+Oijj6hXrx5ubm74+fnx+OOPc+TIEbs+SinGjx9P0aJFcXJyonnz5hw4cCCfIhbZ9dFHH6HRaBg+fLitTcYyd0kiK+7b66+/TtGiRTO0WywWOnbsSFJSEhs3buS3335j3rx5jBo1Kh+iFFk5fPgwVquVr7/+mgMHDvDpp58yc+ZM3nzzTVsfGcuC4ffff2f48OG89dZb7N69myZNmtC+fXvOnj2b36GJO1i/fj0vv/wyW7duZeXKlaSlpdGmTRuSkpJsfSZNmsTUqVP54osviI6OJiAggNatW3P9+vV8jFzcSXR0NN988w3Vq1e3a5exzGVKiPuwZMkSVbFiRXXgwAEFqN27d9tt02q16vz587a2X3/9VRkMBpWQkJAP0YrsmjRpkipVqpTtsYxlwVC/fn01cOBAu7aKFSuq0aNH51NE4l7ExcUpQK1fv14ppZTValUBAQHq448/tvUxmUzK3d1dzZw5M7/CFHdw/fp1Va5cObVy5UrVrFkzNWzYMKWUjGVekBlZcc8uXrxIv379+PHHH3F2ds6wfcuWLVStWtVutrZt27aYzWZ27tz5IEMVOZSQkICXl5ftsYzlwy8lJYWdO3fSpk0bu/Y2bdqwefPmfIpK3IuEhAQA22fw1KlTxMbG2o2twWCgWbNmMrYPqZdffpmOHTvSqlUru3YZy9znkN8BiIJJKUVYWBgDBw6kbt26nD59OkOf2NhY/P397do8PT3R6/XExsY+oEhFTp04cYLp06czZcoUW5uM5cPv8uXLWCyWDOPk7+8vY1SAKKUYOXIkjRs3pmrVqgC28ctsbM+cOfPAYxR39ttvv7Fr1y6io6MzbJOxzH0yIyvsjB8/Ho1Gc8d/O3bsYPr06SQmJjJmzJg7Hk+j0WRoU0pl2i5yV3bH8nYXLlygXbt2PPnkk/Tt29dum4xlwfDf8ZAxKliGDBnC3r17+fXXXzNsk7F9+P39998MGzaMn376CaPRmGU/GcvcIzOyws6QIUN45pln7tgnKCiIDz74gK1bt2a4Z3TdunXp1asXs2fPJiAggG3bttltv3btGqmpqRn+GhW5L7tjecuFCxcIDQ0lODiYb775xq6fjOXDz8fHB51Ol2H2NS4uTsaogHjllVdYsGABGzZs4LHHHrO1BwQEAOmzeYGBgbZ2GduHz86dO4mLi6NOnTq2NovFwoYNG/jiiy9s1ShkLHNRPq7PFQXYmTNn1L59+2z/li9frgA1d+5c9ffffyul/r1A6MKFC7b9fvvtN7lA6CF07tw5Va5cOfXMM8+otLS0DNtlLAuG+vXrq0GDBtm1VapUSS72eshZrVb18ssvq6JFi6qjR49muj0gIEBNnDjR1mY2m+UCoYdQYmKi3e/Gffv2qbp166revXurffv2yVjmAUlkRa44depUhqoFaWlpqmrVqqply5Zq165datWqVeqxxx5TQ4YMyb9ARQbnz59XZcuWVS1atFDnzp1TMTExtn+3yFgWDL/99ptydHRU3333nTp48KAaPny4cnFxUadPn87v0MQdDBo0SLm7u6t169bZff5u3rxp6/Pxxx8rd3d3FRERofbt26eeffZZFRgYqBITE/MxcpEdt1ctUErGMrdJIityRWaJrFLpM7cdO3ZUTk5OysvLSw0ZMkSZTKb8CVJkKjw8XAGZ/rudjGXB8OWXX6qSJUsqvV6vateubSvhJB5eWX3+wsPDbX2sVqsaN26cCggIUAaDQTVt2lTt27cv/4IW2fbfRFbGMndplFIqH1Y0CCGEEEIIcV+kaoEQQgghhCiQJJEVQgghhBAFkiSyQgghhBCiQJJEVgghhBBCFEiSyAohhBBCiAJJElkhhBBCCFEgSSIrhBBCCCEKJElkhRBCCCFEgSSJrBBCPCJmzZqFh4fHfR8nJSWFsmXLsmnTpiz7nD59Go1Gw549e+77fLerV68eERERuXpMIUTBJYmsEOKRERcXx4ABAyhRogQGg4GAgADatm3Lli1b8ju0AuWbb76hZMmShISEPPBzjx07ltGjR2O1Wh/4uYUQDx9JZIUQj4wnnniCv/76i9mzZ3P06FEWLFhA8+bNuXr1an6HVqBMnz6dvn375su5O3bsSEJCAsuXL8+X8wshHi6SyAohHgnx8fFs3LiRiRMnEhoaSsmSJalfvz5jxoyhY8eOdv369++Pv78/RqORqlWrsmjRIgCuXLnCs88+y2OPPYazszPVqlXj119/tTtP8+bNGTp0KK+//jpeXl4EBAQwfvz4O8YWHR1N69at8fHxwd3dnWbNmrFr1y67PhqNhm+//ZZu3brh7OxMuXLlWLBggV2fBQsWUK5cOZycnAgNDWX27NloNBri4+OzPPfChQupU6cORqOR0qVL8+6775KWlpZl/127dnH8+HG71wxg+/bt1KpVC6PRSN26ddm9e3eGfQ8ePEiHDh1wdXXF39+f5557jsuXL9u2X79+nV69euHi4kJgYCCffvopzZs3Z/jw4bY+Op2ODh06ZHjdhRCPJklkhRCPBFdXV1xdXZk/fz5msznTPlarlfbt27N582Z++uknDh48yMcff4xOpwPAZDJRp04dFi1axP79++nfvz/PPfcc27ZtszvO7NmzcXFxYdu2bUyaNIn33nuPlStXZhnb9evX6dOnD1FRUWzdupVy5crRoUMHrl+/btfv3Xff5amnnmLv3r106NCBXr162WaTT58+TY8ePXj88cfZs2cPAwYM4K233rrja7J8+XJ69+7N0KFDOXjwIF9//TWzZs3iww8/zHKfDRs2UL58eYoUKWJrS0pKolOnTlSoUIGdO3cyfvx4Xn31Vbv9YmJiaNasGTVr1mTHjh0sW7aMixcv8tRTT9n6jBw5kk2bNrFgwQJWrlxJVFRUhoQeoH79+kRFRd3xuQkhHhFKCCEeEXPnzlWenp7KaDSqRo0aqTFjxqi//vrLtn358uVKq9WqI0eOZPuYHTp0UKNGjbI9btasmWrcuLFdn3r16qk33ngj28dMS0tTbm5uauHChbY2QL399tu2xzdu3FAajUYtXbpUKaXUG2+8oapWrWp3nLfeeksB6tq1a0oppcLDw5W7u7tte5MmTdSECRPs9vnxxx9VYGBglrENGzZMtWjRwq7t66+/Vl5eXiopKcnWNmPGDAWo3bt3K6WUGjt2rGrTpo3dfn///bcC1JEjR1RiYqJydHRUf/zxh217fHy8cnZ2VsOGDbPb788//1RarVZZLJYs4xRCPBpkRlYI8ch44oknuHDhAgsWLKBt27asW7eO2rVrM2vWLAD27NnDY489Rvny5TPd32Kx8OGHH1K9enW8vb1xdXVlxYoVnD171q5f9erV7R4HBgYSFxeXZVxxcXEMHDiQ8uXL4+7ujru7Ozdu3LjjcV1cXHBzc7Md98iRI9SrV8+uf/369e/4euzcuZP33nvPNlvt6upKv379iImJ4ebNm5nuk5ycjNFotGs7dOgQNWrUwNnZ2dYWHByc4Vxr1661O1fFihUBOHHiBCdPniQ1NdUuZnd3dypUqJAhBicnJ6xWa5Yz60KIR4dDfgcghBAPktFopHXr1rRu3Zp33nmHvn37Mm7cOMLCwnBycrrjvlOmTOHTTz9l2rRpVKtWDRcXF4YPH05KSopdP0dHR7vHGo3mjlfZh4WFcenSJaZNm0bJkiUxGAwEBwfn6LhKKTQajd12pdQdn4/VauXdd9+le/fuGbb9N1m9xcfHh3379uXoPLfO1blzZyZOnJhhW2BgIMeOHQPI1nO4evUqzs7Odx0vIUThJzOyQohHWuXKlUlKSgLSZzzPnTvH0aNHM+0bFRVF165d6d27NzVq1KB06dK2BOx+REVFMXToUDp06ECVKlUwGAx2F0FlR8WKFYmOjrZr27Fjxx33qV27NkeOHKFs2bIZ/mm1mf96qFWrFocPH7ZLMCtXrsxff/1FcnKyrW3r1q0ZznXgwAGCgoIynMvFxYUyZcrg6OjI9u3bbfskJiZm+vru37+f2rVr3/G5CSEeDZLICiEeCVeuXKFFixb89NNP7N27l1OnTvHHH38wadIkunbtCkCzZs1o2rQpTzzxBCtXruTUqVMsXbqUZcuWAVC2bFlWrlzJ5s2bOXToEAMGDCA2Nva+Yytbtiw//vgjhw4dYtu2bfTq1SvHs40DBgzg8OHDvPHGGxw9epQ5c+bYlkz8d5bzlnfeeYcffviB8ePHc+DAAQ4dOsTvv//O22+/neV5QkNDSUpK4sCBA7a2nj17otVqeemllzh48CBLlizhk08+sdvv5Zdf5urVqzz77LNs376dkydPsmLFCl588UUsFgtubm706dOH1157jbVr13LgwAFefPFFtFpthvijoqJo06ZNjl4fIUThJImsEOKR4OrqSoMGDfj0009p2rQpVatWZezYsfTr148vvvjC1m/evHnUq1ePZ599lsqVK/P6669jsViA9GL8tWvXpm3btjRv3pyAgAAef/zx+47t+++/59q1a9SqVYvnnnuOoUOH4ufnl6NjlCpVirlz5xIREUH16tWZMWOGrWqBwWDIdJ+2bduyaNEiVq5cSb169WjYsCFTp06lZMmSWZ7H29ub7t278/PPP9vaXF1dWbhwIQcPHqRWrVq89dZbGZYQFC1alE2bNmGxWGjbti1Vq1Zl2LBhuLu722Z/p06dSnBwMJ06daJVq1aEhIRQqVIlu2UO58+fZ/Pmzbzwwgs5en2EEIWTRmVncZMQQogC58MPP2TmzJn8/fffuXrcffv20apVK44fP46bm1uuHvt2SUlJFCtWjClTpvDSSy8B8Nprr5GQkMA333yTZ+cVQhQccrGXEEIUEl999RX16tXD29ubTZs2MXnyZIYMGZLr56lWrRqTJk3i9OnTVKtWLdeOu3v3bg4fPkz9+vVJSEjgvffeA7At/QDw8/PLUKNWCPHokhlZIYQoJEaMGMHvv//O1atXKVGiBM899xxjxozBwaFgzFns3r2bvn37cuTIEfR6PXXq1GHq1Km5miwLIQoXSWSFEEIIIUSBJBd7CSGEEEKIAkkSWSGEEEIIUSBJIiuEEEIIIQokSWSFEEIIIUSBJImsEEIIIYQokCSRFUIIIYQQBZIksv9vtw5IAAAAAAT9f92OQFcIAMCSyAIAsBQXWvgsInLV0QAAAABJRU5ErkJggg==",
"text/plain": [
""
]
@@ -801,9 +922,18 @@
"rotation_fit = np.polyfit(measured, rotation, 1)\n",
"fig, ax = plt.subplots(figsize=(7, 3.4))\n",
"ax.plot(measured, rotation, color=BLUE, marker=\"o\", ms=3, lw=0, label=\"measured\")\n",
- "ax.plot(measured, np.polyval(rotation_fit, measured), color=ORANGE, label=f\"fit: {rotation_fit[0]:+.3f} x scan angle {rotation_fit[1]:+.2f} deg\")\n",
+ "ax.plot(\n",
+ " measured,\n",
+ " np.polyval(rotation_fit, measured),\n",
+ " color=ORANGE,\n",
+ " label=f\"fit: {rotation_fit[0]:+.3f} x scan angle {rotation_fit[1]:+.2f} deg\",\n",
+ ")\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
- "ax.set(xlabel=\"Scan angle (deg)\", ylabel=\"Rotation of the FOV array (deg)\", title=\"Rotation of the CrIS view array with scan angle\")\n",
+ "ax.set(\n",
+ " xlabel=\"Scan angle (deg)\",\n",
+ " ylabel=\"Rotation of the FOV array (deg)\",\n",
+ " title=\"Rotation of the CrIS view array with scan angle\",\n",
+ ")\n",
"ax.legend(frameon=False)\n",
"fig.tight_layout()\n",
"plt.show()\n",
@@ -811,7 +941,9 @@
" {\n",
" \"rotation per degree of scan angle\": rotation_fit[0],\n",
" \"rotation at nadir (deg)\": rotation_fit[1],\n",
- " \"fit residual, max |x| (deg)\": np.abs(np.polyval(rotation_fit, measured) - rotation).max(),\n",
+ " \"fit residual, max |x| (deg)\": np.abs(\n",
+ " np.polyval(rotation_fit, measured) - rotation\n",
+ " ).max(),\n",
" \"rotation at the left edge (deg)\": rotation[measured.argmax()],\n",
" \"rotation at the right edge (deg)\": rotation[measured.argmin()],\n",
" }\n",
@@ -836,16 +968,16 @@
"id": "dedffedc",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:39.658540Z",
- "iopub.status.busy": "2026-10-15T18:43:39.657490Z",
- "iopub.status.idle": "2026-10-15T18:43:39.787258Z",
- "shell.execute_reply": "2026-10-15T18:43:39.786551Z"
+ "iopub.execute_input": "2026-10-25T13:54:44.601038Z",
+ "iopub.status.busy": "2026-10-25T13:54:44.601038Z",
+ "iopub.status.idle": "2026-10-25T13:54:44.750574Z",
+ "shell.execute_reply": "2026-10-25T13:54:44.750069Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -936,24 +1068,46 @@
" \"\"\"Signed angle (deg) from the horizontal projection of a reference direction to that of a direction.\"\"\"\n",
" a = normalize(reference - np.sum(reference * u2, axis=-1, keepdims=True) * u2)\n",
" b = normalize(direction - np.sum(direction * u2, axis=-1, keepdims=True) * u2)\n",
- " return np.degrees(np.arctan2(np.sum(np.cross(a, b) * u2, axis=-1), np.sum(a * b, axis=-1)))\n",
+ " return np.degrees(\n",
+ " np.arctan2(np.sum(np.cross(a, b) * u2, axis=-1), np.sum(a * b, axis=-1))\n",
+ " )\n",
"\n",
"\n",
"normal2 = normal[i2]\n",
"yaw_earth_fixed = yaw(normal2, sc_vel[i2])\n",
"yaw_inertial = yaw(normal2, inertial_velocity(sc_pos[i2], sc_vel[i2]))\n",
"ascending = sc_vel[i2, 2] > 0\n",
- "out_of_plane = np.degrees(np.abs(np.arcsin(np.einsum(\"kjc,kc->kj\", los_for[i2], normal2))))\n",
+ "out_of_plane = np.degrees(\n",
+ " np.abs(np.arcsin(np.einsum(\"kjc,kc->kj\", los_for[i2], normal2)))\n",
+ ")\n",
"# tilt of the scan plane forward of nadir (positive forward), from the angle between the plane normal and the nadir at each field of regard\n",
- "tilt = -np.degrees(np.arcsin(np.sum(nadir * normal[:, np.newaxis], axis=-1))) # (scan, FOR)\n",
+ "tilt = -np.degrees(\n",
+ " np.arcsin(np.sum(nadir * normal[:, np.newaxis], axis=-1))\n",
+ ") # (scan, FOR)\n",
"scan_plane_tilt = float(np.median(tilt))\n",
"\n",
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
- "for mask, color, label in [(ascending, BLUE, \"ascending\"), (~ascending, AQUA, \"descending\")]:\n",
- " ax.scatter(sub_lat[mask], yaw_earth_fixed[mask], s=6, color=color, label=f\"vs. Earth-fixed velocity, {label}\")\n",
- "ax.scatter(sub_lat, yaw_inertial, s=6, color=ORANGE, label=\"vs. inertial velocity, both\")\n",
+ "for mask, color, label in [\n",
+ " (ascending, BLUE, \"ascending\"),\n",
+ " (~ascending, AQUA, \"descending\"),\n",
+ "]:\n",
+ " ax.scatter(\n",
+ " sub_lat[mask],\n",
+ " yaw_earth_fixed[mask],\n",
+ " s=6,\n",
+ " color=color,\n",
+ " label=f\"vs. Earth-fixed velocity, {label}\",\n",
+ " )\n",
+ "ax.scatter(\n",
+ " sub_lat, yaw_inertial, s=6, color=ORANGE, label=\"vs. inertial velocity, both\"\n",
+ ")\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
- "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Scan plane yaw (deg)\", title=\"Orientation of the CrIS scan plane\", ylim=(-5, 5))\n",
+ "ax.set(\n",
+ " xlabel=\"Sub-satellite latitude (deg)\",\n",
+ " ylabel=\"Scan plane yaw (deg)\",\n",
+ " title=\"Orientation of the CrIS scan plane\",\n",
+ " ylim=(-5, 5),\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, markerscale=3, loc=\"center\")\n",
"fig.tight_layout()\n",
"plt.show()\n",
@@ -972,14 +1126,14 @@
"id": "e6b6df40",
"metadata": {},
"source": [
- "Like ATMS, the scan plane faces the inertial velocity at all latitudes, to within a constant 0.17 deg, whereas the Earth-fixed velocity is rotated by up to about 4 deg, with opposite signs on ascending and descending passes. A `PointedInstrument` with `velocity_frame=VelocityFrame.INERTIAL` reproduces this orientation. The scan plane is also tilted 0.22 deg forward of nadir on both ascending and descending passes (about 3 km on the ground), which `PointedInstrument.pitch_angle` can reproduce.\n",
+ "Like ATMS, the scan plane faces the inertial velocity at all latitudes, to within a constant 0.17 deg, whereas the Earth-fixed velocity is rotated by up to about 4 deg, with opposite signs on ascending and descending passes. A satellite with an inertial velocity frame (`Attitude(velocity_frame=VelocityFrame.INERTIAL)`), whose instruments' views are rotated from its body, reproduces this orientation. The scan plane is also tilted 0.22 deg forward of nadir on both ascending and descending passes (about 3 km on the ground), which `PointedInstrument.pitch_angle` can reproduce.\n",
"\n",
"## Test 3: Geolocation\n",
"\n",
"TAT-C pixel positions from `collect_ground_pixels`, at the middle time of every twentieth scan, are compared with the reported FOV positions. Five configurations isolate the contributions of the along-track reference direction, the scan angles, the scan plane tilt, and scan timing:\n",
"\n",
"* **Nominal, Earth-fixed:** 3.333 deg FOR spacing with TAT-C's default velocity frame.\n",
- "* **Nominal, inertial:** the same with `velocity_frame=VelocityFrame.INERTIAL`.\n",
+ "* **Nominal, inertial:** the same on a satellite with `Attitude(velocity_frame=VelocityFrame.INERTIAL)`.\n",
"* **Fitted, inertial:** the FOR spacing, roll angle, and FOV pitch fitted in Test 2.\n",
"* **Fitted, inertial, tilted:** the same with the scan plane tilt as `pitch_angle`.\n",
"* **Fitted, inertial, tilted, sample times:** the same, but with each FOR projected at its own sample time rather than all at the scan's middle time. TAT-C does not do this; it is computed here (with `PointedInstrument.compute_projected_pixel_position`) to quantify the effect of scan timing.\n",
@@ -993,10 +1147,10 @@
"id": "853055c9",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:39.788820Z",
- "iopub.status.busy": "2026-10-15T18:43:39.788820Z",
- "iopub.status.idle": "2026-10-15T18:43:43.540486Z",
- "shell.execute_reply": "2026-10-15T18:43:43.540486Z"
+ "iopub.execute_input": "2026-10-25T13:54:44.752578Z",
+ "iopub.status.busy": "2026-10-25T13:54:44.752578Z",
+ "iopub.status.idle": "2026-10-25T13:54:50.505191Z",
+ "shell.execute_reply": "2026-10-25T13:54:50.505191Z"
}
},
"outputs": [
@@ -1033,38 +1187,38 @@
"
along
\n",
"
-2.83
\n",
"
59.21
\n",
- "
93.66
\n",
+ "
93.67
\n",
"
\n",
"
\n",
"
right
\n",
"
-1.73
\n",
"
4.25
\n",
- "
6.02
\n",
+ "
6.00
\n",
"
\n",
"
\n",
"
horizontal
\n",
"
12.42
\n",
"
59.33
\n",
- "
93.69
\n",
+ "
93.70
\n",
"
\n",
"
\n",
"
nominal, inertial
\n",
"
along
\n",
- "
-2.88
\n",
+ "
-2.89
\n",
"
21.22
\n",
"
22.95
\n",
"
\n",
"
\n",
"
right
\n",
- "
-1.77
\n",
+ "
-1.76
\n",
"
3.61
\n",
- "
4.90
\n",
+ "
4.91
\n",
"
\n",
"
\n",
"
horizontal
\n",
- "
10.13
\n",
+ "
10.12
\n",
"
21.51
\n",
- "
23.45
\n",
+ "
23.46
\n",
"
\n",
"
\n",
"
fitted, inertial
\n",
@@ -1088,34 +1242,34 @@
"
\n",
"
fitted, inertial, tilted
\n",
"
along
\n",
- "
0.40
\n",
- "
19.04
\n",
- "
19.59
\n",
+ "
0.39
\n",
+ "
19.05
\n",
+ "
19.58
\n",
"
\n",
"
\n",
"
right
\n",
"
0.01
\n",
- "
0.35
\n",
- "
0.70
\n",
+ "
0.36
\n",
+ "
0.72
\n",
"
\n",
"
\n",
"
horizontal
\n",
- "
10.00
\n",
- "
19.04
\n",
- "
19.59
\n",
+ "
9.99
\n",
+ "
19.05
\n",
+ "
19.58
\n",
"
\n",
"
\n",
"
fitted, inertial, tilted, sample times
\n",
"
along
\n",
- "
0.45
\n",
+ "
0.44
\n",
"
0.67
\n",
- "
0.72
\n",
+ "
0.71
\n",
"
\n",
"
\n",
"
right
\n",
"
0.01
\n",
- "
0.33
\n",
- "
0.65
\n",
+ "
0.35
\n",
+ "
0.66
\n",
"
\n",
"
\n",
"
horizontal
\n",
@@ -1129,20 +1283,20 @@
],
"text/plain": [
" median p95 |x| max |x|\n",
- "nominal, Earth-fixed along -2.83 59.21 93.66\n",
- " right -1.73 4.25 6.02\n",
- " horizontal 12.42 59.33 93.69\n",
- "nominal, inertial along -2.88 21.22 22.95\n",
- " right -1.77 3.61 4.90\n",
- " horizontal 10.13 21.51 23.45\n",
+ "nominal, Earth-fixed along -2.83 59.21 93.67\n",
+ " right -1.73 4.25 6.00\n",
+ " horizontal 12.42 59.33 93.70\n",
+ "nominal, inertial along -2.89 21.22 22.95\n",
+ " right -1.76 3.61 4.91\n",
+ " horizontal 10.12 21.51 23.46\n",
"fitted, inertial along -2.92 21.23 22.86\n",
" right -0.04 0.26 0.49\n",
" horizontal 9.96 21.23 22.86\n",
- "fitted, inertial, tilted along 0.40 19.04 19.59\n",
- " right 0.01 0.35 0.70\n",
- " horizontal 10.00 19.04 19.59\n",
- "fitted, inertial, tilted, sample times along 0.45 0.67 0.72\n",
- " right 0.01 0.33 0.65\n",
+ "fitted, inertial, tilted along 0.39 19.05 19.58\n",
+ " right 0.01 0.36 0.72\n",
+ " horizontal 9.99 19.05 19.58\n",
+ "fitted, inertial, tilted, sample times along 0.44 0.67 0.71\n",
+ " right 0.01 0.35 0.66\n",
" horizontal 0.48 0.70 0.74"
]
},
@@ -1180,15 +1334,15 @@
"
\n",
@@ -1664,15 +1869,15 @@
"rectangular, 128 s (53.36 deg along track) 80.827 \n",
"\n",
" TAT-C (10^6 km^2) IoU \\\n",
- "field of regard, 60 s 79.852 0.986 \n",
- "rectangular, 8 s (3.60 deg along track) 80.248 0.991 \n",
+ "field of regard, 60 s 79.853 0.986 \n",
+ "rectangular, 8 s (3.60 deg along track) 80.249 0.991 \n",
"rectangular, 32 s (14.32 deg along track) 80.323 0.991 \n",
"rectangular, 128 s (53.36 deg along track) 81.167 0.987 \n",
"\n",
" observed only (%) TAT-C only (%) \n",
"field of regard, 60 s 1.281 0.076 \n",
- "rectangular, 8 s (3.60 deg along track) 0.816 0.101 \n",
- "rectangular, 32 s (14.32 deg along track) 0.763 0.139 \n",
+ "rectangular, 8 s (3.60 deg along track) 0.815 0.101 \n",
+ "rectangular, 32 s (14.32 deg along track) 0.762 0.140 \n",
"rectangular, 128 s (53.36 deg along track) 0.440 0.857 "
]
},
@@ -1702,14 +1907,28 @@
" valid = (kk >= 0) & (kk < len(cells) - 1) & (jj >= 0) & (jj < N_FOR - 1)\n",
" kk, jj = np.clip(kk, 0, len(cells) - 2), np.clip(jj, 0, N_FOR - 2)\n",
" valid &= contiguous[k0 + kk]\n",
- " corners = [cells[kk, jj], cells[kk, jj + 1], cells[kk + 1, jj + 1], cells[kk + 1, jj]]\n",
- " signs = np.stack([np.sum(np.cross(a, b) * points, axis=-1) for a, b in zip(corners, corners[1:] + corners[:1])])\n",
- " inside |= valid & (np.all(signs >= 0, axis=0) | np.all(signs <= 0, axis=0))\n",
+ " corners = [\n",
+ " cells[kk, jj],\n",
+ " cells[kk, jj + 1],\n",
+ " cells[kk + 1, jj + 1],\n",
+ " cells[kk + 1, jj],\n",
+ " ]\n",
+ " signs = np.stack(\n",
+ " [\n",
+ " np.sum(np.cross(a, b) * points, axis=-1)\n",
+ " for a, b in zip(corners, corners[1:] + corners[:1])\n",
+ " ]\n",
+ " )\n",
+ " inside |= valid & (\n",
+ " np.all(signs >= 0, axis=0) | np.all(signs <= 0, axis=0)\n",
+ " )\n",
" return inside\n",
"\n",
"\n",
"STEP4, MARGIN4 = 0.1, 128 # deg, s\n",
- "lon_grid, lat_grid = np.meshgrid(np.arange(-180 + STEP4 / 2, 180, STEP4), np.arange(-90 + STEP4 / 2, 90, STEP4))\n",
+ "lon_grid, lat_grid = np.meshgrid(\n",
+ " np.arange(-180 + STEP4 / 2, 180, STEP4), np.arange(-90 + STEP4 / 2, 90, STEP4)\n",
+ ")\n",
"cell_area = np.cos(np.radians(lat_grid)) * (STEP4 * 111.32) ** 2 # km^2\n",
"grid_unit = up(lon_grid.ravel(), lat_grid.ravel())\n",
"orbit_start = datetime(2026, 10, 2, 12, tzinfo=timezone.utc)\n",
@@ -1717,22 +1936,36 @@
"scans4 = np.nonzero((t_mid >= orbit_start) & (t_mid < orbit_start + orbit_period))[0]\n",
"first, last = t_mid[scans4[0]].to_pydatetime(), t_mid[scans4[-1]].to_pydatetime()\n",
"# grid cells near fields of regard sampled away from the ends of the orbit\n",
- "distance, nearest = cKDTree(for_unit[scans4].reshape(-1, 3)).query(grid_unit, workers=-1)\n",
- "nearest_time = (t_mid[scans4] - t_mid[scans4[0]]).total_seconds().to_numpy()[nearest // N_FOR]\n",
- "compared = (distance < 2 * np.sin(300 / 6371 / 2)) & (nearest_time > MARGIN4) & (nearest_time < (last - first).total_seconds() - MARGIN4)\n",
+ "distance, nearest = cKDTree(for_unit[scans4].reshape(-1, 3)).query(\n",
+ " grid_unit, workers=-1\n",
+ ")\n",
+ "nearest_time = (\n",
+ " (t_mid[scans4] - t_mid[scans4[0]]).total_seconds().to_numpy()[nearest // N_FOR]\n",
+ ")\n",
+ "compared = (\n",
+ " (distance < 2 * np.sin(300 / 6371 / 2))\n",
+ " & (nearest_time > MARGIN4)\n",
+ " & (nearest_time < (last - first).total_seconds() - MARGIN4)\n",
+ ")\n",
"observed4 = np.zeros(len(grid_unit), dtype=bool)\n",
"observed4[compared] = observed_cells(grid_unit[compared], scans4)\n",
- "ground_speed = 2 * np.pi * 6371.0088e3 / orbit_period.total_seconds() # m/s, approximate\n",
+ "ground_speed = (\n",
+ " 2 * np.pi * 6371.0088e3 / orbit_period.total_seconds()\n",
+ ") # m/s, approximate\n",
"altitude = float(np.median(sub4.elevation.m))\n",
"\n",
"\n",
- "def swath_area_row(instrument, time_step):\n",
+ "def swath_area_row(instrument, time_step, attitude=None):\n",
" n = int((last - first).total_seconds() // time_step)\n",
" times = [first + timedelta(seconds=time_step * (i + 0.5)) for i in range(n)]\n",
- " geometry = compute_ground_track(noaa20.model_copy(update={\"instruments\": [instrument]}), times).geometry.iloc[0]\n",
+ " geometry = compute_ground_track(\n",
+ " on_noaa20(instrument, attitude), times\n",
+ " ).geometry.iloc[0]\n",
" prepare(geometry)\n",
" predicted = np.zeros(len(grid_unit), dtype=bool)\n",
- " predicted[compared] = contains_xy(geometry, lon_grid.ravel()[compared], lat_grid.ravel()[compared])\n",
+ " predicted[compared] = contains_xy(\n",
+ " geometry, lon_grid.ravel()[compared], lat_grid.ravel()[compared]\n",
+ " )\n",
" area = lambda mask: np.sum(mask * cell_area.ravel()) / 1e6\n",
" return predicted.reshape(lat_grid.shape), {\n",
" \"observed (10^6 km^2)\": area(observed4),\n",
@@ -1744,8 +1977,8 @@
"\n",
"\n",
"swath_rows, swath_maps = {}, {}\n",
- "swath_maps[\"field of regard, 60 s\"], swath_rows[\"field of regard, 60 s\"] = swath_area_row(\n",
- " Instrument(name=\"CrIS\", field_of_regard=2 * EDGE), 60\n",
+ "swath_maps[\"field of regard, 60 s\"], swath_rows[\"field of regard, 60 s\"] = (\n",
+ " swath_area_row(Instrument(name=\"CrIS\", field_of_regard=2 * EDGE), 60)\n",
")\n",
"for time_step in [SCAN_PERIOD, 32, 128]:\n",
" along_fov = np.degrees(2 * np.arctan(ground_speed * time_step / 2 / altitude))\n",
@@ -1757,9 +1990,9 @@
" cross_track_field_of_view=2 * EDGE,\n",
" along_track_field_of_view=along_fov,\n",
" is_rectangular=True,\n",
- " velocity_frame=VelocityFrame.INERTIAL,\n",
" ),\n",
" time_step,\n",
+ " INERTIAL,\n",
" )\n",
"observed4 = observed4.reshape(lat_grid.shape)\n",
"pd.DataFrame(swath_rows).T.round(3)"
@@ -1771,16 +2004,16 @@
"id": "8856aebf",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:43:56.704305Z",
- "iopub.status.busy": "2026-10-15T18:43:56.704305Z",
- "iopub.status.idle": "2026-10-15T18:44:02.789270Z",
- "shell.execute_reply": "2026-10-15T18:44:02.789270Z"
+ "iopub.execute_input": "2026-10-25T13:55:04.648989Z",
+ "iopub.status.busy": "2026-10-25T13:55:04.648989Z",
+ "iopub.status.idle": "2026-10-25T13:55:11.663760Z",
+ "shell.execute_reply": "2026-10-25T13:55:11.663760Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -1790,15 +2023,24 @@
}
],
"source": [
- "fig, axes = plt.subplots(1, 2, figsize=(12, 4.2), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, axes = plt.subplots(\n",
+ " 1, 2, figsize=(12, 4.2), subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"for ax, name in zip(axes, [list(swath_maps)[0], list(swath_maps)[-1]]):\n",
" predicted = swath_maps[name]\n",
- " category = np.where(observed4 & predicted, 1, np.where(observed4, 2, np.where(predicted, 3, 0))).astype(float)\n",
+ " category = np.where(\n",
+ " observed4 & predicted, 1, np.where(observed4, 2, np.where(predicted, 3, 0))\n",
+ " ).astype(float)\n",
" category[~compared.reshape(lat_grid.shape)] = 0\n",
" ax.pcolormesh(\n",
- " lon_grid, lat_grid, np.ma.masked_equal(category, 0),\n",
- " cmap=plt.matplotlib.colors.ListedColormap([\"#d9d8d3\", ORANGE, BLUE]), vmin=0.5, vmax=3.5,\n",
- " transform=ccrs.PlateCarree(), shading=\"auto\",\n",
+ " lon_grid,\n",
+ " lat_grid,\n",
+ " np.ma.masked_equal(category, 0),\n",
+ " cmap=plt.matplotlib.colors.ListedColormap([\"#d9d8d3\", ORANGE, BLUE]),\n",
+ " vmin=0.5,\n",
+ " vmax=3.5,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " shading=\"auto\",\n",
" )\n",
" ax.coastlines(color=GRAY, linewidth=0.5)\n",
" ax.set_global()\n",
@@ -1831,10 +2073,10 @@
"id": "b3cfb72c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:44:02.791185Z",
- "iopub.status.busy": "2026-10-15T18:44:02.791185Z",
- "iopub.status.idle": "2026-10-15T18:44:10.430323Z",
- "shell.execute_reply": "2026-10-15T18:44:10.430323Z"
+ "iopub.execute_input": "2026-10-25T13:55:11.666765Z",
+ "iopub.status.busy": "2026-10-25T13:55:11.666765Z",
+ "iopub.status.idle": "2026-10-25T13:55:21.336698Z",
+ "shell.execute_reply": "2026-10-25T13:55:21.336192Z"
}
},
"outputs": [
@@ -1911,12 +2153,16 @@
"source": [
"# TAT-C's satellite zenith angle (from the satellite's elevation as seen from each pixel) at the fitted configuration\n",
"track5 = collect_ground_pixels(\n",
- " noaa20.model_copy(update={\"instruments\": [configs[\"fitted, inertial, tilted\"]]}), times3, sat_altaz=True\n",
+ " configs[\"fitted, inertial, tilted\"], times3, sat_altaz=True\n",
")\n",
- "zenith_model = per_for(90 - track5[\"sat_alt\"].to_numpy().reshape(len(i3), N_PIX, 3))[:, :, center] # (scan, FOR)\n",
+ "zenith_model = per_for(90 - track5[\"sat_alt\"].to_numpy().reshape(len(i3), N_PIX, 3))[\n",
+ " :, :, center\n",
+ "] # (scan, FOR)\n",
"zenith_reported = fov_zenith[i3][:, :, center]\n",
"range_reported = fov_range[i3][:, :, center]\n",
- "altitude5 = wgs84.geographic_position_of(orbit.get_orbit_track(times3)).elevation.m # of the sub-satellite point\n",
+ "altitude5 = wgs84.geographic_position_of(\n",
+ " orbit.get_orbit_track(times3)\n",
+ ").elevation.m # of the sub-satellite point\n",
"\n",
"\n",
"def slant_range(altitude, zenith, radius=EARTH_MEAN_RADIUS):\n",
@@ -1935,10 +2181,15 @@
"range_model = slant_range(altitude5[:, np.newaxis], zenith_model)\n",
"test5 = pd.DataFrame(\n",
" {\n",
- " \"zenith angle, TAT-C minus reported (deg)\": summarize(zenith_model - zenith_reported),\n",
- " \"slant range, TAT-C minus reported (km)\": summarize((range_model - range_reported) / 1e3),\n",
+ " \"zenith angle, TAT-C minus reported (deg)\": summarize(\n",
+ " zenith_model - zenith_reported\n",
+ " ),\n",
+ " \"slant range, TAT-C minus reported (km)\": summarize(\n",
+ " (range_model - range_reported) / 1e3\n",
+ " ),\n",
" \"equivalent diameter, TAT-C minus reported (km)\": summarize(\n",
- " footprint_size(range_model, zenith_model, FOV_DIAMETER)[2] - footprint_size(range_reported, zenith_reported, FOV_DIAMETER)[2]\n",
+ " footprint_size(range_model, zenith_model, FOV_DIAMETER)[2]\n",
+ " - footprint_size(range_reported, zenith_reported, FOV_DIAMETER)[2]\n",
" ),\n",
" }\n",
").T\n",
@@ -1951,10 +2202,10 @@
"id": "4ca6f3ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:44:10.430323Z",
- "iopub.status.busy": "2026-10-15T18:44:10.430323Z",
- "iopub.status.idle": "2026-10-15T18:44:10.445787Z",
- "shell.execute_reply": "2026-10-15T18:44:10.445787Z"
+ "iopub.execute_input": "2026-10-25T13:55:21.338699Z",
+ "iopub.status.busy": "2026-10-25T13:55:21.337699Z",
+ "iopub.status.idle": "2026-10-25T13:55:21.353277Z",
+ "shell.execute_reply": "2026-10-25T13:55:21.353277Z"
}
},
"outputs": [
@@ -2125,9 +2376,22 @@
],
"source": [
"# measured spacing (km) of the views of an array: the mean distance from the central view to its four nearest neighbors\n",
- "neighbors = [m for m in range(N_FOV) if abs(pixel_cross[m] - 1) + abs(pixel_along[m] - 1) == 1]\n",
- "view_spacing = np.mean([np.linalg.norm(fov_xyz[:, :, m] - fov_xyz[:, :, center], axis=-1) for m in neighbors], axis=0) / 1e3 # (scan, FOR)\n",
- "along_r, cross_r, equivalent_r = footprint_size(range_reported, zenith_reported, FOV_DIAMETER)\n",
+ "neighbors = [\n",
+ " m for m in range(N_FOV) if abs(pixel_cross[m] - 1) + abs(pixel_along[m] - 1) == 1\n",
+ "]\n",
+ "view_spacing = (\n",
+ " np.mean(\n",
+ " [\n",
+ " np.linalg.norm(fov_xyz[:, :, m] - fov_xyz[:, :, center], axis=-1)\n",
+ " for m in neighbors\n",
+ " ],\n",
+ " axis=0,\n",
+ " )\n",
+ " / 1e3\n",
+ ") # (scan, FOR)\n",
+ "along_r, cross_r, equivalent_r = footprint_size(\n",
+ " range_reported, zenith_reported, FOV_DIAMETER\n",
+ ")\n",
"by_for = lambda a: a.mean(axis=0) # mean over scans of each field of regard\n",
"rows = [0, 4, 9, 14, 15, 19, 24, 29]\n",
"table5 = pd.DataFrame(\n",
@@ -2151,16 +2415,16 @@
"id": "2391013a",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:44:10.447832Z",
- "iopub.status.busy": "2026-10-15T18:44:10.446796Z",
- "iopub.status.idle": "2026-10-15T18:44:10.644708Z",
- "shell.execute_reply": "2026-10-15T18:44:10.643381Z"
+ "iopub.execute_input": "2026-10-25T13:55:21.355791Z",
+ "iopub.status.busy": "2026-10-25T13:55:21.354786Z",
+ "iopub.status.idle": "2026-10-25T13:55:21.491876Z",
+ "shell.execute_reply": "2026-10-25T13:55:21.491876Z"
}
},
"outputs": [
{
"data": {
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",
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",
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""
]
@@ -2174,8 +2438,18 @@
"ax.plot(np.arange(N_FOR), by_for(along_r), color=BLUE, label=\"along-track size\")\n",
"ax.plot(np.arange(N_FOR), by_for(cross_r), color=ORANGE, label=\"cross-track size\")\n",
"ax.plot(np.arange(N_FOR), by_for(equivalent_r), color=INK, label=\"equivalent diameter\")\n",
- "ax.plot(np.arange(N_FOR), by_for(view_spacing), color=GRAY, ls=\"--\", label=\"spacing of views\")\n",
- "ax.set(xlabel=\"CrIS field of regard index (0 = first scanned)\", ylabel=\"Size or spacing (km)\", title=\"CrIS footprint size\")\n",
+ "ax.plot(\n",
+ " np.arange(N_FOR),\n",
+ " by_for(view_spacing),\n",
+ " color=GRAY,\n",
+ " ls=\"--\",\n",
+ " label=\"spacing of views\",\n",
+ ")\n",
+ "ax.set(\n",
+ " xlabel=\"CrIS field of regard index (0 = first scanned)\",\n",
+ " ylabel=\"Size or spacing (km)\",\n",
+ " title=\"CrIS footprint size\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -2205,10 +2479,10 @@
"id": "724c596c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-15T18:44:10.646692Z",
- "iopub.status.busy": "2026-10-15T18:44:10.646692Z",
- "iopub.status.idle": "2026-10-15T18:44:10.658820Z",
- "shell.execute_reply": "2026-10-15T18:44:10.658820Z"
+ "iopub.execute_input": "2026-10-25T13:55:21.495307Z",
+ "iopub.status.busy": "2026-10-25T13:55:21.494309Z",
+ "iopub.status.idle": "2026-10-25T13:55:21.501443Z",
+ "shell.execute_reply": "2026-10-25T13:55:21.501443Z"
}
},
"outputs": [
@@ -2224,7 +2498,7 @@
"field of regard, between outermost FOR centers (deg) 96.685708\n",
"footprint equivalent diameter at nadir (km) 14.10401\n",
"footprint equivalent diameter at the swath edge (km) 31.942358\n",
- "center geolocation error, 95th percentile (km) 0.696837\n",
+ "center geolocation error, 95th percentile (km) 0.697072\n",
"dtype: object"
]
},
@@ -2242,11 +2516,21 @@
" \"scan plane tilt, pitch angle (deg)\": scan_plane_tilt,\n",
" \"FOV pitch (deg)\": fov_pitch,\n",
" \"FOV diameter, published (deg)\": FOV_DIAMETER,\n",
- " \"field of regard, between outermost FOR centers (deg)\": measured.max() - measured.min(),\n",
- " \"footprint equivalent diameter at nadir (km)\": by_for(equivalent_r)[N_FOR // 2 - 1 : N_FOR // 2 + 1].mean(),\n",
- " \"footprint equivalent diameter at the swath edge (km)\": by_for(equivalent_r)[[0, -1]].mean(),\n",
+ " \"field of regard, between outermost FOR centers (deg)\": measured.max()\n",
+ " - measured.min(),\n",
+ " \"footprint equivalent diameter at nadir (km)\": by_for(equivalent_r)[\n",
+ " N_FOR // 2 - 1 : N_FOR // 2 + 1\n",
+ " ].mean(),\n",
+ " \"footprint equivalent diameter at the swath edge (km)\": by_for(equivalent_r)[\n",
+ " [0, -1]\n",
+ " ].mean(),\n",
" \"center geolocation error, 95th percentile (km)\": summarize(\n",
- " np.hypot(*[per_for(v)[:, :, center] for v in residuals3[\"fitted, inertial, tilted, sample times\"]])\n",
+ " np.hypot(\n",
+ " *[\n",
+ " per_for(v)[:, :, center]\n",
+ " for v in residuals3[\"fitted, inertial, tilted, sample times\"]\n",
+ " ]\n",
+ " )\n",
" ).loc[\"p95 |x|\"],\n",
" }\n",
").round(3)"
@@ -2269,7 +2553,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.16"
+ "version": "3.12.8"
}
},
"nbformat": 4,
diff --git a/docs/validation/ValidateImagerVIIRS.ipynb b/docs/validation/ValidateImagerVIIRS.ipynb
index 70fa81bd..60030667 100644
--- a/docs/validation/ValidateImagerVIIRS.ipynb
+++ b/docs/validation/ValidateImagerVIIRS.ipynb
@@ -9,7 +9,7 @@
"\n",
"This notebook validates TAT-C's representation of horizontal imagers against the [Visible Infrared Imaging Radiometer Suite (VIIRS)](https://www.star.nesdis.noaa.gov/jpss/VIIRS.php) aboard NOAA-20 (JPSS-1). VIIRS is a 22-band visible and infrared radiometer with a rotating telescope that scans perpendicular to the flight direction every 1.79 s, within 56.28 deg of nadir (a swath about 3,040 km wide). Each scan images 16 rows of moderate-resolution (M-band) detectors at once, about 750 m apart at nadir, so a scan covers a strip about 12 km long along track: about the distance NOAA-20's ground track advances in one scan period. Across track, the samples of each row are aggregated on board into 3,200 pixels, in three zones that keep the pixel size more uniform across the scan.\n",
"\n",
- "As described in the companion notebook, [Imager Validation: NOAA-20 ATMS](ValidateImagerATMS.ipynb), TAT-C represents a nadir-pointing imager as an `Instrument` with a field of regard or a `PointedInstrument` with cross-track and along-track fields of view, and does not model the scan mechanism: the along-track field of view stands in for the time the instrument takes to sweep the ground. VIIRS is a natural test of this convention, since its 16-detector array is designed to cover the ground traveled in one scan. The ATMS notebook found that NOAA-20 scans perpendicular to its *inertial* velocity and that `PointedInstrument.velocity_frame=VelocityFrame.INERTIAL` reproduces this. This notebook asks five questions:\n",
+ "As described in the companion notebook, [Imager Validation: NOAA-20 ATMS](ValidateImagerATMS.ipynb), TAT-C represents a nadir-pointing imager as an `Instrument` with a field of regard or a `PointedInstrument` with cross-track and along-track fields of view, and does not model the scan mechanism: the along-track field of view stands in for the time the instrument takes to sweep the ground. VIIRS is a natural test of this convention, since its 16-detector array is designed to cover the ground traveled in one scan. The ATMS notebook found that NOAA-20 scans perpendicular to its *inertial* velocity and that a satellite with an inertial velocity frame (`Attitude(velocity_frame=VelocityFrame.INERTIAL)`), whose instruments' views are rotated from its body, reproduces this. This notebook asks five questions:\n",
"\n",
"1. **Orbit.** Does TAT-C's propagation of the operational two-line element (TLE) set reproduce NOAA-20's position?\n",
"2. **Scan geometry.** What are VIIRS's scan angles and the angular extent of its detector array, and is its scan perpendicular to the inertial velocity like that of ATMS?\n",
@@ -39,10 +39,10 @@
"id": "49f40a34",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:19.249243Z",
- "iopub.status.busy": "2026-10-08T17:41:19.248243Z",
- "iopub.status.idle": "2026-10-08T17:41:21.522460Z",
- "shell.execute_reply": "2026-10-08T17:41:21.522460Z"
+ "iopub.execute_input": "2026-10-25T13:56:18.204395Z",
+ "iopub.status.busy": "2026-10-25T13:56:18.203395Z",
+ "iopub.status.idle": "2026-10-25T13:56:19.134412Z",
+ "shell.execute_reply": "2026-10-25T13:56:19.134412Z"
}
},
"outputs": [
@@ -95,7 +95,9 @@
" params[\"continuation-token\"] = token\n",
" listing = requests.get(BUCKET, params=params, timeout=60).text\n",
" keys += re.findall(r\"(.*?)\", listing)\n",
- " match = re.search(r\"(.*?)\", listing)\n",
+ " match = re.search(\n",
+ " r\"(.*?)\", listing\n",
+ " )\n",
" if match is None:\n",
" return keys\n",
" token = match.group(1)\n",
@@ -117,9 +119,16 @@
" \"\"\"Read-only file object that downloads blocks of a remote object with HTTP range requests on demand.\"\"\"\n",
"\n",
" def __init__(self, key, block_size=2**16):\n",
- " self.url, self.block_size, self.blocks, self.position = BUCKET + key, block_size, {}, 0\n",
+ " self.url, self.block_size, self.blocks, self.position = (\n",
+ " BUCKET + key,\n",
+ " block_size,\n",
+ " {},\n",
+ " 0,\n",
+ " )\n",
" self.session = requests.Session()\n",
- " self.size = int(self.session.head(self.url, timeout=60).headers[\"Content-Length\"])\n",
+ " self.size = int(\n",
+ " self.session.head(self.url, timeout=60).headers[\"Content-Length\"]\n",
+ " )\n",
"\n",
" def readable(self):\n",
" return True\n",
@@ -131,30 +140,48 @@
" return self.position\n",
"\n",
" def seek(self, offset, whence=io.SEEK_SET):\n",
- " self.position = {io.SEEK_SET: 0, io.SEEK_CUR: self.position, io.SEEK_END: self.size}[whence] + offset\n",
+ " self.position = {\n",
+ " io.SEEK_SET: 0,\n",
+ " io.SEEK_CUR: self.position,\n",
+ " io.SEEK_END: self.size,\n",
+ " }[whence] + offset\n",
" return self.position\n",
"\n",
" def fetch(self, start, end):\n",
" \"\"\"Download (block-aligned) bytes [start, end) into the cache.\"\"\"\n",
" for attempt in range(5):\n",
" try:\n",
- " response = self.session.get(self.url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=120)\n",
+ " response = self.session.get(\n",
+ " self.url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=120\n",
+ " )\n",
" response.raise_for_status()\n",
" break\n",
" except requests.RequestException:\n",
" if attempt == 4:\n",
" raise\n",
- " for i, block in enumerate(range(start // self.block_size, (end - 1) // self.block_size + 1)):\n",
- " self.blocks[block] = response.content[i * self.block_size : (i + 1) * self.block_size]\n",
+ " for i, block in enumerate(\n",
+ " range(start // self.block_size, (end - 1) // self.block_size + 1)\n",
+ " ):\n",
+ " self.blocks[block] = response.content[\n",
+ " i * self.block_size : (i + 1) * self.block_size\n",
+ " ]\n",
"\n",
" def readinto(self, buffer):\n",
" n = min(len(buffer), self.size - self.position)\n",
" if n <= 0:\n",
" return 0\n",
- " first, last = self.position // self.block_size, (self.position + n - 1) // self.block_size\n",
- " missing = [block for block in range(first, last + 1) if block not in self.blocks]\n",
+ " first, last = (\n",
+ " self.position // self.block_size,\n",
+ " (self.position + n - 1) // self.block_size,\n",
+ " )\n",
+ " missing = [\n",
+ " block for block in range(first, last + 1) if block not in self.blocks\n",
+ " ]\n",
" if missing:\n",
- " self.fetch(missing[0] * self.block_size, min((missing[-1] + 1) * self.block_size, self.size))\n",
+ " self.fetch(\n",
+ " missing[0] * self.block_size,\n",
+ " min((missing[-1] + 1) * self.block_size, self.size),\n",
+ " )\n",
" data = b\"\".join(self.blocks[block] for block in range(first, last + 1))\n",
" start = self.position - first * self.block_size\n",
" buffer[:n] = data[start : start + n]\n",
@@ -166,16 +193,24 @@
" \"\"\"Open a remote granule, prefetching the metadata at the start and end of the file.\"\"\"\n",
" remote = RemoteFile(key)\n",
" remote.fetch(0, remote.block_size)\n",
- " remote.fetch((remote.size - 2**19) // remote.block_size * remote.block_size, remote.size)\n",
+ " remote.fetch(\n",
+ " (remote.size - 2**19) // remote.block_size * remote.block_size, remote.size\n",
+ " )\n",
" return h5py.File(remote)\n",
"\n",
"\n",
"TLE_FILE = DATA_DIR / f\"n20_tle_{DAY:%Y%m%d}.txt\"\n",
"if not TLE_FILE.exists():\n",
" # the element set with an epoch at the start of the day\n",
- " key = next(k for k in list_keys(f\"TLE-AUX/{DAY:%Y/%m/%d}/\") if f\"Z_{DAY:%Y%m%d}000000Z_\" in k)\n",
+ " key = next(\n",
+ " k\n",
+ " for k in list_keys(f\"TLE-AUX/{DAY:%Y/%m/%d}/\")\n",
+ " if f\"Z_{DAY:%Y%m%d}000000Z_\" in k\n",
+ " )\n",
" with h5py.File(io.BytesIO(fetch(key))) as f:\n",
- " TLE_FILE.write_text(bytes(f[\"All_Data/TLE-AUX_All/Dataset_Array_0\"][:]).decode().strip() + \"\\n\")\n",
+ " TLE_FILE.write_text(\n",
+ " bytes(f[\"All_Data/TLE-AUX_All/Dataset_Array_0\"][:]).decode().strip() + \"\\n\"\n",
+ " )\n",
"tle = TLE_FILE.read_text().splitlines()\n",
"\n",
"GEO = \"All_Data/VIIRS-MOD-GEO-TC_All/\"\n",
@@ -184,7 +219,9 @@
"PIXELS_FILE = DATA_DIR / f\"viirs_gmtco_pixels_{DAY:%Y%m%d}.npz\"\n",
"N_SCANS, N_DETECTORS, N_SAMPLES = 48, 16, 3200\n",
"PIXEL_GRANULES = 10 # every tenth granule\n",
- "COLUMNS = np.unique(np.r_[np.arange(0, N_SAMPLES, 8), N_SAMPLES - 1]) # subsampled columns\n",
+ "COLUMNS = np.unique(\n",
+ " np.r_[np.arange(0, N_SAMPLES, 8), N_SAMPLES - 1]\n",
+ ") # subsampled columns\n",
"ROWS = np.array([0, 7, 8, 15]) # detectors kept for every scan\n",
"\n",
"if not (SCANS_FILE.exists() and PIXELS_FILE.exists()):\n",
@@ -197,12 +234,18 @@
" \"gring_lat\": granule.attrs[\"G-Ring_Latitude\"].ravel(),\n",
" \"gring_lon\": granule.attrs[\"G-Ring_Longitude\"].ravel(),\n",
" \"n_scans\": int(granule.attrs[\"N_Number_Of_Scans\"].ravel()[0]),\n",
- " **{field: f[GEO + field][:] for field in [\"StartTime\", \"MidTime\", \"SCPosition\", \"SCVelocity\"]},\n",
+ " **{\n",
+ " field: f[GEO + field][:]\n",
+ " for field in [\"StartTime\", \"MidTime\", \"SCPosition\", \"SCVelocity\"]\n",
+ " },\n",
" }\n",
"\n",
" def read_pixels(key):\n",
" with open_granule(key) as f:\n",
- " lat, lon, height = (f[GEO + field][:].reshape(N_SCANS, N_DETECTORS, N_SAMPLES) for field in [\"Latitude\", \"Longitude\", \"Height\"])\n",
+ " lat, lon, height = (\n",
+ " f[GEO + field][:].reshape(N_SCANS, N_DETECTORS, N_SAMPLES)\n",
+ " for field in [\"Latitude\", \"Longitude\", \"Height\"]\n",
+ " )\n",
" center = N_SCANS // 2\n",
" return {\n",
" # selected detectors of every scan, at subsampled columns\n",
@@ -218,7 +261,11 @@
" with concurrent.futures.ThreadPoolExecutor(16) as pool:\n",
" scans = list(pool.map(read_scans, keys))\n",
" pixels = list(pool.map(read_pixels, keys[::PIXEL_GRANULES]))\n",
- " np.savez_compressed(SCANS_FILE, keys=np.array(keys), **{field: np.stack([s[field] for s in scans]) for field in scans[0]})\n",
+ " np.savez_compressed(\n",
+ " SCANS_FILE,\n",
+ " keys=np.array(keys),\n",
+ " **{field: np.stack([s[field] for s in scans]) for field in scans[0]},\n",
+ " )\n",
" np.savez_compressed(\n",
" PIXELS_FILE,\n",
" granule=np.arange(len(keys))[::PIXEL_GRANULES],\n",
@@ -255,10 +302,10 @@
"id": "61503429",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:21.525466Z",
- "iopub.status.busy": "2026-10-08T17:41:21.524466Z",
- "iopub.status.idle": "2026-10-08T17:41:24.610803Z",
- "shell.execute_reply": "2026-10-08T17:41:24.609797Z"
+ "iopub.execute_input": "2026-10-25T13:56:19.136416Z",
+ "iopub.status.busy": "2026-10-25T13:56:19.136416Z",
+ "iopub.status.idle": "2026-10-25T13:56:20.785405Z",
+ "shell.execute_reply": "2026-10-25T13:56:20.784901Z"
}
},
"outputs": [
@@ -289,7 +336,14 @@
"from tatc.analysis import collect_observations\n",
"from tatc.constants import EARTH_MEAN_RADIUS, EARTH_ROTATION_RATE\n",
"from tatc.generation import generate_points_fibonacci_lattice\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, Point, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Instrument,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"from tatc.utils import (\n",
" VelocityFrame,\n",
" ViewGeometry,\n",
@@ -307,16 +361,24 @@
"\n",
"\n",
"def iet_to_utc(iet):\n",
- " return IET_EPOCH + pd.to_timedelta(np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\")\n",
+ " return IET_EPOCH + pd.to_timedelta(\n",
+ " np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\"\n",
+ " )\n",
"\n",
"\n",
"# scans (granule, scan) of every granule, in time order\n",
"valid_scan = np.arange(N_SCANS)[np.newaxis, :] < viirs[\"n_scans\"][:, np.newaxis]\n",
"scan_mid = iet_to_utc(viirs[\"MidTime\"][valid_scan])\n",
- "scan_pos, scan_vel = viirs[\"SCPosition\"][valid_scan].astype(float), viirs[\"SCVelocity\"][valid_scan].astype(float)\n",
+ "scan_pos, scan_vel = viirs[\"SCPosition\"][valid_scan].astype(float), viirs[\"SCVelocity\"][\n",
+ " valid_scan\n",
+ "].astype(float)\n",
"scan_period = np.median(np.diff(viirs[\"StartTime\"][valid_scan])) / 1e6\n",
"\n",
- "noaa20 = Satellite(name=\"NOAA-20\", orbit=GeneralPerturbationsOrbit.from_tle(tle))\n",
+ "# not yaw-steered: the body (and its instruments' views) aligned with the inertial velocity\n",
+ "INERTIAL = Attitude(velocity_frame=VelocityFrame.INERTIAL)\n",
+ "noaa20 = Satellite(\n",
+ " name=\"NOAA-20\", orbit=GeneralPerturbationsOrbit.from_tle(tle), attitude=INERTIAL\n",
+ ")\n",
"orbit = noaa20.orbit.to_gp_orbit()\n",
"\n",
"TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
@@ -325,13 +387,17 @@
"\n",
"def ecef(lon, lat, height=0.0):\n",
" \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
- " return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+ " return np.stack(\n",
+ " TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1\n",
+ " )\n",
"\n",
"\n",
"def up(lon, lat):\n",
" \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
" lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -347,7 +413,9 @@
"\n",
"def view_tangents(position, velocity, target):\n",
" \"\"\"Along-track and cross-track (positive left) view angle tangents of targets in TAT-C's instrument frame.\"\"\"\n",
- " lon, lat, _ = TO_GEODETIC.transform(position[..., 0], position[..., 1], position[..., 2])\n",
+ " lon, lat, _ = TO_GEODETIC.transform(\n",
+ " position[..., 0], position[..., 1], position[..., 2]\n",
+ " )\n",
" n = -up(lon, lat)\n",
" v = normalize(velocity)\n",
" c = np.cross(v, n)\n",
@@ -360,13 +428,25 @@
" \"\"\"Summary statistics of a residual sample.\"\"\"\n",
" x = np.asarray(x, dtype=float)\n",
" x = x[np.isfinite(x)]\n",
- " return pd.Series({\"n\": x.size, \"median\": np.median(x), \"p95 |x|\": np.percentile(np.abs(x), 95), \"max |x|\": np.abs(x).max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": x.size,\n",
+ " \"median\": np.median(x),\n",
+ " \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+ " \"max |x|\": np.abs(x).max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"def binned(x, y, edges, q=(5, 50, 95)):\n",
" \"\"\"Percentiles of y in bins of x.\"\"\"\n",
" i = np.digitize(x, edges) - 1\n",
- " return np.array([np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q) for k in range(len(edges) - 1)])\n",
+ " return np.array(\n",
+ " [\n",
+ " np.percentile(y[i == k], q) if np.any(i == k) else [np.nan] * len(q)\n",
+ " for k in range(len(edges) - 1)\n",
+ " ]\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -379,7 +459,14 @@
" \"grid.linewidth\": 0.6,\n",
" }\n",
")\n",
- "pd.Series({\"scans\": len(scan_mid), \"first scan (UTC)\": scan_mid[0], \"last scan (UTC)\": scan_mid[-1], \"scan period (s)\": scan_period})"
+ "pd.Series(\n",
+ " {\n",
+ " \"scans\": len(scan_mid),\n",
+ " \"first scan (UTC)\": scan_mid[0],\n",
+ " \"last scan (UTC)\": scan_mid[-1],\n",
+ " \"scan period (s)\": scan_period,\n",
+ " }\n",
+ ")"
]
},
{
@@ -398,10 +485,10 @@
"id": "a43b7848",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:24.612802Z",
- "iopub.status.busy": "2026-10-08T17:41:24.611803Z",
- "iopub.status.idle": "2026-10-08T17:41:24.683187Z",
- "shell.execute_reply": "2026-10-08T17:41:24.682681Z"
+ "iopub.execute_input": "2026-10-25T13:56:20.787407Z",
+ "iopub.status.busy": "2026-10-25T13:56:20.787407Z",
+ "iopub.status.idle": "2026-10-25T13:56:20.832378Z",
+ "shell.execute_reply": "2026-10-25T13:56:20.832378Z"
}
},
"outputs": [
@@ -483,13 +570,19 @@
"p1, v1 = orbit.get_orbit_track(list(scan_mid[i1])).frame_xyz_and_velocity(itrs)\n",
"offset1 = np.array(p1.m).T - scan_pos[i1]\n",
"radial1 = normalize(scan_pos[i1])\n",
- "along1 = normalize(scan_vel[i1] - np.sum(scan_vel[i1] * radial1, axis=-1, keepdims=True) * radial1)\n",
+ "along1 = normalize(\n",
+ " scan_vel[i1] - np.sum(scan_vel[i1] * radial1, axis=-1, keepdims=True) * radial1\n",
+ ")\n",
"pd.DataFrame(\n",
" {\n",
" \"along-track (km)\": summarize(np.sum(offset1 * along1, axis=-1) / 1e3),\n",
- " \"cross-track, right (km)\": summarize(np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3),\n",
+ " \"cross-track, right (km)\": summarize(\n",
+ " np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3\n",
+ " ),\n",
" \"radial (km)\": summarize(np.sum(offset1 * radial1, axis=-1) / 1e3),\n",
- " \"velocity (m/s)\": summarize(np.linalg.norm(np.array(v1.m_per_s).T - scan_vel[i1], axis=-1)),\n",
+ " \"velocity (m/s)\": summarize(\n",
+ " np.linalg.norm(np.array(v1.m_per_s).T - scan_vel[i1], axis=-1)\n",
+ " ),\n",
" }\n",
").T.round(3)"
]
@@ -516,10 +609,10 @@
"id": "3a8f939c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:24.685193Z",
- "iopub.status.busy": "2026-10-08T17:41:24.685193Z",
- "iopub.status.idle": "2026-10-08T17:41:24.911936Z",
- "shell.execute_reply": "2026-10-08T17:41:24.911936Z"
+ "iopub.execute_input": "2026-10-25T13:56:20.834383Z",
+ "iopub.status.busy": "2026-10-25T13:56:20.834383Z",
+ "iopub.status.idle": "2026-10-25T13:56:21.002905Z",
+ "shell.execute_reply": "2026-10-25T13:56:21.002905Z"
}
},
"outputs": [
@@ -543,8 +636,14 @@
"source": [
"granules = viirs_pixels[\"granule\"]\n",
"center = N_SCANS // 2\n",
- "full_xyz = ecef(viirs_pixels[\"lon_full\"], viirs_pixels[\"lat_full\"], viirs_pixels[\"height_full\"]).mean(axis=1) # (granule, sample, 3)\n",
- "full_ok = np.all(np.abs(viirs_pixels[\"lat_full\"]) <= 90, axis=(1, 2)) & (center < viirs[\"n_scans\"][granules])\n",
+ "full_xyz = ecef(\n",
+ " viirs_pixels[\"lon_full\"], viirs_pixels[\"lat_full\"], viirs_pixels[\"height_full\"]\n",
+ ").mean(\n",
+ " axis=1\n",
+ ") # (granule, sample, 3)\n",
+ "full_ok = np.all(np.abs(viirs_pixels[\"lat_full\"]) <= 90, axis=(1, 2)) & (\n",
+ " center < viirs[\"n_scans\"][granules]\n",
+ ")\n",
"pos_c = viirs[\"SCPosition\"][granules, center].astype(float)\n",
"vel_c = inertial_velocity(pos_c, viirs[\"SCVelocity\"][granules, center].astype(float))\n",
"_, cross_full = view_tangents(pos_c[:, np.newaxis], vel_c[:, np.newaxis], full_xyz)\n",
@@ -558,10 +657,17 @@
" {\n",
" \"first sample, measured (deg)\": measured_angle[0],\n",
" \"last sample, measured (deg)\": measured_angle[-1],\n",
- " \"field of regard, measured (deg)\": np.abs(measured_angle[0] - measured_angle[-1]),\n",
+ " \"field of regard, measured (deg)\": np.abs(\n",
+ " measured_angle[0] - measured_angle[-1]\n",
+ " ),\n",
" \"field of regard, nominal (deg)\": 2 * EDGE,\n",
- " \"granule-to-granule std. dev., median (deg)\": np.median(np.std(sample_angle, axis=0)),\n",
- " \"uniform 3,200-pixel grid, max |error| (deg)\": np.abs(measured_angle - np.linspace(measured_angle[0], measured_angle[-1], N_SAMPLES)).max(),\n",
+ " \"granule-to-granule std. dev., median (deg)\": np.median(\n",
+ " np.std(sample_angle, axis=0)\n",
+ " ),\n",
+ " \"uniform 3,200-pixel grid, max |error| (deg)\": np.abs(\n",
+ " measured_angle\n",
+ " - np.linspace(measured_angle[0], measured_angle[-1], N_SAMPLES)\n",
+ " ).max(),\n",
" }\n",
").round(4)"
]
@@ -572,10 +678,10 @@
"id": "dad2115e",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:24.914942Z",
- "iopub.status.busy": "2026-10-08T17:41:24.913943Z",
- "iopub.status.idle": "2026-10-08T17:41:25.273124Z",
- "shell.execute_reply": "2026-10-08T17:41:25.272117Z"
+ "iopub.execute_input": "2026-10-25T13:56:21.004910Z",
+ "iopub.status.busy": "2026-10-25T13:56:21.004910Z",
+ "iopub.status.idle": "2026-10-25T13:56:21.273350Z",
+ "shell.execute_reply": "2026-10-25T13:56:21.273350Z"
}
},
"outputs": [
@@ -685,17 +791,40 @@
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(11, 3.6))\n",
- "axes[0].plot(np.arange(N_SAMPLES), measured_angle, color=BLUE, label=\"measured (median)\")\n",
- "axes[0].plot(np.arange(N_SAMPLES), np.linspace(measured_angle[0], measured_angle[-1], N_SAMPLES), color=ORANGE, ls=\"--\", label=\"uniform 3,200-pixel grid\")\n",
- "axes[0].set(xlabel=\"Sample index\", ylabel=\"Scan angle (deg, positive left)\", title=\"VIIRS M-band scan angles\")\n",
+ "axes[0].plot(\n",
+ " np.arange(N_SAMPLES), measured_angle, color=BLUE, label=\"measured (median)\"\n",
+ ")\n",
+ "axes[0].plot(\n",
+ " np.arange(N_SAMPLES),\n",
+ " np.linspace(measured_angle[0], measured_angle[-1], N_SAMPLES),\n",
+ " color=ORANGE,\n",
+ " ls=\"--\",\n",
+ " label=\"uniform 3,200-pixel grid\",\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Sample index\",\n",
+ " ylabel=\"Scan angle (deg, positive left)\",\n",
+ " title=\"VIIRS M-band scan angles\",\n",
+ ")\n",
"axes[1].plot(np.arange(N_SAMPLES - 1) + 0.5, spacing * 1e3, color=BLUE)\n",
- "axes[1].set(xlabel=\"Sample index\", ylabel=\"Sample spacing (mdeg)\", title=\"Sample spacing (aggregation zones)\", ylim=(0, None))\n",
+ "axes[1].set(\n",
+ " xlabel=\"Sample index\",\n",
+ " ylabel=\"Sample spacing (mdeg)\",\n",
+ " title=\"Sample spacing (aggregation zones)\",\n",
+ " ylim=(0, None),\n",
+ ")\n",
"axes[0].legend(frameon=False)\n",
"fig.tight_layout()\n",
"plt.show()\n",
"pd.DataFrame(\n",
" [\n",
- " {\"first sample\": a, \"last sample\": b, \"samples aggregated\": aggregation[a], \"spacing (mdeg)\": 1e3 * np.median(spacing[a:b]), \"scan angles (deg)\": f\"{measured_angle[a]:.2f} to {measured_angle[b]:.2f}\"}\n",
+ " {\n",
+ " \"first sample\": a,\n",
+ " \"last sample\": b,\n",
+ " \"samples aggregated\": aggregation[a],\n",
+ " \"spacing (mdeg)\": 1e3 * np.median(spacing[a:b]),\n",
+ " \"scan angles (deg)\": f\"{measured_angle[a]:.2f} to {measured_angle[b]:.2f}\",\n",
+ " }\n",
" for a, b in zip(zones[:-1], zones[1:])\n",
" ]\n",
").round(2)"
@@ -721,10 +850,10 @@
"id": "65806c1b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:25.275425Z",
- "iopub.status.busy": "2026-10-08T17:41:25.275124Z",
- "iopub.status.idle": "2026-10-08T17:41:29.986792Z",
- "shell.execute_reply": "2026-10-08T17:41:29.986792Z"
+ "iopub.execute_input": "2026-10-25T13:56:21.275354Z",
+ "iopub.status.busy": "2026-10-25T13:56:21.275354Z",
+ "iopub.status.idle": "2026-10-25T13:56:24.650515Z",
+ "shell.execute_reply": "2026-10-25T13:56:24.649510Z"
}
},
"outputs": [
@@ -745,23 +874,45 @@
}
],
"source": [
- "px_ok = np.all(np.abs(viirs_pixels[\"lat\"]) <= 90, axis=(2, 3)) & (np.arange(N_SCANS)[np.newaxis, :] < viirs[\"n_scans\"][granules, np.newaxis])\n",
+ "px_ok = np.all(np.abs(viirs_pixels[\"lat\"]) <= 90, axis=(2, 3)) & (\n",
+ " np.arange(N_SCANS)[np.newaxis, :] < viirs[\"n_scans\"][granules, np.newaxis]\n",
+ ")\n",
"pos_p = viirs[\"SCPosition\"][granules].astype(float)[px_ok]\n",
"vel_p = inertial_velocity(pos_p, viirs[\"SCVelocity\"][granules].astype(float)[px_ok])\n",
- "px_xyz = ecef(viirs_pixels[\"lon\"], viirs_pixels[\"lat\"], viirs_pixels[\"height\"])[px_ok] # (scan, detector, column, 3)\n",
- "along_t, cross_t = view_tangents(pos_p[:, np.newaxis, np.newaxis], vel_p[:, np.newaxis, np.newaxis], px_xyz)\n",
- "scan_angle_p = np.degrees(np.arctan((cross_t[:, 1] + cross_t[:, 2]) / 2)) # center detectors\n",
+ "px_xyz = ecef(viirs_pixels[\"lon\"], viirs_pixels[\"lat\"], viirs_pixels[\"height\"])[\n",
+ " px_ok\n",
+ "] # (scan, detector, column, 3)\n",
+ "along_t, cross_t = view_tangents(\n",
+ " pos_p[:, np.newaxis, np.newaxis], vel_p[:, np.newaxis, np.newaxis], px_xyz\n",
+ ")\n",
+ "scan_angle_p = np.degrees(\n",
+ " np.arctan((cross_t[:, 1] + cross_t[:, 2]) / 2)\n",
+ ") # center detectors\n",
"# angle between the lines of sight of detectors 0 and 15, scaled to the full array\n",
"los = normalize(px_xyz - pos_p[:, np.newaxis, np.newaxis])\n",
- "array_angle = 16 / 15 * np.degrees(np.arccos(np.clip(np.sum(los[:, 0] * los[:, 3], axis=-1), -1, 1)))\n",
+ "array_angle = (\n",
+ " 16\n",
+ " / 15\n",
+ " * np.degrees(np.arccos(np.clip(np.sum(los[:, 0] * los[:, 3], axis=-1), -1, 1)))\n",
+ ")\n",
"nadir = np.abs(scan_angle_p) < 5\n",
- "ground_speed = np.median(np.linalg.norm(np.diff(ecef(*TO_GEODETIC.transform(*scan_pos[:2000].T)[:2]), axis=0), axis=-1)) / scan_period\n",
+ "ground_speed = (\n",
+ " np.median(\n",
+ " np.linalg.norm(\n",
+ " np.diff(ecef(*TO_GEODETIC.transform(*scan_pos[:2000].T)[:2]), axis=0),\n",
+ " axis=-1,\n",
+ " )\n",
+ " )\n",
+ " / scan_period\n",
+ ")\n",
"altitude = np.median(TO_GEODETIC.transform(*scan_pos[::100].T)[2])\n",
"tailored = np.degrees(2 * np.arctan(ground_speed * scan_period / 2 / altitude))\n",
"pd.Series(\n",
" {\n",
" \"detector array, near nadir (deg)\": np.median(array_angle[nadir]),\n",
- " \"detector array, |scan angle| > 50 deg (deg)\": np.median(array_angle[np.abs(scan_angle_p) > 50]),\n",
+ " \"detector array, |scan angle| > 50 deg (deg)\": np.median(\n",
+ " array_angle[np.abs(scan_angle_p) > 50]\n",
+ " ),\n",
" \"detector spacing, near nadir (mdeg)\": 1e3 * np.median(array_angle[nadir]) / 16,\n",
" \"ground track advance per scan (km)\": ground_speed * scan_period / 1e3,\n",
" \"along-track field of view tailored to one scan (deg)\": tailored,\n",
@@ -787,10 +938,10 @@
"id": "5ca76d6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:29.989800Z",
- "iopub.status.busy": "2026-10-08T17:41:29.988800Z",
- "iopub.status.idle": "2026-10-08T17:41:30.984707Z",
- "shell.execute_reply": "2026-10-08T17:41:30.984707Z"
+ "iopub.execute_input": "2026-10-25T13:56:24.652516Z",
+ "iopub.status.busy": "2026-10-25T13:56:24.652516Z",
+ "iopub.status.idle": "2026-10-25T13:56:25.502569Z",
+ "shell.execute_reply": "2026-10-25T13:56:25.502059Z"
}
},
"outputs": [
@@ -873,14 +1024,18 @@
" \"\"\"Signed angle (deg) from the horizontal projection of a reference direction to that of a direction.\"\"\"\n",
" a = normalize(reference - np.sum(reference * u2, axis=-1, keepdims=True) * u2)\n",
" b = normalize(direction - np.sum(direction * u2, axis=-1, keepdims=True) * u2)\n",
- " return np.degrees(np.arctan2(np.sum(np.cross(a, b) * u2, axis=-1), np.sum(a * b, axis=-1)))\n",
+ " return np.degrees(\n",
+ " np.arctan2(np.sum(np.cross(a, b) * u2, axis=-1), np.sum(a * b, axis=-1))\n",
+ " )\n",
"\n",
"\n",
"pd.DataFrame(\n",
" {\n",
" \"yaw vs. Earth-fixed velocity (deg)\": summarize(yaw(normal, vel_e)),\n",
" \"yaw vs. inertial velocity (deg)\": summarize(yaw(normal, vel_p[i2])),\n",
- " \"line of sight out of plane (deg)\": summarize(np.degrees(np.abs(np.arcsin(np.einsum(\"kjc,kc->kj\", center_los, normal))))),\n",
+ " \"line of sight out of plane (deg)\": summarize(\n",
+ " np.degrees(np.abs(np.arcsin(np.einsum(\"kjc,kc->kj\", center_los, normal))))\n",
+ " ),\n",
" }\n",
").T.round(3)"
]
@@ -905,10 +1060,10 @@
"id": "18c7a1f3",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:30.987228Z",
- "iopub.status.busy": "2026-10-08T17:41:30.987228Z",
- "iopub.status.idle": "2026-10-08T17:41:32.155350Z",
- "shell.execute_reply": "2026-10-08T17:41:32.155350Z"
+ "iopub.execute_input": "2026-10-25T13:56:25.504576Z",
+ "iopub.status.busy": "2026-10-25T13:56:25.504576Z",
+ "iopub.status.idle": "2026-10-25T13:56:26.280304Z",
+ "shell.execute_reply": "2026-10-25T13:56:26.280304Z"
}
},
"outputs": [
@@ -1008,27 +1163,45 @@
" cross_track_field_of_view=2 * EDGE,\n",
" along_track_field_of_view=tailored,\n",
" is_rectangular=True,\n",
- " velocity_frame=VelocityFrame.INERTIAL,\n",
")\n",
"viirs_scan_view = viirs_view.model_copy(update={\"view_geometry\": ViewGeometry.SCAN})\n",
"# observed length and advance (km)\n",
"length = 16 / 15 * np.linalg.norm(px_xyz[:, 0] - px_xyz[:, 3], axis=-1) / 1e3\n",
"scan_center = (px_xyz[:, 1] + px_xyz[:, 2]) / 2\n",
"scan_index = np.flatnonzero(px_ok) # granule * N_SCANS + scan\n",
- "consecutive = np.nonzero((np.diff(scan_index) == 1) & (np.diff(scan_index // N_SCANS) == 0))[0] # scan pairs within a granule\n",
- "advance = np.linalg.norm(scan_center[consecutive + 1] - scan_center[consecutive], axis=-1) / 1e3\n",
+ "consecutive = np.nonzero(\n",
+ " (np.diff(scan_index) == 1) & (np.diff(scan_index // N_SCANS) == 0)\n",
+ ")[\n",
+ " 0\n",
+ "] # scan pairs within a granule\n",
+ "advance = (\n",
+ " np.linalg.norm(scan_center[consecutive + 1] - scan_center[consecutive], axis=-1)\n",
+ " / 1e3\n",
+ ")\n",
"\n",
"# TAT-C lengths and advances at a sample of scans and scan angles\n",
"i3 = np.arange(0, len(pos_p), 25)\n",
"scan_times3 = iet_to_utc(viirs[\"MidTime\"][granules][px_ok][i3])\n",
"angles3 = np.arange(-56, 57, 4.0)\n",
"track3 = orbit.get_orbit_track(list(scan_times3))\n",
- "track3_next = orbit.get_orbit_track([t + timedelta(seconds=scan_period) for t in scan_times3])\n",
- "tatc_length, angular_length, tatc_advance = (np.zeros((len(i3), len(angles3))) for _ in range(3))\n",
+ "track3_next = orbit.get_orbit_track(\n",
+ " [t + timedelta(seconds=scan_period) for t in scan_times3]\n",
+ ")\n",
+ "tatc_length, angular_length, tatc_advance = (\n",
+ " np.zeros((len(i3), len(angles3))) for _ in range(3)\n",
+ ")\n",
"\n",
"\n",
"def distance(a, b):\n",
- " return GEOD.inv(a.longitude.degrees, a.latitude.degrees, b.longitude.degrees, b.latitude.degrees)[2] / 1e3\n",
+ " return (\n",
+ " GEOD.inv(\n",
+ " a.longitude.degrees,\n",
+ " a.latitude.degrees,\n",
+ " b.longitude.degrees,\n",
+ " b.latitude.degrees,\n",
+ " )[2]\n",
+ " / 1e3\n",
+ " )\n",
"\n",
"\n",
"half = viirs_view.along_track_field_of_view / 2\n",
@@ -1037,12 +1210,20 @@
" # rectangular boundary points at the cross-track position of the scan angle (clock angles from the left cross-track axis)\n",
" clock = np.degrees(np.arctan2(tan_half, np.tan(np.radians(angle))))\n",
" edge = lambda c: compute_projected_ray_position(\n",
- " track3, viirs_view.cross_track_field_of_view, viirs_view.along_track_field_of_view, 0, 0, True, c,\n",
+ " track3,\n",
+ " viirs_view.cross_track_field_of_view,\n",
+ " viirs_view.along_track_field_of_view,\n",
+ " 0,\n",
+ " 0,\n",
+ " True,\n",
+ " c,\n",
" velocity_frame=VelocityFrame.INERTIAL,\n",
" )\n",
" tatc_length[:, k] = distance(edge(clock), edge(360 - clock))\n",
" # scan geometry: rays at the along-track edges of the view, rolled to the scan angle\n",
- " ray = lambda track, pitch: compute_projected_ray_position(track, 0, 0, angle, pitch, velocity_frame=viirs_scan_view.velocity_frame)\n",
+ " ray = lambda track, pitch: compute_projected_ray_position(\n",
+ " track, 0, 0, angle, pitch, velocity_frame=VelocityFrame.INERTIAL\n",
+ " )\n",
" angular_length[:, k] = distance(ray(track3, half), ray(track3, -half))\n",
" tatc_advance[:, k] = distance(ray(track3, 0), ray(track3_next, 0))\n",
"\n",
@@ -1054,16 +1235,57 @@
"axes[0].fill_between(mid3, q_len[:, 0], q_len[:, 2], color=INK, alpha=0.12, lw=0)\n",
"axes[0].plot(mid3, q_len[:, 1], color=INK, marker=\"o\", ms=3, label=\"VIIRS scan length\")\n",
"axes[0].plot(mid3, q_adv[:, 1], color=INK, ls=\":\", label=\"VIIRS scan advance\")\n",
- "axes[0].plot(angles3, np.median(tatc_length, axis=0), color=BLUE, label=\"TAT-C frame view length\")\n",
- "axes[0].plot(angles3, np.median(angular_length, axis=0), color=ORANGE, ls=\"--\", label=\"TAT-C scan view length\")\n",
- "axes[0].plot(angles3, np.median(tatc_advance, axis=0), color=BLUE, ls=\":\", label=\"TAT-C advance per scan period\")\n",
- "axes[0].set(xlabel=\"Scan angle (deg, positive left)\", ylabel=\"Along-track distance (km)\", title=\"Along-track length of one scan\", ylim=(0, None))\n",
+ "axes[0].plot(\n",
+ " angles3, np.median(tatc_length, axis=0), color=BLUE, label=\"TAT-C frame view length\"\n",
+ ")\n",
+ "axes[0].plot(\n",
+ " angles3,\n",
+ " np.median(angular_length, axis=0),\n",
+ " color=ORANGE,\n",
+ " ls=\"--\",\n",
+ " label=\"TAT-C scan view length\",\n",
+ ")\n",
+ "axes[0].plot(\n",
+ " angles3,\n",
+ " np.median(tatc_advance, axis=0),\n",
+ " color=BLUE,\n",
+ " ls=\":\",\n",
+ " label=\"TAT-C advance per scan period\",\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Scan angle (deg, positive left)\",\n",
+ " ylabel=\"Along-track distance (km)\",\n",
+ " title=\"Along-track length of one scan\",\n",
+ " ylim=(0, None),\n",
+ ")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
- "axes[1].plot(mid3, 100 * (1 - q_adv[:, 1] / q_len[:, 1]), color=INK, marker=\"o\", ms=3, label=\"VIIRS\")\n",
- "axes[1].plot(angles3, 100 * (1 - np.median(tatc_advance, axis=0) / np.median(tatc_length, axis=0)), color=BLUE, label=\"TAT-C frame view\")\n",
- "axes[1].plot(angles3, 100 * (1 - np.median(tatc_advance, axis=0) / np.median(angular_length, axis=0)), color=ORANGE, ls=\"--\", label=\"TAT-C scan view\")\n",
+ "axes[1].plot(\n",
+ " mid3,\n",
+ " 100 * (1 - q_adv[:, 1] / q_len[:, 1]),\n",
+ " color=INK,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " label=\"VIIRS\",\n",
+ ")\n",
+ "axes[1].plot(\n",
+ " angles3,\n",
+ " 100 * (1 - np.median(tatc_advance, axis=0) / np.median(tatc_length, axis=0)),\n",
+ " color=BLUE,\n",
+ " label=\"TAT-C frame view\",\n",
+ ")\n",
+ "axes[1].plot(\n",
+ " angles3,\n",
+ " 100 * (1 - np.median(tatc_advance, axis=0) / np.median(angular_length, axis=0)),\n",
+ " color=ORANGE,\n",
+ " ls=\"--\",\n",
+ " label=\"TAT-C scan view\",\n",
+ ")\n",
"axes[1].axhline(0, color=GRAY, lw=0.8)\n",
- "axes[1].set(xlabel=\"Scan angle (deg, positive left)\", ylabel=\"Overlap of consecutive scans (%)\", title=\"Scan-to-scan overlap (bow tie)\")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Scan angle (deg, positive left)\",\n",
+ " ylabel=\"Overlap of consecutive scans (%)\",\n",
+ " title=\"Scan-to-scan overlap (bow tie)\",\n",
+ ")\n",
"axes[1].legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()\n",
@@ -1071,9 +1293,15 @@
" {\n",
" \"VIIRS length (km)\": q_len[[len(mid3) // 2, 0, -1], 1],\n",
" \"VIIRS advance (km)\": q_adv[[len(mid3) // 2, 0, -1], 1],\n",
- " \"TAT-C frame view length (km)\": np.median(tatc_length, axis=0)[[len(angles3) // 2, 0, -1]],\n",
- " \"TAT-C scan view length (km)\": np.median(angular_length, axis=0)[[len(angles3) // 2, 0, -1]],\n",
- " \"TAT-C advance (km)\": np.median(tatc_advance, axis=0)[[len(angles3) // 2, 0, -1]],\n",
+ " \"TAT-C frame view length (km)\": np.median(tatc_length, axis=0)[\n",
+ " [len(angles3) // 2, 0, -1]\n",
+ " ],\n",
+ " \"TAT-C scan view length (km)\": np.median(angular_length, axis=0)[\n",
+ " [len(angles3) // 2, 0, -1]\n",
+ " ],\n",
+ " \"TAT-C advance (km)\": np.median(tatc_advance, axis=0)[\n",
+ " [len(angles3) // 2, 0, -1]\n",
+ " ],\n",
" },\n",
" index=[\"nadir\", \"left edge\", \"right edge\"],\n",
").round(2)"
@@ -1097,10 +1325,10 @@
"id": "3506ef67",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:32.158358Z",
- "iopub.status.busy": "2026-10-08T17:41:32.158358Z",
- "iopub.status.idle": "2026-10-08T17:41:32.715941Z",
- "shell.execute_reply": "2026-10-08T17:41:32.715941Z"
+ "iopub.execute_input": "2026-10-25T13:56:26.282873Z",
+ "iopub.status.busy": "2026-10-25T13:56:26.281869Z",
+ "iopub.status.idle": "2026-10-25T13:56:26.590393Z",
+ "shell.execute_reply": "2026-10-25T13:56:26.590393Z"
}
},
"outputs": [
@@ -1185,28 +1413,55 @@
}
],
"source": [
- "observed_width = np.sum(np.linalg.norm(np.diff(scan_center, axis=1), axis=-1), axis=1) / 1e3\n",
- "sub_lon4, sub_lat4, sub_alt4 = TO_GEODETIC.transform(pos_p[:, 0], pos_p[:, 1], pos_p[:, 2])\n",
+ "observed_width = (\n",
+ " np.sum(np.linalg.norm(np.diff(scan_center, axis=1), axis=-1), axis=1) / 1e3\n",
+ ")\n",
+ "sub_lon4, sub_lat4, sub_alt4 = TO_GEODETIC.transform(\n",
+ " pos_p[:, 0], pos_p[:, 1], pos_p[:, 2]\n",
+ ")\n",
"measured_for = np.abs(measured_angle[0] - measured_angle[-1])\n",
- "apogee_altitude = compute_apoapsis_radius(noaa20.orbit.get_semimajor_axis(), noaa20.orbit.get_eccentricity()) - EARTH_MEAN_RADIUS\n",
+ "apogee_altitude = (\n",
+ " compute_apoapsis_radius(\n",
+ " noaa20.orbit.get_semimajor_axis(), noaa20.orbit.get_eccentricity()\n",
+ " )\n",
+ " - EARTH_MEAN_RADIUS\n",
+ ")\n",
"min_elevation_angle = compute_min_elevation_angle(apogee_altitude, 2 * EDGE)\n",
"swath_width = np.vectorize(field_of_regard_to_swath_width)\n",
"widths = {\n",
" \"nominal field of regard\": swath_width(sub_alt4, 2 * EDGE) / 1e3,\n",
- " \"nominal field of regard, collect_observations\": swath_width(sub_alt4, np.vectorize(compute_field_of_regard)(sub_alt4, min_elevation_angle)) / 1e3,\n",
+ " \"nominal field of regard, collect_observations\": swath_width(\n",
+ " sub_alt4, np.vectorize(compute_field_of_regard)(sub_alt4, min_elevation_angle)\n",
+ " )\n",
+ " / 1e3,\n",
" \"measured field of regard\": swath_width(sub_alt4, measured_for) / 1e3,\n",
"}\n",
"edges4 = np.arange(-90, 91, 10)\n",
"mid4 = (edges4[:-1] + edges4[1:]) / 2\n",
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
- "ax.plot(mid4, binned(sub_lat4, observed_width, edges4)[:, 1], color=INK, marker=\"o\", ms=3, label=\"observed (VIIRS)\")\n",
+ "ax.plot(\n",
+ " mid4,\n",
+ " binned(sub_lat4, observed_width, edges4)[:, 1],\n",
+ " color=INK,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " label=\"observed (VIIRS)\",\n",
+ ")\n",
"for (name, width), color in zip(widths.items(), [BLUE, ORANGE, AQUA]):\n",
- " ax.plot(mid4, binned(sub_lat4, width, edges4)[:, 1], color=color, label=f\"TAT-C: {name}\")\n",
- "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Swath width (km)\", title=\"VIIRS swath width\")\n",
+ " ax.plot(\n",
+ " mid4, binned(sub_lat4, width, edges4)[:, 1], color=color, label=f\"TAT-C: {name}\"\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Sub-satellite latitude (deg)\",\n",
+ " ylabel=\"Swath width (km)\",\n",
+ " title=\"VIIRS swath width\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()\n",
- "pd.DataFrame({name: summarize(width - observed_width) for name, width in widths.items()}).T.round(1)"
+ "pd.DataFrame(\n",
+ " {name: summarize(width - observed_width) for name, width in widths.items()}\n",
+ ").T.round(1)"
]
},
{
@@ -1231,10 +1486,10 @@
"id": "fd46c4ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:41:32.719019Z",
- "iopub.status.busy": "2026-10-08T17:41:32.719019Z",
- "iopub.status.idle": "2026-10-08T17:50:44.259151Z",
- "shell.execute_reply": "2026-10-08T17:50:44.258144Z"
+ "iopub.execute_input": "2026-10-25T13:56:26.592398Z",
+ "iopub.status.busy": "2026-10-25T13:56:26.592398Z",
+ "iopub.status.idle": "2026-10-25T13:59:42.759086Z",
+ "shell.execute_reply": "2026-10-25T13:59:42.759086Z"
}
},
"outputs": [
@@ -1371,7 +1626,10 @@
" if not inside.any():\n",
" continue\n",
" # scan lines through G-ring vertices 2-4 and 6-0; the first is the one nearest the first scan's nadir\n",
- " lines = [normalize(np.cross(vertices[2], vertices[4])), normalize(np.cross(vertices[6], vertices[0]))]\n",
+ " lines = [\n",
+ " normalize(np.cross(vertices[2], vertices[4])),\n",
+ " normalize(np.cross(vertices[6], vertices[0])),\n",
+ " ]\n",
" if np.abs(lines[1] @ first_sub[g]) < np.abs(lines[0] @ first_sub[g]):\n",
" lines = lines[::-1]\n",
" d_first, d_last = (np.abs(point_xyz[inside] @ line) for line in lines)\n",
@@ -1379,24 +1637,40 @@
" times = gran_first[g] + (gran_last[g] - gran_first[g]) * fraction\n",
" for p, t in zip(np.flatnonzero(inside), times):\n",
" observed_rows.append({\"point_id\": p, \"granule\": g, \"time\": t})\n",
- "observed5 = pd.DataFrame(observed_rows).astype({\"point_id\": \"int64\"}).sort_values([\"point_id\", \"time\"])\n",
+ "observed5 = (\n",
+ " pd.DataFrame(observed_rows)\n",
+ " .astype({\"point_id\": \"int64\"})\n",
+ " .sort_values([\"point_id\", \"time\"])\n",
+ ")\n",
"# one observation per pass (granules less than 20 minutes apart)\n",
- "new_pass = observed5.groupby(\"point_id\").time.diff().fillna(pd.Timedelta(days=1)) > pd.Timedelta(minutes=20)\n",
+ "new_pass = observed5.groupby(\"point_id\").time.diff().fillna(\n",
+ " pd.Timedelta(days=1)\n",
+ ") > pd.Timedelta(minutes=20)\n",
"observed5 = observed5[new_pass.to_numpy()].reset_index(drop=True)\n",
"# elevation angle from the reported spacecraft position, interpolated linearly in time between scans\n",
"scan_seconds = (scan_mid - scan_mid[0]).total_seconds().to_numpy()\n",
"obs_seconds = (observed5.time - scan_mid[0]).dt.total_seconds().to_numpy()\n",
"k = np.clip(np.searchsorted(scan_seconds, obs_seconds) - 1, 0, len(scan_seconds) - 2)\n",
"position = scan_pos[k] + scan_vel[k] * (obs_seconds - scan_seconds[k])[:, np.newaxis]\n",
- "target = ecef(points5.geometry.x.to_numpy()[observed5.point_id], points5.geometry.y.to_numpy()[observed5.point_id])\n",
- "observed5[\"elevation\"] = np.degrees(np.arcsin(np.sum(normalize(position - target) * point_xyz[observed5.point_id], axis=-1)))\n",
+ "target = ecef(\n",
+ " points5.geometry.x.to_numpy()[observed5.point_id],\n",
+ " points5.geometry.y.to_numpy()[observed5.point_id],\n",
+ ")\n",
+ "observed5[\"elevation\"] = np.degrees(\n",
+ " np.arcsin(\n",
+ " np.sum(normalize(position - target) * point_xyz[observed5.point_id], axis=-1)\n",
+ " )\n",
+ ")\n",
"\n",
"start5, end5 = scan_mid[0].to_pydatetime(), scan_mid[-1].to_pydatetime()\n",
"instruments5 = {\n",
" \"nadir Instrument\": Instrument(name=\"VIIRS\", field_of_regard=2 * EDGE),\n",
" \"PointedInstrument, inertial\": viirs_view,\n",
" \"PointedInstrument, inertial, measured field of regard\": viirs_view.model_copy(\n",
- " update={\"field_of_regard\": measured_for, \"cross_track_field_of_view\": measured_for}\n",
+ " update={\n",
+ " \"field_of_regard\": measured_for,\n",
+ " \"cross_track_field_of_view\": measured_for,\n",
+ " }\n",
" ),\n",
"}\n",
"\n",
@@ -1406,8 +1680,11 @@
" return pd.merge_asof(\n",
" observed5.sort_values(\"time\"),\n",
" predicted[[\"point_id\", \"epoch\", \"sat_alt\"]].sort_values(\"epoch\"),\n",
- " left_on=\"time\", right_on=\"epoch\", by=\"point_id\",\n",
- " direction=\"nearest\", tolerance=pd.Timedelta(minutes=10),\n",
+ " left_on=\"time\",\n",
+ " right_on=\"epoch\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=10),\n",
" )\n",
"\n",
"\n",
@@ -1415,7 +1692,12 @@
"for name, instrument in instruments5.items():\n",
" satellite = noaa20.model_copy(update={\"instruments\": [instrument]})\n",
" predicted5[name] = pd.concat(\n",
- " [collect_observations(Point(latitude=g.y, longitude=g.x), satellite, start5, end5).assign(point_id=p) for p, g in enumerate(points5.geometry)]\n",
+ " [\n",
+ " collect_observations(\n",
+ " Point(latitude=g.y, longitude=g.x), satellite, start5, end5\n",
+ " ).assign(point_id=p)\n",
+ " for p, g in enumerate(points5.geometry)\n",
+ " ]\n",
" ).reset_index(drop=True)\n",
" predicted5[name][\"point_id\"] = predicted5[name].point_id.astype(\"int64\")\n",
" matched5[name] = match(predicted5[name])\n",
@@ -1429,8 +1711,12 @@
" \"predicted only\": len(predicted5[name]) - int(hit.sum()),\n",
" \"time, median (s)\": np.median(dt),\n",
" \"time, p95 |x| (s)\": np.percentile(np.abs(dt), 95),\n",
- " \"elevation, median (deg)\": np.median((matched5[name].sat_alt - matched5[name].elevation)[hit]),\n",
- " \"elevation, p95 |x| (deg)\": np.percentile(np.abs(matched5[name].sat_alt - matched5[name].elevation)[hit], 95),\n",
+ " \"elevation, median (deg)\": np.median(\n",
+ " (matched5[name].sat_alt - matched5[name].elevation)[hit]\n",
+ " ),\n",
+ " \"elevation, p95 |x| (deg)\": np.percentile(\n",
+ " np.abs(matched5[name].sat_alt - matched5[name].elevation)[hit], 95\n",
+ " ),\n",
" }\n",
"pd.DataFrame(rows5).T.round(2)"
]
@@ -1441,10 +1727,10 @@
"id": "7f814559",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:50:44.262663Z",
- "iopub.status.busy": "2026-10-08T17:50:44.262663Z",
- "iopub.status.idle": "2026-10-08T17:50:44.676088Z",
- "shell.execute_reply": "2026-10-08T17:50:44.675082Z"
+ "iopub.execute_input": "2026-10-25T13:59:42.761090Z",
+ "iopub.status.busy": "2026-10-25T13:59:42.761090Z",
+ "iopub.status.idle": "2026-10-25T13:59:43.139997Z",
+ "shell.execute_reply": "2026-10-25T13:59:43.139997Z"
}
},
"outputs": [
@@ -1464,16 +1750,40 @@
"for (name, matched), color in zip(matched5.items(), [BLUE, AQUA, ORANGE]):\n",
" hit = matched.epoch.notna()\n",
" dt = (matched.epoch - matched.time).dt.total_seconds()\n",
- " axes[0].hist(dt[hit], bins=np.arange(-20, 20.5, 0.5), color=color, histtype=\"step\", lw=1.5, label=name)\n",
+ " axes[0].hist(\n",
+ " dt[hit],\n",
+ " bins=np.arange(-20, 20.5, 0.5),\n",
+ " color=color,\n",
+ " histtype=\"step\",\n",
+ " lw=1.5,\n",
+ " label=name,\n",
+ " )\n",
"axes[0].set(xlabel=\"TAT-C minus VIIRS (s)\", ylabel=\"Passes\", title=\"Observation time\")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
"lat_edges = np.arange(-90, 91, 10)\n",
"point_lat = points5.geometry.y.to_numpy()\n",
"n_points = np.histogram(point_lat, lat_edges)[0]\n",
- "axes[1].plot((lat_edges[:-1] + lat_edges[1:]) / 2, np.histogram(point_lat[observed5.point_id], lat_edges)[0] / n_points, color=INK, marker=\"o\", ms=3, label=\"observed (VIIRS)\")\n",
+ "axes[1].plot(\n",
+ " (lat_edges[:-1] + lat_edges[1:]) / 2,\n",
+ " np.histogram(point_lat[observed5.point_id], lat_edges)[0] / n_points,\n",
+ " color=INK,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " label=\"observed (VIIRS)\",\n",
+ ")\n",
"for (name, predicted), color in zip(predicted5.items(), [BLUE, AQUA, ORANGE]):\n",
- " axes[1].plot((lat_edges[:-1] + lat_edges[1:]) / 2, np.histogram(point_lat[predicted.point_id], lat_edges)[0] / n_points, color=color, ls=\"--\", label=name)\n",
- "axes[1].set(xlabel=\"Point latitude (deg)\", ylabel=\"Observations per point per day\", title=\"Daily observations\")\n",
+ " axes[1].plot(\n",
+ " (lat_edges[:-1] + lat_edges[1:]) / 2,\n",
+ " np.histogram(point_lat[predicted.point_id], lat_edges)[0] / n_points,\n",
+ " color=color,\n",
+ " ls=\"--\",\n",
+ " label=name,\n",
+ " )\n",
+ "axes[1].set(\n",
+ " xlabel=\"Point latitude (deg)\",\n",
+ " ylabel=\"Observations per point per day\",\n",
+ " title=\"Daily observations\",\n",
+ ")\n",
"axes[1].legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1498,7 +1808,7 @@
"| 4. Swath width | 47 km too wide with the nominal field of regard, 3 km with the measured one (median) |\n",
"| 5. Daily observations (`collect_observations`) | all observed passes predicted with the nominal field of regard (2 to 3% extra), 99.8% with the measured one (1.6% extra); epochs within 2 s (95th percentile) for an inertial-frame `PointedInstrument`, vs. 14 s for a nadir `Instrument` |\n",
"\n",
- "**Scan orientation and timing.** VIIRS confirms the ATMS findings: NOAA-20's instruments scan perpendicular to the inertial velocity, so `velocity_frame=VelocityFrame.INERTIAL` is the appropriate setting for a `PointedInstrument` representing either instrument, and it makes the observation epochs of `collect_observations` match the actual scan times. VIIRS's Earth view lasts only 0.56 s, so the along-track error from observing a whole view at one instant is under 2 km at the swath edges.\n",
+ "**Scan orientation and timing.** VIIRS confirms the ATMS findings: NOAA-20's instruments scan perpendicular to the inertial velocity, so the inertial velocity frame (`Attitude(velocity_frame=VelocityFrame.INERTIAL)` of the satellite) is the appropriate setting for either instrument, and it makes the observation epochs of `collect_observations` match the actual scan times. VIIRS's Earth view lasts only 0.56 s, so the along-track error from observing a whole view at one instant is under 2 km at the swath edges.\n",
"\n",
"**Along-track field of view as integration time.** VIIRS's 16-detector array is designed to cover the ground traveled in one scan period, and the along-track field of view tailored to one scan period (0.814 deg) matches its angular extent (0.817 deg). At nadir, TAT-C's footprint therefore matches a VIIRS scan. Toward the swath edges, however, TAT-C's default (frame geometry) view is a rectangle in the plane perpendicular to the boresight, like the focal plane of a framing camera or pushbroom imager, so its angular along-track extent shrinks as the cosine of the scan angle. A scanning imager such as VIIRS keeps a constant angular extent, so its scans grow to about twice their nadir length at the edges and overlap (the bow tie effect). The scan geometry (`view_geometry=ViewGeometry.SCAN`) reproduces this; with the frame geometry, footprints still tile the swath, so coverage and revisit are unaffected, but footprint and pixel sizes near the edges of a scanning imager's swath are underestimated by up to about 40%.\n",
"\n",
diff --git a/docs/validation/ValidateLatencyNOAA20.ipynb b/docs/validation/ValidateLatencyNOAA20.ipynb
index e49dfec5..95554ebb 100644
--- a/docs/validation/ValidateLatencyNOAA20.ipynb
+++ b/docs/validation/ValidateLatencyNOAA20.ipynb
@@ -87,7 +87,10 @@
" response = requests.get(BUCKET, params=params, timeout=120)\n",
" response.raise_for_status()\n",
" root = ET.fromstring(response.content)\n",
- " keys += [c.find(\"s:Key\", namespace).text for c in root.findall(\"s:Contents\", namespace)]\n",
+ " keys += [\n",
+ " c.find(\"s:Key\", namespace).text\n",
+ " for c in root.findall(\"s:Contents\", namespace)\n",
+ " ]\n",
" following = root.find(\"s:NextContinuationToken\", namespace)\n",
" if following is None:\n",
" return keys\n",
@@ -100,24 +103,47 @@
" for day in [DAY, DAY + timedelta(days=1)]:\n",
" for key in listing(f\"ATMS-SDR/{day:%Y/%m/%d}/\"):\n",
" date, start, end, orbit, created = pattern.search(key).groups()\n",
- " rows.append({\"date\": date, \"start\": start, \"end\": end, \"orbit\": int(orbit), \"created\": created})\n",
+ " rows.append(\n",
+ " {\n",
+ " \"date\": date,\n",
+ " \"start\": start,\n",
+ " \"end\": end,\n",
+ " \"orbit\": int(orbit),\n",
+ " \"created\": created,\n",
+ " }\n",
+ " )\n",
" pd.DataFrame(rows).to_csv(GRANULES_FILE, index=False)\n",
"granules = pd.read_csv(GRANULES_FILE, dtype=str)\n",
- "parse = lambda date, clock: pd.to_datetime(date + clock.str[:6], format=\"%Y%m%d%H%M%S\", utc=True)\n",
+ "parse = lambda date, clock: pd.to_datetime(\n",
+ " date + clock.str[:6], format=\"%Y%m%d%H%M%S\", utc=True\n",
+ ")\n",
"granules[\"start\"] = parse(granules.date, granules.start)\n",
"granules[\"end\"] = parse(granules.date, granules.end)\n",
"granules.loc[granules.end < granules.start, \"end\"] += pd.Timedelta(days=1)\n",
"granules[\"created\"] = pd.to_datetime(granules.created, format=\"%Y%m%d%H%M%S\", utc=True)\n",
"granules = granules.drop_duplicates(\"start\").sort_values(\"end\")\n",
- "granules = granules[(granules.end >= DAY) & (granules.end < DAY + timedelta(days=1))].reset_index(drop=True)\n",
+ "granules = granules[\n",
+ " (granules.end >= DAY) & (granules.end < DAY + timedelta(days=1))\n",
+ "].reset_index(drop=True)\n",
"granules[\"latency (min)\"] = (granules.created - granules.end).dt.total_seconds() / 60\n",
"TLE_FILE = ORBIT_DIR / \"weather.tle\"\n",
"if not TLE_FILE.exists():\n",
- " TLE_FILE.write_text(requests.get(\"https://celestrak.org/NORAD/elements/gp.php\", params={\"GROUP\": \"weather\", \"FORMAT\": \"tle\"}, timeout=120).text)\n",
+ " TLE_FILE.write_text(\n",
+ " requests.get(\n",
+ " \"https://celestrak.org/NORAD/elements/gp.php\",\n",
+ " params={\"GROUP\": \"weather\", \"FORMAT\": \"tle\"},\n",
+ " timeout=120,\n",
+ " ).text\n",
+ " )\n",
"lines = [line.rstrip() for line in TLE_FILE.read_text().splitlines() if line.strip()]\n",
"k = next(i for i, line in enumerate(lines) if line.startswith(\"NOAA 20\"))\n",
"TLE = lines[k + 1 : k + 3]\n",
- "pd.concat([pd.Series({\"granules\": len(granules), \"orbits\": granules.orbit.nunique()}), granules[\"latency (min)\"].describe(percentiles=[0.05, 0.5, 0.95])[1:]]).round(1)"
+ "pd.concat(\n",
+ " [\n",
+ " pd.Series({\"granules\": len(granules), \"orbits\": granules.orbit.nunique()}),\n",
+ " granules[\"latency (min)\"].describe(percentiles=[0.05, 0.5, 0.95])[1:],\n",
+ " ]\n",
+ ").round(1)"
]
},
{
@@ -226,19 +252,48 @@
" \"Fairbanks\": (64.86, -147.85),\n",
" \"Inuvik\": (68.40, -133.50),\n",
"}\n",
- "stations = {name: GroundStation(name=name, latitude=lat, longitude=lon, min_elevation_angle=5) for name, (lat, lon) in STATIONS.items()}\n",
- "contacts = {name: collect_downlinks(station, noaa20, DAY - timedelta(hours=3), DAY + timedelta(days=1, hours=3)) for name, station in stations.items()}\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
+ "stations = {\n",
+ " name: GroundStation(name=name, latitude=lat, longitude=lon, min_elevation_angle=5)\n",
+ " for name, (lat, lon) in STATIONS.items()\n",
+ "}\n",
+ "contacts = {\n",
+ " name: collect_downlinks(\n",
+ " station, noaa20, DAY - timedelta(hours=3), DAY + timedelta(days=1, hours=3)\n",
+ " )\n",
+ " for name, station in stations.items()\n",
+ "}\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
"STATION_COLORS = dict(zip(STATIONS, [BLUE, ORANGE, AQUA, PURPLE, GRAY]))\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"\n",
"def downlinking_contact(station_names):\n",
" \"\"\"For each granule, the first contact (of the given stations) that has not ended when the granule's observation ends.\"\"\"\n",
- " c = pd.concat([contacts[name] for name in station_names]).sort_values(\"end\").reset_index(drop=True)\n",
+ " c = (\n",
+ " pd.concat([contacts[name] for name in station_names])\n",
+ " .sort_values(\"end\")\n",
+ " .reset_index(drop=True)\n",
+ " )\n",
" return pd.merge_asof(\n",
" granules.sort_values(\"end\"),\n",
- " c[[\"station\", \"start\", \"end\"]].rename(columns={\"start\": \"contact start\", \"end\": \"contact end\"}).sort_values(\"contact end\"),\n",
+ " c[[\"station\", \"start\", \"end\"]]\n",
+ " .rename(columns={\"start\": \"contact start\", \"end\": \"contact end\"})\n",
+ " .sort_values(\"contact end\"),\n",
" left_on=\"end\",\n",
" right_on=\"contact end\",\n",
" direction=\"forward\",\n",
@@ -248,8 +303,12 @@
"pd.DataFrame(\n",
" {\n",
" name: {\n",
- " \"contacts on 3 October\": int(((c.start >= DAY) & (c.start < DAY + timedelta(days=1))).sum()),\n",
- " \"duration, median (min)\": ((c.end - c.start).dt.total_seconds() / 60).median(),\n",
+ " \"contacts on 3 October\": int(\n",
+ " ((c.start >= DAY) & (c.start < DAY + timedelta(days=1))).sum()\n",
+ " ),\n",
+ " \"duration, median (min)\": (\n",
+ " (c.end - c.start).dt.total_seconds() / 60\n",
+ " ).median(),\n",
" }\n",
" for name, c in contacts.items()\n",
" }\n",
@@ -382,13 +441,20 @@
}
],
"source": [
- "SETS = [[\"Svalbard\"], [\"Svalbard\", \"McMurdo\"], [\"Svalbard\", \"McMurdo\", \"Troll\"], [\"Svalbard\", \"McMurdo\", \"Fairbanks\"], [\"Svalbard\", \"McMurdo\", \"Troll\", \"Fairbanks\", \"Inuvik\"]]\n",
+ "SETS = [\n",
+ " [\"Svalbard\"],\n",
+ " [\"Svalbard\", \"McMurdo\"],\n",
+ " [\"Svalbard\", \"McMurdo\", \"Troll\"],\n",
+ " [\"Svalbard\", \"McMurdo\", \"Fairbanks\"],\n",
+ " [\"Svalbard\", \"McMurdo\", \"Troll\", \"Fairbanks\", \"Inuvik\"],\n",
+ "]\n",
"rows1 = {}\n",
"for station_set in SETS:\n",
" merged = downlinking_contact(station_set)\n",
" delay = (merged.created - merged[\"contact start\"]).dt.total_seconds() / 60\n",
" rows1[\" + \".join(station_set)] = {\n",
- " \"created 0 to 20 min after contact start (%)\": 100 * np.mean((delay >= 0) & (delay <= 20)),\n",
+ " \"created 0 to 20 min after contact start (%)\": 100\n",
+ " * np.mean((delay >= 0) & (delay <= 20)),\n",
" \"created before contact start (%)\": 100 * np.mean(delay < 0),\n",
" \"delay after contact start, median (min)\": np.median(delay),\n",
" \"delay after contact start, p90 (min)\": np.percentile(delay, 90),\n",
@@ -427,11 +493,36 @@
"for name in STATIONS:\n",
" for _, c in contacts[name].iterrows():\n",
" if c.end > window[0] and c.start < window[1]:\n",
- " ax.axvspan(c.start, c.end, ymin=0, ymax=1, color=STATION_COLORS[name], alpha=0.25 if name in (\"Svalbard\", \"McMurdo\") else 0.08, lw=0)\n",
- " ax.plot([], [], color=STATION_COLORS[name], lw=6, alpha=0.4, label=f\"{name} contact (TAT-C)\")\n",
+ " ax.axvspan(\n",
+ " c.start,\n",
+ " c.end,\n",
+ " ymin=0,\n",
+ " ymax=1,\n",
+ " color=STATION_COLORS[name],\n",
+ " alpha=0.25 if name in (\"Svalbard\", \"McMurdo\") else 0.08,\n",
+ " lw=0,\n",
+ " )\n",
+ " ax.plot(\n",
+ " [],\n",
+ " [],\n",
+ " color=STATION_COLORS[name],\n",
+ " lw=6,\n",
+ " alpha=0.4,\n",
+ " label=f\"{name} contact (TAT-C)\",\n",
+ " )\n",
"shown = granules[(granules.end > window[0]) & (granules.end < window[1])]\n",
- "ax.scatter(shown.created, (shown.created - shown.end).dt.total_seconds() / 60, s=4, color=INK, label=\"granule creation (latency)\")\n",
- "ax.set(xlim=window, ylabel=\"Latency at creation (min)\", title=\"NOAA-20 ATMS granules and predicted ground station contacts, 3 October 2026, 06:00 to 12:00 UTC\")\n",
+ "ax.scatter(\n",
+ " shown.created,\n",
+ " (shown.created - shown.end).dt.total_seconds() / 60,\n",
+ " s=4,\n",
+ " color=INK,\n",
+ " label=\"granule creation (latency)\",\n",
+ ")\n",
+ "ax.set(\n",
+ " xlim=window,\n",
+ " ylabel=\"Latency at creation (min)\",\n",
+ " title=\"NOAA-20 ATMS granules and predicted ground station contacts, 3 October 2026, 06:00 to 12:00 UTC\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=7, ncol=3, loc=\"upper left\")\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -547,17 +638,26 @@
}
],
"source": [
- "merged[\"delay (min)\"] = (merged.created - merged[\"contact start\"]).dt.total_seconds() / 60\n",
- "merged[\"contact duration (min)\"] = (merged[\"contact end\"] - merged[\"contact start\"]).dt.total_seconds() / 60\n",
+ "merged[\"delay (min)\"] = (\n",
+ " merged.created - merged[\"contact start\"]\n",
+ ").dt.total_seconds() / 60\n",
+ "merged[\"contact duration (min)\"] = (\n",
+ " merged[\"contact end\"] - merged[\"contact start\"]\n",
+ ").dt.total_seconds() / 60\n",
"merged[\"observed during contact\"] = merged.end >= merged[\"contact start\"]\n",
"rows2 = {}\n",
- "for label, subset in [(\"all granules\", merged), (\"observed before the contact\", merged[~merged[\"observed during contact\"]]), (\"observed during the contact\", merged[merged[\"observed during contact\"]])]:\n",
+ "for label, subset in [\n",
+ " (\"all granules\", merged),\n",
+ " (\"observed before the contact\", merged[~merged[\"observed during contact\"]]),\n",
+ " (\"observed during the contact\", merged[merged[\"observed during contact\"]]),\n",
+ "]:\n",
" rows2[label] = {\n",
" \"granules\": len(subset),\n",
" \"delay after contact start, p10 (min)\": subset[\"delay (min)\"].quantile(0.1),\n",
" \"delay after contact start, median (min)\": subset[\"delay (min)\"].median(),\n",
" \"delay after contact start, p90 (min)\": subset[\"delay (min)\"].quantile(0.9),\n",
- " \"created after contact end (%)\": 100 * np.mean(subset.created > subset[\"contact end\"]),\n",
+ " \"created after contact end (%)\": 100\n",
+ " * np.mean(subset.created > subset[\"contact end\"]),\n",
" }\n",
"pd.DataFrame(rows2).T.round(1)"
]
@@ -590,9 +690,25 @@
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
"for station, color in [(\"Svalbard\", BLUE), (\"McMurdo\", ORANGE)]:\n",
" s = merged[merged.station == station]\n",
- " ax.hist(s[\"delay (min)\"].clip(-5, 60), bins=np.arange(-5, 61, 1), color=color, alpha=0.6, label=station)\n",
- "ax.axvline(merged[\"contact duration (min)\"].median(), color=INK, lw=0.8, ls=\"--\", label=\"median contact duration\")\n",
- "ax.set(xlabel=\"Granule creation after contact start (min)\", ylabel=\"Granules\", title=\"Creation of NOAA-20 ATMS granules within contacts\")\n",
+ " ax.hist(\n",
+ " s[\"delay (min)\"].clip(-5, 60),\n",
+ " bins=np.arange(-5, 61, 1),\n",
+ " color=color,\n",
+ " alpha=0.6,\n",
+ " label=station,\n",
+ " )\n",
+ "ax.axvline(\n",
+ " merged[\"contact duration (min)\"].median(),\n",
+ " color=INK,\n",
+ " lw=0.8,\n",
+ " ls=\"--\",\n",
+ " label=\"median contact duration\",\n",
+ ")\n",
+ "ax.set(\n",
+ " xlabel=\"Granule creation after contact start (min)\",\n",
+ " ylabel=\"Granules\",\n",
+ " title=\"Creation of NOAA-20 ATMS granules within contacts\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -764,16 +880,44 @@
"from shapely.geometry import Point as ShapelyPoint\n",
"\n",
"observations[\"geometry\"] = [ShapelyPoint(0, 0)] * len(observations)\n",
- "downlinks = pd.concat([contacts[\"Svalbard\"], contacts[\"McMurdo\"]]).sort_values(\"start\").reset_index(drop=True)\n",
- "latencies = compute_latencies(gpd.GeoDataFrame(observations, crs=\"EPSG:4326\"), downlinks, during_contact=\"next\").sort_values(\"target_hash\").reset_index(drop=True)\n",
- "latencies_end = compute_latencies(gpd.GeoDataFrame(observations, crs=\"EPSG:4326\"), downlinks).sort_values(\"target_hash\").reset_index(drop=True)\n",
+ "downlinks = (\n",
+ " pd.concat([contacts[\"Svalbard\"], contacts[\"McMurdo\"]])\n",
+ " .sort_values(\"start\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
+ "latencies = (\n",
+ " compute_latencies(\n",
+ " gpd.GeoDataFrame(observations, crs=\"EPSG:4326\"),\n",
+ " downlinks,\n",
+ " during_contact=\"next\",\n",
+ " )\n",
+ " .sort_values(\"target_hash\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
+ "latencies_end = (\n",
+ " compute_latencies(gpd.GeoDataFrame(observations, crs=\"EPSG:4326\"), downlinks)\n",
+ " .sort_values(\"target_hash\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
"actual = granules[\"latency (min)\"].to_numpy()\n",
"tatc = latencies.latency.dt.total_seconds().to_numpy() / 60\n",
- "current = ((merged[\"contact start\"] + (merged[\"contact end\"] - merged[\"contact start\"]) / 2 - merged.end).dt.total_seconds() / 60).to_numpy()\n",
+ "current = (\n",
+ " (\n",
+ " merged[\"contact start\"]\n",
+ " + (merged[\"contact end\"] - merged[\"contact start\"]) / 2\n",
+ " - merged.end\n",
+ " ).dt.total_seconds()\n",
+ " / 60\n",
+ ").to_numpy()\n",
"during = merged[\"observed during contact\"].to_numpy()\n",
"playback = latencies_end.latency.dt.total_seconds().to_numpy() / 60\n",
"rows3 = {}\n",
- "for label, values in [(\"actual (creation)\", actual), ('compute_latencies, during_contact=\"next\"', tatc), (\"first contact not ended (midpoint)\", current), ('compute_latencies (default, \"end\")', playback)]:\n",
+ "for label, values in [\n",
+ " (\"actual (creation)\", actual),\n",
+ " ('compute_latencies, during_contact=\"next\"', tatc),\n",
+ " (\"first contact not ended (midpoint)\", current),\n",
+ " ('compute_latencies (default, \"end\")', playback),\n",
+ "]:\n",
" rows3[label] = {\n",
" \"mean (min)\": np.nanmean(values),\n",
" \"median (min)\": np.nanmedian(values),\n",
@@ -783,8 +927,18 @@
" \"mean, observed before contact (min)\": np.nanmean(values[~during]),\n",
" }\n",
"table3 = pd.DataFrame(rows3).T\n",
- "table3[\"difference from actual, median (min)\"] = [0, np.nanmedian(tatc - actual), np.nanmedian(current - actual), np.nanmedian(playback - actual)]\n",
- "table3[\"difference from actual, p95 |x| (min)\"] = [0, np.nanpercentile(np.abs(tatc - actual), 95), np.nanpercentile(np.abs(current - actual), 95), np.nanpercentile(np.abs(playback - actual), 95)]\n",
+ "table3[\"difference from actual, median (min)\"] = [\n",
+ " 0,\n",
+ " np.nanmedian(tatc - actual),\n",
+ " np.nanmedian(current - actual),\n",
+ " np.nanmedian(playback - actual),\n",
+ "]\n",
+ "table3[\"difference from actual, p95 |x| (min)\"] = [\n",
+ " 0,\n",
+ " np.nanpercentile(np.abs(tatc - actual), 95),\n",
+ " np.nanpercentile(np.abs(current - actual), 95),\n",
+ " np.nanpercentile(np.abs(playback - actual), 95),\n",
+ "]\n",
"table3.round(1)"
]
},
@@ -815,16 +969,39 @@
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.6))\n",
"bins = np.arange(0, 75, 2)\n",
- "axes[0].hist(np.clip(actual, 0, 74), bins=bins, color=GRAY, alpha=0.6, label=\"actual (creation)\")\n",
- "axes[0].hist(np.clip(tatc, 0, 74), bins=bins, histtype=\"step\", color=BLUE, lw=1.5, label='during_contact=\"next\"')\n",
- "axes[0].hist(np.clip(playback, 0, 74), bins=bins, histtype=\"step\", color=ORANGE, lw=1.5, label='during_contact=\"end\" (default)')\n",
+ "axes[0].hist(\n",
+ " np.clip(actual, 0, 74), bins=bins, color=GRAY, alpha=0.6, label=\"actual (creation)\"\n",
+ ")\n",
+ "axes[0].hist(\n",
+ " np.clip(tatc, 0, 74),\n",
+ " bins=bins,\n",
+ " histtype=\"step\",\n",
+ " color=BLUE,\n",
+ " lw=1.5,\n",
+ " label='during_contact=\"next\"',\n",
+ ")\n",
+ "axes[0].hist(\n",
+ " np.clip(playback, 0, 74),\n",
+ " bins=bins,\n",
+ " histtype=\"step\",\n",
+ " color=ORANGE,\n",
+ " lw=1.5,\n",
+ " label='during_contact=\"end\" (default)',\n",
+ ")\n",
"axes[0].set(xlabel=\"Latency (min)\", ylabel=\"Granules\", title=\"Latency distributions\")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
"hours = [(t - DAY) / timedelta(hours=1) for t in granules.end]\n",
"axes[1].plot(hours, actual, \".\", ms=2, color=GRAY, label=\"actual (creation)\")\n",
"axes[1].plot(hours, tatc, \".\", ms=2, color=BLUE, label='during_contact=\"next\"')\n",
- "axes[1].plot(hours, playback, \".\", ms=1, color=ORANGE, label='during_contact=\"end\" (default)')\n",
- "axes[1].set(xlim=(6, 12), xlabel=\"Observation time (hours UTC)\", ylabel=\"Latency (min)\", title=\"Latency by observation time\")\n",
+ "axes[1].plot(\n",
+ " hours, playback, \".\", ms=1, color=ORANGE, label='during_contact=\"end\" (default)'\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlim=(6, 12),\n",
+ " xlabel=\"Observation time (hours UTC)\",\n",
+ " ylabel=\"Latency (min)\",\n",
+ " title=\"Latency by observation time\",\n",
+ ")\n",
"axes[1].legend(frameon=False, fontsize=8, markerscale=4)\n",
"fig.tight_layout()\n",
"plt.show()"
diff --git a/docs/validation/ValidateLidarICESat2.ipynb b/docs/validation/ValidateLidarICESat2.ipynb
index 8f269f41..f782ea02 100644
--- a/docs/validation/ValidateLidarICESat2.ipynb
+++ b/docs/validation/ValidateLidarICESat2.ipynb
@@ -87,14 +87,20 @@
"GPS_EPOCH = datetime(1980, 1, 6, tzinfo=timezone.utc)\n",
"GPS_UTC = 18 # s\n",
"BEAMS = [\"gt1l\", \"gt1r\", \"gt2l\", \"gt2r\", \"gt3l\", \"gt3r\"]\n",
- "CROSSINGS_FILE = DATA_DIR / f\"atl08ql_crossings_{START:%Y%m%d}_{END - timedelta(days=1):%Y%m%d}.csv\"\n",
- "SEGMENTS_FILE = DATA_DIR / f\"atl08ql_segments_{START:%Y%m%d}_{END - timedelta(days=1):%Y%m%d}.csv\"\n",
+ "CROSSINGS_FILE = (\n",
+ " DATA_DIR / f\"atl08ql_crossings_{START:%Y%m%d}_{END - timedelta(days=1):%Y%m%d}.csv\"\n",
+ ")\n",
+ "SEGMENTS_FILE = (\n",
+ " DATA_DIR / f\"atl08ql_segments_{START:%Y%m%d}_{END - timedelta(days=1):%Y%m%d}.csv\"\n",
+ ")\n",
"geod = Geod(ellps=\"WGS84\")\n",
"\n",
"\n",
"def gps_seconds_to_utc(seconds):\n",
" \"\"\"UTC times of GPS seconds since 6 January 1980.\"\"\"\n",
- " return pd.to_datetime(GPS_EPOCH) + pd.to_timedelta(np.asarray(seconds) - GPS_UTC, unit=\"s\")\n",
+ " return pd.to_datetime(GPS_EPOCH) + pd.to_timedelta(\n",
+ " np.asarray(seconds) - GPS_UTC, unit=\"s\"\n",
+ " )\n",
"\n",
"\n",
"_sessions = []\n",
@@ -117,13 +123,17 @@
" self.block_size, self.blocks, self.position = block_size, {}, 0\n",
" # follow the Earthdata Login redirects while prefetching the first blocks (file metadata)\n",
" response = self._get(url, 0, prefetch * block_size)\n",
- " self.url, self.size = response.url, int(response.headers[\"Content-Range\"].split(\"/\")[1])\n",
+ " self.url, self.size = response.url, int(\n",
+ " response.headers[\"Content-Range\"].split(\"/\")[1]\n",
+ " )\n",
" self._store(0, response.content)\n",
"\n",
" def _get(self, url, start, end):\n",
" for attempt in range(5):\n",
" try:\n",
- " response = https_session().get(url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300)\n",
+ " response = https_session().get(\n",
+ " url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300\n",
+ " )\n",
" response.raise_for_status()\n",
" return response\n",
" except requests.RequestException:\n",
@@ -144,18 +154,30 @@
" return self.position\n",
"\n",
" def seek(self, offset, whence=io.SEEK_SET):\n",
- " self.position = {io.SEEK_SET: 0, io.SEEK_CUR: self.position, io.SEEK_END: self.size}[whence] + offset\n",
+ " self.position = {\n",
+ " io.SEEK_SET: 0,\n",
+ " io.SEEK_CUR: self.position,\n",
+ " io.SEEK_END: self.size,\n",
+ " }[whence] + offset\n",
" return self.position\n",
"\n",
" def readinto(self, buffer):\n",
" n = min(len(buffer), self.size - self.position)\n",
" if n <= 0:\n",
" return 0\n",
- " first, last = self.position // self.block_size, (self.position + n - 1) // self.block_size\n",
- " missing = [block for block in range(first, last + 1) if block not in self.blocks]\n",
+ " first, last = (\n",
+ " self.position // self.block_size,\n",
+ " (self.position + n - 1) // self.block_size,\n",
+ " )\n",
+ " missing = [\n",
+ " block for block in range(first, last + 1) if block not in self.blocks\n",
+ " ]\n",
" if missing:\n",
" end = min((missing[-1] + 1) * self.block_size, self.size)\n",
- " self._store(missing[0], self._get(self.url, missing[0] * self.block_size, end).content)\n",
+ " self._store(\n",
+ " missing[0],\n",
+ " self._get(self.url, missing[0] * self.block_size, end).content,\n",
+ " )\n",
" data = b\"\".join(self.blocks[block] for block in range(first, last + 1))\n",
" offset = self.position - first * self.block_size\n",
" buffer[:n] = data[offset : offset + n]\n",
@@ -166,13 +188,28 @@
"if not (CROSSINGS_FILE.exists() and SEGMENTS_FILE.exists()):\n",
" import earthaccess\n",
"\n",
- "\n",
- " results = earthaccess.search_data(short_name=\"ATL08QL\", version=\"007\", temporal=(START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"), (END - timedelta(seconds=1)).strftime(\"%Y-%m-%dT%H:%M:%SZ\")))\n",
+ " results = earthaccess.search_data(\n",
+ " short_name=\"ATL08QL\",\n",
+ " version=\"007\",\n",
+ " temporal=(\n",
+ " START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " (END - timedelta(seconds=1)).strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " ),\n",
+ " )\n",
" names = [r[\"umm\"][\"GranuleUR\"] for r in results]\n",
" rgts = [int(re.search(r\"_(\\d{4})(\\d{2})(\\d{2})_\", n).group(1)) for n in names]\n",
- " urls = [next(link[\"URL\"] for link in r[\"umm\"][\"RelatedUrls\"] if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".h5\")) for r in results]\n",
+ " urls = [\n",
+ " next(\n",
+ " link[\"URL\"]\n",
+ " for link in r[\"umm\"][\"RelatedUrls\"]\n",
+ " if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".h5\")\n",
+ " )\n",
+ " for r in results\n",
+ " ]\n",
" # one file per RGT for the orbit information, and every 10th file for the segments\n",
- " first_of_rgt = sorted({rgt: i for i, rgt in reversed(list(enumerate(rgts)))}.values())\n",
+ " first_of_rgt = sorted(\n",
+ " {rgt: i for i, rgt in reversed(list(enumerate(rgts)))}.values()\n",
+ " )\n",
" sample = sorted(set(range(0, len(results), 10)) | set(first_of_rgt))\n",
"\n",
" def read(i):\n",
@@ -198,7 +235,13 @@
" {\n",
" \"file\": names[i],\n",
" \"beam\": beam,\n",
- " \"beam_type\": f[beam].attrs[\"atlas_beam_type\"].decode() if isinstance(f[beam].attrs[\"atlas_beam_type\"], bytes) else str(f[beam].attrs[\"atlas_beam_type\"]),\n",
+ " \"beam_type\": (\n",
+ " f[beam].attrs[\"atlas_beam_type\"].decode()\n",
+ " if isinstance(\n",
+ " f[beam].attrs[\"atlas_beam_type\"], bytes\n",
+ " )\n",
+ " else str(f[beam].attrs[\"atlas_beam_type\"])\n",
+ " ),\n",
" # every 10th segment (about 1 km apart)\n",
" \"latitude\": group[\"latitude\"][::10],\n",
" \"longitude\": group[\"longitude\"][::10],\n",
@@ -210,20 +253,41 @@
"\n",
" with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
" outputs = list(pool.map(read, sample))\n",
- " pd.DataFrame([o for o, _ in outputs]).drop_duplicates(\"rgt\").to_csv(CROSSINGS_FILE, index=False)\n",
- " pd.concat([s for _, segments in outputs for s in segments]).to_csv(SEGMENTS_FILE, index=False)\n",
+ " pd.DataFrame([o for o, _ in outputs]).drop_duplicates(\"rgt\").to_csv(\n",
+ " CROSSINGS_FILE, index=False\n",
+ " )\n",
+ " pd.concat([s for _, segments in outputs for s in segments]).to_csv(\n",
+ " SEGMENTS_FILE, index=False\n",
+ " )\n",
"crossings = pd.read_csv(CROSSINGS_FILE)\n",
"crossings[\"time\"] = gps_seconds_to_utc(crossings.crossing_gps_seconds)\n",
"segments = pd.read_csv(SEGMENTS_FILE)\n",
"segments[\"time\"] = gps_seconds_to_utc(segments.gps_seconds)\n",
"icesat2 = GeneralPerturbationsOrbit.from_tle(TLE)\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"pd.Series(\n",
" {\n",
" \"orbits (RGTs)\": len(crossings),\n",
" \"cycle\": \", \".join(map(str, sorted(crossings.cycle.unique()))),\n",
- " \"spacecraft orientation (0: backward, 1: forward)\": \", \".join(map(str, sorted(crossings.sc_orient.unique()))),\n",
+ " \"spacecraft orientation (0: backward, 1: forward)\": \", \".join(\n",
+ " map(str, sorted(crossings.sc_orient.unique()))\n",
+ " ),\n",
" \"files with segments\": segments.file.nunique(),\n",
" \"segments (every 10th)\": len(segments),\n",
" }\n",
@@ -383,27 +447,44 @@
"def ascending_node(time):\n",
" \"\"\"Time and longitude of TAT-C's northward equator crossing nearest a time.\"\"\"\n",
" offsets = np.arange(-120, 120.01, 0.25)\n",
- " times = [time.floor(\"us\").to_pydatetime() + timedelta(seconds=float(s)) for s in offsets]\n",
- " latitude = wgs84.subpoint_of(satrec.at(timescale.from_datetimes(times))).latitude.degrees\n",
+ " times = [\n",
+ " time.floor(\"us\").to_pydatetime() + timedelta(seconds=float(s)) for s in offsets\n",
+ " ]\n",
+ " latitude = wgs84.subpoint_of(\n",
+ " satrec.at(timescale.from_datetimes(times))\n",
+ " ).latitude.degrees\n",
" k = np.nonzero((latitude[:-1] < 0) & (latitude[1:] >= 0))[0][0]\n",
" fraction = -latitude[k] / (latitude[k + 1] - latitude[k])\n",
" crossing = times[k] + (times[k + 1] - times[k]) * fraction\n",
- " longitude = wgs84.subpoint_of(satrec.at(timescale.from_datetime(crossing))).longitude.degrees\n",
+ " longitude = wgs84.subpoint_of(\n",
+ " satrec.at(timescale.from_datetime(crossing))\n",
+ " ).longitude.degrees\n",
" return crossing, float(longitude)\n",
"\n",
"\n",
"nodes = [ascending_node(t) for t in crossings.time]\n",
"crossings[\"days before TLE epoch\"] = (TLE_EPOCH - crossings.time) / timedelta(days=1)\n",
- "crossings[\"time difference (s)\"] = [(t - c).total_seconds() for (t, _), c in zip(nodes, crossings.time)]\n",
- "crossings[\"longitude difference (km)\"] = [((lon - lan + 180) % 360 - 180) * np.pi / 180 * 6378.137 for (_, lon), lan in zip(nodes, crossings.lan)]\n",
+ "crossings[\"time difference (s)\"] = [\n",
+ " (t - c).total_seconds() for (t, _), c in zip(nodes, crossings.time)\n",
+ "]\n",
+ "crossings[\"longitude difference (km)\"] = [\n",
+ " ((lon - lan + 180) % 360 - 180) * np.pi / 180 * 6378.137\n",
+ " for (_, lon), lan in zip(nodes, crossings.lan)\n",
+ "]\n",
"crossings[\"bin\"] = pd.cut(crossings[\"days before TLE epoch\"], [0, 4, 6, 8, 10, 12, 14])\n",
"crossings.groupby(\"bin\", observed=True).agg(\n",
" **{\n",
" \"orbits\": (\"rgt\", \"size\"),\n",
" \"time difference, median (s)\": (\"time difference (s)\", \"median\"),\n",
- " \"time difference, max |x| (s)\": (\"time difference (s)\", lambda x: x.abs().max()),\n",
+ " \"time difference, max |x| (s)\": (\n",
+ " \"time difference (s)\",\n",
+ " lambda x: x.abs().max(),\n",
+ " ),\n",
" \"longitude difference, median (km)\": (\"longitude difference (km)\", \"median\"),\n",
- " \"longitude difference, max |x| (km)\": (\"longitude difference (km)\", lambda x: x.abs().max()),\n",
+ " \"longitude difference, max |x| (km)\": (\n",
+ " \"longitude difference (km)\",\n",
+ " lambda x: x.abs().max(),\n",
+ " ),\n",
" }\n",
").round(3)"
]
@@ -458,8 +539,12 @@
}
],
"source": [
- "default_cycle = GeneralPerturbationsOrbit.from_tle(TLE, remove_drag=True, repeat_cycle=\"auto\").get_repeat_cycle()\n",
- "long_cycle = GeneralPerturbationsOrbit.from_tle(TLE, remove_drag=True, repeat_cycle=\"auto\").get_repeat_cycle(max_search_duration=timedelta(days=100))\n",
+ "default_cycle = GeneralPerturbationsOrbit.from_tle(\n",
+ " TLE, remove_drag=True, repeat_cycle=\"auto\"\n",
+ ").get_repeat_cycle()\n",
+ "long_cycle = GeneralPerturbationsOrbit.from_tle(\n",
+ " TLE, remove_drag=True, repeat_cycle=\"auto\"\n",
+ ").get_repeat_cycle(max_search_duration=timedelta(days=100))\n",
"\n",
"\n",
"def closest_returns(orbit, max_days=100):\n",
@@ -473,40 +558,76 @@
" out = {}\n",
" for days in range(1, max_days + 1):\n",
" center = epoch + timedelta(seconds=days * nodal_day)\n",
- " times = timescale.from_datetimes([center + timedelta(seconds=float(x)) for x in np.arange(-period / 2, period / 2, 5)])\n",
- " out[days] = np.linalg.norm(np.array(satellite.at(times).frame_xyz(itrs).m).T - initial, axis=1).min() / 1e3\n",
+ " times = timescale.from_datetimes(\n",
+ " [\n",
+ " center + timedelta(seconds=float(x))\n",
+ " for x in np.arange(-period / 2, period / 2, 5)\n",
+ " ]\n",
+ " )\n",
+ " out[days] = (\n",
+ " np.linalg.norm(\n",
+ " np.array(satellite.at(times).frame_xyz(itrs).m).T - initial, axis=1\n",
+ " ).min()\n",
+ " / 1e3\n",
+ " )\n",
" return pd.Series(out), 91 * nodal_day / period\n",
"\n",
"\n",
"returns_drag, _ = closest_returns(icesat2)\n",
- "returns_no_drag, orbits_91 = closest_returns(GeneralPerturbationsOrbit.from_tle(TLE, remove_drag=True))\n",
- "declared = GeneralPerturbationsOrbit.from_tle(TLE, remove_drag=True, repeat_cycle=timedelta(days=91)).get_repeat_cycle()\n",
+ "returns_no_drag, orbits_91 = closest_returns(\n",
+ " GeneralPerturbationsOrbit.from_tle(TLE, remove_drag=True)\n",
+ ")\n",
+ "declared = GeneralPerturbationsOrbit.from_tle(\n",
+ " TLE, remove_drag=True, repeat_cycle=timedelta(days=91)\n",
+ ").get_repeat_cycle()\n",
"\n",
"# measured repeat cycle: equator crossings of the same RGTs in consecutive cycles\n",
"REPEAT_FILE = DATA_DIR / \"atl08_rgt_crossings.csv\"\n",
"if not REPEAT_FILE.exists():\n",
" rows = []\n",
" for rgt in [277, 500, 1000]:\n",
- " for short_name, cycle in [(\"ATL08QL\", 33), (\"ATL08QL\", 32), (\"ATL08\", 32), (\"ATL08\", 31)]:\n",
+ " for short_name, cycle in [\n",
+ " (\"ATL08QL\", 33),\n",
+ " (\"ATL08QL\", 32),\n",
+ " (\"ATL08\", 32),\n",
+ " (\"ATL08\", 31),\n",
+ " ]:\n",
" if any(r[\"rgt\"] == rgt and r[\"cycle\"] == cycle for r in rows):\n",
" continue\n",
" response = requests.get(\n",
" \"https://cmr.earthdata.nasa.gov/search/granules.umm_json\",\n",
- " params={\"short_name\": short_name, \"version\": \"007\", \"readable_granule_name\": f\"{short_name}_*_{rgt:04d}{cycle:02d}*\", \"options[readable_granule_name][pattern]\": \"true\", \"page_size\": 1},\n",
+ " params={\n",
+ " \"short_name\": short_name,\n",
+ " \"version\": \"007\",\n",
+ " \"readable_granule_name\": f\"{short_name}_*_{rgt:04d}{cycle:02d}*\",\n",
+ " \"options[readable_granule_name][pattern]\": \"true\",\n",
+ " \"page_size\": 1,\n",
+ " },\n",
" timeout=120,\n",
" )\n",
" items = response.json().get(\"items\", [])\n",
" if not items:\n",
" continue\n",
- " url = next(link[\"URL\"] for link in items[0][\"umm\"][\"RelatedUrls\"] if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".h5\"))\n",
+ " url = next(\n",
+ " link[\"URL\"]\n",
+ " for link in items[0][\"umm\"][\"RelatedUrls\"]\n",
+ " if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".h5\")\n",
+ " )\n",
" with h5py.File(RemoteFile(url), \"r\") as f:\n",
- " gps_seconds = float(f[\"ancillary_data/atlas_sdp_gps_epoch\"][0]) + float(f[\"orbit_info/crossing_time\"][0])\n",
- " rows.append({\"rgt\": rgt, \"cycle\": cycle, \"crossing_gps_seconds\": gps_seconds})\n",
+ " gps_seconds = float(f[\"ancillary_data/atlas_sdp_gps_epoch\"][0]) + float(\n",
+ " f[\"orbit_info/crossing_time\"][0]\n",
+ " )\n",
+ " rows.append(\n",
+ " {\"rgt\": rgt, \"cycle\": cycle, \"crossing_gps_seconds\": gps_seconds}\n",
+ " )\n",
" pd.DataFrame(rows).to_csv(REPEAT_FILE, index=False)\n",
"rgt_crossings = pd.read_csv(REPEAT_FILE).sort_values([\"rgt\", \"cycle\"])\n",
"rgt_crossings[\"time\"] = gps_seconds_to_utc(rgt_crossings.crossing_gps_seconds)\n",
"measured = rgt_crossings.groupby(\"rgt\").time.diff().dropna()\n",
- "print(\"best returns within 100 nodal days without drag (km):\", {d: round(v, 1) for d, v in returns_no_drag.nsmallest(4).items()})\n",
+ "print(\n",
+ " \"best returns within 100 nodal days without drag (km):\",\n",
+ " {d: round(v, 1) for d, v in returns_no_drag.nsmallest(4).items()},\n",
+ ")\n",
"pd.Series(\n",
" {\n",
" \"repeat cycle, 30-day search (default)\": str(default_cycle),\n",
@@ -516,7 +637,9 @@
" \"closest return after 91 nodal days, without drag (km)\": returns_no_drag[91],\n",
" \"measured repeat cycle (consecutive cycles of 3 RGTs)\": f\"{measured.min()} to {measured.max()}\",\n",
" \"declared repeat cycle (91 days), refined to nodal days\": str(declared),\n",
- " \"declared minus measured (median, s)\": (declared - measured.median()).total_seconds(),\n",
+ " \"declared minus measured (median, s)\": (\n",
+ " declared - measured.median()\n",
+ " ).total_seconds(),\n",
" }\n",
")"
]
@@ -877,21 +1000,46 @@
" track = satrec.at(times)\n",
" ahead = satrec.at(timescale.tt_jd(times.tt + 1 / 86400))\n",
" now, later = wgs84.subpoint_of(track), wgs84.subpoint_of(ahead)\n",
- " heading = geod.inv(now.longitude.degrees, now.latitude.degrees, later.longitude.degrees, later.latitude.degrees)[0]\n",
- " azimuth, _, distance = geod.inv(now.longitude.degrees, now.latitude.degrees, group.longitude.to_numpy(), group.latitude.to_numpy())\n",
+ " heading = geod.inv(\n",
+ " now.longitude.degrees,\n",
+ " now.latitude.degrees,\n",
+ " later.longitude.degrees,\n",
+ " later.latitude.degrees,\n",
+ " )[0]\n",
+ " azimuth, _, distance = geod.inv(\n",
+ " now.longitude.degrees,\n",
+ " now.latitude.degrees,\n",
+ " group.longitude.to_numpy(),\n",
+ " group.latitude.to_numpy(),\n",
+ " )\n",
" angle = np.radians(azimuth - heading)\n",
- " return group.assign(**{\"cross-track (km)\": -distance * np.sin(angle) / 1e3, \"along-track (km)\": distance * np.cos(angle) / 1e3})\n",
+ " return group.assign(\n",
+ " **{\n",
+ " \"cross-track (km)\": -distance * np.sin(angle) / 1e3,\n",
+ " \"along-track (km)\": distance * np.cos(angle) / 1e3,\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"segments = pd.concat([beam_offsets(g) for _, g in segments.groupby(\"file\")])\n",
"segments[\"days before TLE epoch\"] = (TLE_EPOCH - segments.time) / timedelta(days=1)\n",
- "segments[\"region\"] = np.where(segments.latitude < -60, \"Antarctica (south of 60 deg S)\", np.where(segments.latitude > 60, \"north of 60 deg N\", \"60 deg S to 60 deg N\"))\n",
+ "segments[\"region\"] = np.where(\n",
+ " segments.latitude < -60,\n",
+ " \"Antarctica (south of 60 deg S)\",\n",
+ " np.where(segments.latitude > 60, \"north of 60 deg N\", \"60 deg S to 60 deg N\"),\n",
+ ")\n",
"table2 = segments.groupby([\"region\", \"beam\", \"beam_type\"]).agg(\n",
" **{\n",
" \"segments\": (\"latitude\", \"size\"),\n",
" \"cross-track, median (km)\": (\"cross-track (km)\", \"median\"),\n",
- " \"cross-track, 5th percentile (km)\": (\"cross-track (km)\", lambda x: x.quantile(0.05)),\n",
- " \"cross-track, 95th percentile (km)\": (\"cross-track (km)\", lambda x: x.quantile(0.95)),\n",
+ " \"cross-track, 5th percentile (km)\": (\n",
+ " \"cross-track (km)\",\n",
+ " lambda x: x.quantile(0.05),\n",
+ " ),\n",
+ " \"cross-track, 95th percentile (km)\": (\n",
+ " \"cross-track (km)\",\n",
+ " lambda x: x.quantile(0.95),\n",
+ " ),\n",
" \"along-track, median (km)\": (\"along-track (km)\", \"median\"),\n",
" }\n",
")\n",
@@ -927,14 +1075,42 @@
"central = antarctica[antarctica.beam.isin([\"gt2l\", \"gt2r\"])]\n",
"fig, axes = plt.subplots(1, 3, figsize=(15, 3.6))\n",
"for beam, color in zip(BEAMS, [BLUE, BLUE, ORANGE, ORANGE, AQUA, AQUA]):\n",
- " axes[0].hist(antarctica[antarctica.beam == beam][\"cross-track (km)\"], bins=np.arange(-7, 7, 0.05), histtype=\"step\", color=color, label=beam[:3] if beam.endswith(\"l\") else None)\n",
- "axes[0].set(xlabel=\"Distance from TAT-C's ground track (km, positive to the left)\", ylabel=\"Segments\", title=\"Antarctica: beam segments across track\")\n",
+ " axes[0].hist(\n",
+ " antarctica[antarctica.beam == beam][\"cross-track (km)\"],\n",
+ " bins=np.arange(-7, 7, 0.05),\n",
+ " histtype=\"step\",\n",
+ " color=color,\n",
+ " label=beam[:3] if beam.endswith(\"l\") else None,\n",
+ " )\n",
+ "axes[0].set(\n",
+ " xlabel=\"Distance from TAT-C's ground track (km, positive to the left)\",\n",
+ " ylabel=\"Segments\",\n",
+ " title=\"Antarctica: beam segments across track\",\n",
+ ")\n",
"axes[0].legend(frameon=False, fontsize=8, title=\"beam pair\", title_fontsize=8)\n",
- "others = segments[~segments.region.str.startswith(\"Antarctica\") & segments.beam.isin([\"gt2l\", \"gt2r\"])]\n",
+ "others = segments[\n",
+ " ~segments.region.str.startswith(\"Antarctica\") & segments.beam.isin([\"gt2l\", \"gt2r\"])\n",
+ "]\n",
"axes[1].scatter(others.latitude, others[\"cross-track (km)\"], s=1, color=GRAY, alpha=0.3)\n",
- "axes[1].set(xlabel=\"Latitude (deg)\", ylabel=\"Cross-track offset (km)\", title=\"Central pair outside Antarctica (off-pointing)\", ylim=(-20, 20))\n",
- "axes[2].scatter(central[\"days before TLE epoch\"], central[\"cross-track (km)\"], s=1, color=ORANGE, alpha=0.3)\n",
- "axes[2].set(xlabel=\"Days before TLE epoch\", ylabel=\"Cross-track offset (km)\", title=\"Antarctica: central pair\", ylim=(-4, 4))\n",
+ "axes[1].set(\n",
+ " xlabel=\"Latitude (deg)\",\n",
+ " ylabel=\"Cross-track offset (km)\",\n",
+ " title=\"Central pair outside Antarctica (off-pointing)\",\n",
+ " ylim=(-20, 20),\n",
+ ")\n",
+ "axes[2].scatter(\n",
+ " central[\"days before TLE epoch\"],\n",
+ " central[\"cross-track (km)\"],\n",
+ " s=1,\n",
+ " color=ORANGE,\n",
+ " alpha=0.3,\n",
+ ")\n",
+ "axes[2].set(\n",
+ " xlabel=\"Days before TLE epoch\",\n",
+ " ylabel=\"Cross-track offset (km)\",\n",
+ " title=\"Antarctica: central pair\",\n",
+ " ylim=(-4, 4),\n",
+ ")\n",
"axes[2].invert_xaxis()\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1073,11 +1249,21 @@
"def angle(distance):\n",
" \"\"\"Off-nadir angle (deg) of a signed ground distance from nadir, on a spherical Earth.\"\"\"\n",
" theta = distance / EARTH_MEAN_RADIUS\n",
- " return np.degrees(np.arctan2(EARTH_MEAN_RADIUS * np.sin(theta), EARTH_MEAN_RADIUS + ALTITUDE - EARTH_MEAN_RADIUS * np.cos(theta)))\n",
+ " return np.degrees(\n",
+ " np.arctan2(\n",
+ " EARTH_MEAN_RADIUS * np.sin(theta),\n",
+ " EARTH_MEAN_RADIUS + ALTITUDE - EARTH_MEAN_RADIUS * np.cos(theta),\n",
+ " )\n",
+ " )\n",
"\n",
"\n",
"WIDTHS = [100, 250, 500, 1000, 2000, 4000]\n",
- "sample3 = pd.concat([antarctica[antarctica.beam.str[:3] == pair].sample(600, random_state=1) for pair in PAIRS])\n",
+ "sample3 = pd.concat(\n",
+ " [\n",
+ " antarctica[antarctica.beam.str[:3] == pair].sample(600, random_state=1)\n",
+ " for pair in PAIRS\n",
+ " ]\n",
+ ")\n",
"\n",
"\n",
"def predicted(row):\n",
@@ -1089,12 +1275,18 @@
" for width in WIDTHS:\n",
" view = PointedInstrument(\n",
" name=\"pair\",\n",
- " cross_track_field_of_view=angle(offset + width / 2) - angle(offset - width / 2),\n",
+ " cross_track_field_of_view=angle(offset + width / 2)\n",
+ " - angle(offset - width / 2),\n",
" along_track_field_of_view=1,\n",
" roll_angle=(angle(offset + width / 2) + angle(offset - width / 2)) / 2,\n",
" is_rectangular=True,\n",
" )\n",
- " obs = collect_observations(point, Satellite(name=\"ICESat-2\", orbit=icesat2, instruments=[view]), time - timedelta(minutes=2), time + timedelta(minutes=2))\n",
+ " obs = collect_observations(\n",
+ " point,\n",
+ " Satellite(name=\"ICESat-2\", orbit=icesat2, instruments=[view]),\n",
+ " time - timedelta(minutes=2),\n",
+ " time + timedelta(minutes=2),\n",
+ " )\n",
" out[width] = not obs.empty\n",
" return out\n",
"\n",
diff --git a/docs/validation/ValidateLimbMLS.ipynb b/docs/validation/ValidateLimbMLS.ipynb
index 6ec6fc1d..7871f8c2 100644
--- a/docs/validation/ValidateLimbMLS.ipynb
+++ b/docs/validation/ValidateLimbMLS.ipynb
@@ -306,8 +306,12 @@
"\n",
"\n",
"mif_tai = l1[\"sc/MIF_TAI\"][:]\n",
- "tp_lat, tp_lon, tp_alt = (l1[\"GHz/\" + v][:].astype(float) for v in [\"GeodLat\", \"Lon\", \"GeodAlt\"])\n",
- "sc_lat, sc_lon, sc_alt = (l1[\"sc/\" + v][:].astype(float) for v in [\"GeodLat\", \"Lon\", \"GeodAlt\"])\n",
+ "tp_lat, tp_lon, tp_alt = (\n",
+ " l1[\"GHz/\" + v][:].astype(float) for v in [\"GeodLat\", \"Lon\", \"GeodAlt\"]\n",
+ ")\n",
+ "sc_lat, sc_lon, sc_alt = (\n",
+ " l1[\"sc/\" + v][:].astype(float) for v in [\"GeodLat\", \"Lon\", \"GeodAlt\"]\n",
+ ")\n",
"orb_y = l1[\"GHz/OrbY\"][:].astype(float)\n",
"sc_heading = GEOD.inv(sc_lon[:, 0], sc_lat[:, 0], sc_lon[:, 1], sc_lat[:, 1])[0]\n",
"\n",
@@ -330,8 +334,12 @@
" \"upward sweep duration (s)\": elapsed_median[top],\n",
" \"major frames on 14 Aug\": in_day.sum(),\n",
" \"off-nominal major frames\": (~nominal).sum(),\n",
- " \"off-nominal start (UTC)\": maf_utc[np.nonzero(~nominal)[0][0]].strftime(\"%H:%M:%S\"),\n",
- " \"off-nominal end (UTC)\": maf_utc[np.nonzero(~nominal)[0][-1]].strftime(\"%H:%M:%S\"),\n",
+ " \"off-nominal start (UTC)\": maf_utc[np.nonzero(~nominal)[0][0]].strftime(\n",
+ " \"%H:%M:%S\"\n",
+ " ),\n",
+ " \"off-nominal end (UTC)\": maf_utc[np.nonzero(~nominal)[0][-1]].strftime(\n",
+ " \"%H:%M:%S\"\n",
+ " ),\n",
" \"max off-nominal OrbY (km)\": np.abs(orb_y).max() / 1e3,\n",
" }\n",
").to_frame(\"MLS\")"
@@ -625,14 +633,34 @@
"northbound = np.cos(np.radians(sc_heading[mi])) > 0\n",
"north = np.where(northbound, 1, -1)\n",
"fig, axes = plt.subplots(1, 2, figsize=(10, 3.6))\n",
- "axes[0].plot(*binned_median(tp_lat[mi, ji], (alt2 - target) / 1e3, edges), color=BLUE, marker=\"o\", ms=3, label=\"TAT-C\")\n",
- "axes[0].set(xlabel=\"Tangent point latitude (deg)\", ylabel=\"Altitude error (km)\", title=\"Achieved vs. requested altitude\")\n",
+ "axes[0].plot(\n",
+ " *binned_median(tp_lat[mi, ji], (alt2 - target) / 1e3, edges),\n",
+ " color=BLUE,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " label=\"TAT-C\",\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Tangent point latitude (deg)\",\n",
+ " ylabel=\"Altitude error (km)\",\n",
+ " title=\"Achieved vs. requested altitude\",\n",
+ ")\n",
"for y, color, label in [\n",
" (north * along2, BLUE, \"TAT-C, northward\"),\n",
" (right2, ORANGE, \"TAT-C, cross-track (right)\"),\n",
"]:\n",
- " axes[1].plot(*binned_median(tp_lat[mi, ji], y, edges), color=color, marker=\"o\", ms=3, label=label)\n",
- "axes[1].set(xlabel=\"Tangent point latitude (deg)\", ylabel=\"TAT-C minus MLS (km)\", title=\"Horizontal offset at matched altitude\")\n",
+ " axes[1].plot(\n",
+ " *binned_median(tp_lat[mi, ji], y, edges),\n",
+ " color=color,\n",
+ " marker=\"o\",\n",
+ " ms=3,\n",
+ " label=label,\n",
+ " )\n",
+ "axes[1].set(\n",
+ " xlabel=\"Tangent point latitude (deg)\",\n",
+ " ylabel=\"TAT-C minus MLS (km)\",\n",
+ " title=\"Horizontal offset at matched altitude\",\n",
+ ")\n",
"for ax in axes:\n",
" ax.axhline(0, color=GRAY, lw=0.8)\n",
" ax.legend(frameon=False)\n",
@@ -742,7 +770,9 @@
" def minimize(offsets):\n",
" # offset (m) of minimum geodetic altitude among candidates (shape (N, K))\n",
" points = p[:, :, np.newaxis] + d[:, :, np.newaxis] * offsets\n",
- " altitudes = rectangular_to_geodetic(points.reshape(3, -1))[2].reshape(offsets.shape)\n",
+ " altitudes = rectangular_to_geodetic(points.reshape(3, -1))[2].reshape(\n",
+ " offsets.shape\n",
+ " )\n",
" return offsets[np.arange(len(offsets)), np.argmin(altitudes, axis=1)]\n",
"\n",
" coarse = minimize(np.tile(np.linspace(-1000e3, 1000e3, 401), (p.shape[1], 1)))\n",
@@ -755,8 +785,12 @@
" \"MLS OrbY, nominal (km)\": summarize(orb_y[nominal, :N_GHZ].ravel() / 1e3),\n",
" \"MLS tangency defect (km)\": summarize(\n",
" tangency_defect(\n",
- " sc_lon[mi, ji][sample], sc_lat[mi, ji][sample], sc_alt[mi, ji][sample],\n",
- " tp_lon[mi, ji][sample], tp_lat[mi, ji][sample], tp_alt[mi, ji][sample],\n",
+ " sc_lon[mi, ji][sample],\n",
+ " sc_lat[mi, ji][sample],\n",
+ " sc_alt[mi, ji][sample],\n",
+ " tp_lon[mi, ji][sample],\n",
+ " tp_lat[mi, ji][sample],\n",
+ " tp_alt[mi, ji][sample],\n",
" )\n",
" ),\n",
" }\n",
@@ -977,10 +1011,13 @@
"source": [
"day_idx = np.nonzero(in_day & nominal)[0]\n",
"example_elevations = sorted(\n",
- " pressure_to_altitude(p * 100) for p in [1000, 300, 100, 30, 10, 3, 1, 0.3, 0.1, 0.03, 0.01]\n",
+ " pressure_to_altitude(p * 100)\n",
+ " for p in [1000, 300, 100, 30, 10, 3, 1, 0.3, 0.1, 0.03, 0.01]\n",
")\n",
"configs = {\n",
- " \"example\": dict(scan_elevations=example_elevations, scan_duration=timedelta(seconds=25)),\n",
+ " \"example\": dict(\n",
+ " scan_elevations=example_elevations, scan_duration=timedelta(seconds=25)\n",
+ " ),\n",
" \"matched\": dict(\n",
" scan_elevations=list(np.linspace(alt_median[0], alt_median[top], 11)),\n",
" scan_duration=timedelta(seconds=float(elapsed_median[top])),\n",
@@ -1007,15 +1044,22 @@
"for name, config in configs.items():\n",
" dh, dt_same_alt, dz_same_time, profile = [], [], [], []\n",
" for i in subset:\n",
- " points = _sample_limb_scan(aura, maf_utc[i], 0.0, config[\"scan_elevations\"], config[\"scan_duration\"])\n",
+ " points = _sample_limb_scan(\n",
+ " aura, maf_utc[i], 0.0, config[\"scan_elevations\"], config[\"scan_duration\"]\n",
+ " )\n",
" t, a, la, lo, o = mls_scan(i)\n",
" for p in points:\n",
" elapsed = (p[\"time\"] - maf_utc[i]).total_seconds()\n",
" profile.append((elapsed, p[\"elevation\"] / 1e3))\n",
" if a.min() <= p[\"elevation\"] <= a.max():\n",
" dh.append(\n",
- " GEOD.inv(np.interp(p[\"elevation\"], a[o], lo[o]), np.interp(p[\"elevation\"], a[o], la[o]),\n",
- " p[\"longitude\"], p[\"latitude\"])[2] / 1e3\n",
+ " GEOD.inv(\n",
+ " np.interp(p[\"elevation\"], a[o], lo[o]),\n",
+ " np.interp(p[\"elevation\"], a[o], la[o]),\n",
+ " p[\"longitude\"],\n",
+ " p[\"latitude\"],\n",
+ " )[2]\n",
+ " / 1e3\n",
" )\n",
" dt_same_alt.append(elapsed - np.interp(p[\"elevation\"], a[o], t[o]))\n",
" if t.min() <= elapsed <= t.max():\n",
@@ -1030,9 +1074,18 @@
" t, a, la, lo, o = mls_scan(i)\n",
" z = row.position.z\n",
" position_dh.append(\n",
- " GEOD.inv(np.interp(z, a[o], lo[o]), np.interp(z, a[o], la[o]), row.position.x, row.position.y)[2] / 1e3\n",
+ " GEOD.inv(\n",
+ " np.interp(z, a[o], lo[o]),\n",
+ " np.interp(z, a[o], la[o]),\n",
+ " row.position.x,\n",
+ " row.position.y,\n",
+ " )[2]\n",
+ " / 1e3\n",
+ " )\n",
+ " position_dt.append(\n",
+ " (row.time.to_pydatetime() - maf_utc[i]).total_seconds()\n",
+ " - np.interp(z, a[o], t[o])\n",
" )\n",
- " position_dt.append((row.time.to_pydatetime() - maf_utc[i]).total_seconds() - np.interp(z, a[o], t[o]))\n",
" test3[name] = pd.DataFrame(\n",
" {\n",
" \"sample time offset at same altitude (s)\": summarize(dt_same_alt),\n",
@@ -1080,10 +1133,24 @@
"source": [
"fig, ax = plt.subplots(figsize=(5.5, 3.8))\n",
"ax.plot(elapsed_median, alt_median / 1e3, color=INK, lw=2, label=\"MLS (median scan)\")\n",
- "for name, color, label in [(\"example\", BLUE, \"TAT-C, example configuration\"), (\"matched\", ORANGE, \"TAT-C, matched configuration\")]:\n",
+ "for name, color, label in [\n",
+ " (\"example\", BLUE, \"TAT-C, example configuration\"),\n",
+ " (\"matched\", ORANGE, \"TAT-C, matched configuration\"),\n",
+ "]:\n",
" median_profile = np.median(profiles[name], axis=0)\n",
- " ax.plot(median_profile[:, 0], median_profile[:, 1], color=color, marker=\"o\", ms=4, label=label)\n",
- "ax.set(xlabel=\"Time since scan start (s)\", ylabel=\"Tangent point altitude (km)\", title=\"Vertical scan timing\")\n",
+ " ax.plot(\n",
+ " median_profile[:, 0],\n",
+ " median_profile[:, 1],\n",
+ " color=color,\n",
+ " marker=\"o\",\n",
+ " ms=4,\n",
+ " label=label,\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Time since scan start (s)\",\n",
+ " ylabel=\"Tangent point altitude (km)\",\n",
+ " title=\"Vertical scan timing\",\n",
+ ")\n",
"ax.legend(frameon=False)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1247,24 +1314,45 @@
"l2.set_auto_mask(False)\n",
"geo = l2[\"HDFEOS/SWATHS/Temperature/Geolocation Fields\"]\n",
"l2_time = np.array([tai_to_utc(x) for x in geo[\"Time\"][:]])\n",
- "l2_lat, l2_lon, l2_lst = (geo[v][:].astype(float) for v in [\"Latitude\", \"Longitude\", \"LocalSolarTime\"])\n",
- "keep = (np.abs(l2_lat) <= 90) & np.array([DAY0 <= t < DAY0 + timedelta(days=1) for t in l2_time])\n",
- "l2_time, l2_lat, l2_lon, l2_lst = l2_time[keep], l2_lat[keep], l2_lon[keep], l2_lst[keep]\n",
+ "l2_lat, l2_lon, l2_lst = (\n",
+ " geo[v][:].astype(float) for v in [\"Latitude\", \"Longitude\", \"LocalSolarTime\"]\n",
+ ")\n",
+ "keep = (np.abs(l2_lat) <= 90) & np.array(\n",
+ " [DAY0 <= t < DAY0 + timedelta(days=1) for t in l2_time]\n",
+ ")\n",
+ "l2_time, l2_lat, l2_lon, l2_lst = (\n",
+ " l2_time[keep],\n",
+ " l2_lat[keep],\n",
+ " l2_lon[keep],\n",
+ " l2_lst[keep],\n",
+ ")\n",
"l2_s = np.array([(t - DAY0).total_seconds() for t in l2_time])\n",
"\n",
- "times = [DAY0 + i * timedelta(seconds=25) for i in range(timedelta(days=1) // timedelta(seconds=25))]\n",
- "limb_obs = collect_limb_observations(aura, times, 0, example_elevations, timedelta(seconds=25))\n",
+ "times = [\n",
+ " DAY0 + i * timedelta(seconds=25)\n",
+ " for i in range(timedelta(days=1) // timedelta(seconds=25))\n",
+ "]\n",
+ "limb_obs = collect_limb_observations(\n",
+ " aura, times, 0, example_elevations, timedelta(seconds=25)\n",
+ ")\n",
"obs_s = np.array([(t.to_pydatetime() - DAY0).total_seconds() for t in limb_obs.time])\n",
"obs_lat = np.array([p.y for p in limb_obs.position])\n",
"obs_lon = np.array([p.x for p in limb_obs.position])\n",
"\n",
"dense_s = np.arange(obs_s[0], obs_s[-1], 1.0)\n",
"dense_lat = np.interp(dense_s, obs_s, obs_lat)\n",
- "dense_lon = (np.interp(dense_s, obs_s, np.unwrap(obs_lon, period=360)) + 180) % 360 - 180\n",
+ "dense_lon = (\n",
+ " np.interp(dense_s, obs_s, np.unwrap(obs_lon, period=360)) + 180\n",
+ ") % 360 - 180\n",
"nearest_km, nearest_s = [], []\n",
"for s, la, lo in zip(l2_s, l2_lat, l2_lon):\n",
" w = np.abs(dense_s - s) < 120\n",
- " dist = GEOD.inv(np.full(w.sum(), lo), np.full(w.sum(), la), dense_lon[w], dense_lat[w])[2] / 1e3\n",
+ " dist = (\n",
+ " GEOD.inv(\n",
+ " np.full(w.sum(), lo), np.full(w.sum(), la), dense_lon[w], dense_lat[w]\n",
+ " )[2]\n",
+ " / 1e3\n",
+ " )\n",
" k = np.argmin(dist)\n",
" nearest_km.append(dist[k])\n",
" nearest_s.append(dense_s[w][k] - s)\n",
@@ -1294,7 +1382,10 @@
").T\n",
"display(test4.round(2))\n",
"pd.DataFrame(\n",
- " {\"distance to TAT-C track (km)\": summarize(nearest_km), \"TAT-C time minus L2 time (s)\": summarize(nearest_s)}\n",
+ " {\n",
+ " \"distance to TAT-C track (km)\": summarize(nearest_km),\n",
+ " \"TAT-C time minus L2 time (s)\": summarize(nearest_s),\n",
+ " }\n",
").T.round(3)"
]
},
@@ -1334,22 +1425,49 @@
],
"source": [
"fig, axes = plt.subplots(\n",
- " 1, 2, figsize=(11, 3.6), gridspec_kw={\"width_ratios\": [1.6, 1]},\n",
+ " 1,\n",
+ " 2,\n",
+ " figsize=(11, 3.6),\n",
+ " gridspec_kw={\"width_ratios\": [1.6, 1]},\n",
")\n",
"axes[0].remove()\n",
"ax_map = fig.add_subplot(1, 2, 1, projection=ccrs.PlateCarree())\n",
"half = obs_s < 12 * 3600\n",
- "ax_map.scatter(obs_lon[half], obs_lat[half], s=8, color=BLUE, label=\"TAT-C positions\", transform=ccrs.PlateCarree())\n",
- "ax_map.scatter(l2_lon[l2_s < 12 * 3600], l2_lat[l2_s < 12 * 3600], s=2, color=INK, label=\"MLS Level 2 profiles\", transform=ccrs.PlateCarree())\n",
+ "ax_map.scatter(\n",
+ " obs_lon[half],\n",
+ " obs_lat[half],\n",
+ " s=8,\n",
+ " color=BLUE,\n",
+ " label=\"TAT-C positions\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
+ "ax_map.scatter(\n",
+ " l2_lon[l2_s < 12 * 3600],\n",
+ " l2_lat[l2_s < 12 * 3600],\n",
+ " s=2,\n",
+ " color=INK,\n",
+ " label=\"MLS Level 2 profiles\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
"ax_map.coastlines(color=GRAY, linewidth=0.5)\n",
"ax_map.set_global()\n",
"ax_map.set_title(\"Profile locations, 00:00-12:00 UTC\")\n",
"ax_map.legend(frameon=False, loc=\"upper center\", bbox_to_anchor=(0.5, -0.02), ncol=2)\n",
"bins = np.arange(-90, 91, 10)\n",
"centers = (bins[1:] + bins[:-1]) / 2\n",
- "axes[1].bar(centers - 2.1, np.histogram(l2_lat, bins)[0], width=4, color=INK, label=\"MLS Level 2\")\n",
- "axes[1].bar(centers + 2.1, np.histogram(obs_lat, bins)[0], width=4, color=BLUE, label=\"TAT-C\")\n",
- "axes[1].set(xlabel=\"Latitude (deg)\", ylabel=\"Profiles per day\", title=\"Daily latitude sampling\")\n",
+ "axes[1].bar(\n",
+ " centers - 2.1,\n",
+ " np.histogram(l2_lat, bins)[0],\n",
+ " width=4,\n",
+ " color=INK,\n",
+ " label=\"MLS Level 2\",\n",
+ ")\n",
+ "axes[1].bar(\n",
+ " centers + 2.1, np.histogram(obs_lat, bins)[0], width=4, color=BLUE, label=\"TAT-C\"\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Latitude (deg)\", ylabel=\"Profiles per day\", title=\"Daily latitude sampling\"\n",
+ ")\n",
"axes[1].grid(axis=\"x\", visible=False)\n",
"axes[1].legend(frameon=False, loc=\"upper center\", ncol=2)\n",
"fig.tight_layout()\n",
diff --git a/docs/validation/ValidateLimbSABER.ipynb b/docs/validation/ValidateLimbSABER.ipynb
index ab6ca31f..f40f6bc3 100644
--- a/docs/validation/ValidateLimbSABER.ipynb
+++ b/docs/validation/ValidateLimbSABER.ipynb
@@ -55,7 +55,8 @@
"DATA_DIR.mkdir(exist_ok=True)\n",
"SABER_URL = \"https://data.gats-inc.com/saber/Version2_0/Level2A/2026\"\n",
"saber_files = [\n",
- " DATA_DIR / f\"SABER_L2A_{2026263 if orbit_number == 134529 else 2026264}_{orbit_number}_02.08.nc\"\n",
+ " DATA_DIR\n",
+ " / f\"SABER_L2A_{2026263 if orbit_number == 134529 else 2026264}_{orbit_number}_02.08.nc\"\n",
" for orbit_number in range(134529, 134545)\n",
"]\n",
"for file in saber_files:\n",
@@ -277,12 +278,22 @@
}
],
"source": [
- "FIELDS = [\"time\", \"tpaltitude\", \"tplatitude\", \"tplongitude\", \"sclatitude\", \"sclongitude\", \"scaltitude\"]\n",
+ "FIELDS = [\n",
+ " \"time\",\n",
+ " \"tpaltitude\",\n",
+ " \"tplatitude\",\n",
+ " \"tplongitude\",\n",
+ " \"sclatitude\",\n",
+ " \"sclongitude\",\n",
+ " \"scaltitude\",\n",
+ "]\n",
"events = []\n",
"for file in saber_files:\n",
" ds = nc.Dataset(file)\n",
" ds.set_auto_mask(False)\n",
- " orbit_start_date = datetime.strptime(ds.Data_Start_Time[:7], \"%Y%j\").replace(tzinfo=timezone.utc)\n",
+ " orbit_start_date = datetime.strptime(ds.Data_Start_Time[:7], \"%Y%j\").replace(\n",
+ " tzinfo=timezone.utc\n",
+ " )\n",
" data = {name: ds[name][:] for name in FIELDS}\n",
" for e, mode in enumerate(ds[\"mode\"][:]):\n",
" ok = (\n",
@@ -295,7 +306,9 @@
" continue\n",
" order = np.argsort(data[\"time\"][e][ok])\n",
" event = {name: data[name][e][ok][order].astype(float) for name in FIELDS}\n",
- " event[\"datetime\"] = [orbit_start_date + timedelta(milliseconds=ms) for ms in event[\"time\"]]\n",
+ " event[\"datetime\"] = [\n",
+ " orbit_start_date + timedelta(milliseconds=ms) for ms in event[\"time\"]\n",
+ " ]\n",
" event[\"elapsed\"] = (event[\"time\"] - event[\"time\"][0]) / 1e3\n",
" event[\"upward\"] = bool(mode == 1)\n",
" event[\"northbound\"] = event[\"sclatitude\"][-1] > event[\"sclatitude\"][0]\n",
@@ -307,7 +320,10 @@
" \"\"\"Spacecraft ground track heading (deg) at sample i of an event.\"\"\"\n",
" j = min(i + 1, len(event[\"time\"]) - 1)\n",
" return GEOD.inv(\n",
- " event[\"sclongitude\"][j - 1], event[\"sclatitude\"][j - 1], event[\"sclongitude\"][j], event[\"sclatitude\"][j]\n",
+ " event[\"sclongitude\"][j - 1],\n",
+ " event[\"sclatitude\"][j - 1],\n",
+ " event[\"sclongitude\"][j],\n",
+ " event[\"sclatitude\"][j],\n",
" )[0]\n",
"\n",
"\n",
@@ -319,7 +335,13 @@
"mid = [len(e[\"time\"]) // 2 for e in events]\n",
"view_azimuth = np.array(\n",
" [\n",
- " GEOD.inv(e[\"sclongitude\"][m], e[\"sclatitude\"][m], e[\"tplongitude\"][m], e[\"tplatitude\"][m])[0] - heading(e, m)\n",
+ " GEOD.inv(\n",
+ " e[\"sclongitude\"][m],\n",
+ " e[\"sclatitude\"][m],\n",
+ " e[\"tplongitude\"][m],\n",
+ " e[\"tplatitude\"][m],\n",
+ " )[0]\n",
+ " - heading(e, m)\n",
" for e, m in zip(events, mid)\n",
" ]\n",
")\n",
@@ -332,8 +354,12 @@
" \"scan duration (s)\": np.median(durations),\n",
" \"scan bottom (km)\": np.median(bottoms),\n",
" \"scan top (km)\": np.median(tops),\n",
- " \"start spacing after an upward scan (s)\": np.median(np.diff(starts)[upward[:-1]]),\n",
- " \"start spacing after a downward scan (s)\": np.median(np.diff(starts)[~upward[:-1]]),\n",
+ " \"start spacing after an upward scan (s)\": np.median(\n",
+ " np.diff(starts)[upward[:-1]]\n",
+ " ),\n",
+ " \"start spacing after a downward scan (s)\": np.median(\n",
+ " np.diff(starts)[~upward[:-1]]\n",
+ " ),\n",
" \"tangent point azimuth from heading (deg)\": np.median(view_azimuth),\n",
" \"range of that azimuth (deg)\": np.ptp(view_azimuth),\n",
" }\n",
@@ -458,7 +484,9 @@
" {\n",
" \"along-track (km)\": summarize(along1),\n",
" \"cross-track (km)\": summarize(right1),\n",
- " \"altitude (km)\": summarize(g1.elevation.km - [e[\"scaltitude\"][m] for e, m in zip(events, mid)]),\n",
+ " \"altitude (km)\": summarize(\n",
+ " g1.elevation.km - [e[\"scaltitude\"][m] for e, m in zip(events, mid)]\n",
+ " ),\n",
" }\n",
").T\n",
"test1.round(3)"
@@ -603,10 +631,18 @@
"tp_p, in_domain = _limb_tangent_point(orbit.get_orbit_track(t2), 90.0, target)\n",
"g2 = wgs84.geographic_position_of(Geocentric(Distance(m=tp_p).au, None, ts2))\n",
"along2, right2 = along_right(\n",
- " ref_lon, ref_lat, g2.longitude.degrees, g2.latitude.degrees, np.array([heading(e, i) for e, i in samples])\n",
+ " ref_lon,\n",
+ " ref_lat,\n",
+ " g2.longitude.degrees,\n",
+ " g2.latitude.degrees,\n",
+ " np.array([heading(e, i) for e, i in samples]),\n",
")\n",
"d_north = (g2.latitude.degrees - ref_lat) * 111.2\n",
- "d_east = ((g2.longitude.degrees - ref_lon + 180) % 360 - 180) * 111.2 * np.cos(np.radians(ref_lat))\n",
+ "d_east = (\n",
+ " ((g2.longitude.degrees - ref_lon + 180) % 360 - 180)\n",
+ " * 111.2\n",
+ " * np.cos(np.radians(ref_lat))\n",
+ ")\n",
"test2 = pd.DataFrame(\n",
" {\n",
" \"altitude error (km)\": summarize((g2.elevation.m - target) / 1e3),\n",
@@ -655,9 +691,24 @@
"source": [
"edges = np.arange(-60, 91, 10)\n",
"fig, axes = plt.subplots(1, 2, figsize=(10, 3.6), sharey=True)\n",
- "for ax, mask, title in [(axes[0], northbound, \"Ascending passes\"), (axes[1], ~northbound, \"Descending passes\")]:\n",
- " ax.plot(*binned_median(ref_lat[mask], d_east[mask], edges), color=BLUE, marker=\"o\", ms=4, label=\"Eastward\")\n",
- " ax.plot(*binned_median(ref_lat[mask], d_north[mask], edges), color=ORANGE, marker=\"o\", ms=4, label=\"Northward\")\n",
+ "for ax, mask, title in [\n",
+ " (axes[0], northbound, \"Ascending passes\"),\n",
+ " (axes[1], ~northbound, \"Descending passes\"),\n",
+ "]:\n",
+ " ax.plot(\n",
+ " *binned_median(ref_lat[mask], d_east[mask], edges),\n",
+ " color=BLUE,\n",
+ " marker=\"o\",\n",
+ " ms=4,\n",
+ " label=\"Eastward\",\n",
+ " )\n",
+ " ax.plot(\n",
+ " *binned_median(ref_lat[mask], d_north[mask], edges),\n",
+ " color=ORANGE,\n",
+ " marker=\"o\",\n",
+ " ms=4,\n",
+ " label=\"Northward\",\n",
+ " )\n",
" ax.axhline(0, color=GRAY, lw=0.8)\n",
" ax.set(xlabel=\"Tangent point latitude (deg)\", title=title)\n",
" ax.legend(frameon=False)\n",
@@ -781,7 +832,9 @@
" def minimize(offsets):\n",
" # offset (m) of minimum geodetic altitude among candidates (shape (N, K))\n",
" points = p[:, :, np.newaxis] + d[:, :, np.newaxis] * offsets\n",
- " altitudes = rectangular_to_geodetic(points.reshape(3, -1))[2].reshape(offsets.shape)\n",
+ " altitudes = rectangular_to_geodetic(points.reshape(3, -1))[2].reshape(\n",
+ " offsets.shape\n",
+ " )\n",
" return offsets[np.arange(len(offsets)), np.argmin(altitudes, axis=1)]\n",
"\n",
" coarse = minimize(np.tile(np.linspace(-1000e3, 1000e3, 401), (p.shape[1], 1)))\n",
@@ -790,9 +843,15 @@
"\n",
"check = samples[::5]\n",
"defect = tangency_defect(\n",
- " *(np.array([e[name][i] for e, i in check]) for name in [\"sclongitude\", \"sclatitude\"]),\n",
+ " *(\n",
+ " np.array([e[name][i] for e, i in check])\n",
+ " for name in [\"sclongitude\", \"sclatitude\"]\n",
+ " ),\n",
" np.array([e[\"scaltitude\"][i] * 1e3 for e, i in check]),\n",
- " *(np.array([e[name][i] for e, i in check]) for name in [\"tplongitude\", \"tplatitude\"]),\n",
+ " *(\n",
+ " np.array([e[name][i] for e, i in check])\n",
+ " for name in [\"tplongitude\", \"tplatitude\"]\n",
+ " ),\n",
" np.array([e[\"tpaltitude\"][i] * 1e3 for e, i in check]),\n",
")\n",
"check_alt = np.array([e[\"tpaltitude\"][i] for e, i in check])\n",
@@ -950,13 +1009,16 @@
"TO_GEODETIC = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
"precession = compute_precession(ts2[len(ts2) // 2].tdb)\n",
"# small-angle rotation vector (rad) of the precession since J2000\n",
- "w = np.array(\n",
- " [\n",
- " precession[2, 1] - precession[1, 2],\n",
- " precession[0, 2] - precession[2, 0],\n",
- " precession[1, 0] - precession[0, 1],\n",
- " ]\n",
- ") / 2\n",
+ "w = (\n",
+ " np.array(\n",
+ " [\n",
+ " precession[2, 1] - precession[1, 2],\n",
+ " precession[0, 2] - precession[2, 0],\n",
+ " precession[1, 0] - precession[0, 1],\n",
+ " ]\n",
+ " )\n",
+ " / 2\n",
+ ")\n",
"print(f\"precession rotation vector (deg): {np.degrees(w).round(3)}\")\n",
"\n",
"\n",
@@ -970,16 +1032,27 @@
"\n",
"fix_lon, fix_lat, fix_alt = remove_precession(ref_lon, ref_lat, target, ts2)\n",
"fixed_defect = tangency_defect(\n",
- " *(np.array([e[name][i] for e, i in check]) for name in [\"sclongitude\", \"sclatitude\"]),\n",
+ " *(\n",
+ " np.array([e[name][i] for e, i in check])\n",
+ " for name in [\"sclongitude\", \"sclatitude\"]\n",
+ " ),\n",
" np.array([e[\"scaltitude\"][i] * 1e3 for e, i in check]),\n",
" fix_lon[::5],\n",
" fix_lat[::5],\n",
" fix_alt[::5],\n",
")\n",
"along2f, right2f = along_right(\n",
- " fix_lon, fix_lat, g2.longitude.degrees, g2.latitude.degrees, np.array([heading(e, i) for e, i in samples])\n",
+ " fix_lon,\n",
+ " fix_lat,\n",
+ " g2.longitude.degrees,\n",
+ " g2.latitude.degrees,\n",
+ " np.array([heading(e, i) for e, i in samples]),\n",
+ ")\n",
+ "d_east_f = (\n",
+ " ((g2.longitude.degrees - fix_lon + 180) % 360 - 180)\n",
+ " * 111.2\n",
+ " * np.cos(np.radians(fix_lat))\n",
")\n",
- "d_east_f = ((g2.longitude.degrees - fix_lon + 180) % 360 - 180) * 111.2 * np.cos(np.radians(fix_lat))\n",
"d_north_f = (g2.latitude.degrees - fix_lat) * 111.2\n",
"pd.DataFrame(\n",
" {\n",
@@ -1165,7 +1238,9 @@
"test3, profiles = {}, {}\n",
"for direction in [ScanDirection.UPWARD, ScanDirection.DOWNWARD]:\n",
" dh, dt_same_alt, dz_same_time, profile = [], [], [], []\n",
- " selected = [e for e in events if e[\"upward\"] == (direction == ScanDirection.UPWARD)][::4]\n",
+ " selected = [\n",
+ " e for e in events if e[\"upward\"] == (direction == ScanDirection.UPWARD)\n",
+ " ][::4]\n",
" for e in selected:\n",
" points = _sample_limb_scan(\n",
" timed,\n",
@@ -1182,12 +1257,21 @@
" profile.append((elapsed, p[\"elevation\"] / 1e3))\n",
" if a.min() <= p[\"elevation\"] <= a.max():\n",
" dh.append(\n",
- " GEOD.inv(np.interp(p[\"elevation\"], a[o], lon[o]), np.interp(p[\"elevation\"], a[o], e[\"tplatitude\"][o]),\n",
- " p[\"longitude\"], p[\"latitude\"])[2] / 1e3\n",
+ " GEOD.inv(\n",
+ " np.interp(p[\"elevation\"], a[o], lon[o]),\n",
+ " np.interp(p[\"elevation\"], a[o], e[\"tplatitude\"][o]),\n",
+ " p[\"longitude\"],\n",
+ " p[\"latitude\"],\n",
+ " )[2]\n",
+ " / 1e3\n",
+ " )\n",
+ " dt_same_alt.append(\n",
+ " elapsed - np.interp(p[\"elevation\"], a[o], e[\"elapsed\"][o])\n",
" )\n",
- " dt_same_alt.append(elapsed - np.interp(p[\"elevation\"], a[o], e[\"elapsed\"][o]))\n",
" if e[\"elapsed\"][0] <= elapsed <= e[\"elapsed\"][-1]:\n",
- " dz_same_time.append((p[\"elevation\"] - np.interp(elapsed, e[\"elapsed\"], a)) / 1e3)\n",
+ " dz_same_time.append(\n",
+ " (p[\"elevation\"] - np.interp(elapsed, e[\"elapsed\"], a)) / 1e3\n",
+ " )\n",
" profiles[direction.value] = np.array(profile).reshape(len(selected), -1, 2)\n",
" test3[direction.value] = pd.DataFrame(\n",
" {\n",
@@ -1236,12 +1320,30 @@
"grid = np.linspace(0, np.median(durations), 50)\n",
"for up, color, label in [(True, BLUE, \"upward\"), (False, ORANGE, \"downward\")]:\n",
" measured = np.median(\n",
- " [np.interp(grid, e[\"elapsed\"], e[\"tpaltitude\"]) for e in events if e[\"upward\"] == up], axis=0\n",
+ " [\n",
+ " np.interp(grid, e[\"elapsed\"], e[\"tpaltitude\"])\n",
+ " for e in events\n",
+ " if e[\"upward\"] == up\n",
+ " ],\n",
+ " axis=0,\n",
" )\n",
" ax.plot(grid, measured, color=color, lw=2, label=f\"SABER, {label} (median)\")\n",
" simulated = np.median(profiles[label], axis=0)\n",
- " ax.plot(simulated[:, 0], simulated[:, 1], color=color, lw=0, marker=\"o\", ms=5, mfc=\"white\", label=f\"TAT-C, {label}\")\n",
- "ax.set(xlabel=\"Time since scan start (s)\", ylabel=\"Tangent point altitude (km)\", title=\"Vertical scan timing\")\n",
+ " ax.plot(\n",
+ " simulated[:, 0],\n",
+ " simulated[:, 1],\n",
+ " color=color,\n",
+ " lw=0,\n",
+ " marker=\"o\",\n",
+ " ms=5,\n",
+ " mfc=\"white\",\n",
+ " label=f\"TAT-C, {label}\",\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Time since scan start (s)\",\n",
+ " ylabel=\"Tangent point altitude (km)\",\n",
+ " title=\"Vertical scan timing\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1402,8 +1504,17 @@
" t += gap_after_down\n",
"limb_obs = pd.concat(\n",
" [\n",
- " collect_limb_observations(timed, up_times, 90.0, scan_elevations, scan_duration, ScanDirection.UPWARD),\n",
- " collect_limb_observations(timed, down_times, 90.0, scan_elevations, scan_duration, ScanDirection.DOWNWARD),\n",
+ " collect_limb_observations(\n",
+ " timed, up_times, 90.0, scan_elevations, scan_duration, ScanDirection.UPWARD\n",
+ " ),\n",
+ " collect_limb_observations(\n",
+ " timed,\n",
+ " down_times,\n",
+ " 90.0,\n",
+ " scan_elevations,\n",
+ " scan_duration,\n",
+ " ScanDirection.DOWNWARD,\n",
+ " ),\n",
" ]\n",
").sort_values(\"time\", ignore_index=True)\n",
"sample_elevation = (min(scan_elevations) + max(scan_elevations)) / 2\n",
@@ -1417,19 +1528,35 @@
" a = e[\"tpaltitude\"] * 1e3\n",
" o = np.argsort(a)\n",
" if a.min() <= sample_elevation <= a.max():\n",
- " ref_s.append((e[\"datetime\"][0] - DAY0).total_seconds() + np.interp(sample_elevation, a[o], e[\"elapsed\"][o]))\n",
+ " ref_s.append(\n",
+ " (e[\"datetime\"][0] - DAY0).total_seconds()\n",
+ " + np.interp(sample_elevation, a[o], e[\"elapsed\"][o])\n",
+ " )\n",
" ref_lat4.append(np.interp(sample_elevation, a[o], e[\"tplatitude\"][o]))\n",
- " ref_lon4.append(np.interp(sample_elevation, a[o], np.unwrap(e[\"tplongitude\"], period=360)[o]))\n",
+ " ref_lon4.append(\n",
+ " np.interp(\n",
+ " sample_elevation, a[o], np.unwrap(e[\"tplongitude\"], period=360)[o]\n",
+ " )\n",
+ " )\n",
"ref_s, ref_lat4 = np.array(ref_s), np.array(ref_lat4)\n",
"ref_lon4 = (np.array(ref_lon4) + 180) % 360 - 180\n",
"\n",
"dense_s = np.arange(obs_s[0], obs_s[-1], 1.0)\n",
"dense_lat = np.interp(dense_s, obs_s, obs_lat)\n",
- "dense_lon = (np.interp(dense_s, obs_s, np.unwrap(obs_lon, period=360)) + 180) % 360 - 180\n",
+ "dense_lon = (\n",
+ " np.interp(dense_s, obs_s, np.unwrap(obs_lon, period=360)) + 180\n",
+ ") % 360 - 180\n",
"nearest_km = []\n",
"for s, la, lo in zip(ref_s, ref_lat4, ref_lon4):\n",
" w = np.abs(dense_s - s) < 300\n",
- " nearest_km.append(np.min(GEOD.inv(np.full(w.sum(), lo), np.full(w.sum(), la), dense_lon[w], dense_lat[w])[2]) / 1e3)\n",
+ " nearest_km.append(\n",
+ " np.min(\n",
+ " GEOD.inv(\n",
+ " np.full(w.sum(), lo), np.full(w.sum(), la), dense_lon[w], dense_lat[w]\n",
+ " )[2]\n",
+ " )\n",
+ " / 1e3\n",
+ " )\n",
"\n",
"\n",
"def equator_lst(seconds, lat, lon, ascending):\n",
@@ -1482,21 +1609,45 @@
}
],
"source": [
- "fig, axes = plt.subplots(1, 2, figsize=(11, 3.6), gridspec_kw={\"width_ratios\": [1.6, 1]})\n",
+ "fig, axes = plt.subplots(\n",
+ " 1, 2, figsize=(11, 3.6), gridspec_kw={\"width_ratios\": [1.6, 1]}\n",
+ ")\n",
"axes[0].remove()\n",
"ax_map = fig.add_subplot(1, 2, 1, projection=ccrs.PlateCarree())\n",
"half = obs_s < 12 * 3600\n",
- "ax_map.scatter(obs_lon[half], obs_lat[half], s=10, color=BLUE, label=\"TAT-C positions\", transform=ccrs.PlateCarree())\n",
- "ax_map.scatter(ref_lon4[ref_s < 12 * 3600], ref_lat4[ref_s < 12 * 3600], s=3, color=INK, label=\"SABER profiles\", transform=ccrs.PlateCarree())\n",
+ "ax_map.scatter(\n",
+ " obs_lon[half],\n",
+ " obs_lat[half],\n",
+ " s=10,\n",
+ " color=BLUE,\n",
+ " label=\"TAT-C positions\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
+ "ax_map.scatter(\n",
+ " ref_lon4[ref_s < 12 * 3600],\n",
+ " ref_lat4[ref_s < 12 * 3600],\n",
+ " s=3,\n",
+ " color=INK,\n",
+ " label=\"SABER profiles\",\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
"ax_map.coastlines(color=GRAY, linewidth=0.5)\n",
"ax_map.set_global()\n",
- "ax_map.set_title(f\"Profile locations at {sample_elevation / 1e3:.0f} km, 00:00-12:00 UTC\")\n",
+ "ax_map.set_title(\n",
+ " f\"Profile locations at {sample_elevation / 1e3:.0f} km, 00:00-12:00 UTC\"\n",
+ ")\n",
"ax_map.legend(frameon=False, loc=\"upper center\", bbox_to_anchor=(0.5, -0.02), ncol=2)\n",
"bins = np.arange(-90, 91, 10)\n",
"centers = (bins[1:] + bins[:-1]) / 2\n",
- "axes[1].bar(centers - 2.1, np.histogram(ref_lat4, bins)[0], width=4, color=INK, label=\"SABER\")\n",
- "axes[1].bar(centers + 2.1, np.histogram(obs_lat, bins)[0], width=4, color=BLUE, label=\"TAT-C\")\n",
- "axes[1].set(xlabel=\"Latitude (deg)\", ylabel=\"Profiles per day\", title=\"Daily latitude sampling\")\n",
+ "axes[1].bar(\n",
+ " centers - 2.1, np.histogram(ref_lat4, bins)[0], width=4, color=INK, label=\"SABER\"\n",
+ ")\n",
+ "axes[1].bar(\n",
+ " centers + 2.1, np.histogram(obs_lat, bins)[0], width=4, color=BLUE, label=\"TAT-C\"\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Latitude (deg)\", ylabel=\"Profiles per day\", title=\"Daily latitude sampling\"\n",
+ ")\n",
"axes[1].grid(axis=\"x\", visible=False)\n",
"axes[1].legend(frameon=False, loc=\"upper left\")\n",
"fig.tight_layout()\n",
diff --git a/docs/validation/ValidateMaintainedOrbitLandsat9.ipynb b/docs/validation/ValidateMaintainedOrbitLandsat9.ipynb
index c2d60def..34bf3f88 100644
--- a/docs/validation/ValidateMaintainedOrbitLandsat9.ipynb
+++ b/docs/validation/ValidateMaintainedOrbitLandsat9.ipynb
@@ -75,27 +75,61 @@
"\n",
"HISTORY_FILE = Path(\"data\") / \"orbits\" / \"landsat9_gp_history.tle\"\n",
"if not HISTORY_FILE.exists():\n",
- " raise FileNotFoundError(f\"Save the Landsat 9 TLE history from Space-Track.org as {HISTORY_FILE} (see above).\")\n",
- "lines = [line.rstrip() for line in HISTORY_FILE.read_text().splitlines() if line.strip()]\n",
+ " raise FileNotFoundError(\n",
+ " f\"Save the Landsat 9 TLE history from Space-Track.org as {HISTORY_FILE} (see above).\"\n",
+ " )\n",
+ "lines = [\n",
+ " line.rstrip() for line in HISTORY_FILE.read_text().splitlines() if line.strip()\n",
+ "]\n",
"tles = [lines[i + 1 : i + 3] for i in range(0, len(lines), 3)]\n",
"rows = []\n",
"for tle in tles:\n",
" satrec = Satrec.twoline2rv(*tle)\n",
" rows.append(\n",
" {\n",
- " \"epoch\": pd.Timestamp(satrec.jdsatepoch + satrec.jdsatepochF - 2440587.5, unit=\"D\", tz=\"UTC\"),\n",
+ " \"epoch\": pd.Timestamp(\n",
+ " satrec.jdsatepoch + satrec.jdsatepochF - 2440587.5, unit=\"D\", tz=\"UTC\"\n",
+ " ),\n",
" \"semimajor axis (km)\": satrec.a * satrec.radiusearthkm,\n",
" \"inclination (deg)\": np.degrees(satrec.inclo),\n",
" }\n",
" )\n",
"history = pd.DataFrame(rows)\n",
- "history = history.assign(tle=tles).drop_duplicates(\"epoch\").sort_values(\"epoch\").reset_index(drop=True)\n",
+ "history = (\n",
+ " history.assign(tle=tles)\n",
+ " .drop_duplicates(\"epoch\")\n",
+ " .sort_values(\"epoch\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
"orbits = [GeneralPerturbationsOrbit.from_tle(tle) for tle in history.tle]\n",
"START, END = history.epoch.iloc[0], history.epoch.iloc[-1]\n",
"first = history.tle.iloc[0]\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
- "pd.Series({\"TLEs\": len(tles), \"unique epochs\": len(history), \"first epoch\": f\"{START:%Y-%m-%d %H:%M}\", \"last epoch\": f\"{END:%Y-%m-%d %H:%M}\", \"median interval (h)\": history.epoch.diff().median() / pd.Timedelta(hours=1)})"
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
+ "pd.Series(\n",
+ " {\n",
+ " \"TLEs\": len(tles),\n",
+ " \"unique epochs\": len(history),\n",
+ " \"first epoch\": f\"{START:%Y-%m-%d %H:%M}\",\n",
+ " \"last epoch\": f\"{END:%Y-%m-%d %H:%M}\",\n",
+ " \"median interval (h)\": history.epoch.diff().median() / pd.Timedelta(hours=1),\n",
+ " }\n",
+ ")"
]
},
{
@@ -154,29 +188,66 @@
"\n",
"def ltan(time, longitude):\n",
" \"\"\"Local mean solar time (h) of a crossing.\"\"\"\n",
- " return (time.hour + time.minute / 60 + (time.second + time.microsecond / 1e6) / 3600 + longitude / 15) % 24\n",
+ " return (\n",
+ " time.hour\n",
+ " + time.minute / 60\n",
+ " + (time.second + time.microsecond / 1e6) / 3600\n",
+ " + longitude / 15\n",
+ " ) % 24\n",
"\n",
"\n",
"# reference crossings: one per day, from the TLE closest to noon\n",
- "days = pd.date_range(START.normalize() + pd.Timedelta(days=1), END.normalize(), freq=\"D\", tz=\"UTC\")\n",
- "reference = sorted({int(np.argmin(np.abs((history.epoch - (d + pd.Timedelta(hours=12))).dt.total_seconds()))) for d in days})\n",
+ "days = pd.date_range(\n",
+ " START.normalize() + pd.Timedelta(days=1), END.normalize(), freq=\"D\", tz=\"UTC\"\n",
+ ")\n",
+ "reference = sorted(\n",
+ " {\n",
+ " int(\n",
+ " np.argmin(\n",
+ " np.abs(\n",
+ " (history.epoch - (d + pd.Timedelta(hours=12))).dt.total_seconds()\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " for d in days\n",
+ " }\n",
+ ")\n",
"nodes = []\n",
"for i in reference:\n",
- " time, longitude = ascending_node(orbits[i], (history.epoch[i] + pd.Timedelta(minutes=50)).floor(\"us\").to_pydatetime())\n",
- " nodes.append({\"time\": time, \"longitude\": longitude, \"LTAN (h)\": ltan(time, longitude)})\n",
+ " time, longitude = ascending_node(\n",
+ " orbits[i],\n",
+ " (history.epoch[i] + pd.Timedelta(minutes=50)).floor(\"us\").to_pydatetime(),\n",
+ " )\n",
+ " nodes.append(\n",
+ " {\"time\": time, \"longitude\": longitude, \"LTAN (h)\": ltan(time, longitude)}\n",
+ " )\n",
"nodes = pd.DataFrame(nodes)\n",
"nodes[\"elapsed (days)\"] = (pd.to_datetime(nodes.time) - START) / pd.Timedelta(days=1)\n",
"\n",
- "daily = history.set_index(\"epoch\")[[\"semimajor axis (km)\", \"inclination (deg)\"]].resample(\"D\").median().dropna()\n",
+ "daily = (\n",
+ " history.set_index(\"epoch\")[[\"semimajor axis (km)\", \"inclination (deg)\"]]\n",
+ " .resample(\"D\")\n",
+ " .median()\n",
+ " .dropna()\n",
+ ")\n",
"burns = daily[daily[\"semimajor axis (km)\"].diff() > 0.030]\n",
"inclination_maneuvers = daily[daily[\"inclination (deg)\"].diff().abs() > 0.01]\n",
- "drift = daily[\"inclination (deg)\"].diff()[daily[\"inclination (deg)\"].diff().abs() < 0.005].mean() * 365.25\n",
+ "drift = (\n",
+ " daily[\"inclination (deg)\"]\n",
+ " .diff()[daily[\"inclination (deg)\"].diff().abs() < 0.005]\n",
+ " .mean()\n",
+ " * 365.25\n",
+ ")\n",
"pd.Series(\n",
" {\n",
" \"semimajor axis, range (km)\": f\"{history['semimajor axis (km)'].min():.3f} to {history['semimajor axis (km)'].max():.3f}\",\n",
" \"semimajor axis maneuvers (daily increase > 30 m)\": len(burns),\n",
- " \"semimajor axis maneuvers, median increase (m)\": 1e3 * daily[\"semimajor axis (km)\"].diff()[burns.index].median(),\n",
- " \"inclination maneuvers\": \", \".join(f\"{t:%Y-%m-%d} ({daily['inclination (deg)'].diff()[t]:+.3f} deg)\" for t in inclination_maneuvers.index),\n",
+ " \"semimajor axis maneuvers, median increase (m)\": 1e3\n",
+ " * daily[\"semimajor axis (km)\"].diff()[burns.index].median(),\n",
+ " \"inclination maneuvers\": \", \".join(\n",
+ " f\"{t:%Y-%m-%d} ({daily['inclination (deg)'].diff()[t]:+.3f} deg)\"\n",
+ " for t in inclination_maneuvers.index\n",
+ " ),\n",
" \"inclination drift between maneuvers (deg/yr)\": drift,\n",
" \"LTAN, range (minutes after 22:00)\": f\"{(nodes['LTAN (h)'].min() - 22) * 60:.2f} to {(nodes['LTAN (h)'].max() - 22) * 60:.2f}\",\n",
" }\n",
@@ -209,12 +280,21 @@
],
"source": [
"fig, axes = plt.subplots(1, 3, figsize=(15, 3.6))\n",
- "axes[0].plot(history.epoch, history[\"semimajor axis (km)\"] - 7077, \".\", ms=1.5, color=BLUE)\n",
- "axes[0].set(ylabel=\"Semimajor axis minus 7,077 km (km)\", title=\"Mean semimajor axis (drag and maneuvers)\")\n",
+ "axes[0].plot(\n",
+ " history.epoch, history[\"semimajor axis (km)\"] - 7077, \".\", ms=1.5, color=BLUE\n",
+ ")\n",
+ "axes[0].set(\n",
+ " ylabel=\"Semimajor axis minus 7,077 km (km)\",\n",
+ " title=\"Mean semimajor axis (drag and maneuvers)\",\n",
+ ")\n",
"axes[1].plot(history.epoch, history[\"inclination (deg)\"], \".\", ms=1.5, color=ORANGE)\n",
"axes[1].set(ylabel=\"Inclination (deg)\", title=\"Mean inclination\")\n",
- "axes[2].plot(pd.to_datetime(nodes.time), nodes[\"LTAN (h)\"] * 60 - 22 * 60, \".\", ms=2, color=AQUA)\n",
- "axes[2].set(ylabel=\"Minutes after 22:00\", title=\"Local mean solar time of ascending node\")\n",
+ "axes[2].plot(\n",
+ " pd.to_datetime(nodes.time), nodes[\"LTAN (h)\"] * 60 - 22 * 60, \".\", ms=2, color=AQUA\n",
+ ")\n",
+ "axes[2].set(\n",
+ " ylabel=\"Minutes after 22:00\", title=\"Local mean solar time of ascending node\"\n",
+ ")\n",
"for ax in axes:\n",
" ax.tick_params(axis=\"x\", rotation=30)\n",
"fig.tight_layout()\n",
@@ -461,32 +541,67 @@
"import warnings\n",
"\n",
"# the SGP4 models propagate the first TLE directly, with drag, for 19 months on purpose\n",
- "warnings.filterwarnings(\"ignore\", message=\"Propagating general perturbations elements with drag\")\n",
+ "warnings.filterwarnings(\n",
+ " \"ignore\", message=\"Propagating general perturbations elements with drag\"\n",
+ ")\n",
"models = {\n",
" \"SGP4\": GeneralPerturbationsOrbit.from_tle(first),\n",
" \"SGP4 without drag\": GeneralPerturbationsOrbit.from_tle(first, remove_drag=True),\n",
- " \"detected repeat cycle\": GeneralPerturbationsOrbit.from_tle(first, remove_drag=True, repeat_cycle=\"auto\"),\n",
- " \"declared repeat cycle\": GeneralPerturbationsOrbit.from_tle(first, remove_drag=True, repeat_cycle=timedelta(days=16)),\n",
+ " \"detected repeat cycle\": GeneralPerturbationsOrbit.from_tle(\n",
+ " first, remove_drag=True, repeat_cycle=\"auto\"\n",
+ " ),\n",
+ " \"declared repeat cycle\": GeneralPerturbationsOrbit.from_tle(\n",
+ " first, remove_drag=True, repeat_cycle=timedelta(days=16)\n",
+ " ),\n",
"}\n",
"period = orbits[0].get_orbit_period().total_seconds()\n",
"for name, orbit in models.items():\n",
" offsets = []\n",
- " for reference_time, reference_longitude, reference_ltan in zip(nodes.time, nodes.longitude, nodes[\"LTAN (h)\"]):\n",
+ " for reference_time, reference_longitude, reference_ltan in zip(\n",
+ " nodes.time, nodes.longitude, nodes[\"LTAN (h)\"]\n",
+ " ):\n",
" time, longitude = ascending_node(orbit, reference_time)\n",
- " offsets.append(((time - reference_time).total_seconds(), ((longitude - reference_longitude + 180) % 360 - 180) * 111.32, (ltan(time, longitude) - reference_ltan) * 60))\n",
+ " offsets.append(\n",
+ " (\n",
+ " (time - reference_time).total_seconds(),\n",
+ " ((longitude - reference_longitude + 180) % 360 - 180) * 111.32,\n",
+ " (ltan(time, longitude) - reference_ltan) * 60,\n",
+ " )\n",
+ " )\n",
" offsets = np.array(offsets)\n",
" valid = np.abs(offsets[:, 0]) < period / 2 - 60\n",
" nodes[f\"{name}: time (s)\"] = np.where(valid, offsets[:, 0], np.nan)\n",
" nodes[f\"{name}: ground track (km)\"] = np.where(valid, offsets[:, 1], np.nan)\n",
" nodes[f\"{name}: LTAN (min)\"] = np.where(valid, offsets[:, 2], np.nan)\n",
- "print(\"repeat cycles:\", {name: str(orbit.get_repeat_cycle()) for name, orbit in models.items() if orbit.repeat_cycle is not None})\n",
- "bins = pd.cut(nodes[\"elapsed (days)\"], [0, 30, 91, 182, 365, 600], labels=[\"first month\", \"1 to 3 months\", \"3 to 6 months\", \"6 to 12 months\", \"12 to 19 months\"])\n",
+ "print(\n",
+ " \"repeat cycles:\",\n",
+ " {\n",
+ " name: str(orbit.get_repeat_cycle())\n",
+ " for name, orbit in models.items()\n",
+ " if orbit.repeat_cycle is not None\n",
+ " },\n",
+ ")\n",
+ "bins = pd.cut(\n",
+ " nodes[\"elapsed (days)\"],\n",
+ " [0, 30, 91, 182, 365, 600],\n",
+ " labels=[\n",
+ " \"first month\",\n",
+ " \"1 to 3 months\",\n",
+ " \"3 to 6 months\",\n",
+ " \"6 to 12 months\",\n",
+ " \"12 to 19 months\",\n",
+ " ],\n",
+ ")\n",
"table2 = {}\n",
"for name in models:\n",
" for quantity in [\"time (s)\", \"ground track (km)\"]:\n",
" column = nodes[f\"{name}: {quantity}\"]\n",
- " table2[(name, f\"{quantity}, max |x|\")] = column.abs().groupby(bins, observed=False).max()\n",
- " table2[(name, \"excluded (%)\")] = 100 * column.isna().groupby(bins, observed=False).mean()\n",
+ " table2[(name, f\"{quantity}, max |x|\")] = (\n",
+ " column.abs().groupby(bins, observed=False).max()\n",
+ " )\n",
+ " table2[(name, \"excluded (%)\")] = (\n",
+ " 100 * column.isna().groupby(bins, observed=False).mean()\n",
+ " )\n",
"pd.DataFrame(table2).T.round(1)"
]
},
@@ -518,12 +633,48 @@
"fig, axes = plt.subplots(1, 3, figsize=(15, 3.6))\n",
"colors = dict(zip(models, [GRAY, PURPLE, BLUE, ORANGE]))\n",
"for name in models:\n",
- " axes[0].plot(nodes[\"elapsed (days)\"], nodes[f\"{name}: time (s)\"], \".\", ms=2, color=colors[name], label=name)\n",
- " axes[1].plot(nodes[\"elapsed (days)\"], nodes[f\"{name}: ground track (km)\"], \".\", ms=2, color=colors[name])\n",
- " axes[2].plot(nodes[\"elapsed (days)\"], nodes[f\"{name}: LTAN (min)\"], \".\", ms=2, color=colors[name])\n",
- "axes[0].set(xlabel=\"Days since first TLE\", ylabel=\"TAT-C minus Landsat 9 (s)\", title=\"Ascending node time\", yscale=\"symlog\", ylim=(-3000, 3000))\n",
- "axes[1].set(xlabel=\"Days since first TLE\", ylabel=\"TAT-C minus Landsat 9 (km)\", title=\"Ascending node longitude (at the equator)\", yscale=\"symlog\", ylim=(-2000, 2000))\n",
- "axes[2].set(xlabel=\"Days since first TLE\", ylabel=\"TAT-C minus Landsat 9 (min)\", title=\"LTAN\", ylim=(-6, 6))\n",
+ " axes[0].plot(\n",
+ " nodes[\"elapsed (days)\"],\n",
+ " nodes[f\"{name}: time (s)\"],\n",
+ " \".\",\n",
+ " ms=2,\n",
+ " color=colors[name],\n",
+ " label=name,\n",
+ " )\n",
+ " axes[1].plot(\n",
+ " nodes[\"elapsed (days)\"],\n",
+ " nodes[f\"{name}: ground track (km)\"],\n",
+ " \".\",\n",
+ " ms=2,\n",
+ " color=colors[name],\n",
+ " )\n",
+ " axes[2].plot(\n",
+ " nodes[\"elapsed (days)\"],\n",
+ " nodes[f\"{name}: LTAN (min)\"],\n",
+ " \".\",\n",
+ " ms=2,\n",
+ " color=colors[name],\n",
+ " )\n",
+ "axes[0].set(\n",
+ " xlabel=\"Days since first TLE\",\n",
+ " ylabel=\"TAT-C minus Landsat 9 (s)\",\n",
+ " title=\"Ascending node time\",\n",
+ " yscale=\"symlog\",\n",
+ " ylim=(-3000, 3000),\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Days since first TLE\",\n",
+ " ylabel=\"TAT-C minus Landsat 9 (km)\",\n",
+ " title=\"Ascending node longitude (at the equator)\",\n",
+ " yscale=\"symlog\",\n",
+ " ylim=(-2000, 2000),\n",
+ ")\n",
+ "axes[2].set(\n",
+ " xlabel=\"Days since first TLE\",\n",
+ " ylabel=\"TAT-C minus Landsat 9 (min)\",\n",
+ " title=\"LTAN\",\n",
+ " ylim=(-6, 6),\n",
+ ")\n",
"axes[0].legend(frameon=False, fontsize=8, markerscale=4)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -632,24 +783,57 @@
}
],
"source": [
- "oli = Instrument(name=\"OLI\", field_of_regard=utils.swath_width_to_field_of_regard(705e3, 185e3))\n",
+ "oli = Instrument(\n",
+ " name=\"OLI\", field_of_regard=utils.swath_width_to_field_of_regard(705e3, 185e3)\n",
+ ")\n",
"lattice = generate_points_fibonacci_lattice(3000e3)\n",
"lattice = lattice[lattice.geometry.y.abs() < 81].reset_index(drop=True)\n",
- "points = [Point(id=i, latitude=g.y, longitude=g.x) for i, g in enumerate(lattice.geometry)]\n",
- "reference_orbit = GeneralPerturbationsOrbit.from_tle([line for tle in history.tle for line in tle])\n",
- "predictions = {\"reference\": reference_orbit, \"detected repeat cycle\": models[\"detected repeat cycle\"], \"declared repeat cycle\": models[\"declared repeat cycle\"]}\n",
- "start, end = (START + pd.Timedelta(hours=1)).floor(\"us\").to_pydatetime(), END.floor(\"us\").to_pydatetime()\n",
+ "points = [\n",
+ " Point(id=i, latitude=g.y, longitude=g.x) for i, g in enumerate(lattice.geometry)\n",
+ "]\n",
+ "reference_orbit = GeneralPerturbationsOrbit.from_tle(\n",
+ " [line for tle in history.tle for line in tle]\n",
+ ")\n",
+ "predictions = {\n",
+ " \"reference\": reference_orbit,\n",
+ " \"detected repeat cycle\": models[\"detected repeat cycle\"],\n",
+ " \"declared repeat cycle\": models[\"declared repeat cycle\"],\n",
+ "}\n",
+ "start, end = (START + pd.Timedelta(hours=1)).floor(\"us\").to_pydatetime(), END.floor(\n",
+ " \"us\"\n",
+ ").to_pydatetime()\n",
"observations = {}\n",
"for name, orbit in predictions.items():\n",
" satellite = Satellite(name=\"Landsat 9\", orbit=orbit, instruments=[oli])\n",
- " frames = Parallel(n_jobs=-1)(delayed(collect_observations)(point, satellite, start, end) for point in points)\n",
- " observations[name] = pd.concat([frame.assign(point_id=point.id) for point, frame in zip(points, frames)], ignore_index=True)\n",
+ " frames = Parallel(n_jobs=-1)(\n",
+ " delayed(collect_observations)(point, satellite, start, end) for point in points\n",
+ " )\n",
+ " observations[name] = pd.concat(\n",
+ " [frame.assign(point_id=point.id) for point, frame in zip(points, frames)],\n",
+ " ignore_index=True,\n",
+ " )\n",
"rows3 = {}\n",
"reference_obs = observations[\"reference\"].sort_values(\"epoch\")\n",
"for name in [\"detected repeat cycle\", \"declared repeat cycle\"]:\n",
" predicted = observations[name].sort_values(\"epoch\")\n",
- " pairs = pd.merge_asof(reference_obs[[\"point_id\", \"epoch\"]], predicted[[\"point_id\", \"epoch\"]].rename(columns={\"epoch\": \"predicted\"}), left_on=\"epoch\", right_on=\"predicted\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=10))\n",
- " back = pd.merge_asof(predicted[[\"point_id\", \"epoch\"]], reference_obs[[\"point_id\", \"epoch\"]].rename(columns={\"epoch\": \"actual\"}), left_on=\"epoch\", right_on=\"actual\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=10))\n",
+ " pairs = pd.merge_asof(\n",
+ " reference_obs[[\"point_id\", \"epoch\"]],\n",
+ " predicted[[\"point_id\", \"epoch\"]].rename(columns={\"epoch\": \"predicted\"}),\n",
+ " left_on=\"epoch\",\n",
+ " right_on=\"predicted\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=10),\n",
+ " )\n",
+ " back = pd.merge_asof(\n",
+ " predicted[[\"point_id\", \"epoch\"]],\n",
+ " reference_obs[[\"point_id\", \"epoch\"]].rename(columns={\"epoch\": \"actual\"}),\n",
+ " left_on=\"epoch\",\n",
+ " right_on=\"actual\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=10),\n",
+ " )\n",
" error = (pairs.predicted - pairs.epoch).dt.total_seconds()\n",
" elapsed = (pairs.epoch - START) / pd.Timedelta(days=1)\n",
" rows3[name] = {\n",
@@ -693,7 +877,11 @@
"for name, color in [(\"detected repeat cycle\", BLUE), (\"declared repeat cycle\", ORANGE)]:\n",
" pairs = observations[name + \" pairs\"]\n",
" ax.plot(pairs.elapsed, pairs.error, \".\", ms=2, color=color, label=name)\n",
- "ax.set(xlabel=\"Days since first TLE\", ylabel=\"Predicted minus reference (s)\", title=\"Observation epoch errors\")\n",
+ "ax.set(\n",
+ " xlabel=\"Days since first TLE\",\n",
+ " ylabel=\"Predicted minus reference (s)\",\n",
+ " title=\"Observation epoch errors\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, markerscale=4)\n",
"plt.show()"
]
diff --git a/docs/validation/ValidateOrbitGeneration.ipynb b/docs/validation/ValidateOrbitGeneration.ipynb
index e7ab7ca7..555cbbcc 100644
--- a/docs/validation/ValidateOrbitGeneration.ipynb
+++ b/docs/validation/ValidateOrbitGeneration.ipynb
@@ -74,24 +74,57 @@
"DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
"T = datetime(2026, 10, 4, 12, tzinfo=timezone.utc)\n",
"CELESTRAK = \"https://celestrak.org/NORAD/elements/gp.php\"\n",
- "GROUPS = [\"weather\", \"resource\", \"galileo\", \"beidou\", \"glo-ops\", \"gps-ops\", \"oneweb\", \"goes\"]\n",
- "NAMES = {\"meridian\": [\"MERIDIAN\", \"COSMOS 2510\", \"COSMOS 2518\", \"COSMOS 2541\", \"COSMOS 2552\", \"COSMOS 2563\"], \"qzss\": [\"QZS\"]}\n",
+ "GROUPS = [\n",
+ " \"weather\",\n",
+ " \"resource\",\n",
+ " \"galileo\",\n",
+ " \"beidou\",\n",
+ " \"glo-ops\",\n",
+ " \"gps-ops\",\n",
+ " \"oneweb\",\n",
+ " \"goes\",\n",
+ "]\n",
+ "NAMES = {\n",
+ " \"meridian\": [\n",
+ " \"MERIDIAN\",\n",
+ " \"COSMOS 2510\",\n",
+ " \"COSMOS 2518\",\n",
+ " \"COSMOS 2541\",\n",
+ " \"COSMOS 2552\",\n",
+ " \"COSMOS 2563\",\n",
+ " ],\n",
+ " \"qzss\": [\"QZS\"],\n",
+ "}\n",
"\n",
"\n",
"def fetch(path, params_list):\n",
" if not path.exists():\n",
" text = \"\"\n",
" for params in params_list:\n",
- " response = requests.get(CELESTRAK, params={**params, \"FORMAT\": \"tle\"}, timeout=120)\n",
+ " response = requests.get(\n",
+ " CELESTRAK, params={**params, \"FORMAT\": \"tle\"}, timeout=120\n",
+ " )\n",
" response.raise_for_status()\n",
- " text += \"\\n\".join(line for line in response.text.splitlines() if \"No GP data\" not in line) + \"\\n\"\n",
+ " text += (\n",
+ " \"\\n\".join(\n",
+ " line\n",
+ " for line in response.text.splitlines()\n",
+ " if \"No GP data\" not in line\n",
+ " )\n",
+ " + \"\\n\"\n",
+ " )\n",
" path.write_text(text)\n",
" lines = [line.rstrip() for line in path.read_text().splitlines() if line.strip()]\n",
" return {lines[i].strip(): lines[i + 1 : i + 3] for i in range(0, len(lines) - 2, 3)}\n",
"\n",
"\n",
"TLES = {group: fetch(DATA_DIR / f\"{group}.tle\", [{\"GROUP\": group}]) for group in GROUPS}\n",
- "TLES.update({key: fetch(DATA_DIR / f\"{key}.tle\", [{\"NAME\": name} for name in names]) for key, names in NAMES.items()})\n",
+ "TLES.update(\n",
+ " {\n",
+ " key: fetch(DATA_DIR / f\"{key}.tle\", [{\"NAME\": name} for name in names])\n",
+ " for key, names in NAMES.items()\n",
+ " }\n",
+ ")\n",
"pd.Series({group: len(tles) for group, tles in TLES.items()}, name=\"satellites\")"
]
},
@@ -153,19 +186,43 @@
" hn = np.linalg.norm(h, axis=0)\n",
" inclination = np.degrees(np.arccos(h[2] / hn))\n",
" raan = np.degrees(np.arctan2(h[0], -h[1])) % 360\n",
- " node = np.array([np.cos(np.radians(raan)), np.sin(np.radians(raan)), np.zeros_like(raan)])\n",
- " u = np.degrees(np.arctan2(np.sum(np.cross(node, r, axis=0) * h, axis=0) / hn, np.sum(node * r, axis=0))) % 360\n",
+ " node = np.array(\n",
+ " [np.cos(np.radians(raan)), np.sin(np.radians(raan)), np.zeros_like(raan)]\n",
+ " )\n",
+ " u = (\n",
+ " np.degrees(\n",
+ " np.arctan2(\n",
+ " np.sum(np.cross(node, r, axis=0) * h, axis=0) / hn,\n",
+ " np.sum(node * r, axis=0),\n",
+ " )\n",
+ " )\n",
+ " % 360\n",
+ " )\n",
" return inclination, raan, u, np.linalg.norm(r, axis=0)\n",
"\n",
"\n",
"def node_local_time(at, start, days=1, step=20):\n",
" \"\"\"Median local mean solar time (hours) of northbound equator crossings over a period.\"\"\"\n",
" seconds = np.arange(0, days * 86400, step)\n",
- " p = at(timescale.from_datetimes([start + timedelta(seconds=float(s)) for s in seconds])).frame_xyz(itrs).m\n",
+ " p = (\n",
+ " at(\n",
+ " timescale.from_datetimes(\n",
+ " [start + timedelta(seconds=float(s)) for s in seconds]\n",
+ " )\n",
+ " )\n",
+ " .frame_xyz(itrs)\n",
+ " .m\n",
+ " )\n",
" k = np.nonzero((p[2][:-1] < 0) & (p[2][1:] >= 0))[0]\n",
" f = -p[2][k] / (p[2][k + 1] - p[2][k])\n",
- " longitude = np.degrees(np.arctan2(p[1][k] + f * (p[1][k + 1] - p[1][k]), p[0][k] + f * (p[0][k + 1] - p[0][k])))\n",
- " hours = (start - start.replace(hour=0, minute=0, second=0, microsecond=0)) / timedelta(hours=1) + (seconds[k] + f * step) / 3600\n",
+ " longitude = np.degrees(\n",
+ " np.arctan2(\n",
+ " p[1][k] + f * (p[1][k + 1] - p[1][k]), p[0][k] + f * (p[0][k + 1] - p[0][k])\n",
+ " )\n",
+ " )\n",
+ " hours = (\n",
+ " start - start.replace(hour=0, minute=0, second=0, microsecond=0)\n",
+ " ) / timedelta(hours=1) + (seconds[k] + f * step) / 3600\n",
" return float(np.median(np.mod(hours + longitude / 15, 24)))\n",
"\n",
"\n",
@@ -175,11 +232,25 @@
"\n",
"def summarize(x):\n",
" x = np.abs(np.asarray(x, dtype=float))\n",
- " return pd.Series({\"median |x|\": np.median(x), \"p90 |x|\": np.percentile(x, 90), \"max |x|\": x.max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"median |x|\": np.median(x),\n",
+ " \"p90 |x|\": np.percentile(x, 90),\n",
+ " \"max |x|\": x.max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})"
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")"
]
},
{
@@ -410,16 +481,28 @@
],
"source": [
"SSO_MISSIONS = [\n",
- " (\"weather\", \"SUOMI NPP\"), (\"weather\", \"NOAA 20 (JPSS-1)\"), (\"weather\", \"NOAA 21 (JPSS-2)\"), (\"weather\", \"METOP-B\"), (\"weather\", \"METOP-C\"),\n",
- " (\"resource\", \"LANDSAT 8\"), (\"resource\", \"LANDSAT 9\"), (\"resource\", \"SENTINEL-2A\"), (\"resource\", \"SENTINEL-2B\"), (\"resource\", \"SENTINEL-2C\"),\n",
- " (\"resource\", \"SENTINEL-3A\"), (\"resource\", \"SENTINEL-3B\"), (\"resource\", \"SENTINEL-5P\"),\n",
+ " (\"weather\", \"SUOMI NPP\"),\n",
+ " (\"weather\", \"NOAA 20 (JPSS-1)\"),\n",
+ " (\"weather\", \"NOAA 21 (JPSS-2)\"),\n",
+ " (\"weather\", \"METOP-B\"),\n",
+ " (\"weather\", \"METOP-C\"),\n",
+ " (\"resource\", \"LANDSAT 8\"),\n",
+ " (\"resource\", \"LANDSAT 9\"),\n",
+ " (\"resource\", \"SENTINEL-2A\"),\n",
+ " (\"resource\", \"SENTINEL-2B\"),\n",
+ " (\"resource\", \"SENTINEL-2C\"),\n",
+ " (\"resource\", \"SENTINEL-3A\"),\n",
+ " (\"resource\", \"SENTINEL-3B\"),\n",
+ " (\"resource\", \"SENTINEL-5P\"),\n",
"]\n",
"rows1 = {}\n",
"for group, name in SSO_MISSIONS:\n",
" sat = satellite(group, name)\n",
" epoch = sat.epoch.utc_datetime()\n",
" ltan = node_local_time(sat.at, epoch)\n",
- " altitude = (EARTH_MU / (sat.model.no_kozai / 60) ** 2) ** (1 / 3) - EARTH_MEAN_RADIUS\n",
+ " altitude = (EARTH_MU / (sat.model.no_kozai / 60) ** 2) ** (\n",
+ " 1 / 3\n",
+ " ) - EARTH_MEAN_RADIUS\n",
" hours, minutes = divmod(ltan * 60, 60)\n",
" orbit = SunSynchronousOrbit(\n",
" mean_altitude=altitude,\n",
@@ -436,7 +519,9 @@
" \"TAT-C inclination (deg)\": orbit.get_inclination(),\n",
" \"local time of ascending node (h)\": ltan,\n",
" \"TAT-C minus mission (min)\": 60 * wrap(15 * (ltan_generated - ltan)) / 15,\n",
- " \"TAT-C drift over a year (min)\": 60 * wrap(15 * (ltan_year - ltan_generated)) / 15,\n",
+ " \"TAT-C drift over a year (min)\": 60\n",
+ " * wrap(15 * (ltan_year - ltan_generated))\n",
+ " / 15,\n",
" }\n",
"table1 = pd.DataFrame(rows1).T\n",
"table1.round(3)"
@@ -481,13 +566,20 @@
" epoch = day.to_pydatetime()\n",
" t = timescale.from_datetime(epoch)\n",
" ra, _, _ = de421[\"earth\"].at(t).observe(de421[\"sun\"]).radec()\n",
- " legacy = (ra._degrees + 360 * 13.5 / 24 + 180) % 360 # pylint: disable=protected-access\n",
- " current = SunSynchronousOrbit(mean_altitude=800e3, equator_crossing_time=time(13, 30), epoch=epoch).get_right_ascension_ascending_node()\n",
+ " legacy = (\n",
+ " ra._degrees + 360 * 13.5 / 24 + 180\n",
+ " ) % 360 # pylint: disable=protected-access\n",
+ " current = SunSynchronousOrbit(\n",
+ " mean_altitude=800e3, equator_crossing_time=time(13, 30), epoch=epoch\n",
+ " ).get_right_ascension_ascending_node()\n",
" offsets.append(4 * wrap(legacy - current)) # 4 minutes per degree\n",
"fig, ax = plt.subplots(figsize=(7, 3.2))\n",
"ax.plot(days, offsets, color=BLUE)\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
- "ax.set(ylabel=\"Node local time offset (min)\", title=\"Ascending node offset of the previous convention (apparent Sun, J2000)\")\n",
+ "ax.set(\n",
+ " ylabel=\"Node local time offset (min)\",\n",
+ " title=\"Ascending node offset of the previous convention (apparent Sun, J2000)\",\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()"
]
@@ -669,9 +761,23 @@
" for name in TLES[group]:\n",
" sat = satellite(group, name)\n",
" inclination, raan, u, radius = state_elements(sat.at(t_ref))\n",
- " rows.append({\"name\": name, \"inclination\": inclination, \"raan\": raan, \"u\": u, \"radius\": radius, \"eccentricity\": sat.model.ecco, \"n\": sat.model.no_kozai})\n",
+ " rows.append(\n",
+ " {\n",
+ " \"name\": name,\n",
+ " \"inclination\": inclination,\n",
+ " \"raan\": raan,\n",
+ " \"u\": u,\n",
+ " \"radius\": radius,\n",
+ " \"eccentricity\": sat.model.ecco,\n",
+ " \"n\": sat.model.no_kozai,\n",
+ " }\n",
+ " )\n",
" frame = pd.DataFrame(rows)\n",
- " nominal = (frame.eccentricity < 0.02) & ((frame.n / frame.n.median() - 1).abs() < 0.003) & ((frame.inclination - frame.inclination.median()).abs() < 5)\n",
+ " nominal = (\n",
+ " (frame.eccentricity < 0.02)\n",
+ " & ((frame.n / frame.n.median() - 1).abs() < 0.003)\n",
+ " & ((frame.inclination - frame.inclination.median()).abs() < 5)\n",
+ " )\n",
" return frame[nominal].reset_index(drop=True)\n",
"\n",
"\n",
@@ -688,14 +794,29 @@
" true_anomaly=float(frame.u[0] + offset_u) % 360,\n",
" epoch=T,\n",
" )\n",
- " walker = WalkerConstellation(name=\"walker\", orbit=lead, number_satellites=total, number_planes=planes, relative_spacing=spacing, configuration=configuration)\n",
+ " walker = WalkerConstellation(\n",
+ " name=\"walker\",\n",
+ " orbit=lead,\n",
+ " number_satellites=total,\n",
+ " number_planes=planes,\n",
+ " relative_spacing=spacing,\n",
+ " configuration=configuration,\n",
+ " )\n",
" members = walker.generate_members()\n",
- " slot_raan = np.array([m.orbit.get_right_ascension_ascending_node() for m in members])\n",
+ " slot_raan = np.array(\n",
+ " [m.orbit.get_right_ascension_ascending_node() for m in members]\n",
+ " )\n",
" slot_u = np.array([m.orbit.true_anomaly for m in members])\n",
" d_raan = wrap(frame.raan.to_numpy()[:, None] - slot_raan[None, :])\n",
" d_u = wrap(frame.u.to_numpy()[:, None] - slot_u[None, :])\n",
- " slot = np.argmin(np.hypot(d_raan * np.sin(np.radians(frame.inclination.median())), d_u), axis=1)\n",
- " du, draan = d_u[np.arange(len(slot)), slot], d_raan[np.arange(len(slot)), slot]\n",
+ " slot = np.argmin(\n",
+ " np.hypot(d_raan * np.sin(np.radians(frame.inclination.median())), d_u),\n",
+ " axis=1,\n",
+ " )\n",
+ " du, draan = (\n",
+ " d_u[np.arange(len(slot)), slot],\n",
+ " d_raan[np.arange(len(slot)), slot],\n",
+ " )\n",
" offset_u += float(np.median(du))\n",
" offset_raan += float(np.median(draan))\n",
" if best is None or np.median(np.abs(du)) < best[0]:\n",
@@ -752,9 +873,28 @@
"for ax, label in zip(axes, [\"Galileo\", \"GPS\", \"OneWeb\"]):\n",
" frame, walker, _, _, _ = fits[label]\n",
" members = walker.generate_members()\n",
- " ax.scatter([m.orbit.get_right_ascension_ascending_node() for m in members], [m.orbit.true_anomaly for m in members], s=30 if label != \"OneWeb\" else 6, facecolors=\"none\", edgecolors=GRAY, label=\"Walker slots\")\n",
- " ax.scatter(frame.raan, frame.u, s=10 if label != \"OneWeb\" else 2, color=BLUE, label=\"operational satellites\")\n",
- " ax.set(xlabel=\"Right ascension of ascending node (deg)\", ylabel=\"Argument of latitude (deg)\", title=f\"{label}: {rows2[label]['pattern (T/P/F)']}\", xlim=(0, 360), ylim=(0, 360))\n",
+ " ax.scatter(\n",
+ " [m.orbit.get_right_ascension_ascending_node() for m in members],\n",
+ " [m.orbit.true_anomaly for m in members],\n",
+ " s=30 if label != \"OneWeb\" else 6,\n",
+ " facecolors=\"none\",\n",
+ " edgecolors=GRAY,\n",
+ " label=\"Walker slots\",\n",
+ " )\n",
+ " ax.scatter(\n",
+ " frame.raan,\n",
+ " frame.u,\n",
+ " s=10 if label != \"OneWeb\" else 2,\n",
+ " color=BLUE,\n",
+ " label=\"operational satellites\",\n",
+ " )\n",
+ " ax.set(\n",
+ " xlabel=\"Right ascension of ascending node (deg)\",\n",
+ " ylabel=\"Argument of latitude (deg)\",\n",
+ " title=f\"{label}: {rows2[label]['pattern (T/P/F)']}\",\n",
+ " xlim=(0, 360),\n",
+ " ylim=(0, 360),\n",
+ " )\n",
"axes[0].legend(frameon=False, fontsize=8, loc=\"lower left\")\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1000,14 +1140,20 @@
"\n",
"def mean_visible(tracks, times, min_elevation=10):\n",
" \"\"\"Mean number of satellites above an elevation angle at each latitude.\"\"\"\n",
- " position = np.stack([track.frame_xyz(itrs).m for track in tracks], axis=1) # (3, satellites, times)\n",
+ " position = np.stack(\n",
+ " [track.frame_xyz(itrs).m for track in tracks], axis=1\n",
+ " ) # (3, satellites, times)\n",
" counts = []\n",
" for latitude in LATITUDES:\n",
" site = wgs84.latlon(latitude, 0)\n",
" p = np.array(site.itrs_xyz.m)[:, None, None]\n",
- " up = np.array([np.cos(np.radians(latitude)), 0, np.sin(np.radians(latitude))])[:, None, None]\n",
+ " up = np.array([np.cos(np.radians(latitude)), 0, np.sin(np.radians(latitude))])[\n",
+ " :, None, None\n",
+ " ]\n",
" line = position - p\n",
- " elevation = np.degrees(np.arcsin(np.sum(line * up, axis=0) / np.linalg.norm(line, axis=0)))\n",
+ " elevation = np.degrees(\n",
+ " np.arcsin(np.sum(line * up, axis=0) / np.linalg.norm(line, axis=0))\n",
+ " )\n",
" counts.append(np.mean(np.sum(elevation >= min_elevation, axis=0)))\n",
" return np.array(counts)\n",
"\n",
@@ -1019,13 +1165,31 @@
" # the operational satellite closest to each occupied slot\n",
" cost = np.abs(fits[label][2])\n",
" closest = pd.Series(cost).groupby(slot).idxmin()\n",
- " duration, step = (timedelta(days=1), timedelta(minutes=10)) if label != \"OneWeb\" else (timedelta(hours=2), timedelta(minutes=2))\n",
- " times = timescale.from_datetimes([T + k * step for k in range(int(duration / step))])\n",
- " real_tracks = [satellite(group, frame.name[i]).at(times) for i in closest.to_numpy()]\n",
- " walker_tracks = [members[k].orbit.to_gp_orbit().elements[0].to_skyfield().at(times) for k in closest.index]\n",
- " visible[label] = (mean_visible(real_tracks, times), mean_visible(walker_tracks, times))\n",
+ " duration, step = (\n",
+ " (timedelta(days=1), timedelta(minutes=10))\n",
+ " if label != \"OneWeb\"\n",
+ " else (timedelta(hours=2), timedelta(minutes=2))\n",
+ " )\n",
+ " times = timescale.from_datetimes(\n",
+ " [T + k * step for k in range(int(duration / step))]\n",
+ " )\n",
+ " real_tracks = [\n",
+ " satellite(group, frame.name[i]).at(times) for i in closest.to_numpy()\n",
+ " ]\n",
+ " walker_tracks = [\n",
+ " members[k].orbit.to_gp_orbit().elements[0].to_skyfield().at(times)\n",
+ " for k in closest.index\n",
+ " ]\n",
+ " visible[label] = (\n",
+ " mean_visible(real_tracks, times),\n",
+ " mean_visible(walker_tracks, times),\n",
+ " )\n",
"pd.DataFrame(\n",
- " {(label, kind): counts for label, (real_counts, walker_counts) in visible.items() for kind, counts in [(\"operational\", real_counts), (\"Walker\", walker_counts)]},\n",
+ " {\n",
+ " (label, kind): counts\n",
+ " for label, (real_counts, walker_counts) in visible.items()\n",
+ " for kind, counts in [(\"operational\", real_counts), (\"Walker\", walker_counts)]\n",
+ " },\n",
" index=pd.Index(LATITUDES, name=\"latitude (deg)\"),\n",
").round(1)"
]
@@ -1216,28 +1380,66 @@
" (\"resource\", \"LANDSAT 8\", \"LANDSAT 9\", timedelta(days=8), True),\n",
"]\n",
"SIDEREAL_DAY = 86164.0905\n",
- "day_times = timescale.from_datetimes([T + timedelta(minutes=k) for k in range(0, 1440, 5)])\n",
+ "day_times = timescale.from_datetimes(\n",
+ " [T + timedelta(minutes=k) for k in range(0, 1440, 5)]\n",
+ ")\n",
"rows3 = {}\n",
"for group, lead_name, follow_name, interval, repeat in TRAIN_CASES:\n",
" lead_sat, follow_sat = satellite(group, lead_name), satellite(group, follow_name)\n",
" inclination, raan, u, _ = state_elements(lead_sat.at(t_ref))\n",
" _, raan_follow, u_follow, _ = state_elements(follow_sat.at(t_ref))\n",
" semimajor_axis = (EARTH_MU / (lead_sat.model.no_kozai / 60) ** 2) ** (1 / 3)\n",
- " lead = KeplerianOrbit(semimajor_axis=semimajor_axis, eccentricity=0, inclination=float(inclination), right_ascension_ascending_node=float(raan), perigee_argument=0, true_anomaly=float(u), epoch=T)\n",
+ " lead = KeplerianOrbit(\n",
+ " semimajor_axis=semimajor_axis,\n",
+ " eccentricity=0,\n",
+ " inclination=float(inclination),\n",
+ " right_ascension_ascending_node=float(raan),\n",
+ " perigee_argument=0,\n",
+ " true_anomaly=float(u),\n",
+ " epoch=T,\n",
+ " )\n",
" if interval is None:\n",
" interval = lead.get_orbit_period() * (float(wrap(u - u_follow)) % 360 / 360)\n",
- " train = TrainConstellation(name=\"train\", orbit=lead, number_satellites=2, interval=interval, repeat_ground_track=repeat)\n",
+ " train = TrainConstellation(\n",
+ " name=\"train\",\n",
+ " orbit=lead,\n",
+ " number_satellites=2,\n",
+ " interval=interval,\n",
+ " repeat_ground_track=repeat,\n",
+ " )\n",
" member = train.generate_members()[1].orbit\n",
- " legacy = lead.get_derived_orbit(-360 * interval / lead.get_orbit_period(), 360 * interval.total_seconds() / SIDEREAL_DAY if repeat else 0)\n",
+ " legacy = lead.get_derived_orbit(\n",
+ " -360 * interval / lead.get_orbit_period(),\n",
+ " 360 * interval.total_seconds() / SIDEREAL_DAY if repeat else 0,\n",
+ " )\n",
" real_points = wgs84.subpoint_of(follow_sat.at(day_times))\n",
" for version, orbit in [(\"TAT-C\", member), (\"previous\", legacy)]:\n",
- " generated_points = wgs84.subpoint_of(orbit.to_gp_orbit().elements[0].to_skyfield().at(day_times))\n",
- " distance = np.array([geodesic_distance(*a) for a in zip(real_points.longitude.degrees, real_points.latitude.degrees, generated_points.longitude.degrees, generated_points.latitude.degrees)]) / 1e3\n",
+ " generated_points = wgs84.subpoint_of(\n",
+ " orbit.to_gp_orbit().elements[0].to_skyfield().at(day_times)\n",
+ " )\n",
+ " distance = (\n",
+ " np.array(\n",
+ " [\n",
+ " geodesic_distance(*a)\n",
+ " for a in zip(\n",
+ " real_points.longitude.degrees,\n",
+ " real_points.latitude.degrees,\n",
+ " generated_points.longitude.degrees,\n",
+ " generated_points.latitude.degrees,\n",
+ " )\n",
+ " ]\n",
+ " )\n",
+ " / 1e3\n",
+ " )\n",
" rows3[(f\"{lead_name} → {follow_name}\", version)] = {\n",
" \"interval\": str(interval).split(\".\")[0],\n",
" \"repeat ground track\": repeat,\n",
- " \"right ascension difference (deg)\": float(wrap(orbit.get_right_ascension_ascending_node() - raan_follow)),\n",
- " \"argument of latitude difference (deg)\": float(wrap(orbit.true_anomaly - u_follow)),\n",
+ " \"right ascension difference (deg)\": float(\n",
+ " wrap(orbit.get_right_ascension_ascending_node() - raan_follow)\n",
+ " ),\n",
+ " \"argument of latitude difference (deg)\": float(\n",
+ " wrap(orbit.true_anomaly - u_follow)\n",
+ " ),\n",
" \"position difference, median (km)\": np.median(distance),\n",
" \"position difference, max (km)\": distance.max(),\n",
" }\n",
@@ -1530,18 +1732,30 @@
"source": [
"def apogee_drift(at, revolutions, days=30):\n",
" \"\"\"Apogee longitudes and their drift rate (deg/day, mean over each daily apogee).\"\"\"\n",
- " times = timescale.from_datetimes([T + timedelta(minutes=k) for k in range(0, days * 1440, 2)])\n",
+ " times = timescale.from_datetimes(\n",
+ " [T + timedelta(minutes=k) for k in range(0, days * 1440, 2)]\n",
+ " )\n",
" track = at(times)\n",
" radius = np.linalg.norm(track.position.m, axis=0)\n",
" k = np.nonzero((radius[1:-1] > radius[:-2]) & (radius[1:-1] >= radius[2:]))[0] + 1\n",
" points = wgs84.subpoint_of(track[k])\n",
" elapsed = times.tt[k] - t_ref.tt\n",
- " rates = [np.polyfit(elapsed[i::revolutions], np.degrees(np.unwrap(np.radians(points.longitude.degrees[i::revolutions]))), 1)[0] for i in range(revolutions)]\n",
+ " rates = [\n",
+ " np.polyfit(\n",
+ " elapsed[i::revolutions],\n",
+ " np.degrees(np.unwrap(np.radians(points.longitude.degrees[i::revolutions]))),\n",
+ " 1,\n",
+ " )[0]\n",
+ " for i in range(revolutions)\n",
+ " ]\n",
" return float(np.mean(rates)), float(np.median(points.latitude.degrees))\n",
"\n",
"\n",
"rows4 = {}\n",
- "for group, cls, revolutions in [(\"meridian\", MolniyaOrbit, 2), (\"qzss\", TundraOrbit, 1)]:\n",
+ "for group, cls, revolutions in [\n",
+ " (\"meridian\", MolniyaOrbit, 2),\n",
+ " (\"qzss\", TundraOrbit, 1),\n",
+ "]:\n",
" for name in TLES[group]:\n",
" sat = satellite(group, name)\n",
" m = sat.model\n",
@@ -1553,12 +1767,15 @@
" orbit = cls(\n",
" perigee_altitude=semimajor_axis * (1 - m.ecco) - EARTH_MEAN_RADIUS,\n",
" inclination=math.degrees(m.inclo),\n",
- " right_ascension_ascending_node=math.degrees(m.nodeo + m.nodedot * minutes) % 360,\n",
+ " right_ascension_ascending_node=math.degrees(m.nodeo + m.nodedot * minutes)\n",
+ " % 360,\n",
" true_anomaly=float(mean_anomaly_to_true_anomaly(mean_anomaly, m.ecco)),\n",
" epoch=T,\n",
" )\n",
" real_drift, real_latitude = apogee_drift(sat.at, revolutions)\n",
- " tatc_drift, tatc_latitude = apogee_drift(orbit.to_gp_orbit().elements[0].to_skyfield().at, revolutions)\n",
+ " tatc_drift, tatc_latitude = apogee_drift(\n",
+ " orbit.to_gp_orbit().elements[0].to_skyfield().at, revolutions\n",
+ " )\n",
" rows4[name] = {\n",
" \"TAT-C orbit\": cls.__name__,\n",
" \"semimajor axis (km)\": semimajor_axis / 1e3,\n",
@@ -1708,7 +1925,9 @@
"source": [
"def apogee_longitudes(at, days=30):\n",
" \"\"\"Longitudes (deg) of the apogees of a propagated orbit, sampled every 20 s.\"\"\"\n",
- " times = timescale.from_datetimes([T + timedelta(seconds=20 * k) for k in range(days * 4320)])\n",
+ " times = timescale.from_datetimes(\n",
+ " [T + timedelta(seconds=20 * k) for k in range(days * 4320)]\n",
+ " )\n",
" track = at(times)\n",
" radius = np.linalg.norm(track.position.m, axis=0)\n",
" k = np.nonzero((radius[1:-1] > radius[:-2]) & (radius[1:-1] >= radius[2:]))[0] + 1\n",
@@ -1728,7 +1947,11 @@
" float(real[0]),\n",
" perigee_altitude=semimajor_axis * (1 - m.ecco) - EARTH_MEAN_RADIUS,\n",
" inclination=math.degrees(m.inclo),\n",
- " true_anomaly=float(mean_anomaly_to_true_anomaly(math.degrees(m.mo + m.mdot * minutes) % 360, m.ecco)),\n",
+ " true_anomaly=float(\n",
+ " mean_anomaly_to_true_anomaly(\n",
+ " math.degrees(m.mo + m.mdot * minutes) % 360, m.ecco\n",
+ " )\n",
+ " ),\n",
" epoch=T,\n",
" )\n",
" generated = apogee_longitudes(orbit.to_gp_orbit().elements[0].to_skyfield().at)\n",
@@ -1739,7 +1962,11 @@
" \"argument of perigee (deg)\": math.degrees(m.argpo),\n",
" \"right ascension (deg)\": raan,\n",
" \"TAT-C right ascension (deg)\": orbit.right_ascension_ascending_node,\n",
- " \"right ascension difference (deg)\": (orbit.right_ascension_ascending_node - raan + 180) % 360 - 180,\n",
+ " \"right ascension difference (deg)\": (\n",
+ " orbit.right_ascension_ascending_node - raan + 180\n",
+ " )\n",
+ " % 360\n",
+ " - 180,\n",
" \"apogee longitude range over 30 days (deg)\": np.ptp(real),\n",
" \"TAT-C apogee longitude range over 30 days (deg)\": np.ptp(generated),\n",
" }\n",
@@ -1854,18 +2081,37 @@
],
"source": [
"rows5 = {}\n",
- "times30 = timescale.from_datetimes([T + timedelta(hours=k) for k in range(0, 30 * 24 + 1, 6)])\n",
- "for label, longitude, name in [(\"GOES 19\", None, \"GOES 19\"), (\"GOES 18\", None, \"GOES 18\"), (\"0 deg\", 0.0, None), (\"105 deg E\", 105.0, None)]:\n",
+ "times30 = timescale.from_datetimes(\n",
+ " [T + timedelta(hours=k) for k in range(0, 30 * 24 + 1, 6)]\n",
+ ")\n",
+ "for label, longitude, name in [\n",
+ " (\"GOES 19\", None, \"GOES 19\"),\n",
+ " (\"GOES 18\", None, \"GOES 18\"),\n",
+ " (\"0 deg\", 0.0, None),\n",
+ " (\"105 deg E\", 105.0, None),\n",
+ "]:\n",
" if name is not None:\n",
" sat = satellite(\"goes\", name)\n",
- " real = np.degrees(np.unwrap(np.radians(wgs84.subpoint_of(sat.at(times30)).longitude.degrees)))\n",
+ " real = np.degrees(\n",
+ " np.unwrap(np.radians(wgs84.subpoint_of(sat.at(times30)).longitude.degrees))\n",
+ " )\n",
" longitude = float(real[0])\n",
" orbit = GeosynchronousOrbit(longitude=longitude, epoch=T)\n",
- " generated = np.degrees(np.unwrap(np.radians(wgs84.subpoint_of(orbit.to_gp_orbit().elements[0].to_skyfield().at(times30)).longitude.degrees)))\n",
+ " generated = np.degrees(\n",
+ " np.unwrap(\n",
+ " np.radians(\n",
+ " wgs84.subpoint_of(\n",
+ " orbit.to_gp_orbit().elements[0].to_skyfield().at(times30)\n",
+ " ).longitude.degrees\n",
+ " )\n",
+ " )\n",
+ " )\n",
" rows5[label] = {\n",
" \"longitude (deg)\": longitude,\n",
" \"TAT-C longitude at epoch (deg)\": generated[0],\n",
- " \"operational longitude range over 30 days (deg)\": np.ptp(real) if name is not None else np.nan,\n",
+ " \"operational longitude range over 30 days (deg)\": (\n",
+ " np.ptp(real) if name is not None else np.nan\n",
+ " ),\n",
" \"TAT-C longitude change over 30 days (deg)\": generated[-1] - generated[0],\n",
" }\n",
"pd.DataFrame(rows5).T.round(3)"
diff --git a/docs/validation/ValidateOrbitISS.ipynb b/docs/validation/ValidateOrbitISS.ipynb
index 8fbcadd9..2e5a89df 100644
--- a/docs/validation/ValidateOrbitISS.ipynb
+++ b/docs/validation/ValidateOrbitISS.ipynb
@@ -32,10 +32,10 @@
"id": "d5bf30de",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:56:51.047486Z",
- "iopub.status.busy": "2026-10-08T17:56:51.046485Z",
- "iopub.status.idle": "2026-10-08T17:56:54.856547Z",
- "shell.execute_reply": "2026-10-08T17:56:54.856041Z"
+ "iopub.execute_input": "2026-10-25T13:31:29.969550Z",
+ "iopub.status.busy": "2026-10-25T13:31:29.968551Z",
+ "iopub.status.idle": "2026-10-25T13:31:32.459854Z",
+ "shell.execute_reply": "2026-10-25T13:31:32.459183Z"
}
},
"outputs": [
@@ -73,7 +73,15 @@
"from tatc import utils\n",
"from tatc.analysis import collect_observations\n",
"from tatc.constants import EARTH_MEAN_RADIUS, timescale\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, Point, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " AttitudeSchedule,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Instrument,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"\n",
"DATA_DIR = Path(\"data\") / \"iss\"\n",
"DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
@@ -93,26 +101,61 @@
" import earthaccess\n",
"\n",
" earthaccess.login()\n",
- " results = earthaccess.search_data(short_name=\"ECO_L1B_ATT\", version=\"002\", temporal=(START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"), END.strftime(\"%Y-%m-%dT%H:%M:%SZ\")))\n",
+ " results = earthaccess.search_data(\n",
+ " short_name=\"ECO_L1B_ATT\",\n",
+ " version=\"002\",\n",
+ " temporal=(\n",
+ " START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " END.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " ),\n",
+ " )\n",
" earthaccess.download(results[::4], str(DATA_DIR))\n",
" attitude_files = sorted(DATA_DIR.glob(\"ECOv002_L1B_ATT_*.h5\"))\n",
"frames, attitudes = [], []\n",
"for path in attitude_files:\n",
" with h5py.File(path, \"r\") as f:\n",
- " frames.append(pd.DataFrame(np.c_[f[\"Ephemeris/time_j2000\"][::30], f[\"Ephemeris/eci_position\"][::30], f[\"Ephemeris/eci_velocity\"][::30]], columns=[\"tt_j2000\", \"x\", \"y\", \"z\", \"vx\", \"vy\", \"vz\"]))\n",
+ " frames.append(\n",
+ " pd.DataFrame(\n",
+ " np.c_[\n",
+ " f[\"Ephemeris/time_j2000\"][::30],\n",
+ " f[\"Ephemeris/eci_position\"][::30],\n",
+ " f[\"Ephemeris/eci_velocity\"][::30],\n",
+ " ],\n",
+ " columns=[\"tt_j2000\", \"x\", \"y\", \"z\", \"vx\", \"vy\", \"vz\"],\n",
+ " )\n",
+ " )\n",
" # attitude quaternions (scalar first, spacecraft to J2000), every 5 minutes\n",
- " attitudes.append(pd.DataFrame(np.c_[f[\"Attitude/time_j2000\"][::300], f[\"Attitude/quaternion\"][::300]], columns=[\"tt_j2000\", \"qw\", \"qx\", \"qy\", \"qz\"]))\n",
- "ephemeris = pd.concat(frames).drop_duplicates(\"tt_j2000\").sort_values(\"tt_j2000\").reset_index(drop=True)\n",
+ " attitudes.append(\n",
+ " pd.DataFrame(\n",
+ " np.c_[f[\"Attitude/time_j2000\"][::300], f[\"Attitude/quaternion\"][::300]],\n",
+ " columns=[\"tt_j2000\", \"qw\", \"qx\", \"qy\", \"qz\"],\n",
+ " )\n",
+ " )\n",
+ "ephemeris = (\n",
+ " pd.concat(frames)\n",
+ " .drop_duplicates(\"tt_j2000\")\n",
+ " .sort_values(\"tt_j2000\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
"times = timescale.tt_jd(2451545.0 + ephemeris.tt_j2000.to_numpy() / 86400)\n",
"ephemeris[\"time\"] = pd.to_datetime(times.utc_datetime())\n",
"\n",
- "SCENES_FILE = DATA_DIR / f\"emit_scenes_{SCENES_START:%Y%m%d}_{SCENES_END - timedelta(days=1):%Y%m%d}.json\"\n",
+ "SCENES_FILE = (\n",
+ " DATA_DIR\n",
+ " / f\"emit_scenes_{SCENES_START:%Y%m%d}_{SCENES_END - timedelta(days=1):%Y%m%d}.json\"\n",
+ ")\n",
"if not SCENES_FILE.exists():\n",
" items, page = [], 1\n",
" while True:\n",
" response = requests.get(\n",
" \"https://cmr.earthdata.nasa.gov/search/granules.umm_json\",\n",
- " params={\"short_name\": \"EMITL1BRAD\", \"version\": \"002\", \"temporal\": f\"{SCENES_START:%Y-%m-%dT%H:%M:%SZ},{SCENES_END:%Y-%m-%dT%H:%M:%SZ}\", \"page_size\": 2000, \"page_num\": page},\n",
+ " params={\n",
+ " \"short_name\": \"EMITL1BRAD\",\n",
+ " \"version\": \"002\",\n",
+ " \"temporal\": f\"{SCENES_START:%Y-%m-%dT%H:%M:%SZ},{SCENES_END:%Y-%m-%dT%H:%M:%SZ}\",\n",
+ " \"page_size\": 2000,\n",
+ " \"page_num\": page,\n",
+ " },\n",
" timeout=300,\n",
" )\n",
" response.raise_for_status()\n",
@@ -120,10 +163,26 @@
" items += [\n",
" {\n",
" \"id\": item[\"umm\"][\"GranuleUR\"],\n",
- " \"start\": item[\"umm\"][\"TemporalExtent\"][\"RangeDateTime\"][\"BeginningDateTime\"],\n",
+ " \"start\": item[\"umm\"][\"TemporalExtent\"][\"RangeDateTime\"][\n",
+ " \"BeginningDateTime\"\n",
+ " ],\n",
" \"end\": item[\"umm\"][\"TemporalExtent\"][\"RangeDateTime\"][\"EndingDateTime\"],\n",
- " \"polygon\": [[p[\"Longitude\"], p[\"Latitude\"]] for p in item[\"umm\"][\"SpatialExtent\"][\"HorizontalSpatialDomain\"][\"Geometry\"][\"GPolygons\"][0][\"Boundary\"][\"Points\"]],\n",
- " \"solar_zenith\": float(next((a[\"Values\"][0] for a in item[\"umm\"].get(\"AdditionalAttributes\", []) if a[\"Name\"] == \"SOLAR_ZENITH\"), \"nan\")),\n",
+ " \"polygon\": [\n",
+ " [p[\"Longitude\"], p[\"Latitude\"]]\n",
+ " for p in item[\"umm\"][\"SpatialExtent\"][\"HorizontalSpatialDomain\"][\n",
+ " \"Geometry\"\n",
+ " ][\"GPolygons\"][0][\"Boundary\"][\"Points\"]\n",
+ " ],\n",
+ " \"solar_zenith\": float(\n",
+ " next(\n",
+ " (\n",
+ " a[\"Values\"][0]\n",
+ " for a in item[\"umm\"].get(\"AdditionalAttributes\", [])\n",
+ " if a[\"Name\"] == \"SOLAR_ZENITH\"\n",
+ " ),\n",
+ " \"nan\",\n",
+ " )\n",
+ " ),\n",
" }\n",
" for item in batch\n",
" ]\n",
@@ -137,8 +196,23 @@
"scenes[\"mid\"] = scenes.start + (scenes.end - scenes.start) / 2\n",
"iss = GeneralPerturbationsOrbit.from_tle(TLE)\n",
"satrec = iss.elements[0].to_skyfield()\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"pd.Series(\n",
" {\n",
" \"ephemeris files (orbits)\": len(attitude_files),\n",
@@ -165,10 +239,10 @@
"id": "7d983c44",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:56:54.860066Z",
- "iopub.status.busy": "2026-10-08T17:56:54.859066Z",
- "iopub.status.idle": "2026-10-08T17:56:55.076462Z",
- "shell.execute_reply": "2026-10-08T17:56:55.075455Z"
+ "iopub.execute_input": "2026-10-25T13:31:32.461188Z",
+ "iopub.status.busy": "2026-10-25T13:31:32.461188Z",
+ "iopub.status.idle": "2026-10-25T13:31:32.600992Z",
+ "shell.execute_reply": "2026-10-25T13:31:32.600992Z"
}
},
"outputs": [
@@ -531,20 +605,29 @@
"ephemeris[\"radial (km)\"] = np.sum(difference * radial, axis=1) / 1e3\n",
"ephemeris[\"along-track (km)\"] = np.sum(difference * along, axis=1) / 1e3\n",
"ephemeris[\"cross-track (km)\"] = np.sum(difference * normal, axis=1) / 1e3\n",
- "ephemeris[\"along-track (s)\"] = ephemeris[\"along-track (km)\"] * 1e3 / np.linalg.norm(velocity, axis=1)\n",
- "ephemeris[\"days before TLE epoch\"] = (pd.Timestamp(TLE_EPOCH) - ephemeris.time) / pd.Timedelta(days=1)\n",
+ "ephemeris[\"along-track (s)\"] = (\n",
+ " ephemeris[\"along-track (km)\"] * 1e3 / np.linalg.norm(velocity, axis=1)\n",
+ ")\n",
+ "ephemeris[\"days before TLE epoch\"] = (\n",
+ " pd.Timestamp(TLE_EPOCH) - ephemeris.time\n",
+ ") / pd.Timedelta(days=1)\n",
"ephemeris[\"day\"] = ephemeris.time.dt.floor(\"D\")\n",
"table1 = ephemeris.groupby(\"day\").agg(\n",
" **{\n",
" \"days before TLE epoch\": (\"days before TLE epoch\", \"mean\"),\n",
" \"along-track, median (km)\": (\"along-track (km)\", \"median\"),\n",
" \"along-track, median (s)\": (\"along-track (s)\", \"median\"),\n",
- " \"cross-track, p95 |x| (km)\": (\"cross-track (km)\", lambda x: x.abs().quantile(0.95)),\n",
+ " \"cross-track, p95 |x| (km)\": (\n",
+ " \"cross-track (km)\",\n",
+ " lambda x: x.abs().quantile(0.95),\n",
+ " ),\n",
" }\n",
")\n",
"table1.index = table1.index.strftime(\"%Y-%m-%d\")\n",
"departure = ephemeris[ephemeris[\"along-track (km)\"].abs() > 20].time.max()\n",
- "print(f\"last ephemeris time with an along-track offset above 20 km: {departure:%Y-%m-%d %H:%M} UTC\")\n",
+ "print(\n",
+ " f\"last ephemeris time with an along-track offset above 20 km: {departure:%Y-%m-%d %H:%M} UTC\"\n",
+ ")\n",
"table1.round(2)"
]
},
@@ -554,10 +637,10 @@
"id": "24c0c799",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:56:55.079466Z",
- "iopub.status.busy": "2026-10-08T17:56:55.079466Z",
- "iopub.status.idle": "2026-10-08T17:56:55.801998Z",
- "shell.execute_reply": "2026-10-08T17:56:55.800990Z"
+ "iopub.execute_input": "2026-10-25T13:31:32.603507Z",
+ "iopub.status.busy": "2026-10-25T13:31:32.602500Z",
+ "iopub.status.idle": "2026-10-25T13:31:32.986786Z",
+ "shell.execute_reply": "2026-10-25T13:31:32.986786Z"
}
},
"outputs": [
@@ -574,11 +657,33 @@
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.6))\n",
- "axes[0].plot(ephemeris[\"days before TLE epoch\"], ephemeris[\"along-track (km)\"], \".\", ms=1, color=BLUE)\n",
- "axes[0].set(xlabel=\"Days before TLE epoch\", ylabel=\"TAT-C minus ISS (km)\", title=\"Along-track offset\", yscale=\"symlog\")\n",
+ "axes[0].plot(\n",
+ " ephemeris[\"days before TLE epoch\"],\n",
+ " ephemeris[\"along-track (km)\"],\n",
+ " \".\",\n",
+ " ms=1,\n",
+ " color=BLUE,\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Days before TLE epoch\",\n",
+ " ylabel=\"TAT-C minus ISS (km)\",\n",
+ " title=\"Along-track offset\",\n",
+ " yscale=\"symlog\",\n",
+ ")\n",
"axes[0].invert_xaxis()\n",
- "axes[1].plot(ephemeris[\"days before TLE epoch\"], ephemeris[\"cross-track (km)\"], \".\", ms=1, color=ORANGE)\n",
- "axes[1].set(xlabel=\"Days before TLE epoch\", ylabel=\"TAT-C minus ISS (km)\", title=\"Cross-track offset\", ylim=(-6, 6))\n",
+ "axes[1].plot(\n",
+ " ephemeris[\"days before TLE epoch\"],\n",
+ " ephemeris[\"cross-track (km)\"],\n",
+ " \".\",\n",
+ " ms=1,\n",
+ " color=ORANGE,\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Days before TLE epoch\",\n",
+ " ylabel=\"TAT-C minus ISS (km)\",\n",
+ " title=\"Cross-track offset\",\n",
+ " ylim=(-6, 6),\n",
+ ")\n",
"axes[1].invert_xaxis()\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -602,10 +707,10 @@
"id": "381b115c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:56:55.805132Z",
- "iopub.status.busy": "2026-10-08T17:56:55.805132Z",
- "iopub.status.idle": "2026-10-08T18:00:12.745589Z",
- "shell.execute_reply": "2026-10-08T18:00:12.744580Z"
+ "iopub.execute_input": "2026-10-25T13:31:32.989717Z",
+ "iopub.status.busy": "2026-10-25T13:31:32.988791Z",
+ "iopub.status.idle": "2026-10-25T13:33:28.017059Z",
+ "shell.execute_reply": "2026-10-25T13:33:28.017059Z"
}
},
"outputs": [
@@ -751,7 +856,12 @@
"scenes[\"width (km)\"] = scenes.polygon.map(scene_width)\n",
"scenes[\"center\"] = scenes.polygon.map(lambda p: Polygon(p).centroid)\n",
"ALTITUDE = float(np.median(np.linalg.norm(recorded, axis=1))) - 6378137.0\n",
- "emit = Instrument(name=\"EMIT\", field_of_regard=utils.swath_width_to_field_of_regard(ALTITUDE, scenes[\"width (km)\"].median() * 1e3))\n",
+ "emit = Instrument(\n",
+ " name=\"EMIT\",\n",
+ " field_of_regard=utils.swath_width_to_field_of_regard(\n",
+ " ALTITUDE, scenes[\"width (km)\"].median() * 1e3\n",
+ " ),\n",
+ ")\n",
"satellite = Satellite(name=\"ISS\", orbit=iss, instruments=[emit])\n",
"\n",
"\n",
@@ -759,7 +869,13 @@
" \"\"\"TAT-C's observation of a scene's center near its midpoint time.\"\"\"\n",
" point = Point(id=0, latitude=scene.center.y, longitude=scene.center.x)\n",
" mid = scene.mid.floor(\"us\").to_pydatetime()\n",
- " obs = collect_observations(point, satellite, mid - timedelta(minutes=30), mid + timedelta(minutes=30), omit_solar=False)\n",
+ " obs = collect_observations(\n",
+ " point,\n",
+ " satellite,\n",
+ " mid - timedelta(minutes=30),\n",
+ " mid + timedelta(minutes=30),\n",
+ " omit_solar=False,\n",
+ " )\n",
" if obs.empty:\n",
" return {\"id\": scene.id, \"observed\": False}\n",
" o = obs.iloc[np.argmin(np.abs((obs.epoch - scene.mid).dt.total_seconds()))]\n",
@@ -775,18 +891,28 @@
"with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
" observed = pd.DataFrame(list(pool.map(observe, scenes.itertuples())))\n",
"scenes = scenes.merge(observed, on=\"id\")\n",
- "scenes[\"days before TLE epoch\"] = (pd.Timestamp(TLE_EPOCH) - scenes.mid) / pd.Timedelta(days=1)\n",
+ "scenes[\"days before TLE epoch\"] = (pd.Timestamp(TLE_EPOCH) - scenes.mid) / pd.Timedelta(\n",
+ " days=1\n",
+ ")\n",
"scenes[\"bin\"] = pd.cut(scenes[\"days before TLE epoch\"], [1, 3, 5, 7, 9, 11, 13, 15])\n",
"table2 = scenes.groupby(\"bin\", observed=True).agg(\n",
" **{\n",
" \"scenes\": (\"id\", \"size\"),\n",
" \"observed by TAT-C (%)\": (\"observed\", lambda x: 100 * x.mean()),\n",
" \"epoch minus midpoint, median (s)\": (\"epoch minus midpoint (s)\", \"median\"),\n",
- " \"solar zenith difference, max |x| (deg)\": (\"solar zenith difference (deg)\", lambda x: x.abs().max()),\n",
- " \"local solar time, range (h)\": (\"local solar time (h)\", lambda x: f\"{x.min():.1f} to {x.max():.1f}\"),\n",
+ " \"solar zenith difference, max |x| (deg)\": (\n",
+ " \"solar zenith difference (deg)\",\n",
+ " lambda x: x.abs().max(),\n",
+ " ),\n",
+ " \"local solar time, range (h)\": (\n",
+ " \"local solar time (h)\",\n",
+ " lambda x: f\"{x.min():.1f} to {x.max():.1f}\",\n",
+ " ),\n",
" }\n",
")\n",
- "print(f\"median scene width {scenes['width (km)'].median():.1f} km; station altitude {ALTITUDE / 1e3:.0f} km; field of regard {emit.field_of_regard:.2f} deg\")\n",
+ "print(\n",
+ " f\"median scene width {scenes['width (km)'].median():.1f} km; station altitude {ALTITUDE / 1e3:.0f} km; field of regard {emit.field_of_regard:.2f} deg\"\n",
+ ")\n",
"table2.round(2)"
]
},
@@ -796,10 +922,10 @@
"id": "ccbab319",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:00:12.747588Z",
- "iopub.status.busy": "2026-10-08T18:00:12.747588Z",
- "iopub.status.idle": "2026-10-08T18:00:13.226787Z",
- "shell.execute_reply": "2026-10-08T18:00:13.226279Z"
+ "iopub.execute_input": "2026-10-25T13:33:28.019065Z",
+ "iopub.status.busy": "2026-10-25T13:33:28.019065Z",
+ "iopub.status.idle": "2026-10-25T13:33:28.246801Z",
+ "shell.execute_reply": "2026-10-25T13:33:28.246447Z"
}
},
"outputs": [
@@ -816,14 +942,37 @@
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.6))\n",
- "axes[0].plot(scenes[\"days before TLE epoch\"], scenes[\"epoch minus midpoint (s)\"], \".\", ms=3, color=BLUE, label=\"EMIT scenes (TAT-C epoch minus scene midpoint)\")\n",
+ "axes[0].plot(\n",
+ " scenes[\"days before TLE epoch\"],\n",
+ " scenes[\"epoch minus midpoint (s)\"],\n",
+ " \".\",\n",
+ " ms=3,\n",
+ " color=BLUE,\n",
+ " label=\"EMIT scenes (TAT-C epoch minus scene midpoint)\",\n",
+ ")\n",
"hourly = ephemeris[ephemeris[\"days before TLE epoch\"] < 16]\n",
- "axes[0].plot(hourly[\"days before TLE epoch\"], -hourly[\"along-track (s)\"], \".\", ms=1, color=GRAY, label=\"minus along-track offset (Test 1)\")\n",
- "axes[0].set(xlabel=\"Days before TLE epoch\", ylabel=\"Time (s)\", title=\"Observation time offset\")\n",
+ "axes[0].plot(\n",
+ " hourly[\"days before TLE epoch\"],\n",
+ " -hourly[\"along-track (s)\"],\n",
+ " \".\",\n",
+ " ms=1,\n",
+ " color=GRAY,\n",
+ " label=\"minus along-track offset (Test 1)\",\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Days before TLE epoch\", ylabel=\"Time (s)\", title=\"Observation time offset\"\n",
+ ")\n",
"axes[0].invert_xaxis()\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
- "axes[1].scatter(scenes.mid.dt.tz_convert(None), scenes[\"local solar time (h)\"], s=3, color=ORANGE)\n",
- "axes[1].set(ylabel=\"Local solar time (h)\", title=\"Local solar time of EMIT scenes (TAT-C)\", ylim=(0, 24), yticks=range(0, 25, 4))\n",
+ "axes[1].scatter(\n",
+ " scenes.mid.dt.tz_convert(None), scenes[\"local solar time (h)\"], s=3, color=ORANGE\n",
+ ")\n",
+ "axes[1].set(\n",
+ " ylabel=\"Local solar time (h)\",\n",
+ " title=\"Local solar time of EMIT scenes (TAT-C)\",\n",
+ " ylim=(0, 24),\n",
+ " yticks=range(0, 25, 4),\n",
+ ")\n",
"axes[1].tick_params(axis=\"x\", rotation=30)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -838,7 +987,7 @@
"\n",
"The observation epochs follow the orbit's along-track offset, except for a constant offset: where the orbit is accurate, TAT-C observes the scene centers 8.5 s (7.2 to 9.0 s) before the scenes' midpoint times, which is about 60 km along track.\n",
"\n",
- "The station does not fly level: its attitude, recorded with the ephemeris, is expressed relative to the local orbital frame (forward, cross-track, and nadir axes) as yaw, pitch, and roll angles. EMIT is then modeled as a rectangular `PointedInstrument` with the same cross-track field of view, tilted by the station's median pitch angle (positive pitch looks forward), and the observation epochs of the scenes are compared again."
+ "The station does not fly level: its attitude, recorded with the ephemeris, is expressed relative to the local orbital frame (forward, cross-track, and nadir axes) as yaw, pitch, and roll angles. EMIT is then modeled as a rectangular `PointedInstrument` with the same cross-track field of view, tilted by the station's median pitch angle (positive pitch looks forward), and the observation epochs of the scenes are compared again. The station's attitude is also represented directly, as the satellite's: an `AttitudeSchedule` of its recorded yaw, pitch, and roll angles (every 5 minutes, in the orbits with data, each from 2.5 minutes before its sample; TAT-C's yaw is positive to the left, the station's to the right), with EMIT viewing along the station's vertical axis."
]
},
{
@@ -847,10 +996,10 @@
"id": "3ed3beb1",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:00:13.232798Z",
- "iopub.status.busy": "2026-10-08T18:00:13.231796Z",
- "iopub.status.idle": "2026-10-08T18:00:13.293468Z",
- "shell.execute_reply": "2026-10-08T18:00:13.292455Z"
+ "iopub.execute_input": "2026-10-25T13:33:28.248754Z",
+ "iopub.status.busy": "2026-10-25T13:33:28.248754Z",
+ "iopub.status.idle": "2026-10-25T13:33:28.268322Z",
+ "shell.execute_reply": "2026-10-25T13:33:28.268322Z"
}
},
"outputs": [
@@ -923,23 +1072,39 @@
}
],
"source": [
- "attitude = pd.concat(attitudes).drop_duplicates(\"tt_j2000\").sort_values(\"tt_j2000\").reset_index(drop=True)\n",
+ "attitude = (\n",
+ " pd.concat(attitudes)\n",
+ " .drop_duplicates(\"tt_j2000\")\n",
+ " .sort_values(\"tt_j2000\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
"attitude_times = timescale.tt_jd(2451545.0 + attitude.tt_j2000.to_numpy() / 86400)\n",
- "nearest = np.searchsorted(ephemeris.tt_j2000.to_numpy(), attitude.tt_j2000.to_numpy()).clip(0, len(ephemeris) - 1)\n",
+ "nearest = np.searchsorted(\n",
+ " ephemeris.tt_j2000.to_numpy(), attitude.tt_j2000.to_numpy()\n",
+ ").clip(0, len(ephemeris) - 1)\n",
"r_eci, v_eci = recorded[nearest], velocity[nearest]\n",
"nadir = -r_eci / np.linalg.norm(r_eci, axis=1, keepdims=True)\n",
"cross = -np.cross(r_eci, v_eci)\n",
"cross /= np.linalg.norm(cross, axis=1, keepdims=True)\n",
"forward = np.cross(cross, nadir)\n",
"orbital = np.stack([forward, cross, nadir], axis=1) # rows: orbital frame axes in J2000\n",
- "body = Rotation.from_quat(attitude[[\"qx\", \"qy\", \"qz\", \"qw\"]].to_numpy()).as_matrix() # columns: body axes in J2000\n",
+ "body = Rotation.from_quat(\n",
+ " attitude[[\"qx\", \"qy\", \"qz\", \"qw\"]].to_numpy()\n",
+ ").as_matrix() # columns: body axes in J2000\n",
"angles = Rotation.from_matrix(orbital @ body).as_euler(\"ZYX\", degrees=True)\n",
"attitude[\"yaw (deg)\"], attitude[\"pitch (deg)\"], attitude[\"roll (deg)\"] = angles.T\n",
"attitude[\"time\"] = pd.to_datetime(attitude_times.utc_datetime())\n",
- "in_scenes = attitude[(attitude.time >= pd.Timestamp(SCENES_START)) & (attitude.time < pd.Timestamp(SCENES_END))]\n",
+ "in_scenes = attitude[\n",
+ " (attitude.time >= pd.Timestamp(SCENES_START))\n",
+ " & (attitude.time < pd.Timestamp(SCENES_END))\n",
+ "]\n",
"PITCH = float(in_scenes[\"pitch (deg)\"].median())\n",
- "print(f\"station attitude, {SCENES_START:%d %b} to {SCENES_END - timedelta(days=1):%d %b} (median and 5th to 95th percentiles):\")\n",
- "in_scenes[[\"yaw (deg)\", \"pitch (deg)\", \"roll (deg)\"]].describe(percentiles=[0.05, 0.5, 0.95]).loc[[\"5%\", \"50%\", \"95%\"]].round(2)"
+ "print(\n",
+ " f\"station attitude, {SCENES_START:%d %b} to {SCENES_END - timedelta(days=1):%d %b} (median and 5th to 95th percentiles):\"\n",
+ ")\n",
+ "in_scenes[[\"yaw (deg)\", \"pitch (deg)\", \"roll (deg)\"]].describe(\n",
+ " percentiles=[0.05, 0.5, 0.95]\n",
+ ").loc[[\"5%\", \"50%\", \"95%\"]].round(2)"
]
},
{
@@ -948,13 +1113,20 @@
"id": "7d5136de",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:00:13.298465Z",
- "iopub.status.busy": "2026-10-08T18:00:13.297465Z",
- "iopub.status.idle": "2026-10-08T18:02:11.771635Z",
- "shell.execute_reply": "2026-10-08T18:02:11.771129Z"
+ "iopub.execute_input": "2026-10-25T13:33:28.270760Z",
+ "iopub.status.busy": "2026-10-25T13:33:28.269760Z",
+ "iopub.status.idle": "2026-10-25T13:35:51.327203Z",
+ "shell.execute_reply": "2026-10-25T13:35:51.327203Z"
}
},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "214 attitude segments\n"
+ ]
+ },
{
"data": {
"text/html": [
@@ -978,6 +1150,7 @@
"
\n",
"
nadir instrument
\n",
"
instrument pitched -8.9 deg
\n",
+ "
station attitude schedule
\n",
"
\n",
" \n",
" \n",
@@ -985,21 +1158,25 @@
"
scenes observed (within 9 days of the TLE epoch)
\n",
"
1698.00
\n",
"
1698.00
\n",
+ "
1691.00
\n",
" \n",
"
\n",
"
epoch minus midpoint, 5% (s)
\n",
"
-9.04
\n",
"
0.65
\n",
+ "
0.47
\n",
"
\n",
"
\n",
"
epoch minus midpoint, 50% (s)
\n",
"
-8.47
\n",
"
1.22
\n",
+ "
1.14
\n",
"
\n",
"
\n",
"
epoch minus midpoint, 95% (s)
\n",
"
-7.18
\n",
"
2.51
\n",
+ "
1.77
\n",
"
\n",
" \n",
"\n",
@@ -1012,11 +1189,17 @@
"epoch minus midpoint, 50% (s) -8.47 \n",
"epoch minus midpoint, 95% (s) -7.18 \n",
"\n",
- " instrument pitched -8.9 deg \n",
- "scenes observed (within 9 days of the TLE epoch) 1698.00 \n",
- "epoch minus midpoint, 5% (s) 0.65 \n",
- "epoch minus midpoint, 50% (s) 1.22 \n",
- "epoch minus midpoint, 95% (s) 2.51 "
+ " instrument pitched -8.9 deg \\\n",
+ "scenes observed (within 9 days of the TLE epoch) 1698.00 \n",
+ "epoch minus midpoint, 5% (s) 0.65 \n",
+ "epoch minus midpoint, 50% (s) 1.22 \n",
+ "epoch minus midpoint, 95% (s) 2.51 \n",
+ "\n",
+ " station attitude schedule \n",
+ "scenes observed (within 9 days of the TLE epoch) 1691.00 \n",
+ "epoch minus midpoint, 5% (s) 0.47 \n",
+ "epoch minus midpoint, 50% (s) 1.14 \n",
+ "epoch minus midpoint, 95% (s) 1.77 "
]
},
"execution_count": 7,
@@ -1025,15 +1208,50 @@
}
],
"source": [
- "emit_pitched = PointedInstrument(name=\"EMIT\", cross_track_field_of_view=emit.field_of_regard, along_track_field_of_view=1, pitch_angle=PITCH, is_rectangular=True)\n",
+ "emit_pitched = PointedInstrument(\n",
+ " name=\"EMIT\",\n",
+ " cross_track_field_of_view=emit.field_of_regard,\n",
+ " along_track_field_of_view=1,\n",
+ " pitch_angle=PITCH,\n",
+ " is_rectangular=True,\n",
+ ")\n",
"pitched_satellite = Satellite(name=\"ISS\", orbit=iss, instruments=[emit_pitched])\n",
"\n",
"\n",
- "def observe_pitched(scene):\n",
- " \"\"\"Observation epoch of a scene's center by the pitched instrument, relative to its midpoint time.\"\"\"\n",
+ "# the station's recorded attitude as the satellite's: a schedule of the attitude of each sample\n",
+ "samples = attitude[\n",
+ " (attitude.time >= pd.Timestamp(SCENES_START) - pd.Timedelta(hours=1))\n",
+ " & (attitude.time < pd.Timestamp(SCENES_END))\n",
+ "]\n",
+ "station_attitude = AttitudeSchedule.from_attitudes(\n",
+ " [t.floor(\"us\").to_pydatetime() - timedelta(minutes=2.5) for t in samples.time],\n",
+ " [\n",
+ " Attitude(\n",
+ " yaw_angle=-row[\"yaw (deg)\"],\n",
+ " pitch_angle=row[\"pitch (deg)\"],\n",
+ " roll_angle=row[\"roll (deg)\"],\n",
+ " )\n",
+ " for _, row in samples.iterrows()\n",
+ " ],\n",
+ ")\n",
+ "emit_body = PointedInstrument(\n",
+ " name=\"EMIT\",\n",
+ " cross_track_field_of_view=emit.field_of_regard,\n",
+ " along_track_field_of_view=1,\n",
+ " is_rectangular=True,\n",
+ ")\n",
+ "scheduled_satellite = Satellite(\n",
+ " name=\"ISS\", orbit=iss, instruments=[emit_body], attitude=station_attitude\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def observe_offset(satellite, scene):\n",
+ " \"\"\"Observation epoch of a scene's center by a satellite's instrument, relative to its midpoint time.\"\"\"\n",
" point = Point(id=0, latitude=scene.center.y, longitude=scene.center.x)\n",
" mid = scene.mid.floor(\"us\").to_pydatetime()\n",
- " obs = collect_observations(point, pitched_satellite, mid - timedelta(minutes=30), mid + timedelta(minutes=30))\n",
+ " obs = collect_observations(\n",
+ " point, satellite, mid - timedelta(minutes=30), mid + timedelta(minutes=30)\n",
+ " )\n",
" if obs.empty:\n",
" return np.nan\n",
" offsets = (obs.epoch - scene.mid).dt.total_seconds().to_numpy()\n",
@@ -1042,13 +1260,42 @@
"\n",
"recent = scenes[scenes[\"days before TLE epoch\"] < 9]\n",
"with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
- " pitched_offsets = np.array(list(pool.map(observe_pitched, recent.itertuples())))\n",
+ " pitched_offsets = np.array(\n",
+ " list(\n",
+ " pool.map(\n",
+ " lambda scene: observe_offset(pitched_satellite, scene),\n",
+ " recent.itertuples(),\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " scheduled_offsets = np.array(\n",
+ " list(\n",
+ " pool.map(\n",
+ " lambda scene: observe_offset(scheduled_satellite, scene),\n",
+ " recent.itertuples(),\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ "print(f\"{len(station_attitude.segments)} attitude segments\")\n",
"pd.DataFrame(\n",
" {\n",
- " \"nadir instrument\": recent[\"epoch minus midpoint (s)\"].describe(percentiles=[0.05, 0.5, 0.95]),\n",
- " f\"instrument pitched {PITCH:.1f} deg\": pd.Series(pitched_offsets).describe(percentiles=[0.05, 0.5, 0.95]),\n",
+ " \"nadir instrument\": recent[\"epoch minus midpoint (s)\"].describe(\n",
+ " percentiles=[0.05, 0.5, 0.95]\n",
+ " ),\n",
+ " f\"instrument pitched {PITCH:.1f} deg\": pd.Series(pitched_offsets).describe(\n",
+ " percentiles=[0.05, 0.5, 0.95]\n",
+ " ),\n",
+ " \"station attitude schedule\": pd.Series(scheduled_offsets).describe(\n",
+ " percentiles=[0.05, 0.5, 0.95]\n",
+ " ),\n",
" }\n",
- ").loc[[\"count\", \"5%\", \"50%\", \"95%\"]].round(2).rename(index=lambda i: f\"epoch minus midpoint, {i} (s)\" if i != \"count\" else \"scenes observed (within 9 days of the TLE epoch)\")"
+ ").loc[[\"count\", \"5%\", \"50%\", \"95%\"]].round(2).rename(\n",
+ " index=lambda i: (\n",
+ " f\"epoch minus midpoint, {i} (s)\"\n",
+ " if i != \"count\"\n",
+ " else \"scenes observed (within 9 days of the TLE epoch)\"\n",
+ " )\n",
+ ")"
]
},
{
@@ -1056,18 +1303,18 @@
"id": "3f919cba",
"metadata": {},
"source": [
- "The station flies pitched by -8.9 deg (between -9.4 and -7.6 deg) relative to its local orbital frame, with a yaw of -4 deg and a roll of 0.9 deg, as the station's attitude is chosen to minimize the torques on it. EMIT, fixed to the station, thus views about 9 deg aft of nadir. With this pitch, TAT-C's observation epochs agree with the scenes' midpoint times to within 1.2 s (median; 0.7 to 2.5 s), instead of 8.5 s, the remainder reflecting the variations of the station's attitude and EMIT's alignment.\n",
+ "The station flies pitched by -8.9 deg (between -9.4 and -7.6 deg) relative to its local orbital frame, with a yaw of -4 deg and a roll of 0.9 deg, as the station's attitude is chosen to minimize the torques on it. EMIT, fixed to the station, thus views about 9 deg aft of nadir. With this pitch, TAT-C's observation epochs agree with the scenes' midpoint times to within 1.2 s (median; 0.7 to 2.5 s), instead of 8.5 s, the remainder reflecting the variations of the station's attitude and EMIT's alignment. With the station's recorded attitude as an `AttitudeSchedule`, which follows these variations, the epochs agree within 1.1 s (median; 0.5 to 1.8 s). Seven of the 1,698 scenes are then missed, all on 26 September, after a 14-hour gap in the attitude samples during which the station turned from flying backward (a yaw of 176 deg, around its maneuver of 25 September) to flying forward: the schedule holds the backward attitude through the gap. With attitude samples throughout the period, the schedule represents such changes of the station's attitude, which a fixed pitch angle does not.\n",
"\n",
"## Summary\n",
"\n",
"| Test | Result |\n",
"| --- | --- |\n",
"| 1. Orbit | TLE within 6 km along track and 2 km across track back to the station's last maneuver (9 days before the TLE epoch); along-track errors grow by about 250 km per day before it |\n",
- "| 2. Observations | all EMIT scenes predicted within 9 days of the TLE epoch, with solar zenith angles within 0.05 deg; observation epochs within 1.2 s (median) when EMIT is modeled with the station's 8.9 deg pitch, 8.5 s when viewing nadir |\n",
+ "| 2. Observations | all EMIT scenes predicted within 9 days of the TLE epoch, with solar zenith angles within 0.05 deg; observation epochs within 1.1 s (median; 1.8 s at the 95th percentile) with the station's recorded attitude as an `AttitudeSchedule`, 1.2 s (2.5 s) with its median 8.9 deg pitch, and 8.5 s when viewing nadir |\n",
"\n",
"**Orbit.** A TLE propagated by SGP4 represents the ISS's low orbit well (a few kilometers) until the station's last maneuver, but not before it: the station is reboosted every few weeks, and a TLE describes the orbit only since the last reboost. Analyses of past ISS observations need TLEs that bracket the period (a multi-element `GeneralPerturbationsOrbit`), and predictions for the future are limited by the next maneuver and by drag.\n",
"\n",
- "**Observations.** TAT-C predicts the scenes of an ISS instrument and their solar geometry, including the drift of local solar time of a precessing orbit. Instruments fixed to the station view along the station's axes rather than nadir: the station's attitude (here, a pitch of about 9 deg) shifts their observations along track and can be modeled with the pitch (and roll) angles of a `PointedInstrument`."
+ "**Observations.** TAT-C predicts the scenes of an ISS instrument and their solar geometry, including the drift of local solar time of a precessing orbit. Instruments fixed to the station view along the station's axes rather than nadir: the station's attitude (here, a pitch of about 9 deg, and at times flying backward) shifts their observations along track. It is modeled as the satellite's `Attitude` (or an `AttitudeSchedule` of the recorded attitude), from which the instruments' views are rotated, or approximately with the pitch (and roll) angles of a `PointedInstrument`."
]
}
],
diff --git a/docs/validation/ValidatePointingOCO2.ipynb b/docs/validation/ValidatePointingOCO2.ipynb
index 17cea3a9..d99df60b 100644
--- a/docs/validation/ValidatePointingOCO2.ipynb
+++ b/docs/validation/ValidatePointingOCO2.ipynb
@@ -15,12 +15,14 @@
"\n",
"Throughout, the spacecraft yaws so that the slit stays perpendicular to the plane containing the Sun and the line of sight (the solar principal plane), which keeps the polarization of the measured light constant.\n",
"\n",
- "TAT-C represents a pointed instrument as a `PointedInstrument` whose roll and pitch angles may vary around the orbit (`roll_angle_profile` and `pitch_angle_profile`, as functions of the argument of latitude), and whose field of view is aligned with the ground track. This notebook asks:\n",
+ "TAT-C represents a pointed instrument as a `PointedInstrument`, whose view is rotated from the axes of the spacecraft's body, and the spacecraft's attitude: an `Attitude` whose yaw, roll, and pitch angles may vary around the orbit (as functions of the argument of latitude), a `TargetAttitude` that points at ground targets, a `GlintAttitude` that points at or near the sun glint (the specular point), or an `AttitudeSchedule` that switches between them over time. This notebook asks:\n",
"\n",
"1. **Operating modes.** How does OCO-2 schedule its modes?\n",
- "2. **Nadir mode.** Does a nadir `PointedInstrument` reproduce OCO-2's footprints and swath?\n",
- "3. **Glint mode.** Can roll and pitch angle profiles represent glint pointing, and for how long?\n",
- "4. **Repeat cycle.** Does TAT-C find OCO-2's 16-day repeat cycle, and does the ground track repeat?"
+ "2. **Nadir mode.** Does a nadir `PointedInstrument` reproduce OCO-2's footprints, and a yaw angle profile its slit?\n",
+ "3. **Glint mode.** Does TAT-C locate the glint spot, and does a `GlintAttitude` reproduce OCO-2's glint pointing over weeks (compared with roll and pitch angle profiles)?\n",
+ "4. **Repeat cycle.** Does TAT-C find OCO-2's 16-day repeat cycle, and does the ground track repeat?\n",
+ "5. **Target mode.** Does a `TargetAttitude` reproduce OCO-2's target observations?\n",
+ "6. **Mode schedule.** Does one satellite with an `AttitudeSchedule` reproduce all of OCO-2's modes?"
]
},
{
@@ -39,10 +41,10 @@
"id": "03090d73",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:29:30.772166Z",
- "iopub.status.busy": "2026-10-08T18:29:30.771167Z",
- "iopub.status.idle": "2026-10-08T18:29:32.978805Z",
- "shell.execute_reply": "2026-10-08T18:29:32.978805Z"
+ "iopub.execute_input": "2026-10-25T18:16:09.986445Z",
+ "iopub.status.busy": "2026-10-25T18:16:09.986445Z",
+ "iopub.status.idle": "2026-10-25T18:16:13.743312Z",
+ "shell.execute_reply": "2026-10-25T18:16:13.742305Z"
}
},
"outputs": [
@@ -160,13 +162,17 @@
" def __init__(self, url, block_size=2**20, prefetch=1):\n",
" self.block_size, self.blocks, self.position = block_size, {}, 0\n",
" response = self._get(url, 0, prefetch * block_size)\n",
- " self.url, self.size = response.url, int(response.headers[\"Content-Range\"].split(\"/\")[1])\n",
+ " self.url, self.size = response.url, int(\n",
+ " response.headers[\"Content-Range\"].split(\"/\")[1]\n",
+ " )\n",
" self._store(0, response.content)\n",
"\n",
" def _get(self, url, start, end):\n",
" for attempt in range(5):\n",
" try:\n",
- " response = https_session().get(url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300)\n",
+ " response = https_session().get(\n",
+ " url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300\n",
+ " )\n",
" response.raise_for_status()\n",
" return response\n",
" except requests.RequestException:\n",
@@ -187,18 +193,30 @@
" return self.position\n",
"\n",
" def seek(self, offset, whence=io.SEEK_SET):\n",
- " self.position = {io.SEEK_SET: 0, io.SEEK_CUR: self.position, io.SEEK_END: self.size}[whence] + offset\n",
+ " self.position = {\n",
+ " io.SEEK_SET: 0,\n",
+ " io.SEEK_CUR: self.position,\n",
+ " io.SEEK_END: self.size,\n",
+ " }[whence] + offset\n",
" return self.position\n",
"\n",
" def readinto(self, buffer):\n",
" n = min(len(buffer), self.size - self.position)\n",
" if n <= 0:\n",
" return 0\n",
- " first, last = self.position // self.block_size, (self.position + n - 1) // self.block_size\n",
- " missing = [block for block in range(first, last + 1) if block not in self.blocks]\n",
+ " first, last = (\n",
+ " self.position // self.block_size,\n",
+ " (self.position + n - 1) // self.block_size,\n",
+ " )\n",
+ " missing = [\n",
+ " block for block in range(first, last + 1) if block not in self.blocks\n",
+ " ]\n",
" if missing:\n",
" end = min((missing[-1] + 1) * self.block_size, self.size)\n",
- " self._store(missing[0], self._get(self.url, missing[0] * self.block_size, end).content)\n",
+ " self._store(\n",
+ " missing[0],\n",
+ " self._get(self.url, missing[0] * self.block_size, end).content,\n",
+ " )\n",
" data = b\"\".join(self.blocks[block] for block in range(first, last + 1))\n",
" offset = self.position - first * self.block_size\n",
" buffer[:n] = data[offset : offset + n]\n",
@@ -217,11 +235,44 @@
" np.savez_compressed(\n",
" path,\n",
" frame_time_tai93=f[\"FrameHeader/frame_time_tai93\"][:][k],\n",
- " **{name: fg[name][:][k] for name in [\"spacecraft_position\", \"spacecraft_velocity\", \"spacecraft_lat\", \"spacecraft_lon\", \"spacecraft_alt\", \"roll\", \"pitch\", \"yaw\", \"glint_spot_latitude\", \"glint_spot_longitude\", \"glint_off_pointing_angle\", \"ground_track\"]},\n",
- " **{name: sg[name][:][k] for name in [\"sounding_time_tai93\", \"sounding_latitude\", \"sounding_longitude\", \"sounding_altitude\", \"sounding_zenith\", \"sounding_azimuth\", \"sounding_solar_zenith\", \"sounding_solar_azimuth\", \"sounding_polarization_angle\"]},\n",
+ " **{\n",
+ " name: fg[name][:][k]\n",
+ " for name in [\n",
+ " \"spacecraft_position\",\n",
+ " \"spacecraft_velocity\",\n",
+ " \"spacecraft_lat\",\n",
+ " \"spacecraft_lon\",\n",
+ " \"spacecraft_alt\",\n",
+ " \"roll\",\n",
+ " \"pitch\",\n",
+ " \"yaw\",\n",
+ " \"glint_spot_latitude\",\n",
+ " \"glint_spot_longitude\",\n",
+ " \"glint_off_pointing_angle\",\n",
+ " \"ground_track\",\n",
+ " ]\n",
+ " },\n",
+ " **{\n",
+ " name: sg[name][:][k]\n",
+ " for name in [\n",
+ " \"sounding_time_tai93\",\n",
+ " \"sounding_latitude\",\n",
+ " \"sounding_longitude\",\n",
+ " \"sounding_altitude\",\n",
+ " \"sounding_zenith\",\n",
+ " \"sounding_azimuth\",\n",
+ " \"sounding_solar_zenith\",\n",
+ " \"sounding_solar_azimuth\",\n",
+ " \"sounding_polarization_angle\",\n",
+ " ]\n",
+ " },\n",
" sounding_operation_mode=sg[\"sounding_operation_mode\"][:][k].astype(\"U3\"),\n",
- " vertex_latitude=f[\"FootprintGeometry/footprint_vertex_latitude\"][::100, :, 0, :],\n",
- " vertex_longitude=f[\"FootprintGeometry/footprint_vertex_longitude\"][::100, :, 0, :],\n",
+ " vertex_latitude=f[\"FootprintGeometry/footprint_vertex_latitude\"][\n",
+ " ::100, :, 0, :\n",
+ " ],\n",
+ " vertex_longitude=f[\"FootprintGeometry/footprint_vertex_longitude\"][\n",
+ " ::100, :, 0, :\n",
+ " ],\n",
" )\n",
" return path\n",
"\n",
@@ -229,8 +280,18 @@
"if not any(DATA_DIR.glob(\"oco2_L1bSc*.npz\")):\n",
" import earthaccess\n",
"\n",
- " results = earthaccess.search_data(short_name=\"OCO2_L1B_Science\", temporal=(\"2026-09-14T00:00:00Z\", \"2026-09-30T23:59:59Z\"))\n",
- " urls = sorted(next(link[\"URL\"] for link in r[\"umm\"][\"RelatedUrls\"] if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".h5\")) for r in results)\n",
+ " results = earthaccess.search_data(\n",
+ " short_name=\"OCO2_L1B_Science\",\n",
+ " temporal=(\"2026-09-14T00:00:00Z\", \"2026-09-30T23:59:59Z\"),\n",
+ " )\n",
+ " urls = sorted(\n",
+ " next(\n",
+ " link[\"URL\"]\n",
+ " for link in r[\"umm\"][\"RelatedUrls\"]\n",
+ " if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".h5\")\n",
+ " )\n",
+ " for r in results\n",
+ " )\n",
" with concurrent.futures.ThreadPoolExecutor(6) as pool:\n",
" list(pool.map(reduce_file, urls))\n",
"\n",
@@ -239,13 +300,45 @@
" match = re.match(r\"oco2_L1bSc(\\w\\w)_(\\d+)(\\w)_\", path.name)\n",
" with np.load(path) as d:\n",
" record = {key: d[key] for key in d.files}\n",
- " valid = (record[\"frame_time_tai93\"] > 0) & np.all(np.abs(record[\"sounding_latitude\"]) <= 90, axis=1)\n",
- " record = {key: (value[valid] if key not in (\"vertex_latitude\", \"vertex_longitude\") and len(value) == len(valid) else value) for key, value in record.items()}\n",
- " record.update({\"name\": path.stem, \"mode\": match.group(1), \"orbit\": int(match.group(2)), \"segment\": match.group(3)})\n",
+ " valid = (record[\"frame_time_tai93\"] > 0) & np.all(\n",
+ " np.abs(record[\"sounding_latitude\"]) <= 90, axis=1\n",
+ " )\n",
+ " record = {\n",
+ " key: (\n",
+ " value[valid]\n",
+ " if key not in (\"vertex_latitude\", \"vertex_longitude\")\n",
+ " and len(value) == len(valid)\n",
+ " else value\n",
+ " )\n",
+ " for key, value in record.items()\n",
+ " }\n",
+ " record.update(\n",
+ " {\n",
+ " \"name\": path.stem,\n",
+ " \"mode\": match.group(1),\n",
+ " \"orbit\": int(match.group(2)),\n",
+ " \"segment\": match.group(3),\n",
+ " }\n",
+ " )\n",
" if len(record[\"frame_time_tai93\"]):\n",
" files.append(record)\n",
- "catalog = pd.DataFrame([{\"mode\": f[\"mode\"], \"orbit\": f[\"orbit\"], \"frames\": len(f[\"frame_time_tai93\"]), \"start\": pd.Timestamp(\"1993-01-01\", tz=\"UTC\") + pd.to_timedelta(f[\"frame_time_tai93\"][0] - 11, unit=\"s\")} for f in files])\n",
- "catalog.groupby(\"mode\").agg(files=(\"orbit\", \"size\"), orbits=(\"orbit\", \"nunique\"), hours=(\"frames\", lambda x: x.sum() * FRAME_STEP / 3 / 3600)).round(1)"
+ "catalog = pd.DataFrame(\n",
+ " [\n",
+ " {\n",
+ " \"mode\": f[\"mode\"],\n",
+ " \"orbit\": f[\"orbit\"],\n",
+ " \"frames\": len(f[\"frame_time_tai93\"]),\n",
+ " \"start\": pd.Timestamp(\"1993-01-01\", tz=\"UTC\")\n",
+ " + pd.to_timedelta(f[\"frame_time_tai93\"][0] - 11, unit=\"s\"),\n",
+ " }\n",
+ " for f in files\n",
+ " ]\n",
+ ")\n",
+ "catalog.groupby(\"mode\").agg(\n",
+ " files=(\"orbit\", \"size\"),\n",
+ " orbits=(\"orbit\", \"nunique\"),\n",
+ " hours=(\"frames\", lambda x: x.sum() * FRAME_STEP / 3 / 3600),\n",
+ ").round(1)"
]
},
{
@@ -255,7 +348,7 @@
"source": [
"## Setup\n",
"\n",
- "Frame times are given in seconds since 1 January 1993 in International Atomic Time (TAI), converted to UTC with Skyfield. The footprint of a frame is represented by the mean location of its eight soundings, and its pointing by TAT-C's roll (positive left) and pitch (positive forward) angles of that location from the spacecraft (`compute_view_angles`), relative to the geodetic nadir and the Earth-fixed velocity."
+ "Frame times are given in seconds of International Atomic Time (TAI) since 1 January 1993 00:00:00 UTC, when TAI was 27 s ahead of UTC, converted to UTC with Skyfield. The footprint of a frame is represented by the mean location of its eight soundings, and its pointing by TAT-C's roll (positive left) and pitch (positive forward) angles of that location from the spacecraft (`compute_view_angles`), relative to the geodetic nadir and the Earth-fixed velocity."
]
},
{
@@ -264,10 +357,10 @@
"id": "64bf4c92",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:29:32.981813Z",
- "iopub.status.busy": "2026-10-08T18:29:32.980812Z",
- "iopub.status.idle": "2026-10-08T18:29:45.342238Z",
- "shell.execute_reply": "2026-10-08T18:29:45.341231Z"
+ "iopub.execute_input": "2026-10-25T18:16:13.745309Z",
+ "iopub.status.busy": "2026-10-25T18:16:13.745309Z",
+ "iopub.status.idle": "2026-10-25T18:16:29.675942Z",
+ "shell.execute_reply": "2026-10-25T18:16:29.675942Z"
}
},
"outputs": [
@@ -287,18 +380,57 @@
"from skyfield.units import Distance, Velocity\n",
"\n",
"from tatc.constants import timescale\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, PointedInstrument\n",
- "from tatc.utils import compute_argument_of_latitude, compute_projected_ray_position, compute_view_angles, geodesic_distance\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " AttitudeSchedule,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " GlintAttitude,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ " TargetAttitude,\n",
+ ")\n",
+ "from tatc.utils import (\n",
+ " compute_argument_of_latitude,\n",
+ " compute_projected_ray_position,\n",
+ " compute_specular_point,\n",
+ " compute_view_angles,\n",
+ " geodesic_distance,\n",
+ ")\n",
"\n",
"orbit = GeneralPerturbationsOrbit.from_tle(TLE)\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"\n",
"def frame_track(record, k):\n",
" \"\"\"OCO-2's reported (inertial) position and velocity at frames k.\"\"\"\n",
- " t = timescale.tai(1993, 1, 1, 0, 0, 26 + record[\"frame_time_tai93\"][k].astype(float))\n",
- " return Geocentric(Distance(m=record[\"spacecraft_position\"][k].T.astype(float)).au, Velocity(km_per_s=record[\"spacecraft_velocity\"][k].T.astype(float) / 1e3).au_per_d, t)\n",
+ " # TAI seconds since 1993-01-01 00:00:00 UTC, when TAI - UTC was 27 s\n",
+ " t = timescale.tai(\n",
+ " 1993, 1, 1, 0, 0, 27 + record[\"frame_time_tai93\"][k].astype(float)\n",
+ " )\n",
+ " return Geocentric(\n",
+ " Distance(m=record[\"spacecraft_position\"][k].T.astype(float)).au,\n",
+ " Velocity(\n",
+ " km_per_s=record[\"spacecraft_velocity\"][k].T.astype(float) / 1e3\n",
+ " ).au_per_d,\n",
+ " t,\n",
+ " )\n",
"\n",
"\n",
"def frame_geometry(record, step=3):\n",
@@ -310,18 +442,36 @@
" center_lon = np.degrees(np.angle(np.exp(1j * np.radians(lon)).mean(axis=1)))\n",
" angles = np.array(\n",
" [\n",
- " [compute_view_angles(track[i], wgs84.latlon(a, b)) for a, b in ((center_lat[i], center_lon[i]), (lat[i, 0], lon[i, 0]), (lat[i, 7], lon[i, 7]))]\n",
+ " [\n",
+ " compute_view_angles(track[i], wgs84.latlon(a, b))\n",
+ " for a, b in (\n",
+ " (center_lat[i], center_lon[i]),\n",
+ " (lat[i, 0], lon[i, 0]),\n",
+ " (lat[i, 7], lon[i, 7]),\n",
+ " )\n",
+ " ]\n",
" for i in range(len(k))\n",
" ]\n",
" ) # (frame, footprint, roll/pitch)\n",
" return pd.DataFrame(\n",
" {\n",
- " \"file\": record[\"name\"], \"mode\": record[\"mode\"], \"orbit\": record[\"orbit\"], \"index\": k,\n",
+ " \"file\": record[\"name\"],\n",
+ " \"mode\": record[\"mode\"],\n",
+ " \"orbit\": record[\"orbit\"],\n",
+ " \"index\": k,\n",
" \"time\": track.t.utc_datetime(),\n",
- " \"argument of latitude (deg)\": compute_argument_of_latitude(track.position.m, track.velocity.m_per_s),\n",
- " \"latitude (deg)\": center_lat, \"longitude (deg)\": center_lon,\n",
- " \"roll (deg)\": angles[:, 0, 0], \"pitch (deg)\": angles[:, 0, 1],\n",
- " \"slit angle from cross-track (deg)\": np.degrees(np.arctan2(angles[:, 2, 1] - angles[:, 1, 1], angles[:, 2, 0] - angles[:, 1, 0])),\n",
+ " \"argument of latitude (deg)\": compute_argument_of_latitude(\n",
+ " track.position.m, track.velocity.m_per_s\n",
+ " ),\n",
+ " \"latitude (deg)\": center_lat,\n",
+ " \"longitude (deg)\": center_lon,\n",
+ " \"roll (deg)\": angles[:, 0, 0],\n",
+ " \"pitch (deg)\": angles[:, 0, 1],\n",
+ " \"slit angle from cross-track (deg)\": np.degrees(\n",
+ " np.arctan2(\n",
+ " angles[:, 2, 1] - angles[:, 1, 1], angles[:, 2, 0] - angles[:, 1, 0]\n",
+ " )\n",
+ " ),\n",
" \"spacecraft yaw (deg)\": record[\"yaw\"][k],\n",
" \"glint off-pointing (deg)\": record[\"glint_off_pointing_angle\"][k],\n",
" \"solar zenith (deg)\": record[\"sounding_solar_zenith\"][k].mean(axis=1),\n",
@@ -329,7 +479,11 @@
" )\n",
"\n",
"\n",
- "frames = Parallel(n_jobs=-1)(delayed(frame_geometry)(record) for record in files if record[\"mode\"] in (\"GL\", \"ND\"))\n",
+ "frames = Parallel(n_jobs=-1)(\n",
+ " delayed(frame_geometry)(record)\n",
+ " for record in files\n",
+ " if record[\"mode\"] in (\"GL\", \"ND\")\n",
+ ")\n",
"frames = pd.concat(frames).sort_values(\"time\").reset_index(drop=True)\n",
"frames[\"day\"] = pd.DatetimeIndex(frames.time).strftime(\"%Y-%m-%d\")\n",
"record_by_name = {record[\"name\"]: record for record in files}\n",
@@ -352,10 +506,10 @@
"id": "32aef3ff",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:29:45.343748Z",
- "iopub.status.busy": "2026-10-08T18:29:45.343748Z",
- "iopub.status.idle": "2026-10-08T18:29:45.368699Z",
- "shell.execute_reply": "2026-10-08T18:29:45.367692Z"
+ "iopub.execute_input": "2026-10-25T18:16:29.677947Z",
+ "iopub.status.busy": "2026-10-25T18:16:29.677947Z",
+ "iopub.status.idle": "2026-10-25T18:16:29.701505Z",
+ "shell.execute_reply": "2026-10-25T18:16:29.700497Z"
}
},
"outputs": [
@@ -564,10 +718,21 @@
}
],
"source": [
- "orbit_modes = catalog[catalog[\"mode\"].isin([\"GL\", \"ND\"])].groupby(\"orbit\")[\"mode\"].agg(lambda x: \"+\".join(sorted(set(x))))\n",
+ "orbit_modes = (\n",
+ " catalog[catalog[\"mode\"].isin([\"GL\", \"ND\"])]\n",
+ " .groupby(\"orbit\")[\"mode\"]\n",
+ " .agg(lambda x: \"+\".join(sorted(set(x))))\n",
+ ")\n",
"catalog[\"day\"] = catalog.start.dt.strftime(\"%Y-%m-%d\")\n",
"table1 = catalog.groupby([\"day\", \"mode\"]).orbit.nunique().unstack(fill_value=0)\n",
- "print(\"consecutive orbits:\", \" \".join(orbit_modes.loc[orbit_modes.index[:40]].str.replace(\"GL\", \"G\").str.replace(\"ND\", \"N\")))\n",
+ "print(\n",
+ " \"consecutive orbits:\",\n",
+ " \" \".join(\n",
+ " orbit_modes.loc[orbit_modes.index[:40]]\n",
+ " .str.replace(\"GL\", \"G\")\n",
+ " .str.replace(\"ND\", \"N\")\n",
+ " ),\n",
+ ")\n",
"parity = pd.crosstab(orbit_modes.index % 2, orbit_modes)\n",
"print(parity)\n",
"table1"
@@ -578,7 +743,7 @@
"id": "fecc7b5d",
"metadata": {},
"source": [
- "OCO-2 alternates its modes by orbit: odd-numbered orbits are in glint mode, and most even-numbered orbits in nadir mode (86 of 124), with others in glint mode (34) or both (4). Over the 17 days, it observed for 119 hours in glint mode and 68 hours in nadir mode, with ten target observations (about 4 minutes each) and short special segments. TAT-C does not schedule modes: a mode can be modeled as a separate instrument (or satellite) for the orbits in which it is used, or a single analysis can represent one mode.\n",
+ "OCO-2 alternates its modes by orbit: odd-numbered orbits are in glint mode, and most even-numbered orbits in nadir mode (86 of 124), with others in glint mode (34) or both (4). Over the 17 days, it observed for 119 hours in glint mode and 68 hours in nadir mode, with ten target observations (about 4 minutes each) and short special segments. TAT-C represents such a schedule with an `AttitudeSchedule` of the spacecraft's attitudes in each mode (Test 6).\n",
"\n",
"## Test 2: Nadir Mode\n",
"\n",
@@ -591,10 +756,10 @@
"id": "c55f705f",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:29:45.370698Z",
- "iopub.status.busy": "2026-10-08T18:29:45.370698Z",
- "iopub.status.idle": "2026-10-08T18:29:47.210694Z",
- "shell.execute_reply": "2026-10-08T18:29:47.210694Z"
+ "iopub.execute_input": "2026-10-25T18:16:29.703505Z",
+ "iopub.status.busy": "2026-10-25T18:16:29.703505Z",
+ "iopub.status.idle": "2026-10-25T18:16:31.199884Z",
+ "shell.execute_reply": "2026-10-25T18:16:31.199884Z"
}
},
"outputs": [
@@ -603,19 +768,19 @@
"output_type": "stream",
"text": [
" reported orbit (km) TLE orbit (km)\n",
- "50% 2.068 5.956\n",
- "95% 2.778 9.314\n",
- "max 2.972 10.583\n"
+ "50% 2.115 2.250\n",
+ "95% 2.761 6.406\n",
+ "max 2.949 8.358\n"
]
},
{
"data": {
"text/plain": [
"days before TLE epoch (bin)\n",
- "(5, 10] 6.85\n",
- "(10, 15] 6.07\n",
- "(15, 20] 5.42\n",
- "(20, 25] 4.68\n",
+ "(5, 10] 2.32\n",
+ "(10, 15] 2.27\n",
+ "(15, 20] 2.05\n",
+ "(20, 25] 2.28\n",
"Name: TLE orbit (km), dtype: float64"
]
},
@@ -626,25 +791,57 @@
],
"source": [
"nadir = frames[frames[\"mode\"] == \"ND\"].iloc[::5]\n",
- "nadir_view = PointedInstrument(name=\"OCO-2 nadir\", field_of_regard=5, cross_track_field_of_view=0.8, along_track_field_of_view=0.1, is_rectangular=True)\n",
+ "nadir_view = PointedInstrument(\n",
+ " name=\"OCO-2 nadir\",\n",
+ " field_of_regard=5,\n",
+ " cross_track_field_of_view=0.8,\n",
+ " along_track_field_of_view=0.1,\n",
+ " is_rectangular=True,\n",
+ ")\n",
"rows2 = []\n",
"for name, group in nadir.groupby(\"file\"):\n",
" record = record_by_name[name]\n",
" track = frame_track(record, group[\"index\"].to_numpy())\n",
" reported = nadir_view.compute_footprint_center(track)\n",
- " tle = nadir_view.compute_footprint_center(orbit.get_orbit_track(list(track.t.utc_datetime())))\n",
- " for i, (lat, lon) in enumerate(zip(group[\"latitude (deg)\"], group[\"longitude (deg)\"])):\n",
+ " tle = nadir_view.compute_footprint_center(\n",
+ " orbit.get_orbit_track(list(track.t.utc_datetime()))\n",
+ " )\n",
+ " for i, (lat, lon) in enumerate(\n",
+ " zip(group[\"latitude (deg)\"], group[\"longitude (deg)\"])\n",
+ " ):\n",
" rows2.append(\n",
" {\n",
- " \"reported orbit (km)\": geodesic_distance(lon, lat, reported.longitude.degrees[i], reported.latitude.degrees[i]) / 1e3,\n",
- " \"TLE orbit (km)\": geodesic_distance(lon, lat, tle.longitude.degrees[i], tle.latitude.degrees[i]) / 1e3,\n",
- " \"days before TLE epoch\": (orbit.get_epoch() - track.t.utc_datetime()[i]).total_seconds() / 86400,\n",
+ " \"reported orbit (km)\": geodesic_distance(\n",
+ " lon,\n",
+ " lat,\n",
+ " reported.longitude.degrees[i],\n",
+ " reported.latitude.degrees[i],\n",
+ " )\n",
+ " / 1e3,\n",
+ " \"TLE orbit (km)\": geodesic_distance(\n",
+ " lon, lat, tle.longitude.degrees[i], tle.latitude.degrees[i]\n",
+ " )\n",
+ " / 1e3,\n",
+ " \"days before TLE epoch\": (\n",
+ " orbit.get_epoch() - track.t.utc_datetime()[i]\n",
+ " ).total_seconds()\n",
+ " / 86400,\n",
" }\n",
" )\n",
"footprints = pd.DataFrame(rows2)\n",
- "footprints[\"days before TLE epoch (bin)\"] = pd.cut(footprints[\"days before TLE epoch\"], [5, 10, 15, 20, 25])\n",
- "print(footprints[[\"reported orbit (km)\", \"TLE orbit (km)\"]].describe(percentiles=[0.5, 0.95]).loc[[\"50%\", \"95%\", \"max\"]].round(3).to_string())\n",
- "footprints.groupby(\"days before TLE epoch (bin)\", observed=True)[\"TLE orbit (km)\"].median().round(2)"
+ "footprints[\"days before TLE epoch (bin)\"] = pd.cut(\n",
+ " footprints[\"days before TLE epoch\"], [5, 10, 15, 20, 25]\n",
+ ")\n",
+ "print(\n",
+ " footprints[[\"reported orbit (km)\", \"TLE orbit (km)\"]]\n",
+ " .describe(percentiles=[0.5, 0.95])\n",
+ " .loc[[\"50%\", \"95%\", \"max\"]]\n",
+ " .round(3)\n",
+ " .to_string()\n",
+ ")\n",
+ "footprints.groupby(\"days before TLE epoch (bin)\", observed=True)[\n",
+ " \"TLE orbit (km)\"\n",
+ "].median().round(2)"
]
},
{
@@ -652,7 +849,7 @@
"id": "34be11f5",
"metadata": {},
"source": [
- "In nadir mode, TAT-C's nadir view, projected from the reported orbit, is within 2.1 km (median) and 3.0 km (maximum) of the center of the eight footprints, which lies up to 0.2 deg from the geodetic nadir along track, within a footprint length. With the TLE orbit, propagated 5 to 25 days back from its epoch, the differences are 6 km (median) and 11 km (maximum), mostly along track.\n",
+ "In nadir mode, TAT-C's nadir view, projected from the reported orbit, is within 2.1 km (median) and 2.9 km (maximum) of the center of the eight footprints, which lies up to 0.2 deg from the geodetic nadir along track, within a footprint length. With the TLE orbit, propagated 5 to 25 days back from its epoch, the differences are 2.3 km (median) and 8.4 km (maximum), whatever the TLE's age.\n",
"\n",
"**Swath.** The slit's orientation is the direction from the first to the eighth footprint, in TAT-C's view angles (0 deg: across track, to the left; 90 deg: forward). TAT-C aligns a `PointedInstrument`'s field of view with the ground track, so the cross-track extent of the eight footprints is compared with the length of the slit on the ground."
]
@@ -663,10 +860,10 @@
"id": "51acef3f",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:29:47.212700Z",
- "iopub.status.busy": "2026-10-08T18:29:47.212700Z",
- "iopub.status.idle": "2026-10-08T18:29:47.300786Z",
- "shell.execute_reply": "2026-10-08T18:29:47.300786Z"
+ "iopub.execute_input": "2026-10-25T18:16:31.202397Z",
+ "iopub.status.busy": "2026-10-25T18:16:31.202397Z",
+ "iopub.status.idle": "2026-10-25T18:16:31.287972Z",
+ "shell.execute_reply": "2026-10-25T18:16:31.287467Z"
}
},
"outputs": [
@@ -735,14 +932,14 @@
" \n",
"
"
]
@@ -840,11 +1053,35 @@
"for mode, color in [(\"ND\", BLUE), (\"GL\", ORANGE)]:\n",
" subset = frames[frames[\"mode\"] == mode].iloc[::20]\n",
" u = (subset[\"argument of latitude (deg)\"] + 90) % 360 - 90\n",
- " axes[0].scatter(u, subset[\"slit angle from cross-track (deg)\"], s=2, color=color, label={\"ND\": \"nadir\", \"GL\": \"glint\"}[mode])\n",
- "axes[0].set(xlabel=\"Argument of latitude (deg)\", ylabel=\"Slit angle from cross-track (deg)\", title=\"Slit orientation\")\n",
+ " axes[0].scatter(\n",
+ " u,\n",
+ " subset[\"slit angle from cross-track (deg)\"],\n",
+ " s=2,\n",
+ " color=color,\n",
+ " label={\"ND\": \"nadir\", \"GL\": \"glint\"}[mode],\n",
+ " )\n",
+ "axes[0].set(\n",
+ " xlabel=\"Argument of latitude (deg)\",\n",
+ " ylabel=\"Slit angle from cross-track (deg)\",\n",
+ " title=\"Slit orientation\",\n",
+ ")\n",
"axes[0].legend(frameon=False, markerscale=5)\n",
- "axes[1].plot((swath[\"argument of latitude (deg)\"] + 90) % 360 - 90, swath[\"cross-track extent (km)\"], \".\", ms=2, color=BLUE, label=\"cross-track extent of the footprints\")\n",
- "axes[1].plot((swath[\"argument of latitude (deg)\"] + 90) % 360 - 90, swath[\"slit length (km)\"], \".\", ms=2, color=GRAY, label=\"slit length (TAT-C swath without yaw)\")\n",
+ "axes[1].plot(\n",
+ " (swath[\"argument of latitude (deg)\"] + 90) % 360 - 90,\n",
+ " swath[\"cross-track extent (km)\"],\n",
+ " \".\",\n",
+ " ms=2,\n",
+ " color=BLUE,\n",
+ " label=\"cross-track extent of the footprints\",\n",
+ ")\n",
+ "axes[1].plot(\n",
+ " (swath[\"argument of latitude (deg)\"] + 90) % 360 - 90,\n",
+ " swath[\"slit length (km)\"],\n",
+ " \".\",\n",
+ " ms=2,\n",
+ " color=GRAY,\n",
+ " label=\"slit length (TAT-C swath without yaw)\",\n",
+ ")\n",
"axes[1].set(xlabel=\"Argument of latitude (deg)\", ylabel=\"km\", title=\"Nadir swath width\")\n",
"axes[1].legend(frameon=False, markerscale=5)\n",
"plt.show()"
@@ -855,32 +1092,265 @@
"id": "bdc349ee",
"metadata": {},
"source": [
- "OCO-2 rotates its slit with the spacecraft's yaw to keep it perpendicular to the solar principal plane. Over the southern part of the day side, the slit lies within about 13 deg of the cross-track direction, so the eight footprints span 10 km across track, as for TAT-C's view. North of the equator, the slit turns toward the ground track: it lies along the track near an argument of latitude of 20 deg (about 20 deg north), where the footprints cover only one footprint width across track (about 1.3 km), and at 115 to 130 deg from the cross-track direction at higher latitudes, where they span 4 to 6 km. Since a `PointedInstrument`'s view is aligned with the ground track, TAT-C overestimates OCO-2's nadir swath by a factor of 3.7 (median) between the equator and 30 deg north, and by 1.5 to 2.4 at higher northern latitudes. Representing it requires a rotation of the view about its boresight (a yaw angle) that varies around the orbit, which TAT-C does not model.\n",
+ "OCO-2 rotates its slit with the spacecraft's yaw to keep it perpendicular to the solar principal plane. Over the southern part of the day side, the slit lies within about 13 deg of the cross-track direction, so the eight footprints span 10 km across track, as for TAT-C's view. North of the equator, the slit turns toward the ground track: it lies along the track near an argument of latitude of 20 deg (about 20 deg north), where the footprints cover only one footprint width across track (about 1.3 km), and at 115 to 130 deg from the cross-track direction at higher latitudes, where they span 4 to 6 km. Since a `PointedInstrument`'s view is aligned with the ground track, TAT-C overestimates OCO-2's nadir swath by a factor of 3.7 (median) between the equator and 30 deg north, and by 1.5 to 2.4 at higher northern latitudes. The slit's rotation is the spacecraft's yaw, so it is represented by an `Attitude` with a yaw angle profile (`yaw_angle_profile`): the negative of the slit angle from the cross-track direction (TAT-C's positive yaw turns the body's front to the left), in 2 deg bins of argument of latitude, fitted to the nadir orbits of the first day and of each day. The slit is a view of eight pixels across track (0.82 deg), and the first and eighth footprints are compared with TAT-C's first and eighth pixels (`compute_projected_pixel_position`), with the reported orbit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a5e84eb9",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T18:16:31.598980Z",
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+ "iopub.status.idle": "2026-10-25T18:16:32.889908Z",
+ "shell.execute_reply": "2026-10-25T18:16:32.889403Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
slit ends, median (km)
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+ "
slit ends, 95th percentile (km)
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
no yaw
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+ "
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+ "
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+ "
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+ "
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+ "
first-day yaw profile
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+ "
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+ "
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+ "
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+ "
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+ " slit ends, median (km) slit ends, 95th percentile (km)\n",
+ "no yaw 4.13 8.18\n",
+ "first-day yaw profile 2.08 3.19\n",
+ "same-day yaw profile 2.08 3.16"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# the slit: a view of eight pixels across track, rotated by the spacecraft's yaw (TAT-C's pixel 0 is the first footprint)\n",
+ "slit = PointedInstrument(\n",
+ " name=\"OCO-2\",\n",
+ " field_of_regard=120,\n",
+ " cross_track_field_of_view=0.82,\n",
+ " along_track_field_of_view=0.1,\n",
+ " cross_track_pixels=8,\n",
+ " is_rectangular=True,\n",
+ ")\n",
+ "nadir_frames = frames[frames[\"mode\"] == \"ND\"]\n",
+ "nadir_days = sorted(nadir_frames.day.unique())\n",
+ "\n",
+ "\n",
+ "def fit_yaw_profile(subset):\n",
+ " \"\"\"Yaw angle profile: the negative of the median slit angle in 2 deg bins of argument of latitude.\"\"\"\n",
+ " bins = (subset[\"argument of latitude (deg)\"] // 2) * 2 + 1\n",
+ " return [\n",
+ " (float(u), float(-a))\n",
+ " for u, a in subset.groupby(bins)[\"slit angle from cross-track (deg)\"]\n",
+ " .median()\n",
+ " .items()\n",
+ " ]\n",
+ "\n",
+ "\n",
+ "def slit_end_errors(attitude, subset):\n",
+ " \"\"\"Distances (km) of the first and eighth footprints from TAT-C's first and eighth pixels.\"\"\"\n",
+ " instrument = Satellite(\n",
+ " name=\"OCO-2\", orbit=orbit, instruments=[slit], attitude=attitude\n",
+ " ).get_instrument()\n",
+ " distances = []\n",
+ " for name, group in subset.groupby(\"file\"):\n",
+ " record = record_by_name[name]\n",
+ " k = group[\"index\"].to_numpy()\n",
+ " track = frame_track(record, k)\n",
+ " for footprint in (0, 7):\n",
+ " position = instrument.compute_projected_pixel_position(track, footprint, 0)\n",
+ " distances += list(\n",
+ " geodesic_distance(\n",
+ " record[\"sounding_longitude\"][k, footprint],\n",
+ " record[\"sounding_latitude\"][k, footprint],\n",
+ " position.longitude.degrees,\n",
+ " position.latitude.degrees,\n",
+ " )\n",
+ " / 1e3\n",
+ " )\n",
+ " return np.array(distances)\n",
+ "\n",
"\n",
+ "first_yaw = fit_yaw_profile(nadir_frames[nadir_frames.day == nadir_days[1]])\n",
+ "errors2c = {\"no yaw\": [], \"first-day yaw profile\": [], \"same-day yaw profile\": []}\n",
+ "for day in nadir_days[1:]:\n",
+ " subset = nadir_frames[nadir_frames.day == day].iloc[::6]\n",
+ " errors2c[\"no yaw\"].append(slit_end_errors(None, subset))\n",
+ " errors2c[\"first-day yaw profile\"].append(\n",
+ " slit_end_errors(Attitude(yaw_angle_profile=first_yaw), subset)\n",
+ " )\n",
+ " errors2c[\"same-day yaw profile\"].append(\n",
+ " slit_end_errors(\n",
+ " Attitude(\n",
+ " yaw_angle_profile=fit_yaw_profile(nadir_frames[nadir_frames.day == day])\n",
+ " ),\n",
+ " subset,\n",
+ " )\n",
+ " )\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " label: {\n",
+ " \"slit ends, median (km)\": np.median(np.concatenate(e)),\n",
+ " \"slit ends, 95th percentile (km)\": np.quantile(np.concatenate(e), 0.95),\n",
+ " }\n",
+ " for label, e in errors2c.items()\n",
+ " }\n",
+ ").T.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "36e9b223",
+ "metadata": {},
+ "source": [
+ "The yaw angle profile rotates TAT-C's slit with OCO-2's: the first and eighth footprints are within 2.1 km (median) and 3.2 km (95th percentile) of TAT-C's first and eighth pixels, instead of 4.1 and 8.2 km with the view aligned with the ground track; the remainder is the footprint center's offset along track from nadir (above). Unlike the glint mode's roll and pitch (Test 3), the slit's yaw does not drift: the first day's profile is as accurate as each day's over the 17 days. With the yaw, TAT-C's nadir swath is the cross-track extent of the eight footprints, rather than 3.7 times wider (median between the equator and 30 deg north)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab12961f",
+ "metadata": {},
+ "source": [
"## Test 3: Glint Mode\n",
"\n",
- "In glint mode, the footprint's roll and pitch angles vary around the orbit. Roll and pitch angle profiles (`roll_angle_profile`, `pitch_angle_profile`) are fitted to the glint orbits of the first day (the median angles in 2 deg bins of argument of latitude), and a `PointedInstrument` with these profiles is evaluated (`compute_footprint_center`) on the glint orbits of every day, with the reported orbit; its footprint centers are compared with the reported ones. As a baseline, profiles fitted to each day are evaluated on the same day."
+ "**Glint spot.** TAT-C's specular point (`compute_specular_point`) is the point on the WGS 84 ellipsoid where the surface normal bisects the directions toward the satellite and the Sun. It is computed from the reported orbit at each glint frame and compared with the glint spot reported by OCO-2.\n",
+ "\n",
+ "**Pointing.** OCO-2 points its footprints along the line from nadir to the glint spot, but short of the spot. The offset is the difference between the specular point's angle from nadir and the footprint's (both from TAT-C's roll and pitch angles from the spacecraft).\n",
+ "\n",
+ "A `GlintAttitude` points the spacecraft's body at the specular point, or short of it toward nadir by an offset angle. Its offset can be a function of the specular point's angle from nadir (`offset_angle_profile`), fitted here to the glint orbits of the first complete day (the median offset in 2 deg bins).\n",
+ "\n",
+ "A nadir `PointedInstrument` on a satellite with this attitude is evaluated (`compute_footprint_center`) on the glint orbits of every day, with the reported orbit, and its view centers are compared with the reported footprint centers. Two alternatives that do not compute the glint are compared with it:\n",
+ "* roll and pitch angle profiles of the spacecraft's `Attitude` (`roll_angle_profile` and `pitch_angle_profile`, the median angles in 2 deg bins of argument of latitude) fitted to the first day; and\n",
+ "* the same profiles fitted to each day and evaluated on the same day."
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "07e4d443",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:29:47.615141Z",
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- "iopub.status.idle": "2026-10-08T18:29:49.388201Z",
- "shell.execute_reply": "2026-10-08T18:29:49.387695Z"
+ "iopub.execute_input": "2026-10-25T18:16:32.891909Z",
+ "iopub.status.busy": "2026-10-25T18:16:32.891909Z",
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+ "shell.execute_reply": "2026-10-25T18:16:38.453645Z"
}
},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "glint off-pointing angle: median 7.0 deg, range 4.0 to 9.3 deg (5th to 95th percentile)\n"
- ]
+ "data": {
+ "text/html": [
+ "
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+ "\n",
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ " \n",
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+ "
specular point from reported glint spot (km)
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42831.0
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0.016
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0.053
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0.152
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0.186
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offset toward nadir (deg)
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+ " \n",
+ "
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+ ],
+ "text/plain": [
+ " count min 50% 95% \\\n",
+ "specular point from reported glint spot (km) 42831.0 0.016 0.053 0.152 \n",
+ "offset toward nadir (deg) 42831.0 3.793 7.013 9.304 \n",
+ "offset minus first day's profile (deg) 42831.0 0.000 0.018 0.065 \n",
+ "\n",
+ " max \n",
+ "specular point from reported glint spot (km) 0.186 \n",
+ "offset toward nadir (deg) 9.558 \n",
+ "offset minus first day's profile (deg) 0.220 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
"data": {
@@ -903,7 +1373,9 @@
" \n",
"
"
]
},
"metadata": {},
@@ -1199,15 +1847,38 @@
}
],
"source": [
- "fig, axes = plt.subplots(1, 2, figsize=(12, 3.8))\n",
+ "fig, axes = plt.subplots(1, 3, figsize=(15, 3.8))\n",
"for day, color in [(days[1], BLUE), (days[len(days) // 2], PURPLE), (days[-1], ORANGE)]:\n",
" subset = glint[glint.day == day].iloc[::10]\n",
" u = (subset[\"argument of latitude (deg)\"] + 90) % 360 - 90\n",
" axes[0].scatter(u, subset[\"roll (deg)\"], s=2, color=color, label=day)\n",
" axes[1].scatter(u, subset[\"pitch (deg)\"], s=2, color=color, label=day)\n",
- "axes[0].set(xlabel=\"Argument of latitude (deg)\", ylabel=\"Roll (deg, positive left)\", title=\"Glint mode roll\")\n",
- "axes[1].set(xlabel=\"Argument of latitude (deg)\", ylabel=\"Pitch (deg, positive forward)\", title=\"Glint mode pitch\")\n",
+ " axes[2].scatter(\n",
+ " subset[\"specular point off nadir (deg)\"],\n",
+ " subset[\"offset (deg)\"],\n",
+ " s=2,\n",
+ " color=color,\n",
+ " label=day,\n",
+ " )\n",
+ "axes[2].plot(*zip(*offset_profile), color=INK, lw=1, label=\"profile (first day)\")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Argument of latitude (deg)\",\n",
+ " ylabel=\"Roll (deg, positive left)\",\n",
+ " title=\"Glint mode roll\",\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Argument of latitude (deg)\",\n",
+ " ylabel=\"Pitch (deg, positive forward)\",\n",
+ " title=\"Glint mode pitch\",\n",
+ ")\n",
+ "axes[2].set(\n",
+ " xlabel=\"Specular point's angle from nadir (deg)\",\n",
+ " ylabel=\"Offset toward nadir (deg)\",\n",
+ " title=\"Offset from the specular point\",\n",
+ ")\n",
"axes[0].legend(frameon=False, markerscale=5)\n",
+ "axes[2].legend(frameon=False, markerscale=5, fontsize=8)\n",
+ "fig.tight_layout()\n",
"plt.show()"
]
},
@@ -1216,7 +1887,13 @@
"id": "cdca695d",
"metadata": {},
"source": [
- "In glint mode, OCO-2 points 16 to 28 deg to the left of its ground track and from 45 deg forward (in the south) to 45 deg aft (in the north), following the glint spot, which lies on the Sun's side (west) of the afternoon orbit. It does not point exactly at the glint spot but 4.0 to 9.3 deg from it (median 7.0 deg), toward nadir. The angles vary smoothly around the orbit, so roll and pitch angle profiles fitted to one day reproduce the footprint centers of that day's other glint orbits within 1.3 km (median) and 3.3 km (95th percentile), on every day. As the Sun's declination changes, however, the glint geometry moves: the profiles of the first day are 4 km off (median) the next day, 30 km after a week, and 68 km after 16 days, as both angles change (the roll angle by about 0.1 deg per day, and the pitch angle by up to several degrees, in steps). Glint-mode analyses of more than a few days need profiles fitted to each day, or pointing computed from the Sun's position (toward the glint spot), which TAT-C does not model.\n",
+ "**Glint spot.** TAT-C's specular point is within 0.05 km (median) and 0.19 km (maximum) of OCO-2's reported glint spot.\n",
+ "\n",
+ "**Pointing.** In glint mode, OCO-2 points 16 to 28 deg to the left of its ground track, and from 45 deg forward (in the south) to 45 deg aft (in the north). It follows the glint, which lies on the Sun's side (west) of the afternoon orbit. It points 3.8 to 9.6 deg short of the specular point, toward nadir. This offset depends on the specular point's angle from nadir alone: it grows from about 4 deg at 20 deg to 9 deg at 60 deg, and the first day's profile matches the offsets of every day within 0.02 deg (median) and 0.07 deg (95th percentile).\n",
+ "\n",
+ "The `GlintAttitude` with the first day's offsets places the view within 1.4 to 1.6 km (median) and 2.0 to 2.3 km (95th percentile) of the footprint centers on every day, with no drift over the 17 days. That is as close as roll and pitch angle profiles fitted to each day (1.3 to 1.5 km median, 3.1 to 3.4 km 95th percentile).\n",
+ "\n",
+ "Profiles of argument of latitude cannot follow the Sun, however. As the Sun's declination changes, the glint geometry moves: the first day's profiles are 4 km off (median) the next day, 30 km after a week, and 68 km after 16 days. Pointing computed from the Sun's position follows the glint over any period.\n",
"\n",
"## Test 4: Repeat Cycle\n",
"\n",
@@ -1225,14 +1902,14 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"id": "57ef3edc",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:29:49.849820Z",
- "iopub.status.busy": "2026-10-08T18:29:49.849820Z",
- "iopub.status.idle": "2026-10-08T18:29:50.053071Z",
- "shell.execute_reply": "2026-10-08T18:29:50.053071Z"
+ "iopub.execute_input": "2026-10-25T18:16:38.836991Z",
+ "iopub.status.busy": "2026-10-25T18:16:38.836991Z",
+ "iopub.status.idle": "2026-10-25T18:16:39.051473Z",
+ "shell.execute_reply": "2026-10-25T18:16:39.050958Z"
}
},
"outputs": [
@@ -1247,18 +1924,28 @@
"dtype: object"
]
},
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the repeat cycle of the orbit maintained on its repeat ground track (without drag)\n",
- "repeat_cycle = GeneralPerturbationsOrbit.from_tle(TLE, remove_drag=True, repeat_cycle=\"auto\").get_repeat_cycle()\n",
+ "repeat_cycle = GeneralPerturbationsOrbit.from_tle(\n",
+ " TLE, remove_drag=True, repeat_cycle=\"auto\"\n",
+ ").get_repeat_cycle()\n",
"positions = []\n",
"for record in files:\n",
" k = np.arange(0, len(record[\"frame_time_tai93\"]), 6)\n",
- " positions.append(pd.DataFrame({\"orbit\": record[\"orbit\"], \"latitude\": record[\"spacecraft_lat\"][k], \"longitude\": record[\"spacecraft_lon\"][k]}))\n",
+ " positions.append(\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"orbit\": record[\"orbit\"],\n",
+ " \"latitude\": record[\"spacecraft_lat\"][k],\n",
+ " \"longitude\": record[\"spacecraft_lon\"][k],\n",
+ " }\n",
+ " )\n",
+ " )\n",
"positions = pd.concat(positions).drop_duplicates()\n",
"rows4 = []\n",
"for number, group in positions.groupby(\"orbit\"):\n",
@@ -1266,17 +1953,33 @@
" if later.empty:\n",
" continue\n",
" for lat in np.arange(-60, 61, 10):\n",
- " a, b = group.iloc[(group.latitude - lat).abs().argsort()[:1]], later.iloc[(later.latitude - lat).abs().argsort()[:1]]\n",
+ " a, b = (\n",
+ " group.iloc[(group.latitude - lat).abs().argsort()[:1]],\n",
+ " later.iloc[(later.latitude - lat).abs().argsort()[:1]],\n",
+ " )\n",
" if abs(a.latitude.iloc[0] - lat) < 0.2 and abs(b.latitude.iloc[0] - lat) < 0.2:\n",
- " rows4.append({\"orbit\": number, \"latitude\": lat, \"distance (km)\": geodesic_distance(a.longitude.iloc[0], lat, b.longitude.iloc[0], lat) / 1e3})\n",
+ " rows4.append(\n",
+ " {\n",
+ " \"orbit\": number,\n",
+ " \"latitude\": lat,\n",
+ " \"distance (km)\": geodesic_distance(\n",
+ " a.longitude.iloc[0], lat, b.longitude.iloc[0], lat\n",
+ " )\n",
+ " / 1e3,\n",
+ " }\n",
+ " )\n",
"repeat = pd.DataFrame(rows4)\n",
"pd.Series(\n",
" {\n",
" \"TAT-C repeat cycle (TLE)\": repeat_cycle,\n",
- " \"TAT-C repeat cycle (days)\": repeat_cycle / timedelta(days=1) if repeat_cycle else np.nan,\n",
+ " \"TAT-C repeat cycle (days)\": (\n",
+ " repeat_cycle / timedelta(days=1) if repeat_cycle else np.nan\n",
+ " ),\n",
" \"orbit pairs 233 revolutions apart\": repeat.orbit.nunique(),\n",
" \"ground track repeat distance, median (km)\": repeat[\"distance (km)\"].median(),\n",
- " \"ground track repeat distance, 95th percentile (km)\": repeat[\"distance (km)\"].quantile(0.95),\n",
+ " \"ground track repeat distance, 95th percentile (km)\": repeat[\n",
+ " \"distance (km)\"\n",
+ " ].quantile(0.95),\n",
" }\n",
")"
]
@@ -1286,24 +1989,540 @@
"id": "5d888d4c",
"metadata": {},
"source": [
- "`get_repeat_cycle` finds OCO-2's 16-day repeat cycle (233 orbits) from the TLE. The reported sub-satellite points of the six orbits whose repeats lie within the data are within 9 km (median) and 13 km (95th percentile) of those 233 orbits earlier, consistent with OCO-2's maintenance of the A-Train's reference ground track. Since the cycle has an odd number of orbits, an orbit observed in glint mode in one cycle is usually observed in nadir mode in the next.\n",
+ "`get_repeat_cycle` finds OCO-2's 16-day repeat cycle (233 orbits) from the TLE. The reported sub-satellite points of the six orbits whose repeats lie within the data are within 9 km (median) and 13 km (95th percentile) of those 233 orbits earlier, consistent with OCO-2's maintenance of the A-Train's reference ground track. Since the cycle has an odd number of orbits, an orbit observed in glint mode in one cycle is usually observed in nadir mode in the next."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a36e003b",
+ "metadata": {},
+ "source": [
+ "## Test 5: Target Mode\n",
+ "\n",
+ "In target mode, OCO-2 points at a ground site (in these data, calibration and validation sites such as Sodankylä, East Trout Lake, Edwards, Wollongong, Gobabeb, Saga, and Izaña) during an overpass. Each target observation's site is taken as the median of its footprint centers, and a satellite with a `TargetAttitude` pointing at the site is evaluated with the reported orbit: the center of its nadir `PointedInstrument`'s view (`compute_footprint_center`) is compared with the reported footprint centers, and the site's angle from nadir at the start and end of the observation is compared with the `TargetAttitude`'s maximum off-nadir angle. The duration of the target pointing predicted with the TLE orbit (the time the site lies within the maximum off-nadir angle) is compared with the observation's."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "c984ebb7",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T18:16:39.053273Z",
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+ "shell.execute_reply": "2026-10-25T18:16:39.180229Z"
+ }
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+ {
+ "data": {
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+ "
site (deg)
\n",
+ "
observed (s)
\n",
+ "
predicted (s)
\n",
+ "
site off nadir at start, end (deg)
\n",
+ "
TAT-C view to footprints, median (km)
\n",
+ "
TAT-C view to footprints, 95th percentile (km)
\n",
+ "
nadir to footprints, median (km)
\n",
+ "
\n",
+ "
\n",
+ "
orbit
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
64907
\n",
+ "
67.37, 26.64
\n",
+ "
256.7
\n",
+ "
286.0
\n",
+ "
49.5, 47.4
\n",
+ "
5.1
\n",
+ "
11.8
\n",
+ "
440.5
\n",
+ "
\n",
+ "
\n",
+ "
64942
\n",
+ "
39.91, -104.24
\n",
+ "
266.7
\n",
+ "
283.0
\n",
+ "
49.4, 49.8
\n",
+ "
5.0
\n",
+ "
13.2
\n",
+ "
465.4
\n",
+ "
\n",
+ "
\n",
+ "
64967
\n",
+ "
-23.57, 15.04
\n",
+ "
260.0
\n",
+ "
279.0
\n",
+ "
49.1, 50.0
\n",
+ "
5.4
\n",
+ "
12.3
\n",
+ "
494.3
\n",
+ "
\n",
+ "
\n",
+ "
64976
\n",
+ "
-34.46, 150.86
\n",
+ "
263.3
\n",
+ "
282.0
\n",
+ "
48.9, 50.1
\n",
+ "
5.4
\n",
+ "
12.3
\n",
+ "
489.7
\n",
+ "
\n",
+ "
\n",
+ "
65000
\n",
+ "
54.36, -104.99
\n",
+ "
266.7
\n",
+ "
284.0
\n",
+ "
49.5, 49.8
\n",
+ "
5.1
\n",
+ "
12.8
\n",
+ "
476.5
\n",
+ "
\n",
+ "
\n",
+ "
65030
\n",
+ "
34.95, -117.88
\n",
+ "
266.7
\n",
+ "
284.0
\n",
+ "
49.5, 49.8
\n",
+ "
5.1
\n",
+ "
12.7
\n",
+ "
470.0
\n",
+ "
\n",
+ "
\n",
+ "
65049
\n",
+ "
33.24, 130.29
\n",
+ "
246.7
\n",
+ "
276.0
\n",
+ "
49.5, 47.7
\n",
+ "
5.2
\n",
+ "
11.8
\n",
+ "
481.5
\n",
+ "
\n",
+ "
\n",
+ "
65073
\n",
+ "
39.92, -104.24
\n",
+ "
250.0
\n",
+ "
281.0
\n",
+ "
49.3, 47.4
\n",
+ "
4.7
\n",
+ "
11.5
\n",
+ "
456.4
\n",
+ "
\n",
+ "
\n",
+ "
65102
\n",
+ "
54.36, -104.99
\n",
+ "
266.7
\n",
+ "
285.0
\n",
+ "
49.4, 49.6
\n",
+ "
5.2
\n",
+ "
12.1
\n",
+ "
459.7
\n",
+ "
\n",
+ "
\n",
+ "
65128
\n",
+ "
28.29, -16.52
\n",
+ "
256.7
\n",
+ "
274.0
\n",
+ "
49.2, 49.9
\n",
+ "
5.4
\n",
+ "
12.4
\n",
+ "
506.2
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " site (deg) observed (s) predicted (s) \\\n",
+ "orbit \n",
+ "64907 67.37, 26.64 256.7 286.0 \n",
+ "64942 39.91, -104.24 266.7 283.0 \n",
+ "64967 -23.57, 15.04 260.0 279.0 \n",
+ "64976 -34.46, 150.86 263.3 282.0 \n",
+ "65000 54.36, -104.99 266.7 284.0 \n",
+ "65030 34.95, -117.88 266.7 284.0 \n",
+ "65049 33.24, 130.29 246.7 276.0 \n",
+ "65073 39.92, -104.24 250.0 281.0 \n",
+ "65102 54.36, -104.99 266.7 285.0 \n",
+ "65128 28.29, -16.52 256.7 274.0 \n",
+ "\n",
+ " site off nadir at start, end (deg) \\\n",
+ "orbit \n",
+ "64907 49.5, 47.4 \n",
+ "64942 49.4, 49.8 \n",
+ "64967 49.1, 50.0 \n",
+ "64976 48.9, 50.1 \n",
+ "65000 49.5, 49.8 \n",
+ "65030 49.5, 49.8 \n",
+ "65049 49.5, 47.7 \n",
+ "65073 49.3, 47.4 \n",
+ "65102 49.4, 49.6 \n",
+ "65128 49.2, 49.9 \n",
+ "\n",
+ " TAT-C view to footprints, median (km) \\\n",
+ "orbit \n",
+ "64907 5.1 \n",
+ "64942 5.0 \n",
+ "64967 5.4 \n",
+ "64976 5.4 \n",
+ "65000 5.1 \n",
+ "65030 5.1 \n",
+ "65049 5.2 \n",
+ "65073 4.7 \n",
+ "65102 5.2 \n",
+ "65128 5.4 \n",
+ "\n",
+ " TAT-C view to footprints, 95th percentile (km) \\\n",
+ "orbit \n",
+ "64907 11.8 \n",
+ "64942 13.2 \n",
+ "64967 12.3 \n",
+ "64976 12.3 \n",
+ "65000 12.8 \n",
+ "65030 12.7 \n",
+ "65049 11.8 \n",
+ "65073 11.5 \n",
+ "65102 12.1 \n",
+ "65128 12.4 \n",
+ "\n",
+ " nadir to footprints, median (km) \n",
+ "orbit \n",
+ "64907 440.5 \n",
+ "64942 465.4 \n",
+ "64967 494.3 \n",
+ "64976 489.7 \n",
+ "65000 476.5 \n",
+ "65030 470.0 \n",
+ "65049 481.5 \n",
+ "65073 456.4 \n",
+ "65102 459.7 \n",
+ "65128 506.2 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "MAX_OFF_NADIR = (\n",
+ " 51 # deg, from the site's angle from nadir at the start and end of the observations\n",
+ ")\n",
+ "\n",
"\n",
+ "def frame_centers(record, k):\n",
+ " \"\"\"Mean latitudes and longitudes of the eight footprints of frames k.\"\"\"\n",
+ " lat = record[\"sounding_latitude\"][k].mean(axis=1).astype(float)\n",
+ " lon = np.degrees(\n",
+ " np.angle(np.exp(1j * np.radians(record[\"sounding_longitude\"][k])).mean(axis=1))\n",
+ " )\n",
+ " return lat, lon\n",
+ "\n",
+ "\n",
+ "targets, rows5 = {}, []\n",
+ "for record in [r for r in files if r[\"mode\"] == \"TG\"]:\n",
+ " k = np.arange(len(record[\"frame_time_tai93\"]))\n",
+ " track = frame_track(record, k)\n",
+ " lat, lon = frame_centers(record, k)\n",
+ " site = Point(latitude=float(np.median(lat)), longitude=float(np.median(lon)))\n",
+ " targets[record[\"name\"]] = site\n",
+ " attitude = TargetAttitude(targets=[site], max_off_nadir_angle=MAX_OFF_NADIR)\n",
+ " center = (\n",
+ " Satellite(name=\"OCO-2\", orbit=orbit, instruments=[view], attitude=attitude)\n",
+ " .get_instrument()\n",
+ " .compute_footprint_center(track)\n",
+ " )\n",
+ " nadir_point = wgs84.subpoint_of(track)\n",
+ " to_target = (\n",
+ " geodesic_distance(lon, lat, center.longitude.degrees, center.latitude.degrees)\n",
+ " / 1e3\n",
+ " )\n",
+ " to_nadir = (\n",
+ " geodesic_distance(\n",
+ " lon, lat, nadir_point.longitude.degrees, nadir_point.latitude.degrees\n",
+ " )\n",
+ " / 1e3\n",
+ " )\n",
+ " roll, pitch = np.array(\n",
+ " [\n",
+ " compute_view_angles(track[i], wgs84.latlon(site.latitude, site.longitude))\n",
+ " for i in (0, len(k) - 1)\n",
+ " ]\n",
+ " ).T\n",
+ " off_nadir = np.degrees(\n",
+ " np.arccos(np.cos(np.radians(roll)) * np.cos(np.radians(pitch)))\n",
+ " )\n",
+ " # the target pointing predicted with the TLE orbit, at 1 s steps around the observation\n",
+ " times = track.t.utc_datetime()\n",
+ " window = [\n",
+ " times[0] + timedelta(seconds=s)\n",
+ " for s in range(-180, int((times[-1] - times[0]).total_seconds()) + 181)\n",
+ " ]\n",
+ " predicted = np.sum(attitude.get_target_index(orbit.get_orbit_track(window)) >= 0)\n",
+ " rows5.append(\n",
+ " {\n",
+ " \"orbit\": record[\"orbit\"],\n",
+ " \"site (deg)\": f\"{site.latitude:.2f}, {site.longitude:.2f}\",\n",
+ " \"observed (s)\": float(\n",
+ " record[\"frame_time_tai93\"][-1] - record[\"frame_time_tai93\"][0]\n",
+ " ),\n",
+ " \"predicted (s)\": float(predicted),\n",
+ " \"site off nadir at start, end (deg)\": f\"{off_nadir[0]:.1f}, {off_nadir[1]:.1f}\",\n",
+ " \"TAT-C view to footprints, median (km)\": np.median(to_target),\n",
+ " \"TAT-C view to footprints, 95th percentile (km)\": np.quantile(\n",
+ " to_target, 0.95\n",
+ " ),\n",
+ " \"nadir to footprints, median (km)\": np.median(to_nadir),\n",
+ " }\n",
+ " )\n",
+ "pd.DataFrame(rows5).set_index(\"orbit\").round(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b3eb7b44",
+ "metadata": {},
+ "source": [
+ "In target mode, OCO-2 points at its site from when the site is about 49.5 deg from nadir (48.9 to 49.5 deg at the first sampled frames) until it is 47 to 50 deg from nadir again, for 247 to 267 s (between the first and last sampled frames). A `TargetAttitude` pointing at the site within 51 deg of nadir places the view within 4.7 to 5.4 km (median) and 12 to 13 km (95th percentile) of the footprints of each observation, the extent of OCO-2's sweeps of its slit across the site, rather than 440 to 510 km for a nadir view. Its predicted pointing, with the TLE orbit, lasts 274 to 286 s, 16 to 31 s longer than observed (whose durations, between sampled frames, are up to 7 s short), as OCO-2 starts pointing within the maximum off-nadir angle. TAT-C's view stays on the site, rather than sweeping across it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "add73009",
+ "metadata": {},
+ "source": [
+ "## Test 6: Mode Schedule\n",
+ "\n",
+ "A single satellite represents OCO-2's 17 days of operations with an `AttitudeSchedule` whose segments start with each file (and so follow the modes by orbit, Test 1): in glint mode, the `GlintAttitude` with the first day's offsets (Test 3); in nadir mode, the yaw angle profile of its day (Test 2), from the nearest complete day for the partial first day; in target mode, a `TargetAttitude` at its site (Test 5); and in the short special segments, nadir. Its instrument is the slit of Test 2. The center of its view is compared with the reported footprint centers of every ninth sampled frame (every 30 s) of all glint, nadir, and target files, with the reported orbit."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "a32d5f68",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T18:16:39.183269Z",
+ "iopub.status.busy": "2026-10-25T18:16:39.183269Z",
+ "iopub.status.idle": "2026-10-25T18:16:40.714198Z",
+ "shell.execute_reply": "2026-10-25T18:16:40.713896Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "358 segments\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
count
\n",
+ "
50%
\n",
+ "
95%
\n",
+ "
max
\n",
+ "
\n",
+ "
\n",
+ "
mode
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
glint
\n",
+ "
14299.0
\n",
+ "
1.48
\n",
+ "
2.14
\n",
+ "
9.69
\n",
+ "
\n",
+ "
\n",
+ "
nadir
\n",
+ "
8176.0
\n",
+ "
2.12
\n",
+ "
2.76
\n",
+ "
2.98
\n",
+ "
\n",
+ "
\n",
+ "
target
\n",
+ "
90.0
\n",
+ "
5.53
\n",
+ "
13.77
\n",
+ "
16.11
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " count 50% 95% max\n",
+ "mode \n",
+ "glint 14299.0 1.48 2.14 9.69\n",
+ "nadir 8176.0 2.12 2.76 2.98\n",
+ "target 90.0 5.53 13.77 16.11"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# yaw profiles of the complete days (from the second), to cover the whole orbit\n",
+ "nadir_profiles = {\n",
+ " day: fit_yaw_profile(nadir_frames[nadir_frames.day == day])\n",
+ " for day in nadir_days[1:]\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def nearest(profiles, day):\n",
+ " \"\"\"The profiles of the nearest complete day.\"\"\"\n",
+ " return profiles[\n",
+ " min(profiles, key=lambda d: abs(pd.Timestamp(d) - pd.Timestamp(day)))\n",
+ " ]\n",
+ "\n",
+ "\n",
+ "def mode_attitude(record, day):\n",
+ " \"\"\"The spacecraft's attitude in the mode of an orbit file.\"\"\"\n",
+ " if record[\"mode\"] == \"GL\":\n",
+ " # pointing near the glint, with the first day's offsets (Test 3)\n",
+ " return glint_attitude\n",
+ " if record[\"mode\"] == \"ND\":\n",
+ " return Attitude(yaw_angle_profile=nearest(nadir_profiles, day))\n",
+ " if record[\"mode\"] == \"TG\":\n",
+ " return TargetAttitude(\n",
+ " targets=[targets[record[\"name\"]]], max_off_nadir_angle=MAX_OFF_NADIR\n",
+ " )\n",
+ " return Attitude()\n",
+ "\n",
+ "\n",
+ "# each segment starts a second before its file's first frame (whose time, as a datetime, is rounded to a microsecond)\n",
+ "starts = [\n",
+ " frame_track(record, np.array([0])).t.utc_datetime()[0] - timedelta(seconds=1)\n",
+ " for record in files\n",
+ "]\n",
+ "order = np.argsort(starts)\n",
+ "schedule = AttitudeSchedule.from_attitudes(\n",
+ " [starts[i] for i in order],\n",
+ " [mode_attitude(files[i], starts[i].strftime(\"%Y-%m-%d\")) for i in order],\n",
+ ")\n",
+ "oco2 = Satellite(name=\"OCO-2\", orbit=orbit, instruments=[slit], attitude=schedule)\n",
+ "instrument = oco2.get_instrument()\n",
+ "rows6 = []\n",
+ "for record in files:\n",
+ " if record[\"mode\"] not in (\"GL\", \"ND\", \"TG\"):\n",
+ " continue\n",
+ " k = np.arange(0, len(record[\"frame_time_tai93\"]), 9)\n",
+ " lat, lon = frame_centers(record, k)\n",
+ " center = instrument.compute_footprint_center(frame_track(record, k))\n",
+ " rows6.append(\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"mode\": record[\"mode\"],\n",
+ " \"distance (km)\": geodesic_distance(\n",
+ " lon, lat, center.longitude.degrees, center.latitude.degrees\n",
+ " )\n",
+ " / 1e3,\n",
+ " }\n",
+ " )\n",
+ " )\n",
+ "errors6 = pd.concat(rows6)\n",
+ "print(f\"{len(schedule.segments)} segments\")\n",
+ "errors6.groupby(\"mode\")[\"distance (km)\"].describe(percentiles=[0.5, 0.95])[\n",
+ " [\"count\", \"50%\", \"95%\", \"max\"]\n",
+ "].rename(index={\"GL\": \"glint\", \"ND\": \"nadir\", \"TG\": \"target\"}).round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2cb0fe7a",
+ "metadata": {},
+ "source": [
+ "One satellite with a schedule of 358 attitudes reproduces OCO-2's 17 days of operations. The centers of its views are within these distances of the reported footprint centers:\n",
+ "\n",
+ "| Mode | Median | 95th percentile |\n",
+ "| --- | --- | --- |\n",
+ "| Glint | 1.5 km | 2.1 km |\n",
+ "| Nadir | 2.1 km | 2.8 km |\n",
+ "| Target | 5.5 km | 13.8 km |\n",
+ "\n",
+ "These match the tests of each mode, and the glint mode is modeled with a single attitude for the 17 days.\n",
+ "\n",
+ "TAT-C thus models OCO-2's schedule of modes, each with its attitude, in a single analysis (for example, of its observations of points with `collect_observations`). The schedule is built here from the reported modes. A prediction would need the planned schedule of modes and targets, while the glint pointing follows from the Sun's position."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25fa5071",
+ "metadata": {},
+ "source": [
"## Summary\n",
"\n",
"| Test | Result |\n",
"| --- | --- |\n",
- "| 1. Operating modes | glint and nadir modes alternate by orbit (odd orbits in glint mode); not scheduled by TAT-C |\n",
- "| 2. Nadir mode (`PointedInstrument`) | footprint centers within 2.1 km (median) with the reported orbit and 6 km with the TLE; the slit rotates with the spacecraft's yaw, so TAT-C overestimates the swath by a factor of 3.7 between the equator and 30 deg north |\n",
- "| 3. Glint mode (`roll_angle_profile`, `pitch_angle_profile`) | same-day profiles within 1.3 km (median); the first day's profiles drift by 4 km per day (68 km after 16 days) |\n",
+ "| 1. Operating modes | glint and nadir modes alternate by orbit (odd orbits in glint mode), with ten target observations |\n",
+ "| 2. Nadir mode (`PointedInstrument`, `Attitude.yaw_angle_profile`) | footprint centers within 2.1 km (median) with the reported orbit and 2.3 km with the TLE; with a yaw angle profile, the slit's ends within 2.1 km (4.1 km without) and the swath its true width (3.7 times narrower between the equator and 30 deg north) |\n",
+ "| 3. Glint mode (`compute_specular_point`, `GlintAttitude`) | specular point within 0.05 km (median) of the reported glint spot; pointing 3.8 to 9.6 deg short of it toward nadir, as a function of its angle from nadir; a `GlintAttitude` with the first day's offsets within 1.5 km (median) and 2.3 km (95th percentile) on every day, where roll and pitch angle profiles drift by 4 km per day |\n",
"| 4. Repeat cycle (`get_repeat_cycle`) | 16 days (233 orbits), as designed; ground track repeats within 9 km |\n",
+ "| 5. Target mode (`TargetAttitude`) | views within 5 km (median) of the footprints at ten sites (440 to 510 km for a nadir view); pointing from about 50 deg from nadir |\n",
+ "| 6. Mode schedule (`AttitudeSchedule`) | one satellite reproduces all modes over 17 days: 1.5 km (glint), 2.1 km (nadir), and 5.5 km (target), median |\n",
"\n",
- "**Pointing profiles.** Roll and pitch angle profiles in argument of latitude represent pointing that varies around the orbit, such as OCO-2's glint mode, for analyses of about a day; over longer periods, pointing that follows the Sun moves with the season.\n",
- "\n",
- "**Limitations and possible additions.** Three features of OCO-2's operations are not modeled by TAT-C:\n",
+ "**Pointing.** The spacecraft's attitude represents OCO-2's pointing in each mode, with a schedule of the modes over time:\n",
+ "* **Nadir mode:** a yaw angle profile in argument of latitude for the slit's yaw, which is stable over weeks.\n",
+ "* **Glint mode:** a `GlintAttitude` pointing near the glint, computed from the Sun's position, which follows the season, unlike roll and pitch angle profiles.\n",
+ "* **Target mode:** pointing at ground sites.\n",
"\n",
- "* a rotation of the view about its boresight (yaw), which varies around the orbit and narrows OCO-2's nadir swath by a factor of up to about 7 north of the equator;\n",
- "* pointing computed from the Sun's position (toward or near the glint spot), which would follow the seasonal motion of the glint geometry;\n",
- "* a schedule of modes (such as alternating glint and nadir orbits)."
+ "**Limitations.** TAT-C models target mode as a stare at the site rather than OCO-2's sweeps of its slit across it, and turns instantly between attitudes."
]
}
],
diff --git a/docs/validation/ValidatePushbroomMSI.ipynb b/docs/validation/ValidatePushbroomMSI.ipynb
index 3464ebe4..fae41ba5 100644
--- a/docs/validation/ValidatePushbroomMSI.ipynb
+++ b/docs/validation/ValidatePushbroomMSI.ipynb
@@ -41,10 +41,10 @@
"id": "32bd3918",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:46.810548Z",
- "iopub.status.busy": "2026-10-08T17:59:46.810548Z",
- "iopub.status.idle": "2026-10-08T17:59:47.776357Z",
- "shell.execute_reply": "2026-10-08T17:59:47.775850Z"
+ "iopub.execute_input": "2026-10-25T14:01:55.757294Z",
+ "iopub.status.busy": "2026-10-25T14:01:55.757294Z",
+ "iopub.status.idle": "2026-10-25T14:01:56.606358Z",
+ "shell.execute_reply": "2026-10-25T14:01:56.606358Z"
}
},
"outputs": [
@@ -169,13 +169,21 @@
"\n",
"ITEMS_FILE = DATA_DIR / f\"s2_items_{DAY:%Y%m%d}.json\"\n",
"if not ITEMS_FILE.exists():\n",
- " body = {\"collections\": [\"sentinel-2-l2a\"], \"datetime\": f\"{DAY:%Y-%m-%d}T00:00:00Z/{DAY:%Y-%m-%d}T23:59:59Z\", \"limit\": 1000}\n",
+ " body = {\n",
+ " \"collections\": [\"sentinel-2-l2a\"],\n",
+ " \"datetime\": f\"{DAY:%Y-%m-%d}T00:00:00Z/{DAY:%Y-%m-%d}T23:59:59Z\",\n",
+ " \"limit\": 1000,\n",
+ " }\n",
" items, url = [], STAC\n",
" while url:\n",
" page = requests.post(url, json=body, timeout=300).json()\n",
" items += page[\"features\"]\n",
" following = [link for link in page[\"links\"] if link[\"rel\"] == \"next\"]\n",
- " url, body = (following[0][\"href\"], following[0].get(\"body\", body)) if following else (None, None)\n",
+ " url, body = (\n",
+ " (following[0][\"href\"], following[0].get(\"body\", body))\n",
+ " if following\n",
+ " else (None, None)\n",
+ " )\n",
" ITEMS_FILE.write_text(\n",
" json.dumps(\n",
" [\n",
@@ -195,7 +203,11 @@
" )\n",
"tiles = pd.DataFrame(json.loads(ITEMS_FILE.read_text()))\n",
"tiles[\"datetime\"] = pd.to_datetime(tiles[\"datetime\"], utc=True)\n",
- "tiles.groupby(\"platform\").agg(tiles=(\"id\", \"size\"), datatakes=(\"datatake\", \"nunique\"), datastrips=(\"datastrip\", \"nunique\"))"
+ "tiles.groupby(\"platform\").agg(\n",
+ " tiles=(\"id\", \"size\"),\n",
+ " datatakes=(\"datatake\", \"nunique\"),\n",
+ " datastrips=(\"datastrip\", \"nunique\"),\n",
+ ")"
]
},
{
@@ -212,10 +224,10 @@
"id": "2489c788",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:47.779874Z",
- "iopub.status.busy": "2026-10-08T17:59:47.779874Z",
- "iopub.status.idle": "2026-10-08T17:59:47.825472Z",
- "shell.execute_reply": "2026-10-08T17:59:47.824464Z"
+ "iopub.execute_input": "2026-10-25T14:01:56.609365Z",
+ "iopub.status.busy": "2026-10-25T14:01:56.609365Z",
+ "iopub.status.idle": "2026-10-25T14:01:56.638122Z",
+ "shell.execute_reply": "2026-10-25T14:01:56.638122Z"
}
},
"outputs": [
@@ -286,17 +298,41 @@
" token = get(TOKEN).json()[\"token\"]\n",
"\n",
" def read_times(row):\n",
- " head = get(f\"{row.datastrip_metadata}?{token}\", headers={\"Range\": \"bytes=0-16383\"}).text\n",
- " times = {tag: re.search(f\"<{tag}>(.*?)\", head).group(1) for tag in [\"DATASTRIP_SENSING_START\", \"DATASTRIP_SENSING_STOP\"]}\n",
- " return {\"datastrip\": row.datastrip, \"start\": times[\"DATASTRIP_SENSING_START\"], \"stop\": times[\"DATASTRIP_SENSING_STOP\"]}\n",
+ " head = get(\n",
+ " f\"{row.datastrip_metadata}?{token}\", headers={\"Range\": \"bytes=0-16383\"}\n",
+ " ).text\n",
+ " times = {\n",
+ " tag: re.search(f\"<{tag}>(.*?)\", head).group(1)\n",
+ " for tag in [\"DATASTRIP_SENSING_START\", \"DATASTRIP_SENSING_STOP\"]\n",
+ " }\n",
+ " return {\n",
+ " \"datastrip\": row.datastrip,\n",
+ " \"start\": times[\"DATASTRIP_SENSING_START\"],\n",
+ " \"stop\": times[\"DATASTRIP_SENSING_STOP\"],\n",
+ " }\n",
"\n",
" first = tiles.drop_duplicates(\"datastrip\")\n",
" with concurrent.futures.ThreadPoolExecutor(16) as pool:\n",
- " pd.DataFrame(pool.map(read_times, first.itertuples())).to_csv(DATASTRIPS_FILE, index=False)\n",
+ " pd.DataFrame(pool.map(read_times, first.itertuples())).to_csv(\n",
+ " DATASTRIPS_FILE, index=False\n",
+ " )\n",
"datastrips = pd.read_csv(DATASTRIPS_FILE, parse_dates=[\"start\", \"stop\"]).merge(\n",
- " tiles.groupby(\"datastrip\").agg(platform=(\"platform\", \"first\"), datatake=(\"datatake\", \"first\"), tiles=(\"id\", \"size\")).reset_index()\n",
+ " tiles.groupby(\"datastrip\")\n",
+ " .agg(\n",
+ " platform=(\"platform\", \"first\"),\n",
+ " datatake=(\"datatake\", \"first\"),\n",
+ " tiles=(\"id\", \"size\"),\n",
+ " )\n",
+ " .reset_index()\n",
+ ")\n",
+ "selected = (\n",
+ " tiles.groupby([\"platform\", \"datatake\"])\n",
+ " .size()\n",
+ " .reset_index(name=\"tiles\")\n",
+ " .sort_values(\"tiles\")\n",
+ " .groupby(\"platform\")\n",
+ " .tail(1)\n",
")\n",
- "selected = tiles.groupby([\"platform\", \"datatake\"]).size().reset_index(name=\"tiles\").sort_values(\"tiles\").groupby(\"platform\").tail(1)\n",
"selected"
]
},
@@ -314,10 +350,10 @@
"id": "a0ed02ee",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:47.828473Z",
- "iopub.status.busy": "2026-10-08T17:59:47.828473Z",
- "iopub.status.idle": "2026-10-08T17:59:48.195243Z",
- "shell.execute_reply": "2026-10-08T17:59:48.194235Z"
+ "iopub.execute_input": "2026-10-25T14:01:56.640126Z",
+ "iopub.status.busy": "2026-10-25T14:01:56.640126Z",
+ "iopub.status.idle": "2026-10-25T14:01:56.855916Z",
+ "shell.execute_reply": "2026-10-25T14:01:56.855916Z"
}
},
"outputs": [
@@ -345,14 +381,23 @@
" chosen = tiles[tiles.datatake.isin(selected.datatake)]\n",
"\n",
" def read_ephemeris(row):\n",
- " root = ET.fromstring(strip_namespaces(get(f\"{row.datastrip_metadata}?{token}\").text))\n",
+ " root = ET.fromstring(\n",
+ " strip_namespaces(get(f\"{row.datastrip_metadata}?{token}\").text)\n",
+ " )\n",
" return pd.DataFrame(\n",
" {\n",
" \"platform\": row.platform,\n",
" \"datastrip\": row.datastrip,\n",
- " \"time\": [pd.Timestamp(p.find(\"GPS_TIME\").text, tz=\"UTC\") - pd.Timedelta(seconds=GPS_UTC) for p in root.findall(\".//GPS_Point\")],\n",
+ " \"time\": [\n",
+ " pd.Timestamp(p.find(\"GPS_TIME\").text, tz=\"UTC\")\n",
+ " - pd.Timedelta(seconds=GPS_UTC)\n",
+ " for p in root.findall(\".//GPS_Point\")\n",
+ " ],\n",
" **{\n",
- " f\"{kind}_{axis}\": [float(p.find(f\"{kind.upper()}_VALUES\").text.split()[i]) / 1e3 for p in root.findall(\".//GPS_Point\")]\n",
+ " f\"{kind}_{axis}\": [\n",
+ " float(p.find(f\"{kind.upper()}_VALUES\").text.split()[i]) / 1e3\n",
+ " for p in root.findall(\".//GPS_Point\")\n",
+ " ]\n",
" for kind in [\"position\", \"velocity\"]\n",
" for i, axis in enumerate(\"xyz\")\n",
" },\n",
@@ -360,31 +405,59 @@
" )\n",
"\n",
" def read_grids(row):\n",
- " root = ET.fromstring(strip_namespaces(get(f\"{row.granule_metadata}?{token}\").text))\n",
+ " root = ET.fromstring(\n",
+ " strip_namespaces(get(f\"{row.granule_metadata}?{token}\").text)\n",
+ " )\n",
" epsg = int(root.find(\".//HORIZONTAL_CS_CODE\").text.split(\":\")[1])\n",
- " geoposition = next(g for g in root.findall(\".//Geoposition\") if g.get(\"resolution\") == \"10\")\n",
- " ulx, uly = float(geoposition.find(\"ULX\").text), float(geoposition.find(\"ULY\").text)\n",
+ " geoposition = next(\n",
+ " g for g in root.findall(\".//Geoposition\") if g.get(\"resolution\") == \"10\"\n",
+ " )\n",
+ " ulx, uly = float(geoposition.find(\"ULX\").text), float(\n",
+ " geoposition.find(\"ULY\").text\n",
+ " )\n",
" to_lonlat = Transformer.from_crs(epsg, 4326, always_xy=True)\n",
" records = []\n",
" for grid in root.findall(\".//Viewing_Incidence_Angles_Grids\"):\n",
" if grid.get(\"bandId\") != BAND:\n",
" continue\n",
- " zenith = np.array([[float(v) for v in r.text.split()] for r in grid.find(\"Zenith/Values_List\")])\n",
- " azimuth = np.array([[float(v) for v in r.text.split()] for r in grid.find(\"Azimuth/Values_List\")])\n",
+ " zenith = np.array(\n",
+ " [\n",
+ " [float(v) for v in r.text.split()]\n",
+ " for r in grid.find(\"Zenith/Values_List\")\n",
+ " ]\n",
+ " )\n",
+ " azimuth = np.array(\n",
+ " [\n",
+ " [float(v) for v in r.text.split()]\n",
+ " for r in grid.find(\"Azimuth/Values_List\")\n",
+ " ]\n",
+ " )\n",
" i, j = np.nonzero(np.isfinite(zenith))\n",
" lon, lat = to_lonlat.transform(ulx + j * 5000.0, uly - i * 5000.0)\n",
" records.append(\n",
" pd.DataFrame(\n",
- " {\"tile\": row.id, \"platform\": row.platform, \"datastrip\": row.datastrip, \"detector\": int(grid.get(\"detectorId\")),\n",
- " \"lon\": lon, \"lat\": lat, \"zenith\": zenith[i, j], \"azimuth\": azimuth[i, j]}\n",
+ " {\n",
+ " \"tile\": row.id,\n",
+ " \"platform\": row.platform,\n",
+ " \"datastrip\": row.datastrip,\n",
+ " \"detector\": int(grid.get(\"detectorId\")),\n",
+ " \"lon\": lon,\n",
+ " \"lat\": lat,\n",
+ " \"zenith\": zenith[i, j],\n",
+ " \"azimuth\": azimuth[i, j],\n",
+ " }\n",
" )\n",
" )\n",
" # tiles without valid data have no viewing grids\n",
" return pd.concat(records) if records else None\n",
"\n",
" with concurrent.futures.ThreadPoolExecutor(16) as pool:\n",
- " ephemeris = pd.concat(pool.map(read_ephemeris, chosen.drop_duplicates(\"datastrip\").itertuples()))\n",
- " sampled = pd.concat([g.sort_values(\"datetime\").iloc[::5] for _, g in chosen.groupby(\"datatake\")])\n",
+ " ephemeris = pd.concat(\n",
+ " pool.map(read_ephemeris, chosen.drop_duplicates(\"datastrip\").itertuples())\n",
+ " )\n",
+ " sampled = pd.concat(\n",
+ " [g.sort_values(\"datetime\").iloc[::5] for _, g in chosen.groupby(\"datatake\")]\n",
+ " )\n",
" grids = pd.concat(pool.map(read_grids, sampled.itertuples()))\n",
" ephemeris.to_csv(EPHEMERIS_FILE, index=False)\n",
" grids.to_csv(GRIDS_FILE, index=False)\n",
@@ -416,10 +489,10 @@
"id": "16320023",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:48.198750Z",
- "iopub.status.busy": "2026-10-08T17:59:48.198750Z",
- "iopub.status.idle": "2026-10-08T17:59:51.832883Z",
- "shell.execute_reply": "2026-10-08T17:59:51.831871Z"
+ "iopub.execute_input": "2026-10-25T14:01:56.858920Z",
+ "iopub.status.busy": "2026-10-25T14:01:56.857921Z",
+ "iopub.status.idle": "2026-10-25T14:01:58.844191Z",
+ "shell.execute_reply": "2026-10-25T14:01:58.843186Z"
}
},
"outputs": [],
@@ -436,19 +509,33 @@
"\n",
"from tatc.analysis import collect_observations, compute_ground_track\n",
"from tatc.constants import EARTH_MEAN_RADIUS, EARTH_ROTATION_RATE\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Instrument, Point, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Instrument,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"from tatc.utils import NadirReference, VelocityFrame, compute_along_track_field_of_view\n",
"\n",
"TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
"TO_GEODETIC = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
- "satellites = {name: Satellite(name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle)) for name, tle in TLES.items()}\n",
+ "satellites = {\n",
+ " name: Satellite(name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle))\n",
+ " for name, tle in TLES.items()\n",
+ "}\n",
"PLATFORMS = list(TLES)\n",
"\n",
"\n",
"def seconds(times):\n",
" \"\"\"Seconds since the start of the analysis day.\"\"\"\n",
" delta = pd.to_datetime(times, utc=True) - pd.Timestamp(DAY)\n",
- " return delta.dt.total_seconds().to_numpy() if isinstance(delta, pd.Series) else delta.total_seconds()\n",
+ " return (\n",
+ " delta.dt.total_seconds().to_numpy()\n",
+ " if isinstance(delta, pd.Series)\n",
+ " else delta.total_seconds()\n",
+ " )\n",
"\n",
"\n",
"def to_utc(values):\n",
@@ -457,13 +544,17 @@
"\n",
"def ecef(lon, lat, height=0.0):\n",
" \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
- " return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+ " return np.stack(\n",
+ " TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1\n",
+ " )\n",
"\n",
"\n",
"def up(lon, lat):\n",
" \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
" lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -472,14 +563,23 @@
"\n",
"def inertial_velocity(position, velocity):\n",
" \"\"\"Inertial velocity expressed in Earth-fixed coordinates.\"\"\"\n",
- " return velocity + EARTH_ROTATION_RATE * np.stack([-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1)\n",
+ " return velocity + EARTH_ROTATION_RATE * np.stack(\n",
+ " [-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def summarize(x):\n",
" \"\"\"Summary statistics of a residual sample.\"\"\"\n",
" x = np.asarray(x, dtype=float)\n",
" x = x[np.isfinite(x)]\n",
- " return pd.Series({\"n\": x.size, \"median\": np.median(x), \"p95 |x|\": np.percentile(np.abs(x), 95), \"max |x|\": np.abs(x).max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": x.size,\n",
+ " \"median\": np.median(x),\n",
+ " \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+ " \"max |x|\": np.abs(x).max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"ephemeris[\"t\"] = seconds(ephemeris[\"time\"])\n",
@@ -512,10 +612,10 @@
"id": "c8128abc",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:51.837880Z",
- "iopub.status.busy": "2026-10-08T17:59:51.836879Z",
- "iopub.status.idle": "2026-10-08T17:59:51.918777Z",
- "shell.execute_reply": "2026-10-08T17:59:51.917261Z"
+ "iopub.execute_input": "2026-10-25T14:01:58.846227Z",
+ "iopub.status.busy": "2026-10-25T14:01:58.846227Z",
+ "iopub.status.idle": "2026-10-25T14:01:58.888167Z",
+ "shell.execute_reply": "2026-10-25T14:01:58.888167Z"
}
},
"outputs": [
@@ -642,12 +742,26 @@
" e = ephemeris[ephemeris.platform == platform].iloc[::10]\n",
" position = e[[f\"position_{a}\" for a in \"xyz\"]].to_numpy()\n",
" velocity = e[[f\"velocity_{a}\" for a in \"xyz\"]].to_numpy()\n",
- " predicted = np.array(satellites[platform].orbit.to_gp_orbit().get_orbit_track(to_utc(e.t)).frame_xyz_and_velocity(itrs)[0].m).T\n",
+ " predicted = np.array(\n",
+ " satellites[platform]\n",
+ " .orbit.to_gp_orbit()\n",
+ " .get_orbit_track(to_utc(e.t))\n",
+ " .frame_xyz_and_velocity(itrs)[0]\n",
+ " .m\n",
+ " ).T\n",
" offset = predicted - position\n",
" radial = normalize(position)\n",
- " along = normalize(velocity - np.sum(velocity * radial, axis=-1, keepdims=True) * radial)\n",
- " for name, component in [(\"along-track\", along), (\"cross-track, right\", np.cross(along, radial)), (\"radial\", radial)]:\n",
- " rows1[(platform, f\"{name} (km)\")] = summarize(np.sum(offset * component, axis=-1) / 1e3)\n",
+ " along = normalize(\n",
+ " velocity - np.sum(velocity * radial, axis=-1, keepdims=True) * radial\n",
+ " )\n",
+ " for name, component in [\n",
+ " (\"along-track\", along),\n",
+ " (\"cross-track, right\", np.cross(along, radial)),\n",
+ " (\"radial\", radial),\n",
+ " ]:\n",
+ " rows1[(platform, f\"{name} (km)\")] = summarize(\n",
+ " np.sum(offset * component, axis=-1) / 1e3\n",
+ " )\n",
"pd.DataFrame(rows1).T.round(3)"
]
},
@@ -671,10 +785,10 @@
"id": "b19e8078",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:51.922776Z",
- "iopub.status.busy": "2026-10-08T17:59:51.922776Z",
- "iopub.status.idle": "2026-10-08T17:59:52.691649Z",
- "shell.execute_reply": "2026-10-08T17:59:52.690132Z"
+ "iopub.execute_input": "2026-10-25T14:01:58.890177Z",
+ "iopub.status.busy": "2026-10-25T14:01:58.890177Z",
+ "iopub.status.idle": "2026-10-25T14:01:59.386347Z",
+ "shell.execute_reply": "2026-10-25T14:01:59.386347Z"
}
},
"outputs": [
@@ -811,31 +925,55 @@
" t_e = e.t.to_numpy()\n",
" position_e = e[[f\"position_{a}\" for a in \"xyz\"]].to_numpy()\n",
" velocity_e = e[[f\"velocity_{a}\" for a in \"xyz\"]].to_numpy()\n",
- " interpolate = lambda t, values: np.stack([np.interp(t, t_e, values[:, k]) for k in range(3)], axis=-1)\n",
+ " interpolate = lambda t, values: np.stack(\n",
+ " [np.interp(t, t_e, values[:, k]) for k in range(3)], axis=-1\n",
+ " )\n",
" lon, lat = platform_grids.lon.to_numpy(), platform_grids.lat.to_numpy()\n",
" point = ecef(lon, lat)\n",
- " u, east = up(lon, lat), normalize(np.stack([-np.sin(np.radians(lon)), np.cos(np.radians(lon)), 0 * lon], axis=-1))\n",
+ " u, east = up(lon, lat), normalize(\n",
+ " np.stack([-np.sin(np.radians(lon)), np.cos(np.radians(lon)), 0 * lon], axis=-1)\n",
+ " )\n",
" north = np.cross(u, east)\n",
- " zenith, azimuth = np.radians(platform_grids.zenith.to_numpy())[:, None], np.radians(platform_grids.azimuth.to_numpy())[:, None]\n",
- " toward = np.cos(zenith) * u + np.sin(zenith) * (np.sin(azimuth) * east + np.cos(azimuth) * north)\n",
+ " zenith, azimuth = (\n",
+ " np.radians(platform_grids.zenith.to_numpy())[:, None],\n",
+ " np.radians(platform_grids.azimuth.to_numpy())[:, None],\n",
+ " )\n",
+ " toward = np.cos(zenith) * u + np.sin(zenith) * (\n",
+ " np.sin(azimuth) * east + np.cos(azimuth) * north\n",
+ " )\n",
" b = np.sum(point * toward, axis=1)\n",
" t = np.full(len(point), np.median(t_e))\n",
" for _ in range(8):\n",
" p, v = interpolate(t, position_e), interpolate(t, velocity_e)\n",
" radius = np.linalg.norm(p, axis=1)\n",
- " location = point + (-b + np.sqrt(b**2 - np.sum(point**2, axis=1) + radius**2))[:, None] * toward\n",
+ " location = (\n",
+ " point\n",
+ " + (-b + np.sqrt(b**2 - np.sum(point**2, axis=1) + radius**2))[:, None]\n",
+ " * toward\n",
+ " )\n",
" t = t - np.sum((p - location) * v, axis=1) / np.sum(v * v, axis=1)\n",
" p, v = interpolate(t, position_e), interpolate(t, velocity_e)\n",
" los = normalize(point - p)\n",
" sub_lon, sub_lat, _ = TO_GEODETIC.transform(*p.T)\n",
" nadir = -up(sub_lon, sub_lat)\n",
- " result = {\"t\": t, \"miss\": np.linalg.norm(np.cross(p - location, normalize(v)), axis=1) / 1e3}\n",
- " for frame, velocity, nadir in [(\"earth_fixed\", v, nadir), (\"inertial\", inertial_velocity(p, v), nadir), (\"geocentric\", v, -normalize(p))]:\n",
+ " result = {\n",
+ " \"t\": t,\n",
+ " \"miss\": np.linalg.norm(np.cross(p - location, normalize(v)), axis=1) / 1e3,\n",
+ " }\n",
+ " for frame, velocity, nadir in [\n",
+ " (\"earth_fixed\", v, nadir),\n",
+ " (\"inertial\", inertial_velocity(p, v), nadir),\n",
+ " (\"geocentric\", v, -normalize(p)),\n",
+ " ]:\n",
" cross_axis = normalize(np.cross(velocity, nadir))\n",
" along_axis = np.cross(nadir, cross_axis)\n",
" down = np.sum(los * nadir, axis=1)\n",
- " result[f\"along_{frame}\"] = np.degrees(np.arctan(np.sum(los * along_axis, axis=1) / down))\n",
- " result[f\"cross_{frame}\"] = np.degrees(np.arctan(np.sum(los * cross_axis, axis=1) / down))\n",
+ " result[f\"along_{frame}\"] = np.degrees(\n",
+ " np.arctan(np.sum(los * along_axis, axis=1) / down)\n",
+ " )\n",
+ " result[f\"cross_{frame}\"] = np.degrees(\n",
+ " np.arctan(np.sum(los * cross_axis, axis=1) / down)\n",
+ " )\n",
" return pd.DataFrame(result, index=platform_grids.index)\n",
"\n",
"\n",
@@ -847,13 +985,19 @@
" \"\"\"Per-detector along-track angle offsets plus common linear (yaw) and quadratic terms in the cross-track angle.\"\"\"\n",
" x = grids[f\"cross_{frame}\"].to_numpy()\n",
" design = np.column_stack([pd.get_dummies(grids.detector).to_numpy(float), x, x**2])\n",
- " coefficients, *_ = np.linalg.lstsq(design, grids[f\"along_{frame}\"].to_numpy(), rcond=None)\n",
+ " coefficients, *_ = np.linalg.lstsq(\n",
+ " design, grids[f\"along_{frame}\"].to_numpy(), rcond=None\n",
+ " )\n",
" residual = grids[f\"along_{frame}\"].to_numpy() - design @ coefficients\n",
" return coefficients, residual\n",
"\n",
"\n",
"rows2 = {}\n",
- "for frame, label in [(\"earth_fixed\", \"earth_fixed\"), (\"inertial\", \"inertial\"), (\"geocentric\", \"earth_fixed, geocentric nadir\")]:\n",
+ "for frame, label in [\n",
+ " (\"earth_fixed\", \"earth_fixed\"),\n",
+ " (\"inertial\", \"inertial\"),\n",
+ " (\"geocentric\", \"earth_fixed, geocentric nadir\"),\n",
+ "]:\n",
" coefficients, residual = fit(frame)\n",
" grids[f\"residual_{frame}\"] = residual\n",
" rows2[label] = {\n",
@@ -862,7 +1006,10 @@
" \"fit residual, p95 (deg)\": np.percentile(np.abs(residual), 95),\n",
" }\n",
"detectors = grids.groupby(\"detector\").agg(\n",
- " along=(\"along_earth_fixed\", \"median\"), cross_min=(\"cross_earth_fixed\", \"min\"), cross_max=(\"cross_earth_fixed\", \"max\"), points=(\"t\", \"size\")\n",
+ " along=(\"along_earth_fixed\", \"median\"),\n",
+ " cross_min=(\"cross_earth_fixed\", \"min\"),\n",
+ " cross_max=(\"cross_earth_fixed\", \"max\"),\n",
+ " points=(\"t\", \"size\"),\n",
")\n",
"pd.concat(\n",
" [\n",
@@ -870,11 +1017,17 @@
" pd.DataFrame(\n",
" {\n",
" \"value\": {\n",
- " \"imaging time recovery, miss distance p95 (km)\": np.percentile(grids.miss, 95),\n",
+ " \"imaging time recovery, miss distance p95 (km)\": np.percentile(\n",
+ " grids.miss, 95\n",
+ " ),\n",
" \"cross-track view angle, minimum (deg)\": grids.cross_earth_fixed.min(),\n",
" \"cross-track view angle, maximum (deg)\": grids.cross_earth_fixed.max(),\n",
- " \"odd detectors, along-track angle (deg)\": grids[grids.odd].along_earth_fixed.median(),\n",
- " \"even detectors, along-track angle (deg)\": grids[~grids.odd].along_earth_fixed.median(),\n",
+ " \"odd detectors, along-track angle (deg)\": grids[\n",
+ " grids.odd\n",
+ " ].along_earth_fixed.median(),\n",
+ " \"even detectors, along-track angle (deg)\": grids[\n",
+ " ~grids.odd\n",
+ " ].along_earth_fixed.median(),\n",
" }\n",
" }\n",
" ).round(3),\n",
@@ -888,10 +1041,10 @@
"id": "63bd637c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:52.694653Z",
- "iopub.status.busy": "2026-10-08T17:59:52.694653Z",
- "iopub.status.idle": "2026-10-08T17:59:53.592728Z",
- "shell.execute_reply": "2026-10-08T17:59:53.592219Z"
+ "iopub.execute_input": "2026-10-25T14:01:59.389351Z",
+ "iopub.status.busy": "2026-10-25T14:01:59.388352Z",
+ "iopub.status.idle": "2026-10-25T14:02:00.024640Z",
+ "shell.execute_reply": "2026-10-25T14:02:00.023633Z"
}
},
"outputs": [
@@ -1025,19 +1178,38 @@
}
],
"source": [
- "fig, axes = plt.subplots(1, 3, figsize=(14, 3.8), gridspec_kw={\"width_ratios\": [1, 1, 0.8]})\n",
+ "fig, axes = plt.subplots(\n",
+ " 1, 3, figsize=(14, 3.8), gridspec_kw={\"width_ratios\": [1, 1, 0.8]}\n",
+ ")\n",
"for ax, frame in zip(axes[:2], [\"earth_fixed\", \"inertial\"]):\n",
- " for odd, color, label in [(True, BLUE, \"odd detectors\"), (False, ORANGE, \"even detectors\")]:\n",
- " g = grids[grids.odd == odd].sample(min(4000, int((grids.odd == odd).sum())), random_state=0)\n",
- " ax.scatter(g[f\"cross_{frame}\"], g[f\"along_{frame}\"], s=2, color=color, label=label)\n",
- " ax.set(xlabel=\"Cross-track view angle (deg, positive left)\", title=f\"{frame.replace('_', '-')} velocity frame\")\n",
+ " for odd, color, label in [\n",
+ " (True, BLUE, \"odd detectors\"),\n",
+ " (False, ORANGE, \"even detectors\"),\n",
+ " ]:\n",
+ " g = grids[grids.odd == odd].sample(\n",
+ " min(4000, int((grids.odd == odd).sum())), random_state=0\n",
+ " )\n",
+ " ax.scatter(\n",
+ " g[f\"cross_{frame}\"], g[f\"along_{frame}\"], s=2, color=color, label=label\n",
+ " )\n",
+ " ax.set(\n",
+ " xlabel=\"Cross-track view angle (deg, positive left)\",\n",
+ " title=f\"{frame.replace('_', '-')} velocity frame\",\n",
+ " )\n",
"axes[1].sharey(axes[0])\n",
"axes[0].set_ylabel(\"Along-track view angle (deg, positive forward)\")\n",
"axes[0].legend(frameon=False, markerscale=4)\n",
"g = grids.sample(6000, random_state=0)\n",
- "for frame, color, label in [(\"earth_fixed\", BLUE, \"geodetic nadir (TAT-C)\"), (\"geocentric\", ORANGE, \"geocentric nadir\")]:\n",
+ "for frame, color, label in [\n",
+ " (\"earth_fixed\", BLUE, \"geodetic nadir (TAT-C)\"),\n",
+ " (\"geocentric\", ORANGE, \"geocentric nadir\"),\n",
+ "]:\n",
" axes[2].scatter(g.lat, g[f\"residual_{frame}\"], s=2, color=color, label=label)\n",
- "axes[2].set(xlabel=\"Latitude (deg)\", ylabel=\"Along-track fit residual (deg)\", title=\"Nadir reference (Earth-fixed frame)\")\n",
+ "axes[2].set(\n",
+ " xlabel=\"Latitude (deg)\",\n",
+ " ylabel=\"Along-track fit residual (deg)\",\n",
+ " title=\"Nadir reference (Earth-fixed frame)\",\n",
+ ")\n",
"axes[2].legend(frameon=False, markerscale=4, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()\n",
@@ -1066,10 +1238,10 @@
"id": "5e1316a2",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T17:59:53.596738Z",
- "iopub.status.busy": "2026-10-08T17:59:53.596738Z",
- "iopub.status.idle": "2026-10-08T18:00:05.688722Z",
- "shell.execute_reply": "2026-10-08T18:00:05.687714Z"
+ "iopub.execute_input": "2026-10-25T14:02:00.027148Z",
+ "iopub.status.busy": "2026-10-25T14:02:00.025642Z",
+ "iopub.status.idle": "2026-10-25T14:02:08.334093Z",
+ "shell.execute_reply": "2026-10-25T14:02:08.334093Z"
}
},
"outputs": [
@@ -1190,11 +1362,20 @@
"TIME_STEP = 10 # s\n",
"cross_fov = grids.cross_earth_fixed.max() - grids.cross_earth_fixed.min()\n",
"roll = (grids.cross_earth_fixed.max() + grids.cross_earth_fixed.min()) / 2\n",
- "altitude = float(np.median(np.linalg.norm(ephemeris[[f\"position_{a}\" for a in \"xyz\"]].to_numpy(), axis=1))) - 6371.0088e3\n",
+ "altitude = (\n",
+ " float(\n",
+ " np.median(\n",
+ " np.linalg.norm(\n",
+ " ephemeris[[f\"position_{a}\" for a in \"xyz\"]].to_numpy(), axis=1\n",
+ " )\n",
+ " )\n",
+ " )\n",
+ " - 6371.0088e3\n",
+ ")\n",
"inclination = float(TLES[\"Sentinel-2A\"][1][8:16])\n",
"\n",
"\n",
- "def msi_view(time_step, velocity_frame=VelocityFrame.EARTH_FIXED):\n",
+ "def msi_view(time_step):\n",
" return PointedInstrument(\n",
" name=\"MSI\",\n",
" field_of_regard=cross_fov + 10,\n",
@@ -1207,34 +1388,65 @@
" ),\n",
" roll_angle=roll,\n",
" is_rectangular=True,\n",
- " velocity_frame=velocity_frame,\n",
" )\n",
"\n",
"\n",
"def unwrap(geometry):\n",
" \"\"\"Shift longitudes of geometries that cross the antimeridian to be continuous.\"\"\"\n",
- " lon = np.concatenate([np.array(g.exterior.coords)[:, 0] for g in getattr(geometry, \"geoms\", [geometry])])\n",
+ " lon = np.concatenate(\n",
+ " [\n",
+ " np.array(g.exterior.coords)[:, 0]\n",
+ " for g in getattr(geometry, \"geoms\", [geometry])\n",
+ " ]\n",
+ " )\n",
" if lon.max() - lon.min() > 180:\n",
- " return shapely.ops.transform(lambda x, y, z=None: (np.where(np.asarray(x) < 0, np.asarray(x) + 360, x), y), geometry)\n",
+ " return shapely.ops.transform(\n",
+ " lambda x, y, z=None: (\n",
+ " np.where(np.asarray(x) < 0, np.asarray(x) + 360, x),\n",
+ " y,\n",
+ " ),\n",
+ " geometry,\n",
+ " )\n",
" return geometry\n",
"\n",
"\n",
"def footprint_row(row):\n",
- " observed = unwrap(shapely.unary_union([shape(g) for g in tiles.geometry[tiles.datastrip == row.datastrip]]))\n",
+ " observed = unwrap(\n",
+ " shapely.unary_union(\n",
+ " [shape(g) for g in tiles.geometry[tiles.datastrip == row.datastrip]]\n",
+ " )\n",
+ " )\n",
" center = observed.centroid\n",
- " project = Transformer.from_crs(\"EPSG:4326\", f\"+proj=aeqd +lat_0={center.y} +lon_0={center.x} +units=m\", always_xy=True).transform\n",
+ " project = Transformer.from_crs(\n",
+ " \"EPSG:4326\",\n",
+ " f\"+proj=aeqd +lat_0={center.y} +lon_0={center.x} +units=m\",\n",
+ " always_xy=True,\n",
+ " ).transform\n",
" a = shapely.ops.transform(project, observed).buffer(0)\n",
" start, stop = seconds(row.start), seconds(row.stop)\n",
" n = max(1, int(round((stop - start) / TIME_STEP)))\n",
" step = (stop - start) / n\n",
" times = to_utc(start + step * (np.arange(n) + 0.5))\n",
- " result = {\"datastrip\": row.datastrip, \"platform\": row.platform, \"tiles\": row.tiles, \"duration (s)\": stop - start, \"latitude\": center.y}\n",
+ " result = {\n",
+ " \"datastrip\": row.datastrip,\n",
+ " \"platform\": row.platform,\n",
+ " \"tiles\": row.tiles,\n",
+ " \"duration (s)\": stop - start,\n",
+ " \"latitude\": center.y,\n",
+ " }\n",
" for frame in [VelocityFrame.EARTH_FIXED, VelocityFrame.INERTIAL]:\n",
- " satellite = satellites[row.platform].model_copy(update={\"instruments\": [msi_view(step, frame)]})\n",
+ " satellite = satellites[row.platform].model_copy(\n",
+ " update={\n",
+ " \"instruments\": [msi_view(step)],\n",
+ " \"attitude\": Attitude(velocity_frame=frame),\n",
+ " }\n",
+ " )\n",
" predicted = compute_ground_track(satellite, times).geometry.iloc[0]\n",
" b = shapely.ops.transform(project, unwrap(predicted)).buffer(0)\n",
" result[f\"IoU, {frame.value}\"] = a.intersection(b).area / a.union(b).area\n",
- " result[f\"observed only (%), {frame.value}\"] = 100 * a.difference(b).area / a.area\n",
+ " result[f\"observed only (%), {frame.value}\"] = (\n",
+ " 100 * a.difference(b).area / a.area\n",
+ " )\n",
" result[f\"TAT-C only (%), {frame.value}\"] = 100 * b.difference(a).area / b.area\n",
" return result\n",
"\n",
@@ -1242,7 +1454,9 @@
"footprints = pd.DataFrame([footprint_row(row) for row in datastrips.itertuples()])\n",
"pd.concat(\n",
" [\n",
- " footprints.drop(columns=[\"datastrip\", \"platform\"]).describe(percentiles=[0.05, 0.5]).T[[\"count\", \"5%\", \"50%\", \"mean\"]],\n",
+ " footprints.drop(columns=[\"datastrip\", \"platform\"])\n",
+ " .describe(percentiles=[0.05, 0.5])\n",
+ " .T[[\"count\", \"5%\", \"50%\", \"mean\"]],\n",
" ]\n",
").round(3)"
]
@@ -1253,10 +1467,10 @@
"id": "71fad055",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:00:05.693240Z",
- "iopub.status.busy": "2026-10-08T18:00:05.692241Z",
- "iopub.status.idle": "2026-10-08T18:00:18.982781Z",
- "shell.execute_reply": "2026-10-08T18:00:18.981773Z"
+ "iopub.execute_input": "2026-10-25T14:02:08.337097Z",
+ "iopub.status.busy": "2026-10-25T14:02:08.337097Z",
+ "iopub.status.idle": "2026-10-25T14:02:18.876942Z",
+ "shell.execute_reply": "2026-10-25T14:02:18.876942Z"
}
},
"outputs": [
@@ -1278,15 +1492,32 @@
"ax.coastlines(color=GRAY, linewidth=0.5)\n",
"for platform in PLATFORMS:\n",
" geometries = [shape(g) for g in tiles.geometry[tiles.platform == platform]]\n",
- " ax.add_geometries(geometries, crs=ccrs.PlateCarree(), facecolor=PLATFORM_COLORS[platform], edgecolor=\"none\", alpha=0.6)\n",
+ " ax.add_geometries(\n",
+ " geometries,\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=PLATFORM_COLORS[platform],\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.6,\n",
+ " )\n",
" ax.plot([], [], color=PLATFORM_COLORS[platform], lw=6, alpha=0.6, label=platform)\n",
"ax.legend(frameon=False, fontsize=8, loc=\"lower left\")\n",
"ax.set_title(f\"Sentinel-2 tiles, {DAY:%d %B %Y}\", fontsize=9)\n",
"ax2 = fig.add_subplot(1, 2, 2)\n",
"for platform in PLATFORMS:\n",
" f = footprints[footprints.platform == platform]\n",
- " ax2.scatter(f[\"duration (s)\"], f[\"IoU, earth_fixed\"], s=10, color=PLATFORM_COLORS[platform], label=platform)\n",
- "ax2.set(xscale=\"log\", xlabel=\"Datastrip duration (s)\", ylabel=\"IoU (TAT-C vs. tile footprints)\", title=\"Datastrip footprint agreement (Earth-fixed frame)\")\n",
+ " ax2.scatter(\n",
+ " f[\"duration (s)\"],\n",
+ " f[\"IoU, earth_fixed\"],\n",
+ " s=10,\n",
+ " color=PLATFORM_COLORS[platform],\n",
+ " label=platform,\n",
+ " )\n",
+ "ax2.set(\n",
+ " xscale=\"log\",\n",
+ " xlabel=\"Datastrip duration (s)\",\n",
+ " ylabel=\"IoU (TAT-C vs. tile footprints)\",\n",
+ " title=\"Datastrip footprint agreement (Earth-fixed frame)\",\n",
+ ")\n",
"ax2.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1321,10 +1552,10 @@
"id": "bea7887f",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:00:18.986782Z",
- "iopub.status.busy": "2026-10-08T18:00:18.986782Z",
- "iopub.status.idle": "2026-10-08T18:19:10.549339Z",
- "shell.execute_reply": "2026-10-08T18:19:10.549339Z"
+ "iopub.execute_input": "2026-10-25T14:02:18.880455Z",
+ "iopub.status.busy": "2026-10-25T14:02:18.879451Z",
+ "iopub.status.idle": "2026-10-25T14:13:23.659015Z",
+ "shell.execute_reply": "2026-10-25T14:13:23.659015Z"
}
},
"outputs": [
@@ -1423,8 +1654,12 @@
}
],
"source": [
- "single = {frame.value: msi_view(TIME_STEP, frame) for frame in [VelocityFrame.EARTH_FIXED, VelocityFrame.INERTIAL]}\n",
- "nadir = Instrument(name=\"MSI\", field_of_regard=2 * max(abs(grids.cross_earth_fixed.min()), grids.cross_earth_fixed.max()))\n",
+ "single = msi_view(TIME_STEP)\n",
+ "nadir = Instrument(\n",
+ " name=\"MSI\",\n",
+ " field_of_regard=2\n",
+ " * max(abs(grids.cross_earth_fixed.min()), grids.cross_earth_fixed.max()),\n",
+ ")\n",
"modules = [\n",
" PointedInstrument(\n",
" name=f\"MSI detector {d}\",\n",
@@ -1438,7 +1673,11 @@
" for d, row in detectors.iterrows()\n",
"]\n",
"# the same, with the view angles measured from (and the views rotated from) the geocentric nadir\n",
- "detectors_geocentric = grids.groupby(\"detector\").agg(along=(\"along_geocentric\", \"median\"), cross_min=(\"cross_geocentric\", \"min\"), cross_max=(\"cross_geocentric\", \"max\"))\n",
+ "detectors_geocentric = grids.groupby(\"detector\").agg(\n",
+ " along=(\"along_geocentric\", \"median\"),\n",
+ " cross_min=(\"cross_geocentric\", \"min\"),\n",
+ " cross_max=(\"cross_geocentric\", \"max\"),\n",
+ ")\n",
"modules_geocentric = [\n",
" PointedInstrument(\n",
" name=f\"MSI detector {d}\",\n",
@@ -1454,19 +1693,31 @@
"]\n",
"sample4 = grids.iloc[:: max(1, len(grids) // 1000)]\n",
"rows4, dt4 = {}, {}\n",
- "for name, instruments in [\n",
- " (\"single view, earth_fixed\", [single[\"earth_fixed\"]]),\n",
- " (\"single view, inertial\", [single[\"inertial\"]]),\n",
- " (\"nadir field of regard\", [nadir]),\n",
- " (\"detector views\", modules),\n",
- " (\"detector views, geocentric nadir\", modules_geocentric),\n",
+ "for name, instruments, attitude in [\n",
+ " (\"single view, earth_fixed\", [single], None),\n",
+ " (\n",
+ " \"single view, inertial\",\n",
+ " [single],\n",
+ " Attitude(velocity_frame=VelocityFrame.INERTIAL),\n",
+ " ),\n",
+ " (\"nadir field of regard\", [nadir], None),\n",
+ " (\"detector views\", modules, None),\n",
+ " (\"detector views, geocentric nadir\", modules_geocentric, None),\n",
"]:\n",
" differences, missed = [], 0\n",
" for point in sample4.itertuples():\n",
" truth = pd.Timestamp(DAY) + pd.Timedelta(seconds=float(point.t))\n",
- " satellite = satellites[point.platform].model_copy(update={\"instruments\": instruments})\n",
+ " satellite = satellites[point.platform].model_copy(\n",
+ " update={\"instruments\": instruments, \"attitude\": attitude}\n",
+ " )\n",
" epochs = [\n",
- " collect_observations(Point(id=0, latitude=point.lat, longitude=point.lon), satellite, truth - pd.Timedelta(minutes=5), truth + pd.Timedelta(minutes=5), index).epoch\n",
+ " collect_observations(\n",
+ " Point(id=0, latitude=point.lat, longitude=point.lon),\n",
+ " satellite,\n",
+ " truth - pd.Timedelta(minutes=5),\n",
+ " truth + pd.Timedelta(minutes=5),\n",
+ " index,\n",
+ " ).epoch\n",
" for index in range(len(instruments))\n",
" ]\n",
" epochs = pd.concat(epochs) if epochs else pd.Series(dtype=\"datetime64[ns, UTC]\")\n",
@@ -1477,8 +1728,19 @@
" differences.append(delta[np.argmin(np.abs(delta))])\n",
" dt4[name] = np.array(differences)\n",
" lat4 = sample4.lat.to_numpy()\n",
- " rows4[name] = pd.concat([pd.Series({\"points\": len(sample4), \"missed\": missed}), summarize(differences).drop(\"n\")])\n",
- "pd.DataFrame(rows4).T.rename(columns={\"median\": \"time, median (s)\", \"p95 |x|\": \"time, p95 |x| (s)\", \"max |x|\": \"time, max |x| (s)\"}).round(3)"
+ " rows4[name] = pd.concat(\n",
+ " [\n",
+ " pd.Series({\"points\": len(sample4), \"missed\": missed}),\n",
+ " summarize(differences).drop(\"n\"),\n",
+ " ]\n",
+ " )\n",
+ "pd.DataFrame(rows4).T.rename(\n",
+ " columns={\n",
+ " \"median\": \"time, median (s)\",\n",
+ " \"p95 |x|\": \"time, p95 |x| (s)\",\n",
+ " \"max |x|\": \"time, max |x| (s)\",\n",
+ " }\n",
+ ").round(3)"
]
},
{
@@ -1487,10 +1749,10 @@
"id": "f7097a93",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:19:10.551347Z",
- "iopub.status.busy": "2026-10-08T18:19:10.551347Z",
- "iopub.status.idle": "2026-10-08T18:19:10.830120Z",
- "shell.execute_reply": "2026-10-08T18:19:10.830120Z"
+ "iopub.execute_input": "2026-10-25T14:13:23.662019Z",
+ "iopub.status.busy": "2026-10-25T14:13:23.661019Z",
+ "iopub.status.idle": "2026-10-25T14:13:23.939211Z",
+ "shell.execute_reply": "2026-10-25T14:13:23.939211Z"
}
},
"outputs": [
@@ -1511,7 +1773,11 @@
" if len(dt4[name]) == len(lat4):\n",
" ax.scatter(lat4, dt4[name], s=4, color=color, label=name)\n",
"ax.axhline(0, color=INK, lw=0.6)\n",
- "ax.set(xlabel=\"Latitude (deg)\", ylabel=\"TAT-C epoch minus imaging time (s)\", title=\"Observation time error\")\n",
+ "ax.set(\n",
+ " xlabel=\"Latitude (deg)\",\n",
+ " ylabel=\"TAT-C epoch minus imaging time (s)\",\n",
+ " title=\"Observation time error\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, markerscale=3)\n",
"fig.tight_layout()\n",
"plt.show()"
diff --git a/docs/validation/ValidateROCOSMIC2.ipynb b/docs/validation/ValidateROCOSMIC2.ipynb
index 9478ac3f..4f2be8a7 100644
--- a/docs/validation/ValidateROCOSMIC2.ipynb
+++ b/docs/validation/ValidateROCOSMIC2.ipynb
@@ -37,10 +37,10 @@
"id": "36f949b1",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:01:25.814887Z",
- "iopub.status.busy": "2026-10-08T18:01:25.814887Z",
- "iopub.status.idle": "2026-10-08T18:02:43.539926Z",
- "shell.execute_reply": "2026-10-08T18:02:43.539926Z"
+ "iopub.execute_input": "2026-10-25T20:49:49.903347Z",
+ "iopub.status.busy": "2026-10-25T20:49:49.903187Z",
+ "iopub.status.idle": "2026-10-25T20:51:01.936272Z",
+ "shell.execute_reply": "2026-10-25T20:51:01.935492Z"
}
},
"outputs": [
@@ -64,7 +64,9 @@
"CONSTELLATIONS = {\"G\": \"GPS\", \"R\": \"GLONASS\", \"E\": \"Galileo\"}\n",
"\n",
"DATA_DIR = os.path.join(\"data\", \"ro\")\n",
- "rodb = RODatabaseClient(metadata_root=os.path.join(DATA_DIR, \"metadata\"), version=\"v1.1\")\n",
+ "rodb = RODatabaseClient(\n",
+ " metadata_root=os.path.join(DATA_DIR, \"metadata\"), version=\"v1.1\"\n",
+ ")\n",
"occultations = rodb.query(\n",
" missions=\"cosmic2\", datetimerange=(\"2026-05-14\", \"2026-05-14T23:59:59\"), silent=True\n",
")\n",
@@ -77,12 +79,20 @@
" availablefiletypes=[\"ucar_calibratedPhase\", \"ucar_refractivityRetrieval\"],\n",
" )\n",
" rng = np.random.default_rng(20260514 + k)\n",
- " sample._data += [pool._data[i] for i in sorted(rng.choice(pool.size, 200, replace=False))]\n",
- " print(f\"{CONSTELLATIONS[code]}: {pool.size} occultations with a retrieval, 200 sampled\")\n",
+ " sample._data += [\n",
+ " pool._data[i] for i in sorted(rng.choice(pool.size, 200, replace=False))\n",
+ " ]\n",
+ " print(\n",
+ " f\"{CONSTELLATIONS[code]}: {pool.size} occultations with a retrieval, 200 sampled\"\n",
+ " )\n",
"sample.size = len(sample._data)\n",
"# flat layout keeps local paths short (Windows limits paths to 260 characters)\n",
- "sample.download(\"ucar_calibratedPhase\", data_root=DATA_DIR, keep_aws_structure=False, silent=True)\n",
- "print(f\"{occultations.size} COSMIC-2 occultations on 14 May 2026, {sample.size} sampled\")"
+ "sample.download(\n",
+ " \"ucar_calibratedPhase\", data_root=DATA_DIR, keep_aws_structure=False, silent=True\n",
+ ")\n",
+ "print(\n",
+ " f\"{occultations.size} COSMIC-2 occultations on 14 May 2026, {sample.size} sampled\"\n",
+ ")"
]
},
{
@@ -112,10 +122,10 @@
"id": "f77450b8",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:02:43.543770Z",
- "iopub.status.busy": "2026-10-08T18:02:43.543770Z",
- "iopub.status.idle": "2026-10-08T18:03:01.029710Z",
- "shell.execute_reply": "2026-10-08T18:03:01.028701Z"
+ "iopub.execute_input": "2026-10-25T20:51:01.938710Z",
+ "iopub.status.busy": "2026-10-25T20:51:01.938419Z",
+ "iopub.status.idle": "2026-10-25T20:51:10.342171Z",
+ "shell.execute_reply": "2026-10-25T20:51:10.341473Z"
}
},
"outputs": [
@@ -175,9 +185,9 @@
"
\n",
" \n",
"\n",
@@ -273,17 +283,17 @@
"text/plain": [
" satellites median error (km) max error (km) excluded sets \\\n",
"group \n",
- "Galileo 26 1.73 15.62 0 \n",
- "GPS 32 1.25 4.71 1 \n",
- "GLONASS 19 1.23 6.91 0 \n",
- "receivers 5 0.52 1.21 0 \n",
+ "Galileo 26 1.70 15.67 0 \n",
+ "GPS 32 1.28 4.66 1 \n",
+ "GLONASS 19 1.23 6.88 0 \n",
+ "receivers 5 0.53 1.19 0 \n",
"\n",
" closest next-best satellite (km) \n",
"group \n",
- "Galileo 7121.80 \n",
- "GPS 2150.95 \n",
- "GLONASS 3901.38 \n",
- "receivers 3338.25 "
+ "Galileo 7121.83 \n",
+ "GPS 2151.02 \n",
+ "GLONASS 3901.41 \n",
+ "receivers 3338.26 "
]
},
"execution_count": 2,
@@ -311,7 +321,12 @@
"# element sets per NORAD catalog number, keyed by epoch (later duplicates win)\n",
"tle_lines = [\n",
" line.rstrip()\n",
- " for path in [\"gps_2026-05-14.3le\", \"glonass_2026-05-14.3le\", \"galileo_2026-05-14.3le\", \"cosmic2_2026-05-14.3le\"]\n",
+ " for path in [\n",
+ " \"gps_2026-05-14.3le\",\n",
+ " \"glonass_2026-05-14.3le\",\n",
+ " \"galileo_2026-05-14.3le\",\n",
+ " \"cosmic2_2026-05-14.3le\",\n",
+ " ]\n",
" for line in open(path)\n",
" if line.strip()\n",
"]\n",
@@ -342,35 +357,68 @@
" recorded_m = np.array([p for _, p in positions]).T\n",
"\n",
" def errors(sets):\n",
- " orbit = GeneralPerturbationsOrbit.from_tle(sum((sets[e] for e in sorted(sets)), []))\n",
- " p = np.einsum(\"ij...,j...->i...\", rotation, np.array(orbit.get_orbit_track_at_time(ts).position.m))\n",
+ " orbit = GeneralPerturbationsOrbit.from_tle(\n",
+ " sum((sets[e] for e in sorted(sets)), [])\n",
+ " )\n",
+ " p = np.einsum(\n",
+ " \"ij...,j...->i...\",\n",
+ " rotation,\n",
+ " np.array(orbit.get_orbit_track_at_time(ts).position.m),\n",
+ " )\n",
" return np.linalg.norm(p - recorded_m, axis=0) / 1e3\n",
"\n",
- " best = {norad: min(np.median(errors({e: s})) for e, s in sets.items()) for norad, sets in element_sets.items()}\n",
+ " best = {\n",
+ " norad: min(np.median(errors({e: s})) for e, s in sets.items())\n",
+ " for norad, sets in element_sets.items()\n",
+ " }\n",
" norad = min(best, key=best.get)\n",
- " kept = {e: s for e, s in element_sets[norad].items() if errors({e: s}).max() <= max_error}\n",
- " return norad, kept, errors(kept) if kept else None, sorted(best.values())[1], len(element_sets[norad]) - len(kept)\n",
+ " kept = {\n",
+ " e: s\n",
+ " for e, s in element_sets[norad].items()\n",
+ " if errors({e: s}).max() <= max_error\n",
+ " }\n",
+ " return (\n",
+ " norad,\n",
+ " kept,\n",
+ " errors(kept) if kept else None,\n",
+ " sorted(best.values())[1],\n",
+ " len(element_sets[norad]) - len(kept),\n",
+ " )\n",
"\n",
"\n",
"# recorded positions of each receiver and transmitter\n",
"recorded = collections.defaultdict(list)\n",
"for record in sample._data:\n",
- " l1 = nc.Dataset(os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"])))\n",
+ " l1 = nc.Dataset(\n",
+ " os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"]))\n",
+ " )\n",
" l1.set_auto_mask(False)\n",
" for k in [0, l1[\"time\"].size // 2, l1[\"time\"].size - 1]:\n",
- " t = GPS_EPOCH + timedelta(seconds=float(l1[\"startTime\"][...]) - GPS_UTC_OFFSET + float(l1[\"time\"][k]))\n",
+ " t = GPS_EPOCH + timedelta(\n",
+ " seconds=float(l1[\"startTime\"][...]) - GPS_UTC_OFFSET + float(l1[\"time\"][k])\n",
+ " )\n",
" recorded[record[\"transmitter\"]].append((t, l1[\"positionGNSS\"][k].astype(float)))\n",
" recorded[record[\"receiver\"]].append((t, l1[\"positionLEO\"][k].astype(float)))\n",
"\n",
"\n",
"keys = sorted(recorded)\n",
- "results = Parallel(n_jobs=N_JOBS)(delayed(identify)(recorded[key], element_sets, MAX_ERROR) for key in keys)\n",
+ "results = Parallel(n_jobs=N_JOBS)(\n",
+ " delayed(identify)(recorded[key], element_sets, MAX_ERROR) for key in keys\n",
+ ")\n",
"rows, satellites = [], {}\n",
"for key, (norad, kept, errors, next_best, excluded) in zip(keys, results):\n",
- " row = {\"key\": key, \"NORAD\": norad, \"name\": names[norad], \"element sets\": len(kept), \"excluded sets\": excluded}\n",
+ " row = {\n",
+ " \"key\": key,\n",
+ " \"NORAD\": norad,\n",
+ " \"name\": names[norad],\n",
+ " \"element sets\": len(kept),\n",
+ " \"excluded sets\": excluded,\n",
+ " }\n",
" if kept: # identified: at least one element set reproduces the recorded positions\n",
" satellites[key] = Satellite(name=key, orbit=to_orbit(kept))\n",
- " row.update({\"median error (km)\": np.median(errors), \"max error (km)\": errors.max()})\n",
+ " row.update(\n",
+ " {\"median error (km)\": np.median(errors), \"max error (km)\": errors.max()}\n",
+ " )\n",
" row[\"next-best satellite (km)\"] = next_best\n",
" rows.append(row)\n",
"orbit_check = pd.DataFrame(rows).set_index(\"key\")\n",
@@ -378,8 +426,12 @@
"receivers = [satellites[k] for k in sorted(satellites) if k.startswith(\"cosmic2\")]\n",
"transmitters = [satellites[k] for k in sorted(satellites) if k[0] in CONSTELLATIONS]\n",
"unidentified = sorted(k for k in recorded if k not in satellites)\n",
- "print(f\"{len(receivers)} receivers and {len(transmitters)} transmitters identified; unidentified: {unidentified or 'none'}\")\n",
- "display(orbit_check[orbit_check[\"excluded sets\"] > 0].round(2)) # element sets excluded as poor\n",
+ "print(\n",
+ " f\"{len(receivers)} receivers and {len(transmitters)} transmitters identified; unidentified: {unidentified or 'none'}\"\n",
+ ")\n",
+ "display(\n",
+ " orbit_check[orbit_check[\"excluded sets\"] > 0].round(2)\n",
+ ") # element sets excluded as poor\n",
"orbit_check.groupby(\"group\", sort=False).agg(\n",
" **{\n",
" \"satellites\": (\"NORAD\", \"size\"),\n",
@@ -409,10 +461,10 @@
"id": "7eaf75b3",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:01.033225Z",
- "iopub.status.busy": "2026-10-08T18:03:01.032224Z",
- "iopub.status.idle": "2026-10-08T18:03:09.491589Z",
- "shell.execute_reply": "2026-10-08T18:03:09.491589Z"
+ "iopub.execute_input": "2026-10-25T20:51:10.344121Z",
+ "iopub.status.busy": "2026-10-25T20:51:10.343828Z",
+ "iopub.status.idle": "2026-10-25T20:51:15.326551Z",
+ "shell.execute_reply": "2026-10-25T20:51:15.325778Z"
}
},
"outputs": [
@@ -511,7 +563,8 @@
"source": [
"def crossing_times(receiver, transmitter, stamps, window=600):\n",
" \"\"\"Time at which the straight line reaches -80 km, and the transmitter yaw angle in the\n",
- " receiver frame at that time, within window (s) of each time stamp (NaT/NaN if not reached).\"\"\"\n",
+ " receiver frame at that time, within window (s) of each time stamp (NaT/NaN if not reached).\n",
+ " \"\"\"\n",
" from datetime import timedelta\n",
"\n",
" import numpy as np\n",
@@ -529,9 +582,15 @@
" ts = timescale.from_datetimes([stamp + timedelta(seconds=s) for s in offsets])\n",
" rx_pv = receiver.orbit.to_gp_orbit().get_orbit_track_at_time(ts)\n",
" tx_pv = transmitter.orbit.to_gp_orbit().get_orbit_track_at_time(ts)\n",
- " tp, tp_sign, _, yaw = _tangent_point_geometry(tx_pv, rx_pv, *compute_vnb_frame(rx_pv))\n",
- " height = wgs84.geographic_position_of(Geocentric(Distance(m=tp).au, None, ts)).elevation.m\n",
- " height[tp_sign > 0] = np.nan # tangent point not between receiver and transmitter\n",
+ " tp, tp_sign, _, yaw = _tangent_point_geometry(\n",
+ " tx_pv, rx_pv, *compute_vnb_frame(rx_pv)\n",
+ " )\n",
+ " height = wgs84.geographic_position_of(\n",
+ " Geocentric(Distance(m=tp).au, None, ts)\n",
+ " ).elevation.m\n",
+ " height[tp_sign > 0] = (\n",
+ " np.nan\n",
+ " ) # tangent point not between receiver and transmitter\n",
" s = np.sign(height + 80e3)\n",
" crossings = np.nonzero(s[:-1] * s[1:] < 0)[0]\n",
" if len(crossings) == 0:\n",
@@ -540,7 +599,10 @@
" j = crossings[0]\n",
" w = (height[j] + 80e3) / (height[j] - height[j + 1])\n",
" results.append(\n",
- " (stamp + timedelta(seconds=offsets[j] + w), yaw[j] + w * (((yaw[j + 1] - yaw[j] + 180) % 360) - 180))\n",
+ " (\n",
+ " stamp + timedelta(seconds=offsets[j] + w),\n",
+ " yaw[j] + w * (((yaw[j + 1] - yaw[j] + 180) % 360) - 180),\n",
+ " )\n",
" )\n",
" return results\n",
"\n",
@@ -560,13 +622,18 @@
" ]\n",
")\n",
"# occultations of satellites without an orbit cannot be compared\n",
- "without_orbit = ~observed.transmitter.isin(satellites.keys()) | ~observed.receiver.isin(satellites.keys())\n",
- "print(f\"{int(without_orbit.sum())} observed occultations without an identified orbit, excluded\")\n",
+ "without_orbit = ~observed.transmitter.isin(satellites.keys()) | ~observed.receiver.isin(\n",
+ " satellites.keys()\n",
+ ")\n",
+ "print(\n",
+ " f\"{int(without_orbit.sum())} observed occultations without an identified orbit, excluded\"\n",
+ ")\n",
"observed = observed[~without_orbit].reset_index(drop=True)\n",
"# one parallel task per receiver-transmitter combination\n",
"groups = list(observed.groupby([\"receiver\", \"transmitter\"]).groups.items())\n",
"results = Parallel(n_jobs=N_JOBS)(\n",
- " delayed(crossing_times)(satellites[rx], satellites[tx], list(observed.stamp[index])) for (rx, tx), index in groups\n",
+ " delayed(crossing_times)(satellites[rx], satellites[tx], list(observed.stamp[index]))\n",
+ " for (rx, tx), index in groups\n",
")\n",
"t80, yaw80 = [pd.NaT] * len(observed), [np.nan] * len(observed)\n",
"for (_, index), group_results in zip(groups, results):\n",
@@ -575,7 +642,9 @@
"observed[\"t80\"] = pd.to_datetime(t80)\n",
"observed[\"yaw80\"] = yaw80\n",
"unresolved = int(observed.t80.isna().sum())\n",
- "observed = observed[(observed.t80 >= DAY0) & (observed.t80 < DAY0 + timedelta(days=1))].reset_index(drop=True)\n",
+ "observed = observed[\n",
+ " (observed.t80 >= DAY0) & (observed.t80 < DAY0 + timedelta(days=1))\n",
+ "].reset_index(drop=True)\n",
"\n",
"\n",
"def folded(yaw):\n",
@@ -584,10 +653,14 @@
" return np.minimum(yaw, 180 - yaw)\n",
"\n",
"\n",
- "print(f\"{len(observed)} observed occultations reach -80 km on 14 May ({unresolved} not resolved within the search window)\")\n",
+ "print(\n",
+ " f\"{len(observed)} observed occultations reach -80 km on 14 May ({unresolved} not resolved within the search window)\"\n",
+ ")\n",
"pd.DataFrame(\n",
" {\n",
- " c: np.percentile(folded(observed.yaw80[observed.constellation == c]), [50, 90, 95, 99, 100])\n",
+ " c: np.percentile(\n",
+ " folded(observed.yaw80[observed.constellation == c]), [50, 90, 95, 99, 100]\n",
+ " )\n",
" for c in CONSTELLATIONS.values()\n",
" },\n",
" index=[\"50%\", \"90%\", \"95%\", \"99%\", \"max\"],\n",
@@ -612,10 +685,10 @@
"id": "c2238b02",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:09.495596Z",
- "iopub.status.busy": "2026-10-08T18:03:09.495596Z",
- "iopub.status.idle": "2026-10-08T18:03:11.149190Z",
- "shell.execute_reply": "2026-10-08T18:03:11.148685Z"
+ "iopub.execute_input": "2026-10-25T20:51:15.328356Z",
+ "iopub.status.busy": "2026-10-25T20:51:15.328124Z",
+ "iopub.status.idle": "2026-10-25T20:51:35.451885Z",
+ "shell.execute_reply": "2026-10-25T20:51:35.451099Z"
}
},
"outputs": [
@@ -750,7 +823,12 @@
" # order (as a single collect_ro_observations call over all transmitters would return them)\n",
" results = Parallel(n_jobs=N_JOBS)(\n",
" delayed(collect_ro_observations)(\n",
- " receiver, transmitter, DAY0, DAY0 + timedelta(days=1), time_step=timedelta(seconds=1), max_yaw=MAX_YAW\n",
+ " receiver,\n",
+ " transmitter,\n",
+ " DAY0,\n",
+ " DAY0 + timedelta(days=1),\n",
+ " time_step=timedelta(seconds=1),\n",
+ " max_yaw=MAX_YAW,\n",
" )\n",
" for receiver in receivers\n",
" for transmitter in transmitters\n",
@@ -758,9 +836,9 @@
" n = len(transmitters)\n",
" pd.concat(\n",
" [\n",
- " pd.concat([r for r in results[k * n : (k + 1) * n] if len(r)], ignore_index=True).sort_values(\n",
- " \"time\", ignore_index=True\n",
- " )\n",
+ " pd.concat(\n",
+ " [r for r in results[k * n : (k + 1) * n] if len(r)], ignore_index=True\n",
+ " ).sort_values(\"time\", ignore_index=True)\n",
" for k in range(len(receivers))\n",
" ],\n",
" ignore_index=True,\n",
@@ -774,8 +852,13 @@
" candidates = []\n",
" for (rx, tx), p in pred.groupby([\"receiver\", \"transmitter\"]):\n",
" o = obs[(obs.receiver == rx) & (obs.transmitter == tx)]\n",
- " dt = np.abs((p.time.values[:, None] - o.t80.values[None, :]) / np.timedelta64(1, \"s\"))\n",
- " candidates += [(dt[i, j], p.index[i], o.index[j]) for i, j in zip(*np.nonzero(dt < tolerance))]\n",
+ " dt = np.abs(\n",
+ " (p.time.values[:, None] - o.t80.values[None, :]) / np.timedelta64(1, \"s\")\n",
+ " )\n",
+ " candidates += [\n",
+ " (dt[i, j], p.index[i], o.index[j])\n",
+ " for i, j in zip(*np.nonzero(dt < tolerance))\n",
+ " ]\n",
" used_p, used_o, pairs = set(), set(), []\n",
" for _, i, j in sorted(candidates):\n",
" if i not in used_p and j not in used_o:\n",
@@ -799,7 +882,8 @@
" \"share of observed that were predicted\": len(m) / len(o),\n",
" \"share of predicted that were observed\": len(m) / len(p),\n",
" \"rising/setting agreement (matched)\": np.mean(\n",
- " predicted.loc[m.pred, \"is_rising\"].values == ~observed.loc[m.obs, \"setting\"].values\n",
+ " predicted.loc[m.pred, \"is_rising\"].values\n",
+ " == ~observed.loc[m.obs, \"setting\"].values\n",
" ),\n",
" }\n",
"print(f\"max_yaw = {MAX_YAW} deg\")\n",
@@ -824,16 +908,16 @@
"id": "39cb1dbb",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:11.152197Z",
- "iopub.status.busy": "2026-10-08T18:03:11.152197Z",
- "iopub.status.idle": "2026-10-08T18:03:25.630975Z",
- "shell.execute_reply": "2026-10-08T18:03:25.630468Z"
+ "iopub.execute_input": "2026-10-25T20:51:35.453657Z",
+ "iopub.status.busy": "2026-10-25T20:51:35.453415Z",
+ "iopub.status.idle": "2026-10-25T20:51:43.481280Z",
+ "shell.execute_reply": "2026-10-25T20:51:43.480545Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
"text/plain": [
""
]
@@ -846,7 +930,13 @@
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams.update(\n",
- " {\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6}\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
")\n",
"\n",
"pred = predicted.copy()\n",
@@ -854,28 +944,38 @@
"pred.loc[pairs.pred, \"seen\"] = True\n",
"pred[\"yaw\"] = folded(pred.rx_tx_yaw)\n",
"pred[\"hour\"] = pred.time.dt.hour\n",
- "pred[\"concurrent\"] = [ # other predicted occultations of the same receiver and direction overlapping in time\n",
- " int(\n",
- " (\n",
- " (pred.receiver == r.receiver)\n",
- " & (pred.is_rising == r.is_rising)\n",
- " & (pred.start < r.end)\n",
- " & (pred.end > r.start)\n",
- " & (pred.index != r.Index)\n",
- " ).sum()\n",
- " )\n",
- " for r in pred.itertuples()\n",
- "]\n",
+ "pred[\"concurrent\"] = (\n",
+ " [ # other predicted occultations of the same receiver and direction overlapping in time\n",
+ " int(\n",
+ " (\n",
+ " (pred.receiver == r.receiver)\n",
+ " & (pred.is_rising == r.is_rising)\n",
+ " & (pred.start < r.end)\n",
+ " & (pred.end > r.start)\n",
+ " & (pred.index != r.Index)\n",
+ " ).sum()\n",
+ " )\n",
+ " for r in pred.itertuples()\n",
+ " ]\n",
+ ")\n",
"order = sorted(pred.transmitter.unique(), key=lambda tx: (\"GRE\".index(tx[0]), tx))\n",
- "by_pair = pred.pivot_table(index=\"receiver\", columns=\"transmitter\", values=\"seen\", aggfunc=\"mean\")[order]\n",
- "hourly = pred.groupby([\"hour\", \"receiver\"])[\"seen\"].mean().unstack() # hours x receivers\n",
- "\n",
- "fig, axes = plt.subplots(2, 1, figsize=(13, 5.4), gridspec_kw={\"height_ratios\": [1.2, 1]})\n",
+ "by_pair = pred.pivot_table(\n",
+ " index=\"receiver\", columns=\"transmitter\", values=\"seen\", aggfunc=\"mean\"\n",
+ ")[order]\n",
+ "hourly = (\n",
+ " pred.groupby([\"hour\", \"receiver\"])[\"seen\"].mean().unstack()\n",
+ ") # hours x receivers\n",
+ "\n",
+ "fig, axes = plt.subplots(\n",
+ " 2, 1, figsize=(13, 5.4), gridspec_kw={\"height_ratios\": [1.2, 1]}\n",
+ ")\n",
"im = axes[0].imshow(by_pair.values, cmap=\"Blues\", vmin=0, vmax=1, aspect=\"auto\")\n",
"axes[0].set_xticks(range(by_pair.shape[1]), by_pair.columns, rotation=90, fontsize=7)\n",
"axes[0].set_yticks(range(by_pair.shape[0]), by_pair.index, fontsize=8)\n",
"axes[0].grid(False)\n",
- "axes[0].set_title(\"Share of predicted occultations observed, by receiver and transmitter\")\n",
+ "axes[0].set_title(\n",
+ " \"Share of predicted occultations observed, by receiver and transmitter\"\n",
+ ")\n",
"fig.colorbar(im, ax=axes[0], pad=0.01)\n",
"im = axes[1].imshow(hourly.T.values, cmap=\"Blues\", vmin=0, vmax=1, aspect=\"auto\")\n",
"axes[1].set_xticks(range(hourly.shape[0]), hourly.index, fontsize=8)\n",
@@ -901,10 +1001,10 @@
"id": "53e49c95",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:25.633983Z",
- "iopub.status.busy": "2026-10-08T18:03:25.633983Z",
- "iopub.status.idle": "2026-10-08T18:03:25.677316Z",
- "shell.execute_reply": "2026-10-08T18:03:25.676810Z"
+ "iopub.execute_input": "2026-10-25T20:51:43.483866Z",
+ "iopub.status.busy": "2026-10-25T20:51:43.483440Z",
+ "iopub.status.idle": "2026-10-25T20:51:43.508824Z",
+ "shell.execute_reply": "2026-10-25T20:51:43.508130Z"
}
},
"outputs": [
@@ -1070,19 +1170,28 @@
"seen_rate = pred.groupby([\"receiver\", \"transmitter\"])[\"seen\"].transform(\"mean\")\n",
"tracked = pred[seen_rate > 0.1]\n",
"groups = {\n",
- " \"constellation\": pd.Categorical(tracked.constellation, categories=list(CONSTELLATIONS.values())),\n",
+ " \"constellation\": pd.Categorical(\n",
+ " tracked.constellation, categories=list(CONSTELLATIONS.values())\n",
+ " ),\n",
" # arcs cut off by the yaw limit report a yaw of exactly max_yaw (up to round-off)\n",
" \"yaw from fore/aft (deg)\": pd.cut(\n",
- " tracked.yaw.clip(upper=MAX_YAW), [b for b in [0, 15, 30, 45, 55, 60, 65, 70, 75, 80] if b < MAX_YAW] + [MAX_YAW]\n",
+ " tracked.yaw.clip(upper=MAX_YAW),\n",
+ " [b for b in [0, 15, 30, 45, 55, 60, 65, 70, 75, 80] if b < MAX_YAW] + [MAX_YAW],\n",
" ),\n",
" \"direction\": tracked.is_rising.map({True: \"rising\", False: \"setting\"}),\n",
" \"other same-direction occultations at the same time\": pd.Categorical(\n",
- " tracked.concurrent.clip(upper=3).astype(str).replace(\"3\", \"3 or more\"), categories=[\"0\", \"1\", \"2\", \"3 or more\"]\n",
+ " tracked.concurrent.clip(upper=3).astype(str).replace(\"3\", \"3 or more\"),\n",
+ " categories=[\"0\", \"1\", \"2\", \"3 or more\"],\n",
" ),\n",
"}\n",
- "print(f\"{len(tracked)} of {len(pred)} predicted occultations in receiver-transmitter combinations observed at least 10% of the time\")\n",
+ "print(\n",
+ " f\"{len(tracked)} of {len(pred)} predicted occultations in receiver-transmitter combinations observed at least 10% of the time\"\n",
+ ")\n",
"pd.concat(\n",
- " {name: tracked.groupby(key, observed=True)[\"seen\"].agg([\"mean\", \"size\"]) for name, key in groups.items()}\n",
+ " {\n",
+ " name: tracked.groupby(key, observed=True)[\"seen\"].agg([\"mean\", \"size\"])\n",
+ " for name, key in groups.items()\n",
+ " }\n",
").rename(columns={\"mean\": \"share observed\", \"size\": \"predicted\"}).round(2)"
]
},
@@ -1106,10 +1215,10 @@
"id": "9bc440a5",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:25.680218Z",
- "iopub.status.busy": "2026-10-08T18:03:25.680218Z",
- "iopub.status.idle": "2026-10-08T18:03:32.843257Z",
- "shell.execute_reply": "2026-10-08T18:03:32.842752Z"
+ "iopub.execute_input": "2026-10-25T20:51:43.510576Z",
+ "iopub.status.busy": "2026-10-25T20:51:43.510389Z",
+ "iopub.status.idle": "2026-10-25T20:51:47.875253Z",
+ "shell.execute_reply": "2026-10-25T20:51:47.874529Z"
}
},
"outputs": [
@@ -1146,7 +1255,7 @@
" \n",
" \n",
"
\n",
- "
GPS
\n",
+ "
GPS
\n",
"
TAT-C time minus true time at -80 km (s)
\n",
"
199.0
\n",
"
-0.053
\n",
@@ -1157,17 +1266,27 @@
"
0.267
\n",
"
\n",
"
\n",
+ "
|TAT-C time minus true time| at -80 km (s)
\n",
+ "
199.0
\n",
+ "
0.101
\n",
+ "
0.082
\n",
+ "
0.000
\n",
+ "
0.085
\n",
+ "
0.249
\n",
+ "
0.499
\n",
+ "
\n",
+ "
\n",
"
TAT-C position: -80 km point to truth (km)
\n",
"
199.0
\n",
- "
0.480
\n",
- "
0.410
\n",
- "
0.009
\n",
- "
0.378
\n",
- "
1.195
\n",
- "
2.777
\n",
+ "
0.476
\n",
+ "
0.405
\n",
+ "
0.017
\n",
+ "
0.384
\n",
+ "
1.180
\n",
+ "
2.759
\n",
"
\n",
"
\n",
- "
GLONASS
\n",
+ "
GLONASS
\n",
"
TAT-C time minus true time at -80 km (s)
\n",
"
198.0
\n",
"
-0.019
\n",
@@ -1178,17 +1297,27 @@
"
0.351
\n",
"
\n",
"
\n",
+ "
|TAT-C time minus true time| at -80 km (s)
\n",
+ "
198.0
\n",
+ "
0.094
\n",
+ "
0.080
\n",
+ "
0.001
\n",
+ "
0.075
\n",
+ "
0.252
\n",
+ "
0.409
\n",
+ "
\n",
+ "
\n",
"
TAT-C position: -80 km point to truth (km)
\n",
"
198.0
\n",
- "
0.480
\n",
+ "
0.476
\n",
"
0.441
\n",
- "
0.022
\n",
- "
0.344
\n",
- "
1.303
\n",
- "
2.497
\n",
+ "
0.021
\n",
+ "
0.343
\n",
+ "
1.284
\n",
+ "
2.516
\n",
"
\n",
"
\n",
- "
Galileo
\n",
+ "
Galileo
\n",
"
TAT-C time minus true time at -80 km (s)
\n",
"
197.0
\n",
"
-0.062
\n",
@@ -1199,14 +1328,24 @@
"
0.462
\n",
"
\n",
"
\n",
+ "
|TAT-C time minus true time| at -80 km (s)
\n",
+ "
197.0
\n",
+ "
0.118
\n",
+ "
0.095
\n",
+ "
0.001
\n",
+ "
0.093
\n",
+ "
0.295
\n",
+ "
0.554
\n",
+ "
\n",
+ "
\n",
"
TAT-C position: -80 km point to truth (km)
\n",
"
197.0
\n",
- "
0.559
\n",
+ "
0.550
\n",
"
0.554
\n",
- "
0.052
\n",
- "
0.372
\n",
- "
1.659
\n",
- "
3.207
\n",
+ "
0.043
\n",
+ "
0.365
\n",
+ "
1.673
\n",
+ "
3.225
\n",
"
\n",
" \n",
"\n",
@@ -1215,19 +1354,25 @@
"text/plain": [
" count mean std \\\n",
"GPS TAT-C time minus true time at -80 km (s) 199.0 -0.053 0.119 \n",
- " TAT-C position: -80 km point to truth (km) 199.0 0.480 0.410 \n",
+ " |TAT-C time minus true time| at -80 km (s) 199.0 0.101 0.082 \n",
+ " TAT-C position: -80 km point to truth (km) 199.0 0.476 0.405 \n",
"GLONASS TAT-C time minus true time at -80 km (s) 198.0 -0.019 0.122 \n",
- " TAT-C position: -80 km point to truth (km) 198.0 0.480 0.441 \n",
+ " |TAT-C time minus true time| at -80 km (s) 198.0 0.094 0.080 \n",
+ " TAT-C position: -80 km point to truth (km) 198.0 0.476 0.441 \n",
"Galileo TAT-C time minus true time at -80 km (s) 197.0 -0.062 0.138 \n",
- " TAT-C position: -80 km point to truth (km) 197.0 0.559 0.554 \n",
+ " |TAT-C time minus true time| at -80 km (s) 197.0 0.118 0.095 \n",
+ " TAT-C position: -80 km point to truth (km) 197.0 0.550 0.554 \n",
"\n",
" min 50% 95% max \n",
"GPS TAT-C time minus true time at -80 km (s) -0.499 -0.049 0.137 0.267 \n",
- " TAT-C position: -80 km point to truth (km) 0.009 0.378 1.195 2.777 \n",
+ " |TAT-C time minus true time| at -80 km (s) 0.000 0.085 0.249 0.499 \n",
+ " TAT-C position: -80 km point to truth (km) 0.017 0.384 1.180 2.759 \n",
"GLONASS TAT-C time minus true time at -80 km (s) -0.409 -0.011 0.179 0.351 \n",
- " TAT-C position: -80 km point to truth (km) 0.022 0.344 1.303 2.497 \n",
+ " |TAT-C time minus true time| at -80 km (s) 0.001 0.075 0.252 0.409 \n",
+ " TAT-C position: -80 km point to truth (km) 0.021 0.343 1.284 2.516 \n",
"Galileo TAT-C time minus true time at -80 km (s) -0.554 -0.067 0.151 0.462 \n",
- " TAT-C position: -80 km point to truth (km) 0.052 0.372 1.659 3.207 "
+ " |TAT-C time minus true time| at -80 km (s) 0.001 0.093 0.295 0.554 \n",
+ " TAT-C position: -80 km point to truth (km) 0.043 0.365 1.673 3.225 "
]
},
"execution_count": 7,
@@ -1272,10 +1417,16 @@
"# from the recorded receiver and transmitter positions (subsampled to 10 Hz)\n",
"truth = {}\n",
"for record in sample._data:\n",
- " l1 = nc.Dataset(os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"])))\n",
+ " l1 = nc.Dataset(\n",
+ " os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"]))\n",
+ " )\n",
" l1.set_auto_mask(False)\n",
" t, rx, tx = l1[\"time\"][:], l1[\"positionLEO\"][:].T, l1[\"positionGNSS\"][:].T\n",
- " ok = (np.abs(t) < 1e10) & np.all(np.abs(rx) < 1e10, axis=0) & np.all(np.abs(tx) < 1e10, axis=0)\n",
+ " ok = (\n",
+ " (np.abs(t) < 1e10)\n",
+ " & np.all(np.abs(rx) < 1e10, axis=0)\n",
+ " & np.all(np.abs(tx) < 1e10, axis=0)\n",
+ " )\n",
" i = np.nonzero(ok)[0][::10]\n",
" start = float(l1[\"startTime\"][...])\n",
" # GPS seconds since 1980-01-06 are TAI - 19 s\n",
@@ -1283,7 +1434,11 @@
" t_80 = crossing(t[i], to_lla(points)[2], -80e3)\n",
" if np.isfinite(t_80):\n",
" lon, lat, _ = to_lla(interpolate(t[i], points, [t_80]))\n",
- " truth[record[\"occid\"]] = (GPS_EPOCH + timedelta(seconds=start - GPS_UTC_OFFSET + t_80), lon[0], lat[0])\n",
+ " truth[record[\"occid\"]] = (\n",
+ " GPS_EPOCH + timedelta(seconds=start - GPS_UTC_OFFSET + t_80),\n",
+ " lon[0],\n",
+ " lat[0],\n",
+ " )\n",
"\n",
"rows = []\n",
"for i, j in zip(pairs.pred, pairs.obs):\n",
@@ -1293,14 +1448,25 @@
" rows.append(\n",
" {\n",
" \"constellation\": o.constellation,\n",
- " \"TAT-C time minus true time at -80 km (s)\": (p.time.to_pydatetime() - true_time).total_seconds(),\n",
- " \"TAT-C position: -80 km point to truth (km)\": GEOD.inv(p.position.x, p.position.y, lon, lat)[2] / 1e3,\n",
+ " \"TAT-C time minus true time at -80 km (s)\": (\n",
+ " p.time.to_pydatetime() - true_time\n",
+ " ).total_seconds(),\n",
+ " \"|TAT-C time minus true time| at -80 km (s)\": abs(\n",
+ " (p.time.to_pydatetime() - true_time).total_seconds()\n",
+ " ),\n",
+ " \"TAT-C position: -80 km point to truth (km)\": GEOD.inv(\n",
+ " p.position.x, p.position.y, lon, lat\n",
+ " )[2]\n",
+ " / 1e3,\n",
" }\n",
" )\n",
"accuracy = pd.DataFrame(rows)\n",
"pd.concat(\n",
" {\n",
- " c: accuracy[accuracy.constellation == c].drop(columns=\"constellation\").describe(percentiles=[0.5, 0.95]).T\n",
+ " c: accuracy[accuracy.constellation == c]\n",
+ " .drop(columns=\"constellation\")\n",
+ " .describe(percentiles=[0.5, 0.95])\n",
+ " .T\n",
" for c in CONSTELLATIONS.values()\n",
" }\n",
").round(3)"
@@ -1311,7 +1477,7 @@
"id": "610f189a",
"metadata": {},
"source": [
- "For all three constellations, TAT-C's `time` agrees with the true time at which the straight line reaches -80 km to within 0.14 to 0.18 s (95th percentile), and its `position` with the true straight-line tangent point to 0.34 to 0.38 km (median) and 1.2 to 1.7 km (95th percentile), limited by orbit accuracy; Galileo, with the least accurate element sets, has the largest position errors."
+ "For all three constellations, TAT-C's `time` agrees with the true time at which the straight line reaches -80 km to within 0.25 to 0.30 s (95th percentile of the absolute difference; on average, TAT-C's `time` is 0.02 to 0.06 s early), and its `position` with the true straight-line tangent point to 0.34 to 0.38 km (median) and 1.2 to 1.7 km (95th percentile), limited by orbit accuracy; Galileo, with the least accurate element sets, has the largest position errors."
]
},
{
@@ -1326,7 +1492,7 @@
"| 1. Orbits (TLEs vs. recorded positions) | All 77 transmitters (32 GPS, 19 GLONASS, 26 Galileo) and 5 receivers identified; within about 5 km (GPS), 7 km (GLONASS), and 16 km (Galileo), after excluding one poor GPS element set |\n",
"| 2. Predicted vs. observed (default `max_yaw` = 65 deg) | All 5,699 observed occultations predicted, with correct rising/setting; recorded share of predictions 69% (GPS), 81% (GLONASS), and 37% (Galileo) |\n",
"| 3. Detection | Unrecorded predictions mostly explained by receiver operations (untracked transmitters, data gaps, selective Galileo tracking) and the antenna's coverage edge (60-65 deg yaw) |\n",
- "| 4. Timing and location | `time` within 0.14 to 0.18 s (95th percentile); `position` 0.34 to 0.38 km (median) and 1.2 to 1.7 km (95th percentile) from the true tangent point |\n",
+ "| 4. Timing and location | `time` within 0.25 to 0.30 s (95th percentile); `position` 0.34 to 0.38 km (median) and 1.2 to 1.7 km (95th percentile) from the true tangent point |\n",
"\n",
"**TAT-C's RO geometry is validated end to end for GPS, GLONASS, and Galileo.** With published orbits, `collect_ro_observations` predicts every occultation COSMIC-2 recorded from the three constellations on this day, labels each correctly as rising or setting, and reports its time to a fraction of a second and its position to within a few hundred meters (median), limited by orbit accuracy. The default `max_yaw` of 65 deg matches COSMIC-2's observed coverage, which extends to about 64 deg from the forward or rearward direction for every constellation. Predicted counts are upper bounds on recorded counts: COSMIC-2 recorded 69% of the predicted GPS occultations, 81% of GLONASS, and 37% of Galileo, for operational reasons outside TAT-C's model.\n",
"\n",
@@ -1356,7 +1522,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.8"
+ "version": "3.14.9"
}
},
"nbformat": 4,
diff --git a/docs/validation/ValidateROPlanetiQ.ipynb b/docs/validation/ValidateROPlanetiQ.ipynb
index a8b172dd..943d47ca 100644
--- a/docs/validation/ValidateROPlanetiQ.ipynb
+++ b/docs/validation/ValidateROPlanetiQ.ipynb
@@ -35,10 +35,10 @@
"id": "13ba3bde",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:02:19.167028Z",
- "iopub.status.busy": "2026-10-08T18:02:19.167028Z",
- "iopub.status.idle": "2026-10-08T18:03:35.929237Z",
- "shell.execute_reply": "2026-10-08T18:03:35.929237Z"
+ "iopub.execute_input": "2026-10-25T20:51:51.756967Z",
+ "iopub.status.busy": "2026-10-25T20:51:51.756760Z",
+ "iopub.status.idle": "2026-10-25T20:53:01.587114Z",
+ "shell.execute_reply": "2026-10-25T20:53:01.586374Z"
}
},
"outputs": [
@@ -62,9 +62,13 @@
"CONSTELLATIONS = {\"G\": \"GPS\", \"R\": \"GLONASS\", \"E\": \"Galileo\"}\n",
"\n",
"DATA_DIR = os.path.join(\"data\", \"ro\")\n",
- "rodb = RODatabaseClient(metadata_root=os.path.join(DATA_DIR, \"metadata\"), version=\"v1.1\")\n",
+ "rodb = RODatabaseClient(\n",
+ " metadata_root=os.path.join(DATA_DIR, \"metadata\"), version=\"v1.1\"\n",
+ ")\n",
"occultations = rodb.query(\n",
- " missions=\"planetiq\", datetimerange=(\"2026-05-14\", \"2026-05-14T23:59:59\"), silent=True\n",
+ " missions=\"planetiq\",\n",
+ " datetimerange=(\"2026-05-14\", \"2026-05-14T23:59:59\"),\n",
+ " silent=True,\n",
")\n",
"# seeded random sample of 200 occultations with a retrieval from each constellation\n",
"sample = occultations[0:0]\n",
@@ -75,12 +79,20 @@
" availablefiletypes=[\"ucar_calibratedPhase\", \"ucar_refractivityRetrieval\"],\n",
" )\n",
" rng = np.random.default_rng(20260514 + k)\n",
- " sample._data += [pool._data[i] for i in sorted(rng.choice(pool.size, 200, replace=False))]\n",
- " print(f\"{CONSTELLATIONS[code]}: {pool.size} occultations with a retrieval, 200 sampled\")\n",
+ " sample._data += [\n",
+ " pool._data[i] for i in sorted(rng.choice(pool.size, 200, replace=False))\n",
+ " ]\n",
+ " print(\n",
+ " f\"{CONSTELLATIONS[code]}: {pool.size} occultations with a retrieval, 200 sampled\"\n",
+ " )\n",
"sample.size = len(sample._data)\n",
"# flat layout keeps local paths short (Windows limits paths to 260 characters)\n",
- "sample.download(\"ucar_calibratedPhase\", data_root=DATA_DIR, keep_aws_structure=False, silent=True)\n",
- "print(f\"{occultations.size} PlanetiQ occultations on 14 May 2026, {sample.size} sampled\")"
+ "sample.download(\n",
+ " \"ucar_calibratedPhase\", data_root=DATA_DIR, keep_aws_structure=False, silent=True\n",
+ ")\n",
+ "print(\n",
+ " f\"{occultations.size} PlanetiQ occultations on 14 May 2026, {sample.size} sampled\"\n",
+ ")"
]
},
{
@@ -107,10 +119,10 @@
"id": "76972489",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:35.932247Z",
- "iopub.status.busy": "2026-10-08T18:03:35.932247Z",
- "iopub.status.idle": "2026-10-08T18:03:51.675486Z",
- "shell.execute_reply": "2026-10-08T18:03:51.675486Z"
+ "iopub.execute_input": "2026-10-25T20:53:01.589198Z",
+ "iopub.status.busy": "2026-10-25T20:53:01.588865Z",
+ "iopub.status.idle": "2026-10-25T20:53:10.424592Z",
+ "shell.execute_reply": "2026-10-25T20:53:10.423736Z"
}
},
"outputs": [
@@ -217,26 +229,26 @@
"
\n",
"
Galileo
\n",
"
26
\n",
- "
1.53
\n",
- "
15.58
\n",
+ "
1.47
\n",
+ "
15.63
\n",
"
0
\n",
- "
5397.50
\n",
+ "
5397.44
\n",
"
\n",
"
\n",
"
GPS
\n",
"
26
\n",
- "
1.32
\n",
- "
4.65
\n",
+ "
1.35
\n",
+ "
4.60
\n",
"
0
\n",
- "
1829.68
\n",
+ "
1829.62
\n",
"
\n",
"
\n",
"
GLONASS
\n",
"
19
\n",
- "
1.46
\n",
- "
6.81
\n",
+ "
1.45
\n",
+ "
6.77
\n",
"
0
\n",
- "
3838.61
\n",
+ "
3838.64
\n",
"
\n",
"
\n",
"
receivers
\n",
@@ -244,7 +256,7 @@
"
0.62
\n",
"
1.39
\n",
"
0
\n",
- "
2330.33
\n",
+ "
2330.34
\n",
"
\n",
" \n",
"\n",
@@ -253,17 +265,17 @@
"text/plain": [
" satellites median error (km) max error (km) excluded sets \\\n",
"group \n",
- "Galileo 26 1.53 15.58 0 \n",
- "GPS 26 1.32 4.65 0 \n",
- "GLONASS 19 1.46 6.81 0 \n",
+ "Galileo 26 1.47 15.63 0 \n",
+ "GPS 26 1.35 4.60 0 \n",
+ "GLONASS 19 1.45 6.77 0 \n",
"receivers 2 0.62 1.39 0 \n",
"\n",
" closest next-best satellite (km) \n",
"group \n",
- "Galileo 5397.50 \n",
- "GPS 1829.68 \n",
- "GLONASS 3838.61 \n",
- "receivers 2330.33 "
+ "Galileo 5397.44 \n",
+ "GPS 1829.62 \n",
+ "GLONASS 3838.64 \n",
+ "receivers 2330.34 "
]
},
"execution_count": 2,
@@ -291,7 +303,12 @@
"# element sets per NORAD catalog number, keyed by epoch (later duplicates win)\n",
"tle_lines = [\n",
" line.rstrip()\n",
- " for path in [\"gps_2026-05-14.3le\", \"glonass_2026-05-14.3le\", \"galileo_2026-05-14.3le\", \"planetiq_2026-05-14.3le\"]\n",
+ " for path in [\n",
+ " \"gps_2026-05-14.3le\",\n",
+ " \"glonass_2026-05-14.3le\",\n",
+ " \"galileo_2026-05-14.3le\",\n",
+ " \"planetiq_2026-05-14.3le\",\n",
+ " ]\n",
" for line in open(path)\n",
" if line.strip()\n",
"]\n",
@@ -322,35 +339,68 @@
" recorded_m = np.array([p for _, p in positions]).T\n",
"\n",
" def errors(sets):\n",
- " orbit = GeneralPerturbationsOrbit.from_tle(sum((sets[e] for e in sorted(sets)), []))\n",
- " p = np.einsum(\"ij...,j...->i...\", rotation, np.array(orbit.get_orbit_track_at_time(ts).position.m))\n",
+ " orbit = GeneralPerturbationsOrbit.from_tle(\n",
+ " sum((sets[e] for e in sorted(sets)), [])\n",
+ " )\n",
+ " p = np.einsum(\n",
+ " \"ij...,j...->i...\",\n",
+ " rotation,\n",
+ " np.array(orbit.get_orbit_track_at_time(ts).position.m),\n",
+ " )\n",
" return np.linalg.norm(p - recorded_m, axis=0) / 1e3\n",
"\n",
- " best = {norad: min(np.median(errors({e: s})) for e, s in sets.items()) for norad, sets in element_sets.items()}\n",
+ " best = {\n",
+ " norad: min(np.median(errors({e: s})) for e, s in sets.items())\n",
+ " for norad, sets in element_sets.items()\n",
+ " }\n",
" norad = min(best, key=best.get)\n",
- " kept = {e: s for e, s in element_sets[norad].items() if errors({e: s}).max() <= max_error}\n",
- " return norad, kept, errors(kept) if kept else None, sorted(best.values())[1], len(element_sets[norad]) - len(kept)\n",
+ " kept = {\n",
+ " e: s\n",
+ " for e, s in element_sets[norad].items()\n",
+ " if errors({e: s}).max() <= max_error\n",
+ " }\n",
+ " return (\n",
+ " norad,\n",
+ " kept,\n",
+ " errors(kept) if kept else None,\n",
+ " sorted(best.values())[1],\n",
+ " len(element_sets[norad]) - len(kept),\n",
+ " )\n",
"\n",
"\n",
"# recorded positions of each receiver and transmitter\n",
"recorded = collections.defaultdict(list)\n",
"for record in sample._data:\n",
- " l1 = nc.Dataset(os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"])))\n",
+ " l1 = nc.Dataset(\n",
+ " os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"]))\n",
+ " )\n",
" l1.set_auto_mask(False)\n",
" for k in [0, l1[\"time\"].size // 2, l1[\"time\"].size - 1]:\n",
- " t = GPS_EPOCH + timedelta(seconds=float(l1[\"startTime\"][...]) - GPS_UTC_OFFSET + float(l1[\"time\"][k]))\n",
+ " t = GPS_EPOCH + timedelta(\n",
+ " seconds=float(l1[\"startTime\"][...]) - GPS_UTC_OFFSET + float(l1[\"time\"][k])\n",
+ " )\n",
" recorded[record[\"transmitter\"]].append((t, l1[\"positionGNSS\"][k].astype(float)))\n",
" recorded[record[\"receiver\"]].append((t, l1[\"positionLEO\"][k].astype(float)))\n",
"\n",
"\n",
"keys = sorted(recorded)\n",
- "results = Parallel(n_jobs=N_JOBS)(delayed(identify)(recorded[key], element_sets, MAX_ERROR) for key in keys)\n",
+ "results = Parallel(n_jobs=N_JOBS)(\n",
+ " delayed(identify)(recorded[key], element_sets, MAX_ERROR) for key in keys\n",
+ ")\n",
"rows, satellites = [], {}\n",
"for key, (norad, kept, errors, next_best, excluded) in zip(keys, results):\n",
- " row = {\"key\": key, \"NORAD\": norad, \"name\": names[norad], \"element sets\": len(kept), \"excluded sets\": excluded}\n",
+ " row = {\n",
+ " \"key\": key,\n",
+ " \"NORAD\": norad,\n",
+ " \"name\": names[norad],\n",
+ " \"element sets\": len(kept),\n",
+ " \"excluded sets\": excluded,\n",
+ " }\n",
" if kept: # identified: at least one element set reproduces the recorded positions\n",
" satellites[key] = Satellite(name=key, orbit=to_orbit(kept))\n",
- " row.update({\"median error (km)\": np.median(errors), \"max error (km)\": errors.max()})\n",
+ " row.update(\n",
+ " {\"median error (km)\": np.median(errors), \"max error (km)\": errors.max()}\n",
+ " )\n",
" row[\"next-best satellite (km)\"] = next_best\n",
" rows.append(row)\n",
"orbit_check = pd.DataFrame(rows).set_index(\"key\")\n",
@@ -358,8 +408,12 @@
"receivers = [satellites[k] for k in sorted(satellites) if k.startswith(\"planetiq\")]\n",
"transmitters = [satellites[k] for k in sorted(satellites) if k[0] in CONSTELLATIONS]\n",
"unidentified = sorted(k for k in recorded if k not in satellites)\n",
- "print(f\"{len(receivers)} receivers and {len(transmitters)} transmitters identified; unidentified: {unidentified or 'none'}\")\n",
- "display(orbit_check[orbit_check[\"excluded sets\"] > 0].round(2)) # element sets excluded as poor\n",
+ "print(\n",
+ " f\"{len(receivers)} receivers and {len(transmitters)} transmitters identified; unidentified: {unidentified or 'none'}\"\n",
+ ")\n",
+ "display(\n",
+ " orbit_check[orbit_check[\"excluded sets\"] > 0].round(2)\n",
+ ") # element sets excluded as poor\n",
"orbit_check.groupby(\"group\", sort=False).agg(\n",
" **{\n",
" \"satellites\": (\"NORAD\", \"size\"),\n",
@@ -389,10 +443,10 @@
"id": "dc491949",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:51.678493Z",
- "iopub.status.busy": "2026-10-08T18:03:51.678493Z",
- "iopub.status.idle": "2026-10-08T18:03:57.327130Z",
- "shell.execute_reply": "2026-10-08T18:03:57.327130Z"
+ "iopub.execute_input": "2026-10-25T20:53:10.426924Z",
+ "iopub.status.busy": "2026-10-25T20:53:10.426537Z",
+ "iopub.status.idle": "2026-10-25T20:53:13.937506Z",
+ "shell.execute_reply": "2026-10-25T20:53:13.936787Z"
}
},
"outputs": [
@@ -491,7 +545,8 @@
"source": [
"def crossing_times(receiver, transmitter, stamps, window=600):\n",
" \"\"\"Time at which the straight line reaches -80 km, and the transmitter yaw angle in the\n",
- " receiver frame at that time, within window (s) of each time stamp (NaT/NaN if not reached).\"\"\"\n",
+ " receiver frame at that time, within window (s) of each time stamp (NaT/NaN if not reached).\n",
+ " \"\"\"\n",
" from datetime import timedelta\n",
"\n",
" import numpy as np\n",
@@ -509,9 +564,15 @@
" ts = timescale.from_datetimes([stamp + timedelta(seconds=s) for s in offsets])\n",
" rx_pv = receiver.orbit.to_gp_orbit().get_orbit_track_at_time(ts)\n",
" tx_pv = transmitter.orbit.to_gp_orbit().get_orbit_track_at_time(ts)\n",
- " tp, tp_sign, _, yaw = _tangent_point_geometry(tx_pv, rx_pv, *compute_vnb_frame(rx_pv))\n",
- " height = wgs84.geographic_position_of(Geocentric(Distance(m=tp).au, None, ts)).elevation.m\n",
- " height[tp_sign > 0] = np.nan # tangent point not between receiver and transmitter\n",
+ " tp, tp_sign, _, yaw = _tangent_point_geometry(\n",
+ " tx_pv, rx_pv, *compute_vnb_frame(rx_pv)\n",
+ " )\n",
+ " height = wgs84.geographic_position_of(\n",
+ " Geocentric(Distance(m=tp).au, None, ts)\n",
+ " ).elevation.m\n",
+ " height[tp_sign > 0] = (\n",
+ " np.nan\n",
+ " ) # tangent point not between receiver and transmitter\n",
" s = np.sign(height + 80e3)\n",
" crossings = np.nonzero(s[:-1] * s[1:] < 0)[0]\n",
" if len(crossings) == 0:\n",
@@ -520,7 +581,10 @@
" j = crossings[0]\n",
" w = (height[j] + 80e3) / (height[j] - height[j + 1])\n",
" results.append(\n",
- " (stamp + timedelta(seconds=offsets[j] + w), yaw[j] + w * (((yaw[j + 1] - yaw[j] + 180) % 360) - 180))\n",
+ " (\n",
+ " stamp + timedelta(seconds=offsets[j] + w),\n",
+ " yaw[j] + w * (((yaw[j + 1] - yaw[j] + 180) % 360) - 180),\n",
+ " )\n",
" )\n",
" return results\n",
"\n",
@@ -540,13 +604,18 @@
" ]\n",
")\n",
"# occultations of satellites without an orbit cannot be compared\n",
- "without_orbit = ~observed.transmitter.isin(satellites.keys()) | ~observed.receiver.isin(satellites.keys())\n",
- "print(f\"{int(without_orbit.sum())} observed occultations without an identified orbit, excluded\")\n",
+ "without_orbit = ~observed.transmitter.isin(satellites.keys()) | ~observed.receiver.isin(\n",
+ " satellites.keys()\n",
+ ")\n",
+ "print(\n",
+ " f\"{int(without_orbit.sum())} observed occultations without an identified orbit, excluded\"\n",
+ ")\n",
"observed = observed[~without_orbit].reset_index(drop=True)\n",
"# one parallel task per receiver-transmitter combination\n",
"groups = list(observed.groupby([\"receiver\", \"transmitter\"]).groups.items())\n",
"results = Parallel(n_jobs=N_JOBS)(\n",
- " delayed(crossing_times)(satellites[rx], satellites[tx], list(observed.stamp[index])) for (rx, tx), index in groups\n",
+ " delayed(crossing_times)(satellites[rx], satellites[tx], list(observed.stamp[index]))\n",
+ " for (rx, tx), index in groups\n",
")\n",
"t80, yaw80 = [pd.NaT] * len(observed), [np.nan] * len(observed)\n",
"for (_, index), group_results in zip(groups, results):\n",
@@ -555,7 +624,9 @@
"observed[\"t80\"] = pd.to_datetime(t80)\n",
"observed[\"yaw80\"] = yaw80\n",
"unresolved = int(observed.t80.isna().sum())\n",
- "observed = observed[(observed.t80 >= DAY0) & (observed.t80 < DAY0 + timedelta(days=1))].reset_index(drop=True)\n",
+ "observed = observed[\n",
+ " (observed.t80 >= DAY0) & (observed.t80 < DAY0 + timedelta(days=1))\n",
+ "].reset_index(drop=True)\n",
"\n",
"\n",
"def folded(yaw):\n",
@@ -564,10 +635,14 @@
" return np.minimum(yaw, 180 - yaw)\n",
"\n",
"\n",
- "print(f\"{len(observed)} observed occultations reach -80 km on 14 May ({unresolved} not resolved within the search window)\")\n",
+ "print(\n",
+ " f\"{len(observed)} observed occultations reach -80 km on 14 May ({unresolved} not resolved within the search window)\"\n",
+ ")\n",
"pd.DataFrame(\n",
" {\n",
- " c: np.percentile(folded(observed.yaw80[observed.constellation == c]), [50, 90, 95, 99, 100])\n",
+ " c: np.percentile(\n",
+ " folded(observed.yaw80[observed.constellation == c]), [50, 90, 95, 99, 100]\n",
+ " )\n",
" for c in CONSTELLATIONS.values()\n",
" },\n",
" index=[\"50%\", \"90%\", \"95%\", \"99%\", \"max\"],\n",
@@ -592,10 +667,10 @@
"id": "b83fed25",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:57.330652Z",
- "iopub.status.busy": "2026-10-08T18:03:57.329652Z",
- "iopub.status.idle": "2026-10-08T18:03:57.939566Z",
- "shell.execute_reply": "2026-10-08T18:03:57.939566Z"
+ "iopub.execute_input": "2026-10-25T20:53:13.939299Z",
+ "iopub.status.busy": "2026-10-25T20:53:13.939065Z",
+ "iopub.status.idle": "2026-10-25T20:53:24.118236Z",
+ "shell.execute_reply": "2026-10-25T20:53:24.117479Z"
}
},
"outputs": [
@@ -647,11 +722,11 @@
" \n",
"
\n",
"
GPS
\n",
- "
1578
\n",
+ "
1582
\n",
"
1382
\n",
"
1382
\n",
"
1.0
\n",
- "
0.876
\n",
+ "
0.874
\n",
"
1.0
\n",
"
\n",
"
\n",
@@ -665,20 +740,20 @@
"
\n",
"
\n",
"
Galileo
\n",
- "
1581
\n",
+ "
1585
\n",
"
1384
\n",
"
1384
\n",
"
1.0
\n",
- "
0.875
\n",
+ "
0.873
\n",
"
1.0
\n",
"
\n",
"
\n",
"
all
\n",
- "
4360
\n",
+ "
4368
\n",
"
3838
\n",
"
3838
\n",
"
1.0
\n",
- "
0.880
\n",
+ "
0.879
\n",
"
1.0
\n",
"
\n",
" \n",
@@ -688,10 +763,10 @@
"text/plain": [
" predicted observed matched \\\n",
"constellation \n",
- "GPS 1578 1382 1382 \n",
+ "GPS 1582 1382 1382 \n",
"GLONASS 1201 1072 1072 \n",
- "Galileo 1581 1384 1384 \n",
- "all 4360 3838 3838 \n",
+ "Galileo 1585 1384 1384 \n",
+ "all 4368 3838 3838 \n",
"\n",
" share of observed that were predicted \\\n",
"constellation \n",
@@ -702,10 +777,10 @@
"\n",
" share of predicted that were observed \\\n",
"constellation \n",
- "GPS 0.876 \n",
+ "GPS 0.874 \n",
"GLONASS 0.893 \n",
- "Galileo 0.875 \n",
- "all 0.880 \n",
+ "Galileo 0.873 \n",
+ "all 0.879 \n",
"\n",
" rising/setting agreement (matched) \n",
"constellation \n",
@@ -730,7 +805,12 @@
" # order (as a single collect_ro_observations call over all transmitters would return them)\n",
" results = Parallel(n_jobs=N_JOBS)(\n",
" delayed(collect_ro_observations)(\n",
- " receiver, transmitter, DAY0, DAY0 + timedelta(days=1), time_step=timedelta(seconds=1), max_yaw=MAX_YAW\n",
+ " receiver,\n",
+ " transmitter,\n",
+ " DAY0,\n",
+ " DAY0 + timedelta(days=1),\n",
+ " time_step=timedelta(seconds=1),\n",
+ " max_yaw=MAX_YAW,\n",
" )\n",
" for receiver in receivers\n",
" for transmitter in transmitters\n",
@@ -738,9 +818,9 @@
" n = len(transmitters)\n",
" pd.concat(\n",
" [\n",
- " pd.concat([r for r in results[k * n : (k + 1) * n] if len(r)], ignore_index=True).sort_values(\n",
- " \"time\", ignore_index=True\n",
- " )\n",
+ " pd.concat(\n",
+ " [r for r in results[k * n : (k + 1) * n] if len(r)], ignore_index=True\n",
+ " ).sort_values(\"time\", ignore_index=True)\n",
" for k in range(len(receivers))\n",
" ],\n",
" ignore_index=True,\n",
@@ -754,8 +834,13 @@
" candidates = []\n",
" for (rx, tx), p in pred.groupby([\"receiver\", \"transmitter\"]):\n",
" o = obs[(obs.receiver == rx) & (obs.transmitter == tx)]\n",
- " dt = np.abs((p.time.values[:, None] - o.t80.values[None, :]) / np.timedelta64(1, \"s\"))\n",
- " candidates += [(dt[i, j], p.index[i], o.index[j]) for i, j in zip(*np.nonzero(dt < tolerance))]\n",
+ " dt = np.abs(\n",
+ " (p.time.values[:, None] - o.t80.values[None, :]) / np.timedelta64(1, \"s\")\n",
+ " )\n",
+ " candidates += [\n",
+ " (dt[i, j], p.index[i], o.index[j])\n",
+ " for i, j in zip(*np.nonzero(dt < tolerance))\n",
+ " ]\n",
" used_p, used_o, pairs = set(), set(), []\n",
" for _, i, j in sorted(candidates):\n",
" if i not in used_p and j not in used_o:\n",
@@ -779,7 +864,8 @@
" \"share of observed that were predicted\": len(m) / len(o),\n",
" \"share of predicted that were observed\": len(m) / len(p),\n",
" \"rising/setting agreement (matched)\": np.mean(\n",
- " predicted.loc[m.pred, \"is_rising\"].values == ~observed.loc[m.obs, \"setting\"].values\n",
+ " predicted.loc[m.pred, \"is_rising\"].values\n",
+ " == ~observed.loc[m.obs, \"setting\"].values\n",
" ),\n",
" }\n",
"print(f\"max_yaw = {MAX_YAW} deg\")\n",
@@ -791,7 +877,7 @@
"id": "1b8fb905",
"metadata": {},
"source": [
- "With a `max_yaw` of 85 deg, TAT-C predicts every one of the 3,838 observed occultations (1,382 from GPS, 1,072 from GLONASS, and 1,384 from Galileo) and labels each correctly as rising or setting. TAT-C predicts 4,360 occultations, of which 88% were recorded (88% for GPS and Galileo, 89% for GLONASS), a much higher share than for COSMIC-2 (61% overall); Test 3 examines the rest.\n",
+ "With a `max_yaw` of 85 deg, TAT-C predicts every one of the 3,838 observed occultations (1,382 from GPS, 1,072 from GLONASS, and 1,384 from Galileo) and labels each correctly as rising or setting. TAT-C predicts 4,368 occultations, of which 88% were recorded (87% for GPS and Galileo, 89% for GLONASS), a much higher share than for COSMIC-2 (61% overall); Test 3 examines the rest.\n",
"\n",
"## Test 3: Which Predicted Occultations Are Observed?\n",
"\n",
@@ -804,16 +890,16 @@
"id": "12496669",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:03:57.942571Z",
- "iopub.status.busy": "2026-10-08T18:03:57.942571Z",
- "iopub.status.idle": "2026-10-08T18:04:02.581506Z",
- "shell.execute_reply": "2026-10-08T18:04:02.581506Z"
+ "iopub.execute_input": "2026-10-25T20:53:24.120254Z",
+ "iopub.status.busy": "2026-10-25T20:53:24.120004Z",
+ "iopub.status.idle": "2026-10-25T20:53:28.005959Z",
+ "shell.execute_reply": "2026-10-25T20:53:28.005236Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"
other same-direction occultations at the same time
\n",
@@ -1009,17 +1111,17 @@
"
\n",
"
1
\n",
"
0.90
\n",
- "
693
\n",
+ "
690
\n",
"
\n",
"
\n",
"
2
\n",
"
0.90
\n",
- "
1136
\n",
+ "
1142
\n",
"
\n",
"
\n",
"
3 or more
\n",
"
0.86
\n",
- "
2335
\n",
+ "
2340
\n",
"
\n",
" \n",
"\n",
@@ -1027,9 +1129,9 @@
],
"text/plain": [
" share observed \\\n",
- "constellation GPS 0.88 \n",
+ "constellation GPS 0.87 \n",
" GLONASS 0.89 \n",
- " Galileo 0.88 \n",
+ " Galileo 0.87 \n",
"yaw from fore/aft (deg) (0, 15] 0.90 \n",
" (15, 30] 0.92 \n",
" (30, 45] 0.90 \n",
@@ -1039,7 +1141,7 @@
" (65, 70] 0.82 \n",
" (70, 75] 0.74 \n",
" (75, 80] 0.67 \n",
- " (80, 85] 0.37 \n",
+ " (80, 85] 0.32 \n",
"direction rising 0.85 \n",
" setting 0.91 \n",
"other same-direction occultations at the same time 0 0.92 \n",
@@ -1048,9 +1150,9 @@
" 3 or more 0.86 \n",
"\n",
" predicted \n",
- "constellation GPS 1578 \n",
+ "constellation GPS 1582 \n",
" GLONASS 1201 \n",
- " Galileo 1581 \n",
+ " Galileo 1585 \n",
"yaw from fore/aft (deg) (0, 15] 945 \n",
" (15, 30] 1087 \n",
" (30, 45] 1148 \n",
@@ -1060,13 +1162,13 @@
" (65, 70] 92 \n",
" (70, 75] 105 \n",
" (75, 80] 64 \n",
- " (80, 85] 51 \n",
- "direction rising 2183 \n",
- " setting 2177 \n",
+ " (80, 85] 59 \n",
+ "direction rising 2186 \n",
+ " setting 2182 \n",
"other same-direction occultations at the same time 0 196 \n",
- " 1 693 \n",
- " 2 1136 \n",
- " 3 or more 2335 "
+ " 1 690 \n",
+ " 2 1142 \n",
+ " 3 or more 2340 "
]
},
"execution_count": 6,
@@ -1078,19 +1180,28 @@
"seen_rate = pred.groupby([\"receiver\", \"transmitter\"])[\"seen\"].transform(\"mean\")\n",
"tracked = pred[seen_rate > 0.1]\n",
"groups = {\n",
- " \"constellation\": pd.Categorical(tracked.constellation, categories=list(CONSTELLATIONS.values())),\n",
+ " \"constellation\": pd.Categorical(\n",
+ " tracked.constellation, categories=list(CONSTELLATIONS.values())\n",
+ " ),\n",
" # arcs cut off by the yaw limit report a yaw of exactly max_yaw (up to round-off)\n",
" \"yaw from fore/aft (deg)\": pd.cut(\n",
- " tracked.yaw.clip(upper=MAX_YAW), [b for b in [0, 15, 30, 45, 55, 60, 65, 70, 75, 80] if b < MAX_YAW] + [MAX_YAW]\n",
+ " tracked.yaw.clip(upper=MAX_YAW),\n",
+ " [b for b in [0, 15, 30, 45, 55, 60, 65, 70, 75, 80] if b < MAX_YAW] + [MAX_YAW],\n",
" ),\n",
" \"direction\": tracked.is_rising.map({True: \"rising\", False: \"setting\"}),\n",
" \"other same-direction occultations at the same time\": pd.Categorical(\n",
- " tracked.concurrent.clip(upper=3).astype(str).replace(\"3\", \"3 or more\"), categories=[\"0\", \"1\", \"2\", \"3 or more\"]\n",
+ " tracked.concurrent.clip(upper=3).astype(str).replace(\"3\", \"3 or more\"),\n",
+ " categories=[\"0\", \"1\", \"2\", \"3 or more\"],\n",
" ),\n",
"}\n",
- "print(f\"{len(tracked)} of {len(pred)} predicted occultations in receiver-transmitter combinations observed at least 10% of the time\")\n",
+ "print(\n",
+ " f\"{len(tracked)} of {len(pred)} predicted occultations in receiver-transmitter combinations observed at least 10% of the time\"\n",
+ ")\n",
"pd.concat(\n",
- " {name: tracked.groupby(key, observed=True)[\"seen\"].agg([\"mean\", \"size\"]) for name, key in groups.items()}\n",
+ " {\n",
+ " name: tracked.groupby(key, observed=True)[\"seen\"].agg([\"mean\", \"size\"])\n",
+ " for name, key in groups.items()\n",
+ " }\n",
").rename(columns={\"mean\": \"share observed\", \"size\": \"predicted\"}).round(2)"
]
},
@@ -1099,7 +1210,7 @@
"id": "cedb8daf",
"metadata": {},
"source": [
- "Unlike COSMIC-2, PlanetiQ tracks the three constellations alike (86 to 90% of predicted occultations recorded for every receiver and constellation) and shows no extended data gaps: both receivers recorded at least 64% of the predicted occultations in every hour. The one exception is GLONASS R19, which both receivers recorded for only 18% of its predicted occultations, although COSMIC-2 recorded most of them; every other transmitter was recorded at least 68% of the time (lowest for Galileo E19 and GPS G25). The recorded share is nearly independent of yaw angle up to 65 deg (81 to 93% for each constellation) and falls beyond it as PlanetiQ's antenna coverage tapers off: to 71 to 89% between 65 and 75 deg, 59 to 76% between 75 and 80 deg, and 37% between 80 and 85 deg, where GLONASS (71%) fares much better than GPS (31%) and Galileo (11%). Setting occultations were recorded more often than rising ones for GPS and Galileo (91 to 92% vs 83 to 84%) but hardly for GLONASS (90% vs 88%), and the share decreases only slightly when several occultations compete for the same receiver. Since TAT-C predicts all of PlanetiQ's recorded occultations, PlanetiQ recorded 88% (GPS and Galileo) to 89% (GLONASS) as many occultations as TAT-C predicts with an 85 deg `max_yaw`.\n",
+ "Unlike COSMIC-2, PlanetiQ tracks the three constellations alike (85 to 90% of predicted occultations recorded for every receiver and constellation) and shows no extended data gaps: both receivers recorded at least 64% of the predicted occultations in every hour. The one exception is GLONASS R19, which both receivers recorded for only 18% of its predicted occultations, although COSMIC-2 recorded most of them; every other transmitter was recorded at least 66% of the time (lowest for Galileo E19, then GPS G17 and G25). The recorded share is nearly independent of yaw angle up to 65 deg (81 to 93% for each constellation) and falls beyond it as PlanetiQ's antenna coverage tapers off: to 74 to 86% between 65 and 75 deg, 59 to 77% between 75 and 80 deg, and 32% between 80 and 85 deg, where GLONASS (71%) fares much better than GPS (25%) and Galileo (9%). Setting occultations were recorded more often than rising ones for GPS and Galileo (91 to 92% vs 83 to 84%) but hardly for GLONASS (90% vs 88%), and the share decreases only slightly when several occultations compete for the same receiver. Since TAT-C predicts all of PlanetiQ's recorded occultations, PlanetiQ recorded 87% (GPS and Galileo) to 89% (GLONASS) as many occultations as TAT-C predicts with an 85 deg `max_yaw`.\n",
"\n",
"## Test 4: Timing and Location of Matched Occultations\n",
"\n",
@@ -1112,10 +1223,10 @@
"id": "cbe65f5a",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:02.612371Z",
- "iopub.status.busy": "2026-10-08T18:04:02.612371Z",
- "iopub.status.idle": "2026-10-08T18:04:08.729104Z",
- "shell.execute_reply": "2026-10-08T18:04:08.729104Z"
+ "iopub.execute_input": "2026-10-25T20:53:28.031794Z",
+ "iopub.status.busy": "2026-10-25T20:53:28.031536Z",
+ "iopub.status.idle": "2026-10-25T20:53:33.396577Z",
+ "shell.execute_reply": "2026-10-25T20:53:33.395762Z"
}
},
"outputs": [
@@ -1152,7 +1263,7 @@
" \n",
" \n",
"
\n",
- "
GPS
\n",
+ "
GPS
\n",
"
TAT-C time minus true time at -80 km (s)
\n",
"
200.0
\n",
"
0.013
\n",
@@ -1163,17 +1274,27 @@
"
0.366
\n",
"
\n",
"
\n",
+ "
|TAT-C time minus true time| at -80 km (s)
\n",
+ "
200.0
\n",
+ "
0.103
\n",
+ "
0.079
\n",
+ "
0.000
\n",
+ "
0.090
\n",
+ "
0.251
\n",
+ "
0.371
\n",
+ "
\n",
+ "
\n",
"
TAT-C position: -80 km point to truth (km)
\n",
"
200.0
\n",
"
0.454
\n",
"
0.364
\n",
"
0.012
\n",
- "
0.332
\n",
- "
1.164
\n",
- "
1.851
\n",
+ "
0.335
\n",
+ "
1.162
\n",
+ "
1.846
\n",
"
\n",
"
\n",
- "
GLONASS
\n",
+ "
GLONASS
\n",
"
TAT-C time minus true time at -80 km (s)
\n",
"
200.0
\n",
"
0.018
\n",
@@ -1184,17 +1305,27 @@
"
0.508
\n",
"
\n",
"
\n",
+ "
|TAT-C time minus true time| at -80 km (s)
\n",
+ "
200.0
\n",
+ "
0.103
\n",
+ "
0.081
\n",
+ "
0.001
\n",
+ "
0.084
\n",
+ "
0.245
\n",
+ "
0.508
\n",
+ "
\n",
+ "
\n",
"
TAT-C position: -80 km point to truth (km)
\n",
"
200.0
\n",
- "
0.498
\n",
- "
0.438
\n",
- "
0.008
\n",
- "
0.368
\n",
- "
1.220
\n",
- "
3.075
\n",
+ "
0.497
\n",
+ "
0.437
\n",
+ "
0.009
\n",
+ "
0.372
\n",
+ "
1.213
\n",
+ "
3.080
\n",
"
\n",
"
\n",
- "
Galileo
\n",
+ "
Galileo
\n",
"
TAT-C time minus true time at -80 km (s)
\n",
"
200.0
\n",
"
-0.003
\n",
@@ -1205,13 +1336,23 @@
"
0.578
\n",
"
\n",
"
\n",
+ "
|TAT-C time minus true time| at -80 km (s)
\n",
+ "
200.0
\n",
+ "
0.099
\n",
+ "
0.098
\n",
+ "
0.000
\n",
+ "
0.075
\n",
+ "
0.263
\n",
+ "
0.692
\n",
+ "
\n",
+ "
\n",
"
TAT-C position: -80 km point to truth (km)
\n",
"
200.0
\n",
- "
0.466
\n",
+ "
0.465
\n",
"
0.554
\n",
- "
0.020
\n",
- "
0.335
\n",
- "
1.179
\n",
+ "
0.025
\n",
+ "
0.334
\n",
+ "
1.184
\n",
"
4.108
\n",
"
\n",
" \n",
@@ -1221,19 +1362,25 @@
"text/plain": [
" count mean std \\\n",
"GPS TAT-C time minus true time at -80 km (s) 200.0 0.013 0.129 \n",
+ " |TAT-C time minus true time| at -80 km (s) 200.0 0.103 0.079 \n",
" TAT-C position: -80 km point to truth (km) 200.0 0.454 0.364 \n",
"GLONASS TAT-C time minus true time at -80 km (s) 200.0 0.018 0.129 \n",
- " TAT-C position: -80 km point to truth (km) 200.0 0.498 0.438 \n",
+ " |TAT-C time minus true time| at -80 km (s) 200.0 0.103 0.081 \n",
+ " TAT-C position: -80 km point to truth (km) 200.0 0.497 0.437 \n",
"Galileo TAT-C time minus true time at -80 km (s) 200.0 -0.003 0.140 \n",
- " TAT-C position: -80 km point to truth (km) 200.0 0.466 0.554 \n",
+ " |TAT-C time minus true time| at -80 km (s) 200.0 0.099 0.098 \n",
+ " TAT-C position: -80 km point to truth (km) 200.0 0.465 0.554 \n",
"\n",
" min 50% 95% max \n",
"GPS TAT-C time minus true time at -80 km (s) -0.371 0.004 0.212 0.366 \n",
- " TAT-C position: -80 km point to truth (km) 0.012 0.332 1.164 1.851 \n",
+ " |TAT-C time minus true time| at -80 km (s) 0.000 0.090 0.251 0.371 \n",
+ " TAT-C position: -80 km point to truth (km) 0.012 0.335 1.162 1.846 \n",
"GLONASS TAT-C time minus true time at -80 km (s) -0.229 0.007 0.245 0.508 \n",
- " TAT-C position: -80 km point to truth (km) 0.008 0.368 1.220 3.075 \n",
+ " |TAT-C time minus true time| at -80 km (s) 0.001 0.084 0.245 0.508 \n",
+ " TAT-C position: -80 km point to truth (km) 0.009 0.372 1.213 3.080 \n",
"Galileo TAT-C time minus true time at -80 km (s) -0.692 0.004 0.184 0.578 \n",
- " TAT-C position: -80 km point to truth (km) 0.020 0.335 1.179 4.108 "
+ " |TAT-C time minus true time| at -80 km (s) 0.000 0.075 0.263 0.692 \n",
+ " TAT-C position: -80 km point to truth (km) 0.025 0.334 1.184 4.108 "
]
},
"execution_count": 7,
@@ -1278,10 +1425,16 @@
"# from the recorded receiver and transmitter positions (subsampled to 10 Hz)\n",
"truth = {}\n",
"for record in sample._data:\n",
- " l1 = nc.Dataset(os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"])))\n",
+ " l1 = nc.Dataset(\n",
+ " os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"]))\n",
+ " )\n",
" l1.set_auto_mask(False)\n",
" t, rx, tx = l1[\"time\"][:], l1[\"positionLEO\"][:].T, l1[\"positionGNSS\"][:].T\n",
- " ok = (np.abs(t) < 1e10) & np.all(np.abs(rx) < 1e10, axis=0) & np.all(np.abs(tx) < 1e10, axis=0)\n",
+ " ok = (\n",
+ " (np.abs(t) < 1e10)\n",
+ " & np.all(np.abs(rx) < 1e10, axis=0)\n",
+ " & np.all(np.abs(tx) < 1e10, axis=0)\n",
+ " )\n",
" i = np.nonzero(ok)[0][::10]\n",
" start = float(l1[\"startTime\"][...])\n",
" # GPS seconds since 1980-01-06 are TAI - 19 s\n",
@@ -1289,7 +1442,11 @@
" t_80 = crossing(t[i], to_lla(points)[2], -80e3)\n",
" if np.isfinite(t_80):\n",
" lon, lat, _ = to_lla(interpolate(t[i], points, [t_80]))\n",
- " truth[record[\"occid\"]] = (GPS_EPOCH + timedelta(seconds=start - GPS_UTC_OFFSET + t_80), lon[0], lat[0])\n",
+ " truth[record[\"occid\"]] = (\n",
+ " GPS_EPOCH + timedelta(seconds=start - GPS_UTC_OFFSET + t_80),\n",
+ " lon[0],\n",
+ " lat[0],\n",
+ " )\n",
"\n",
"rows = []\n",
"for i, j in zip(pairs.pred, pairs.obs):\n",
@@ -1299,14 +1456,25 @@
" rows.append(\n",
" {\n",
" \"constellation\": o.constellation,\n",
- " \"TAT-C time minus true time at -80 km (s)\": (p.time.to_pydatetime() - true_time).total_seconds(),\n",
- " \"TAT-C position: -80 km point to truth (km)\": GEOD.inv(p.position.x, p.position.y, lon, lat)[2] / 1e3,\n",
+ " \"TAT-C time minus true time at -80 km (s)\": (\n",
+ " p.time.to_pydatetime() - true_time\n",
+ " ).total_seconds(),\n",
+ " \"|TAT-C time minus true time| at -80 km (s)\": abs(\n",
+ " (p.time.to_pydatetime() - true_time).total_seconds()\n",
+ " ),\n",
+ " \"TAT-C position: -80 km point to truth (km)\": GEOD.inv(\n",
+ " p.position.x, p.position.y, lon, lat\n",
+ " )[2]\n",
+ " / 1e3,\n",
" }\n",
" )\n",
"accuracy = pd.DataFrame(rows)\n",
"pd.concat(\n",
" {\n",
- " c: accuracy[accuracy.constellation == c].drop(columns=\"constellation\").describe(percentiles=[0.5, 0.95]).T\n",
+ " c: accuracy[accuracy.constellation == c]\n",
+ " .drop(columns=\"constellation\")\n",
+ " .describe(percentiles=[0.5, 0.95])\n",
+ " .T\n",
" for c in CONSTELLATIONS.values()\n",
" }\n",
").round(3)"
@@ -1317,7 +1485,7 @@
"id": "ca89b353",
"metadata": {},
"source": [
- "For all three constellations, TAT-C's `time` agrees with the true time at which the straight line reaches -80 km to within 0.18 to 0.25 s (95th percentile), and its `position` with the true straight-line tangent point to 0.33 to 0.37 km (median) and 1.2 km (95th percentile), for all 200 sampled occultations of each constellation, limited by orbit accuracy."
+ "For all three constellations, TAT-C's `time` agrees with the true time at which the straight line reaches -80 km to within 0.25 to 0.26 s (95th percentile of the absolute difference, with a mean difference of at most 0.02 s), and its `position` with the true straight-line tangent point to 0.33 to 0.37 km (median) and 1.2 km (95th percentile), for all 200 sampled occultations of each constellation, limited by orbit accuracy."
]
},
{
@@ -1330,13 +1498,13 @@
"| Test | Result |\n",
"| --- | --- |\n",
"| 1. Orbits (TLEs vs. recorded positions) | All 71 transmitters (26 GPS, 19 GLONASS, 26 Galileo) and both receivers identified; within about 5 km (GPS), 7 km (GLONASS), and 16 km (Galileo), with no element sets excluded |\n",
- "| 2. Predicted vs. observed (`max_yaw` = 85 deg) | All 3,838 observed occultations predicted, with correct rising/setting; 88% of the 4,360 predicted occultations recorded (88 to 89% per constellation) |\n",
- "| 3. Detection | Constellations tracked alike, with no untracked combinations or data gaps (except GLONASS R19, 18% recorded); recorded share nearly independent of yaw angle up to 65 deg, falling to 37% at 80-85 deg |\n",
- "| 4. Timing and location | `time` within 0.18 to 0.25 s (95th percentile); `position` 0.33 to 0.37 km (median) and 1.2 km (95th percentile) from the true tangent point |\n",
+ "| 2. Predicted vs. observed (`max_yaw` = 85 deg) | All 3,838 observed occultations predicted, with correct rising/setting; 88% of the 4,368 predicted occultations recorded (87 to 89% per constellation) |\n",
+ "| 3. Detection | Constellations tracked alike, with no untracked combinations or data gaps (except GLONASS R19, 18% recorded); recorded share nearly independent of yaw angle up to 65 deg, falling to 32% at 80-85 deg |\n",
+ "| 4. Timing and location | `time` within 0.25 to 0.26 s (95th percentile); `position` 0.33 to 0.37 km (median) and 1.2 km (95th percentile) from the true tangent point |\n",
"\n",
"**TAT-C's RO geometry is validated for a polar orbit and three constellations.** With published orbits and a `max_yaw` of 85 deg, `collect_ro_observations` predicts every GPS, GLONASS, and Galileo occultation PlanetiQ recorded on this day, labels each correctly as rising or setting, and reports its time to a fraction of a second and its position to a third of a kilometer (median), limited by orbit accuracy. PlanetiQ recorded 88% of TAT-C's predictions, with no untracked transmitters or data gaps apart from GLONASS R19, so predicted counts are close to recorded counts for every constellation.\n",
"\n",
- "**PlanetiQ's coverage extends beyond the default `max_yaw`.** The 65 deg default matches COSMIC-2's coverage, but PlanetiQ observes occultations up to 84 deg from its forward or rearward direction for all three constellations, and a `max_yaw` of 85 deg predicts all of them. Predictions beyond 65 deg are recorded progressively less often as the antenna coverage tapers off (59 to 76% between 75 and 80 deg and 37% between 80 and 85 deg), so they add more predicted than recorded occultations; a smaller `max_yaw` trades a few missed occultations for a closer match between predicted and recorded counts.\n",
+ "**PlanetiQ's coverage extends beyond the default `max_yaw`.** The 65 deg default matches COSMIC-2's coverage, but PlanetiQ observes occultations up to 84 deg from its forward or rearward direction for all three constellations, and a `max_yaw` of 85 deg predicts all of them. Predictions beyond 65 deg are recorded progressively less often as the antenna coverage tapers off (59 to 77% between 75 and 80 deg and 32% between 80 and 85 deg), so they add more predicted than recorded occultations; a smaller `max_yaw` trades a few missed occultations for a closer match between predicted and recorded counts.\n",
"\n",
"**Next steps:**\n",
"\n",
@@ -1364,7 +1532,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.8"
+ "version": "3.14.9"
}
},
"nbformat": 4,
diff --git a/docs/validation/ValidateRadarNEXRAD.ipynb b/docs/validation/ValidateRadarNEXRAD.ipynb
index fe9a4904..9300f0fc 100644
--- a/docs/validation/ValidateRadarNEXRAD.ipynb
+++ b/docs/validation/ValidateRadarNEXRAD.ipynb
@@ -84,7 +84,9 @@
" params[\"continuation-token\"] = token\n",
" listing = requests.get(BUCKET, params=params, timeout=60).text\n",
" keys += re.findall(r\"(.*?)\", listing)\n",
- " match = re.search(r\"(.*?)\", listing)\n",
+ " match = re.search(\n",
+ " r\"(.*?)\", listing\n",
+ " )\n",
" if match is None:\n",
" return [key for key in keys if key.endswith(\"_V06\")]\n",
" token = match.group(1)\n",
@@ -101,7 +103,9 @@
" data, chunk = b\"\", 2**21\n",
" while True:\n",
" response = requests.get(\n",
- " BUCKET + key, headers={\"Range\": f\"bytes={len(data)}-{len(data) + chunk - 1}\"}, timeout=120\n",
+ " BUCKET + key,\n",
+ " headers={\"Range\": f\"bytes={len(data)}-{len(data) + chunk - 1}\"},\n",
+ " timeout=120,\n",
" )\n",
" if response.status_code == 206:\n",
" data += response.content\n",
@@ -112,7 +116,11 @@
" if end + 4 + size > len(data):\n",
" break\n",
" end, count = end + 4 + size, count + 1\n",
- " if count == n_records or response.status_code != 206 or len(response.content) < chunk:\n",
+ " if (\n",
+ " count == n_records\n",
+ " or response.status_code != 206\n",
+ " or len(response.content) < chunk\n",
+ " ):\n",
" return data[:end]\n",
"\n",
"\n",
@@ -125,7 +133,9 @@
" \"\"\"Number of records from the start of a volume through the first cut at or above an elevation angle.\"\"\"\n",
" n_records = 1 # metadata record\n",
" for cut in Level2File(io.BytesIO(raw)).vcp_info.els:\n",
- " n_records += (720 if \"0.5 azimuth and 0.25km range res.\" in cut.super_res else 360) // 120\n",
+ " n_records += (\n",
+ " 720 if \"0.5 azimuth and 0.25km range res.\" in cut.super_res else 360\n",
+ " ) // 120\n",
" if wrap(cut.el_angle) >= angle - 0.1:\n",
" break\n",
" return n_records + 1 # one record of margin\n",
@@ -148,8 +158,12 @@
" header = sweep[0][4][b\"REF\"][0]\n",
" sweeps.append(\n",
" {\n",
- " \"commanded\": round(wrap(radar.vcp_info.els[sweep[0][0].el_num - 1].el_angle), 2),\n",
- " \"measured\": float(np.median([wrap(radial[0].el_angle) for radial in sweep])),\n",
+ " \"commanded\": round(\n",
+ " wrap(radar.vcp_info.els[sweep[0][0].el_num - 1].el_angle), 2\n",
+ " ),\n",
+ " \"measured\": float(\n",
+ " np.median([wrap(radial[0].el_angle) for radial in sweep])\n",
+ " ),\n",
" \"radials\": len(sweep),\n",
" \"ref_range\": header.first_gate + header.gate_width * header.num_gates,\n",
" }\n",
@@ -277,7 +291,9 @@
" with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
" reduced = list(pool.map(reduce_volume, jobs))\n",
" pd.DataFrame([volume for volume, _ in reduced]).to_csv(VOLUMES_FILE, index=False)\n",
- " pd.DataFrame([sweep for _, sweeps in reduced for sweep in sweeps]).to_csv(SWEEPS_FILE, index=False)\n",
+ " pd.DataFrame([sweep for _, sweeps in reduced for sweep in sweeps]).to_csv(\n",
+ " SWEEPS_FILE, index=False\n",
+ " )\n",
"\n",
"volumes = pd.read_csv(VOLUMES_FILE, parse_dates=[\"time\"])\n",
"sweeps = pd.read_csv(SWEEPS_FILE, parse_dates=[\"time\"])\n",
@@ -386,12 +402,18 @@
" continue # Doppler-only cut of a split cut, or an incomplete sweep\n",
" header = moments[b\"REF\"][0]\n",
" n_gates = int((RANGE_BINS - header.first_gate) / header.gate_width)\n",
- " range_bin = (header.first_gate + header.gate_width * np.arange(n_gates)).astype(int)\n",
+ " range_bin = (header.first_gate + header.gate_width * np.arange(n_gates)).astype(\n",
+ " int\n",
+ " )\n",
" z, n = np.zeros((360, RANGE_BINS)), np.zeros((360, RANGE_BINS))\n",
" for radial in sweep:\n",
" dbz = radial[4][b\"REF\"][1][:n_gates]\n",
" azimuth = int(radial[0].az_angle) % 360\n",
- " np.add.at(z[azimuth], range_bin[: len(dbz)], np.where(np.isfinite(dbz), 10 ** (dbz / 10), 0))\n",
+ " np.add.at(\n",
+ " z[azimuth],\n",
+ " range_bin[: len(dbz)],\n",
+ " np.where(np.isfinite(dbz), 10 ** (dbz / 10), 0),\n",
+ " )\n",
" np.add.at(n[azimuth], range_bin[: len(dbz)], 1)\n",
" commanded = round(wrap(radar.vcp_info.els[sweep[0][0].el_num - 1].el_angle), 1)\n",
" measured = np.median([wrap(radial[0].el_angle) for radial in sweep])\n",
@@ -405,9 +427,16 @@
" keys = [key for day in EVENTS for key in list_volumes(day, station)[::6]]\n",
" with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
" results = list(pool.map(accumulate, keys))\n",
- " tilts = sorted({tilt for _, volume in results for tilt in volume if tilt <= REFERENCE_TILT})\n",
+ " tilts = sorted(\n",
+ " {tilt for _, volume in results for tilt in volume if tilt <= REFERENCE_TILT}\n",
+ " )\n",
" shape = (len(tilts), 360, RANGE_BINS)\n",
- " z, n, z_ref, n_ref = np.zeros(shape), np.zeros(shape), np.zeros(shape), np.zeros(shape)\n",
+ " z, n, z_ref, n_ref = (\n",
+ " np.zeros(shape),\n",
+ " np.zeros(shape),\n",
+ " np.zeros(shape),\n",
+ " np.zeros(shape),\n",
+ " )\n",
" measured = [[] for _ in tilts]\n",
" for _, volume in results:\n",
" if REFERENCE_TILT not in volume:\n",
@@ -514,13 +543,17 @@
" height = compute_radar_beam_height(slant_range, elevation_angle, antenna)\n",
" theta = np.radians(elevation_angle)\n",
" return EFFECTIVE_RADIUS * np.arcsin(\n",
- " np.minimum(slant_range * np.cos(theta) / (EFFECTIVE_RADIUS + height - antenna), 1)\n",
+ " np.minimum(\n",
+ " slant_range * np.cos(theta) / (EFFECTIVE_RADIUS + height - antenna), 1\n",
+ " )\n",
" )\n",
"\n",
"\n",
"def interpolate_mask(mask):\n",
" \"\"\"Minimum elevation angle (deg) of a terrain mask at 1-deg azimuth bin centers.\"\"\"\n",
- " return np.array([mask.get_min_elevation_angle(azimuth) for azimuth in np.arange(360) + 0.5])\n",
+ " return np.array(\n",
+ " [mask.get_min_elevation_angle(azimuth) for azimuth in np.arange(360) + 0.5]\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -677,14 +710,24 @@
"NCEI_FILE = DATA_DIR / \"nexrad-stations.txt\"\n",
"if not NCEI_FILE.exists():\n",
" NCEI_FILE.write_bytes(\n",
- " requests.get(\"https://www.ncei.noaa.gov/access/homr/file/nexrad-stations.txt\", timeout=60).content\n",
+ " requests.get(\n",
+ " \"https://www.ncei.noaa.gov/access/homr/file/nexrad-stations.txt\", timeout=60\n",
+ " ).content\n",
" )\n",
- "lines = [line for line in NCEI_FILE.read_text(encoding=\"utf-8\").splitlines() if line.strip()]\n",
+ "lines = [\n",
+ " line for line in NCEI_FILE.read_text(encoding=\"utf-8\").splitlines() if line.strip()\n",
+ "]\n",
"spans = [(m.start(), m.end()) for m in re.finditer(r\"-+\", lines[1])]\n",
- "ncei = pd.read_fwf(io.StringIO(\"\\n\".join(lines)), colspecs=spans, skiprows=[1]).set_index(\"ICAO\")\n",
+ "ncei = pd.read_fwf(\n",
+ " io.StringIO(\"\\n\".join(lines)), colspecs=spans, skiprows=[1]\n",
+ ").set_index(\"ICAO\")\n",
"\n",
- "pyproj.network.set_network_enabled(True) # downloads the EGM96 geoid grid if not installed\n",
- "to_ellipsoid = pyproj.Transformer.from_crs(\"EPSG:4326+5773\", \"EPSG:4979\", always_xy=True)\n",
+ "pyproj.network.set_network_enabled(\n",
+ " True\n",
+ ") # downloads the EGM96 geoid grid if not installed\n",
+ "to_ellipsoid = pyproj.Transformer.from_crs(\n",
+ " \"EPSG:4326+5773\", \"EPSG:4979\", always_xy=True\n",
+ ")\n",
"\n",
"site = volumes.groupby(\"station\")[[\"lat\", \"lon\", \"ground\", \"feedhorn\"]].first()\n",
"site[\"antenna\"] = site.ground + site.feedhorn\n",
@@ -693,7 +736,9 @@
" lat, lon, elevation = EXAMPLE[name]\n",
" undulation = to_ellipsoid.transform(site.lon[name], site.lat[name], 0)[2]\n",
" test1[name] = {\n",
- " \"example horizontal offset (m)\": GEOD.inv(lon, lat, site.lon[name], site.lat[name])[2],\n",
+ " \"example horizontal offset (m)\": GEOD.inv(\n",
+ " lon, lat, site.lon[name], site.lat[name]\n",
+ " )[2],\n",
" \"NCEI horizontal offset (m)\": GEOD.inv(\n",
" ncei.LON[name], ncei.LAT[name], site.lon[name], site.lat[name]\n",
" )[2],\n",
@@ -868,22 +913,37 @@
}
],
"source": [
- "cuts = sweeps.groupby([\"station\", \"key\", \"commanded\"]).agg(measured=(\"measured\", \"median\"), ref_range=(\"ref_range\", \"max\")).reset_index()\n",
- "per_volume = cuts.groupby([\"station\", \"key\"]).agg(\n",
- " lowest=(\"measured\", \"min\"), highest=(\"measured\", \"max\"), n_cuts=(\"measured\", \"size\"), ref_range=(\"ref_range\", \"max\")\n",
- ").join(volumes.set_index([\"station\", \"key\"])[[\"vcp\", \"planned_top\"]])\n",
+ "cuts = (\n",
+ " sweeps.groupby([\"station\", \"key\", \"commanded\"])\n",
+ " .agg(measured=(\"measured\", \"median\"), ref_range=(\"ref_range\", \"max\"))\n",
+ " .reset_index()\n",
+ ")\n",
+ "per_volume = (\n",
+ " cuts.groupby([\"station\", \"key\"])\n",
+ " .agg(\n",
+ " lowest=(\"measured\", \"min\"),\n",
+ " highest=(\"measured\", \"max\"),\n",
+ " n_cuts=(\"measured\", \"size\"),\n",
+ " ref_range=(\"ref_range\", \"max\"),\n",
+ " )\n",
+ " .join(volumes.set_index([\"station\", \"key\"])[[\"vcp\", \"planned_top\"]])\n",
+ ")\n",
"test2 = {}\n",
"for name in STATIONS:\n",
" v = per_volume.loc[name]\n",
" vcps = pd.Series(blockage[name][\"vcp\"]).value_counts(normalize=True)\n",
" test2[name] = {\n",
- " \"VCPs used (share)\": \", \".join(f\"{vcp} ({share:.0%})\" for vcp, share in vcps.items()),\n",
+ " \"VCPs used (share)\": \", \".join(\n",
+ " f\"{vcp} ({share:.0%})\" for vcp, share in vcps.items()\n",
+ " ),\n",
" \"lowest tilt (deg)\": v.lowest.median(),\n",
" \"highest tilt, VCP 212: min (deg)\": v.highest[v.vcp == 212].min(),\n",
" \"highest tilt, VCP 212: median (deg)\": v.highest[v.vcp == 212].median(),\n",
" \"highest tilt, VCP 212: max (deg)\": v.highest[v.vcp == 212].max(),\n",
" \"highest tilt, VCP 35: median (deg)\": v.highest[v.vcp == 35].median(),\n",
- " \"VCP 212 volumes ended early\": np.mean(v.highest[v.vcp == 212] < v.planned_top[v.vcp == 212] - 0.2),\n",
+ " \"VCP 212 volumes ended early\": np.mean(\n",
+ " v.highest[v.vcp == 212] < v.planned_top[v.vcp == 212] - 0.2\n",
+ " ),\n",
" \"reflectivity range, low tilts (km)\": v.ref_range.median(),\n",
" }\n",
"pd.DataFrame(test2).round(2)"
@@ -939,18 +999,47 @@
" )\n",
" example = example_stations[name]\n",
" s = np.linspace(0, example.max_range, 300)\n",
- " lowest, highest = example.min_elevation_angle - example.beam_width / 2, example.get_effective_max_elevation_angle()\n",
+ " lowest, highest = (\n",
+ " example.min_elevation_angle - example.beam_width / 2,\n",
+ " example.get_effective_max_elevation_angle(),\n",
+ " )\n",
" arc = np.linspace(lowest, highest, 100)\n",
" for x, y in [\n",
- " *[(ground_range(s, el, example.elevation), compute_radar_beam_height(s, el, example.elevation)) for el in (lowest, highest)],\n",
- " (ground_range(example.max_range, arc, example.elevation), compute_radar_beam_height(example.max_range, arc, example.elevation)),\n",
+ " *[\n",
+ " (\n",
+ " ground_range(s, el, example.elevation),\n",
+ " compute_radar_beam_height(s, el, example.elevation),\n",
+ " )\n",
+ " for el in (lowest, highest)\n",
+ " ],\n",
+ " (\n",
+ " ground_range(example.max_range, arc, example.elevation),\n",
+ " compute_radar_beam_height(example.max_range, arc, example.elevation),\n",
+ " ),\n",
" ]:\n",
" ax.plot(x / 1e3, y / 1e3, color=ORANGE, lw=1.5)\n",
" for height in HEIGHTS:\n",
" ax.axhline(height / 1e3, color=GRAY, ls=\":\", lw=1)\n",
- " ax.plot([], [], color=BLUE, alpha=0.4, lw=6, label=f\"Level II beams ({volume_time(key):%d %b %H:%M} UTC)\")\n",
- " ax.plot([], [], color=ORANGE, label=f\"TAT-C example ({lowest:.2f} to {highest:.2f} deg, {example.max_range / 1e3:.0f} km)\")\n",
- " ax.set(xlim=(0, 470), ylim=(0, 16), xlabel=\"Ground range (km)\", title=f\"{name} (antenna {antenna:.0f} m MSL)\")\n",
+ " ax.plot(\n",
+ " [],\n",
+ " [],\n",
+ " color=BLUE,\n",
+ " alpha=0.4,\n",
+ " lw=6,\n",
+ " label=f\"Level II beams ({volume_time(key):%d %b %H:%M} UTC)\",\n",
+ " )\n",
+ " ax.plot(\n",
+ " [],\n",
+ " [],\n",
+ " color=ORANGE,\n",
+ " label=f\"TAT-C example ({lowest:.2f} to {highest:.2f} deg, {example.max_range / 1e3:.0f} km)\",\n",
+ " )\n",
+ " ax.set(\n",
+ " xlim=(0, 470),\n",
+ " ylim=(0, 16),\n",
+ " xlabel=\"Ground range (km)\",\n",
+ " title=f\"{name} (antenna {antenna:.0f} m MSL)\",\n",
+ " )\n",
" ax.legend(frameon=False, loc=\"upper right\", fontsize=8)\n",
"axes[0].set_ylabel(\"Height (km MSL)\")\n",
"fig.tight_layout()\n",
@@ -1204,7 +1293,10 @@
" warnings.simplefilter(\"ignore\", RuntimeWarning) # all-NaN azimuths\n",
" deficit = np.nanmedian(ratio[lower], axis=2)\n",
" deficit[np.sum(np.isfinite(ratio[lower]), axis=2) < 10] = np.nan\n",
- " return deficit - np.nanpercentile(deficit, 75, axis=1, keepdims=True), stats[\"measured\"][lower]\n",
+ " return (\n",
+ " deficit - np.nanpercentile(deficit, 75, axis=1, keepdims=True),\n",
+ " stats[\"measured\"][lower],\n",
+ " )\n",
"\n",
"\n",
"deficits = {name: blockage_deficit(blockage[name]) for name in STATIONS}\n",
@@ -1266,11 +1358,20 @@
"fig, axes = plt.subplots(4, 1, figsize=(11, 12))\n",
"for ax, name in zip(axes, STATIONS):\n",
" deficit, tilts = deficits[name]\n",
- " edges = np.r_[tilts[0] - (tilts[1] - tilts[0]) / 2, (tilts[1:] + tilts[:-1]) / 2, tilts[-1] + (tilts[-1] - tilts[-2]) / 2]\n",
+ " edges = np.r_[\n",
+ " tilts[0] - (tilts[1] - tilts[0]) / 2,\n",
+ " (tilts[1:] + tilts[:-1]) / 2,\n",
+ " tilts[-1] + (tilts[-1] - tilts[-2]) / 2,\n",
+ " ]\n",
" ax.set_facecolor(\"#d9d8d3\")\n",
" mesh = ax.pcolormesh(np.arange(361), edges, deficit, cmap=\"RdBu\", vmin=-15, vmax=15)\n",
" ax.plot(np.arange(360) + 0.5, mask_angle[name], color=INK, lw=1.2, label=\"DEM mask\")\n",
- " ax.set(xlim=(0, 360), ylim=(edges[0] - 0.3, edges[-1] + 0.6), ylabel=\"Elevation (deg)\", title=name)\n",
+ " ax.set(\n",
+ " xlim=(0, 360),\n",
+ " ylim=(edges[0] - 0.3, edges[-1] + 0.6),\n",
+ " ylabel=\"Elevation (deg)\",\n",
+ " title=name,\n",
+ " )\n",
" ax.set_xticks(range(0, 361, 45))\n",
" ax.grid(False)\n",
" fig.colorbar(mesh, ax=ax, label=\"Deficit (dB)\", pad=0.01)\n",
@@ -1329,7 +1430,12 @@
}
],
"source": [
- "x = np.concatenate([(mask_angle[n][np.newaxis, :] - deficits[n][1][:, np.newaxis]).ravel() for n in STATIONS])\n",
+ "x = np.concatenate(\n",
+ " [\n",
+ " (mask_angle[n][np.newaxis, :] - deficits[n][1][:, np.newaxis]).ravel()\n",
+ " for n in STATIONS\n",
+ " ]\n",
+ ")\n",
"y = np.concatenate([deficits[n][0].ravel() for n in STATIONS])\n",
"valid = np.isfinite(y)\n",
"x, y = x[valid], y[valid]\n",
@@ -1340,19 +1446,46 @@
"\n",
"edges = np.arange(-1.5, 1.51, 0.1)\n",
"centers = (edges[1:] + edges[:-1]) / 2\n",
- "quantiles = np.array([np.percentile(y[(x >= lo) & (x < hi)], [25, 50, 75]) if np.sum((x >= lo) & (x < hi)) > 5 else [np.nan] * 3 for lo, hi in zip(edges[:-1], edges[1:])])\n",
+ "quantiles = np.array(\n",
+ " [\n",
+ " (\n",
+ " np.percentile(y[(x >= lo) & (x < hi)], [25, 50, 75])\n",
+ " if np.sum((x >= lo) & (x < hi)) > 5\n",
+ " else [np.nan] * 3\n",
+ " )\n",
+ " for lo, hi in zip(edges[:-1], edges[1:])\n",
+ " ]\n",
+ ")\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(11, 3.8))\n",
- "axes[0].fill_between(centers, quantiles[:, 0], quantiles[:, 2], color=BLUE, alpha=0.25, lw=0, label=\"interquartile range\")\n",
+ "axes[0].fill_between(\n",
+ " centers,\n",
+ " quantiles[:, 0],\n",
+ " quantiles[:, 2],\n",
+ " color=BLUE,\n",
+ " alpha=0.25,\n",
+ " lw=0,\n",
+ " label=\"interquartile range\",\n",
+ ")\n",
"axes[0].plot(centers, quantiles[:, 1], color=BLUE, marker=\"o\", ms=3, label=\"median\")\n",
"axes[0].axhline(-3, color=GRAY, ls=\":\", lw=1)\n",
"axes[0].axvline(0, color=INK, lw=1, label=\"beam-center criterion\")\n",
- "axes[0].axvline(-BEAMWIDTH / 2, color=ORANGE, lw=1, ls=\"--\", label=\"lower half-power edge\")\n",
- "axes[0].set(xlabel=\"DEM mask angle minus tilt (deg)\", ylabel=\"Observed deficit (dB)\", title=\"Blockage vs. terrain angle\")\n",
+ "axes[0].axvline(\n",
+ " -BEAMWIDTH / 2, color=ORANGE, lw=1, ls=\"--\", label=\"lower half-power edge\"\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"DEM mask angle minus tilt (deg)\",\n",
+ " ylabel=\"Observed deficit (dB)\",\n",
+ " title=\"Blockage vs. terrain angle\",\n",
+ ")\n",
"axes[0].legend(frameon=False, fontsize=8, loc=\"lower left\")\n",
"axes[1].plot(offsets, np.array(agreement) * 100, color=BLUE, marker=\"o\", ms=3)\n",
"axes[1].axvline(0, color=INK, lw=1)\n",
- "axes[1].set(xlabel=\"Offset (deg)\", ylabel=\"Agreement (%)\", title=\"Blocked if mask > tilt - offset\")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Offset (deg)\",\n",
+ " ylabel=\"Agreement (%)\",\n",
+ " title=\"Blocked if mask > tilt - offset\",\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()\n",
"pd.Series(\n",
@@ -1753,7 +1886,13 @@
" spans = (lower <= height) & (upper >= height)\n",
" if spans.any():\n",
" ring = np.zeros(460, dtype=bool)\n",
- " ring[np.clip((ground_range(s[spans], cut.measured, antenna) / 1e3).astype(int), 0, 459)] = True\n",
+ " ring[\n",
+ " np.clip(\n",
+ " (ground_range(s[spans], cut.measured, antenna) / 1e3).astype(int),\n",
+ " 0,\n",
+ " 459,\n",
+ " )\n",
+ " ] = True\n",
" coverage[~blocked_azimuths(name, cut.measured)] |= ring\n",
" return coverage\n",
"\n",
@@ -1783,7 +1922,12 @@
"cell_lonlat = {}\n",
"for name in STATIONS:\n",
" az, rng = np.meshgrid(GRID_AZIMUTH, GRID_RANGE, indexing=\"ij\")\n",
- " lon, lat, _ = GEOD.fwd(np.full(az.shape, site.lon[name]), np.full(az.shape, site.lat[name]), az, rng * 1e3)\n",
+ " lon, lat, _ = GEOD.fwd(\n",
+ " np.full(az.shape, site.lon[name]),\n",
+ " np.full(az.shape, site.lat[name]),\n",
+ " az,\n",
+ " rng * 1e3,\n",
+ " )\n",
" cell_lonlat[name] = (lon, lat)\n",
"\n",
"\n",
@@ -1809,19 +1953,55 @@
"rows = []\n",
"for height in HEIGHTS:\n",
" for name in STATIONS:\n",
- " example_track = rasterize(name, collect_radar_track(example_stations[name], elevation=height).geometry.iloc[0])\n",
+ " example_track = rasterize(\n",
+ " name,\n",
+ " collect_radar_track(example_stations[name], elevation=height).geometry.iloc[\n",
+ " 0\n",
+ " ],\n",
+ " )\n",
" for key in per_volume.loc[name].index:\n",
" observed = observed_coverage(name, key, height)\n",
" for config, predicted in [\n",
" (\"example\", example_track),\n",
- " (\"matched\", rasterize(name, collect_radar_track(matched_station(name, key), elevation=height).geometry.iloc[0])),\n",
- " (\"matched, beam centers\", rasterize(name, collect_radar_track(matched_station(name, key, 0), elevation=height).geometry.iloc[0])),\n",
+ " (\n",
+ " \"matched\",\n",
+ " rasterize(\n",
+ " name,\n",
+ " collect_radar_track(\n",
+ " matched_station(name, key), elevation=height\n",
+ " ).geometry.iloc[0],\n",
+ " ),\n",
+ " ),\n",
+ " (\n",
+ " \"matched, beam centers\",\n",
+ " rasterize(\n",
+ " name,\n",
+ " collect_radar_track(\n",
+ " matched_station(name, key, 0), elevation=height\n",
+ " ).geometry.iloc[0],\n",
+ " ),\n",
+ " ),\n",
" ]:\n",
- " rows.append({\"height (m)\": height, \"station\": name, \"key\": key, \"config\": config, **compare(observed, predicted)})\n",
+ " rows.append(\n",
+ " {\n",
+ " \"height (m)\": height,\n",
+ " \"station\": name,\n",
+ " \"key\": key,\n",
+ " \"config\": config,\n",
+ " **compare(observed, predicted),\n",
+ " }\n",
+ " )\n",
"test4 = pd.DataFrame(rows)\n",
- "summary4 = test4.groupby([\"height (m)\", \"station\", \"config\"], sort=False)[[\"observed area\", \"TAT-C area\", \"IoU\", \"observed inside TAT-C\"]].median()\n",
+ "summary4 = test4.groupby([\"height (m)\", \"station\", \"config\"], sort=False)[\n",
+ " [\"observed area\", \"TAT-C area\", \"IoU\", \"observed inside TAT-C\"]\n",
+ "].median()\n",
"summary4[[\"observed area\", \"TAT-C area\"]] /= 1e3\n",
- "summary4.rename(columns={\"observed area\": \"observed area (1000 km^2)\", \"TAT-C area\": \"TAT-C area (1000 km^2)\"}).unstack(\"config\").round(2)"
+ "summary4.rename(\n",
+ " columns={\n",
+ " \"observed area\": \"observed area (1000 km^2)\",\n",
+ " \"TAT-C area\": \"TAT-C area (1000 km^2)\",\n",
+ " }\n",
+ ").unstack(\"config\").round(2)"
]
},
{
@@ -1949,11 +2129,23 @@
"for height in [3048, 6096]:\n",
" for name in STATIONS:\n",
" for key in per_volume.loc[name].index:\n",
- " station = matched_station(name, key).model_copy(update={\"terrain_mask\": None})\n",
- " predicted = rasterize(name, collect_radar_track(station, elevation=height).geometry.iloc[0])\n",
- " rows.append({\"height (m)\": height, \"station\": name, **compare(observed_coverage(name, key, height), predicted)})\n",
+ " station = matched_station(name, key).model_copy(\n",
+ " update={\"terrain_mask\": None}\n",
+ " )\n",
+ " predicted = rasterize(\n",
+ " name, collect_radar_track(station, elevation=height).geometry.iloc[0]\n",
+ " )\n",
+ " rows.append(\n",
+ " {\n",
+ " \"height (m)\": height,\n",
+ " \"station\": name,\n",
+ " **compare(observed_coverage(name, key, height), predicted),\n",
+ " }\n",
+ " )\n",
"blocked_azimuths = observed_blockage\n",
- "pd.DataFrame(rows).groupby([\"height (m)\", \"station\"], sort=False)[[\"IoU\", \"observed inside TAT-C\"]].median().unstack(\"station\").round(3)"
+ "pd.DataFrame(rows).groupby([\"height (m)\", \"station\"], sort=False)[\n",
+ " [\"IoU\", \"observed inside TAT-C\"]\n",
+ "].median().unstack(\"station\").round(3)"
]
},
{
@@ -2094,21 +2286,37 @@
"source": [
"SNAPSHOT = datetime(2026, 9, 29, 12)\n",
"snapshot = {\n",
- " name: per_volume.loc[name].index[np.argmin(np.abs(volumes.set_index(\"key\").loc[per_volume.loc[name].index, \"time\"] - SNAPSHOT))]\n",
+ " name: per_volume.loc[name].index[\n",
+ " np.argmin(\n",
+ " np.abs(\n",
+ " volumes.set_index(\"key\").loc[per_volume.loc[name].index, \"time\"]\n",
+ " - SNAPSHOT\n",
+ " )\n",
+ " )\n",
+ " ]\n",
" for name in STATIONS\n",
"}\n",
- "lon_grid, lat_grid = np.meshgrid(np.arange(-118, -106.99, 0.02), np.arange(28, 38.01, 0.02))\n",
+ "lon_grid, lat_grid = np.meshgrid(\n",
+ " np.arange(-118, -106.99, 0.02), np.arange(28, 38.01, 0.02)\n",
+ ")\n",
"grid_area = np.cos(np.radians(lat_grid)) * (0.02 * 111.32) ** 2\n",
"range_edges = np.arange(461) * 1e3\n",
"corner_lonlat = {}\n",
"for name in STATIONS:\n",
" az, rng = np.meshgrid(np.arange(361), range_edges, indexing=\"ij\")\n",
- " lon, lat, _ = GEOD.fwd(np.full(az.shape, site.lon[name]), np.full(az.shape, site.lat[name]), az, rng)\n",
+ " lon, lat, _ = GEOD.fwd(\n",
+ " np.full(az.shape, site.lon[name]), np.full(az.shape, site.lat[name]), az, rng\n",
+ " )\n",
" corner_lonlat[name] = (lon, lat)\n",
"\n",
"\n",
"def observed_on_grid(name, coverage):\n",
- " az, _, dist = GEOD.inv(np.full(lon_grid.shape, site.lon[name]), np.full(lon_grid.shape, site.lat[name]), lon_grid, lat_grid)\n",
+ " az, _, dist = GEOD.inv(\n",
+ " np.full(lon_grid.shape, site.lon[name]),\n",
+ " np.full(lon_grid.shape, site.lat[name]),\n",
+ " lon_grid,\n",
+ " lat_grid,\n",
+ " )\n",
" i, j = (np.asarray(az) % 360).astype(int), (dist / 1e3).astype(int)\n",
" inside = j < 460\n",
" out = np.zeros(lon_grid.shape, dtype=bool)\n",
@@ -2116,26 +2324,49 @@
" return out\n",
"\n",
"\n",
- "fig, axes = plt.subplots(1, 3, figsize=(15, 5.6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, axes = plt.subplots(\n",
+ " 1, 3, figsize=(15, 5.6), subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"composite_rows = {}\n",
"for ax, height in zip(axes, HEIGHTS):\n",
" observed_union = np.zeros(lon_grid.shape, dtype=bool)\n",
" for name in STATIONS:\n",
" coverage = observed_coverage(name, snapshot[name], height)\n",
" observed_union |= observed_on_grid(name, coverage)\n",
- " ax.pcolormesh(*corner_lonlat[name], np.ma.masked_where(~coverage, coverage), cmap=ListedColormap([BLUE]), alpha=0.35, transform=ccrs.PlateCarree())\n",
+ " ax.pcolormesh(\n",
+ " *corner_lonlat[name],\n",
+ " np.ma.masked_where(~coverage, coverage),\n",
+ " cmap=ListedColormap([BLUE]),\n",
+ " alpha=0.35,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
" composites = {\n",
- " \"example\": compute_radar_track(list(example_stations.values()), elevation=height),\n",
- " \"matched\": compute_radar_track([matched_station(name, snapshot[name]) for name in STATIONS], elevation=height),\n",
+ " \"example\": compute_radar_track(\n",
+ " list(example_stations.values()), elevation=height\n",
+ " ),\n",
+ " \"matched\": compute_radar_track(\n",
+ " [matched_station(name, snapshot[name]) for name in STATIONS],\n",
+ " elevation=height,\n",
+ " ),\n",
" }\n",
- " for (config, composite), color, style in zip(composites.items(), [ORANGE, INK], [\"--\", \"-\"]):\n",
+ " for (config, composite), color, style in zip(\n",
+ " composites.items(), [ORANGE, INK], [\"--\", \"-\"]\n",
+ " ):\n",
" if not composite.empty:\n",
- " composite.boundary.plot(ax=ax, color=color, ls=style, lw=1.2, transform=ccrs.PlateCarree())\n",
+ " composite.boundary.plot(\n",
+ " ax=ax, color=color, ls=style, lw=1.2, transform=ccrs.PlateCarree()\n",
+ " )\n",
" geometry = composite.geometry.iloc[0]\n",
" else:\n",
" geometry = shapely.Polygon()\n",
- " predicted = shapely.contains_xy(geometry, lon_grid, lat_grid) if not geometry.is_empty else np.zeros(lon_grid.shape, dtype=bool)\n",
- " a_obs, a_pred = (observed_union * grid_area).sum(), (predicted * grid_area).sum()\n",
+ " predicted = (\n",
+ " shapely.contains_xy(geometry, lon_grid, lat_grid)\n",
+ " if not geometry.is_empty\n",
+ " else np.zeros(lon_grid.shape, dtype=bool)\n",
+ " )\n",
+ " a_obs, a_pred = (observed_union * grid_area).sum(), (\n",
+ " predicted * grid_area\n",
+ " ).sum()\n",
" a_both = ((observed_union & predicted) * grid_area).sum()\n",
" composite_rows[(height, config)] = {\n",
" \"observed area (1000 km^2)\": a_obs / 1e3,\n",
@@ -2150,7 +2381,9 @@
"axes[0].plot([], [], color=BLUE, alpha=0.5, lw=6, label=\"observed (Level II)\")\n",
"axes[0].plot([], [], color=ORANGE, ls=\"--\", label=\"TAT-C, example\")\n",
"axes[0].plot([], [], color=INK, label=\"TAT-C, matched\")\n",
- "fig.legend(*axes[0].get_legend_handles_labels(), loc=\"lower center\", ncol=3, frameon=False)\n",
+ "fig.legend(\n",
+ " *axes[0].get_legend_handles_labels(), loc=\"lower center\", ncol=3, frameon=False\n",
+ ")\n",
"fig.tight_layout(rect=(0, 0.06, 1, 1))\n",
"plt.show()\n",
"pd.DataFrame(composite_rows).T.round(2)"
diff --git a/docs/validation/ValidateRegionPolarSentinel1.ipynb b/docs/validation/ValidateRegionPolarSentinel1.ipynb
new file mode 100644
index 00000000..8331ed41
--- /dev/null
+++ b/docs/validation/ValidateRegionPolarSentinel1.ipynb
@@ -0,0 +1,1062 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "735022a9",
+ "metadata": {},
+ "source": [
+ "# Polar Region Validation: Sentinel-1C\n",
+ "\n",
+ "This notebook validates TAT-C's observations of regions (`collect_region_observations`) at the poles and across the anti-meridian, where regions are hardest to represent in longitude and latitude, against the acquisitions of the C-band synthetic aperture radar (SAR) of [Sentinel-1C](https://sentiwiki.copernicus.eu/web/s1-mission) over a week, 29 September to 5 October 2026. Sentinel-1 looks to the right of its sun-synchronous orbit, so it reaches closer to the North Pole (about 87.6 deg) than to the South Pole, and over sea ice and polar oceans it mostly acquires its Extra Wide swath (EW) mode. The [Sentinel-2 region notebook](ValidateRegionSentinel2.ipynb) validates regions at lower latitudes and across the anti-meridian (Fiji); here, four regions test the polar cases:\n",
+ "\n",
+ "* **Antarctica**, a polygon around the South Pole, drawn with edges along the pole and the anti-meridian;\n",
+ "* the **Arctic cap**, everything north of 80 deg, a box around the North Pole;\n",
+ "* **Greenland**, reaching 83.6 deg north, near but not around the pole; and\n",
+ "* the **Chukchi Sea** (64 to 75 deg north, 170 deg east to 170 deg west), a box split in two across the anti-meridian.\n",
+ "\n",
+ "The comparison asks two questions:\n",
+ "\n",
+ "1. **Passes.** Does `collect_region_observations` predict every pass on which Sentinel-1C acquired any part of each region?\n",
+ "2. **Swaths and polar coverage.** Do the predicted swaths contain the acquired products, and reach as close to the poles as Sentinel-1C does?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be7aaefe",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "The Sentinel-1 Level-1 Ground Range Detected (GRD) products of Sentinel-1C over the week are listed in the [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/dataset/sentinel-1-grd) catalog (no account required), with each product's mode, orbit, times, and footprint; the catalog search is cached in a local `data/sentinel1` directory. The EW and Interferometric Wide swath (IW) products are compared (a few stripmap products are not).\n",
+ "\n",
+ "The catalog footprints are polygons of the four corners of each product, with straight edges in longitude and latitude. Near the poles, a straight edge in longitude and latitude between corners tens of degrees of longitude apart lies far from the true edge of the product, which follows (nearly) a great circle toward the pole, so the footprints are redrawn with geodesic edges between their corners."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "4b85e540",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:23:57.936998Z",
+ "iopub.status.busy": "2026-10-25T23:23:57.936830Z",
+ "iopub.status.idle": "2026-10-25T23:23:59.299204Z",
+ "shell.execute_reply": "2026-10-25T23:23:59.298476Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3728 EW and IW products ({'IW': 3028, 'EW': 700}), 29 Sep to 06 Oct 2026\n",
+ "catalog footprints with 4 corners (5 vertices): 99.1%\n"
+ ]
+ }
+ ],
+ "source": [
+ "import json\n",
+ "from datetime import datetime, timezone\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import requests\n",
+ "import shapely\n",
+ "from pyproj import Geod\n",
+ "from shapely.geometry import shape\n",
+ "\n",
+ "DATA_DIR = Path(\"data\") / \"sentinel1\"\n",
+ "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "START = datetime(2026, 9, 29, tzinfo=timezone.utc)\n",
+ "END = datetime(2026, 10, 6, tzinfo=timezone.utc)\n",
+ "STAC = \"https://planetarycomputer.microsoft.com/api/stac/v1/search\"\n",
+ "MODES = [\"EW\", \"IW\"]\n",
+ "GEOD = Geod(ellps=\"WGS84\")\n",
+ "\n",
+ "ITEMS_FILE = DATA_DIR / f\"s1c_grd_{START:%Y%m%d}_{END:%Y%m%d}.json\"\n",
+ "if not ITEMS_FILE.exists():\n",
+ " body = {\n",
+ " \"collections\": [\"sentinel-1-grd\"],\n",
+ " \"datetime\": f\"{START:%Y-%m-%dT%H:%M:%SZ}/{END:%Y-%m-%dT%H:%M:%SZ}\",\n",
+ " \"limit\": 1000,\n",
+ " \"query\": {\"platform\": {\"eq\": \"sentinel-1c\"}},\n",
+ " }\n",
+ " items, url = [], STAC\n",
+ " while url:\n",
+ " page = requests.post(url, json=body, timeout=300).json()\n",
+ " items += page[\"features\"]\n",
+ " following = [link for link in page[\"links\"] if link[\"rel\"] == \"next\"]\n",
+ " url, body = (\n",
+ " (following[0][\"href\"], following[0].get(\"body\", body))\n",
+ " if following\n",
+ " else (None, None)\n",
+ " )\n",
+ " ITEMS_FILE.write_text(json.dumps(items))\n",
+ "\n",
+ "\n",
+ "def geodesic(geometry, points=50):\n",
+ " \"\"\"A footprint with geodesic edges between its corners (rather than straight edges in longitude and latitude).\"\"\"\n",
+ " parts = []\n",
+ " for part in getattr(geometry, \"geoms\", [geometry]):\n",
+ " corners = np.asarray(part.exterior.coords)\n",
+ " ring = []\n",
+ " for (x0, y0), (x1, y1) in zip(corners[:-1], corners[1:]):\n",
+ " ring += [(x0, y0)] + list(GEOD.npts(x0, y0, x1, y1, points))\n",
+ " parts.append(shapely.Polygon(ring))\n",
+ " return parts[0] if len(parts) == 1 else shapely.MultiPolygon(parts)\n",
+ "\n",
+ "\n",
+ "items = [\n",
+ " i\n",
+ " for i in json.loads(ITEMS_FILE.read_text())\n",
+ " if i[\"properties\"][\"sar:instrument_mode\"] in MODES\n",
+ "]\n",
+ "products = pd.DataFrame(\n",
+ " [\n",
+ " {\n",
+ " \"mode\": i[\"properties\"][\"sar:instrument_mode\"],\n",
+ " \"orbit\": i[\"properties\"][\"sat:absolute_orbit\"],\n",
+ " \"start\": pd.Timestamp(i[\"properties\"][\"start_datetime\"]),\n",
+ " \"end\": pd.Timestamp(i[\"properties\"][\"end_datetime\"]),\n",
+ " \"corners\": shape(i[\"geometry\"]),\n",
+ " }\n",
+ " for i in items\n",
+ " ]\n",
+ ")\n",
+ "products[\"footprint\"] = products.corners.apply(geodesic)\n",
+ "vertices = products.corners.apply(lambda g: len(shapely.get_coordinates(g)))\n",
+ "print(\n",
+ " f\"{len(products)} EW and IW products ({products['mode'].value_counts().to_dict()}), {START:%d %b} to {END:%d %b %Y}\"\n",
+ ")\n",
+ "print(f\"catalog footprints with 4 corners (5 vertices): {(vertices == 5).mean():.1%}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6b9b02fc",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "Sentinel-1C is modeled as in the [Sentinel-1 notebook](ValidateSARSentinel1.ipynb): a right-looking, rectangular `PointedInstrument` for each mode, with the roll angle and cross-track field of view fitted to the products there (EW: 28.95 deg and 23.56 deg; IW: 33.76 and 13.27 deg), oriented to the Earth-fixed velocity (the zero-Doppler geometry of SAR images). Sentinel-1 also steers its roll angle around the orbit to keep its slant range nearly constant, which the Sentinel-1 notebook fits as a satellite `Attitude` with a roll angle profile (by argument of latitude) for each mode; both the fixed roll angle and the roll steering are compared. `collect_region_observations` splits each region along the anti-meridian and the poles (`split_polygon`) before observing it, which should preserve its shape."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d82c0951",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:23:59.301190Z",
+ "iopub.status.busy": "2026-10-25T23:23:59.300901Z",
+ "iopub.status.idle": "2026-10-25T23:24:43.044094Z",
+ "shell.execute_reply": "2026-10-25T23:24:43.043337Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
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type
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+ "
bounds
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+ "
area (deg^2)
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+ "
split area (deg^2)
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+ "
symmetric difference (deg^2)
\n",
+ "
observations (fixed roll)
\n",
+ "
observations (roll steering)
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+ "
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+ " \n",
+ " \n",
+ "
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+ "
Antarctica
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+ "
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+ "
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+ "
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Greenland
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Chukchi Sea
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " type bounds area (deg^2) \\\n",
+ "Antarctica MultiPolygon -180.0, -90.0, 180.0, -63.3 6028.836194 \n",
+ "Arctic cap Polygon -180.0, 80.0, 180.0, 90.0 3600.0 \n",
+ "Greenland Polygon -73.3, 60.0, -12.2, 83.6 677.509565 \n",
+ "Chukchi Sea MultiPolygon -180.0, 64.0, 180.0, 75.0 220.0 \n",
+ "\n",
+ " split area (deg^2) symmetric difference (deg^2) \\\n",
+ "Antarctica 6028.836194 0.0 \n",
+ "Arctic cap 3600.0 0.0 \n",
+ "Greenland 677.509565 0.0 \n",
+ "Chukchi Sea 220.0 0.0 \n",
+ "\n",
+ " observations (fixed roll) observations (roll steering) \n",
+ "Antarctica 370 378 \n",
+ "Arctic cap 204 204 \n",
+ "Greenland 127 126 \n",
+ "Chukchi Sea 43 43 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import cartopy.io.shapereader as shpreader\n",
+ "from pyproj import Transformer\n",
+ "from shapely.geometry import MultiPolygon, box\n",
+ "\n",
+ "from tatc.analysis import collect_region_observations\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
+ "from tatc.utils import hash_geometry, split_polygon\n",
+ "\n",
+ "TLE = [\n",
+ " \"1 62261U 24235A 26275.78977191 -.00000047 00000+0 -21660-6 0 9997\",\n",
+ " \"2 62261 98.1826 281.5711 0001343 90.3841 269.7512 14.59198718 97106\",\n",
+ "]\n",
+ "# roll angle and cross-track field of view (deg) of each mode, and the farthest look angle, from ValidateSARSentinel1\n",
+ "VIEWS = {\"EW\": (28.951, 23.563), \"IW\": (33.760, 13.265)}\n",
+ "FAR_LOOK = 40.73\n",
+ "# roll steering (roll angle by argument of latitude, deg), fitted in ValidateSARSentinel1 (Test 6)\n",
+ "ROLL_PROFILES = {\n",
+ " \"EW\": [\n",
+ " (55, -29.25),\n",
+ " (65, -29.099),\n",
+ " (75, -28.824),\n",
+ " (85, -28.978),\n",
+ " (95, -28.909),\n",
+ " (105, -29.02),\n",
+ " (115, -29.184),\n",
+ " (125, -29.385),\n",
+ " (135, -29.668),\n",
+ " (245, -28.101),\n",
+ " (255, -27.764),\n",
+ " (275, -27.997),\n",
+ " (285, -27.89),\n",
+ " (295, -28.191),\n",
+ " ],\n",
+ " \"IW\": [\n",
+ " (5, -33.998),\n",
+ " (15, -33.98),\n",
+ " (25, -34.22),\n",
+ " (35, -34.069),\n",
+ " (45, -33.855),\n",
+ " (55, -33.651),\n",
+ " (65, -33.445),\n",
+ " (75, -33.202),\n",
+ " (95, -33.467),\n",
+ " (105, -33.236),\n",
+ " (115, -33.485),\n",
+ " (125, -33.651),\n",
+ " (135, -33.864),\n",
+ " (145, -34.0),\n",
+ " (155, -34.206),\n",
+ " (165, -33.998),\n",
+ " (175, -33.984),\n",
+ " (185, -33.922),\n",
+ " (195, -34.148),\n",
+ " (205, -33.664),\n",
+ " (215, -33.239),\n",
+ " (225, -32.825),\n",
+ " (255, -32.581),\n",
+ " (275, -32.402),\n",
+ " (305, -32.765),\n",
+ " (315, -33.07),\n",
+ " (325, -33.247),\n",
+ " (335, -33.485),\n",
+ " (345, -33.824),\n",
+ " (355, -34.014),\n",
+ " ],\n",
+ "}\n",
+ "countries = {\n",
+ " r.attributes[\"NAME\"]: r.geometry\n",
+ " for r in shpreader.Reader(\n",
+ " shpreader.natural_earth(\n",
+ " resolution=\"110m\", category=\"cultural\", name=\"admin_0_countries\"\n",
+ " )\n",
+ " ).records()\n",
+ "}\n",
+ "regions = {\n",
+ " \"Antarctica\": countries[\"Antarctica\"],\n",
+ " \"Arctic cap\": box(-180, 80, 180, 90),\n",
+ " \"Greenland\": countries[\"Greenland\"],\n",
+ " \"Chukchi Sea\": MultiPolygon([box(170, 64, 180, 75), box(-180, 64, -170, 75)]),\n",
+ "}\n",
+ "region_names = {hash_geometry(geometry): name for name, geometry in regions.items()}\n",
+ "\n",
+ "\n",
+ "def satellites(steering):\n",
+ " \"\"\"Sentinel-1C in each mode, with a fixed roll angle or roll steering.\"\"\"\n",
+ " return [\n",
+ " Satellite(\n",
+ " name=f\"Sentinel-1C {mode}\",\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(TLE),\n",
+ " attitude=(\n",
+ " Attitude(\n",
+ " roll_angle_profile=[(u, r + roll) for u, r in ROLL_PROFILES[mode]]\n",
+ " )\n",
+ " if steering\n",
+ " else None\n",
+ " ),\n",
+ " instruments=[\n",
+ " PointedInstrument(\n",
+ " name=mode,\n",
+ " field_of_regard=2 * (FAR_LOOK + 1),\n",
+ " cross_track_field_of_view=fov,\n",
+ " along_track_field_of_view=1,\n",
+ " roll_angle=-roll,\n",
+ " is_rectangular=True,\n",
+ " )\n",
+ " ],\n",
+ " )\n",
+ " for mode, (roll, fov) in VIEWS.items()\n",
+ " ]\n",
+ "\n",
+ "\n",
+ "observations = {}\n",
+ "for label, steering in [(\"fixed roll\", False), (\"roll steering\", True)]:\n",
+ " found = collect_region_observations(\n",
+ " list(regions.values()), satellites(steering), START, END\n",
+ " )\n",
+ " found[\"region\"] = found.target_hash.map(region_names)\n",
+ " observations[label] = found\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " name: {\n",
+ " \"type\": geometry.geom_type,\n",
+ " \"bounds\": \", \".join(f\"{b:.1f}\" for b in geometry.bounds),\n",
+ " \"area (deg^2)\": geometry.area,\n",
+ " \"split area (deg^2)\": split_polygon(geometry).area,\n",
+ " \"symmetric difference (deg^2)\": geometry.symmetric_difference(\n",
+ " split_polygon(geometry)\n",
+ " ).area,\n",
+ " \"observations (fixed roll)\": int(\n",
+ " (observations[\"fixed roll\"].region == name).sum()\n",
+ " ),\n",
+ " \"observations (roll steering)\": int(\n",
+ " (observations[\"roll steering\"].region == name).sum()\n",
+ " ),\n",
+ " }\n",
+ " for name, geometry in regions.items()\n",
+ " }\n",
+ ").T.round(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "086da51b",
+ "metadata": {},
+ "source": [
+ "`split_polygon` preserves each region exactly, including Antarctica, whose boundary already includes edges along the South Pole and the anti-meridian, and whose coastline doubles back in longitude around the Antarctic Peninsula and along Victoria Land. (An earlier version reconstructed polygons around a pole by sorting their vertices by longitude, which this validation exposed: it removed or added strips of Antarctica along those coasts, about 160 and 270 deg^2, so that 12% of the area Sentinel-1C acquired in Antarctica fell outside the predicted swaths.)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "979f856a",
+ "metadata": {},
+ "source": [
+ "## Test 1: Passes\n",
+ "\n",
+ "A pass acquired a region if any of its products (by orbit and mode) intersects the region, and is predicted if an observation of the region by the same mode overlaps the products in time. Sentinel-1C acquires its modes according to its acquisition plan, so not every predicted pass is acquired; the comparison is of the acquired passes. The table also counts the products that intersect a region only with their catalog footprints' straight edges."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "678b95f1",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:24:43.046134Z",
+ "iopub.status.busy": "2026-10-25T23:24:43.045823Z",
+ "iopub.status.idle": "2026-10-25T23:24:43.279211Z",
+ "shell.execute_reply": "2026-10-25T23:24:43.278496Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "
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acquired passes
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predicted
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predicted (%)
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observations
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products intersecting only with straight edges
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only with geodesic edges
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Antarctica
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fixed roll
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+ "
Greenland
\n",
+ "
fixed roll
\n",
+ "
59.0
\n",
+ "
59.0
\n",
+ "
100.00
\n",
+ "
127.0
\n",
+ "
9.0
\n",
+ "
2.0
\n",
+ "
\n",
+ "
\n",
+ "
Chukchi Sea
\n",
+ "
fixed roll
\n",
+ "
18.0
\n",
+ "
18.0
\n",
+ "
100.00
\n",
+ "
43.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
\n",
+ "
\n",
+ "
Antarctica
\n",
+ "
roll steering
\n",
+ "
49.0
\n",
+ "
49.0
\n",
+ "
100.00
\n",
+ "
378.0
\n",
+ "
0.0
\n",
+ "
2.0
\n",
+ "
\n",
+ "
\n",
+ "
Arctic cap
\n",
+ "
roll steering
\n",
+ "
84.0
\n",
+ "
84.0
\n",
+ "
100.00
\n",
+ "
204.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
\n",
+ "
\n",
+ "
Greenland
\n",
+ "
roll steering
\n",
+ "
59.0
\n",
+ "
59.0
\n",
+ "
100.00
\n",
+ "
126.0
\n",
+ "
9.0
\n",
+ "
2.0
\n",
+ "
\n",
+ "
\n",
+ "
Chukchi Sea
\n",
+ "
roll steering
\n",
+ "
18.0
\n",
+ "
18.0
\n",
+ "
100.00
\n",
+ "
43.0
\n",
+ "
0.0
\n",
+ "
0.0
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " acquired passes predicted predicted (%) \\\n",
+ "Antarctica fixed roll 49.0 48.0 97.96 \n",
+ "Arctic cap fixed roll 84.0 84.0 100.00 \n",
+ "Greenland fixed roll 59.0 59.0 100.00 \n",
+ "Chukchi Sea fixed roll 18.0 18.0 100.00 \n",
+ "Antarctica roll steering 49.0 49.0 100.00 \n",
+ "Arctic cap roll steering 84.0 84.0 100.00 \n",
+ "Greenland roll steering 59.0 59.0 100.00 \n",
+ "Chukchi Sea roll steering 18.0 18.0 100.00 \n",
+ "\n",
+ " observations \\\n",
+ "Antarctica fixed roll 370.0 \n",
+ "Arctic cap fixed roll 204.0 \n",
+ "Greenland fixed roll 127.0 \n",
+ "Chukchi Sea fixed roll 43.0 \n",
+ "Antarctica roll steering 378.0 \n",
+ "Arctic cap roll steering 204.0 \n",
+ "Greenland roll steering 126.0 \n",
+ "Chukchi Sea roll steering 43.0 \n",
+ "\n",
+ " products intersecting only with straight edges \\\n",
+ "Antarctica fixed roll 0.0 \n",
+ "Arctic cap fixed roll 0.0 \n",
+ "Greenland fixed roll 9.0 \n",
+ "Chukchi Sea fixed roll 0.0 \n",
+ "Antarctica roll steering 0.0 \n",
+ "Arctic cap roll steering 0.0 \n",
+ "Greenland roll steering 9.0 \n",
+ "Chukchi Sea roll steering 0.0 \n",
+ "\n",
+ " only with geodesic edges \n",
+ "Antarctica fixed roll 2.0 \n",
+ "Arctic cap fixed roll 0.0 \n",
+ "Greenland fixed roll 2.0 \n",
+ "Chukchi Sea fixed roll 0.0 \n",
+ "Antarctica roll steering 2.0 \n",
+ "Arctic cap roll steering 0.0 \n",
+ "Greenland roll steering 2.0 \n",
+ "Chukchi Sea roll steering 0.0 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "EQUAL_AREA = {\n",
+ " 1: Transformer.from_crs(\n",
+ " \"EPSG:4326\", \"+proj=laea +lat_0=90 +lon_0=0 +units=m\", always_xy=True\n",
+ " ),\n",
+ " -1: Transformer.from_crs(\n",
+ " \"EPSG:4326\", \"+proj=laea +lat_0=-90 +lon_0=0 +units=m\", always_xy=True\n",
+ " ),\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def project(geometry, pole, densify=True):\n",
+ " \"\"\"A geometry in a polar Lambert azimuthal equal-area projection, with its edges (straight in longitude and\n",
+ " latitude, as TAT-C's regions and swaths) densified first, unless they already are (geodesic footprints).\n",
+ " \"\"\"\n",
+ " if densify:\n",
+ " geometry = shapely.segmentize(geometry, 0.25)\n",
+ " transform = EQUAL_AREA[pole].transform\n",
+ " return shapely.transform(\n",
+ " geometry, lambda xy: np.column_stack(transform(xy[:, 0], xy[:, 1]))\n",
+ " ).buffer(0)\n",
+ "\n",
+ "\n",
+ "def acquired_passes(name):\n",
+ " \"\"\"The passes (by orbit and mode) whose products intersect a region, with their time spans and footprints.\"\"\"\n",
+ " region = regions[name]\n",
+ " hits = products[products.footprint.apply(region.intersects)]\n",
+ " return (\n",
+ " hits.groupby([\"orbit\", \"mode\"])\n",
+ " .agg(\n",
+ " start=(\"start\", \"min\"),\n",
+ " end=(\"end\", \"max\"),\n",
+ " footprint=(\"footprint\", lambda g: shapely.union_all(list(g))),\n",
+ " )\n",
+ " .reset_index()\n",
+ " )\n",
+ "\n",
+ "\n",
+ "passes = {name: acquired_passes(name) for name in regions}\n",
+ "# products intersecting each region with their catalog footprints (straight edges) and with geodesic edges\n",
+ "straight = {\n",
+ " name: products.corners.apply(region.intersects) for name, region in regions.items()\n",
+ "}\n",
+ "true_edges = {\n",
+ " name: products.footprint.apply(region.intersects)\n",
+ " for name, region in regions.items()\n",
+ "}\n",
+ "rows = {}\n",
+ "for label, found in observations.items():\n",
+ " for name in regions:\n",
+ " p, o = passes[name], found[found.region == name]\n",
+ " predicted = [\n",
+ " ((o.instrument == q.mode) & (o.start <= q.end) & (o.end >= q.start)).any()\n",
+ " for q in p.itertuples()\n",
+ " ]\n",
+ " rows[(name, label)] = {\n",
+ " \"acquired passes\": len(p),\n",
+ " \"predicted\": int(np.sum(predicted)),\n",
+ " \"predicted (%)\": 100 * np.mean(predicted),\n",
+ " \"observations\": len(o),\n",
+ " \"products intersecting only with straight edges\": int(\n",
+ " (straight[name] & ~true_edges[name]).sum()\n",
+ " ),\n",
+ " \"only with geodesic edges\": int((true_edges[name] & ~straight[name]).sum()),\n",
+ " }\n",
+ "pd.DataFrame(rows).T.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ebd179d5",
+ "metadata": {},
+ "source": [
+ "With roll steering, `collect_region_observations` predicts every one of the 210 passes on which Sentinel-1C acquired part of a region, including all 84 over the Arctic cap and all 49 over Antarctica. With a fixed roll angle, it misses one Antarctic pass, whose products touch the coast for about 5 km^2 and end less than a second before the predicted observation begins.\n",
+ "\n",
+ "The catalog footprints' straight edges in longitude and latitude would mislead the comparison near the poles: 9 products appear to intersect Greenland only because of them (and 4 intersect a region only with geodesic edges). These products cross the Arctic cap north of Greenland, where a straight edge in longitude and latitude between corners tens of degrees of longitude apart lies up to about 80 km from the product's true edge."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3ae5095a",
+ "metadata": {},
+ "source": [
+ "## Test 2: Swaths and Polar Coverage\n",
+ "\n",
+ "For each region, the part acquired (the union of the acquired products, within the region) is compared with the swaths of the observations predicted for the acquired passes, which cover each whole pass over the region (and so contain the acquired products, if they are accurate). The union of the swaths of all predicted observations gives the part of each region Sentinel-1C can observe in a week, and the farthest latitude reached, its polar gap. Areas are computed in polar Lambert azimuthal equal-area projections."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "64fc3dd3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:24:43.281102Z",
+ "iopub.status.busy": "2026-10-25T23:24:43.280854Z",
+ "iopub.status.idle": "2026-10-25T23:24:57.386895Z",
+ "shell.execute_reply": "2026-10-25T23:24:57.386301Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
region area (10^6 km^2)
\n",
+ "
acquired (%)
\n",
+ "
acquired within predicted swaths (%)
\n",
+ "
predicted swaths of all passes (%)
\n",
+ "
farthest latitude acquired (deg)
\n",
+ "
farthest latitude predicted (deg)
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Antarctica
\n",
+ "
fixed roll
\n",
+ "
12.337
\n",
+ "
18.672
\n",
+ "
99.720
\n",
+ "
71.349
\n",
+ "
-78.633
\n",
+ "
-79.843
\n",
+ "
\n",
+ "
\n",
+ "
Arctic cap
\n",
+ "
fixed roll
\n",
+ "
3.909
\n",
+ "
84.257
\n",
+ "
100.000
\n",
+ "
94.111
\n",
+ "
87.570
\n",
+ "
87.577
\n",
+ "
\n",
+ "
\n",
+ "
Greenland
\n",
+ "
fixed roll
\n",
+ "
2.207
\n",
+ "
100.000
\n",
+ "
100.000
\n",
+ "
100.000
\n",
+ "
83.645
\n",
+ "
83.645
\n",
+ "
\n",
+ "
\n",
+ "
Chukchi Sea
\n",
+ "
fixed roll
\n",
+ "
0.958
\n",
+ "
89.701
\n",
+ "
99.944
\n",
+ "
100.000
\n",
+ "
75.000
\n",
+ "
75.000
\n",
+ "
\n",
+ "
\n",
+ "
Antarctica
\n",
+ "
roll steering
\n",
+ "
12.337
\n",
+ "
18.672
\n",
+ "
100.000
\n",
+ "
71.957
\n",
+ "
-78.633
\n",
+ "
-79.970
\n",
+ "
\n",
+ "
\n",
+ "
Arctic cap
\n",
+ "
roll steering
\n",
+ "
3.909
\n",
+ "
84.257
\n",
+ "
100.000
\n",
+ "
94.103
\n",
+ "
87.570
\n",
+ "
87.575
\n",
+ "
\n",
+ "
\n",
+ "
Greenland
\n",
+ "
roll steering
\n",
+ "
2.207
\n",
+ "
100.000
\n",
+ "
100.000
\n",
+ "
100.000
\n",
+ "
83.645
\n",
+ "
83.645
\n",
+ "
\n",
+ "
\n",
+ "
Chukchi Sea
\n",
+ "
roll steering
\n",
+ "
0.958
\n",
+ "
89.701
\n",
+ "
100.000
\n",
+ "
100.000
\n",
+ "
75.000
\n",
+ "
75.000
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " region area (10^6 km^2) acquired (%) \\\n",
+ "Antarctica fixed roll 12.337 18.672 \n",
+ "Arctic cap fixed roll 3.909 84.257 \n",
+ "Greenland fixed roll 2.207 100.000 \n",
+ "Chukchi Sea fixed roll 0.958 89.701 \n",
+ "Antarctica roll steering 12.337 18.672 \n",
+ "Arctic cap roll steering 3.909 84.257 \n",
+ "Greenland roll steering 2.207 100.000 \n",
+ "Chukchi Sea roll steering 0.958 89.701 \n",
+ "\n",
+ " acquired within predicted swaths (%) \\\n",
+ "Antarctica fixed roll 99.720 \n",
+ "Arctic cap fixed roll 100.000 \n",
+ "Greenland fixed roll 100.000 \n",
+ "Chukchi Sea fixed roll 99.944 \n",
+ "Antarctica roll steering 100.000 \n",
+ "Arctic cap roll steering 100.000 \n",
+ "Greenland roll steering 100.000 \n",
+ "Chukchi Sea roll steering 100.000 \n",
+ "\n",
+ " predicted swaths of all passes (%) \\\n",
+ "Antarctica fixed roll 71.349 \n",
+ "Arctic cap fixed roll 94.111 \n",
+ "Greenland fixed roll 100.000 \n",
+ "Chukchi Sea fixed roll 100.000 \n",
+ "Antarctica roll steering 71.957 \n",
+ "Arctic cap roll steering 94.103 \n",
+ "Greenland roll steering 100.000 \n",
+ "Chukchi Sea roll steering 100.000 \n",
+ "\n",
+ " farthest latitude acquired (deg) \\\n",
+ "Antarctica fixed roll -78.633 \n",
+ "Arctic cap fixed roll 87.570 \n",
+ "Greenland fixed roll 83.645 \n",
+ "Chukchi Sea fixed roll 75.000 \n",
+ "Antarctica roll steering -78.633 \n",
+ "Arctic cap roll steering 87.570 \n",
+ "Greenland roll steering 83.645 \n",
+ "Chukchi Sea roll steering 75.000 \n",
+ "\n",
+ " farthest latitude predicted (deg) \n",
+ "Antarctica fixed roll -79.843 \n",
+ "Arctic cap fixed roll 87.577 \n",
+ "Greenland fixed roll 83.645 \n",
+ "Chukchi Sea fixed roll 75.000 \n",
+ "Antarctica roll steering -79.970 \n",
+ "Arctic cap roll steering 87.575 \n",
+ "Greenland roll steering 83.645 \n",
+ "Chukchi Sea roll steering 75.000 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def latitudes(geometry):\n",
+ " return np.concatenate(\n",
+ " [\n",
+ " np.asarray(p.exterior.coords)[:, 1]\n",
+ " for p in getattr(geometry, \"geoms\", [geometry])\n",
+ " if not p.is_empty\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ "\n",
+ "rows = {}\n",
+ "for label, found in observations.items():\n",
+ " for name, region in regions.items():\n",
+ " pole = -1 if region.bounds[1] < 0 else 1\n",
+ " area = project(region, pole)\n",
+ " p, o = passes[name], found[found.region == name]\n",
+ " acquired = shapely.union_all(\n",
+ " [project(g, pole, densify=False) for g in p.footprint]\n",
+ " ).intersection(area)\n",
+ " matched = [\n",
+ " o[(o.instrument == q.mode) & (o.start <= q.end) & (o.end >= q.start)]\n",
+ " for q in p.itertuples()\n",
+ " ]\n",
+ " swaths = shapely.union_all(\n",
+ " [project(g, pole) for m in matched for g in m.geometry]\n",
+ " )\n",
+ " everything = shapely.union_all(\n",
+ " [project(g, pole) for g in o.geometry]\n",
+ " ).intersection(area)\n",
+ " reach = np.nanmax if pole > 0 else np.nanmin\n",
+ " rows[(name, label)] = {\n",
+ " \"region area (10^6 km^2)\": area.area / 1e12,\n",
+ " \"acquired (%)\": 100 * acquired.area / area.area,\n",
+ " \"acquired within predicted swaths (%)\": 100\n",
+ " * acquired.intersection(swaths).area\n",
+ " / acquired.area,\n",
+ " \"predicted swaths of all passes (%)\": 100 * everything.area / area.area,\n",
+ " \"farthest latitude acquired (deg)\": reach(\n",
+ " np.concatenate([latitudes(g.intersection(region)) for g in p.footprint])\n",
+ " ),\n",
+ " \"farthest latitude predicted (deg)\": reach(\n",
+ " np.concatenate([latitudes(g) for g in o.geometry])\n",
+ " ),\n",
+ " }\n",
+ "coverage = pd.DataFrame(rows).T\n",
+ "coverage.round(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0ffd63ed",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T23:24:57.388801Z",
+ "iopub.status.busy": "2026-10-25T23:24:57.388551Z",
+ "iopub.status.idle": "2026-10-25T23:25:12.257461Z",
+ "shell.execute_reply": "2026-10-25T23:25:12.256709Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "D:\\github\\tatc\\.venv\\Lib\\site-packages\\cartopy\\mpl\\geoaxes.py:792: UserWarning: Approximating coordinate system \n",
+ "Name: unknown\n",
+ "Axis Info [ellipsoidal]:\n",
+ "- lon[east]: Longitude (degree)\n",
+ "- lat[north]: Latitude (degree)\n",
+ "Area of Use:\n",
+ "- undefined\n",
+ "Datum: World Geodetic System 1984\n",
+ "- Ellipsoid: WGS 84\n",
+ "- Prime Meridian: Greenwich\n",
+ " with the PlateCarree projection.\n",
+ " warnings.warn(f'Approximating coordinate system {crs!r} with '\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import cartopy.crs as ccrs\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "fig = plt.figure(figsize=(12, 6))\n",
+ "for k, (pole, title) in enumerate([(1, \"Arctic\"), (-1, \"Antarctic\")]):\n",
+ " ax = fig.add_subplot(\n",
+ " 1,\n",
+ " 2,\n",
+ " k + 1,\n",
+ " projection=ccrs.NorthPolarStereo() if pole > 0 else ccrs.SouthPolarStereo(),\n",
+ " )\n",
+ " ax.set_extent([-180, 180, 60 * pole, 90 * pole], ccrs.PlateCarree())\n",
+ " ax.coastlines(linewidth=0.4, color=\"#888780\")\n",
+ " names = [n for n, r in regions.items() if (r.bounds[1] < 0) == (pole < 0)]\n",
+ " found = observations[\"roll steering\"]\n",
+ " swaths = [g for g in found[found.region.isin(names)].geometry]\n",
+ " ax.add_geometries(\n",
+ " swaths, ccrs.PlateCarree(), facecolor=\"#93c5fd\", edgecolor=\"none\", alpha=0.35\n",
+ " )\n",
+ " footprints = [\n",
+ " shapely.segmentize(g, 0.5) for n in names for g in passes[n].footprint\n",
+ " ]\n",
+ " ax.add_geometries(\n",
+ " footprints,\n",
+ " ccrs.Geodetic(),\n",
+ " facecolor=\"none\",\n",
+ " edgecolor=\"#1f4e79\",\n",
+ " linewidth=0.3,\n",
+ " )\n",
+ " for n in names:\n",
+ " ax.add_geometries(\n",
+ " [shapely.segmentize(regions[n], 0.5)],\n",
+ " ccrs.PlateCarree(),\n",
+ " facecolor=\"none\",\n",
+ " edgecolor=\"#b91c1c\",\n",
+ " linewidth=0.8,\n",
+ " )\n",
+ " ax.set_title(\n",
+ " f\"{title}: predicted swaths (blue fill), acquired products (outlines), regions (red)\",\n",
+ " fontsize=9,\n",
+ " )\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5819665b",
+ "metadata": {},
+ "source": [
+ "The predicted swaths contain all the area Sentinel-1C acquired in every region with roll steering (99.7% of Antarctica's with a fixed roll angle), on both sides of the anti-meridian and around both poles. Over the week, Sentinel-1C could observe 94.1% of the Arctic cap: its swaths reach 87.58 deg north, as its products do (87.57 deg), leaving a gap of about 270 km around the North Pole. They reach only about 80 deg south (looking away from the South Pole), so it could observe 72% of Antarctica, of which it acquired about a quarter (19% of the continent) in this week's acquisition plan, along the coasts. Greenland and the Chukchi Sea are observed entirely."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20af5099",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| Regions | Each region preserved exactly by `split_polygon`, including Antarctica around the South Pole (after a correction exposed by this validation) |\n",
+ "| 1. Passes | All 210 acquired passes predicted with roll steering (one marginal Antarctic pass missed with a fixed roll angle) |\n",
+ "| 2. Swaths and polar coverage | All acquired area within the predicted swaths with roll steering (99.7% for Antarctica with a fixed roll angle); swaths reach 87.58 deg north (products 87.57 deg) |\n",
+ "\n",
+ "**TAT-C's region observations are validated at the poles and across the anti-meridian.** Over a week of Sentinel-1C acquisitions, `collect_region_observations` predicts every pass over regions around both poles, near the North Pole, and split across the anti-meridian, and its swaths contain the acquired products and reproduce Sentinel-1's polar gap.\n",
+ "\n",
+ "**Findings.** The validation exposed an error in `split_polygon`, which reconstructed a polygon around a pole by sorting its vertices by longitude, valid only for boundaries monotonic in longitude: Antarctica lost and gained strips along its coasts, and 12% of the acquired area fell outside the predicted swaths. `split_polygon` now keeps the order of the vertices, unrolled across the anti-meridian. The catalog footprints of the products, by contrast, need care near the poles: their edges are straight in longitude and latitude, and only geodesic edges represent the products there.\n",
+ "\n",
+ "**Next steps:**\n",
+ "\n",
+ "1. *Seasonal acquisitions.* Repeat in another season, when Sentinel-1's acquisition plan covers more of Antarctica.\n",
+ "2. *Other instruments.* Compare left-looking or nadir-viewing instruments, whose polar gaps differ (for example, a left-looking radar reaches closer to the South Pole).\n",
+ "\n",
+ "**Limitations.** This study covers one week, one satellite, and coarse (1:110 million) region outlines. The roll steering profiles were fitted to one day of products in the Sentinel-1 notebook, partly from other parts of the orbit."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateRegionSentinel2.ipynb b/docs/validation/ValidateRegionSentinel2.ipynb
index e1ce6fa5..988273a1 100644
--- a/docs/validation/ValidateRegionSentinel2.ipynb
+++ b/docs/validation/ValidateRegionSentinel2.ipynb
@@ -207,8 +207,16 @@
"PLATFORMS = list(TLES)\n",
"REGION_NAMES = [\"Iceland\", \"Japan\", \"Chile\", \"Fiji\", \"Norway\", \"Switzerland\"]\n",
"\n",
- "countries = shpreader.Reader(shpreader.natural_earth(resolution=\"110m\", category=\"cultural\", name=\"admin_0_countries\"))\n",
- "regions = {r.attributes[\"NAME\"]: r.geometry for r in countries.records() if r.attributes[\"NAME\"] in REGION_NAMES}\n",
+ "countries = shpreader.Reader(\n",
+ " shpreader.natural_earth(\n",
+ " resolution=\"110m\", category=\"cultural\", name=\"admin_0_countries\"\n",
+ " )\n",
+ ")\n",
+ "regions = {\n",
+ " r.attributes[\"NAME\"]: r.geometry\n",
+ " for r in countries.records()\n",
+ " if r.attributes[\"NAME\"] in REGION_NAMES\n",
+ "}\n",
"regions = {name: regions[name] for name in REGION_NAMES}\n",
"REGION_IDS = {name: i for i, name in enumerate(REGION_NAMES)}\n",
"\n",
@@ -223,7 +231,10 @@
" \"intersects\": region.__geo_interface__,\n",
" \"datetime\": f\"{START:%Y-%m-%dT%H:%M:%SZ}/{END - timedelta(seconds=1):%Y-%m-%dT%H:%M:%SZ}\",\n",
" \"limit\": 1000,\n",
- " \"fields\": {\"include\": [\"id\", \"properties.platform\", \"properties.s2:datatake_id\"], \"exclude\": [\"geometry\"]},\n",
+ " \"fields\": {\n",
+ " \"include\": [\"id\", \"properties.platform\", \"properties.s2:datatake_id\"],\n",
+ " \"exclude\": [\"geometry\"],\n",
+ " },\n",
" }\n",
" rows = []\n",
" while body is not None:\n",
@@ -239,24 +250,39 @@
" # back off (the catalog limits request rates)\n",
" time.sleep(2**attempt)\n",
" rows += [\n",
- " {\"region\": name, \"product\": f[\"id\"], \"platform\": f[\"properties\"][\"platform\"], \"datatake\": f[\"properties\"][\"s2:datatake_id\"]}\n",
+ " {\n",
+ " \"region\": name,\n",
+ " \"product\": f[\"id\"],\n",
+ " \"platform\": f[\"properties\"][\"platform\"],\n",
+ " \"datatake\": f[\"properties\"][\"s2:datatake_id\"],\n",
+ " }\n",
" for f in page[\"features\"]\n",
" ]\n",
- " following = [link for link in page.get(\"links\", []) if link.get(\"rel\") == \"next\"]\n",
+ " following = [\n",
+ " link for link in page.get(\"links\", []) if link.get(\"rel\") == \"next\"\n",
+ " ]\n",
" body = following[0].get(\"body\") if following else None\n",
" cache.write_text(json.dumps(rows))\n",
" return rows\n",
"\n",
"\n",
- "products = pd.DataFrame([row for name, region in regions.items() for row in search(name, region)])\n",
- "products[\"datatake_start\"] = pd.to_datetime(products.datatake.str.extract(r\"_(\\d{8}T\\d{6})_\")[0], format=\"%Y%m%dT%H%M%S\", utc=True)\n",
+ "products = pd.DataFrame(\n",
+ " [row for name, region in regions.items() for row in search(name, region)]\n",
+ ")\n",
+ "products[\"datatake_start\"] = pd.to_datetime(\n",
+ " products.datatake.str.extract(r\"_(\\d{8}T\\d{6})_\")[0],\n",
+ " format=\"%Y%m%dT%H%M%S\",\n",
+ " utc=True,\n",
+ ")\n",
"observed = (\n",
" products[products.platform.isin(PLATFORMS)]\n",
" .groupby([\"region\", \"platform\", \"datatake\"], as_index=False)\n",
" .agg(datatake_start=(\"datatake_start\", \"first\"), products=(\"product\", \"size\"))\n",
" .sort_values([\"region\", \"datatake_start\"], ignore_index=True)\n",
")\n",
- "products.groupby([\"region\", \"platform\"]).agg(products=(\"product\", \"size\"), passes=(\"datatake\", \"nunique\")).unstack(fill_value=0).reindex(REGION_NAMES)"
+ "products.groupby([\"region\", \"platform\"]).agg(\n",
+ " products=(\"product\", \"size\"), passes=(\"datatake\", \"nunique\")\n",
+ ").unstack(fill_value=0).reindex(REGION_NAMES)"
]
},
{
@@ -296,7 +322,12 @@
"import geopandas as gpd\n",
"import matplotlib.pyplot as plt\n",
"\n",
- "from tatc.analysis import aggregate_observations, collect_ground_track, collect_multi_region_observations, reduce_observations\n",
+ "from tatc.analysis import (\n",
+ " aggregate_observations,\n",
+ " collect_ground_track,\n",
+ " collect_multi_region_observations,\n",
+ " reduce_observations,\n",
+ ")\n",
"from skyfield.api import wgs84\n",
"\n",
"from tatc.constants import de421, timescale\n",
@@ -314,14 +345,26 @@
")\n",
"SPLIT_REGIONS = {name: split_polygon(region) for name, region in regions.items()}\n",
"REGION_NAMES_BY_HASH = {hash_geometry(region): name for name, region in regions.items()}\n",
- "satellites = {name: Satellite(name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle), instruments=[msi]) for name, tle in TLES.items()}\n",
+ "satellites = {\n",
+ " name: Satellite(\n",
+ " name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle), instruments=[msi]\n",
+ " )\n",
+ " for name, tle in TLES.items()\n",
+ "}\n",
"\n",
"\n",
"def summarize(x):\n",
" \"\"\"Summary statistics of a residual sample.\"\"\"\n",
" x = np.asarray(x, dtype=float)\n",
" x = x[np.isfinite(x)]\n",
- " return pd.Series({\"n\": x.size, \"median\": np.median(x), \"p95 |x|\": np.percentile(np.abs(x), 95), \"max |x|\": np.abs(x).max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": x.size,\n",
+ " \"median\": np.median(x),\n",
+ " \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+ " \"max |x|\": np.abs(x).max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -340,10 +383,23 @@
"ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson(central_longitude=150))\n",
"ax.set_global()\n",
"ax.coastlines(color=GRAY, linewidth=0.5)\n",
- "ax.add_geometries(list(regions.values()), crs=ccrs.PlateCarree(), facecolor=BLUE, edgecolor=INK, linewidth=0.4)\n",
+ "ax.add_geometries(\n",
+ " list(regions.values()),\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=BLUE,\n",
+ " edgecolor=INK,\n",
+ " linewidth=0.4,\n",
+ ")\n",
"for name, region in regions.items():\n",
" point = region.representative_point()\n",
- " ax.annotate(name, (point.x, point.y), xytext=(6, 4), textcoords=\"offset points\", fontsize=8, transform=ccrs.PlateCarree())\n",
+ " ax.annotate(\n",
+ " name,\n",
+ " (point.x, point.y),\n",
+ " xytext=(6, 4),\n",
+ " textcoords=\"offset points\",\n",
+ " fontsize=8,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
"ax.set_title(\"Regions of interest\", fontsize=9)\n",
"plt.show()"
]
@@ -497,56 +553,94 @@
"def solar_elevation(row, instrument):\n",
" \"\"\"Solar elevation (deg) at an observation's midpoint, at the point of the region within the footprint at which TAT-C checks illumination.\"\"\"\n",
" midpoint = (row.start + (row.end - row.start) / 2).round(\"us\").to_pydatetime()\n",
- " orbit_track = satellites[row.platform].orbit.to_gp_orbit().get_orbit_track([midpoint])\n",
+ " orbit_track = (\n",
+ " satellites[row.platform].orbit.to_gp_orbit().get_orbit_track([midpoint])\n",
+ " )\n",
" region = SPLIT_REGIONS[row.region]\n",
" view = instrument.compute_footprint(orbit_track)[0].intersection(region)\n",
" point = (view if not view.is_empty else region).representative_point()\n",
" topos = de421[\"earth\"] + wgs84.latlon(point.y, point.x)\n",
- " return topos.at(timescale.from_datetime(midpoint)).observe(de421[\"sun\"]).apparent().altaz()[0].degrees\n",
+ " return (\n",
+ " topos.at(timescale.from_datetime(midpoint))\n",
+ " .observe(de421[\"sun\"])\n",
+ " .apparent()\n",
+ " .altaz()[0]\n",
+ " .degrees\n",
+ " )\n",
"\n",
"\n",
"def predict(instrument=msi):\n",
" \"\"\"Predicted observations of each region by each satellite, grouped into passes.\"\"\"\n",
- " members = [s.model_copy(update={\"instruments\": [instrument]}) for s in satellites.values()]\n",
+ " members = [\n",
+ " s.model_copy(update={\"instruments\": [instrument]}) for s in satellites.values()\n",
+ " ]\n",
" frames = []\n",
" for name, region in regions.items():\n",
" o = collect_multi_region_observations(region, members, START, END)\n",
" frames.append(o.assign(region=name).rename(columns={\"satellite\": \"platform\"}))\n",
- " o = pd.concat(frames, ignore_index=True).sort_values([\"region\", \"platform\", \"start\"], ignore_index=True)\n",
+ " o = pd.concat(frames, ignore_index=True).sort_values(\n",
+ " [\"region\", \"platform\", \"start\"], ignore_index=True\n",
+ " )\n",
" o[\"solar_alt\"] = [solar_elevation(row, instrument) for row in o.itertuples()]\n",
- " new_pass = (o.region != o.region.shift()) | (o.platform != o.platform.shift()) | (o.start - o.end.shift() > pd.Timedelta(minutes=10))\n",
+ " new_pass = (\n",
+ " (o.region != o.region.shift())\n",
+ " | (o.platform != o.platform.shift())\n",
+ " | (o.start - o.end.shift() > pd.Timedelta(minutes=10))\n",
+ " )\n",
" o[\"pass\"] = new_pass.cumsum()\n",
" return o\n",
"\n",
"\n",
"def passes(o):\n",
" \"\"\"Predicted passes: the span of each group of observations.\"\"\"\n",
- " return o.groupby(\"pass\").agg(\n",
- " region=(\"region\", \"first\"), platform=(\"platform\", \"first\"), start=(\"start\", \"min\"), end=(\"end\", \"max\"),\n",
- " observations=(\"start\", \"size\"), solar_alt=(\"solar_alt\", \"max\"),\n",
- " ).reset_index()\n",
+ " return (\n",
+ " o.groupby(\"pass\")\n",
+ " .agg(\n",
+ " region=(\"region\", \"first\"),\n",
+ " platform=(\"platform\", \"first\"),\n",
+ " start=(\"start\", \"min\"),\n",
+ " end=(\"end\", \"max\"),\n",
+ " observations=(\"start\", \"size\"),\n",
+ " solar_alt=(\"solar_alt\", \"max\"),\n",
+ " )\n",
+ " .reset_index()\n",
+ " )\n",
"\n",
"\n",
"def match(predicted_passes):\n",
" \"\"\"Match predicted passes to observed passes (datatakes) of the same region and satellite.\"\"\"\n",
" pairs = predicted_passes.merge(observed, on=[\"region\", \"platform\"])\n",
- " pairs = pairs[(pairs.datatake_start >= pairs.start - pd.Timedelta(minutes=45)) & (pairs.datatake_start <= pairs.end + pd.Timedelta(minutes=1))]\n",
+ " pairs = pairs[\n",
+ " (pairs.datatake_start >= pairs.start - pd.Timedelta(minutes=45))\n",
+ " & (pairs.datatake_start <= pairs.end + pd.Timedelta(minutes=1))\n",
+ " ]\n",
" p = predicted_passes.assign(acquired=predicted_passes[\"pass\"].isin(pairs[\"pass\"]))\n",
- " o = observed.assign(predicted=observed.set_index([\"region\", \"datatake\"]).index.isin(pairs.set_index([\"region\", \"datatake\"]).index))\n",
+ " o = observed.assign(\n",
+ " predicted=observed.set_index([\"region\", \"datatake\"]).index.isin(\n",
+ " pairs.set_index([\"region\", \"datatake\"]).index\n",
+ " )\n",
+ " )\n",
" return p, o\n",
"\n",
"\n",
"predicted = predict()\n",
"predicted_passes, observed1 = match(passes(predicted))\n",
- "table1 = pd.DataFrame(\n",
- " {\n",
- " \"observed passes\": observed1.groupby(\"region\").size(),\n",
- " \"observed passes predicted\": observed1.groupby(\"region\").predicted.sum(),\n",
- " \"predicted passes\": predicted_passes.groupby(\"region\").size(),\n",
- " \"predicted passes acquired\": predicted_passes.groupby(\"region\").acquired.sum(),\n",
- " \"predicted observations\": predicted.groupby(\"region\").size(),\n",
- " }\n",
- ").reindex(REGION_NAMES).fillna(0).astype(int)\n",
+ "table1 = (\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"observed passes\": observed1.groupby(\"region\").size(),\n",
+ " \"observed passes predicted\": observed1.groupby(\"region\").predicted.sum(),\n",
+ " \"predicted passes\": predicted_passes.groupby(\"region\").size(),\n",
+ " \"predicted passes acquired\": predicted_passes.groupby(\n",
+ " \"region\"\n",
+ " ).acquired.sum(),\n",
+ " \"predicted observations\": predicted.groupby(\"region\").size(),\n",
+ " }\n",
+ " )\n",
+ " .reindex(REGION_NAMES)\n",
+ " .fillna(0)\n",
+ " .astype(int)\n",
+ ")\n",
"table1.loc[\"total\"] = table1.sum()\n",
"table1"
]
@@ -686,16 +780,38 @@
"source": [
"# swath edge: passes not predicted with the cross-track field of view of the\n",
"# swath in which all bands have valid data (+/-10.2 deg)\n",
- "narrow = predict(msi.model_copy(update={\"name\": \"MSI (valid swath)\", \"cross_track_field_of_view\": 20.4}))\n",
+ "narrow = predict(\n",
+ " msi.model_copy(\n",
+ " update={\"name\": \"MSI (valid swath)\", \"cross_track_field_of_view\": 20.4}\n",
+ " )\n",
+ ")\n",
"narrow_passes = passes(narrow)\n",
- "overlap = predicted_passes.merge(narrow_passes, on=[\"region\", \"platform\"], suffixes=(\"\", \"_narrow\"))\n",
- "overlap = overlap[(overlap.start_narrow <= overlap.end) & (overlap.end_narrow >= overlap.start)]\n",
+ "overlap = predicted_passes.merge(\n",
+ " narrow_passes, on=[\"region\", \"platform\"], suffixes=(\"\", \"_narrow\")\n",
+ ")\n",
+ "overlap = overlap[\n",
+ " (overlap.start_narrow <= overlap.end) & (overlap.end_narrow >= overlap.start)\n",
+ "]\n",
"predicted_passes[\"cause\"] = \"acquired\"\n",
"missed = ~predicted_passes.acquired\n",
"predicted_passes.loc[missed & (predicted_passes.solar_alt < 10), \"cause\"] = \"low Sun\"\n",
- "predicted_passes.loc[missed & (predicted_passes.cause == \"acquired\") & ~predicted_passes[\"pass\"].isin(overlap[\"pass\"]), \"cause\"] = \"swath edge\"\n",
+ "predicted_passes.loc[\n",
+ " missed\n",
+ " & (predicted_passes.cause == \"acquired\")\n",
+ " & ~predicted_passes[\"pass\"].isin(overlap[\"pass\"]),\n",
+ " \"cause\",\n",
+ "] = \"swath edge\"\n",
"predicted_passes.loc[missed & (predicted_passes.cause == \"acquired\"), \"cause\"] = \"other\"\n",
- "causes1 = predicted_passes.groupby([\"region\", \"cause\"]).size().unstack(fill_value=0).reindex(index=REGION_NAMES, columns=[\"acquired\", \"low Sun\", \"swath edge\", \"other\"], fill_value=0)\n",
+ "causes1 = (\n",
+ " predicted_passes.groupby([\"region\", \"cause\"])\n",
+ " .size()\n",
+ " .unstack(fill_value=0)\n",
+ " .reindex(\n",
+ " index=REGION_NAMES,\n",
+ " columns=[\"acquired\", \"low Sun\", \"swath edge\", \"other\"],\n",
+ " fill_value=0,\n",
+ " )\n",
+ ")\n",
"causes1.loc[\"total\"] = causes1.sum()\n",
"causes1"
]
@@ -1242,8 +1358,16 @@
"# solar elevation (deg) at the region within the footprint, of acquired and other predicted passes\n",
"display(predicted_passes.groupby(\"cause\").solar_alt.describe().round(1))\n",
"# predicted passes not acquired, and observed passes not predicted\n",
- "display(predicted_passes[predicted_passes.cause != \"acquired\"][[\"region\", \"platform\", \"start\", \"end\", \"observations\", \"solar_alt\", \"cause\"]])\n",
- "display(observed1[~observed1.predicted][[\"region\", \"platform\", \"datatake\", \"datatake_start\", \"products\"]])"
+ "display(\n",
+ " predicted_passes[predicted_passes.cause != \"acquired\"][\n",
+ " [\"region\", \"platform\", \"start\", \"end\", \"observations\", \"solar_alt\", \"cause\"]\n",
+ " ]\n",
+ ")\n",
+ "display(\n",
+ " observed1[~observed1.predicted][\n",
+ " [\"region\", \"platform\", \"datatake\", \"datatake_start\", \"products\"]\n",
+ " ]\n",
+ ")"
]
},
{
@@ -1345,22 +1469,47 @@
"def footprint_times(row, center):\n",
" \"\"\"Times, at 0.1 s intervals within 2 s of a time, at which the footprint intersects the region.\"\"\"\n",
" center = center.round(\"us\")\n",
- " times = pd.date_range(center - pd.Timedelta(seconds=2), center + pd.Timedelta(seconds=2), freq=\"100ms\").to_pydatetime().tolist()\n",
- " return collect_ground_track(satellites[row.platform], times, mask=regions[row.region]).time\n",
+ " times = (\n",
+ " pd.date_range(\n",
+ " center - pd.Timedelta(seconds=2),\n",
+ " center + pd.Timedelta(seconds=2),\n",
+ " freq=\"100ms\",\n",
+ " )\n",
+ " .to_pydatetime()\n",
+ " .tolist()\n",
+ " )\n",
+ " return collect_ground_track(\n",
+ " satellites[row.platform], times, mask=regions[row.region]\n",
+ " ).time\n",
"\n",
"\n",
"def footprint_period(row):\n",
" \"\"\"First footprint intersecting the region near the start, and last near the end.\"\"\"\n",
" first, last = footprint_times(row, row.start), footprint_times(row, row.end)\n",
- " return pd.Series({\"first\": first.min() if len(first) else pd.NaT, \"last\": last.max() if len(last) else pd.NaT})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"first\": first.min() if len(first) else pd.NaT,\n",
+ " \"last\": last.max() if len(last) else pd.NaT,\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"periods2 = pd.concat([predicted, predicted.apply(footprint_period, axis=1)], axis=1)\n",
- "periods2[\"start difference (s)\"] = (periods2[\"first\"] - periods2.start).dt.total_seconds()\n",
+ "periods2[\"start difference (s)\"] = (\n",
+ " periods2[\"first\"] - periods2.start\n",
+ ").dt.total_seconds()\n",
"periods2[\"end difference (s)\"] = (periods2.end - periods2[\"last\"]).dt.total_seconds()\n",
"periods2[\"duration (s)\"] = (periods2.end - periods2.start).dt.total_seconds()\n",
- "print(\"observations without an intersecting footprint:\", int(periods2[\"first\"].isna().sum()))\n",
- "pd.DataFrame({column: summarize(periods2[column]) for column in [\"duration (s)\", \"start difference (s)\", \"end difference (s)\"]}).T"
+ "print(\n",
+ " \"observations without an intersecting footprint:\",\n",
+ " int(periods2[\"first\"].isna().sum()),\n",
+ ")\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " column: summarize(periods2[column])\n",
+ " for column in [\"duration (s)\", \"start difference (s)\", \"end difference (s)\"]\n",
+ " }\n",
+ ").T"
]
},
{
@@ -1390,11 +1539,25 @@
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
"for k, (name, frame) in enumerate(periods2.groupby(\"region\", sort=False)):\n",
" y = np.full(len(frame), REGION_IDS[name])\n",
- " ax.scatter(frame[\"start difference (s)\"], y - 0.12, s=10, color=BLUE, label=\"first footprint after start\" if k == 0 else None)\n",
- " ax.scatter(frame[\"end difference (s)\"], y + 0.12, s=10, color=ORANGE, label=\"last footprint before end\" if k == 0 else None)\n",
+ " ax.scatter(\n",
+ " frame[\"start difference (s)\"],\n",
+ " y - 0.12,\n",
+ " s=10,\n",
+ " color=BLUE,\n",
+ " label=\"first footprint after start\" if k == 0 else None,\n",
+ " )\n",
+ " ax.scatter(\n",
+ " frame[\"end difference (s)\"],\n",
+ " y + 0.12,\n",
+ " s=10,\n",
+ " color=ORANGE,\n",
+ " label=\"last footprint before end\" if k == 0 else None,\n",
+ " )\n",
"ax.set_yticks(range(len(REGION_NAMES)), REGION_NAMES)\n",
"ax.axvline(0, color=GRAY, lw=0.8)\n",
- "ax.set(xlabel=\"Time (s)\", title=\"Observation periods vs. footprints sampled every 0.1 s\")\n",
+ "ax.set(\n",
+ " xlabel=\"Time (s)\", title=\"Observation periods vs. footprints sampled every 0.1 s\"\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1510,11 +1673,19 @@
"def footprint_union(row):\n",
" \"\"\"Union of the footprints sampled every 1 s over an observation (and at its end), clipped to the region.\"\"\"\n",
" start, end = row.start.round(\"us\"), row.end.round(\"us\")\n",
- " times = pd.date_range(start, end, freq=\"1s\").to_pydatetime().tolist() + [end.to_pydatetime()]\n",
- " track = collect_ground_track(satellites[row.platform], times, mask=regions[row.region])\n",
+ " times = pd.date_range(start, end, freq=\"1s\").to_pydatetime().tolist() + [\n",
+ " end.to_pydatetime()\n",
+ " ]\n",
+ " track = collect_ground_track(\n",
+ " satellites[row.platform], times, mask=regions[row.region]\n",
+ " )\n",
" if not len(track):\n",
" return None\n",
- " return polygonal(shapely.union_all(track.geometry.to_numpy(), grid_size=GRID).intersection(SPLIT_REGIONS[row.region], grid_size=GRID))\n",
+ " return polygonal(\n",
+ " shapely.union_all(track.geometry.to_numpy(), grid_size=GRID).intersection(\n",
+ " SPLIT_REGIONS[row.region], grid_size=GRID\n",
+ " )\n",
+ " )\n",
"\n",
"\n",
"EQUAL_AREA = \"EPSG:6933\"\n",
@@ -1522,7 +1693,10 @@
"\n",
"def area(geometries):\n",
" \"\"\"Area (km2) in an equal-area projection.\"\"\"\n",
- " return gpd.GeoSeries(geometries, crs=\"EPSG:4326\").to_crs(EQUAL_AREA).area.to_numpy() / 1e6\n",
+ " return (\n",
+ " gpd.GeoSeries(geometries, crs=\"EPSG:4326\").to_crs(EQUAL_AREA).area.to_numpy()\n",
+ " / 1e6\n",
+ " )\n",
"\n",
"\n",
"sampled = [footprint_union(row) for row in predicted.itertuples()]\n",
@@ -1568,15 +1742,35 @@
"source": [
"name = \"Japan\"\n",
"frame = predicted[predicted.region == name]\n",
- "fig, axes = plt.subplots(1, 2, figsize=(10, 4.6), subplot_kw={\"projection\": ccrs.PlateCarree()})\n",
+ "fig, axes = plt.subplots(\n",
+ " 1, 2, figsize=(10, 4.6), subplot_kw={\"projection\": ccrs.PlateCarree()}\n",
+ ")\n",
"for ax, platform in zip(axes, PLATFORMS):\n",
" ax.set_extent([127, 147, 29, 46], crs=ccrs.PlateCarree())\n",
" ax.coastlines(color=GRAY, linewidth=0.5)\n",
" swaths = frame[frame.platform == platform]\n",
- " ax.add_geometries(swaths.geometry, crs=ccrs.PlateCarree(), facecolor=PLATFORM_COLORS[platform], edgecolor=\"none\", alpha=0.3)\n",
- " ax.add_geometries([regions[name]], crs=ccrs.PlateCarree(), facecolor=\"none\", edgecolor=INK, linewidth=0.6)\n",
- " ax.set_title(f\"{platform}: {swaths['pass'].nunique()} passes, {len(swaths)} swaths\", fontsize=9)\n",
- "fig.suptitle(f\"Predicted swaths over {name}, {START:%d} to {END - timedelta(days=1):%d %B %Y} (overlaps darker)\", fontsize=10)\n",
+ " ax.add_geometries(\n",
+ " swaths.geometry,\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=PLATFORM_COLORS[platform],\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.3,\n",
+ " )\n",
+ " ax.add_geometries(\n",
+ " [regions[name]],\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=\"none\",\n",
+ " edgecolor=INK,\n",
+ " linewidth=0.6,\n",
+ " )\n",
+ " ax.set_title(\n",
+ " f\"{platform}: {swaths['pass'].nunique()} passes, {len(swaths)} swaths\",\n",
+ " fontsize=9,\n",
+ " )\n",
+ "fig.suptitle(\n",
+ " f\"Predicted swaths over {name}, {START:%d} to {END - timedelta(days=1):%d %B %Y} (overlaps darker)\",\n",
+ " fontsize=10,\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()"
]
@@ -1761,14 +1955,21 @@
" reduced = reduce_observations(aggregate_observations(frame))\n",
" reduced[\"region\"] = reduced.target_hash.map(REGION_NAMES_BY_HASH)\n",
" reduced[\"revisit (days)\"] = reduced.revisit.dt.total_seconds() / 86400\n",
- " return reduced.set_index(\"region\")[[\"samples\", \"revisit (days)\"]].reindex(REGION_NAMES)\n",
+ " return reduced.set_index(\"region\")[[\"samples\", \"revisit (days)\"]].reindex(\n",
+ " REGION_NAMES\n",
+ " )\n",
"\n",
"\n",
- "midpoints = predicted_passes.assign(midpoint=predicted_passes.start + (predicted_passes.end - predicted_passes.start) / 2)\n",
+ "midpoints = predicted_passes.assign(\n",
+ " midpoint=predicted_passes.start\n",
+ " + (predicted_passes.end - predicted_passes.start) / 2\n",
+ ")\n",
"o3 = revisit_statistics(as_observations(observed1, \"datatake_start\"))\n",
"p3 = revisit_statistics(as_observations(midpoints, \"midpoint\"))\n",
"a3 = revisit_statistics(as_observations(midpoints[midpoints.acquired], \"midpoint\"))\n",
- "pd.concat({\"observed\": o3, \"predicted\": p3, \"predicted, acquired passes\": a3}, axis=1).round(2)"
+ "pd.concat(\n",
+ " {\"observed\": o3, \"predicted\": p3, \"predicted, acquired passes\": a3}, axis=1\n",
+ ").round(2)"
]
},
{
@@ -1867,13 +2068,31 @@
"source": [
"def coverage(frame):\n",
" \"\"\"Percentage of the area of each region within the union of its swaths (see reduce_observations).\"\"\"\n",
- " reduced = reduce_observations(aggregate_observations(frame.rename(columns={\"platform\": \"satellite\"})[[\"target_hash\", \"geometry\", \"satellite\", \"instrument\", \"start\", \"end\"]]))\n",
+ " reduced = reduce_observations(\n",
+ " aggregate_observations(\n",
+ " frame.rename(columns={\"platform\": \"satellite\"})[\n",
+ " [\"target_hash\", \"geometry\", \"satellite\", \"instrument\", \"start\", \"end\"]\n",
+ " ]\n",
+ " )\n",
+ " )\n",
" reduced[\"region\"] = reduced.target_hash.map(REGION_NAMES_BY_HASH)\n",
- " return pd.Series(100 * area(reduced.geometry) / area([SPLIT_REGIONS[name] for name in reduced.region]), index=reduced.region).reindex(REGION_NAMES)\n",
+ " return pd.Series(\n",
+ " 100\n",
+ " * area(reduced.geometry)\n",
+ " / area([SPLIT_REGIONS[name] for name in reduced.region]),\n",
+ " index=reduced.region,\n",
+ " ).reindex(REGION_NAMES)\n",
"\n",
"\n",
- "acquired = predicted[\"pass\"].isin(predicted_passes.loc[predicted_passes.acquired, \"pass\"])\n",
- "pd.DataFrame({\"predicted passes\": coverage(predicted), \"predicted passes, acquired\": coverage(predicted[acquired])}).round(1).rename_axis(\"region coverage (%)\", axis=1)"
+ "acquired = predicted[\"pass\"].isin(\n",
+ " predicted_passes.loc[predicted_passes.acquired, \"pass\"]\n",
+ ")\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " \"predicted passes\": coverage(predicted),\n",
+ " \"predicted passes, acquired\": coverage(predicted[acquired]),\n",
+ " }\n",
+ ").round(1).rename_axis(\"region coverage (%)\", axis=1)"
]
},
{
diff --git a/docs/validation/ValidateRevisitSentinel2.ipynb b/docs/validation/ValidateRevisitSentinel2.ipynb
index 6995fcf3..e59c3794 100644
--- a/docs/validation/ValidateRevisitSentinel2.ipynb
+++ b/docs/validation/ValidateRevisitSentinel2.ipynb
@@ -102,10 +102,20 @@
"}\n",
"GPS_UTC = 18 # s\n",
"\n",
- "land = unary_union(list(shpreader.Reader(shpreader.natural_earth(resolution=\"110m\", category=\"physical\", name=\"land\")).geometries()))\n",
+ "land = unary_union(\n",
+ " list(\n",
+ " shpreader.Reader(\n",
+ " shpreader.natural_earth(resolution=\"110m\", category=\"physical\", name=\"land\")\n",
+ " ).geometries()\n",
+ " )\n",
+ ")\n",
"prepare(land)\n",
"lattice = generate_points_fibonacci_lattice(500e3)\n",
- "on_land = contains_xy(land, lattice.geometry.x, lattice.geometry.y) & (lattice.geometry.y > -56) & (lattice.geometry.y < 82.8)\n",
+ "on_land = (\n",
+ " contains_xy(land, lattice.geometry.x, lattice.geometry.y)\n",
+ " & (lattice.geometry.y > -56)\n",
+ " & (lattice.geometry.y < 82.8)\n",
+ ")\n",
"points = lattice[on_land].reset_index(drop=True)\n",
"points[\"point_id\"] = points.index\n",
"\n",
@@ -126,10 +136,22 @@
" \"\"\"Catalog products whose valid data footprint contains a point during the analysis period.\"\"\"\n",
" body = {\n",
" \"collections\": [\"sentinel-2-l2a\"],\n",
- " \"intersects\": {\"type\": \"Point\", \"coordinates\": [point.geometry.x, point.geometry.y]},\n",
+ " \"intersects\": {\n",
+ " \"type\": \"Point\",\n",
+ " \"coordinates\": [point.geometry.x, point.geometry.y],\n",
+ " },\n",
" \"datetime\": f\"{START:%Y-%m-%dT%H:%M:%SZ}/{END - timedelta(seconds=1):%Y-%m-%dT%H:%M:%SZ}\",\n",
" \"limit\": 1000,\n",
- " \"fields\": {\"include\": [\"id\", \"properties.platform\", \"properties.s2:datatake_id\", \"properties.s2:datastrip_id\", \"assets.datastrip-metadata.href\"], \"exclude\": [\"links\", \"geometry\"]},\n",
+ " \"fields\": {\n",
+ " \"include\": [\n",
+ " \"id\",\n",
+ " \"properties.platform\",\n",
+ " \"properties.s2:datatake_id\",\n",
+ " \"properties.s2:datastrip_id\",\n",
+ " \"assets.datastrip-metadata.href\",\n",
+ " ],\n",
+ " \"exclude\": [\"links\", \"geometry\"],\n",
+ " },\n",
" }\n",
" cache = SEARCH_DIR / f\"{point.point_id}.json\"\n",
" if cache.exists():\n",
@@ -164,11 +186,17 @@
"if not PRODUCTS_FILE.exists():\n",
" SEARCH_DIR.mkdir(exist_ok=True)\n",
" with concurrent.futures.ThreadPoolExecutor(4) as pool:\n",
- " pd.DataFrame([r for rows in pool.map(search, points.itertuples()) for r in rows]).to_csv(PRODUCTS_FILE, index=False)\n",
+ " pd.DataFrame(\n",
+ " [r for rows in pool.map(search, points.itertuples()) for r in rows]\n",
+ " ).to_csv(PRODUCTS_FILE, index=False)\n",
"products = pd.read_csv(PRODUCTS_FILE)\n",
"# one observed pass per point and datatake (adjacent tiles overlap)\n",
"observed = products.drop_duplicates([\"point_id\", \"datatake\"]).copy()\n",
- "observed[\"datatake_start\"] = pd.to_datetime(observed.datatake.str.extract(r\"_(\\d{8}T\\d{6})_\")[0], format=\"%Y%m%dT%H%M%S\", utc=True)\n",
+ "observed[\"datatake_start\"] = pd.to_datetime(\n",
+ " observed.datatake.str.extract(r\"_(\\d{8}T\\d{6})_\")[0],\n",
+ " format=\"%Y%m%dT%H%M%S\",\n",
+ " utc=True,\n",
+ ")\n",
"pd.Series(\n",
" {\n",
" \"points\": len(points),\n",
@@ -289,27 +317,54 @@
"if not EPHEMERIS_FILE.exists():\n",
" token = get(TOKEN).json()[\"token\"]\n",
" strips = products.drop_duplicates(\"datastrip\").copy()\n",
- " strips[\"day\"] = pd.to_datetime(strips.datastrip.str.extract(r\"_S(\\d{8})T\")[0], format=\"%Y%m%d\", utc=True)\n",
- " strips = strips[((strips.day - pd.Timestamp(START)).dt.days % 2) == 0].groupby([\"platform\", \"day\"]).head(1)\n",
+ " strips[\"day\"] = pd.to_datetime(\n",
+ " strips.datastrip.str.extract(r\"_S(\\d{8})T\")[0], format=\"%Y%m%d\", utc=True\n",
+ " )\n",
+ " strips = (\n",
+ " strips[((strips.day - pd.Timestamp(START)).dt.days % 2) == 0]\n",
+ " .groupby([\"platform\", \"day\"])\n",
+ " .head(1)\n",
+ " )\n",
"\n",
" def read_ephemeris(row):\n",
- " root = ET.fromstring(strip_namespaces(get(f\"{row.datastrip_metadata}?{token}\").text))\n",
- " values = lambda p, kind: [float(v) / 1e3 for v in p.find(f\"{kind.upper()}_VALUES\").text.split()]\n",
+ " root = ET.fromstring(\n",
+ " strip_namespaces(get(f\"{row.datastrip_metadata}?{token}\").text)\n",
+ " )\n",
+ " values = lambda p, kind: [\n",
+ " float(v) / 1e3 for v in p.find(f\"{kind.upper()}_VALUES\").text.split()\n",
+ " ]\n",
" gps = root.findall(\".//GPS_Point\")\n",
" return pd.DataFrame(\n",
" {\n",
" \"platform\": row.platform,\n",
" \"datastrip\": row.datastrip,\n",
- " \"time\": [pd.Timestamp(p.find(\"GPS_TIME\").text, tz=\"UTC\") - pd.Timedelta(seconds=GPS_UTC) for p in gps],\n",
- " **{f\"position_{a}\": [values(p, \"position\")[i] for p in gps] for i, a in enumerate(\"xyz\")},\n",
- " **{f\"velocity_{a}\": [values(p, \"velocity\")[i] for p in gps] for i, a in enumerate(\"xyz\")},\n",
+ " \"time\": [\n",
+ " pd.Timestamp(p.find(\"GPS_TIME\").text, tz=\"UTC\")\n",
+ " - pd.Timedelta(seconds=GPS_UTC)\n",
+ " for p in gps\n",
+ " ],\n",
+ " **{\n",
+ " f\"position_{a}\": [values(p, \"position\")[i] for p in gps]\n",
+ " for i, a in enumerate(\"xyz\")\n",
+ " },\n",
+ " **{\n",
+ " f\"velocity_{a}\": [values(p, \"velocity\")[i] for p in gps]\n",
+ " for i, a in enumerate(\"xyz\")\n",
+ " },\n",
" }\n",
" )\n",
"\n",
" with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
- " pd.concat(pool.map(read_ephemeris, strips.itertuples())).to_csv(EPHEMERIS_FILE, index=False)\n",
+ " pd.concat(pool.map(read_ephemeris, strips.itertuples())).to_csv(\n",
+ " EPHEMERIS_FILE, index=False\n",
+ " )\n",
"ephemeris = pd.read_csv(EPHEMERIS_FILE, parse_dates=[\"time\"])\n",
- "ephemeris.groupby(\"platform\").agg(datastrips=(\"datastrip\", \"nunique\"), points=(\"time\", \"size\"), first=(\"time\", \"min\"), last=(\"time\", \"max\"))"
+ "ephemeris.groupby(\"platform\").agg(\n",
+ " datastrips=(\"datastrip\", \"nunique\"),\n",
+ " points=(\"time\", \"size\"),\n",
+ " first=(\"time\", \"min\"),\n",
+ " last=(\"time\", \"max\"),\n",
+ ")"
]
},
{
@@ -340,7 +395,11 @@
"import matplotlib.pyplot as plt\n",
"from skyfield.framelib import itrs\n",
"\n",
- "from tatc.analysis import aggregate_observations, collect_multi_observations, reduce_observations\n",
+ "from tatc.analysis import (\n",
+ " aggregate_observations,\n",
+ " collect_multi_observations,\n",
+ " reduce_observations,\n",
+ ")\n",
"from tatc.schemas import GeneralPerturbationsOrbit, Point, PointedInstrument, Satellite\n",
"\n",
"PLATFORMS = list(TLES)\n",
@@ -353,8 +412,15 @@
" is_rectangular=True,\n",
" req_target_sunlit=True,\n",
")\n",
- "satellites = {name: Satellite(name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle), instruments=[msi]) for name, tle in TLES.items()}\n",
- "TLE_EPOCH = {name: s.orbit.to_gp_orbit().elements[0].epoch for name, s in satellites.items()}\n",
+ "satellites = {\n",
+ " name: Satellite(\n",
+ " name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle), instruments=[msi]\n",
+ " )\n",
+ " for name, tle in TLES.items()\n",
+ "}\n",
+ "TLE_EPOCH = {\n",
+ " name: s.orbit.to_gp_orbit().elements[0].epoch for name, s in satellites.items()\n",
+ "}\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -365,7 +431,14 @@
" \"\"\"Summary statistics of a residual sample.\"\"\"\n",
" x = np.asarray(x, dtype=float)\n",
" x = x[np.isfinite(x)]\n",
- " return pd.Series({\"n\": x.size, \"median\": np.median(x), \"p95 |x|\": np.percentile(np.abs(x), 95), \"max |x|\": np.abs(x).max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": x.size,\n",
+ " \"median\": np.median(x),\n",
+ " \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+ " \"max |x|\": np.abs(x).max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -517,31 +590,57 @@
" e = ephemeris[ephemeris.platform == platform].iloc[::10]\n",
" position = e[[f\"position_{a}\" for a in \"xyz\"]].to_numpy()\n",
" velocity = e[[f\"velocity_{a}\" for a in \"xyz\"]].to_numpy()\n",
- " predicted = np.array(satellites[platform].orbit.to_gp_orbit().get_orbit_track(e.time.dt.to_pydatetime().tolist()).frame_xyz_and_velocity(itrs)[0].m).T\n",
+ " predicted = np.array(\n",
+ " satellites[platform]\n",
+ " .orbit.to_gp_orbit()\n",
+ " .get_orbit_track(e.time.dt.to_pydatetime().tolist())\n",
+ " .frame_xyz_and_velocity(itrs)[0]\n",
+ " .m\n",
+ " ).T\n",
" offset = predicted - position\n",
" radial = normalize(position)\n",
- " along = normalize(velocity - np.sum(velocity * radial, axis=-1, keepdims=True) * radial)\n",
+ " along = normalize(\n",
+ " velocity - np.sum(velocity * radial, axis=-1, keepdims=True) * radial\n",
+ " )\n",
" rows1.append(\n",
" pd.DataFrame(\n",
" {\n",
" \"platform\": platform,\n",
" \"datastrip\": e.datastrip.to_numpy(),\n",
- " \"days before TLE epoch\": (TLE_EPOCH[platform] - e.time).dt.total_seconds().to_numpy() / 86400,\n",
+ " \"days before TLE epoch\": (TLE_EPOCH[platform] - e.time)\n",
+ " .dt.total_seconds()\n",
+ " .to_numpy()\n",
+ " / 86400,\n",
" \"along-track (km)\": np.sum(offset * along, axis=-1) / 1e3,\n",
- " \"cross-track, right (km)\": np.sum(offset * np.cross(along, radial), axis=-1) / 1e3,\n",
+ " \"cross-track, right (km)\": np.sum(\n",
+ " offset * np.cross(along, radial), axis=-1\n",
+ " )\n",
+ " / 1e3,\n",
" \"radial (km)\": np.sum(offset * radial, axis=-1) / 1e3,\n",
" }\n",
" )\n",
" )\n",
"orbit1 = pd.concat(rows1)\n",
- "orbit1.groupby([\"platform\", \"datastrip\"]).median(numeric_only=True).reset_index().groupby(\"platform\").agg(\n",
+ "orbit1.groupby([\"platform\", \"datastrip\"]).median(\n",
+ " numeric_only=True\n",
+ ").reset_index().groupby(\"platform\").agg(\n",
" datastrips=(\"datastrip\", \"size\"),\n",
" days_min=(\"days before TLE epoch\", \"min\"),\n",
" days_max=(\"days before TLE epoch\", \"max\"),\n",
" along_max=(\"along-track (km)\", lambda x: x.abs().max()),\n",
" cross_max=(\"cross-track, right (km)\", lambda x: x.abs().max()),\n",
" radial_max=(\"radial (km)\", lambda x: x.abs().max()),\n",
- ").rename(columns={\"days_min\": \"days before epoch, min\", \"days_max\": \"days before epoch, max\", \"along_max\": \"along-track, max |x| (km)\", \"cross_max\": \"cross-track, max |x| (km)\", \"radial_max\": \"radial, max |x| (km)\"}).round(2)"
+ ").rename(\n",
+ " columns={\n",
+ " \"days_min\": \"days before epoch, min\",\n",
+ " \"days_max\": \"days before epoch, max\",\n",
+ " \"along_max\": \"along-track, max |x| (km)\",\n",
+ " \"cross_max\": \"cross-track, max |x| (km)\",\n",
+ " \"radial_max\": \"radial, max |x| (km)\",\n",
+ " }\n",
+ ").round(\n",
+ " 2\n",
+ ")"
]
},
{
@@ -570,13 +669,26 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
- "strips1 = orbit1.groupby([\"platform\", \"datastrip\"]).median(numeric_only=True).reset_index()\n",
+ "strips1 = (\n",
+ " orbit1.groupby([\"platform\", \"datastrip\"]).median(numeric_only=True).reset_index()\n",
+ ")\n",
"for platform in PLATFORMS:\n",
" s = strips1[strips1.platform == platform].sort_values(\"days before TLE epoch\")\n",
- " ax.plot(s[\"days before TLE epoch\"], s[\"along-track (km)\"], \"o-\", ms=4, color=PLATFORM_COLORS[platform], label=platform)\n",
+ " ax.plot(\n",
+ " s[\"days before TLE epoch\"],\n",
+ " s[\"along-track (km)\"],\n",
+ " \"o-\",\n",
+ " ms=4,\n",
+ " color=PLATFORM_COLORS[platform],\n",
+ " label=platform,\n",
+ " )\n",
"ax.axhline(0, color=GRAY, lw=0.8)\n",
"ax.invert_xaxis()\n",
- "ax.set(xlabel=\"Days before TLE epoch\", ylabel=\"Along-track offset, TAT-C minus GPS (km)\", title=\"Orbit propagation error over the analysis period\")\n",
+ "ax.set(\n",
+ " xlabel=\"Days before TLE epoch\",\n",
+ " ylabel=\"Along-track offset, TAT-C minus GPS (km)\",\n",
+ " title=\"Orbit propagation error over the analysis period\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -701,18 +813,45 @@
"source": [
"def predict(point, constellation=None):\n",
" constellation = list((constellation or satellites).values())\n",
- " return collect_multi_observations(Point(latitude=point.geometry.y, longitude=point.geometry.x), constellation, START, END, omit_solar=False).assign(point_id=point.point_id)\n",
+ " return collect_multi_observations(\n",
+ " Point(latitude=point.geometry.y, longitude=point.geometry.x),\n",
+ " constellation,\n",
+ " START,\n",
+ " END,\n",
+ " omit_solar=False,\n",
+ " ).assign(point_id=point.point_id)\n",
"\n",
"\n",
"def match(predicted):\n",
" \"\"\"Match predicted passes (epochs) to observed passes (datatakes) of the same satellite and point.\"\"\"\n",
- " p = predicted[[\"point_id\", \"satellite\", \"epoch\", \"start\", \"end\", \"solar_alt\"]].rename(columns={\"satellite\": \"platform\"}).sort_values(\"epoch\")\n",
- " o = observed[[\"point_id\", \"platform\", \"datatake\", \"datatake_start\"]].sort_values(\"datatake_start\")\n",
+ " p = (\n",
+ " predicted[[\"point_id\", \"satellite\", \"epoch\", \"start\", \"end\", \"solar_alt\"]]\n",
+ " .rename(columns={\"satellite\": \"platform\"})\n",
+ " .sort_values(\"epoch\")\n",
+ " )\n",
+ " o = observed[[\"point_id\", \"platform\", \"datatake\", \"datatake_start\"]].sort_values(\n",
+ " \"datatake_start\"\n",
+ " )\n",
" p[\"point_id\"] = p.point_id.astype(\"int64\")\n",
- " merged = pd.merge_asof(p, o, left_on=\"epoch\", right_on=\"datatake_start\", by=[\"point_id\", \"platform\"], direction=\"backward\", tolerance=pd.Timedelta(minutes=46))\n",
- " merged = merged[merged.datatake.isna() | (merged.epoch - merged.datatake_start <= pd.Timedelta(minutes=45))]\n",
+ " merged = pd.merge_asof(\n",
+ " p,\n",
+ " o,\n",
+ " left_on=\"epoch\",\n",
+ " right_on=\"datatake_start\",\n",
+ " by=[\"point_id\", \"platform\"],\n",
+ " direction=\"backward\",\n",
+ " tolerance=pd.Timedelta(minutes=46),\n",
+ " )\n",
+ " merged = merged[\n",
+ " merged.datatake.isna()\n",
+ " | (merged.epoch - merged.datatake_start <= pd.Timedelta(minutes=45))\n",
+ " ]\n",
" hit = merged.datatake.notna()\n",
- " observed_only = o[~o.set_index([\"point_id\", \"datatake\"]).index.isin(merged[hit].set_index([\"point_id\", \"datatake\"]).index)]\n",
+ " observed_only = o[\n",
+ " ~o.set_index([\"point_id\", \"datatake\"]).index.isin(\n",
+ " merged[hit].set_index([\"point_id\", \"datatake\"]).index\n",
+ " )\n",
+ " ]\n",
" return merged, observed_only\n",
"\n",
"\n",
@@ -730,7 +869,10 @@
" \"observed only\": int((observed_only2.platform == platform).sum()),\n",
" \"observed passes matched (%)\": 100 * m.datatake.notna().sum() / n_observed,\n",
" \"predicted passes acquired (%)\": 100 * m.datatake.notna().sum() / len(m),\n",
- " \"epoch after datatake start, max (min)\": (m.epoch - m.datatake_start).max().total_seconds() / 60,\n",
+ " \"epoch after datatake start, max (min)\": (m.epoch - m.datatake_start)\n",
+ " .max()\n",
+ " .total_seconds()\n",
+ " / 60,\n",
" }\n",
"pd.DataFrame(rows2).T.round(1)"
]
@@ -769,7 +911,16 @@
" m = merged2[merged2.platform == platform]\n",
" acquired = m.groupby(\"point_id\").datatake.apply(lambda d: d.notna().mean())\n",
" xy = points.set_index(\"point_id\").loc[acquired.index].geometry\n",
- " sc = ax.scatter(xy.x, xy.y, c=100 * acquired.to_numpy(), s=8, cmap=\"viridis\", vmin=0, vmax=100, transform=ccrs.PlateCarree())\n",
+ " sc = ax.scatter(\n",
+ " xy.x,\n",
+ " xy.y,\n",
+ " c=100 * acquired.to_numpy(),\n",
+ " s=8,\n",
+ " cmap=\"viridis\",\n",
+ " vmin=0,\n",
+ " vmax=100,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ " )\n",
" ax.set_title(f\"{platform}: predicted passes acquired (%)\", fontsize=9)\n",
"fig.colorbar(sc, ax=fig.axes, shrink=0.6, label=\"Predicted passes acquired (%)\")\n",
"plt.show()"
@@ -935,12 +1086,23 @@
"nominal = merged2[merged2.platform.isin([\"Sentinel-2B\", \"Sentinel-2C\"])].copy()\n",
"nominal[\"acquired\"] = nominal.datatake.notna()\n",
"nominal[\"t\"] = (nominal.epoch - pd.Timestamp(START)).dt.total_seconds()\n",
- "acquired_epochs = {platform: np.sort(g.t[g.acquired].to_numpy()) for platform, g in nominal.groupby(\"platform\")}\n",
+ "acquired_epochs = {\n",
+ " platform: np.sort(g.t[g.acquired].to_numpy())\n",
+ " for platform, g in nominal.groupby(\"platform\")\n",
+ "}\n",
"\n",
"\n",
"def cross_track_angle(row):\n",
- " track = satellites[row.platform].orbit.to_gp_orbit().get_orbit_track(row.epoch.to_pydatetime())\n",
- " _, cross = compute_view_tangents(track, wgs84.latlon(geometry.loc[row.point_id].y, geometry.loc[row.point_id].x), msi.velocity_frame)\n",
+ " track = (\n",
+ " satellites[row.platform]\n",
+ " .orbit.to_gp_orbit()\n",
+ " .get_orbit_track(row.epoch.to_pydatetime())\n",
+ " )\n",
+ " _, cross = compute_view_tangents(\n",
+ " track,\n",
+ " wgs84.latlon(geometry.loc[row.point_id].y, geometry.loc[row.point_id].x),\n",
+ " msi.velocity_frame,\n",
+ " )\n",
" return float(np.degrees(np.arctan(np.ravel(cross)[0])))\n",
"\n",
"\n",
@@ -954,21 +1116,43 @@
"nominal[\"cross\"] = [cross_track_angle(row) for row in nominal.itertuples()]\n",
"missed = ~nominal.acquired\n",
"nominal[\"cause\"] = \"acquired\"\n",
- "gap = missed & np.array([nearest_acquisition(row) > 300 for row in nominal.itertuples()])\n",
+ "gap = missed & np.array(\n",
+ " [nearest_acquisition(row) > 300 for row in nominal.itertuples()]\n",
+ ")\n",
"nominal.loc[gap, \"cause\"] = \"catalog gap\"\n",
"nominal.loc[missed & ~gap & (nominal.solar_alt < 10), \"cause\"] = \"low Sun\"\n",
- "nominal.loc[missed & ~gap & (nominal.solar_alt >= 10) & (nominal.cross.abs() > 10.2), \"cause\"] = \"swath edge\"\n",
+ "nominal.loc[\n",
+ " missed & ~gap & (nominal.solar_alt >= 10) & (nominal.cross.abs() > 10.2), \"cause\"\n",
+ "] = \"swath edge\"\n",
"nominal.loc[(nominal.cause == \"acquired\") & missed, \"cause\"] = \"other\"\n",
- "causes = nominal.groupby([\"platform\", \"cause\"]).size().unstack(fill_value=0)[[\"acquired\", \"catalog gap\", \"low Sun\", \"swath edge\", \"other\"]]\n",
- "causes[\"acquired, excluding catalog gaps (%)\"] = 100 * causes.acquired / (causes.sum(axis=1) - causes[\"catalog gap\"])\n",
+ "causes = (\n",
+ " nominal.groupby([\"platform\", \"cause\"])\n",
+ " .size()\n",
+ " .unstack(fill_value=0)[\n",
+ " [\"acquired\", \"catalog gap\", \"low Sun\", \"swath edge\", \"other\"]\n",
+ " ]\n",
+ ")\n",
+ "causes[\"acquired, excluding catalog gaps (%)\"] = (\n",
+ " 100 * causes.acquired / (causes.sum(axis=1) - causes[\"catalog gap\"])\n",
+ ")\n",
"pd.concat(\n",
" [\n",
" causes,\n",
" pd.DataFrame(\n",
" {\n",
- " \"acquired passes, solar elevation, min (deg)\": nominal[nominal.acquired].groupby(\"platform\").solar_alt.min(),\n",
- " \"acquired passes, |cross-track angle|, max (deg)\": nominal[nominal.acquired].groupby(\"platform\").cross.apply(lambda x: x.abs().max()),\n",
- " \"swath edge passes acquired (%)\": nominal[nominal.cross.abs() > 10.2].groupby(\"platform\").apply(lambda g: 100 * g.acquired.sum() / (g.cause != \"catalog gap\").sum()),\n",
+ " \"acquired passes, solar elevation, min (deg)\": nominal[nominal.acquired]\n",
+ " .groupby(\"platform\")\n",
+ " .solar_alt.min(),\n",
+ " \"acquired passes, |cross-track angle|, max (deg)\": nominal[\n",
+ " nominal.acquired\n",
+ " ]\n",
+ " .groupby(\"platform\")\n",
+ " .cross.apply(lambda x: x.abs().max()),\n",
+ " \"swath edge passes acquired (%)\": nominal[nominal.cross.abs() > 10.2]\n",
+ " .groupby(\"platform\")\n",
+ " .apply(\n",
+ " lambda g: 100 * g.acquired.sum() / (g.cause != \"catalog gap\").sum()\n",
+ " ),\n",
" }\n",
" ),\n",
" ],\n",
@@ -1096,16 +1280,46 @@
}
],
"source": [
- "msi_daylight = PointedInstrument(**{**msi.model_dump(), \"req_target_sunlit\": None, \"min_target_solar_elevation\": 5})\n",
- "daylight = {name: s.model_copy(update={\"instruments\": [msi_daylight]}) for name, s in satellites.items()}\n",
- "predicted5 = pd.concat([predict(p, daylight) for p in points.itertuples()]).reset_index(drop=True)\n",
+ "msi_daylight = PointedInstrument(\n",
+ " **{**msi.model_dump(), \"req_target_sunlit\": None, \"min_target_solar_elevation\": 5}\n",
+ ")\n",
+ "daylight = {\n",
+ " name: s.model_copy(update={\"instruments\": [msi_daylight]})\n",
+ " for name, s in satellites.items()\n",
+ "}\n",
+ "predicted5 = pd.concat([predict(p, daylight) for p in points.itertuples()]).reset_index(\n",
+ " drop=True\n",
+ ")\n",
"merged5, observed_only5 = match(predicted5)\n",
- "key = lambda frame: frame.assign(point_id=frame.point_id.astype(\"int64\")).set_index([\"point_id\", \"platform\", \"epoch\"]).index\n",
- "nominal5 = nominal.set_index([\"point_id\", \"platform\", \"epoch\"]).loc[nominal.set_index([\"point_id\", \"platform\", \"epoch\"]).index.isin(key(merged5))]\n",
- "causes5 = nominal5.groupby([\"platform\", \"cause\"]).size().unstack(fill_value=0).reindex(columns=[\"acquired\", \"catalog gap\", \"low Sun\", \"swath edge\", \"other\"], fill_value=0)\n",
- "causes5[\"acquired, excluding catalog gaps (%)\"] = 100 * causes5.acquired / (causes5.sum(axis=1) - causes5[\"catalog gap\"])\n",
- "causes5[\"predicted passes removed\"] = nominal.groupby(\"platform\").size() - nominal5.groupby(\"platform\").size()\n",
- "causes5[\"observed only\"] = observed_only5[observed_only5.platform.isin(causes5.index)].groupby(\"platform\").size().reindex(causes5.index, fill_value=0)\n",
+ "key = (\n",
+ " lambda frame: frame.assign(point_id=frame.point_id.astype(\"int64\"))\n",
+ " .set_index([\"point_id\", \"platform\", \"epoch\"])\n",
+ " .index\n",
+ ")\n",
+ "nominal5 = nominal.set_index([\"point_id\", \"platform\", \"epoch\"]).loc[\n",
+ " nominal.set_index([\"point_id\", \"platform\", \"epoch\"]).index.isin(key(merged5))\n",
+ "]\n",
+ "causes5 = (\n",
+ " nominal5.groupby([\"platform\", \"cause\"])\n",
+ " .size()\n",
+ " .unstack(fill_value=0)\n",
+ " .reindex(\n",
+ " columns=[\"acquired\", \"catalog gap\", \"low Sun\", \"swath edge\", \"other\"],\n",
+ " fill_value=0,\n",
+ " )\n",
+ ")\n",
+ "causes5[\"acquired, excluding catalog gaps (%)\"] = (\n",
+ " 100 * causes5.acquired / (causes5.sum(axis=1) - causes5[\"catalog gap\"])\n",
+ ")\n",
+ "causes5[\"predicted passes removed\"] = (\n",
+ " nominal.groupby(\"platform\").size() - nominal5.groupby(\"platform\").size()\n",
+ ")\n",
+ "causes5[\"observed only\"] = (\n",
+ " observed_only5[observed_only5.platform.isin(causes5.index)]\n",
+ " .groupby(\"platform\")\n",
+ " .size()\n",
+ " .reindex(causes5.index, fill_value=0)\n",
+ ")\n",
"causes5.round(1)"
]
},
@@ -1277,10 +1491,21 @@
"def as_observations(frame, time_column, satellite_column):\n",
" \"\"\"A data frame of instantaneous observations, as accepted by aggregate_observations.\"\"\"\n",
" t = frame[time_column]\n",
- " geometry = points.set_index(\"point_id\").geometry.loc[frame.point_id.astype(\"int64\")].to_numpy()\n",
+ " geometry = (\n",
+ " points.set_index(\"point_id\")\n",
+ " .geometry.loc[frame.point_id.astype(\"int64\")]\n",
+ " .to_numpy()\n",
+ " )\n",
" return pd.DataFrame(\n",
- " {\"target_hash\": [hash_geometry(g) for g in geometry], \"geometry\": geometry,\n",
- " \"satellite\": frame[satellite_column].to_numpy(), \"instrument\": \"MSI\", \"start\": t.to_numpy(), \"epoch\": t.to_numpy(), \"end\": t.to_numpy()}\n",
+ " {\n",
+ " \"target_hash\": [hash_geometry(g) for g in geometry],\n",
+ " \"geometry\": geometry,\n",
+ " \"satellite\": frame[satellite_column].to_numpy(),\n",
+ " \"instrument\": \"MSI\",\n",
+ " \"start\": t.to_numpy(),\n",
+ " \"epoch\": t.to_numpy(),\n",
+ " \"end\": t.to_numpy(),\n",
+ " }\n",
" )\n",
"\n",
"\n",
@@ -1290,32 +1515,64 @@
" aggregated = aggregate_observations(gpd.GeoDataFrame(frame, crs=\"EPSG:4326\"))\n",
" reduced = reduce_observations(aggregated)\n",
" reduced.index = reduced.target_hash.map(POINT_IDS)\n",
- " reduced[\"max revisit\"] = aggregated.groupby(aggregated.target_hash.map(POINT_IDS)).revisit.max()\n",
+ " reduced[\"max revisit\"] = aggregated.groupby(\n",
+ " aggregated.target_hash.map(POINT_IDS)\n",
+ " ).revisit.max()\n",
" return reduced[[\"samples\", \"revisit\", \"max revisit\"]].reindex(points.point_id)\n",
"\n",
"\n",
- "gaps = nominal[nominal.cause == \"catalog gap\"].set_index([\"point_id\", \"platform\", \"epoch\"]).index\n",
- "in_gap = predicted.assign(point_id=predicted.point_id.astype(\"int64\")).set_index([\"point_id\", \"satellite\", \"epoch\"]).index.isin(gaps)\n",
- "in_gap5 = predicted5.assign(point_id=predicted5.point_id.astype(\"int64\")).set_index([\"point_id\", \"satellite\", \"epoch\"]).index.isin(gaps)\n",
+ "gaps = (\n",
+ " nominal[nominal.cause == \"catalog gap\"]\n",
+ " .set_index([\"point_id\", \"platform\", \"epoch\"])\n",
+ " .index\n",
+ ")\n",
+ "in_gap = (\n",
+ " predicted.assign(point_id=predicted.point_id.astype(\"int64\"))\n",
+ " .set_index([\"point_id\", \"satellite\", \"epoch\"])\n",
+ " .index.isin(gaps)\n",
+ ")\n",
+ "in_gap5 = (\n",
+ " predicted5.assign(point_id=predicted5.point_id.astype(\"int64\"))\n",
+ " .set_index([\"point_id\", \"satellite\", \"epoch\"])\n",
+ " .index.isin(gaps)\n",
+ ")\n",
"rows3, stats3 = {}, {}\n",
"for label, members, frame, frame_in_gap in [\n",
" (\"2B + 2C\", [\"Sentinel-2B\", \"Sentinel-2C\"], predicted, in_gap),\n",
- " (\"2B + 2C, solar elevation >= 5 deg\", [\"Sentinel-2B\", \"Sentinel-2C\"], predicted5, in_gap5),\n",
+ " (\n",
+ " \"2B + 2C, solar elevation >= 5 deg\",\n",
+ " [\"Sentinel-2B\", \"Sentinel-2C\"],\n",
+ " predicted5,\n",
+ " in_gap5,\n",
+ " ),\n",
" (\"2A + 2B + 2C\", PLATFORMS, predicted, in_gap),\n",
"]:\n",
- " o = revisit_statistics(as_observations(observed[observed.platform.isin(members)], \"datatake_start\", \"platform\"))\n",
- " p = revisit_statistics(as_observations(frame[frame.satellite.isin(members) & ~frame_in_gap], \"epoch\", \"satellite\"))\n",
+ " o = revisit_statistics(\n",
+ " as_observations(\n",
+ " observed[observed.platform.isin(members)], \"datatake_start\", \"platform\"\n",
+ " )\n",
+ " )\n",
+ " p = revisit_statistics(\n",
+ " as_observations(\n",
+ " frame[frame.satellite.isin(members) & ~frame_in_gap], \"epoch\", \"satellite\"\n",
+ " )\n",
+ " )\n",
" stats3[label] = (o, p)\n",
" days = lambda x: x.dt.total_seconds() / 86400\n",
" rows3[label] = {\n",
" \"points observed\": int(o.samples.notna().sum()),\n",
" \"samples per point, observed (median)\": o.samples.median(),\n",
" \"samples per point, predicted (median)\": p.samples.median(),\n",
- " \"points with equal samples (%)\": 100 * np.mean(o.samples.fillna(0) == p.samples.fillna(0)),\n",
- " \"samples, predicted minus observed (mean)\": np.mean(p.samples.fillna(0) - o.samples.fillna(0)),\n",
+ " \"points with equal samples (%)\": 100\n",
+ " * np.mean(o.samples.fillna(0) == p.samples.fillna(0)),\n",
+ " \"samples, predicted minus observed (mean)\": np.mean(\n",
+ " p.samples.fillna(0) - o.samples.fillna(0)\n",
+ " ),\n",
" \"mean revisit, observed (median, days)\": days(o.revisit).median(),\n",
" \"mean revisit, predicted (median, days)\": days(p.revisit).median(),\n",
- " \"mean revisit difference, p95 |x| (days)\": np.nanpercentile(np.abs(days(p.revisit) - days(o.revisit)), 95),\n",
+ " \"mean revisit difference, p95 |x| (days)\": np.nanpercentile(\n",
+ " np.abs(days(p.revisit) - days(o.revisit)), 95\n",
+ " ),\n",
" \"max revisit, observed (median, days)\": days(o[\"max revisit\"]).median(),\n",
" \"max revisit, predicted (median, days)\": days(p[\"max revisit\"]).median(),\n",
" }\n",
@@ -1353,15 +1610,36 @@
"ax.set_global()\n",
"ax.coastlines(color=GRAY, linewidth=0.5)\n",
"xy = points.set_index(\"point_id\").geometry\n",
- "sc = ax.scatter(xy.x, xy.y, c=(p.revisit.dt.total_seconds() / 86400).to_numpy(), s=8, cmap=\"viridis_r\", vmin=0, vmax=5, transform=ccrs.PlateCarree())\n",
+ "sc = ax.scatter(\n",
+ " xy.x,\n",
+ " xy.y,\n",
+ " c=(p.revisit.dt.total_seconds() / 86400).to_numpy(),\n",
+ " s=8,\n",
+ " cmap=\"viridis_r\",\n",
+ " vmin=0,\n",
+ " vmax=5,\n",
+ " transform=ccrs.PlateCarree(),\n",
+ ")\n",
"fig.colorbar(sc, ax=ax, shrink=0.7, label=\"Mean revisit, TAT-C (days)\")\n",
"ax.set_title(\"Predicted mean revisit, Sentinel-2B and 2C\", fontsize=9)\n",
"ax2 = fig.add_subplot(1, 2, 2)\n",
"for label, color in [(\"2B + 2C\", BLUE), (\"2A + 2B + 2C\", ORANGE)]:\n",
" o, p = stats3[label]\n",
- " ax2.scatter(o.revisit.dt.total_seconds() / 86400, p.revisit.dt.total_seconds() / 86400, s=6, color=color, label=label)\n",
+ " ax2.scatter(\n",
+ " o.revisit.dt.total_seconds() / 86400,\n",
+ " p.revisit.dt.total_seconds() / 86400,\n",
+ " s=6,\n",
+ " color=color,\n",
+ " label=label,\n",
+ " )\n",
"ax2.plot([0, 6], [0, 6], color=GRAY, lw=0.8)\n",
- "ax2.set(xlim=(0, 6), ylim=(0, 6), xlabel=\"Observed mean revisit (days)\", ylabel=\"Predicted mean revisit (days)\", title=\"Mean revisit per point\")\n",
+ "ax2.set(\n",
+ " xlim=(0, 6),\n",
+ " ylim=(0, 6),\n",
+ " xlabel=\"Observed mean revisit (days)\",\n",
+ " ylabel=\"Predicted mean revisit (days)\",\n",
+ " title=\"Mean revisit per point\",\n",
+ ")\n",
"ax2.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1424,20 +1702,49 @@
],
"source": [
"# the same satellites, propagated maintained on their repeat ground tracks\n",
- "maintained = {name: Satellite(name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle, remove_drag=True, repeat_cycle=\"auto\"), instruments=[msi]) for name, tle in TLES.items()}\n",
- "print(\"repeat cycles:\", {name: str(s.orbit.get_repeat_cycle()) for name, s in maintained.items()})\n",
- "repeated = pd.concat([predict(p, maintained) for p in points.itertuples()]).reset_index(drop=True)\n",
+ "maintained = {\n",
+ " name: Satellite(\n",
+ " name=name,\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(\n",
+ " tle, remove_drag=True, repeat_cycle=\"auto\"\n",
+ " ),\n",
+ " instruments=[msi],\n",
+ " )\n",
+ " for name, tle in TLES.items()\n",
+ "}\n",
+ "print(\n",
+ " \"repeat cycles:\",\n",
+ " {name: str(s.orbit.get_repeat_cycle()) for name, s in maintained.items()},\n",
+ ")\n",
+ "repeated = pd.concat([predict(p, maintained) for p in points.itertuples()]).reset_index(\n",
+ " drop=True\n",
+ ")\n",
"merged4, observed_only4 = match(repeated)\n",
"pair = pd.merge_asof(\n",
- " repeated.assign(point_id=repeated.point_id.astype(\"int64\")).sort_values(\"epoch\")[[\"point_id\", \"satellite\", \"epoch\"]],\n",
- " predicted.assign(point_id=predicted.point_id.astype(\"int64\"), epoch_direct=predicted.epoch).sort_values(\"epoch\")[[\"point_id\", \"satellite\", \"epoch\", \"epoch_direct\"]],\n",
- " on=\"epoch\", by=[\"point_id\", \"satellite\"], direction=\"nearest\", tolerance=pd.Timedelta(minutes=10),\n",
+ " repeated.assign(point_id=repeated.point_id.astype(\"int64\")).sort_values(\"epoch\")[\n",
+ " [\"point_id\", \"satellite\", \"epoch\"]\n",
+ " ],\n",
+ " predicted.assign(\n",
+ " point_id=predicted.point_id.astype(\"int64\"), epoch_direct=predicted.epoch\n",
+ " ).sort_values(\"epoch\")[[\"point_id\", \"satellite\", \"epoch\", \"epoch_direct\"]],\n",
+ " on=\"epoch\",\n",
+ " by=[\"point_id\", \"satellite\"],\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=10),\n",
")\n",
"pair = pair.merge(\n",
- " repeated.assign(point_id=repeated.point_id.astype(\"int64\"))[[\"point_id\", \"satellite\", \"epoch\", \"start\", \"end\"]], on=[\"point_id\", \"satellite\", \"epoch\"]\n",
+ " repeated.assign(point_id=repeated.point_id.astype(\"int64\"))[\n",
+ " [\"point_id\", \"satellite\", \"epoch\", \"start\", \"end\"]\n",
+ " ],\n",
+ " on=[\"point_id\", \"satellite\", \"epoch\"],\n",
").merge(\n",
- " predicted.assign(point_id=predicted.point_id.astype(\"int64\"))[[\"point_id\", \"satellite\", \"epoch\", \"start\", \"end\"]].rename(columns={\"epoch\": \"epoch_direct\", \"start\": \"start_direct\", \"end\": \"end_direct\"}),\n",
- " on=[\"point_id\", \"satellite\", \"epoch_direct\"], how=\"left\",\n",
+ " predicted.assign(point_id=predicted.point_id.astype(\"int64\"))[\n",
+ " [\"point_id\", \"satellite\", \"epoch\", \"start\", \"end\"]\n",
+ " ].rename(\n",
+ " columns={\"epoch\": \"epoch_direct\", \"start\": \"start_direct\", \"end\": \"end_direct\"}\n",
+ " ),\n",
+ " on=[\"point_id\", \"satellite\", \"epoch_direct\"],\n",
+ " how=\"left\",\n",
")\n",
"pair[\"difference (s)\"] = (pair.epoch - pair.epoch_direct).dt.total_seconds()\n",
"pair[\"start difference (s)\"] = (pair.start - pair.start_direct).dt.total_seconds()\n",
@@ -1446,12 +1753,24 @@
"rows4 = {\n",
" \"passes, maintained orbit\": len(repeated),\n",
" \"passes, direct propagation\": len(predicted),\n",
- " \"passes predicted only one way\": int(pair.epoch_direct.isna().sum()) + len(predicted) - int(pair.epoch_direct.notna().sum()),\n",
- " \"epoch difference, first cycle, p95 |x| (s)\": np.percentile(np.abs(pair[\"difference (s)\"][pair.day < 10].dropna()), 95),\n",
- " \"epoch difference, second cycle, p95 |x| (s)\": np.percentile(np.abs(pair[\"difference (s)\"][pair.day >= 10].dropna()), 95),\n",
- " \"access start difference, first cycle, p95 |x| (s)\": np.percentile(np.abs(pair[\"start difference (s)\"][pair.day < 10].dropna()), 95),\n",
- " \"access start difference, second cycle, p95 |x| (s)\": np.percentile(np.abs(pair[\"start difference (s)\"][pair.day >= 10].dropna()), 95),\n",
- " \"access end difference, second cycle, p95 |x| (s)\": np.percentile(np.abs(pair[\"end difference (s)\"][pair.day >= 10].dropna()), 95),\n",
+ " \"passes predicted only one way\": int(pair.epoch_direct.isna().sum())\n",
+ " + len(predicted)\n",
+ " - int(pair.epoch_direct.notna().sum()),\n",
+ " \"epoch difference, first cycle, p95 |x| (s)\": np.percentile(\n",
+ " np.abs(pair[\"difference (s)\"][pair.day < 10].dropna()), 95\n",
+ " ),\n",
+ " \"epoch difference, second cycle, p95 |x| (s)\": np.percentile(\n",
+ " np.abs(pair[\"difference (s)\"][pair.day >= 10].dropna()), 95\n",
+ " ),\n",
+ " \"access start difference, first cycle, p95 |x| (s)\": np.percentile(\n",
+ " np.abs(pair[\"start difference (s)\"][pair.day < 10].dropna()), 95\n",
+ " ),\n",
+ " \"access start difference, second cycle, p95 |x| (s)\": np.percentile(\n",
+ " np.abs(pair[\"start difference (s)\"][pair.day >= 10].dropna()), 95\n",
+ " ),\n",
+ " \"access end difference, second cycle, p95 |x| (s)\": np.percentile(\n",
+ " np.abs(pair[\"end difference (s)\"][pair.day >= 10].dropna()), 95\n",
+ " ),\n",
" \"matched observed passes, maintained orbit\": int(merged4.datatake.notna().sum()),\n",
" \"matched observed passes, direct\": int(merged2.datatake.notna().sum()),\n",
"}\n",
diff --git a/docs/validation/ValidateSARNISAR.ipynb b/docs/validation/ValidateSARNISAR.ipynb
index c5a2e553..4d2b53cf 100644
--- a/docs/validation/ValidateSARNISAR.ipynb
+++ b/docs/validation/ValidateSARNISAR.ipynb
@@ -44,10 +44,10 @@
"id": "43440b02",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:55.946511Z",
- "iopub.status.busy": "2026-10-08T18:04:55.945509Z",
- "iopub.status.idle": "2026-10-08T18:04:56.532744Z",
- "shell.execute_reply": "2026-10-08T18:04:56.532744Z"
+ "iopub.execute_input": "2026-10-25T14:00:45.227032Z",
+ "iopub.status.busy": "2026-10-25T14:00:45.227032Z",
+ "iopub.status.idle": "2026-10-25T14:00:45.897628Z",
+ "shell.execute_reply": "2026-10-25T14:00:45.897628Z"
}
},
"outputs": [
@@ -223,7 +223,9 @@
" ]\n",
")\n",
"# products that start on the analysis day\n",
- "catalog = catalog[catalog.start >= pd.Timestamp(DAY)].sort_values(\"start\", ignore_index=True)\n",
+ "catalog = catalog[catalog.start >= pd.Timestamp(DAY)].sort_values(\n",
+ " \"start\", ignore_index=True\n",
+ ")\n",
"catalog.groupby([\"mode\", \"direction\"]).size().unstack(fill_value=0)"
]
},
@@ -246,10 +248,10 @@
"id": "936bd1bc",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:56.535405Z",
- "iopub.status.busy": "2026-10-08T18:04:56.534399Z",
- "iopub.status.idle": "2026-10-08T18:04:56.652486Z",
- "shell.execute_reply": "2026-10-08T18:04:56.652486Z"
+ "iopub.execute_input": "2026-10-25T14:00:45.900635Z",
+ "iopub.status.busy": "2026-10-25T14:00:45.899632Z",
+ "iopub.status.idle": "2026-10-25T14:00:46.017087Z",
+ "shell.execute_reply": "2026-10-25T14:00:46.017087Z"
}
},
"outputs": [
@@ -273,7 +275,10 @@
"BOUNDS_FILE = DATA_DIR / f\"nisar_rslc_bounds_{DAY:%Y%m%d}.csv\"\n",
"SAMPLE_FILE = DATA_DIR / f\"nisar_rslc_metadata_{DAY:%Y%m%d}.npz\"\n",
"sample = pd.concat(\n",
- " [g.iloc[np.unique(np.linspace(0, len(g) - 1, 4).round().astype(int))] for _, g in catalog.groupby(\"mode\")]\n",
+ " [\n",
+ " g.iloc[np.unique(np.linspace(0, len(g) - 1, 4).round().astype(int))]\n",
+ " for _, g in catalog.groupby(\"mode\")\n",
+ " ]\n",
").sort_values(\"start\", ignore_index=True)\n",
"N_RANGE = 41 # resampled slant ranges across the image\n",
"\n",
@@ -290,13 +295,17 @@
" self.block_size, self.blocks, self.position = block_size, {}, 0\n",
" # follow the Earthdata Login redirects while prefetching the first blocks (file metadata)\n",
" response = self._get(url, 0, prefetch * block_size)\n",
- " self.url, self.size = response.url, int(response.headers[\"Content-Range\"].split(\"/\")[1])\n",
+ " self.url, self.size = response.url, int(\n",
+ " response.headers[\"Content-Range\"].split(\"/\")[1]\n",
+ " )\n",
" self._store(0, response.content)\n",
"\n",
" def _get(self, url, start, end):\n",
" for attempt in range(5):\n",
" try:\n",
- " response = session.get(url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300)\n",
+ " response = session.get(\n",
+ " url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300\n",
+ " )\n",
" response.raise_for_status()\n",
" return response\n",
" except requests.RequestException:\n",
@@ -305,7 +314,9 @@
"\n",
" def _store(self, first, content):\n",
" for i in range(0, len(content), self.block_size):\n",
- " self.blocks[first + i // self.block_size] = content[i : i + self.block_size]\n",
+ " self.blocks[first + i // self.block_size] = content[\n",
+ " i : i + self.block_size\n",
+ " ]\n",
"\n",
" def readable(self):\n",
" return True\n",
@@ -317,18 +328,30 @@
" return self.position\n",
"\n",
" def seek(self, offset, whence=io.SEEK_SET):\n",
- " self.position = {io.SEEK_SET: 0, io.SEEK_CUR: self.position, io.SEEK_END: self.size}[whence] + offset\n",
+ " self.position = {\n",
+ " io.SEEK_SET: 0,\n",
+ " io.SEEK_CUR: self.position,\n",
+ " io.SEEK_END: self.size,\n",
+ " }[whence] + offset\n",
" return self.position\n",
"\n",
" def readinto(self, buffer):\n",
" n = min(len(buffer), self.size - self.position)\n",
" if n <= 0:\n",
" return 0\n",
- " first, last = self.position // self.block_size, (self.position + n - 1) // self.block_size\n",
- " missing = [block for block in range(first, last + 1) if block not in self.blocks]\n",
+ " first, last = (\n",
+ " self.position // self.block_size,\n",
+ " (self.position + n - 1) // self.block_size,\n",
+ " )\n",
+ " missing = [\n",
+ " block for block in range(first, last + 1) if block not in self.blocks\n",
+ " ]\n",
" if missing:\n",
" end = min((missing[-1] + 1) * self.block_size, self.size)\n",
- " self._store(missing[0], self._get(self.url, missing[0] * self.block_size, end).content)\n",
+ " self._store(\n",
+ " missing[0],\n",
+ " self._get(self.url, missing[0] * self.block_size, end).content,\n",
+ " )\n",
" data = b\"\".join(self.blocks[block] for block in range(first, last + 1))\n",
" offset = self.position - first * self.block_size\n",
" buffer[:n] = data[offset : offset + n]\n",
@@ -338,7 +361,10 @@
" def seconds_since_day(dataset):\n",
" \"\"\"Times of a dataset in seconds since the start of the analysis day.\"\"\"\n",
" epoch = re.search(r\"since (\\S+)\", dataset.attrs[\"units\"].decode()).group(1)\n",
- " return dataset[:] + (pd.Timestamp(epoch, tz=\"UTC\") - pd.Timestamp(DAY)).total_seconds()\n",
+ " return (\n",
+ " dataset[:]\n",
+ " + (pd.Timestamp(epoch, tz=\"UTC\") - pd.Timestamp(DAY)).total_seconds()\n",
+ " )\n",
"\n",
" def read_bounds(url):\n",
" with h5py.File(RemoteFile(url)) as f:\n",
@@ -346,7 +372,9 @@
" return {\n",
" \"url\": url,\n",
" \"polygon\": identification[\"boundingPolygon\"][()].decode(),\n",
- " \"zero_doppler_start\": identification[\"zeroDopplerStartTime\"][()].decode(),\n",
+ " \"zero_doppler_start\": identification[\"zeroDopplerStartTime\"][\n",
+ " ()\n",
+ " ].decode(),\n",
" \"zero_doppler_end\": identification[\"zeroDopplerEndTime\"][()].decode(),\n",
" }\n",
"\n",
@@ -356,17 +384,27 @@
" grid = rslc[\"metadata/geolocationGrid\"]\n",
" layer = int(np.argmin(np.abs(grid[\"heightAboveEllipsoid\"][:])))\n",
" grid_range = grid[\"slantRange\"][:]\n",
- " frequency = sorted(k for k in rslc[\"swaths\"] if k.startswith(\"frequency\"))[0]\n",
+ " frequency = sorted(k for k in rslc[\"swaths\"] if k.startswith(\"frequency\"))[\n",
+ " 0\n",
+ " ]\n",
" image_range = rslc[f\"swaths/{frequency}/slantRange\"][[0, -1]]\n",
" # resample the grid to slant ranges spanning the image, from near to far range\n",
" ranges = np.linspace(image_range[0], image_range[1], N_RANGE)\n",
- " resample = lambda x: np.array([np.interp(ranges, grid_range, row) for row in x])\n",
+ " resample = lambda x: np.array(\n",
+ " [np.interp(ranges, grid_range, row) for row in x]\n",
+ " )\n",
" return {\n",
- " \"look_direction\": f[\"science/LSAR/identification/lookDirection\"][()].decode(),\n",
+ " \"look_direction\": f[\"science/LSAR/identification/lookDirection\"][\n",
+ " ()\n",
+ " ].decode(),\n",
" \"time\": seconds_since_day(grid[\"zeroDopplerTime\"]),\n",
" \"slant_range\": ranges,\n",
" # unwrap longitudes so that interpolation does not cross the antimeridian\n",
- " \"lon\": (resample(np.unwrap(grid[\"coordinateX\"][layer], period=360)) + 180) % 360 - 180,\n",
+ " \"lon\": (\n",
+ " resample(np.unwrap(grid[\"coordinateX\"][layer], period=360)) + 180\n",
+ " )\n",
+ " % 360\n",
+ " - 180,\n",
" \"lat\": resample(grid[\"coordinateY\"][layer]),\n",
" \"incidence\": resample(grid[\"incidenceAngle\"][layer]),\n",
" \"orbit_time\": seconds_since_day(rslc[\"metadata/orbit/time\"]),\n",
@@ -385,21 +423,51 @@
" n_time=[len(r[\"time\"]) for r in records],\n",
" n_orbit=[len(r[\"orbit_time\"]) for r in records],\n",
" slant_range=np.stack([r[\"slant_range\"] for r in records]),\n",
- " **{k: np.concatenate([r[k] for r in records]) for k in [\"time\", \"lon\", \"lat\", \"incidence\", \"orbit_time\", \"orbit_position\", \"orbit_velocity\"]},\n",
+ " **{\n",
+ " k: np.concatenate([r[k] for r in records])\n",
+ " for k in [\n",
+ " \"time\",\n",
+ " \"lon\",\n",
+ " \"lat\",\n",
+ " \"incidence\",\n",
+ " \"orbit_time\",\n",
+ " \"orbit_position\",\n",
+ " \"orbit_velocity\",\n",
+ " ]\n",
+ " },\n",
" )\n",
"\n",
"catalog = catalog.merge(pd.read_csv(BOUNDS_FILE), on=\"url\")\n",
"cache = dict(np.load(SAMPLE_FILE))\n",
"split = lambda key, n: np.split(cache[key], np.cumsum(cache[n])[:-1])\n",
"meta = [\n",
- " {\"look_direction\": cache[\"look_direction\"][i], \"slant_range\": cache[\"slant_range\"][i], **dict(zip(keys, values))}\n",
+ " {\n",
+ " \"look_direction\": cache[\"look_direction\"][i],\n",
+ " \"slant_range\": cache[\"slant_range\"][i],\n",
+ " **dict(zip(keys, values)),\n",
+ " }\n",
" for i, values in enumerate(\n",
" zip(\n",
- " split(\"time\", \"n_time\"), split(\"lon\", \"n_time\"), split(\"lat\", \"n_time\"), split(\"incidence\", \"n_time\"),\n",
- " split(\"orbit_time\", \"n_orbit\"), split(\"orbit_position\", \"n_orbit\"), split(\"orbit_velocity\", \"n_orbit\"),\n",
+ " split(\"time\", \"n_time\"),\n",
+ " split(\"lon\", \"n_time\"),\n",
+ " split(\"lat\", \"n_time\"),\n",
+ " split(\"incidence\", \"n_time\"),\n",
+ " split(\"orbit_time\", \"n_orbit\"),\n",
+ " split(\"orbit_position\", \"n_orbit\"),\n",
+ " split(\"orbit_velocity\", \"n_orbit\"),\n",
" )\n",
" )\n",
- " for keys in [(\"time\", \"lon\", \"lat\", \"incidence\", \"orbit_time\", \"orbit_position\", \"orbit_velocity\")]\n",
+ " for keys in [\n",
+ " (\n",
+ " \"time\",\n",
+ " \"lon\",\n",
+ " \"lat\",\n",
+ " \"incidence\",\n",
+ " \"orbit_time\",\n",
+ " \"orbit_position\",\n",
+ " \"orbit_velocity\",\n",
+ " )\n",
+ " ]\n",
"]\n",
"pd.Series(\n",
" {\n",
@@ -428,10 +496,10 @@
"id": "1dcc5ea2",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:56.654868Z",
- "iopub.status.busy": "2026-10-08T18:04:56.654868Z",
- "iopub.status.idle": "2026-10-08T18:04:58.331495Z",
- "shell.execute_reply": "2026-10-08T18:04:58.331495Z"
+ "iopub.execute_input": "2026-10-25T14:00:46.019125Z",
+ "iopub.status.busy": "2026-10-25T14:00:46.019125Z",
+ "iopub.status.idle": "2026-10-25T14:00:47.823582Z",
+ "shell.execute_reply": "2026-10-25T14:00:47.823077Z"
}
},
"outputs": [],
@@ -450,7 +518,13 @@
"\n",
"from tatc.analysis import collect_observations, compute_ground_track\n",
"from tatc.constants import EARTH_ROTATION_RATE\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Point, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"from tatc.utils import VelocityFrame, compute_projected_ray_position\n",
"\n",
"GEOD = Geod(ellps=\"WGS84\")\n",
@@ -466,13 +540,17 @@
"\n",
"def ecef(lon, lat, height=0.0):\n",
" \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
- " return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+ " return np.stack(\n",
+ " TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1\n",
+ " )\n",
"\n",
"\n",
"def up(lon, lat):\n",
" \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
" lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -493,23 +571,42 @@
" k = np.clip(np.searchsorted(t, seconds) - 1, 0, len(t) - 2)\n",
" h = (t[k + 1] - t[k])[:, None]\n",
" s = ((seconds - t[k]) / (t[k + 1] - t[k]))[:, None]\n",
- " h00, h10, h01, h11 = 2 * s**3 - 3 * s**2 + 1, s**3 - 2 * s**2 + s, -2 * s**3 + 3 * s**2, s**3 - s**2\n",
+ " h00, h10, h01, h11 = (\n",
+ " 2 * s**3 - 3 * s**2 + 1,\n",
+ " s**3 - 2 * s**2 + s,\n",
+ " -2 * s**3 + 3 * s**2,\n",
+ " s**3 - s**2,\n",
+ " )\n",
" position = h00 * p[k] + h10 * h * v[k] + h01 * p[k + 1] + h11 * h * v[k + 1]\n",
- " d00, d10, d01, d11 = (6 * s**2 - 6 * s) / h, 3 * s**2 - 4 * s + 1, (-6 * s**2 + 6 * s) / h, 3 * s**2 - 2 * s\n",
+ " d00, d10, d01, d11 = (\n",
+ " (6 * s**2 - 6 * s) / h,\n",
+ " 3 * s**2 - 4 * s + 1,\n",
+ " (-6 * s**2 + 6 * s) / h,\n",
+ " 3 * s**2 - 2 * s,\n",
+ " )\n",
" velocity = d00 * p[k] + d10 * v[k] + d01 * p[k + 1] + d11 * v[k + 1]\n",
" return position, velocity\n",
"\n",
"\n",
"def inertial_velocity(position, velocity):\n",
" \"\"\"Inertial velocity expressed in Earth-fixed coordinates.\"\"\"\n",
- " return velocity + EARTH_ROTATION_RATE * np.stack([-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1)\n",
+ " return velocity + EARTH_ROTATION_RATE * np.stack(\n",
+ " [-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def summarize(x):\n",
" \"\"\"Summary statistics of a residual sample.\"\"\"\n",
" x = np.asarray(x, dtype=float)\n",
" x = x[np.isfinite(x)]\n",
- " return pd.Series({\"n\": x.size, \"median\": np.median(x), \"p95 |x|\": np.percentile(np.abs(x), 95), \"max |x|\": np.abs(x).max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": x.size,\n",
+ " \"median\": np.median(x),\n",
+ " \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+ " \"max |x|\": np.abs(x).max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -540,10 +637,10 @@
"id": "1ab6cc4a",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:58.334503Z",
- "iopub.status.busy": "2026-10-08T18:04:58.334503Z",
- "iopub.status.idle": "2026-10-08T18:04:58.497742Z",
- "shell.execute_reply": "2026-10-08T18:04:58.497742Z"
+ "iopub.execute_input": "2026-10-25T14:00:47.825584Z",
+ "iopub.status.busy": "2026-10-25T14:00:47.825584Z",
+ "iopub.status.idle": "2026-10-25T14:00:48.022251Z",
+ "shell.execute_reply": "2026-10-25T14:00:48.022251Z"
}
},
"outputs": [
@@ -629,19 +726,29 @@
"p1, v1 = orbit.get_orbit_track(to_utc(orbit_time)).frame_xyz_and_velocity(itrs)\n",
"offset1 = np.array(p1.m).T - orbit_position\n",
"radial1 = normalize(orbit_position)\n",
- "along1 = normalize(orbit_velocity - np.sum(orbit_velocity * radial1, axis=-1, keepdims=True) * radial1)\n",
+ "along1 = normalize(\n",
+ " orbit_velocity - np.sum(orbit_velocity * radial1, axis=-1, keepdims=True) * radial1\n",
+ ")\n",
"along1_km = np.sum(offset1 * along1, axis=-1) / 1e3\n",
- "epoch = datetime(2026, 1, 1, tzinfo=timezone.utc) + timedelta(days=float(TLE[0][20:32]) - 1)\n",
+ "epoch = datetime(2026, 1, 1, tzinfo=timezone.utc) + timedelta(\n",
+ " days=float(TLE[0][20:32]) - 1\n",
+ ")\n",
"hours_before_epoch = (epoch - DAY).total_seconds() / 3600 - orbit_time / 3600\n",
"fig, ax = plt.subplots(figsize=(7, 3.2))\n",
"ax.scatter(hours_before_epoch, along1_km, s=4, color=BLUE)\n",
- "ax.set(xlabel=\"Hours before TLE epoch\", ylabel=\"Along-track error (km)\", title=\"TLE vs. NISAR orbit ephemeris\")\n",
+ "ax.set(\n",
+ " xlabel=\"Hours before TLE epoch\",\n",
+ " ylabel=\"Along-track error (km)\",\n",
+ " title=\"TLE vs. NISAR orbit ephemeris\",\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()\n",
"pd.DataFrame(\n",
" {\n",
" \"along-track (km)\": summarize(along1_km),\n",
- " \"cross-track, right (km)\": summarize(np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3),\n",
+ " \"cross-track, right (km)\": summarize(\n",
+ " np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3\n",
+ " ),\n",
" \"radial (km)\": summarize(np.sum(offset1 * radial1, axis=-1) / 1e3),\n",
" }\n",
").T.round(3)"
@@ -669,10 +776,10 @@
"id": "c97aedd0",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:58.500750Z",
- "iopub.status.busy": "2026-10-08T18:04:58.499750Z",
- "iopub.status.idle": "2026-10-08T18:04:58.755512Z",
- "shell.execute_reply": "2026-10-08T18:04:58.755006Z"
+ "iopub.execute_input": "2026-10-25T14:00:48.024767Z",
+ "iopub.status.busy": "2026-10-25T14:00:48.024767Z",
+ "iopub.status.idle": "2026-10-25T14:00:48.227190Z",
+ "shell.execute_reply": "2026-10-25T14:00:48.227190Z"
}
},
"outputs": [
@@ -911,8 +1018,21 @@
" left = normalize(np.cross(normalize(velocity), nadir))\n",
" look = np.degrees(np.arccos(np.sum(los * nadir[:, None], axis=-1)))\n",
" side_all.append(np.sign(np.sum(los * left[:, None], axis=-1)).ravel())\n",
- " plane_earth.append(np.degrees(np.arcsin(np.sum(los * normalize(velocity)[:, None], axis=-1))).ravel())\n",
- " plane_inertial.append(np.degrees(np.arcsin(np.sum(los * normalize(inertial_velocity(position, velocity))[:, None], axis=-1))).ravel())\n",
+ " plane_earth.append(\n",
+ " np.degrees(\n",
+ " np.arcsin(np.sum(los * normalize(velocity)[:, None], axis=-1))\n",
+ " ).ravel()\n",
+ " )\n",
+ " plane_inertial.append(\n",
+ " np.degrees(\n",
+ " np.arcsin(\n",
+ " np.sum(\n",
+ " los * normalize(inertial_velocity(position, velocity))[:, None],\n",
+ " axis=-1,\n",
+ " )\n",
+ " )\n",
+ " ).ravel()\n",
+ " )\n",
" rows2.append(\n",
" {\n",
" \"latitude\": np.median(sub_lat),\n",
@@ -923,10 +1043,16 @@
" \"far incidence (deg)\": np.median(m[\"incidence\"][:, -1]),\n",
" }\n",
" )\n",
- "geometry = pd.concat([sample[[\"granule\", \"mode\", \"direction\"]], pd.DataFrame(rows2)], axis=1)\n",
- "side_all, plane_earth, plane_inertial = map(np.concatenate, (side_all, plane_earth, plane_inertial))\n",
+ "geometry = pd.concat(\n",
+ " [sample[[\"granule\", \"mode\", \"direction\"]], pd.DataFrame(rows2)], axis=1\n",
+ ")\n",
+ "side_all, plane_earth, plane_inertial = map(\n",
+ " np.concatenate, (side_all, plane_earth, plane_inertial)\n",
+ ")\n",
"# roll angle and cross-track field of view of each mode, from the median edge look angles\n",
- "modes = geometry.groupby(\"mode\")[[\"near look (deg)\", \"far look (deg)\", \"near incidence (deg)\", \"far incidence (deg)\"]].median()\n",
+ "modes = geometry.groupby(\"mode\")[\n",
+ " [\"near look (deg)\", \"far look (deg)\", \"near incidence (deg)\", \"far incidence (deg)\"]\n",
+ "].median()\n",
"modes[\"roll (deg)\"] = (modes[\"near look (deg)\"] + modes[\"far look (deg)\"]) / 2\n",
"modes[\"field of view (deg)\"] = modes[\"far look (deg)\"] - modes[\"near look (deg)\"]\n",
"modes[\"products\"] = catalog.groupby(\"mode\").size()\n",
@@ -934,12 +1060,22 @@
" pd.Series(\n",
" {\n",
" \"lines of sight on the left (%)\": 100 * np.mean(side_all > 0),\n",
- " \"angle to Earth-fixed zero-Doppler plane, max |x| (deg)\": np.abs(plane_earth).max(),\n",
- " \"angle to inertial zero-Doppler plane, p95 |x| (deg)\": np.percentile(np.abs(plane_inertial), 95),\n",
- " \"near look angle, range within modes (deg)\": (geometry.groupby(\"mode\")[\"near look (deg)\"].agg(np.ptp)).max(),\n",
- " \"far look angle, range within modes (deg)\": (geometry.groupby(\"mode\")[\"far look (deg)\"].agg(np.ptp)).max(),\n",
+ " \"angle to Earth-fixed zero-Doppler plane, max |x| (deg)\": np.abs(\n",
+ " plane_earth\n",
+ " ).max(),\n",
+ " \"angle to inertial zero-Doppler plane, p95 |x| (deg)\": np.percentile(\n",
+ " np.abs(plane_inertial), 95\n",
+ " ),\n",
+ " \"near look angle, range within modes (deg)\": (\n",
+ " geometry.groupby(\"mode\")[\"near look (deg)\"].agg(np.ptp)\n",
+ " ).max(),\n",
+ " \"far look angle, range within modes (deg)\": (\n",
+ " geometry.groupby(\"mode\")[\"far look (deg)\"].agg(np.ptp)\n",
+ " ).max(),\n",
" }\n",
- " ).round(5).to_string()\n",
+ " )\n",
+ " .round(5)\n",
+ " .to_string()\n",
")\n",
"modes.round(2)"
]
@@ -950,10 +1086,10 @@
"id": "17b83820",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:58.757524Z",
- "iopub.status.busy": "2026-10-08T18:04:58.757524Z",
- "iopub.status.idle": "2026-10-08T18:04:58.909062Z",
- "shell.execute_reply": "2026-10-08T18:04:58.908055Z"
+ "iopub.execute_input": "2026-10-25T14:00:48.230315Z",
+ "iopub.status.busy": "2026-10-25T14:00:48.229151Z",
+ "iopub.status.idle": "2026-10-25T14:00:48.518458Z",
+ "shell.execute_reply": "2026-10-25T14:00:48.517448Z"
}
},
"outputs": [
@@ -970,9 +1106,25 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
- "for column, color in [(\"near look (deg)\", BLUE), (\"far look (deg)\", BLUE), (\"near incidence (deg)\", ORANGE), (\"far incidence (deg)\", ORANGE)]:\n",
- " ax.scatter(geometry.latitude, geometry[column], s=12, color=color, marker=\"o\" if \"near\" in column else \"^\", label=column.replace(\" (deg)\", \"\"))\n",
- "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Angle (deg)\", title=\"NISAR look and incidence angles at the image edges\")\n",
+ "for column, color in [\n",
+ " (\"near look (deg)\", BLUE),\n",
+ " (\"far look (deg)\", BLUE),\n",
+ " (\"near incidence (deg)\", ORANGE),\n",
+ " (\"far incidence (deg)\", ORANGE),\n",
+ "]:\n",
+ " ax.scatter(\n",
+ " geometry.latitude,\n",
+ " geometry[column],\n",
+ " s=12,\n",
+ " color=color,\n",
+ " marker=\"o\" if \"near\" in column else \"^\",\n",
+ " label=column.replace(\" (deg)\", \"\"),\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Sub-satellite latitude (deg)\",\n",
+ " ylabel=\"Angle (deg)\",\n",
+ " title=\"NISAR look and incidence angles at the image edges\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, ncols=2)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -998,10 +1150,10 @@
"id": "7008ab69",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:58.911062Z",
- "iopub.status.busy": "2026-10-08T18:04:58.911062Z",
- "iopub.status.idle": "2026-10-08T18:04:59.418013Z",
- "shell.execute_reply": "2026-10-08T18:04:59.418013Z"
+ "iopub.execute_input": "2026-10-25T14:00:48.521459Z",
+ "iopub.status.busy": "2026-10-25T14:00:48.521459Z",
+ "iopub.status.idle": "2026-10-25T14:00:49.370331Z",
+ "shell.execute_reply": "2026-10-25T14:00:49.369314Z"
}
},
"outputs": [
@@ -1127,9 +1279,20 @@
" look_angle = modes.loc[mode, f\"{edge} look (deg)\"]\n",
" times = m[\"time\"][::4]\n",
" _, velocity = interpolate_orbit(m, times)\n",
- " ray = compute_projected_ray_position(orbit.get_orbit_track(to_utc(times)), 0, 0, look_angle, velocity_frame=frame)\n",
+ " ray = compute_projected_ray_position(\n",
+ " orbit.get_orbit_track(to_utc(times)),\n",
+ " 0,\n",
+ " 0,\n",
+ " look_angle,\n",
+ " velocity_frame=frame,\n",
+ " )\n",
" lon, lat = m[\"lon\"][::4, column], m[\"lat\"][::4, column]\n",
- " along, right = along_right(lon, lat, ecef(ray.longitude.degrees, ray.latitude.degrees) - ecef(lon, lat), velocity)\n",
+ " along, right = along_right(\n",
+ " lon,\n",
+ " lat,\n",
+ " ecef(ray.longitude.degrees, ray.latitude.degrees) - ecef(lon, lat),\n",
+ " velocity,\n",
+ " )\n",
" along_all.append(along)\n",
" right_all.append(right)\n",
" rows3[(frame.value, edge, \"along\")] = summarize(np.concatenate(along_all))\n",
@@ -1155,10 +1318,10 @@
"id": "f80b0a1d",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:04:59.420019Z",
- "iopub.status.busy": "2026-10-08T18:04:59.420019Z",
- "iopub.status.idle": "2026-10-08T18:05:17.398683Z",
- "shell.execute_reply": "2026-10-08T18:05:17.398683Z"
+ "iopub.execute_input": "2026-10-25T14:00:49.372860Z",
+ "iopub.status.busy": "2026-10-25T14:00:49.372860Z",
+ "iopub.status.idle": "2026-10-25T14:01:06.296143Z",
+ "shell.execute_reply": "2026-10-25T14:01:06.296143Z"
}
},
"outputs": [
@@ -1405,16 +1568,17 @@
"altitude = float(np.median(geometry[\"altitude (km)\"])) * 1e3\n",
"\n",
"\n",
- "def nisar_view(mode, time_step=TIME_STEP, velocity_frame=VelocityFrame.EARTH_FIXED):\n",
+ "def nisar_view(mode, time_step=TIME_STEP):\n",
" \"\"\"Rectangular, left-looking view of a NISAR mode, with the along-track field of view tailored to a time step (s).\"\"\"\n",
" return PointedInstrument(\n",
" name=\"NISAR L-SAR\",\n",
" field_of_regard=2 * (modes[\"far look (deg)\"].max() + 1),\n",
" cross_track_field_of_view=modes.loc[mode, \"field of view (deg)\"],\n",
- " along_track_field_of_view=np.degrees(2 * np.arctan(ground_speed * time_step / 2 / altitude)),\n",
+ " along_track_field_of_view=np.degrees(\n",
+ " 2 * np.arctan(ground_speed * time_step / 2 / altitude)\n",
+ " ),\n",
" roll_angle=modes.loc[mode, \"roll (deg)\"],\n",
" is_rectangular=True,\n",
- " velocity_frame=velocity_frame,\n",
" )\n",
"\n",
"\n",
@@ -1422,21 +1586,37 @@
" \"\"\"Shift longitudes of a polygon that crosses the antimeridian to be continuous.\"\"\"\n",
" lon = np.array(geometry.exterior.coords)[:, 0]\n",
" if lon.max() - lon.min() > 180:\n",
- " return shapely.ops.transform(lambda x, y, z=None: (np.where(np.asarray(x) < 0, np.asarray(x) + 360, x), y), geometry)\n",
+ " return shapely.ops.transform(\n",
+ " lambda x, y, z=None: (\n",
+ " np.where(np.asarray(x) < 0, np.asarray(x) + 360, x),\n",
+ " y,\n",
+ " ),\n",
+ " geometry,\n",
+ " )\n",
" return geometry\n",
"\n",
"\n",
"def footprint_row(product):\n",
- " start, end = pd.Timestamp(product.zero_doppler_start, tz=\"UTC\"), pd.Timestamp(product.zero_doppler_end, tz=\"UTC\")\n",
+ " start, end = pd.Timestamp(product.zero_doppler_start, tz=\"UTC\"), pd.Timestamp(\n",
+ " product.zero_doppler_end, tz=\"UTC\"\n",
+ " )\n",
" n = max(1, int(round((end - start).total_seconds() / TIME_STEP)))\n",
" step = (end - start) / n\n",
" times = [(start + step * (i + 0.5)).floor(\"us\").to_pydatetime() for i in range(n)]\n",
- " satellite = nisar.model_copy(update={\"instruments\": [nisar_view(product[\"mode\"], step.total_seconds())]})\n",
+ " satellite = nisar.model_copy(\n",
+ " update={\"instruments\": [nisar_view(product[\"mode\"], step.total_seconds())]}\n",
+ " )\n",
" predicted = compute_ground_track(satellite, times).geometry.iloc[0]\n",
" observed = unwrap(shapely.force_2d(shapely.from_wkt(product.polygon)))\n",
" center = observed.centroid\n",
- " project = Transformer.from_crs(\"EPSG:4326\", f\"+proj=aeqd +lat_0={center.y} +lon_0={center.x} +units=m\", always_xy=True).transform\n",
- " a, b = transform_geometry(project, observed).buffer(0), transform_geometry(project, predicted).buffer(0)\n",
+ " project = Transformer.from_crs(\n",
+ " \"EPSG:4326\",\n",
+ " f\"+proj=aeqd +lat_0={center.y} +lon_0={center.x} +units=m\",\n",
+ " always_xy=True,\n",
+ " ).transform\n",
+ " a, b = transform_geometry(project, observed).buffer(0), transform_geometry(\n",
+ " project, predicted\n",
+ " ).buffer(0)\n",
" return {\n",
" \"granule\": product.granule,\n",
" \"observed (km^2)\": a.area / 1e6,\n",
@@ -1446,12 +1626,18 @@
" }\n",
"\n",
"\n",
- "footprints = pd.DataFrame([footprint_row(product) for _, product in catalog.iterrows()]).merge(catalog[[\"granule\", \"mode\", \"direction\", \"coverage\"]])\n",
+ "footprints = pd.DataFrame(\n",
+ " [footprint_row(product) for _, product in catalog.iterrows()]\n",
+ ").merge(catalog[[\"granule\", \"mode\", \"direction\", \"coverage\"]])\n",
"pd.concat(\n",
" [\n",
" footprints[[\"IoU\"]].describe(percentiles=[0.05, 0.5]).T.assign(group=\"all\"),\n",
- " footprints.groupby(\"coverage\").IoU.describe(percentiles=[0.05, 0.5]).assign(group=\"coverage\"),\n",
- " footprints.groupby(\"mode\").IoU.describe(percentiles=[0.05, 0.5]).assign(group=\"mode\"),\n",
+ " footprints.groupby(\"coverage\")\n",
+ " .IoU.describe(percentiles=[0.05, 0.5])\n",
+ " .assign(group=\"coverage\"),\n",
+ " footprints.groupby(\"mode\")\n",
+ " .IoU.describe(percentiles=[0.05, 0.5])\n",
+ " .assign(group=\"mode\"),\n",
" ]\n",
").round(3)"
]
@@ -1462,10 +1648,10 @@
"id": "b45eb238",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:17.400722Z",
- "iopub.status.busy": "2026-10-08T18:05:17.400722Z",
- "iopub.status.idle": "2026-10-08T18:05:27.052101Z",
- "shell.execute_reply": "2026-10-08T18:05:27.051094Z"
+ "iopub.execute_input": "2026-10-25T14:01:06.298147Z",
+ "iopub.status.busy": "2026-10-25T14:01:06.298147Z",
+ "iopub.status.idle": "2026-10-25T14:01:14.369320Z",
+ "shell.execute_reply": "2026-10-25T14:01:14.369320Z"
}
},
"outputs": [
@@ -1488,12 +1674,27 @@
"for polygon in catalog.polygon:\n",
" # split footprints that cross the antimeridian for plotting\n",
" footprint = unwrap(shapely.force_2d(shapely.from_wkt(polygon)))\n",
- " parts = [footprint.intersection(shapely.box(-180, -90, 180, 90)), shapely.affinity.translate(footprint.intersection(shapely.box(180, -90, 540, 90)), -360)]\n",
- " ax.add_geometries([part for part in parts if not part.is_empty], crs=ccrs.PlateCarree(), facecolor=BLUE, edgecolor=\"none\", alpha=0.6)\n",
+ " parts = [\n",
+ " footprint.intersection(shapely.box(-180, -90, 180, 90)),\n",
+ " shapely.affinity.translate(\n",
+ " footprint.intersection(shapely.box(180, -90, 540, 90)), -360\n",
+ " ),\n",
+ " ]\n",
+ " ax.add_geometries(\n",
+ " [part for part in parts if not part.is_empty],\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=BLUE,\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.6,\n",
+ " )\n",
"ax.set_title(f\"NISAR RSLC frames, {DAY:%d %B %Y}\", fontsize=9)\n",
"ax2 = fig.add_subplot(1, 2, 2)\n",
"ax2.scatter(footprints.latitude, footprints.IoU, s=6, color=BLUE)\n",
- "ax2.set(xlabel=\"Frame center latitude (deg)\", ylabel=\"IoU (TAT-C vs. bounding polygon)\", title=\"Frame footprint agreement\")\n",
+ "ax2.set(\n",
+ " xlabel=\"Frame center latitude (deg)\",\n",
+ " ylabel=\"IoU (TAT-C vs. bounding polygon)\",\n",
+ " title=\"Frame footprint agreement\",\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()"
]
@@ -1516,10 +1717,10 @@
"id": "f20d1904",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:27.054101Z",
- "iopub.status.busy": "2026-10-08T18:05:27.053101Z",
- "iopub.status.idle": "2026-10-08T18:06:24.539159Z",
- "shell.execute_reply": "2026-10-08T18:06:24.539159Z"
+ "iopub.execute_input": "2026-10-25T14:01:14.373326Z",
+ "iopub.status.busy": "2026-10-25T14:01:14.373326Z",
+ "iopub.status.idle": "2026-10-25T14:01:50.095134Z",
+ "shell.execute_reply": "2026-10-25T14:01:50.094626Z"
}
},
"outputs": [
@@ -1592,19 +1793,52 @@
"for frame in [VelocityFrame.EARTH_FIXED, VelocityFrame.INERTIAL]:\n",
" differences, missed = [], 0\n",
" for mode, m in zip(geometry[\"mode\"], meta):\n",
- " satellite = nisar.model_copy(update={\"instruments\": [nisar_view(mode, velocity_frame=frame)]})\n",
+ " satellite = nisar.model_copy(\n",
+ " update={\n",
+ " \"instruments\": [nisar_view(mode)],\n",
+ " \"attitude\": Attitude(velocity_frame=frame),\n",
+ " }\n",
+ " )\n",
" for i in np.linspace(1, len(m[\"time\"]) - 2, 3).astype(int):\n",
- " for j in (np.linspace(0.1, 0.9, 5) * (len(m[\"slant_range\"]) - 1)).round().astype(int):\n",
- " point = Point(id=0, latitude=float(m[\"lat\"][i, j]), longitude=float(m[\"lon\"][i, j]))\n",
+ " for j in (\n",
+ " (np.linspace(0.1, 0.9, 5) * (len(m[\"slant_range\"]) - 1))\n",
+ " .round()\n",
+ " .astype(int)\n",
+ " ):\n",
+ " point = Point(\n",
+ " id=0,\n",
+ " latitude=float(m[\"lat\"][i, j]),\n",
+ " longitude=float(m[\"lon\"][i, j]),\n",
+ " )\n",
" truth = DAY + timedelta(seconds=float(m[\"time\"][i]))\n",
- " observations = collect_observations(point, satellite, truth - timedelta(minutes=5), truth + timedelta(minutes=5))\n",
+ " observations = collect_observations(\n",
+ " point,\n",
+ " satellite,\n",
+ " truth - timedelta(minutes=5),\n",
+ " truth + timedelta(minutes=5),\n",
+ " )\n",
" if observations.empty:\n",
" missed += 1\n",
" continue\n",
- " seconds = (observations.epoch - pd.Timestamp(truth)).dt.total_seconds().to_numpy()\n",
+ " seconds = (\n",
+ " (observations.epoch - pd.Timestamp(truth))\n",
+ " .dt.total_seconds()\n",
+ " .to_numpy()\n",
+ " )\n",
" differences.append(seconds[np.argmin(np.abs(seconds))])\n",
- " rows5[frame.value] = pd.concat([pd.Series({\"points\": len(differences) + missed, \"missed\": missed}), summarize(differences).drop(\"n\")])\n",
- "pd.DataFrame(rows5).T.rename(columns={\"median\": \"time, median (s)\", \"p95 |x|\": \"time, p95 |x| (s)\", \"max |x|\": \"time, max |x| (s)\"}).round(3)"
+ " rows5[frame.value] = pd.concat(\n",
+ " [\n",
+ " pd.Series({\"points\": len(differences) + missed, \"missed\": missed}),\n",
+ " summarize(differences).drop(\"n\"),\n",
+ " ]\n",
+ " )\n",
+ "pd.DataFrame(rows5).T.rename(\n",
+ " columns={\n",
+ " \"median\": \"time, median (s)\",\n",
+ " \"p95 |x|\": \"time, p95 |x| (s)\",\n",
+ " \"max |x|\": \"time, max |x| (s)\",\n",
+ " }\n",
+ ").round(3)"
]
},
{
diff --git a/docs/validation/ValidateSARSentinel1.ipynb b/docs/validation/ValidateSARSentinel1.ipynb
index 281a4d7d..655f9d05 100644
--- a/docs/validation/ValidateSARSentinel1.ipynb
+++ b/docs/validation/ValidateSARSentinel1.ipynb
@@ -18,7 +18,7 @@
"3. **Swath edges.** How closely do the edges of TAT-C's view match the near-range and far-range edges of the images?\n",
"4. **Product footprints.** Does `compute_ground_track` reproduce the footprints of all the products acquired in a day?\n",
"5. **Observation times.** Does `collect_observations` predict when Sentinel-1C images a point?\n",
- "6. **Roll steering.** Does a roll angle that varies around the orbit (`roll_angle_profile`) reproduce Sentinel-1's roll steering?"
+ "6. **Roll steering.** Does a satellite attitude whose roll angle varies around the orbit (an `Attitude` with a `roll_angle_profile`) reproduce Sentinel-1's roll steering?"
]
},
{
@@ -45,10 +45,10 @@
"id": "8fc47306",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:33.924607Z",
- "iopub.status.busy": "2026-10-08T18:05:33.924607Z",
- "iopub.status.idle": "2026-10-08T18:05:34.437920Z",
- "shell.execute_reply": "2026-10-08T18:05:34.437920Z"
+ "iopub.execute_input": "2026-10-25T13:59:23.384863Z",
+ "iopub.status.busy": "2026-10-25T13:59:23.384863Z",
+ "iopub.status.idle": "2026-10-25T13:59:24.029443Z",
+ "shell.execute_reply": "2026-10-25T13:59:24.029443Z"
}
},
"outputs": [
@@ -144,10 +144,20 @@
" page = requests.post(url, json=body, timeout=300).json()\n",
" items += page[\"features\"]\n",
" following = [link for link in page[\"links\"] if link[\"rel\"] == \"next\"]\n",
- " url, body = (following[0][\"href\"], following[0].get(\"body\", body)) if following else (None, None)\n",
+ " url, body = (\n",
+ " (following[0][\"href\"], following[0].get(\"body\", body))\n",
+ " if following\n",
+ " else (None, None)\n",
+ " )\n",
" ITEMS_FILE.write_text(json.dumps(items))\n",
- "items = [item for item in json.loads(ITEMS_FILE.read_text()) if item[\"properties\"][\"sar:instrument_mode\"] in MODES]\n",
- "pd.DataFrame([item[\"properties\"] for item in items]).groupby([\"sar:instrument_mode\", \"sat:orbit_state\"]).size().unstack(fill_value=0)"
+ "items = [\n",
+ " item\n",
+ " for item in json.loads(ITEMS_FILE.read_text())\n",
+ " if item[\"properties\"][\"sar:instrument_mode\"] in MODES\n",
+ "]\n",
+ "pd.DataFrame([item[\"properties\"] for item in items]).groupby(\n",
+ " [\"sar:instrument_mode\", \"sat:orbit_state\"]\n",
+ ").size().unstack(fill_value=0)"
]
},
{
@@ -164,10 +174,10 @@
"id": "67f7f29f",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:34.439928Z",
- "iopub.status.busy": "2026-10-08T18:05:34.439928Z",
- "iopub.status.idle": "2026-10-08T18:05:34.560654Z",
- "shell.execute_reply": "2026-10-08T18:05:34.560654Z"
+ "iopub.execute_input": "2026-10-25T13:59:24.031861Z",
+ "iopub.status.busy": "2026-10-25T13:59:24.031861Z",
+ "iopub.status.idle": "2026-10-25T13:59:24.150707Z",
+ "shell.execute_reply": "2026-10-25T13:59:24.150707Z"
}
},
"outputs": [
@@ -202,7 +212,9 @@
" polarization = item[\"properties\"][\"sar:polarizations\"][0].lower()\n",
" for attempt in range(5):\n",
" try:\n",
- " response = requests.get(f\"{base}/annotation/{mode}-{polarization}.xml?{token}\", timeout=120)\n",
+ " response = requests.get(\n",
+ " f\"{base}/annotation/{mode}-{polarization}.xml?{token}\", timeout=120\n",
+ " )\n",
" response.raise_for_status()\n",
" break\n",
" except requests.RequestException:\n",
@@ -212,7 +224,9 @@
" points = root.findall(\".//geolocationGridPoint\")\n",
" lines = sorted({int(p.find(\"line\").text) for p in points})\n",
" samples = sorted({int(p.find(\"pixel\").text) for p in points})\n",
- " grid = lambda tag, f=float: np.array([f(p.find(tag).text) for p in points]).reshape(len(lines), len(samples))\n",
+ " grid = lambda tag, f=float: np.array(\n",
+ " [f(p.find(tag).text) for p in points]\n",
+ " ).reshape(len(lines), len(samples))\n",
" orbits = root.findall(\".//orbitList/orbit\")\n",
" return {\n",
" \"id\": item[\"id\"],\n",
@@ -224,8 +238,12 @@
" \"lat\": grid(\"latitude\"),\n",
" \"incidence\": grid(\"incidenceAngle\"),\n",
" \"orbit_time\": np.array([seconds(o.find(\"time\").text) for o in orbits]),\n",
- " \"orbit_position\": np.array([[float(o.find(f\"position/{c}\").text) for c in \"xyz\"] for o in orbits]),\n",
- " \"orbit_velocity\": np.array([[float(o.find(f\"velocity/{c}\").text) for c in \"xyz\"] for o in orbits]),\n",
+ " \"orbit_position\": np.array(\n",
+ " [[float(o.find(f\"position/{c}\").text) for c in \"xyz\"] for o in orbits]\n",
+ " ),\n",
+ " \"orbit_velocity\": np.array(\n",
+ " [[float(o.find(f\"velocity/{c}\").text) for c in \"xyz\"] for o in orbits]\n",
+ " ),\n",
" }\n",
"\n",
" with concurrent.futures.ThreadPoolExecutor(16) as pool:\n",
@@ -238,8 +256,14 @@
" n_line=[len(r[\"time\"]) for r in records],\n",
" n_sample=[r[\"time\"].shape[1] for r in records],\n",
" n_orbit=[len(r[\"orbit_time\"]) for r in records],\n",
- " **{k: np.concatenate([r[k].ravel() for r in records]) for k in [\"time\", \"slant_range\", \"lon\", \"lat\", \"incidence\"]},\n",
- " **{k: np.concatenate([r[k] for r in records]) for k in [\"orbit_time\", \"orbit_position\", \"orbit_velocity\"]},\n",
+ " **{\n",
+ " k: np.concatenate([r[k].ravel() for r in records])\n",
+ " for k in [\"time\", \"slant_range\", \"lon\", \"lat\", \"incidence\"]\n",
+ " },\n",
+ " **{\n",
+ " k: np.concatenate([r[k] for r in records])\n",
+ " for k in [\"orbit_time\", \"orbit_position\", \"orbit_velocity\"]\n",
+ " },\n",
" )\n",
"\n",
"cache = dict(np.load(GRID_FILE))\n",
@@ -248,8 +272,14 @@
"meta = []\n",
"for i, (t, sr, lon, lat, inc, ot, op, ov) in enumerate(\n",
" zip(\n",
- " cut(\"time\", size), cut(\"slant_range\", cache[\"n_sample\"]), cut(\"lon\", size), cut(\"lat\", size), cut(\"incidence\", size),\n",
- " cut(\"orbit_time\", cache[\"n_orbit\"]), cut(\"orbit_position\", cache[\"n_orbit\"]), cut(\"orbit_velocity\", cache[\"n_orbit\"]),\n",
+ " cut(\"time\", size),\n",
+ " cut(\"slant_range\", cache[\"n_sample\"]),\n",
+ " cut(\"lon\", size),\n",
+ " cut(\"lat\", size),\n",
+ " cut(\"incidence\", size),\n",
+ " cut(\"orbit_time\", cache[\"n_orbit\"]),\n",
+ " cut(\"orbit_position\", cache[\"n_orbit\"]),\n",
+ " cut(\"orbit_velocity\", cache[\"n_orbit\"]),\n",
" )\n",
"):\n",
" shape2 = (cache[\"n_line\"][i], cache[\"n_sample\"][i])\n",
@@ -273,8 +303,12 @@
" \"\"\"Well-known text of the polygon outlining a product's geolocation grid.\"\"\"\n",
" ring = [(m[\"lon\"][0, j], m[\"lat\"][0, j]) for j in range(m[\"lon\"].shape[1])]\n",
" ring += [(m[\"lon\"][i, -1], m[\"lat\"][i, -1]) for i in range(1, m[\"lon\"].shape[0])]\n",
- " ring += [(m[\"lon\"][-1, j], m[\"lat\"][-1, j]) for j in range(m[\"lon\"].shape[1] - 2, -1, -1)]\n",
- " ring += [(m[\"lon\"][i, 0], m[\"lat\"][i, 0]) for i in range(m[\"lon\"].shape[0] - 2, -1, -1)]\n",
+ " ring += [\n",
+ " (m[\"lon\"][-1, j], m[\"lat\"][-1, j]) for j in range(m[\"lon\"].shape[1] - 2, -1, -1)\n",
+ " ]\n",
+ " ring += [\n",
+ " (m[\"lon\"][i, 0], m[\"lat\"][i, 0]) for i in range(m[\"lon\"].shape[0] - 2, -1, -1)\n",
+ " ]\n",
" return \"POLYGON ((\" + \", \".join(f\"{x} {y}\" for x, y in ring) + \"))\"\n",
"\n",
"\n",
@@ -284,8 +318,12 @@
" \"mode\": cache[\"mode\"],\n",
" \"direction\": cache[\"direction\"],\n",
" \"polygon\": [outline(m) for m in meta],\n",
- " \"zero_doppler_start\": [str(pd.Timestamp(DAY) + pd.Timedelta(seconds=m[\"time\"][0])) for m in meta],\n",
- " \"zero_doppler_end\": [str(pd.Timestamp(DAY) + pd.Timedelta(seconds=m[\"time\"][-1])) for m in meta],\n",
+ " \"zero_doppler_start\": [\n",
+ " str(pd.Timestamp(DAY) + pd.Timedelta(seconds=m[\"time\"][0])) for m in meta\n",
+ " ],\n",
+ " \"zero_doppler_end\": [\n",
+ " str(pd.Timestamp(DAY) + pd.Timedelta(seconds=m[\"time\"][-1])) for m in meta\n",
+ " ],\n",
" }\n",
")\n",
"sample = catalog\n",
@@ -314,10 +352,10 @@
"id": "26a557d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:34.562660Z",
- "iopub.status.busy": "2026-10-08T18:05:34.562660Z",
- "iopub.status.idle": "2026-10-08T18:05:36.212179Z",
- "shell.execute_reply": "2026-10-08T18:05:36.211674Z"
+ "iopub.execute_input": "2026-10-25T13:59:24.152711Z",
+ "iopub.status.busy": "2026-10-25T13:59:24.152711Z",
+ "iopub.status.idle": "2026-10-25T13:59:26.020447Z",
+ "shell.execute_reply": "2026-10-25T13:59:26.020447Z"
}
},
"outputs": [],
@@ -336,7 +374,13 @@
"\n",
"from tatc.analysis import collect_observations, compute_ground_track\n",
"from tatc.constants import EARTH_ROTATION_RATE\n",
- "from tatc.schemas import GeneralPerturbationsOrbit, Point, PointedInstrument, Satellite\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
"from tatc.utils import VelocityFrame, compute_projected_ray_position\n",
"\n",
"GEOD = Geod(ellps=\"WGS84\")\n",
@@ -352,13 +396,17 @@
"\n",
"def ecef(lon, lat, height=0.0):\n",
" \"\"\"Earth-fixed position (m) of geodetic coordinates, stacked on the last axis.\"\"\"\n",
- " return np.stack(TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1)\n",
+ " return np.stack(\n",
+ " TO_ECEF.transform(lon, lat, np.broadcast_to(height, np.shape(lon))), axis=-1\n",
+ " )\n",
"\n",
"\n",
"def up(lon, lat):\n",
" \"\"\"Geodetic up unit vector, stacked on the last axis.\"\"\"\n",
" lon, lat = np.radians(lon), np.radians(lat)\n",
- " return np.stack([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1)\n",
+ " return np.stack(\n",
+ " [np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def normalize(x):\n",
@@ -379,23 +427,42 @@
" k = np.clip(np.searchsorted(t, seconds) - 1, 0, len(t) - 2)\n",
" h = (t[k + 1] - t[k])[:, None]\n",
" s = ((seconds - t[k]) / (t[k + 1] - t[k]))[:, None]\n",
- " h00, h10, h01, h11 = 2 * s**3 - 3 * s**2 + 1, s**3 - 2 * s**2 + s, -2 * s**3 + 3 * s**2, s**3 - s**2\n",
+ " h00, h10, h01, h11 = (\n",
+ " 2 * s**3 - 3 * s**2 + 1,\n",
+ " s**3 - 2 * s**2 + s,\n",
+ " -2 * s**3 + 3 * s**2,\n",
+ " s**3 - s**2,\n",
+ " )\n",
" position = h00 * p[k] + h10 * h * v[k] + h01 * p[k + 1] + h11 * h * v[k + 1]\n",
- " d00, d10, d01, d11 = (6 * s**2 - 6 * s) / h, 3 * s**2 - 4 * s + 1, (-6 * s**2 + 6 * s) / h, 3 * s**2 - 2 * s\n",
+ " d00, d10, d01, d11 = (\n",
+ " (6 * s**2 - 6 * s) / h,\n",
+ " 3 * s**2 - 4 * s + 1,\n",
+ " (-6 * s**2 + 6 * s) / h,\n",
+ " 3 * s**2 - 2 * s,\n",
+ " )\n",
" velocity = d00 * p[k] + d10 * v[k] + d01 * p[k + 1] + d11 * v[k + 1]\n",
" return position, velocity\n",
"\n",
"\n",
"def inertial_velocity(position, velocity):\n",
" \"\"\"Inertial velocity expressed in Earth-fixed coordinates.\"\"\"\n",
- " return velocity + EARTH_ROTATION_RATE * np.stack([-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1)\n",
+ " return velocity + EARTH_ROTATION_RATE * np.stack(\n",
+ " [-position[..., 1], position[..., 0], np.zeros(position.shape[:-1])], axis=-1\n",
+ " )\n",
"\n",
"\n",
"def summarize(x):\n",
" \"\"\"Summary statistics of a residual sample.\"\"\"\n",
" x = np.asarray(x, dtype=float)\n",
" x = x[np.isfinite(x)]\n",
- " return pd.Series({\"n\": x.size, \"median\": np.median(x), \"p95 |x|\": np.percentile(np.abs(x), 95), \"max |x|\": np.abs(x).max()})\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": x.size,\n",
+ " \"median\": np.median(x),\n",
+ " \"p95 |x|\": np.percentile(np.abs(x), 95),\n",
+ " \"max |x|\": np.abs(x).max(),\n",
+ " }\n",
+ " )\n",
"\n",
"\n",
"BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
@@ -426,10 +493,10 @@
"id": "2aef6f21",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:36.215187Z",
- "iopub.status.busy": "2026-10-08T18:05:36.215187Z",
- "iopub.status.idle": "2026-10-08T18:05:36.423890Z",
- "shell.execute_reply": "2026-10-08T18:05:36.423890Z"
+ "iopub.execute_input": "2026-10-25T13:59:26.022964Z",
+ "iopub.status.busy": "2026-10-25T13:59:26.022964Z",
+ "iopub.status.idle": "2026-10-25T13:59:26.250297Z",
+ "shell.execute_reply": "2026-10-25T13:59:26.250297Z"
}
},
"outputs": [
@@ -515,19 +582,29 @@
"p1, v1 = orbit.get_orbit_track(to_utc(orbit_time)).frame_xyz_and_velocity(itrs)\n",
"offset1 = np.array(p1.m).T - orbit_position\n",
"radial1 = normalize(orbit_position)\n",
- "along1 = normalize(orbit_velocity - np.sum(orbit_velocity * radial1, axis=-1, keepdims=True) * radial1)\n",
+ "along1 = normalize(\n",
+ " orbit_velocity - np.sum(orbit_velocity * radial1, axis=-1, keepdims=True) * radial1\n",
+ ")\n",
"along1_km = np.sum(offset1 * along1, axis=-1) / 1e3\n",
- "epoch = datetime(2026, 1, 1, tzinfo=timezone.utc) + timedelta(days=float(TLE[0][20:32]) - 1)\n",
+ "epoch = datetime(2026, 1, 1, tzinfo=timezone.utc) + timedelta(\n",
+ " days=float(TLE[0][20:32]) - 1\n",
+ ")\n",
"hours_before_epoch = (epoch - DAY).total_seconds() / 3600 - orbit_time / 3600\n",
"fig, ax = plt.subplots(figsize=(7, 3.2))\n",
"ax.scatter(hours_before_epoch, along1_km, s=4, color=BLUE)\n",
- "ax.set(xlabel=\"Hours before TLE epoch\", ylabel=\"Along-track error (km)\", title=\"TLE vs. Sentinel-1C restituted orbit\")\n",
+ "ax.set(\n",
+ " xlabel=\"Hours before TLE epoch\",\n",
+ " ylabel=\"Along-track error (km)\",\n",
+ " title=\"TLE vs. Sentinel-1C restituted orbit\",\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()\n",
"pd.DataFrame(\n",
" {\n",
" \"along-track (km)\": summarize(along1_km),\n",
- " \"cross-track, right (km)\": summarize(np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3),\n",
+ " \"cross-track, right (km)\": summarize(\n",
+ " np.sum(offset1 * np.cross(along1, radial1), axis=-1) / 1e3\n",
+ " ),\n",
" \"radial (km)\": summarize(np.sum(offset1 * radial1, axis=-1) / 1e3),\n",
" }\n",
").T.round(3)"
@@ -555,10 +632,10 @@
"id": "74c69429",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:36.426898Z",
- "iopub.status.busy": "2026-10-08T18:05:36.425898Z",
- "iopub.status.idle": "2026-10-08T18:05:36.791157Z",
- "shell.execute_reply": "2026-10-08T18:05:36.790650Z"
+ "iopub.execute_input": "2026-10-25T13:59:26.252302Z",
+ "iopub.status.busy": "2026-10-25T13:59:26.252302Z",
+ "iopub.status.idle": "2026-10-25T13:59:26.570899Z",
+ "shell.execute_reply": "2026-10-25T13:59:26.570392Z"
}
},
"outputs": [
@@ -665,8 +742,21 @@
" left = normalize(np.cross(normalize(velocity), nadir))\n",
" look = np.degrees(np.arccos(np.sum(los * nadir[:, None], axis=-1)))\n",
" side_all.append(np.sign(np.sum(los * left[:, None], axis=-1)).ravel())\n",
- " plane_earth.append(np.degrees(np.arcsin(np.sum(los * normalize(velocity)[:, None], axis=-1))).ravel())\n",
- " plane_inertial.append(np.degrees(np.arcsin(np.sum(los * normalize(inertial_velocity(position, velocity))[:, None], axis=-1))).ravel())\n",
+ " plane_earth.append(\n",
+ " np.degrees(\n",
+ " np.arcsin(np.sum(los * normalize(velocity)[:, None], axis=-1))\n",
+ " ).ravel()\n",
+ " )\n",
+ " plane_inertial.append(\n",
+ " np.degrees(\n",
+ " np.arcsin(\n",
+ " np.sum(\n",
+ " los * normalize(inertial_velocity(position, velocity))[:, None],\n",
+ " axis=-1,\n",
+ " )\n",
+ " )\n",
+ " ).ravel()\n",
+ " )\n",
" rows2.append(\n",
" {\n",
" \"latitude\": np.median(sub_lat),\n",
@@ -677,10 +767,16 @@
" \"far incidence (deg)\": np.median(m[\"incidence\"][:, -1]),\n",
" }\n",
" )\n",
- "geometry = pd.concat([sample[[\"granule\", \"mode\", \"direction\"]], pd.DataFrame(rows2)], axis=1)\n",
- "side_all, plane_earth, plane_inertial = map(np.concatenate, (side_all, plane_earth, plane_inertial))\n",
+ "geometry = pd.concat(\n",
+ " [sample[[\"granule\", \"mode\", \"direction\"]], pd.DataFrame(rows2)], axis=1\n",
+ ")\n",
+ "side_all, plane_earth, plane_inertial = map(\n",
+ " np.concatenate, (side_all, plane_earth, plane_inertial)\n",
+ ")\n",
"# roll angle and cross-track field of view of each mode, from the median edge look angles\n",
- "modes = geometry.groupby(\"mode\")[[\"near look (deg)\", \"far look (deg)\", \"near incidence (deg)\", \"far incidence (deg)\"]].median()\n",
+ "modes = geometry.groupby(\"mode\")[\n",
+ " [\"near look (deg)\", \"far look (deg)\", \"near incidence (deg)\", \"far incidence (deg)\"]\n",
+ "].median()\n",
"modes[\"roll (deg)\"] = (modes[\"near look (deg)\"] + modes[\"far look (deg)\"]) / 2\n",
"modes[\"field of view (deg)\"] = modes[\"far look (deg)\"] - modes[\"near look (deg)\"]\n",
"modes[\"products\"] = catalog.groupby(\"mode\").size()\n",
@@ -688,12 +784,22 @@
" pd.Series(\n",
" {\n",
" \"lines of sight on the right (%)\": 100 * np.mean(side_all < 0),\n",
- " \"angle to Earth-fixed zero-Doppler plane, max |x| (deg)\": np.abs(plane_earth).max(),\n",
- " \"angle to inertial zero-Doppler plane, p95 |x| (deg)\": np.percentile(np.abs(plane_inertial), 95),\n",
- " \"near look angle, range within modes (deg)\": (geometry.groupby(\"mode\")[\"near look (deg)\"].agg(np.ptp)).max(),\n",
- " \"far look angle, range within modes (deg)\": (geometry.groupby(\"mode\")[\"far look (deg)\"].agg(np.ptp)).max(),\n",
+ " \"angle to Earth-fixed zero-Doppler plane, max |x| (deg)\": np.abs(\n",
+ " plane_earth\n",
+ " ).max(),\n",
+ " \"angle to inertial zero-Doppler plane, p95 |x| (deg)\": np.percentile(\n",
+ " np.abs(plane_inertial), 95\n",
+ " ),\n",
+ " \"near look angle, range within modes (deg)\": (\n",
+ " geometry.groupby(\"mode\")[\"near look (deg)\"].agg(np.ptp)\n",
+ " ).max(),\n",
+ " \"far look angle, range within modes (deg)\": (\n",
+ " geometry.groupby(\"mode\")[\"far look (deg)\"].agg(np.ptp)\n",
+ " ).max(),\n",
" }\n",
- " ).round(5).to_string()\n",
+ " )\n",
+ " .round(5)\n",
+ " .to_string()\n",
")\n",
"modes.round(2)"
]
@@ -704,10 +810,10 @@
"id": "744d7fe8",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:36.793581Z",
- "iopub.status.busy": "2026-10-08T18:05:36.793171Z",
- "iopub.status.idle": "2026-10-08T18:05:36.970407Z",
- "shell.execute_reply": "2026-10-08T18:05:36.970407Z"
+ "iopub.execute_input": "2026-10-25T13:59:26.572899Z",
+ "iopub.status.busy": "2026-10-25T13:59:26.572899Z",
+ "iopub.status.idle": "2026-10-25T13:59:26.805526Z",
+ "shell.execute_reply": "2026-10-25T13:59:26.805526Z"
}
},
"outputs": [
@@ -724,9 +830,25 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
- "for column, color in [(\"near look (deg)\", BLUE), (\"far look (deg)\", BLUE), (\"near incidence (deg)\", ORANGE), (\"far incidence (deg)\", ORANGE)]:\n",
- " ax.scatter(geometry.latitude, geometry[column], s=12, color=color, marker=\"o\" if \"near\" in column else \"^\", label=column.replace(\" (deg)\", \"\"))\n",
- "ax.set(xlabel=\"Sub-satellite latitude (deg)\", ylabel=\"Angle (deg)\", title=\"Sentinel-1C look and incidence angles at the image edges\")\n",
+ "for column, color in [\n",
+ " (\"near look (deg)\", BLUE),\n",
+ " (\"far look (deg)\", BLUE),\n",
+ " (\"near incidence (deg)\", ORANGE),\n",
+ " (\"far incidence (deg)\", ORANGE),\n",
+ "]:\n",
+ " ax.scatter(\n",
+ " geometry.latitude,\n",
+ " geometry[column],\n",
+ " s=12,\n",
+ " color=color,\n",
+ " marker=\"o\" if \"near\" in column else \"^\",\n",
+ " label=column.replace(\" (deg)\", \"\"),\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Sub-satellite latitude (deg)\",\n",
+ " ylabel=\"Angle (deg)\",\n",
+ " title=\"Sentinel-1C look and incidence angles at the image edges\",\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, ncols=2)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -752,10 +874,10 @@
"id": "ff0d749c",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:36.973414Z",
- "iopub.status.busy": "2026-10-08T18:05:36.973414Z",
- "iopub.status.idle": "2026-10-08T18:05:41.555919Z",
- "shell.execute_reply": "2026-10-08T18:05:41.555919Z"
+ "iopub.execute_input": "2026-10-25T13:59:26.807557Z",
+ "iopub.status.busy": "2026-10-25T13:59:26.807557Z",
+ "iopub.status.idle": "2026-10-25T13:59:30.955008Z",
+ "shell.execute_reply": "2026-10-25T13:59:30.954501Z"
}
},
"outputs": [
@@ -881,9 +1003,20 @@
" look_angle = modes.loc[mode, f\"{edge} look (deg)\"]\n",
" times = m[\"time\"]\n",
" _, velocity = interpolate_orbit(m, times)\n",
- " ray = compute_projected_ray_position(orbit.get_orbit_track(to_utc(times)), 0, 0, -look_angle, velocity_frame=frame)\n",
+ " ray = compute_projected_ray_position(\n",
+ " orbit.get_orbit_track(to_utc(times)),\n",
+ " 0,\n",
+ " 0,\n",
+ " -look_angle,\n",
+ " velocity_frame=frame,\n",
+ " )\n",
" lon, lat = m[\"lon\"][:, column], m[\"lat\"][:, column]\n",
- " along, right = along_right(lon, lat, ecef(ray.longitude.degrees, ray.latitude.degrees) - ecef(lon, lat), velocity)\n",
+ " along, right = along_right(\n",
+ " lon,\n",
+ " lat,\n",
+ " ecef(ray.longitude.degrees, ray.latitude.degrees) - ecef(lon, lat),\n",
+ " velocity,\n",
+ " )\n",
" along_all.append(along)\n",
" right_all.append(right)\n",
" rows3[(frame.value, edge, \"along\")] = summarize(np.concatenate(along_all))\n",
@@ -909,10 +1042,10 @@
"id": "742aee6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:41.559444Z",
- "iopub.status.busy": "2026-10-08T18:05:41.558444Z",
- "iopub.status.idle": "2026-10-08T18:05:52.821736Z",
- "shell.execute_reply": "2026-10-08T18:05:52.821736Z"
+ "iopub.execute_input": "2026-10-25T13:59:30.957013Z",
+ "iopub.status.busy": "2026-10-25T13:59:30.957013Z",
+ "iopub.status.idle": "2026-10-25T13:59:40.662746Z",
+ "shell.execute_reply": "2026-10-25T13:59:40.661741Z"
}
},
"outputs": [
@@ -1027,16 +1160,17 @@
"altitude = float(np.median(geometry[\"altitude (km)\"])) * 1e3\n",
"\n",
"\n",
- "def sentinel1_view(mode, time_step=TIME_STEP, velocity_frame=VelocityFrame.EARTH_FIXED):\n",
+ "def sentinel1_view(mode, time_step=TIME_STEP):\n",
" \"\"\"Rectangular, right-looking view of a Sentinel-1 mode, with the along-track field of view tailored to a time step (s).\"\"\"\n",
" return PointedInstrument(\n",
" name=\"Sentinel-1C C-SAR\",\n",
" field_of_regard=2 * (modes[\"far look (deg)\"].max() + 1),\n",
" cross_track_field_of_view=modes.loc[mode, \"field of view (deg)\"],\n",
- " along_track_field_of_view=np.degrees(2 * np.arctan(ground_speed * time_step / 2 / altitude)),\n",
+ " along_track_field_of_view=np.degrees(\n",
+ " 2 * np.arctan(ground_speed * time_step / 2 / altitude)\n",
+ " ),\n",
" roll_angle=-modes.loc[mode, \"roll (deg)\"],\n",
" is_rectangular=True,\n",
- " velocity_frame=velocity_frame,\n",
" )\n",
"\n",
"\n",
@@ -1044,21 +1178,41 @@
" \"\"\"Shift longitudes of a polygon that crosses the antimeridian to be continuous.\"\"\"\n",
" lon = np.array(geometry.exterior.coords)[:, 0]\n",
" if lon.max() - lon.min() > 180:\n",
- " return shapely.ops.transform(lambda x, y, z=None: (np.where(np.asarray(x) < 0, np.asarray(x) + 360, x), y), geometry)\n",
+ " return shapely.ops.transform(\n",
+ " lambda x, y, z=None: (\n",
+ " np.where(np.asarray(x) < 0, np.asarray(x) + 360, x),\n",
+ " y,\n",
+ " ),\n",
+ " geometry,\n",
+ " )\n",
" return geometry\n",
"\n",
"\n",
- "def footprint_row(product, view=sentinel1_view):\n",
- " start, end = pd.Timestamp(product.zero_doppler_start, tz=\"UTC\"), pd.Timestamp(product.zero_doppler_end, tz=\"UTC\")\n",
+ "def footprint_row(product, attitudes=None):\n",
+ " \"\"\"Footprint of a product, with the satellite's attitude in each mode (default: nadir, Earth-fixed).\"\"\"\n",
+ " start, end = pd.Timestamp(product.zero_doppler_start, tz=\"UTC\"), pd.Timestamp(\n",
+ " product.zero_doppler_end, tz=\"UTC\"\n",
+ " )\n",
" n = max(1, int(round((end - start).total_seconds() / TIME_STEP)))\n",
" step = (end - start) / n\n",
" times = [(start + step * (i + 0.5)).floor(\"us\").to_pydatetime() for i in range(n)]\n",
- " satellite = sentinel1.model_copy(update={\"instruments\": [view(product[\"mode\"], step.total_seconds())]})\n",
+ " satellite = sentinel1.model_copy(\n",
+ " update={\n",
+ " \"instruments\": [sentinel1_view(product[\"mode\"], step.total_seconds())],\n",
+ " \"attitude\": None if attitudes is None else attitudes[product[\"mode\"]],\n",
+ " }\n",
+ " )\n",
" predicted = compute_ground_track(satellite, times).geometry.iloc[0]\n",
" observed = unwrap(shapely.force_2d(shapely.from_wkt(product.polygon)))\n",
" center = observed.centroid\n",
- " project = Transformer.from_crs(\"EPSG:4326\", f\"+proj=aeqd +lat_0={center.y} +lon_0={center.x} +units=m\", always_xy=True).transform\n",
- " a, b = transform_geometry(project, observed).buffer(0), transform_geometry(project, predicted).buffer(0)\n",
+ " project = Transformer.from_crs(\n",
+ " \"EPSG:4326\",\n",
+ " f\"+proj=aeqd +lat_0={center.y} +lon_0={center.x} +units=m\",\n",
+ " always_xy=True,\n",
+ " ).transform\n",
+ " a, b = transform_geometry(project, observed).buffer(0), transform_geometry(\n",
+ " project, predicted\n",
+ " ).buffer(0)\n",
" return {\n",
" \"granule\": product.granule,\n",
" \"observed (km^2)\": a.area / 1e6,\n",
@@ -1068,12 +1222,18 @@
" }\n",
"\n",
"\n",
- "footprints = pd.DataFrame([footprint_row(product) for _, product in catalog.iterrows()]).merge(catalog[[\"granule\", \"mode\", \"direction\"]])\n",
+ "footprints = pd.DataFrame(\n",
+ " [footprint_row(product) for _, product in catalog.iterrows()]\n",
+ ").merge(catalog[[\"granule\", \"mode\", \"direction\"]])\n",
"pd.concat(\n",
" [\n",
" footprints[[\"IoU\"]].describe(percentiles=[0.05, 0.5]).T.assign(group=\"all\"),\n",
- " footprints.groupby(\"direction\").IoU.describe(percentiles=[0.05, 0.5]).assign(group=\"direction\"),\n",
- " footprints.groupby(\"mode\").IoU.describe(percentiles=[0.05, 0.5]).assign(group=\"mode\"),\n",
+ " footprints.groupby(\"direction\")\n",
+ " .IoU.describe(percentiles=[0.05, 0.5])\n",
+ " .assign(group=\"direction\"),\n",
+ " footprints.groupby(\"mode\")\n",
+ " .IoU.describe(percentiles=[0.05, 0.5])\n",
+ " .assign(group=\"mode\"),\n",
" ]\n",
").round(3)"
]
@@ -1084,10 +1244,10 @@
"id": "c77d816e",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:52.825246Z",
- "iopub.status.busy": "2026-10-08T18:05:52.824742Z",
- "iopub.status.idle": "2026-10-08T18:05:59.157049Z",
- "shell.execute_reply": "2026-10-08T18:05:59.156518Z"
+ "iopub.execute_input": "2026-10-25T13:59:40.664751Z",
+ "iopub.status.busy": "2026-10-25T13:59:40.663750Z",
+ "iopub.status.idle": "2026-10-25T13:59:45.587125Z",
+ "shell.execute_reply": "2026-10-25T13:59:45.587125Z"
}
},
"outputs": [
@@ -1110,12 +1270,27 @@
"for polygon in catalog.polygon:\n",
" # split footprints that cross the antimeridian for plotting\n",
" footprint = unwrap(shapely.force_2d(shapely.from_wkt(polygon)))\n",
- " parts = [footprint.intersection(shapely.box(-180, -90, 180, 90)), shapely.affinity.translate(footprint.intersection(shapely.box(180, -90, 540, 90)), -360)]\n",
- " ax.add_geometries([part for part in parts if not part.is_empty], crs=ccrs.PlateCarree(), facecolor=BLUE, edgecolor=\"none\", alpha=0.6)\n",
+ " parts = [\n",
+ " footprint.intersection(shapely.box(-180, -90, 180, 90)),\n",
+ " shapely.affinity.translate(\n",
+ " footprint.intersection(shapely.box(180, -90, 540, 90)), -360\n",
+ " ),\n",
+ " ]\n",
+ " ax.add_geometries(\n",
+ " [part for part in parts if not part.is_empty],\n",
+ " crs=ccrs.PlateCarree(),\n",
+ " facecolor=BLUE,\n",
+ " edgecolor=\"none\",\n",
+ " alpha=0.6,\n",
+ " )\n",
"ax.set_title(f\"Sentinel-1C IW and EW products, {DAY:%d %B %Y}\", fontsize=9)\n",
"ax2 = fig.add_subplot(1, 2, 2)\n",
"ax2.scatter(footprints.latitude, footprints.IoU, s=6, color=BLUE)\n",
- "ax2.set(xlabel=\"Product center latitude (deg)\", ylabel=\"IoU (TAT-C vs. geolocation grid)\", title=\"Product footprint agreement\")\n",
+ "ax2.set(\n",
+ " xlabel=\"Product center latitude (deg)\",\n",
+ " ylabel=\"IoU (TAT-C vs. geolocation grid)\",\n",
+ " title=\"Product footprint agreement\",\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()"
]
@@ -1138,10 +1313,10 @@
"id": "ae19443f",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:05:59.160667Z",
- "iopub.status.busy": "2026-10-08T18:05:59.160141Z",
- "iopub.status.idle": "2026-10-08T18:07:08.280716Z",
- "shell.execute_reply": "2026-10-08T18:07:08.279709Z"
+ "iopub.execute_input": "2026-10-25T13:59:45.590130Z",
+ "iopub.status.busy": "2026-10-25T13:59:45.590130Z",
+ "iopub.status.idle": "2026-10-25T14:00:26.110700Z",
+ "shell.execute_reply": "2026-10-25T14:00:26.110700Z"
}
},
"outputs": [
@@ -1214,19 +1389,52 @@
"for frame in [VelocityFrame.EARTH_FIXED, VelocityFrame.INERTIAL]:\n",
" differences, missed = [], 0\n",
" for mode, m in zip(geometry[\"mode\"][::10], meta[::10]):\n",
- " satellite = sentinel1.model_copy(update={\"instruments\": [sentinel1_view(mode, velocity_frame=frame)]})\n",
+ " satellite = sentinel1.model_copy(\n",
+ " update={\n",
+ " \"instruments\": [sentinel1_view(mode)],\n",
+ " \"attitude\": Attitude(velocity_frame=frame),\n",
+ " }\n",
+ " )\n",
" for i in np.linspace(1, len(m[\"time\"]) - 2, 3).astype(int):\n",
- " for j in (np.linspace(0.1, 0.9, 5) * (len(m[\"slant_range\"]) - 1)).round().astype(int):\n",
- " point = Point(id=0, latitude=float(m[\"lat\"][i, j]), longitude=float(m[\"lon\"][i, j]))\n",
+ " for j in (\n",
+ " (np.linspace(0.1, 0.9, 5) * (len(m[\"slant_range\"]) - 1))\n",
+ " .round()\n",
+ " .astype(int)\n",
+ " ):\n",
+ " point = Point(\n",
+ " id=0,\n",
+ " latitude=float(m[\"lat\"][i, j]),\n",
+ " longitude=float(m[\"lon\"][i, j]),\n",
+ " )\n",
" truth = DAY + timedelta(seconds=float(m[\"time\"][i]))\n",
- " observations = collect_observations(point, satellite, truth - timedelta(minutes=5), truth + timedelta(minutes=5))\n",
+ " observations = collect_observations(\n",
+ " point,\n",
+ " satellite,\n",
+ " truth - timedelta(minutes=5),\n",
+ " truth + timedelta(minutes=5),\n",
+ " )\n",
" if observations.empty:\n",
" missed += 1\n",
" continue\n",
- " seconds = (observations.epoch - pd.Timestamp(truth)).dt.total_seconds().to_numpy()\n",
+ " seconds = (\n",
+ " (observations.epoch - pd.Timestamp(truth))\n",
+ " .dt.total_seconds()\n",
+ " .to_numpy()\n",
+ " )\n",
" differences.append(seconds[np.argmin(np.abs(seconds))])\n",
- " rows5[frame.value] = pd.concat([pd.Series({\"points\": len(differences) + missed, \"missed\": missed}), summarize(differences).drop(\"n\")])\n",
- "pd.DataFrame(rows5).T.rename(columns={\"median\": \"time, median (s)\", \"p95 |x|\": \"time, p95 |x| (s)\", \"max |x|\": \"time, max |x| (s)\"}).round(3)"
+ " rows5[frame.value] = pd.concat(\n",
+ " [\n",
+ " pd.Series({\"points\": len(differences) + missed, \"missed\": missed}),\n",
+ " summarize(differences).drop(\"n\"),\n",
+ " ]\n",
+ " )\n",
+ "pd.DataFrame(rows5).T.rename(\n",
+ " columns={\n",
+ " \"median\": \"time, median (s)\",\n",
+ " \"p95 |x|\": \"time, p95 |x| (s)\",\n",
+ " \"max |x|\": \"time, max |x| (s)\",\n",
+ " }\n",
+ ").round(3)"
]
},
{
@@ -1238,7 +1446,7 @@
"\n",
"## Test 6: Roll Steering\n",
"\n",
- "A `PointedInstrument` can vary its roll angle around the orbit with a `roll_angle_profile`: a table of roll angles at arguments of latitude (the angle around the orbit from the ascending node), interpolated linearly. For each grid line of the products of Test 2, the satellite's argument of latitude is computed from the TLE at the line's zero-Doppler time, and the roll angle (the mean of the near and far look angles) from the grid. Each mode's profile is the median roll angle in 10 deg bins of argument of latitude, from the even-numbered products; the swath edges of Test 3 are then compared for the odd-numbered products, with the fixed roll angle and with the profile (and the same field of view), and the frame footprints of Test 4 are recomputed with the profiles."
+ "Sentinel-1 steers by rolling the spacecraft, so the steering is represented as the satellite's `Attitude`, whose roll angle can vary around the orbit with a `roll_angle_profile`: a table of roll angles at arguments of latitude (the angle around the orbit from the ascending node), interpolated linearly. The instrument's view is rotated from the satellite's body, so the instrument keeps the fixed roll angle of its mode, and the attitude's profile is the steering relative to it. For each grid line of the products of Test 2, the satellite's argument of latitude is computed from the TLE at the line's zero-Doppler time, and the roll angle (the mean of the near and far look angles) from the grid. Each mode's profile is the median roll angle in 10 deg bins of argument of latitude, from the even-numbered products; the swath edges of Test 3 are then compared for the odd-numbered products, with the fixed roll angle and with the profile (and the same field of view), and the frame footprints of Test 4 are recomputed with the profiles."
]
},
{
@@ -1247,10 +1455,10 @@
"id": "81d83e50",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:07:08.282716Z",
- "iopub.status.busy": "2026-10-08T18:07:08.282716Z",
- "iopub.status.idle": "2026-10-08T18:07:23.078184Z",
- "shell.execute_reply": "2026-10-08T18:07:23.078184Z"
+ "iopub.execute_input": "2026-10-25T14:00:26.112705Z",
+ "iopub.status.busy": "2026-10-25T14:00:26.112705Z",
+ "iopub.status.idle": "2026-10-25T14:00:39.441398Z",
+ "shell.execute_reply": "2026-10-25T14:00:39.441398Z"
}
},
"outputs": [
@@ -1390,7 +1598,9 @@
" {\n",
" \"product\": k,\n",
" \"mode\": mode,\n",
- " \"argument of latitude (deg)\": compute_argument_of_latitude(track.position.m, track.velocity.m_per_s),\n",
+ " \"argument of latitude (deg)\": compute_argument_of_latitude(\n",
+ " track.position.m, track.velocity.m_per_s\n",
+ " ),\n",
" \"roll (deg)\": (look[:, 0] + look[:, -1]) / 2,\n",
" \"field of view (deg)\": look[:, -1] - look[:, 0],\n",
" }\n",
@@ -1406,32 +1616,59 @@
" profiles[mode] = [(float(u), -float(r)) for u, r in medians.items()]\n",
"\n",
"\n",
- "def steered_view(mode, time_step=TIME_STEP, velocity_frame=VelocityFrame.EARTH_FIXED):\n",
- " \"\"\"Right-looking view of a Sentinel-1 mode with its roll angle profile.\"\"\"\n",
- " return sentinel1_view(mode, time_step, velocity_frame).model_copy(update={\"roll_angle_profile\": profiles[mode]})\n",
- "\n",
+ "# roll steering rolls the spacecraft: the satellite's attitude, relative to the instrument's (fixed) roll of each mode\n",
+ "steering = {\n",
+ " mode: Attitude(\n",
+ " roll_angle_profile=[(u, r + modes.loc[mode, \"roll (deg)\"]) for u, r in profile]\n",
+ " )\n",
+ " for mode, profile in profiles.items()\n",
+ "}\n",
"\n",
"rows6 = {}\n",
- "for label, view in [(\"fixed roll\", sentinel1_view), (\"roll profile\", steered_view)]:\n",
+ "for label, attitudes in [(\"fixed roll\", None), (\"roll profile\", steering)]:\n",
" for edge, column, sign in [(\"near\", 0, 1), (\"far\", -1, -1)]:\n",
" along_all, right_all = [], []\n",
" for k, (mode, m) in enumerate(zip(geometry[\"mode\"], meta)):\n",
" if k % 2 == 0:\n",
" continue\n",
- " instrument = view(mode)\n",
+ " instrument = sentinel1_view(mode)\n",
" track = orbit.get_orbit_track(to_utc(m[\"time\"]))\n",
" _, velocity = interpolate_orbit(m, m[\"time\"])\n",
- " # the edge of the view, half the field of view from the (negative) roll angle\n",
- " ray = compute_projected_ray_position(track, 0, 0, instrument.get_roll_angle(track) + sign * instrument.cross_track_field_of_view / 2)\n",
+ " # the edge of the view, half the field of view from the instrument's (negative) roll angle, rotated by the attitude\n",
+ " attitude = None if attitudes is None else attitudes[mode]\n",
+ " ray = compute_projected_ray_position(\n",
+ " track,\n",
+ " 0,\n",
+ " 0,\n",
+ " instrument.roll_angle + sign * instrument.cross_track_field_of_view / 2,\n",
+ " attitude=attitude,\n",
+ " )\n",
" lon, lat = m[\"lon\"][:, column], m[\"lat\"][:, column]\n",
- " along, right = along_right(lon, lat, ecef(ray.longitude.degrees, ray.latitude.degrees) - ecef(lon, lat), velocity)\n",
+ " along, right = along_right(\n",
+ " lon,\n",
+ " lat,\n",
+ " ecef(ray.longitude.degrees, ray.latitude.degrees) - ecef(lon, lat),\n",
+ " velocity,\n",
+ " )\n",
" along_all.append(along)\n",
" right_all.append(right)\n",
" rows6[(label, edge, \"along\")] = summarize(np.concatenate(along_all))\n",
" rows6[(label, edge, \"right\")] = summarize(np.concatenate(right_all))\n",
- "steered_footprints = pd.DataFrame([footprint_row(product, steered_view) for _, product in catalog.iterrows()]).merge(catalog[[\"granule\", \"mode\"]])\n",
- "print(\"product footprint IoU (median): fixed roll\", footprints.groupby(\"mode\").IoU.median().round(3).to_dict(), \"| roll profile\", steered_footprints.groupby(\"mode\").IoU.median().round(3).to_dict())\n",
- "print(\"product footprint IoU (5th percentile): fixed roll\", footprints.groupby(\"mode\").IoU.quantile(0.05).round(3).to_dict(), \"| roll profile\", steered_footprints.groupby(\"mode\").IoU.quantile(0.05).round(3).to_dict())\n",
+ "steered_footprints = pd.DataFrame(\n",
+ " [footprint_row(product, steering) for _, product in catalog.iterrows()]\n",
+ ").merge(catalog[[\"granule\", \"mode\"]])\n",
+ "print(\n",
+ " \"product footprint IoU (median): fixed roll\",\n",
+ " footprints.groupby(\"mode\").IoU.median().round(3).to_dict(),\n",
+ " \"| roll profile\",\n",
+ " steered_footprints.groupby(\"mode\").IoU.median().round(3).to_dict(),\n",
+ ")\n",
+ "print(\n",
+ " \"product footprint IoU (5th percentile): fixed roll\",\n",
+ " footprints.groupby(\"mode\").IoU.quantile(0.05).round(3).to_dict(),\n",
+ " \"| roll profile\",\n",
+ " steered_footprints.groupby(\"mode\").IoU.quantile(0.05).round(3).to_dict(),\n",
+ ")\n",
"pd.DataFrame(rows6).T.round(2)"
]
},
@@ -1441,10 +1678,10 @@
"id": "5e0e8a38",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:07:23.080191Z",
- "iopub.status.busy": "2026-10-08T18:07:23.080191Z",
- "iopub.status.idle": "2026-10-08T18:07:23.467077Z",
- "shell.execute_reply": "2026-10-08T18:07:23.467077Z"
+ "iopub.execute_input": "2026-10-25T14:00:39.443402Z",
+ "iopub.status.busy": "2026-10-25T14:00:39.443402Z",
+ "iopub.status.idle": "2026-10-25T14:00:39.606325Z",
+ "shell.execute_reply": "2026-10-25T14:00:39.606325Z"
}
},
"outputs": [
@@ -1462,10 +1699,23 @@
"source": [
"fig, ax = plt.subplots(figsize=(7, 3.6))\n",
"for (mode, group), color in zip(lines6.groupby(\"mode\"), [BLUE, ORANGE]):\n",
- " ax.scatter(group[\"argument of latitude (deg)\"], group[\"roll (deg)\"], s=3, color=color, alpha=0.4, label=f\"{mode} grid lines\")\n",
+ " ax.scatter(\n",
+ " group[\"argument of latitude (deg)\"],\n",
+ " group[\"roll (deg)\"],\n",
+ " s=3,\n",
+ " color=color,\n",
+ " alpha=0.4,\n",
+ " label=f\"{mode} grid lines\",\n",
+ " )\n",
" u, r = zip(*profiles[mode])\n",
" ax.plot(u, -np.array(r), \"-\", color=INK, lw=1)\n",
- "ax.set(xlabel=\"Argument of latitude (deg)\", ylabel=\"Roll angle to the right (deg)\", title=\"Sentinel-1C roll steering (lines: fitted profiles)\", xlim=(0, 360), xticks=range(0, 361, 90))\n",
+ "ax.set(\n",
+ " xlabel=\"Argument of latitude (deg)\",\n",
+ " ylabel=\"Roll angle to the right (deg)\",\n",
+ " title=\"Sentinel-1C roll steering (lines: fitted profiles)\",\n",
+ " xlim=(0, 360),\n",
+ " xticks=range(0, 361, 90),\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -1487,11 +1737,11 @@
"| 3. Swath edges | 3 km along track and 14 to 20 km across track (95th percentile) with the Earth-fixed frame; 24 to 41 km along track with the inertial frame |\n",
"| 4. Product footprints (`compute_ground_track`) | IoU 0.93 (IW) and 0.96 (EW), median, for all 499 products |\n",
"| 5. Observation times (`collect_observations`) | 0.44 s (95th percentile) with the Earth-fixed frame; up to 6 s with the inertial frame |\n",
- "| 6. Roll steering (`roll_angle_profile`) | swath edges within 6 to 7 km across track (95th percentile), instead of 14 to 20 km with a fixed roll angle; footprint IoU 5th percentile 0.92 (IW) and 0.94 (EW) |\n",
+ "| 6. Roll steering (`Attitude` with a `roll_angle_profile`) | swath edges within 6 to 7 km across track (95th percentile), instead of 14 to 20 km with a fixed roll angle; footprint IoU 5th percentile 0.92 (IW) and 0.94 (EW) |\n",
"\n",
"**Velocity frame.** As for NISAR, Sentinel-1's zero-Doppler images lie in the plane perpendicular to the Earth-fixed velocity, which TAT-C's default `VelocityFrame.EARTH_FIXED` reproduces; the inertial frame would displace swath edges by up to 41 km along track and observation times by up to 6 s.\n",
"\n",
- "**Roll steering.** A right-looking `PointedInstrument` with a negative roll angle and rigid rotation reproduces Sentinel-1C's swath geometry, with per-mode parameters. Sentinel-1 steers its roll angle around the orbit, so its look angles vary by up to 2.5 deg; a fixed roll angle and field of view, set to the median look angles, misplace the swath edges by up to about 20 km (95th percentile) where the look angles depart most from the median. A roll angle profile (`roll_angle_profile`, as a function of argument of latitude) models the roll steering and reduces these errors to about 7 km."
+ "**Roll steering.** A right-looking `PointedInstrument` with a negative roll angle and rigid rotation reproduces Sentinel-1C's swath geometry, with per-mode parameters. Sentinel-1 steers its roll angle around the orbit, so its look angles vary by up to 2.5 deg; a fixed roll angle and field of view, set to the median look angles, misplace the swath edges by up to about 20 km (95th percentile) where the look angles depart most from the median. The satellite's attitude with a roll angle profile (an `Attitude` with a `roll_angle_profile`, as a function of argument of latitude), from which the instrument's fixed view is rotated, models the roll steering and reduces these errors to about 7 km."
]
}
],
diff --git a/docs/validation/ValidateSampleSizes.ipynb b/docs/validation/ValidateSampleSizes.ipynb
new file mode 100644
index 00000000..5729c1be
--- /dev/null
+++ b/docs/validation/ValidateSampleSizes.ipynb
@@ -0,0 +1,2817 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a13219d4",
+ "metadata": {},
+ "source": [
+ "# Sample Size Validation: Cross-Track and Conical Scanners\n",
+ "\n",
+ "This notebook validates TAT-C's sample sizes against operational scanning instruments. `compute_sample_sizes` adds to each observation the size on the ground of the observing instrument's pixel at the target (`sample_along` and `sample_cross`) and the spacing between neighboring pixels (`spacing_along` and `spacing_cross`). Together they bound the detail an observation can resolve, as used by the observing system simulation experiments (OSSEs) in the examples. For a `PointedInstrument`, TAT-C projects angular extents onto the WGS 84 ellipsoid around the line of sight to the target: the instantaneous field of view of a pixel for its size, and the angle between pixels for their spacing. For a `ConicalInstrument`, the size is that of its beam (`beam_width`), the spacing along the scanned arc follows from the azimuth between samples (`scan_sample_spacing`), and the spacing between scans is the distance the arc advances in one scan.\n",
+ "\n",
+ "Geolocation products report where each sample is centered, so the spacing of the samples can be measured directly; the sizes of the footprints cannot, and are instead compared with the values published for each instrument. The comparison uses six instruments, whose scan geometry is validated in the companion notebooks:\n",
+ "\n",
+ "* the cross-track scanners [ATMS](ValidateImagerATMS.ipynb) (a microwave sounder with one beam per scan position) and [CrIS](ValidateImagerCrIS.ipynb) (an infrared sounder) on NOAA-20;\n",
+ "* the cross-track imagers [VIIRS](ValidateImagerVIIRS.ipynb) on NOAA-20 and [MODIS](ValidateDriftMODIS.ipynb) on Terra, whose arrays of detectors image several lines per scan; and\n",
+ "* the conical scanners [AMSR2](ValidateConicalAMSR2.ipynb) on GCOM-W and [GMI](ValidateConicalGMI.ipynb) on GPM.\n",
+ "\n",
+ "The comparison asks four questions:\n",
+ "\n",
+ "1. **Cross-track spacing.** Does TAT-C reproduce the spacing of samples across the scan, which grows toward the swath edges?\n",
+ "2. **Along-track spacing.** Does it reproduce the spacing of samples along track, between detectors and between scans?\n",
+ "3. **Conical spacing.** Does it reproduce the spacing of a conical scanner's samples along the arc and between scans?\n",
+ "4. **Footprint sizes.** Do its sample sizes match the footprint sizes published for each instrument?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a8bd92",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "This notebook reuses the geolocation products downloaded (and cached in `data/`) by the companion notebooks, which must be run first:\n",
+ "\n",
+ "* **ATMS** ([ATMS validation](ValidateImagerATMS.ipynb)): the center of each of the 96 beams of every scan of 2 October 2026 (`GATMO`), with the NOAA-20 two-line element set (TLE) of that day.\n",
+ "* **VIIRS** ([VIIRS validation](ValidateImagerVIIRS.ipynb)): the moderate-resolution (M-band) geolocation (`GMTCO`) of every tenth granule of the same day: all 3,200 samples of the two center detectors of each granule's middle scan, and detectors 1, 8, 9, and 16 of every scan at every eighth sample.\n",
+ "* **MODIS** ([MODIS validation](ValidateDriftMODIS.ipynb)): the 1 km geolocation (`MOD03`) of 4 October 2026: the first and last of the 10 detectors of every scan, at every eighth frame and the last; every twelfth 5-minute granule is used here.\n",
+ "* **AMSR2** ([AMSR2 validation](ValidateConicalAMSR2.ipynb)): the 243 low-resolution samples of every scan of 26 September 2026.\n",
+ "* **GMI** ([GMI validation](ValidateConicalGMI.ipynb)): the 221 samples of each of its two feeds (S1, 10 to 89 GHz; S2, 166 and 183 GHz) of every scan of 3 October 2026."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "01f9c6e1",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T16:05:55.877040Z",
+ "iopub.status.busy": "2026-10-25T16:05:55.877040Z",
+ "iopub.status.idle": "2026-10-25T16:05:57.865316Z",
+ "shell.execute_reply": "2026-10-25T16:05:57.865316Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "ATMS scans 32400\n",
+ "VIIRS granules with pixels 102\n",
+ "MODIS granules 24\n",
+ "AMSR2 scans 49695\n",
+ "GMI scans 46081\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from datetime import datetime, timedelta, timezone\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "DATA_DIR = Path(\"data\")\n",
+ "FILES = {\n",
+ " \"ATMS geolocation (ValidateImagerATMS)\": DATA_DIR\n",
+ " / \"jpss\"\n",
+ " / \"atms_gatmo_20261002.npz\",\n",
+ " \"NOAA-20 TLE (ValidateImagerATMS)\": DATA_DIR / \"jpss\" / \"n20_tle_20261002.txt\",\n",
+ " \"VIIRS geolocation (ValidateImagerVIIRS)\": DATA_DIR\n",
+ " / \"jpss\"\n",
+ " / \"viirs_gmtco_pixels_20261002.npz\",\n",
+ " \"VIIRS scans (ValidateImagerVIIRS)\": DATA_DIR\n",
+ " / \"jpss\"\n",
+ " / \"viirs_gmtco_scans_20261002.npz\",\n",
+ " \"MODIS geolocation (ValidateDriftMODIS)\": DATA_DIR\n",
+ " / \"modis\"\n",
+ " / \"MOD03.A2026277.0000.007.2026277065153.npz\",\n",
+ " \"AMSR2 geolocation (ValidateConicalAMSR2)\": DATA_DIR\n",
+ " / \"amsr2\"\n",
+ " / \"amsr2_l1c_s1_20260926.npz\",\n",
+ " \"GMI geolocation (ValidateConicalGMI)\": DATA_DIR / \"gmi\" / \"gmi_l1c_20261003.npz\",\n",
+ "}\n",
+ "missing = [name for name, path in FILES.items() if not path.exists()]\n",
+ "if missing:\n",
+ " raise FileNotFoundError(\n",
+ " \"run the companion notebooks first, to download: \" + \"; \".join(missing)\n",
+ " )\n",
+ "\n",
+ "tle = FILES[\"NOAA-20 TLE (ValidateImagerATMS)\"].read_text().splitlines()\n",
+ "atms = dict(np.load(FILES[\"ATMS geolocation (ValidateImagerATMS)\"]))\n",
+ "viirs = dict(np.load(FILES[\"VIIRS scans (ValidateImagerVIIRS)\"]))\n",
+ "viirs_pixels = dict(np.load(FILES[\"VIIRS geolocation (ValidateImagerVIIRS)\"]))\n",
+ "# every twelfth MODIS granule (5 minutes each) of 4 October 2026\n",
+ "modis_granules = [\n",
+ " dict(np.load(path))\n",
+ " for path in sorted((DATA_DIR / \"modis\").glob(\"MOD03.A2026277.*.npz\"))[::12]\n",
+ "]\n",
+ "amsr2 = dict(np.load(FILES[\"AMSR2 geolocation (ValidateConicalAMSR2)\"]))\n",
+ "gmi = dict(np.load(FILES[\"GMI geolocation (ValidateConicalGMI)\"]))\n",
+ "pd.Series(\n",
+ " {\n",
+ " \"ATMS scans\": len(atms[\"MidTime\"]),\n",
+ " \"VIIRS granules with pixels\": len(viirs_pixels[\"granule\"]),\n",
+ " \"MODIS granules\": len(modis_granules),\n",
+ " \"AMSR2 scans\": len(amsr2[\"time\"]),\n",
+ " \"GMI scans\": len(gmi[\"time\"]),\n",
+ " }\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e376e0b3",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "The instruments are modeled as in the companion notebooks, with the parameters fitted there, on satellites whose bodies are aligned with the inertial velocity (an inertial `Attitude`, as none of these spacecraft is yaw steered):\n",
+ "\n",
+ "* **ATMS:** a `PointedInstrument` of 96 pixels across track, spaced 1.112 deg and rolled 0.04 deg to the left, with one pixel along track spanning the same angle. The beams of channels 3 to 16 are 2.2 deg wide, about twice their spacing, so the pixels overlap: an oversampling of 1 - 1.112 / 2.2 = 0.49 in each direction. Its scan period of 8/3 s (`scan_period`) spaces its scans along track.\n",
+ "* **MODIS:** a `PointedInstrument` in scan geometry (`ViewGeometry.SCAN`) of 1,354 pixels across track and 10 along track, each 0.0812 deg, with its scan period (1.477 s).\n",
+ "* **VIIRS:** a `PointedInstrument` in scan geometry with 16 pixels (detectors) along track, 0.05104 deg apart, and its scan period (1.787 s), defined in Test 1.\n",
+ "* **AMSR2 and GMI:** `ConicalInstrument`s with the measured cone angles and scan sectors, defined in Test 3.\n",
+ "\n",
+ "The measured spacing at a sample is half the distance between the samples on either side of it (across the scan, between consecutive samples of a scan; along track, between consecutive scans, or between the first and last detectors of a scan divided by the number of detector pitches between them). TAT-C's sizes and spacings are computed by `compute_sample_sizes`, for observations of targets at the sample centers by the instrument at the sample times (the scan times, offset by the beam's time within the scan for ATMS)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "96fb4117",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T16:05:57.867835Z",
+ "iopub.status.busy": "2026-10-25T16:05:57.867835Z",
+ "iopub.status.idle": "2026-10-25T16:05:59.663057Z",
+ "shell.execute_reply": "2026-10-25T16:05:59.663057Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import geopandas as gpd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from pyproj import Transformer\n",
+ "\n",
+ "from tatc.analysis import compute_sample_sizes\n",
+ "from tatc.constants import EARTH_MEAN_RADIUS, timescale\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " CircularOrbit,\n",
+ " ConicalInstrument,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
+ "from tatc.utils import (\n",
+ " VelocityFrame,\n",
+ " ViewGeometry,\n",
+ " compute_along_track_field_of_view,\n",
+ " compute_projected_ray_position,\n",
+ ")\n",
+ "\n",
+ "IET_EPOCH = pd.Timestamp(\"1958-01-01\", tz=\"UTC\")\n",
+ "LEAP_SECONDS = 37\n",
+ "TO_ECEF = Transformer.from_crs(\"EPSG:4979\", \"EPSG:4978\", always_xy=True)\n",
+ "# not yaw-steered: the bodies (and the instruments' views) of NOAA-20, Terra, GCOM-W, and GPM align with the inertial velocity\n",
+ "INERTIAL = Attitude(velocity_frame=VelocityFrame.INERTIAL)\n",
+ "\n",
+ "\n",
+ "def iet_to_utc(iet):\n",
+ " \"\"\"UTC times of IDPS Epoch Times (microseconds since 1958, including leap seconds).\"\"\"\n",
+ " return IET_EPOCH + pd.to_timedelta(\n",
+ " np.asarray(iet) - LEAP_SECONDS * 1_000_000, unit=\"us\"\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def ecef(lon, lat):\n",
+ " \"\"\"Earth-fixed position (m) of geodetic coordinates on the ellipsoid, stacked on the last axis.\"\"\"\n",
+ " return np.stack(TO_ECEF.transform(lon, lat, np.zeros(np.shape(lat))), axis=-1)\n",
+ "\n",
+ "\n",
+ "def neighbor_spacing(xyz, axis):\n",
+ " \"\"\"Spacing (km) between neighboring samples along an axis: half the distance between the samples on either side\n",
+ " of each sample (NaN at the ends).\"\"\"\n",
+ " xyz = np.moveaxis(xyz, axis, 0)\n",
+ " spacing = np.full(xyz.shape[:-1], np.nan)\n",
+ " spacing[1:-1] = np.linalg.norm(xyz[2:] - xyz[:-2], axis=-1) / 2e3\n",
+ " return np.moveaxis(spacing, 0, axis)\n",
+ "\n",
+ "\n",
+ "def sample_sizes(satellite, instrument, lon, lat, epochs):\n",
+ " \"\"\"TAT-C's sample sizes and spacings (km) of an instrument at targets, each observed at an epoch.\"\"\"\n",
+ " observations = gpd.GeoDataFrame(\n",
+ " {\n",
+ " \"satellite\": satellite.name,\n",
+ " \"instrument\": instrument,\n",
+ " \"epoch\": pd.DatetimeIndex(epochs),\n",
+ " },\n",
+ " geometry=gpd.points_from_xy(np.ravel(lon), np.ravel(lat)),\n",
+ " crs=\"EPSG:4326\",\n",
+ " )\n",
+ " sizes = compute_sample_sizes(observations, satellite)\n",
+ " columns = [\n",
+ " \"sample_along\",\n",
+ " \"sample_cross\",\n",
+ " \"spacing_along\",\n",
+ " \"spacing_cross\",\n",
+ " \"sat_range\",\n",
+ " ]\n",
+ " return pd.DataFrame(\n",
+ " {\n",
+ " \"scan_angle\": sizes.scan_angle.to_numpy(),\n",
+ " **{c: sizes[c].to_numpy() / 1e3 for c in columns},\n",
+ " }\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def ratio_summary(predicted, measured):\n",
+ " \"\"\"Percentiles of the ratio of TAT-C's to the measured spacing.\"\"\"\n",
+ " ratio = np.asarray(predicted / measured, dtype=float)\n",
+ " ratio = ratio[np.isfinite(ratio)]\n",
+ " return pd.Series(\n",
+ " {\n",
+ " \"n\": ratio.size,\n",
+ " \"5th percentile\": np.percentile(ratio, 5),\n",
+ " \"median\": np.median(ratio),\n",
+ " \"95th percentile\": np.percentile(ratio, 95),\n",
+ " }\n",
+ " )\n",
+ "\n",
+ "\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\"\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "# ATMS (ValidateImagerATMS): 96 beams on a fitted spacing of 1.112 deg, rolled 0.04 deg left; 2.2 deg beams (channels 3-16)\n",
+ "ATMS_BEAMS, ATMS_SPACING, ATMS_ROLL, ATMS_BEAM_WIDTH = 96, 1.112, 0.04, 2.2\n",
+ "ATMS_SCAN_PERIOD = 8 / 3 # s\n",
+ "atms_instrument = PointedInstrument(\n",
+ " name=\"ATMS\",\n",
+ " field_of_regard=ATMS_BEAMS * ATMS_SPACING,\n",
+ " cross_track_field_of_view=ATMS_BEAMS * ATMS_SPACING,\n",
+ " along_track_field_of_view=ATMS_SPACING,\n",
+ " cross_track_pixels=ATMS_BEAMS,\n",
+ " roll_angle=ATMS_ROLL,\n",
+ " is_rectangular=True,\n",
+ " # beams wider than their spacing overlap: a positive oversampling\n",
+ " cross_track_oversampling=1 - ATMS_SPACING / ATMS_BEAM_WIDTH,\n",
+ " along_track_oversampling=1 - ATMS_SPACING / ATMS_BEAM_WIDTH,\n",
+ " # one beam per scan position: the scans are spaced by their advance in one period\n",
+ " scan_period=ATMS_SCAN_PERIOD,\n",
+ ")\n",
+ "noaa20 = Satellite(\n",
+ " name=\"NOAA-20\",\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(tle),\n",
+ " instruments=[atms_instrument],\n",
+ " attitude=INERTIAL,\n",
+ ")\n",
+ "\n",
+ "# MODIS (ValidateDriftMODIS): 1,354 frames of 0.0812 deg across track, 10 detectors of 0.0812 deg along track, scan geometry\n",
+ "MODIS_SCAN_PERIOD = float(np.median(np.diff(modis_granules[0][\"ev_center_time\"]))) # s\n",
+ "modis_instrument = PointedInstrument(\n",
+ " name=\"MODIS\",\n",
+ " field_of_regard=112,\n",
+ " cross_track_field_of_view=1354 * 0.0812,\n",
+ " along_track_field_of_view=10 * 0.0812,\n",
+ " cross_track_pixels=1354,\n",
+ " along_track_pixels=10,\n",
+ " is_rectangular=True,\n",
+ " view_geometry=ViewGeometry.SCAN,\n",
+ " scan_period=MODIS_SCAN_PERIOD,\n",
+ ")\n",
+ "TERRA_TLE = [\n",
+ " \"1 25994U 99068A 26279.29978334 .00000220 00000+0 53719-4 0 9994\",\n",
+ " \"2 25994 97.9333 324.3740 0001348 204.7527 222.4948 14.61170513425833\",\n",
+ "]\n",
+ "terra = Satellite(\n",
+ " name=\"Terra\",\n",
+ " orbit=GeneralPerturbationsOrbit.from_tle(TERRA_TLE),\n",
+ " instruments=[modis_instrument],\n",
+ " attitude=INERTIAL,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a417b344",
+ "metadata": {},
+ "source": [
+ "## Test 1: Cross-Track Spacing\n",
+ "\n",
+ "**ATMS** samples its 96 beams at a constant angular rate. **MODIS** samples 1,354 frames per scan at a constant angular rate. **VIIRS** samples its detectors at a constant angular rate too, but aggregates the samples on board into 3,200 pixels: three samples per pixel near nadir, two at intermediate scan angles, and one at the swath edges, which keeps its pixels nearly the same size across the swath. The [VIIRS validation](ValidateImagerVIIRS.ipynb) found that a single `PointedInstrument` of 3,200 equal pixels cannot represent these zones. Here, each zone is instead represented by its own `PointedInstrument`, with the zone's pixels, angular spacing, and center (roll angle) taken from the measured scan angles, and each sample is assigned to the instrument of its zone."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4f6867f2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T16:05:59.668703Z",
+ "iopub.status.busy": "2026-10-25T16:05:59.668138Z",
+ "iopub.status.idle": "2026-10-25T16:06:00.261822Z",
+ "shell.execute_reply": "2026-10-25T16:06:00.261822Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ " n TAT-C / measured, 5th percentile \\\n",
+ "ATMS 20304.0 0.9919 \n",
+ "MODIS 44616.0 0.9972 \n",
+ "VIIRS, aggregation zones 325380.0 0.9915 \n",
+ "VIIRS, 3,200 equal pixels 325380.0 0.6564 \n",
+ "\n",
+ " TAT-C / measured, median \\\n",
+ "ATMS 0.9996 \n",
+ "MODIS 0.9994 \n",
+ "VIIRS, aggregation zones 1.0001 \n",
+ "VIIRS, 3,200 equal pixels 0.9852 \n",
+ "\n",
+ " TAT-C / measured, 95th percentile \n",
+ "ATMS 1.0074 \n",
+ "MODIS 1.0038 \n",
+ "VIIRS, aggregation zones 1.0091 \n",
+ "VIIRS, 3,200 equal pixels 1.9737 "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def binned_median(df, value, edges=np.arange(-57, 58, 2.0)):\n",
+ " \"\"\"Median of a value in bins of scan angle.\"\"\"\n",
+ " middle = (edges[:-1] + edges[1:]) / 2\n",
+ " return (\n",
+ " middle,\n",
+ " df.groupby(pd.cut(df.scan_angle, edges), observed=False)[value]\n",
+ " .median()\n",
+ " .to_numpy(),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "viirs_inner = viirs_sizes[~viirs_sizes.boundary]\n",
+ "fig, axes = plt.subplots(1, 3, figsize=(13, 3.8))\n",
+ "for ax, (name, df) in zip(axes, [(\"ATMS\", atms_sizes), (\"MODIS\", modis_sizes)]):\n",
+ " ax.plot(\n",
+ " *binned_median(df, \"measured_cross\"),\n",
+ " \"o\",\n",
+ " color=INK,\n",
+ " ms=3,\n",
+ " label=\"measured (median)\",\n",
+ " )\n",
+ " ax.plot(\n",
+ " *binned_median(df, \"spacing_cross\"), color=BLUE, lw=1.5, label=\"TAT-C (median)\"\n",
+ " )\n",
+ " ax.set(\n",
+ " xlabel=\"Scan angle (deg, positive left)\",\n",
+ " ylabel=\"Cross-track spacing (km)\",\n",
+ " title=name,\n",
+ " ylim=(0, None),\n",
+ " )\n",
+ " ax.legend(frameon=False, fontsize=8)\n",
+ "# VIIRS: medians of each sample, whose spacing jumps at the zone boundaries\n",
+ "by_sample = viirs_inner.groupby(\"sample\")[\n",
+ " [\"scan_angle\", \"measured_cross\", \"uniform_cross\", \"spacing_cross\"]\n",
+ "].median()\n",
+ "axes[2].plot(\n",
+ " by_sample.scan_angle,\n",
+ " by_sample.measured_cross,\n",
+ " \".\",\n",
+ " color=INK,\n",
+ " ms=2,\n",
+ " label=\"measured (median)\",\n",
+ ")\n",
+ "axes[2].plot(\n",
+ " by_sample.scan_angle,\n",
+ " by_sample.spacing_cross,\n",
+ " color=BLUE,\n",
+ " lw=1,\n",
+ " label=\"TAT-C, aggregation zones\",\n",
+ ")\n",
+ "axes[2].plot(\n",
+ " by_sample.scan_angle,\n",
+ " by_sample.uniform_cross,\n",
+ " color=ORANGE,\n",
+ " lw=1.5,\n",
+ " ls=\"--\",\n",
+ " label=\"TAT-C, 3,200 equal pixels\",\n",
+ ")\n",
+ "axes[2].set(\n",
+ " xlabel=\"Scan angle (deg, positive left)\",\n",
+ " ylabel=\"Cross-track spacing (km)\",\n",
+ " title=\"VIIRS (M bands)\",\n",
+ " ylim=(0, 3.5),\n",
+ ")\n",
+ "axes[2].legend(frameon=False, fontsize=8, markerscale=3)\n",
+ "fig.tight_layout()\n",
+ "plt.show()\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " \"ATMS\": ratio_summary(atms_sizes.spacing_cross, atms_sizes.measured_cross),\n",
+ " \"MODIS\": ratio_summary(modis_sizes.spacing_cross, modis_sizes.measured_cross),\n",
+ " \"VIIRS, aggregation zones\": ratio_summary(\n",
+ " viirs_inner.spacing_cross, viirs_inner.measured_cross\n",
+ " ),\n",
+ " \"VIIRS, 3,200 equal pixels\": ratio_summary(\n",
+ " viirs_inner.uniform_cross, viirs_inner.measured_cross\n",
+ " ),\n",
+ " }\n",
+ ").T.rename(columns=lambda c: f\"TAT-C / measured, {c}\" if c != \"n\" else c).round(4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3d2759db",
+ "metadata": {},
+ "source": [
+ "Across the scan, TAT-C's spacing matches the measured spacing of ATMS and MODIS to within 1% (between the 5th and 95th percentiles of the ratio), as both grow toward the swath edges with the slant range and the obliquity of the view: from 16.2 km at nadir to 65 km at the outermost beams for ATMS, and from 0.99 km to 4.6 km for MODIS.\n",
+ "\n",
+ "The VIIRS zones found here match those of the [VIIRS validation](ValidateImagerVIIRS.ipynb). With one `PointedInstrument` per zone, TAT-C reproduces the measured spacing of VIIRS to within 1%: 0.78 km at nadir, rising to 1.17 km at the end of the 3:1 zone (32 deg), dropping when fewer samples are aggregated, and reaching 1.65 km at the edges. A single view of 3,200 equal pixels misses this pattern by -34% to +97%. A satellite with several `PointedInstrument`s, one per aggregation zone, thus represents the sampling of VIIRS; the zones' views tile the swath.\n",
+ "\n",
+ "## Test 2: Along-Track Spacing\n",
+ "\n",
+ "MODIS and VIIRS image several lines per scan with arrays of detectors (10 and 16) at a fixed angular pitch. The measured spacing is the distance between the first and last detectors of a scan, divided by the number of pitches between them (9 and 15). The advance from one scan to the next, per detector, is also measured. ATMS has a single beam per scan position, so its samples are spaced along track by the distance the scan advances in one scan period (8/3 s).\n",
+ "\n",
+ "In TAT-C, the along-track spacing is the angle between pixels along track (the along-track field of view divided by the number of pixels), projected around the line of sight to the target. For a scanning instrument with a scan period (`scan_period`), it is instead the larger of two distances:\n",
+ "* the angular spacing within a scan, if there is more than one pixel along track; and\n",
+ "* the distance the scan advances on the ground at the target in one period, divided among the pixels along track (`compute_ground_advance`).\n",
+ "\n",
+ "The measured spacing is correspondingly the larger of the detector pitch and the scan's advance per detector."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a99fa48e",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T16:06:12.998122Z",
+ "iopub.status.busy": "2026-10-25T16:06:12.998122Z",
+ "iopub.status.idle": "2026-10-25T16:06:13.402243Z",
+ "shell.execute_reply": "2026-10-25T16:06:13.402243Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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U1FT27NnD3Llz8ff3Z8CAAff1/EIIISzTp59+etfHNRoN69ato3v37rRr144JEybQrl07srKy2LJlC9999x2dO3fmyy+/LHDsjRs3OHz4MIqikJKSwpkzZ1i2bBmnT59m3LhxvPzyy3d97ldeeYXExEQGDhxIkyZN0Gg0/Pvvv8yePRu1Wn3PJKMQQoiSadWqFU2bNmXOnDkoivLAU40LY21tzfTp0xk8eDBz587l/fffN8l1TJcuXfjoo49IT0+/Z2LzXn3fvXh7e/PUU08xZcoUvLy8WLFiBTt27GDmzJnG5+7Tpw/r16/n9ddfZ9CgQVy9epVp06bh5eV13+vVT5s2jSeeeILu3bszYcIE9Ho9M2fOxNHRMd8oysmTJ3Pt2jW6detG9erVSUxMZO7cuVhbW9O5c+d85zx8+DDNmzencuXK9/8HEeWeJAmFMKFFixZhY2NT5HD1qlWr0r9/f9auXcuNGzfw9PQslThcXFyYOnUqf/zxB++99x43btxApVJRp04dxo4dyzvvvGPyXwKFEEJYvoYNGxIWFsYXX3zB8uXLmTZtGlZWVjRq1Ig5c+bwyiuvYG1tXeC4tWvXsnbtWtRqNU5OTtSqVYt27dqxcOFC2rZte8/nHT16NKtXr+b7779Hq9WSlpaGu7s77dq1Y9myZcU6hxBCiJJ58cUXGTNmDI0aNaJNmzal8hzPPPMMbdq0YdasWYwePdok1zHDhg3jww8/5Ndff33gKdL30rx5c0aOHMmHH37IhQsX8Pb2ZtasWYwbN85YZ+TIkcTGxrJw4UIWL15M3bp1effdd7l27RpTp069r+ft3r07Gzdu5P3332fIkCFUq1aN119/nYyMjHznbNOmDcePH+edd94hLi6OSpUq0apVK3bt2kXjxo2N9VJTU/njjz+YNm3a/f8xhEVQKbLFqRBCCCGEEEIIISxE37590el0slljMS1atIgxY8Zw9epVGUlYwUmSUAghhBBCCCGEEBbjzJkzBAQEcPDgQVq3bm3ucMo0nU5Ho0aNCAoKYtKkSeYOR5iZbFwihBBCCCGEEEIIi9GkSROWLFlS6G6/Ir+rV6/y/PPPM2HCBHOHIsoAGUkohBBCCCGEEEIIIUQFJyMJhRBCCCGEEEIIIYSo4CRJKIQQQgghhBBCCCFEBSdJQiGEEEIIIYQQQgghKjiLTxIqikJycjKy9KIQQog7zZgxg9atW+Ps7IyHhwf9+vXj3Llz9zxu7969tGzZEjs7O+rWrcvChQsL1Fm3bh2NGjXC1taWRo0asWHDhhLFJn2XEEKI8kb6LiGEKP+szB1AaUtJScHV1ZWkpCRcXFzMHU6htFotERER+Pn54ePjc8/y0NBQRo8ejcFgQK1WM3/+fIKCgkxWvmRpKGMnTERR26CxtuXDqR/T56n+ZOsMbNyyldlz5qOoNKitbHj51f+jw2Od2b33T1b8tDq3XGNN/wGDaN6iJcdPnOLXbb+DSo1KraFbYHdq1KhF6LJlKKhQqTSgzj3mqX790RkUtm3bDmrN7WOsUKmtCGjRgrDTf4NKfcdjGurU9ePSpcu55So1KpUa1GrcPTyJj78FKlXuY6hQqVQ4ObuQmpqaW47q9jEqbGzsUIAcne5/5xF3pSgGUJTcGwrW1lZkZ2eBAigGIPcxFxcnVKhITLwFiiH3H46KHhQDXtU8uX5dCwYDiqIHgwEwUN/Pj3PnzoJB/79yxUDrVi04euQwikGHYtCDokeFQt8+vVGrYMP6tbnlt8+nQmHwoIGsXr0SxaBHMehArwNFz+uvvcJX8+di0Oeg6HNQDDmoFT0ffzSF9959G70uGwy5j6kUPaFLfmD4c0PR52SBPhtFn40ahfB//sbHx6fUP5d5Svp9UVS5yE32vfHGG7Ru3RqdTsekSZPo0aMH4eHhODo6FnpMVFQUTz75JC+//DIrVqzgwIEDvP7667i7uzNw4EAADh06xJAhQ5g2bRr9+/dnw4YNDB48mP3799OmTZtixVZU3xUfF0NV92oP3vgyrqK0E6StlkraKioi6bssv50VoY0g7bQ0FaGdpmyjxe9unJyc/NCThCW5WC9JomDosOFcuKSl0+NPgLUjahsHVNYOaGydmDBxEnO++has7FBbO6Cytkdt48BjXQI5eOQEKit70FjnJr9UKnx9/bh4MQpFpcpNxFnZobayw9bBmRyD6qH8nSoe5XaCTDHeB9BoNADodbp85bmPWaHX6/iv/OWqO8o16PWGO4pzH1Or1bfPLK+tqalQsNaoyExPMSYbFX0OGHT41q7JhfNnUfTZKLpsFF0Gii6TPj27sXnDGvTZGSg56RhyMlDpM5jy/ru8/+4E9FmpKNnpGLJTUekyOHP6ONWrVzdpwlGShwXFxcXh4eHB3r176dSpU6F13nnnHTZv3szZs2eNZa+99hqnT5/m0KFDAAwZMoTk5GS2bdtmrPPEE09QuXJlVq5cWaxYiuq74mKv4+7hfT/NK1cqSjtB2mqppK2iIpK+y/LbWRHaCNJOS1MR2mnKNlr8SMLSUtJRfkuWhjLunQ/BxhErWyeCx7+D/yMteGf+Lzg06ofaxgm1rTOT111jb+rf7DlowGPQ4tvlTsw848Rnk3OnwXk9v7ZAPEtPQaX2owuUn7wOdjUKjly5mgTWVXwLlOcY8t9Xbo+YcnF0wEqtEBcbjaLPBn1O7mgufQ51a9ck8sI5430MOhSDjpYBzTl+LHfUFwb97VFkBvo82Ytff9mCwWAAJXd0lxqFt98aj0YNn0z76H+jvgw6VCqY/P4kpk6ZjEGvM55HrVL48vPPGD9uLAa97vbINgNqFXy78BteefllDAYdKAqKYkCjVhMaupQRw4fnPjfK7foq/vhjJ2oVdO7cKfcxgx5QUKvVbN60kb59+2DQ545yQ8kt/+23bfTs0QPD7boooNGo2blzJ90e73r7OXJpNBp27dpF164Fy8PDwwHw9/cv9jElLS/8OVRorKzYsWMngd0DMRgUckdXqrCysmLr1m30erJ3brkqdySmRmPFho0b6dd/ALnFuSMyNRoNq1b/zJChQzEYFGMyWqOx4tetW1EU6N23L4qiMo7UVFtZ89VXX/PGm6Nzk5e3R4eqNRo+/fQz3g15L3e0qVoDKg1qjRXvTXqfGZ9+hqJSg0qDSq1GrbFi4jshGBT44svZxlGlqDSorawYOnQYq39eg0LuyFQ0Vqg11nTr3pNdu/fC7dGqaKxRa6xp1KQpZ89dALU1Ko01Ko0NKo0NTi6VSc/MRmVlm/8zgopsPahtnQt8nq4kga1n4wLlu6PAufmwAuXzDoHH0wsKlPf6OhlHm39JiPXBc/AyDNlpGLLTmLI5gWNZf7Fp/QWcW47CkJ2KISuFibPWYOXZlPFT56Fy9ESVmYghJ4Pg4GACAwPZuXOnJA8LkZSUBICbm1uRdQ4dOkSPHj3ylfXs2ZNFixaRk5ODtbU1hw4dYty4cQXqzJkzx+QxCyGEEEIIIYSpSJLwHooz+m/OvAV0f2oof12I5t2vfse51Sg0ju5oHN35eJ8z31/6h1vpLfAZ9b9RJauvAlczqNL9owLPeeQa2NdsW2RMhqxUY5JAyUlDycmkQ5sA9u3ajiE7HcPtMvSZjB39f3w582P02em5CbzbSbEvPv+MCRPGY9DrQdFhyMlEreSwckUogwc+nVtflw0oaDQadhqTTO0LJKB+3rWLrl3HFiif9P4uun4xs0D5B4vfoF2lxgQHB6PX69FoNMybN4+gPnUBcI7rWvCxfs1xTBpYoHx4rwAMsaPylc+eN4+BgS1InRKcr3zWvHn06dKC2R9PLFA/oGENAObOnJLvsbnz5tGpXUvmfflp/ueePY92j7Zk/txZ+cvnzqF1q5bMnz+/QKwtWxZenve+KskxJS0v/DnUzJs7h0dbt2T+3Dm3y3W57Z41m/ZtWjJv1kxjfbVGw5xZ8+jSviVzP/uowN+wx2MtmDN9EsHBweju+Ju3bVITgNlTxxc4ZkiPADInvVzIeyEAzc2hBcsHNMc1pWeR7x23W60KPhbUlCbqU4WUP0Ko48lCypsQGnqikPLckXnBwcHoFTVW1rZ8+vmXDBw0mKvaGAJ7PIGiskKlsQJN7lT9z7+YzcR338tNRFrZobKyQ2PryKiX/4+lK1blG/mrsXGkSfPWhJ+/mFtm44ja1gmV2gqDoiIlS8HKtWDSbncUuAQ8V6B8+l6oNmTF/743stPQp8XzSmg0/5xIxLV9MLq0OPTJ0UyY9jWtOjzOsf27CA4u+VRnS6AoCuPHj6djx440adKkyHoxMTF4enrmK/P09ESn0xEfH4+Xl1eRdWJiYoo8b1ZWFllZWcb7ycnJ99kSIYQQAvbt28fnn3/OiRMniI6OZsOGDfTr1++ux2RlZfHRRx+xYsUKYmJiqF69OpMmTWLUqFEPJ2ghhBBmJ9ON7yJfMlBjxUdfLKSGf1uCJ32GlWtNrCrXxMrZG42Te7HWsFMUA4as1Nzphdnp1K3pRcS/f+Um9rLTMGQmo+Sk8cbLQcz9cjr6jGQM2SkYsnKnHB7+8w/8avuwfPmyuycwSqk8729iqnNptVoiIyPx9fUtkHAo6jGtVsvJE0dp0fLRAuVF1S9JuSnPZernNkW7y3L7TNFuc7avtD5/X8xZwNMDhxJ5OZq+A4YalxpQ2ziisXVi1CtvsPTHn1FZO6K2cURt64za1pna9RpxLeYWalvnAqMfi2LIyUCXEo0u+Tq6hMvoEqJY/vV02jWpztpVK+66VmJ598Ybb/Drr7+yf/9+qlevXmS9+vXrM3LkSEJCQoxlBw4coGPHjkRHR1OtWjVsbGwIDQ3l2WefNdb58ccfefHFF8nMzCz0vFOmTGHq1KkFyiMj/sXZ+X+jVHW6HKysrO/ZnujoGC5dukTt2rXx8ip/a7AUt52WQNpqmaStlqU8TlPbtm0bBw4coEWLFgwcOLBYScKnn36aGzdu8PHHH+Pn50dsbCw6nY727dsX6zllurHlt7MitBGknZamIrTTlG2UJGERtFot/v7+VO42GZuq9bGuVPOuF9pWaoVqLhou/HUQXXI0+rRY9Gk3UTJu8dWsabz0/CB0aTdzN2zgf1NAd+7cWeLkWl58pZksexhJmPtVET7khZF2l32m/PwV9vm+34SjQW2LjbMHY0M+ok7DFkycPAO1QxWsnDzQOHth7VwNjZPHXdumS4km51YUmVePkXxqhfE7zBJGFI4ePZqNGzeyb98+6tSpc9e6nTp1IiAggLlz5xrL8jYmSU9Px9rampo1azJu3Lh8U45nz57NnDlzuHz5cqHnLWwkYY0aNe7rQutem9+UB+Xpc/+gpK2WSdoqyhKVSnXPJOFvv/3G0KFDuXjx4l2X3bibB00SlvcZCxXhs1AR2gjSTktTEdopaxI+BBERERgMBmyqNsCmqh8ABl0mXs4Q9defZN+KIifhMrpkLUpaLH8d/5Ma1asTGnqY4OCZ+S7WBwW2IG3mh7cv4sk3BTQoKIjAwMACiYKiyvP4+PhgY92mwBvBx8en0E7VVOWmPpcQlsSUn7/CPt8l/b4oqlz/UvsCScVOXXxp2bEnaidPrFx8sHari00VX7wbPMqtdAUrZy+snL1y1yQ9tQK9Xk9kZGS5/mwrisLo0aPZsGEDe/bsuWeCEKBdu3Zs2bIlX9n27dtp1aoV1tbWxjo7duzIlyTcvn37XUdi2NraYmtbvBGfd6PVahk9ejS2Ndvj0mI4sZvHGNeiLM+vlRBCiNK1efNmWrVqxWeffcby5ctxdHTkqaeeYtq0adjb25f68+f9wKWpVBt9spb5c2eVux+4hBDCVMz5o4kkCYvg5+eHWq0m8dDXKIoeXcIllPQ4dv5zhp07XQkOXpzvArvG7elp95P0k+SaEKK4TJGILOr7KG+9zsyrR+8YkdiQs5HXeOzJZ7GqVBt9WhyQ+2OHr2/BzY/KkzfeeIOffvqJTZs24ezsbFwz0NXV1XhBFBISglarZdmyZUDuTsYLFixg/PjxvPzyyxw6dIhFixbl27V4zJgxdOrUiZkzZ/L000+zadMmdu7cyf79+0u9TRERESgqa9y6vIuVsyeVO47h1p6Z5T6hK4QQ96u8j057WC5evMj+/fuxs7Njw4YNxMfH8/rrr3Pr1i0WL15c6DGmWk837wcu96e/wq56K2K3jJUfuIQQFVZoaChjJ80gJ/GaWWYFSZKwCD4+PkVuAFGcUX6S9BNClGUlSR76+1bny/deKnJDnPLqm2++AaBLly75ypcsWcILL7wAQHR0NFeuXDE+VqdOHbZu3cq4ceP46quv8Pb2Zt68eQwcONBYp3379qxatYr333+fDz74AF9fX1avXk2bNgV3mjc1Pz8/VEoON3dOxbP/1zg/MoisK4fKfUJXCCHuhyUsv/CwGAwGVCoVP/74I66urgDMmjWLQYMG8dVXXxU6mnDGjBmFrqcbHxdNVmaq8b5Ol0Nc7PUin/vE8SMYDAZybkVhV70V9nW7kHHpACdPHMXGuvT7TlO5VzstQUVoI0g7LU15amd0dAxj35uO94iNZMedJ/rnFwgOHk1A80fuus54YW283+nHkiS8i/sZ/SeEEOVZUd9t9/pxpDwqzpK8S5cuLVDWuXNnTp48edfjBg0axKBBg+43tPt25w9cyad+xCXgOWo+9Sm2Lp73PlgIISyIVqtlzNsf4NZzOhlR+0n79xcZnXYXXl5e+Pj4GBOEAP7+/iiKwrVr16hXr16BY0JCQhg/frzxft56ulXdvUq0JmHLVm1Qq9WkX9yDc9NncKjTiSSrz2jR8tFytY6YrHtmOaSdlqU8tTP87AXsfR8HwJCZBIYc9EBiUipNmxXdBlO28d5b8lZwPj4+dOrUSf4xIYSo8OT7sHwICgoiPDycpe/0oE4VNel6ayavjy5WUlQIISxFREQEtjXb4ujXDZfmQwGM6+mKgjp06MD169dJTf3fCMDz58+jVqupfntZpf+ytbXFxcUl3+1+5P3AlRMThj4zGY2DGxNn/CD/3hBCVDh+fn441usBQNqFHcDDX+ZJkoRCCCGEhfHx8aFb107Mer4W1hoVe/9NZc3RRHOHJYQQD42fnx8OtTsCkHEpd01YS1hPt7hSU1MJCwsjLCwMgKioKMLCwoxLaISEhDBixAhj/WHDhlGlShVGjhxJeHg4+/bt4+2332bUqFEPZeOSoKAgws/8Tbu6NgBY1+xY6s8phBBljc7WHRuPhigGHemRu82yzJMkCYUQQggLVb+aHeOecAdg5q83iIrLuscRQghhGTyreVO5QVcA0i8dsJj1dIvr+PHjBAQEEBAQAMD48eMJCAhg8uTJQME1d52cnNixYweJiYm0atWK5557jr59+zJv3ryHFrOPjw/Pds1N4u4KT5ER8EKICuf3v3M3gGpZy5Zf1v9EeHj4Q19LV9YkFEIIISzY8PZu7Ps3lcOR6YxfcYnRzWNoUF92+RRCWLawKxlkGaxwsVPx1aKZ1POzjPV0i6tLly53TbIVtuZuw4YN2bFjRylGdW8d6ztha6Xi6q0czsdk0cDLzqzxCCHEw7Ttr9wkYb/WHnRqXd8sMchIQiGEEMKCqdUqPnnGGzu1jvOxBkZ8sgV/f39CQ0PNHZoQQpSavf/mrq3XqaEzXTrLerrlhYONmnb1HAH4IzzFzNEIIcTDczE2i/MxWVipoVtjZ7PFIUlCIYQQwsLpU2O5tvUDAFxavoDa2Yfg4GC0Wq2ZIxNCiNLx57nbScIGTmaORJRUYKPci+M//pEkoRCi4vjt9ijC9vUcqeSgMVsckiQUQgghLFxERARpF3aScfUoKrUGB79ussunEMJiXU/M4cKNLNQq6FBfkoTlTRd/J9Qq+Dc6i2u3ss0djhBClDpFUdh2ez3CJ5re307xpiJJQiGEEMLC+fn5oVarSb+wEwAH364VapdPIUTFsu/2VOPmtezNOhpD3J/Kjla0rO0AwK7wVDNHI4QQpe/CjSwuxmZjrVHxeCPzTTUGSRIKIYQQFs/Hx4f58+eTdflPFMWArWcjPv5yoazRJYSwSHlJQplqXH7lrccl6xIKISqCvKnGjzVwxNnOvD9uSZJQCCGEqACCgoI4c+IA9d10ALg27GnmiIQQwvQycwwcuZgGSJKwPHu8Ue5rd/JSOrdSdWaORgghSo+iKPz2V+4PIj0fMe9UY5AkoRBCCFFh+Pj4MLBDDQB2/pNs5miEEML0jl1MJzNHoZqrFfWr2Zo7HHGffCrb4O9ti0GBPf/KlGMhhOX6NzqLyzezsbVS0dXf/D9uSZJQCCGEqEDypnCdvJxBfIqMzhBCWJa9d0w1VqlUZo5GPIi8dblkyrEQwpJtuz3VuFNDJxxtzb+OriQJhRBCiArEu5I1jX3sUBTYdVYuvIQQlkNRFPadu50kbGj+0RjiwQTe/lHr4IU00rIMZo5GCCFML3eqcW6SsJeZdzXOI0lCIYQQooLp3iT3wmvnGUkSCiEsx8W4bLQJOdhYqWjj62jucMQDqudpSw03a7J1Ct9vPIpWqzV3SEIIYVK7Tl1Fm5CDnXXZWUfXrEnCffv20bdvX7y9vVGpVGzcuLFAnbNnz/LUU0/h6uqKs7Mzbdu25cqVKw8/WCGEEMJC5I3OOBKZRnKG3szRCCGEaeTtaty6jgMONjIWorxTqVRUI/e6b/aqg/j7+xMaGmrmqIQQwjRCQ0MZOek7AG7+s52fVy43c0S5zNp7pqWl0axZMxYsWFDo45GRkXTs2JGGDRuyZ88eTp8+zQcffICdnd1DjlQIIYSwHHXcbfHztEVn+N/6XUIIUd7tvT3VuLNMNbYIWq2WX374EACHOh0xoCI4OFhGFAohyj2tVsvo0cHY+3YDIO3C9jLz/WZlzifv1asXvXr1KvLxSZMm8eSTT/LZZ58Zy+rWrfswQhNCCCEsWmBjZyJuZLHznxT6BriaOxwhhHggKZl6Tl1KB2Q9QksRERFB5vXT6NNvonGogp1PSzKvHiUyMhIfHx9zhyeEEPctIiICq6oNsHKuhiE7jYzLh0CvLxPfb2V2HL7BYODXX3+lfv369OzZEw8PD9q0aVPolGQhhBBClEzelOP951NJz5YF4YUQ5dvBC2noDFDH3YYabjbmDkeYgJ+fH2oVZETtB8C+zmNoNBp8fX3NHJkQQjwYPz8/HHw7A9xOEGaXme+3MpskjI2NJTU1lU8//ZQnnniC7du3079/fwYMGMDevXuLPC4rK4vk5OR8NyGEEELk19DLluqVrcnMUThwXqYcCyHKt99OxgLQQgaYWQwfHx/mz59P5uXcJKFDnc7MnTvP7KNshBDiQfn4+ODbYSgAGVH70Gg0zJtXNr7fzDrd+G4MhtxRDU8//TTjxo0DoHnz5hw8eJCFCxfSuXPnQo+bMWMGU6dOLVAeHxdNVqZlXQTpdDnExV43dxgPnbS7YpF2WxZ3D29zhyBuU6lUBDZxZumft9j5Twrdm7iYOyQhhLgvS5eGsu2UHxoHN76Z8hK+mS8QFBRk7rCECQQFBdGxczcGfp8MLl6069ne3CEJIcQDu3orm/gsB9QqWDozmKb+c8pEghDKcJKwatWqWFlZ0ahRo3zl/v7+7N+/v8jjQkJCGD9+vPF+cnIyNWrUoKq7Fy4ulnUBFBd7vUJecEu7KxZptxClJ7BxbpJwz9lUsnUGbKzK7AQDIYQolFar5a3p3+M58HsM2alkaE8RHHySwMDAMnPBJR6Mb+3qdGhwlT1nU9kVnkIDL9nEUghRvu0KTwGgVR0HegU+ZuZo8iuzVwM2Nja0bt2ac+fO5Ss/f/48tWrVKvI4W1tbXFxc8t2EEEIIUVCzGvZUddaQmmXgh41HysSOakIIURIRERE4+vcFID1yDxh06G8v/i4sR1f/3M1odp+1rJlhQoiKaXd47nfZ442czRxJQWZNEqamphIWFkZYWBgAUVFRhIWFceXKFQDefvttVq9ezffff09ERAQLFixgy5YtvP7662aMWgghhLAMarWK6prcae2fLtuDv78/oaGhZo5KCCGKz6umLw71ugOQ+s8GgDKz+LswnS4NnVGp4B9tJjeScswdjhBC3LfENB0nLqUD8HgjJzNHU5BZk4THjx8nICCAgIAAAMaPH09AQACTJ08GoH///ixcuJDPPvuMRx55hB9++IF169bRsWNHc4YthBBCWAStVsv20I8AcKjbGYOiIjg4WEYUCiHKjbA4R9TW9uQkXCIr+q8ytfi7MJ2qzlY0rW4PwN5/ZTShEKL82nsuFYMC9avZ4lPZxtzhFGDWNQm7dOmCoih3rTNq1ChGjRr1kCISQgghKo6IiAgyrp1An34LjYMb9nUeI+PiHiIjI+UCWwhRLqw7lgjAG30aEDBsK76+vvL9ZaG6NnLi9NUMdoWnMLhNZXOHI4QQ92VXGZ5qDGV4TUIhhBCitO3bt4++ffvi7e2NSqVi48aNd63/wgsvoFKpCtwaN25srLN06dJC62RmZpZya0rOz88PNQqp4ZsBcG76jEzTE0KUG/9GZ3LmWiZWGni+S006deokCUIL1tU/94L6cGQ6aVkGM0cjhBAll5Vj4MCFvCRh2ZtqDJIkFEIIUYGlpaXRrFkzFixYUKz6c+fOJTo62ni7evUqbm5uPPPMM/nqubi45KsXHR2NnV3Z243Rx8eH+fPnkx6+EcWgx77Go3z4+XdykS2EKBfW3x5F+Li/M1WczDpBSjwEvh421HCzJkevcPCCTDkWQpQ/hyPTyMhWqOZqRSPvsndtAGaebiyEEEKYU69evejVq1ex67u6uuLq6mq8v3HjRhISEhg5cmS+eiqVimrVqpksztIUFBREYGAgb/0cx1+xGjKrdTZ3SEIIcU+ZOQa2nEoCYFDrSuYNRjwUKpWKrv7OLDtwi91nU+nexMXcIQkhRInkTTXu4u+MSqUyczSFk5GEQgghxH1atGgRgYGB1KpVK195amoqtWrVonr16vTp04dTp07d9TxZWVkkJyfnuz1MPj4+vNnbD4BNJ5NkGpcQoszbeSaF5EwD3pWsaefnaO5wypySLqdxpwMHDmBlZUXz5s1LLb771fX29Ly9/6ai0999bXshhChLDAaFPf+mAPC4f9mcagwyklAIIYS4L9HR0Wzbto2ffvopX3nDhg1ZunQpjzzyCMnJycydO5cOHTpw+vRp6tWrV+i5ZsyYwdSpUwuUx8dFk5X5vylVOl0OcbHXTduQ2/xcFapXUnMt0cCqPy/zVFPbUnme4ijNdpY10lbLJG0tfSsP5n439vRXczM+ulSfy93Du1TPXxryltMYOXIkAwcOLPZxSUlJjBgxgm7dunHjxo1SjPD+tKjlgIu9msR0PaevZNCyjoO5QxJCiGL5+1om8Sl6nGzVtK5bdn/ckiShEEIIcR+WLl1KpUqV6NevX77ytm3b0rZtW+P9Dh060KJFC+bPn8+8efMKPVdISAjjx4833k9OTqZGjRpUdffCxeV/06niYq+X6sXqcx1uMfPXG/zyj8Kobl5mmwZR2u0sS6StlknaWroux2cTdi0JlQqe61QD90rWD/X5y4OSLqeR59VXX2XYsGFoNJoSjT58WKw0Kjo3cGJLWDK7z6ZIklAIUW78EZ47ivCxBk7YWJXNqcYg042FEEKIElMUhcWLFzN8+HBsbGzuWletVtO6dWsuXLhQZB1bW1tcXFzy3cyhX0tX7K1VnI/J4uSlDLPEIIQQ97L+eCIAHes54iUJQpNZsmQJkZGRfPjhh8Wqb66lMro2yt3lePdZ2bxECFF+7L6dJOxahqcag4wkFEIIIUps7969RERE8OKLL96zrqIohIWF8cgjjzyEyB6Mi72G3s1dWXsskZWHE2SEhhCizMnRK2w8mQjAQNmwxGQuXLjAu+++y59//omVVfEuEc21VEaDSgpWargUn80Pq37hUX9vvLzMv1lYRVhmoCK0EaSdlqYstPPkuRtcjLNDo1Lwd0slLjbNpOcvrI33O8pfkoRCCCEqrNTUVCIiIoz3o6KiCAsLw83NjZo1axISEoJWq2XZsmX5jlu0aBFt2rShSZMmBc45depU2rZtS7169UhOTmbevHmEhYXx1VdflXp7TGFo28qsPZbIjjPJxCV74O4io3SEEGXHxoOXiU/RU8leRRd/Z3OHYxH0ej3Dhg1j6tSp1K9fv9jHmWupDHeghv0VotIqMfnrX0kN+5H58+cTFBRksue4HxVhmYGK0EaQdloac7czNDSU9xYfoVL70aRdOcqfu1Um/74yZRtlurEQQogK6/jx4wQEBBAQEADA+PHjCQgIYPLkyUDu5iRXrlzJd0xSUhLr1q0rchRhYmIir7zyCv7+/vTo0QOtVsu+fft49NFHS7cxJuLvbUfzWvboDLDmWKK5wxFCCKPQ0FDe+Wo7AFcOhPLTimX3OEIUR0pKCsePH+fNN9/EysoKKysrPvroI06fPo2VlRW7du0q9DhzLZWh1Wo5ufU7AOzrdMZgMBAcHIxWq30ozy+EEMWl1WoZPXo09nU6A5B+cW+Z/76SkYRCCCEqrC5duqAoSpGPL126tECZq6sr6enpRR4ze/ZsZs+ebYrwzObZtpUJu5zBqkM3aagOp0F9P3x8fMwdlhCiAtNqtYwNmYbX8I0ApPyzieDgrwkMDJTvpwfk4uLC33//na/s66+/ZteuXaxdu5Y6deqYKbLCRUREkBa5l0qd3sLOuxkaZy/0KdFERkbKe0EIUaZERESgdqmOrVdTFIOe9Mjd6PX6Mv19JSMJhRBCCJFPjybOOGhyuJmmMHTsF/j7+xMaGmrusIQQFVhERASOjfqhUmvIvHYcXeIV44WWKCg1NZWwsDDCwsKA/y2nkTc6PiQkhBEjRgC5G2w1adIk383DwwM7OzuaNGmCo6OjuZpRKD8/P5SMeDKvHQfAyb83Go0GX19fM0cmhBD5+fn54ezfB4CMK4fQp8WX+e8rSRIKIYQQIp+4G9FEH1kOgHPTwTKVSwhhdrXq+OLUuD8AKX+tASjzF1rmdD/LaZQXPj4+zJ8/n/R/fwXAyb8vc+fOK7OjcoQQFVc1L2982g4DIO3sL2g0GubNK9vfV5IkFEIIIUQ+ERERpPy9HsWgw656K2w8G8mIHSGEWf1zyxmNgxv61FjSo/aWiwstc8pbTuO/t7xlNJYuXcqePXuKPH7KlCnGUYhlUVBQEAfWzsLOyoCVizf+jw0yd0hCCFHAkcg0UnS2ONvCytkTCA8PN/smS/ciSUIhhBBC5OPn54eSHkfav9sAcG01SkbsCCHMatWRBABeeLwGW3/ZUi4utETp8q1dnb4t3ADYeCLJzNEIIURBed9NvQMq061rp3Lxw5YkCYUQQgiRT95UrtRTy1AUAw51O/P+Z9+Xi3/YCCEsz7noTE5eysBKDaO61aBTp/JxoSVKX/+WlQDYfiaZ1Ey9eYMRQog7pGTq+SM8BYB+LVzNHE3xSZJQCCGEEAUEBQXx16Hfae2dDUC0c3szRySEqKhWHc4dRditsTMeLtZmjkaUJU1r2FHX3YbMHIXf/k42dzhCCGH0+9/JZOYo1PWwoUl1O3OHU2ySJBRCCCFEoXx8fJg02B+A7WdSiIzNMnNEQoiKJiVTz5aw3Olaz7atbOZoRFmjUqnod3s04QaZciyEKEPyphr3a1EJlUpl5miKT5KEQgghhChS/Wp2BDZ2RlHg+93x5g5HCFHBbDqRREa2gp+nLa3qOJg7HFEGPRXggkYNYZcziIqTH7OEEOZ3OT6bU5czUKugb4CLucMpEUkSCiGEEOKuXulaBYBfTydz5Wa2maMRQlQUiqIYNywZ2qZ8jcQQD4+7izUd6zkBsoGJEKJs2HgyEYAO9RzL3TIZkiQUQgghxF019rHnsfqOGBT4Ye9Nc4cjhKggjkSmExWXjYONmqfK0aLv4uHr1yr3/bH5VBJ6g2LmaIQQFZneoLD55O2pxreXQyhPJEkohBBCiHt67fGqAGw6mcj1xBwzRyOEqAhW3t6w5KkWrjjaaswcjSjLujR0ppKDhthkHQcvpJk7HCFEBXb0YjoxSTpc7NR09XcydzglZtYk4b59++jbty/e3t6oVCo2btxYZN1XX30VlUrFnDlzHlp8QgghhMjVvJYDbXwd0Olh/tbL7N27F61Wa+6whBAWKuzcVXaF5+5WKxuWiHuxsVLRp3nuul8/7Y+WPkoIYRZarZZvt54HoFczF2yty9+4PKuSVE5KSmLDhg38+eefXLp0ifT0dNzd3QkICKBnz560b9++RE+elpZGs2bNGDlyJAMHDiyy3saNGzly5Aje3t4lOr8QQgjLYup+SJTMq12rciTyCptOpfL12BEomQnMnz+foKAgc4cmhLAgoaGhTFp+BtfWo8jUnuDAb0fxs/DvGenfHly/lpVYcTCBvefS+XHis5CTKn2UEOKhCQ0NJXjCu3i/sBW1NdjEHAAGmTusEitWWjM6OpqXX34ZLy8vPvroI9LS0mjevDndunWjevXq7N69m+7du9OoUSNWr15d7Cfv1asXH3/8MQMGDCiyjlar5c033+THH3/E2rp8LfgohBDCNEqrHxIl42ObQFb0aVRWtri0GI7BYCA4OFhGawghTEar1RI87i2cGj8NQMpfayz6e0b6N9NxUW6SHXcOlcYGxwY9pY8SQjw0Wq2W0aNHY+/bDbW1Hdm3LjLjnRfL5fdPsUYSNmvWjBEjRnD06FGaNGlSaJ2MjAw2btzIrFmzuHr1Km+99dYDB2cwGBg+fDhvv/02jRs3LtYxWVlZZGVlGe8nJyc/cBxCCCHMy1z9kMgvMjKSxKOL8Hx6Hs6PDCL59Cr0KTFERkbi4+Nj7vCEEBYgIiICh0b90ThUQZekJf3iHjDoLfZ7Rvo304mIiCA1fAtunRvg1GQQKX+tRa+33PeOEKLsiIiIwGAw4Nx0MABp4VvK7fdPsZKE//zzD+7u7netY29vz7PPPsuzzz5LXFycSYKbOXMmVlZWBAcHF/uYGTNmMHXq1ALl8XHRZGWmmiSuskKnyyEu9rq5w3jopN0Vi7Tbsrh73N+yEebqh0R+fn5+ZF87Qua149hVb0Xldm+Q8McUfH19zR2aEMJC+NTyxbVl7vTQpOOLwaBHo9FY7PeM9G+m4+fnR/r5rVRq9xo2VepiX7s92VcPW+x7RwhRdvj5+WFfqx02VethyE4nNXxTue27ipUkvFfH9aD1C3PixAnmzp3LyZMnUalUxT4uJCSE8ePHG+8nJydTo0YNqrp74eLi8sBxlSVxsdfv+4K7PJN2VyzSbgHm6YdEQT4+PsyfP58JH83H9pklODZ4gld71Ch3v5AKIcqufVfs0Ti4oUu6Ruq/v6LRaJg3b57Ffs9I/2Y6Pj4+zPvyUz5YuRHngOdxbTGCD94eZrHvHSFE2eHj48OjQ6dxKR1Swzej0qWX276rRBuX5NFqtRw4cIDY2FgMBkO+x0oy6u9u/vzzT2JjY6lZs6axTK/XM2HCBObMmcOlS5cKPc7W1hZbW1uTxCCEEKJsehj9kChcUFAQgYGBfLghlkNaO87QCkVRSvSDnhBCFCYty8DifTcBeHeAH75Dt+Dr61suL7Lul/RvDyYoKIjmba8yPDQFW58WNO9a29whCSEqgHPRmVxKd0WtgrljutPmh1fKbd9V4iThkiVLeO2117CxsaFKlSr5LgpUKpXJOq/hw4cTGBiYr6xnz54MHz6ckSNHmuQ5hBBClD8Pqx8SRfPx8eGT4R70/jKSsMsZbD+TQs9HLGu0vhDi4fvp0C0S0vTUrGLN8K61sdLUMXdID5X0b6bRrEEN+ra4zsYTSSzed5O5z1c3d0hCCAsXuv8WAN2bODPgCX8zR/NgSpwknDx5MpMnTyYkJAS1ulibIxcpNTWViIgI4/2oqCjCwsJwc3OjZs2aVKlSJV99a2trqlWrRoMGDR7oeYUQQpRfpuyHxP3zdLVmZKcqfP1HPLO2xdKloRO21vJ6CCHuT2qmniX7ci+y/u9xd6w0FW90svRvpjPysSpsPJHEH+EpXI7PplZVG3OHJISwULHJOfx6OgmAFzpWuUftsq/EvU96ejpDhw41Scd1/PhxAgICCAgIAGD8+PEEBAQwefLkBz63EEIIy2TKfkg8mJGdquDubMW1hBx+PJRg7nCEEOXYj4cSSMrQU8fdhiebVcyRydK/mY6fpy2dGzqhKLD0z5vmDkcIYcF+PJiATg8tatvTtKa9ucN5YCXugV588UXWrFljkifv0qULiqIUuC1durTQ+pcuXWLs2LEmeW4hhBDlkyn7IfFgHGzUjOmZu4j+t7viuZWqM3NEQojyKCVTb0zkvPZ41Qo5ihCkfzO1UZ1yR/RsPJlEfIr0T0II00vLMvDzkdwfykc+Vv5HEcJ9TDeeMWMGffr04bfffuORRx7B2to63+OzZs0yWXBCCCHEf0k/VLY8HeDKjwdvcfZ6Fp9vvsQTPlr8/PzK7WLNQoiHS6vVMu+36yRnOFHXw4ZeTSvmKEKQ/s3UWta2p2kNO/66msmPh24xpoeHuUMSQliYDccTSc40UKuKDV0aOpk7HJMocZJw+vTp/P7778Z1Af+7oK4QQghRmqQfKlvUahVvP+nJqB+usOl0Bt+88waGpCvMnz+foKAgc4cnhCjDQkNDCZ4QgveIjahtobFVOBq1r7nDMhvp30xLpVIxqlMVxv6oZdWhBF7qXBVHW5nKLYQwDZ1eYdmB3LV0gx5zQ622jO/pEn9Lzpo1i8WLF3P27Fn27NnD7t27jbddu3aVRoxCCCGEkSn7oX379tG3b1+8vb1RqVRs3LjxrvX37NmDSqUqcPv333/z1Vu3bh2NGjXC1taWRo0asWHDhpI2s1ypbpdI+sW9qNRWuHWagMFgIDg4GK1Wa+7QhBBllFarZfTo0Tg1G4ra1pnsm5F88/7wCv29IddZpvd4I2dqVbEhOdPAZ6uOV+j3lxDCtNbsv4w2IQcXOxVPBbiaOxyTKXGS0NbWlg4dOpRGLEIIIcQ9mbIfSktLo1mzZixYsKBEx507d47o6GjjrV69esbHDh06xJAhQxg+fDinT59m+PDhDB48mCNHjpgk5rIoIiKChP1zUXRZ2Ndsi2ODXuj1eiIjI80dmhCijIqIiEDl6IlLwPMAJB35Dr1eV6G/N+Q6y/Q0ahUNrM4BsOpoKv6NmxAaGmrmqIQQ5d3SpaF8EHoMgKt/fs/PK5ebOSLTKXGScMyYMcyfP780YhFCCCHuyZT9UK9evfj4448ZMGBAiY7z8PCgWrVqxptGozE+NmfOHLp3705ISAgNGzYkJCSEbt26MWfOHJPEXBb5+flhSNGSePQHACo/Nh5rp6r4+lbcaYNCiLvz9fWlStd3UVvbk3ntBOmRu9BoNBX6e0Ous0xPq9WyeGoQ+vSbWLl4Ye/XXUa6CyEeiFar5e1PF2Pr2QRFl0Xy6Z8t6nulxGsSHj16lF27dvHLL7/QuHHjAgvqrl+/3mTBCSGEEP9VFvqhgIAAMjMzadSoEe+//z5du3Y1Pnbo0CHGjRuXr37Pnj3vmiTMysoiKyvLeD85OdnkMZcmHx8f5s+fT/DYcTjW745N1fp0G7NCNi8RQhTp71su2Ndqj6LP5ubuGWg0GubNm1ehvzdM2b/t27ePzz//nBMnThAdHc2GDRvo169fkfXXr1/PN998Q1hYGFlZWTRu3JgpU6bQs2fP+21OmRAREYE+J4PksFVUbv8GlR59Be35HURGRlbo95oQ4v5duBCBS5v/AyD17BYMGbm7G1vK90qJk4SVKlUq8YgLIYQQwlTM2Q95eXnx3Xff0bJlS7Kysli+fDndunVjz549dOrUCYCYmBg8PT3zHefp6UlMTEyR550xYwZTp04tUB4fF01WZqrxvk6XQ1zsdRO1xrSe7NWdgOY7OfDPDb46o3A2uQqbD1+mXV3rex/8H2W5naYmbbVM0ta7S8lU+GRTCgCDmqlo3fFjatWqhZdXtTL5d3P38H4oz2PK/i1vOY2RI0cycODAe9bft28f3bt3Z/r06VSqVIklS5bQt29fjhw5QkBAgEliMgc/Pz/UajUpp1fh3HQwVq4+uDYbVKFHrAohHkycphZ23h4YcjJJOrYIwKJGwpc4SbhkyZLSiEMIIYQoFnP2Qw0aNDDuOgnQrl07rl69yhdffGFMEkLBXSgVRbnrzpQhISGMHz/eeD85OZkaNWpQ1d0LFxcXY3lc7PWHdrF6P9w9vGnaDFK33mDpn7eYuyebbgHVcbTV3PvgO5T1dpqStNUySVvv7qsN0SSkK9R1t+H9IQ2wsWpaStGVL6bs33r16kWvXr2KXf+/o92nT5/Opk2b2LJlS7lOEhpHugcHk3T0e6o8/h5enUfjUqWauUMTQpRDOr3CsuN6AFL/WoU+Ld7iRsKXeE3CFStWFPnY22+//UDBCCGEEPdS1vqhtm3bcuHCBeP9atWqFRg1GBsbW2B04Z1sbW1xcXHJdyvP3gh0p3pla2KSdMz9Pc7c4QghypATl9JZczQRgCn9vbCxKvHliMUqS/2bwWAgJSUFNze3IutkZWWRnJyc71YWBQUFER4ezsqZL1O9kpoMvTVL9t00d1hCiHJo08kkLsZm42qv4Y+Fo9m6dSvh4eEEBQWZOzSTKfFIwjfffJNKlSrRp0+ffOXjxo1j1apVfP755yYLTgghhPivstYPnTp1Ci8vL+P9du3asWPHjnzrEm7fvp327ds/1LjMycFGzYf9q/Hy4qv8dDiB1t5Z2KRdws/Pz2J+ZRVClFy2TmHqhmgABrauRMs6DmaOqGwpS/3bl19+SVpaGoMHDy6yTnlaKsPGWkWTRn68Yp3D5F/SCf3zJj38sqniZLokdVloZ2mrCG0EaaelMVU7M3MU5m3PXSrj+UetqeykpnJDPwCz/x0La+P9zmgocZJw1apVDB06lM2bNxunVo0ePZr169eze/fu+wpCCCGEKC5T9kOpqalEREQY70dFRREWFoabmxs1a9YkJCQErVbLsmXLgNzpWLVr16Zx48ZkZ2ezYsUK1q1bx7p164znGDNmDJ06dWLmzJk8/fTTbNq0iZ07d7J//34TtL78aF/PiadbuLLpZBJvfH+G6yufR42e+fPnW9SvrUKI4tFqtcz//TqRsU5UcdIwoZeHuUMqc8rKddbKlSuZMmUKmzZtwsOj6NepPC6VMcBdYd3py5y+msHq0xo+7O9174OKqSy1s7RUhDaCtNPSmKqdP+yNJz41Ge9K1rwUWKtMjYQ35WtZ4lY98cQTLFy4kH79+nH8+HFef/11Y8fVsGFDkwQlhBBCFMWU/dDx48cJCAgwrrc0fvx4AgICmDx5MgDR0dFcuXLFWD87O5u33nqLpk2b8thjj7F//35+/fXXfAvNt2/fnlWrVrFkyRKaNm3K0qVLWb16NW3atDFB68uX4QF69Om3sHarS6VHX8RgMBAcHIxWqzV3aEKIhyg0NJRH2vZg89ncjYzauZzD1b5ka5VWBGXhOmv16tW8+OKL/PzzzwQGBt61bnlcKkOlUjH+doJ63fFEouKyzByREKI8SEzX88Oe3GUKRnd3L1MJQlMr8UhCgKFDh5KQkEDHjh1xd3dn7969+Pn5mTo2IYQQolCm6oe6dOmCoihFPr506dJ89ydOnMjEiRPved5BgwYxaNCgEsdjaWK1F7m1dxbuvT7FpeULZFw5Qtb1U0RGRsq0YyEqCK1Wy+jgsbj3+xqVlS0Zlw+x8OtxBA8Il++BQpjzOmvlypWMGjWKlStX0rt374fynObQqo4DnRs6sfffVOZuj2POc9XNHZIQooz7YU88KZkG6lezpXfzsv+DyIMoVpLwzmHkd/Lw8CAgIICvv/7aWDZr1izTRCaEEELcJv1Q+eTn50fmxd2knv0FJ/8+VO0xjRs/P4+vr6+5QxNCPCQRERE4txqFnXdzDFmp3Nw9A71eLz8W3FZa/VtJl9NYuXIlI0aMYO7cubRt29a4AZe9vT2urq7307QybVxPd/48l8qOMyks3XSA7q1qy/tRCFGoU/9eZfmBFEDF+Cc80KhV5g6pVBUrSXjq1KlCy319fUlOTjY+rlJZ9h9LCCGEeUg/VD75+Pgwf/58xkx4F1uvplhXqknXsavx9rb89W+EELnS7Grj2noUADd3T0efEo1Go5EfC24rrf7t+PHjdO3a1Xg/LxkZFBTE0qVLCyyn8e2336LT6XjjjTd44403jOV59S1NvWp2NHaJ4+8kd6atu0Tw8F6yZq4QooDQ0FA+3BCLo38fMq+dIPLQUR5rYNnfE8VKEsqGJEIIIcxJ+qHyKygoiMDAQHadvMwXh+F8ihtrjyXyzKOVzR2aEKKUJaTp+HxXNiqVmrSzm0m/sAONRsO8efNk1NZtpdW/lXQ5jT179pRKHGWVVqvl93mjqPbcGux8WmBX93GCg4MJDAyU96YQAsj9npjwyfd4DPgWgISD8xiz+Rzdu1v294TlrrYohBBCiDLBx8eH4X3bM6Zn7mLxn/5yg8hYWSxeCEumKArvr4smNllHXXcbds55jq1btxIeHi6jtYTZRUREkJMcQ/LJ5QC4dX4LxdqJyMhIM0cmhCgrzvwbiVvgh6hUalLP/kL2jXDjchmWrFhJwtdee42rV68W64SrV6/mxx9/fKCghBBCiDtJP2QZXujoRns/RzJzFMYuv8TOXXtlp2MhLNSPBxPYczYVGysVnz/rg1/t6nTq1MmiR1/cD+nfzMPPzw+1Wk3SsSVk37yIxqEKbp0myDR4IYTRjuveWLtWR5ccza19XwBUiOUyijXd2N3dnSZNmtC+fXueeuopWrVqhbe3N3Z2diQkJBAeHs7+/ftZtWoVPj4+fPfdd6UdtxBCiApE+iHLoFarmP6MF70+O8vFeGtGfv4HSQf6yjpQQlgQrVbL7pOX+OKwGwBv9fKgoZedmaMqu6R/M4+8NXODg4O5+cdHVBu0CMcGvYhMcUXS2EKI3WdT2BaeAygk7JqGkp1WYZbLKFaScNq0aYwePZpFixaxcOFCzpw5k+9xZ2dnAgMD+eGHH+jRo0epBCqEEKLikn7IcmSnxHJl0zu495mFS/Nnybx2XNaBEsJChIaGEjx+Ip7PhGJd2Q0/pwSGtWto7rDKNOnfzCdvzdzIyEh2RtuzLiybqRuj2TS2Lk52GnOHJ4Qwk5upOj5cHw3AC49VYdibK4iMjMTX17dC/Fu1WElCAA8PD0JCQggJCSExMZHLly+TkZFB1apV8fX1lR0lhRBClCrphyxDREQE6VF/khy2Epfmz1K1x0fErBlFZGRkhfiHlxCWSqvVMnr0aNy6T8O6ci10KTfYt2g41184LJ/te5D+zXx8fHzw8fGhdbaBo1cucvVWDl9ui+XD/l7mDk0IYQaKojBlQzQ3U/XU87QluLs7ttbqCtWP3dfGJZUqVaJZs2a0bdsWPz+/++649u3bR9++ffH29kalUrFx40bjYzk5Obzzzjs88sgjODo64u3tzYgRI7h+/fp9PZcQQgjLYap+SDx8eetAJRyYS+a146htHPHo8yXu3nXMHZoQ4gFERETg3HIUjvV7oOh1xG9/n5z0BItf4N3UpH8zD3sbNVMH5CYGfz6ayNGLaWaOSAjxsGm1Wmb+dJRd4alYaeDTId7YWle8vX7N2uK0tDSaNWvGggULCjyWnp7OyZMn+eCDDzh58iTr16/n/PnzPPXUU2aIVAghhBCmkLcOlEYFcdveRZekxcq1OjN3G8jRK+YOTwhxn7RKLSq1fRWAW3tmkHU9rEIs8C4sRxtfR555tBIAk36+yg7ZXEuICiM0NJQmjz5O6MncpQY6uF2tsOvpmjVJ2KtXLz7++GMGDBhQ4DFXV1d27NjB4MGDadCgAW3btmX+/PmcOHGCK1eumCFaIYQQQphCUFAQ4eHh/LJ+JYtfq4+DjZpjF9OZsSXG3KEJIe7D31cz+OKPTABSwn4iNXxzhVngXViWCb08cLLK5nqSwqjPduDv709oaKi5wxJClKLc5TKCces2GbWNI5nXT/HjR0Mq7I8ExV6TsCxISkpCpVJRqVKlIutkZWWRlZVlvJ+cnPwQIhNCCCFESeStAwXwmXUKo5dfY/WRRNztMvHJOkXLVm0kuSBEORCbYmD06mtk6RQ6N3Di3ddf5VJU9wqzwLuwLMk3Y4ja+C7ufWbh3PxZ0iN2yeZaQli4iIgIHB8ZjJ1PCwzZadzcMRW9LqfCrpddbpKEmZmZvPvuuwwbNgwXF5ci682YMYOpU6cWKI+PiyYrM7U0Q3zodLoc4mIr3hqN0u6KRdptWdw9vM0dgiiDuvo7M7anO7N/i2P+7lRiN80j+/pJ5s+fT1BQkLnDE0IUIeLSNcaujCUuzZ56nrZ8NtQbJzsNNWtUN3doQtyXvM21Uv/9FaeGvakSOJnoVc9V2GSBEBWBfdU6VGr3OgAJB+ahS9ZW6OUyykWSMCcnh6FDh2IwGPj666/vWjckJITx48cb7ycnJ1OjRg2qunvdNblYHsXFXq+QF9zS7opF2i1ExfCEbyYfn9uGY4NeVO31KTFrRsnoDSHKsKVLQ5m6NQ0H367o02/xuOMVnOzqmjssIR6IcXOtfV9iV7011pVrUbndGxU2WSCEpdMbFOb9aUBtbUfm1aOknllf4ZfLKHGSMCAgoNBdtlQqFXZ2dvj5+fHCCy/QtWtXkwSYk5PD4MGDiYqKYteuXfdM9Nna2mJra2uS5xZCCFH2POx+SDwckZGRxP/xMVauNbCt1gTPpxcQs+5lGb0hRBl07do1Jq+OwLn5MBR9NnFb3+aDpf8w8MlO8nl9ANK/mV/e5lrBwcHc3PUJnk/Nxbn5UGKyKyPvbCEsz/IDtzh1OQNHWzXLJz1O6itbK/xyGSXeuOSJJ57g4sWLODo60rVrV7p06YKTkxORkZG0bt2a6OhoAgMD2bRp0wMHl5cgvHDhAjt37qRKlSoPfE4hhBDl28Psh8TD4+fnh1rREfvLeHISLmPl4oVnvwVU8a5j7tCEEP8x//donJsPA+DmHx+TFf0Xer2eyMhIM0dWvkn/Vjbkba61/pv36elvDaiYtPY66dkGc4cmhDChqLgs5m2PA+CtJz1o4V+DTp3kx64SjySMj49nwoQJfPDBB/nKP/74Yy5fvsz27dv58MMPmTZtGk8//fRdz5WamkpERITxflRUFGFhYbi5ueHt7c2gQYM4efIkv/zyC3q9npiY3F0P3dzcsLGxKWnoQgghLIAp+yFRdtw5euPGxjeoNugHrCvXZso2HUte1uNkpzF3iEIIckdd/BLhBMCtvV+Qdm4bQIVev8lUpH8rO/I21wp4VM/pORe5eiuHOb/F8t5T1cwdmhDCBPQGhffXRpOlU2jv58gzrSuZO6Qyo8QjCX/++WeeffbZAuVDhw7l559/BuDZZ5/l3Llz9zzX8ePHCQgIICAgAIDx48cTEBDA5MmTuXbtGps3b+batWs0b94cLy8v4+3gwYMlDVsIIYSFMGU/JMqWvNEbK777gtDXfKnsqCH8eiZvLrtGZo6M4BDC3DYcT+TTX24A0LHqNdL/WQtQ4ddvMhXp38oeZzsNHw3wAuDHQwlsPXKZvXv3otVqzRyZEOJ+abVapiw7RtiV3GnGUwd6FbrUQ0VV4pGEdnZ2HDx4ED8/v3zlBw8exM7ODgCDwVCsdQG7dOmCoihFPn63x4QQQlRMpuyHRNnj4+ODjXUb3D28+a5yBiO/v8KxqHReXxzJ8/Wv06C+nyQihHjItFotP++/yuLTrgAEdXTj7ScbMnloOCdPHKVFy0flc2kC0r+VTR3qO/HMo5VYczSRccsiuP7TUFT6LObPn09QUJC5wxNClEBoaCjjPviCakOWo7KCjpUj8a7UwNxhlSklThKOHj2a1157jRMnTtC6dWtUKhVHjx7lhx9+4L333gPg999/N44OFEIIIUxJ+qGKo5GPPV8F1eDF76M4cknHH7+dIGHX08yfN1cuzIR4SEJDQ3n7sxVU7f0lKg084hrL2082RKVS5Uvqiwcn/VvZ9VxzPSt3XsfKxZvKj43n1q5PCA4OJjAwUBLkQpQTWq2W4HFv4zHwe1RWtmRcPsSir8cxYVC4fI7vUOIk4fvvv0+dOnVYsGABy5cvB6BBgwZ8//33DBuWu4Dxa6+9xv/93/+ZNlIhhBAC6YcqGi+bBKK3vEXVXp/h1LAXoBA8ZpxcmAnxEGi1Wt76dClVe89CpbEm7cIOtu38kOvDz8jnrxRI/1Z2Xb8Syc0/puHR7yucG/cjO/YsqWfWExkZKZ8FIcqJCxciqPz4B9hUrY8+7SY3/5hm3HRLPsf/U+IkIcBzzz3Hc889V+Tj9vb29x2QEEIIcS/SD1UcERERpEf9Sfz296na42OcGj6JysqWcxfkH3RClLYNB6/g3md27oiLSweI3z4ZDDq5oCpF0r+VTX5+fmRfP0nioW+o3P4N3Dq9jSHpqmzWI0Q5cjSxBo71PFD0OcRtm4g+LU423SrEfSUJAbKzs4mNjcVgyL+QeM2aNR84KCGEEOJepB+qGPz8/FCr1aRH/EGcPgf3XjNw9OvGinNWdOhgwNa6xHuwCSGK4fe/k/k+rBIqK0i/uJe4394Dg04uqB4C6d/KHh8fH+bPn09wcDA2VXxxbPAENQfORbF3N3doQohi2BWeQuiRLAAS9n1GVvRfsulWEUr8L+sLFy7w2GOPYW9vT61atahTpw516tShdu3a1KlTpzRiFEIIIYykH6pY8i7MNBoNGVH7iN/6NlYqA0cu6Xhj2TUiLl2TnSaFMLGNJxJ5a6UWnQEaOt/k1vb3QJ8tF1SlTPq3si0oKIjw8HB+GN2Seu5qMvTWvLnsGmlZenOHJoS4i6h4Pe+svg7AsHaVOb5uJlu3biU8PFzWuC5EiUcSvvDCC1hZWfHLL7/g5SVbRQshhHi4TNkP7du3j88//5wTJ04QHR3Nhg0b6NevX5H1169fzzfffENYWBhZWVk0btyYKVOm0LNnT2OdpUuXMnLkyALHZmRkGHenFCUTFBREYGAgkZGR+Pr6os2qxOuhVzkUkUbPfaeI3TwWlT5TdpoU4gFptVoW7dKyOtwZgIGtXPmwf0Nigv42fv4kQVh65Dqr7PPx8cHHx4dHWuQw+KsoLtzI4t2frzOxq4pTJ47SslUb+YwIUYacjbzGhJ9vkZ5tQ+u6Dkzs7Ym1RiWf07socZIwLCyMEydO0LBhw9KIRwghhLgrU/ZDaWlpNGvWjJEjRzJw4MB71t+3bx/du3dn+vTpVKpUiSVLltC3b1+OHDmSb7dJFxcXzp07l+9YSRA+mLwLMwAfYEZfe4J/isXOOwDPfl8Ru2Wc7DQpxAMIDQ3lvcVHqNR+NAAtK8cwpX9D1GpVvs+fKD1ynVV+eLpaM//5GgR9f5ld4alsWLaYxEPfoFar5QcrIcqIJUtD+XiHgl3NNuiSr9NGHYO1pug1X0WuEk83btSoEfHx8aURixBCCHFPpuyHevXqxccff8yAAQOKVX/OnDlMnDiR1q1bU69ePaZPn069evXYsmVLvnoqlYpq1arluwnTskm7xI0N/4c+IxHbak2oNngJKpfqREZGmjs0IcqdK1ev8cHqKGOCMOnYYjZ90o/o6Otmjqxikeus8qVpTXvGdrEFwLXVKBwb9MJgMBAcHCxLYAhhZteuXWPK+uvY1WyDISeD2F8m8O741+WzWQwlThLOnDmTiRMnsmfPHm7evElycnK+mxBCCFGaylI/ZDAYSElJwc3NLV95amoqtWrVonr16vTp04dTp0491LgqAj8/P3Q3zxOz7iVykrRYu1bHa9BiUm1rmTs0IcqVtCw9b62Jx7nZEAASDswj8fA36PV6Sbo/ZKbs3/bt20ffvn3x9vZGpVKxcePGex6zd+9eWrZsiZ2dHXXr1mXhwoX32ZKKo7bVFZJOLAOgSrfJ2NfuIJ8dIcqAL7fG4NS4H4pBT/z2yeTcjJDPZjGVeLpxYGAgAN26dctXrigKKpUKvV4WbhVCCFF6ylI/9OWXX5KWlsbgwYONZQ0bNmTp0qU88sgjJCcnM3fuXDp06MDp06epV69eoefJysoiKyvLeF9+dLu3O3eajPn5BTz6folttaaEbEkn2yqR1tXSiIiIwM/PT6ZJClGE64k5vBF6lfNxthh0mdzc/iHpkbsAZBdjMzBl/1bS5TSioqJ48sknefnll1mxYgUHDhzg9ddfx93dvVjHV1R+fn4kH/4ajWNVnBo+SdVenxL/yzj57AhhRov33WR7lCMAt3bPIOPiHkD6teIqcZJw9+7dpRGHEEIIUSxlpR9auXIlU6ZMYdOmTXh4eBjL27ZtS9u2bY33O3ToQIsWLZg/fz7z5s0r9FwzZsxg6tSpBcrj46LJykw13tfpcoiLtfzpf8Vt55O9uhOwbzeXL1/Gy6cWS/+yZvf5HCatjSbp2A8kHv4WtVrN9E+mMWTIMw8h8pKrKK8pSFvLkujoGP48E8OPEbVJylRR2UFFV4cw5l3aA4BGo+aTjz/Cxlp1z3aU9baagruH90N5HlP2b7169aJXr17Frr9w4UJq1qzJnDlzAPD39+f48eN88cUXkiS8i9wfrOYRPHYcahsnHOp2ovqABSQobsjPU0I8fOuOJfLltlgAOrtf4cdzvwC5CcJ58+bJD8fFUOIkYefOnUsjDiGEEKJYykI/tHr1al588UXWrFljHPlRFLVaTevWrblw4UKRdUJCQhg/frzxfnJyMjVq1KCquxcuLi7G8rjY6w/tYtWcStJOdw9vmjZrAUCLAIVP1l9k1YlsXFu/hJVrDW7+8TGT3p9M/wGDyuQ/DCvKawrS1rIiNDSUibPX4xY4BbW1CnfbdH4a3RTvSg15bWiPEu9iXJbbWt6Ys387dOgQPXr0yFfWs2dPFi1aRE5ODtbW1gWOkVHwuYKCgggMDOTIsWOsvabhtBZeXXKVL/rZkX0rSka0C/EQaLVa1uy/yqLTrgCM6lSFCb38mTQknJMnjtKi5aPyOSymYiUJ//rrL5o0aYJareavv/66a92mTZuaJDAhhBAiT1nqh1auXMmoUaNYuXIlvXv3vmd9RVEICwvjkUceKbKOra0ttra2pgyzQlKrVXRyv8ZXf3yLW5cQHOv3xNqtLnFbJxIZGSn/OBQV3pWr13j/p3+p2utTANIv7Sdsx2SUl49DJR/ZxdgMykr/FhMTg6enZ74yT09PdDod8fHxeHl5FThGRsH/j421inaPBtCylRXj1qZx7oaeF76LImbtSyhpN8r0iPaSqAivJUg7y5vVq9cw9ZvNuPeZhUoD/g7XGN7ChbjY69hYq2jVqgVWVvceGV+eFfZa3u8PeMVKEjZv3pyYmBg8PDxo3rw5KpUKRVEK1JM1CYUQQpSG0uqHUlNTiYiIMN6PiooiLCwMNzc3atasSUhICFqtlmXLchclX7lyJSNGjGDu3Lm0bduWmJgYAOzt7XF1zf3lcurUqbRt25Z69eqRnJzMvHnzCAsL46uvvnqQP4EoJj8/P9L//YWcxGu4PzEdm6r18BqyjFi1x70PFsKCxSbnMHrlTVxajAAg+dSPJByYD4pekuhmVJaus1QqVb77eXH8tzyPjILPLy72Ol4e3nzc5yr9Pv8La7e6ePZbwI31r5bpEe0lUZFeS2ln+aDVapm6YB1V+8xGpbEh7cJOduycTM6oM/j45LbNEtp5L6ZsY7GShFFRUbi7uxv/XwghhHiYSqsfOn78OF27djXez7vYCQoKYunSpURHR3PlyhXj499++y06nY433niDN954w1ieVx8gMTGRV155hZiYGFxdXQkICGDfvn08+uijJotbFO3ODU2iVz+Pe69PsfVqxoe/ZqBNi6V/o2yiLkbK9C9RIWi1WiIiIkixq81nO7O4mWqDITuNmzs/kg1Kyoiycp1VrVo14w9feWJjY7GysqJKlSqFHiOj4AsXp73IjY1vUm3g91hXqonngO+4sfENScYLUQo2H7pC1b5zUVvbkXH5EPHbJ4MhRz5vD6BYScJatWoV+v9CCCHEw1Ba/VCXLl0KHbGRJy/xl2fPnj33POfs2bOZPXv2A0YmHkTe+lCRkZHUql2Xn05bseJgAt/tucm85UeI++19yE5m/vz5BAUFmTtcIUpFaGgoo0cH49RiBJXavIpKraF+NVs6OZxmyg97AVnIvSwoK9dZ7dq1Y8uWLfnKtm/fTqtWrQpdj1AUzc/PDyXjJjc2vo5Hv6+wrlSDaoO+x8attrlDE8Ki7ApP4dtTlVBbQ3rUn8RtexcMOfLj1wNSl/SAGTNmsHjx4gLlixcvZubMmSYJSgghhCiK9EOiOHx8fOjUqRO1alYnpG81QnrYY8hJx65GG7yGrsDKozHBwcFotVpzhyqEyWm1Wsa8M4WqfWZTud3rqNQa0s5u5st+Nox7eSjh4eFs3bqV8PBwSZSXIabs31JTUwkLCyMsLAz433IaeaPjQ0JCGDFihLH+a6+9xuXLlxk/fjxnz55l8eLFLFq0iLfeeuv+G1RB5Y1oV9JucGPdK+QkXMLKyZOJmzI5F51p7vCEsAjb/kpm7I/XyDFAA+db3Po9BPTZ8uOXCZQ4Sfjtt9/SsGHDAuWNGzdm4cKFJglKCCGEKIr0Q+J+1FBfJubnkbkXa86eVBvwHQ5NniEiItLcoQlhcqv/vEq1oT9hX6sdBl0m8Ts/In7nNK5dvgj8L4kuF1Fliyn7t+PHjxMQEEBAQACQu5xGQEAAkydPBiiwnEadOnXYunUre/bsoXnz5kybNo158+YxcODAB2hRxRUUFER4eDhb1oSy4a0mNPSy5Waqnhe+u8zOE5fZu3ev/EglxH3QarV8+uMRJq7SojfAUwGu/PxOe8LP/CU/fplIsaYb3ykmJqbQ3a3c3d2Jjo42SVBCCCFEUaQfEvfDz88PfeIlolcHUaXb+zjW645bpwn8dMGKlm30JN2MISIiQtYqFOVS3tqDHtV9WXxMxdbTrmjsISv2LDe3f0hOQpRMvyoHTNm/lXQ5DYDOnTtz8uTJEj2PKNqdu4UveVnP/y29StiVDEb/FEfsLx+QEx0my14IUQKhoaGEfLuXyp3fBqBZpVg+GdQQtVqV7/MmHkyJRxLWqFGDAwcOFCg/cOAA3t6WvWOMEEII85N+SNyPvOlfakMW8b+9R8K+L1BjYH+kjiem/0XTjn3p3bs3/v7+hIaGmjtcIYotNDQUf39/Bv7fNPrNiWTr6WQ0amhfRUvc+peMCUKZflX2Sf9muVzsNUx9worMa8dQ2zji+dQ87Hy7ybIXQhTT1WvXeH/leWOCMPnUT/w6/Smio6+bOTLLU+KRhC+99BJjx44lJyeHxx9/HIA//viDiRMnMmHCBJMHKIQQQtxJ+iFxv+7c0MTX15ebejeCl18mLtUOj0GLSPhzNqlnNhAcHExgYKAkVESZp9VqGfPWJCp1fhfnJv0ByEm4xLxXGtOtRSBTnv3b+H6X93PZJ/2bZdNeieTG5rG4P/EJDnW74P7EdBIPLyQiQnZhFeJu0rMNvLMmHpeA5wFIPLyQpGOLAGQX41JQ4iThxIkTuXXrFq+//jrZ2dkA2NnZ8c477xASElKic+3bt4/PP/+cEydOEB0dzYYNG+jXr5/xcUVRmDp1Kt999x0JCQm0adOGr776isaNG5c0bCGEEBbClP2QqHjunI7iA7z9aCyjl1zAvlZ7qnQNwbFed27u+kT+0SnKPEVRWLH3GtWGrUbj4AZActhKEg99hfWwDUBNmX5Vzkj/Ztn8/PxQKzritr5D5Q7BuAQ8R6W2r7H2ojVt2xuwtS7xJD8hLFbeMhqVq9Xl4x06/om1RdFnE79jKukXtgPIMhqlpMTfRCqVipkzZxIXF8fhw4c5ffo0t27dMi6CWxJpaWk0a9aMBQsWFPr4Z599xqxZs1iwYAHHjh2jWrVqdO/enZSUlBI/lxBCCMtgyn5IiOaNfIn/ZTy39s3CkJOBXfVWeA9bycmk6uj0ClqtVhaYF2VG3vvxwF9XCPruMsv+dkHj4Eb2zYvErHuFhD9noVZ0ctFUTkn/Ztnylr3QqFUk7J9Dwp5PUaGw63wOLy66wj8RV6W/EYL/LaPRL2gsz8yP5B9tJpUdNTxXJ4Ksi38AyDIapajEIwnzODk50bp16wd68l69etGrV69CH1MUhTlz5jBp0iQGDBgA5L5ZPD09+emnn3j11Vcf6LmFEEKUb6boh4TIvWibR3BwMBlRe6ny+CTsajzK9wez2HzyBKeXvkpW3DnUarUsMC/MKjQ0lODxE3FuORKX5s+h0lhhb63i0UqX+XHhcPQ52XLRZCGkf7Nc/1324kpGJcb9eI1TlzMYMEtL7JZx6JMuS38jKiytVsvo0aOxrdmBqj0/Rm3jQE7CJWYPb0LrRgN54am2soxGKbuvJOGxY8dYs2YNV65cMQ6Fz7N+/XqTBBYVFUVMTAw9evQwltna2tK5c2cOHjwoSUIhhKjAHkY/JCqOOy/a6taty7EYJz79JZobmY54PrOU5LAfSTq2SNYqFGZz5eo13v16B9WGrcHKyR2AjIt7WT6lNy38ezJpiKw9aCmkf7N8/132YvZAe174+hxWrtWpNngpt/Z+Lv2NqLDOnY/Apc1ruLYaCUDG1WPEb3uHjGGrgRqyjMZDUOLpxqtWraJDhw6Eh4ezYcMGcnJyCA8PZ9euXbi6upossJiYGAA8PT3zlXt6ehofK0xWVhbJycn5bkIIISzHw+qHRMXi4+NDp06dqF69Ov1bVWJSu3jSIv5ApbHCtWUQ3s+twbZOFyIiIs0dqqgA8qYVX7t2jT1nUxi1LAG3xydh5eROTpKW2C3jif31LVLjooD/vX/lwql8k/6tYsq+GUX0zy+QcfUYahsHqnb/kMrdpnDm34vmDk2Ihyo+RccP/3gZE4TJp1cTu3k0Kl26LKPxEJV4JOH06dOZPXs2b7zxBs7OzsydO5c6derw6quv4uXlZfIAVSpVvvuKohQou9OMGTOYOnVqgfL4uGiyMlNNHp856XQ5xMVWvC2/pd0Vi7Tbsrh7eD/wOR52PyQqppZN6nLr996kne1A5U4TsHb1wb3Xp3x/RoON2xWyb0Xh5+cnSRlhcqGhoYwePRqrqg2o3GEMdtVbAlboM5NIOrqIlL/XgiFHFmy3QNK/VUx+fn6QnULspjdxaTmCSm1exbHBE3x2VEW1ehlU1dwiIiJC+hxhkfI2KMlwqM2nO7KJS9FjrdIT8/uHpJ77XZbRMIMSJwkjIyPp3bs3kDv9Ny0tDZVKxbhx43j88ccLTdDdj2rVqgG5Iwrv7BRjY2MLjC68U0hICOPHjzfeT05OpkaNGlR198LFxcUksZUVcbHXTXLBXd5IuysWabf4r4fVD4mKLW+B+eDgYKJ/PIprqyDc2rzIqWvw4vIkksN+I+VkKPO+nCHrRgmT0Wq1jJ86H7eeM3D0exwARZfFkEedqZYSwcTFP4NBLxdNFkr6t4rpzv4m+fhScq6fpN7Qr4lJtuW5b6JIOPQNSSdCUauQtQqFRcn7Ucyp2TAqtX8DldoKXw8bZj9XHbs35skyGmZS4unGbm5uxt2FfXx8OHPmDACJiYmkp6ebLLA6depQrVo1duzYYSzLzs5m7969tG/fvsjjbG1tcXFxyXcTQghhOR5WPyREUFAQ4eHh/LplA0eWTWDx8y5kRO27PQV5BN4jNvH+in8Ij7hq7lBFOZY3tXjnicuMXx1HtSHLcfR7HEUxkHr2F7TLB9LVU8uro54nPDycrVu3Eh4eLokCCyT9W8WV199s3bqVsN1r+P29R+hSzwoFFZXavY7n0wtQ2VclODhYdj8WFkGr1TJm4odUefILKnccg0ptRfr535nVzxZfD1tZRsOMSjyS8LHHHmPHjh088sgjDB48mDFjxrBr1y527NhBt27dSnSu1NRUIiIijPejoqIICwvDzc2NmjVrMnbsWKZPn069evWoV68e06dPx8HBgWHDhpU0bCGEEBbClP2QEPdy5wLZERF7if1lAva1O1Kp/ZvYVPHFpdVInluawrD2N+jpl01C9EWZEiaKLTQ0lAnTF+HSaiT2NT0AWxSDnvQLO0g6tpichKh804plwXbLJv1bxfbfz/czda+z9utvcOv8NnY1WuM17Cdu7vqEyMhI+R4Q5d7GQ1eoNnQFGocqKLosbv05m9Qz67g+pgV+daqbO7wKrcRJwgULFpCZmQnkTu21trZm//79DBgwgA8++KBE5zp+/Dhdu3Y13s+bJhwUFMTSpUuZOHEiGRkZvP766yQkJNCmTRu2b9+Os7NzScMWQghhIUzZDwlREn5+fqjVajIu7Sfj0gHs63amUutR4OHP0j9vsWR3Fqnh+0j963VmfzxRRnqJu9p+/DIf7VThOeBbABS9jvTz2xjZsRKzFk5Br5dpxRWN9G/iTvXq+ZFxfivR0aep2vNjbD0b4fHkZ2y5Yo33pWtoL0fKj1KiXNFqtYSfi2BXjA+b/66ExgGy4y8Qv/0Dcm5Gylq7ZYRKURTF3EGUpuTkZFxdXUlKSrK4qccVdc0yaXfFIu0WFVFRfVdFeV+U5XaGhoYSHBxsTODMnTuPSvW78Na3h7D1bgaAohjIvHyQuWN60KdNTa5fv17kovNlua2mVtHbmrc4u965DqtPw/7zaQAo+hxSwzeTdCIUfUo0W7duxdfXt9ysxVSRXldxd9J3mb6dxj5HUVG53f/h0mI4oCIn4RJxv01Cfyvioa5TKK+lZXmY7QwNDWX81Hm4BX6ETZW6ALSsHMOmTwejz84w/ihWGu/livB6mrKNJR5JCKDX69mwYQNnz55FpVLh7+/P008/jZXVfZ1OCCGEKBHph4S5BAUFERgYmC+Bs3fvXmLWvYStTwtcAp7Hoc5j2NfuyLub0vli20nO/z6X1H9/RWXIkUXnK6jQ0FDGT5mHy6Mv41DXAwC1CpLPbCTx2CL0KTEAxlEUMq244pL+Tdzpv33OqWs6Jqy4hHXl2ngNXkrSiWWMGf82gYGB8p0hyqzzUdd4/6d/8Ry0BJXGGl1aPAm7pjHj9yXMeC6s3PwoVlGUuLc5c+YMTz/9NDExMTRo0ACA8+fP4+7uzubNm3nkkUdMHqQQQgiRR/ohYW7/TeDkTUPO0p4kTnsSq0o1cW02lCoBg4jPdsCtawiu7V4n/fx2Jnz8Dd26daN69epotVpOHD9Cy1Zt5B/GFuTO19Xd05t1B3KnFVcbugIgd83B87+x5pMhhB/zJnhvHIBMLRbSv4lC/Xdt3OiVw6jy+CQcfLtS6dEXcWr4JGsPXCX4GW9UKpWZoxXifyPn69T15WiMI1/8koRLixEApEfu5uau6RgyE4mMjJTNScqgEicJX3rpJRo3bszx48epXLkyAAkJCbzwwgu88sorHDp0yORBCiGEEHmkHxJljY+PD/PnzzdOQ1ZStHw0uBYePvGMmrICl6aDsXL1wbnpMzg3fYagpQk0cPiHnz77P3SpsajVahlhaCFCQ0MZPXo0KkcPnJv0x7vN86TpbbCr3jo3ORixk8Sj36NLuEzGuEcLHZkqKi7p38S9+Pn5QXYKcVsnYu/bFbeO47By8eK7U3Am5Sqj2kD2zShZq1CYTV4/aF2tOW6dxmPj3gBQk5NwmYT9s8m4dABA1h8sw0qcJDx9+nS+jgugcuXKfPLJJ7Ru3dqkwQkhhBD/Jf2QKIsKS/ZotVrS/lpFyulV2FVvjZN/Hxx8uxCTZkdMWk28gzaTefUo6RE7Gfv2+8bpYnm/wMtFXtn239cp8tI1Js7dRJXeX2Jfqz0qlZo0PbjaKVz5cwkpZ9ajT70BIDsWi0JJ/ybu5c4fpTIid3Pj2hEGhfzEiaTqHIxI48A5Hcmnfyf15HPM/eIT+fFJPFRarZax732CW49PcKwXCIAhK4XXu1XFMS6WcSsPAzJyvqwrcZKwQYMG3Lhxg8aNG+crj42Nzf1lQwghhChF0g+Jsuq/yZ47L+Yyrx4h5/px3uvzFfE29Vi0IxI77wDsa7XDvlY7FL2OMaviqWl1msUfv4IuI1FGGJZheSMlDIoKh5qP0uX5yUSkuVG15yfGOhlXj5L691oWzH2bK7WbEBz8HSAXR6Jo0r+J4ijsR6lj4VcZ8tFW7Ot0wrXFcJwb92dS6I+079SNenWqmztkUQFk5hiY+9t1qg1bjdrKDsWgJ/XMBhKPLKRZv5V06j2Cnt27ycj5cqDEScLp06cTHBzMlClTaNu2LQCHDx/mo48+YubMmSQnJxvrWtpuwkIIIcxP+iFRnhQ1wvDT17ujdvbGoV53HP0CsXGvz9l4OEsdvEdtI1N7koxLBxg/+ct8axjKCMOH779/94uXr/H25z/i+tgEHP26oXGowj+3v3Z0SVrSzv9G6r9b0SVeQaPRUL/eQrp26STTisU9Sf8miuu/P0qlx10k9pcJ2NVqT+X2b2JTtR6ubV7luSVJ/F+gPZ1rZXD1UqT0H8KktFotFy5EEKOqxaLDOq4nOqG2gsxrx7m170tybkbIyPlySKUoilKSA9Rq9f8Ovr0wat4p7ryvUqnQ6/WmivO+JScn4+rqSlJSksV1phVhK+/CSLsrFmm3+K/y1g/dj6L6roryvqgI7QwNDTWuYajRaJjyxbfEanxZe1B7e/2e/6lir6embTzbV8wk4+oxVIbsfCMMy0vysDy+rnkjBtVO1XCo3YGWT77C1QxXcgz/2xxAn5FA+oWdTH0lEFXCBcaM+d/rOm/ePIsfCVoeX9eyqrz3b9J3ma+dWq0Wf39/DAYDoMKhXiCV2ryKdeVaAOjT4kg8tpj0s5uZP3f2fX8vyWtpWR6knaGhoYyfMhfXDmOwr/EoANVcrXjU8Sxfvz+8TPWDFeH1NGUbSzyScPfu3SZ5YiGEEOJ+SD8kLEHeCMOTJ47SouWjxhGGX43thtrZG/vaHbGv1R676i25mWHDzQxP3PvMQtHnkHXjDO8vP0nVhpeJCd/LuDFvYjAYZHryA7oz2apx8mD7CS2T112n2rCfjRfaF9Ny6+pSbpBx+QAZUfvIuHIYjQp6fPciPj7t6d49/+sqRHFJ/ybu13830Mq6uIuQMf1QfBowc+NFrFy8qdLlHVwDnufdrxbR9fFu1Kwh05BF8fz3x8jfj11m6m/ZVBv6IwCKLouUU8vZ/MM4fGs/xZsDwmXkfDlW4iRh586dSyMOIYQQolikHxKWwsfHBxvrNsZffu+8yEs5vYr0M2v4Ys4CMp38+Dx0J/a1OmDl4oWddwB23gFM3JiOomuK+9NfkXn9NNk3zjB24mTZAOUe/vt30RsUZv+whi8XbcKmWlPsfLKxcs2d1unUuB8AikFHVvRfZFw6wJfvPEdW3GXGLP8s30iJvL/xf19XIYpL+jfxIApb3mLv3r1olw/EqXE/XFuNwsrVB7dukxkZmsDEp11oVDmZi5EyDVkUzbgGr0HBoU5Hmj8zFW2GM/a1O6IoBtIj/iDx4AJ0ydfRXnka39rVZVpxOVfiJOFvv/2Gk5MTHTt2BOCrr77i+++/p1GjRnz11Vf5duMSQgghTM2U/dC+ffv4/PPPOXHiBNHR0WzYsIF+/frd9Zi9e/cyfvx4/vnnH7y9vZk4cSKvvfZavjrr1q3jgw8+MP5D/ZNPPqF///4lbqslSE5OIiM93Xjf3sEBoECZi4ursW5CQjwGg6pAeVH1H2b5vWIvafl/29q/fz9atAjg2rVrVK9enVq1apGSksrMcQvR/7sUvWM11B5NoUpj3Op3IgVb7Kq3wq56K+NzDFuUQFVNJP8c2Ig+8RKqtGjeCX6JF198keTkJC5fvszVq1epUaMGtWrVMnmbHubrerfY/9tOFxdXvv1hCV9+sxycqqOu0oBazbsRr3MlM+cR3B5/xHgexaCnbhX4d/861An/Yoj/B1VOGpqsLDo1n4SzcxNatsz/OgEmaevDej1K43U1VewVlVxniQf13+SMn58fagyk/r2WtLNbcH7kGVxavUAMroz/SUt23L8kHltM1qX9zJ83R0aii3y0Wi3B497CvkFvXJoPw6aqH9oMsFIrJP69iaQTy9AlXQXIt/agKN9KnCR8++23mTlzJgB///0348ePZ8KECezatYvx48ezZMkSkwcphBBC5DFlP5SWlkazZs0YOXIkAwcOvGf9qKgonnzySV5++WVWrFjBgQMHeP3113F3dzcef+jQIYYMGcK0adPo378/GzZsYPDgwezfv582bdrcX6NNwFwJtR++/ybf2llqtbrAeloajYYhQ59n9aoVZbr8Ycd+5u9TxvIxwa+TXxKB3fWs/nUn8Uo14nRuxOkqk2RwIT5DQzzuaJq9jOZ27bkROn6e8Q/qjBic1alYqfSojl1AzXk0akDRo0JBhYJGraJV60c5cfwIGPJiVFCr1bRp056jRw7eXvcKVLfL27XrwOHD+1EM+f9ebds9xqFD+43188rbtet4z3JFyV2DTaXW8Gib9hw5cgi9QbkdjQpUKkCFwaDcjlyFSqWmafMWnDx5EgUVOqxIPRxDqiGZbGs3kjLbYt2jrfE5r2Xk/teKHKpaJVJVc4tq1vG4KTdo1bgp4VgBTW7f8ij53td3vk7l7T1mynJTxf7Sy/8HVMxko1xnCVPLNw1Zl0XaXyuZMrIjNyu1Y9mBBGzcG+Lx5GfoUm8wKXQjLdo9ziP1a8hI9Aos77V3cK/L8j9j8QragsYu9/vUkJ1Kyt/r+fqt3iTU9yZ473WAAiPqRflW4iRhVFQUjRo1AnJHSvTt25fp06dz8uRJnnzySZMHKIQQQtzJlP1Qr1696NWrV7HrL1y4kJo1azJnzhwA/P39OX78OF988YUxSThnzhy6d+9OSEgIACEhIezdu5c5c+awcuXKEsVnKsnJSQWSdQ8jkfB0v4EFFte/MymUR6/Xk5SYUKBuWSs3Z+yFsbWxoopNBlWIooFtFABZBmusPZrw99V04vWVSdI7k2pwRI8V15IBijcF9uCeLKB5gfI//8gEWhQo37sjE2hVoHz39sLL95SwfN8fmUDAvcIG4OjBbPIn9W67/ae1JgcXTSpVNQlUtUqggYcGXeJF1Kr81V1dC08yWep7rKzEHhcby6ZN60o12VhWmfo66+uvv+bzzz8nOjqaxo0bM2fOHB577LEi6//444989tlnXLhwAVdXV5544gm++OILqlSpct9tEuZX1DTkGaHDcGk+DKfG/bBy8sS1zasMW5pCfaf97At9jwztSVnrtoJZsjSUd75chVOTgdjVqopK5YDGDnRJWlLOrCPlzAbU+gxaNn4Tnx4dC7yvhGUocZLQxsaG9Nud7c6dOxkxYgQAbm5uJCcnmzY6IYQQ4j/M2Q8dOnSIHj165Cvr2bMnixYtIicnB2traw4dOsS4ceMK1MlLLBYmKyuLrKws4/0HaUdhI2oy0tPNkkjIysy873aI+2OrzqFdfSfU8aeMZQZFRarBAbVLTa7ezLqdNFRjUFQoqDHcHoVnUNTc3kcVJ2dXklOSc0fs3R7RpwCVK1cmISHh9v3/lVet6k5cfDy5YwshL99WpUoVbt6MLxBnlSpVjeV35uaqVq1KfHxeuWL8b+XKlUlMvGW8r/rP48ZylYK9nR3Zmem5oxxVBpzU6Tip06hZ1YGcxMvYqHJQ3fGkbRo/xqGDFwvE6CZTO80iKyvTJN8/RSUbX3r5/8psotCU/dvq1asZO3YsX3/9NR06dODbb7+lV69ehIeHU7NmzQL19+/fz4gRI5g9ezZ9+/ZFq9Xy2muv8dJLL7Fhw4YHb5wwq8KmIZOdQuLhb0g8+gOOft1wbvYMttWa8m9KFTwGfEtO4lXSzm1j/AefExgYCMCJ40do2aqNJIQsgFarNb6eVk4eLN19lcXHa+DeZ5axTublQzzVzJbF37yJXpdT6Bq88l6wPCVOEnbs2JHx48fToUMHjh49yurVqwE4f/481avLDklCCCFKlzn7oZiYGDw9PfOVeXp6otPpiI+Px8vLq8g6MTExRZ53xowZTJ06tUB5fFw0WZmpxvs6XQ5xsdeLPE9qaiobNqwvcFHcpUvXQusnJxc+Qq20y8tSLJYYu5Um/321SsFFk8Yjda1xSjtb6DH/pdeBxqlguZOdK6nOSQXKa1WrzeWsSwXLvWpzObsE5UWcx8nOhVSn4iVJnJxcSdUUjPGRWk35OzmnQPl//1555D1WvmOPi4suNHl4XXuZrMyqRYVbqIe1CY0p+7dZs2bx4osv8tJLLwG5o9x///13vvnmG2bMmFGg/uHDh6lduzbBwcEA1KlTh1dffZXPPvvsAVslyqL8uyHnkBm5g8/G9EXjdov3vtuLY4MnsK5Ug0ptXoE2rzBkQSQX94WSen47ZCXJ6MJyLjQ0lNHBY7Cp/ijOTWJxrNsJBRVWzl7oM5NIO/sLKX+vRZd0jf6vbeWtf87IiMEKRF3SAxYsWICVlRVr167lm2++Mb5Jtm3bxhNPPGHyAIUQQog7mbsfUqnyz0lUFKVAeWF1/lt2p5CQEJKSkoy3q1dzF4Gu6u6Fu4e38WZlZZ3v/n9vDg7OhV4U29k5FPq8Li6Fj5QyVbm7uxcaTf4MjFqtLlCm0WioUbNumS8vz7FrNBoeaVr4dF2dTlfg/qNt2pBTSLmLiys5OfnLc3J0REfHlmq5i2vlAvHk6HSFxt6ufYdC2/lI04Ay9XpY2nvMVLG7e3hRGFN9L1Wu7H7X79HCbg+Lqfq37OxsTpw4UWDke48ePTh48GChx7Rv355r166xdetWFEXhxo0brF27lt69exf5PFlZWSQnJ+e7ifIjKCiI8PBwtm7dSnh4OEFBQXRtUZvEvZ9ybVFP4n//gIzLB1EMehJwp3Knt6g+aitVe8/infm/EnHpGpA7Im3v3r1otVozt0gU5s7XR1EU9oZd4f2fzuE1fCOeT83FoW5nFFQ0qKrn5o7JXFv8JAn756BLumbcjMTHx4dOnTpJgrCCUCl5VzcWKjk5GVdXV5KSknBxcTF3OCYVF3v9of7DpayQdlcs0m7xsKhUqnvubtypUycCAgKYO3eusSxvY5L09HSsra2pWbMm48aNyzflePbs2cyZM4fLly8XK5ai+q57vS9uxESzbNniAuUDBgw2yRpfploTrLCy/LulxlG5snuZ2b21JLGXtNwcbb18+XK+XXnXrl3LzJkz0ev1aDQa3nnnHUaNepHFixcVKO/Z8wnatHkUOzs747mzsrLYuHETTz/9VKmVHz58hN9//61APEChsRfWzoe74+/9v65Q3nY3/l9bTRE7FNzw6H7XRF2/7mf+a8SIUXhWKzwRaSmuX7+Oj48PBw4coH379sby6dOnExoayrlz5wo9bu3atYwcOZLMzEx0Oh1PPfUUa9euxdrautD6U6ZMKXQUfGTEvzg7Oxvv63Q5WFkVfg5LYintXL16DZPe/wC93oBGo+a5ka+z4Wgcjg2fxNazkbGetdpAbdsbHFzzGemXD6FGYfon0xgy5BkzRm8alvRavjfpA9TOPjg1eIJaHZ4jUedofDx31OCvpPyzgeVfTePy5Sv5XvtPPpbXs7worI33ey0pScJyrKImEaTdFYu0WzwsxUkSvvPOO2zZsoXw8HBj2f/93/8RFhbGoUOHABgyZAgpKSls3brVWKdXr15UqlSp2BuXmDpJOGLEKOPahHkeViLhflSk939ZaatWqy10KlFh5aGhobenqOmN6xMFBQWVenlR8RQVuzmVldf1YSiNtpbW7uplfU1CU8lLEh48eJB27doZyz/55BOWL1/Ov//+W+CY8PBwAgMDGTduHD179iQ6Opq3336b1q1bs2jRokKfp7D1dGvUqFHivstSWFI77/xehdyN2gwGA1aVauHYoCdODZ7AyrWGsb4+I5GMywfIurSffesXUL9O9XK9Q3J5fC3v/HtX9fBi+4mrvP7ht9jVbINN1frGetZqhaTzu0k9/xsZlw6APhuNRkN4eDg+Pj5lsk99UOXx9SwpU7ZRkoTlWEV4sxdG2l2xSLtFaUpNTSUiIgKAgIAAZs2aRdeuXXFzc6NmzZqEhISg1WpZtmwZkLvzZJMmTXj11Vd5+eWXOXToEK+99horV6407m588OBBOnXqxCeffMLTTz/Npk2beP/999m/fz9t2rQpVlz3myQsahfj8nZRXJHe/+W1rSVJKuaVnzxxlBYtHy12/fJ8kVJeX9f7UZbbasofL8qT7OxsHBwcWLNmDf379zeWjxkzhrCwMPbu3VvgmOHDh5OZmcmaNWuMZfv37+exxx7j+vXreHnde/Tl/fZdlsKS2/nfH2/mzp0Hlf344Nv/b+/Ow2M63zeA35NtskhijQQR+xIJ2YTY1yhqaamlqvZSe2m/KEq0tlZRWpSqlNqqKG3V1trXilhqbQmRRRNEgsj+/P7IL6dGJpGJSSaZuT/XNddl3jnnPe9zZsydeefMOftgV7M9zG1LK8uaqwSutgkI/W0lEm+fREZChMY5DIvD5GFRfy6f34fBwd9h4szFsKrkB5vKjWFfpRHS5L/TOUhGGpLu/Ikn13dj7fwxuBtxM8cv44xRUX8+9UGfNep84RIiIiJjcebMGbRu/d9FPSZMmAAg8zw9wcHBiI6ORnh4uPJ41apVsWvXLrz33nv46quvUKFCBSxZskSZIAQyz+u0adMmTJs2DdOnT0f16tWxefPmPE8QvgwHB0cMHfauSX4opsKV0xUNc2u3smyU7Q9YXfsh0oWDg6NJvv9ZWVnB19cX+/bt05gk3LdvH7p166Z1ncTERFhYaH40zDpnpJEfU0J5MGDAALRr107jy57IyEiM7b8QcUcWQe3SADZVm8O2WgugZGXceuKIUi0+QCkA6YkPELTrIh6UvonEiFDM/3A4MlKTYGZmpkweFoeJQ0PQtl++++47jBkzFuYlq8DG1Q/e7fvjxsNacO67QVkvTYCS1hmIDN2FxPCTSAo/iYykeJibm8OjziJ0aNs82/NJlIWThEREZLJatWqV64ef4ODgbG0tW7bE2bNnc+23Z8+e6Nmz58sOL19M9UMxERH9Z8KECejfvz/8/PwQEBCAlStXIjw8HCNGjACAbEfKd+nSBcOGDcPy5cuVnxuPHz8e/v7+qFDBuI/Aobx5/sueZ6+QnBx1Fmn/nsfsN2vBpqwdxs/7HjZVm0Ndvh7MbUvDplpLfHM8GYA7Kg37HSkxV5F89y9MWrIT4fee4rOg/yEjI0Nj4hAoHkcd6kPOk4FjkJGRAXMrW7z/yQrYVPTCst+SUGHwbzC3ybw4061kwNwGyEh9iuSoc3h65xSSbp/EsnVLcauKE8aO3Y+MZ44YzOo/py/viHSeJPz999/Rtm1brY99+eWXGD169EsPioiIKCfMISIiMkb6zLfevXvj/v37mDVrFqKjo+Hh4YFdu3bBzc0NALIdKT9w4EA8evQIX375JSZOnIiSJUuiTZs2mD9//ssVRUYt6wjDZ08PERkZicfnNyAh9HvAzBJWTnVgU8ELAZ0H4q+oVJjbloHapT7ULvUBAJvuAS4Df0XKv38hJeYa/vf5D6jfqA3On/wDY8eOMarJwxdNBpqZmWH+ouWo4tkMU74+hJIt/ge1swcsy1THpjsWwJ1k2FRtAQDISE1CcvQ5JEWGIDnyLJL/vQRkZJ5uxtzcHDVqVEfLli2yPT9EL6LzOQlLliyJffv2oWHDhhrtixcvxkcffYSEhAS9DS4tLQ0zZ87E+vXrcffuXbi4uGDgwIGYNm0azMzM8tQHz0lofFi3aWHd9LzCzCFD4XmdTKNOgLUaK9ZK+VHc843ZZfx15rVGbRegateuHerWrQuzEi6wcvaA2tkT1s6esCxbCyrz7McupT99iJR715F67x+kJUQi4/FdbF+/ApfPHMKEcaN0mjzUdVJRl+dSW98vPDJQXQLT5iyBm7s/xn84F+YOFWBZqgqsSleHuV0ZrdtJe/QvGlS2wdGfv0Vy9EUkx1wGMtJgbm6OoKAgzJgxQ+dzDJrCaxYwjToNek7CRYsWoVOnTjh06BDc3TMvgb5gwQJ8/PHH+PXXX/UyqCzz58/HihUr8N1336FevXo4c+YMBg0aBEdHR4wbN06v2yIiouKhMHOIiIiosDDf6Hk5Xbn7Za/+nZ+rhT9NTERc3D1kZKhe2Mdrr3WHj483IiIiUKlSJbi5ucHBwRGLFy/C/PnzkZ5wDuZPLmJE+xH4fPH7sHGuB7PStaAqWQ1wrArLUlUBm5KwcfWHjau/0v+wDU8A+KHSkN2QJzGQp/fw8U+RuF/qJqJuXsTPPwQj4+kDqFIfYdJ7ozBkyBB8++1qzJs3T5lUnDx5Mnr27Inbt2/jzp07cHV1VcaXkBCP27dv48rli6jr7pmt/fnltfWdmmGGz5euhFg5wMyuPNp16YPSFWtj854kVOy7DipbJ6isSyL4FoBbT1Gq2fhsz3spdQpiboRA9TgCEvcPMu5fRfLDKCw6eQp7yvpj/vwDSHcqA3Nzc0yaNAk9e/ZE27ZtNPZ3Tq+fZ5+3vDyfBfl607VvKhz5urrxggULsHjxYhw9ehSbN2/GnDlz8Ntvv6FJkyZ6Hdyrr76K8uXLY/Xq1Upbjx49YGtri3Xr1uWpDx5JaHxYt2lh3aRNYeWQofBoDNOoE2Ctxoq1Un4V53wrbtml70m1uLhYlCpVTm8TKwDwzarlSE9PV9rNzMygUqk02szNzdG7z1vYvOn7AmkvyG2amZkhIyMDz2vZthM27zqCBJRGfLoDHmXY4lG6LRJhj+SMvB3npEIGSlgJzNIew8YsBZaqVFiq0mAmqVCbZcBClQYLVTpUyIAZBA0b+iPkzEmYIXN6JAMqiKjg49cIZ878iQyokC7mSBULpIkFyrq44XbkXaTDEslihaQMKySLFdLzeByWGskobZMGq9T7cLB4CgezxyhlngA7iUPXzp2wb++ubOu82W9AoT8/RaXvocPeBZC//1f6/r9ZFCcsDX514/fffx/379+Hn58f0tPTsXfv3gK5amOzZs2wYsUKXL9+HbVq1cL58+dx9OhRLF68WO/bIiKi4qOwcoiIiKgwMd/y5mWPVgKyT8AVtYmVbt17aLQB0Dqhlp6ejviHcdmW1Vd7QW5TW98AUMLaAs7qR3DGI432gCbNcfDoSTzKsMOTDBskZtggUWyQblUacYmCJxk2SBI1UsQKAjM8SgEAR8Rr34yG04eTAXhnaz91JBlA/Wzt124DgPafLpshA9aqZNiaPUUJs0SUK2EGs6QY2Jk9hZ1ZIuzNn8BKlYaAJs1x4viZbOtbWWqfpikKz4+h+o6NicGOHVuLxP/NnCYstbUVp4nGLHmaJFyyZEm2NhcXF9ja2qJFixY4deoUTp06BQAYO3as3gY3adIkxMfHo06dOjA3N0d6ejpmz56Nvn375rhOcnIykpOTlftF/dwdRET0YobKISIiooLEfMv0skfY6TopoG0CrqhNrCQnJWXrx9SVLlUKarNUqM0eoiweKu2Zk21HlfsZokKSqFGxaj1c+iccSaJGmlgoRwGmijnSkHlf/v+IQZhZID0jA/L/xxKaQaBCBszNzABJgwoCC6TDQpUGS1UaStrbIvlJHCyQBrVZCqxVKVCrkmFtlgILpEGlwnPju6m1Hsqb5OSkIvN/U9uEZWEcGVlY8jRJuGjRIq3t5ubmOHbsGI4dOwYAUKlUeg2vzZs34/vvv8eGDRtQr149nDt3DuPHj0eFChVyPBHn3LlzERQUlK39Xmw0kpMe621sRUFaWipiY6IMPYxCx7pNC+s2Lvk9DN5QOURERFSQmG+ZE4S6HNWnywRfTpMCnIAzLs9PtpmpBLaqJDTxqIi42yF56sPd3ROXL1/Mc3uAf3OcOH42X+Oj4k3bhGVBHxk5dNi7hTZRmKdJwrCwsIIeh1YffPABJk+ejD59+gAAPD09cfv2bcydOzfHScIpU6ZgwoQJyv2EhAS4urqibDkXnpPQSLBu08K6CTBcDhERERUk5lvmkTQ8wi47tdpa+TVdlpyOVnIsWSrbsvpqL8htGqoebRM6Xt4+WicDc2qvWrUaTp86XiTqKY7Pj659q62tsz0HxZmuR0Y+TUwsWpOEhpKYmAgzMzONNnNz8xzPXQAAarUaarW6oIdGRERERERExZC2CbiiNrFSzskJQ4e9m+fznmlbVl/tWdt89gIQ+u67sOu5fft2tqsvDx8xGrdv38bVK3+hTl2PbO3PL1+U6slPH3l5PguyHl36BlBk/m8a24Tl83SeJOzZsyf8/PwwefJkjfbPPvsMp0+fxpYtW/Q2uC5dumD27NmoXLky6tWrh9DQUCxcuBCDBw/W2zaIiKh4KcwcIiIiKizMt7zRZYIvp0mBnCbgXmbyIy9XUNW1bwBajx7Kqa2g283MBOWcXAqk78Kux9OzPjw962db1tOzPpzLl9X4RU9Wu7bli0o9+elDl+ezKIy7qPzfBLJPWBrTkZE6TxIeOnQIM2bMyNb+yiuvYMGCBXoZVJalS5di+vTpGDlyJGJiYlChQgUMHz4cH330kV63Q0RExUdh5hAREVFhMdV8s7G11csRdroerQTod/JD24TLy/ZNRP8pSv83C/vIyKx1CoPOk4SPHz+GlZVVtnZLS0u9X0nY3t4eixcvxuLFi/XaLxERFV+FmUNERESFxVTzTd9H2OX1aCUiovwyxJGRhcXsxYto8vDwwObNm7O1b9q0Ce7u7noZFBERUU6YQ0REZIxMOd8cHBxR3tlFuT07EaitnYiouCuq73s6H0k4ffp09OjRAzdu3ECbNm0AAL///js2btzI82QQEVGBYw4REZExYr4REZGh6TxJ2LVrV/z000+YM2cOfvzxR9jY2KB+/frYv38/WrZsWRBjJCIiUjCHiIjIGDHfiIjI0HSeJASAzp07o3PnzvoeCxERUZ4wh4iIyBgx34iIyJB0PichERERERERERERGRedjyRMT0/HokWL8MMPPyA8PBwpKSkajz948EBvgyMiInoec4iIiIwR842IiAxN5yMJg4KCsHDhQvTq1Qvx8fGYMGECXn/9dZiZmWHmzJkFMEQiIqL/MIeIiMgYMd+IiMjQdJ4kXL9+PVatWoX3338fFhYW6Nu3L7755ht89NFHOHnyZEGMkYiISMEcIiIiY8R8IyIiQ9N5kvDu3bvw9PQEAJQoUQLx8fEAgFdffRW//vqrfkdHRET0HOYQEREZI+YbEREZms6ThJUqVUJ0dDQAoEaNGti7dy8A4M8//4Rardbv6IiIiJ7DHCIiImPEfCMiIkPTeZLwtddew++//w4AGDduHKZPn46aNWvi7bffxuDBg/U+QCIiomcxh4iIyBgx34iIyNB0vrrxvHnzlH/37NkTrq6uOHbsGGrUqIGuXbvqdXBERETPYw4REZExYr4REZGh6XQkYWpqKgYNGoSbN28qbY0aNcKECRMYXEREVOAKIoeWLVuGqlWrwtraGr6+vjhy5EiOyw4cOBAqlSrbrV69esoywcHBWpdJSkrK1/iIiMj48XMWEREVBTpNElpaWmL79u0FNRYiIqJc6TuHNm/ejPHjx2Pq1KkIDQ1F8+bN0bFjR4SHh2td/osvvkB0dLRyu3PnDkqXLo033nhDYzkHBweN5aKjo2Ftba23cRMRkXHh5ywiIioK8nVOwp9++qkAhkJERPRi+syhhQsXYsiQIRg6dCjq1q2LxYsXw9XVFcuXL9e6vKOjI5ydnZXbmTNnEBcXh0GDBmksp1KpNJZzdnbWy3iJiMh46ftzli5HygNAcnIypk6dCjc3N6jValSvXh3ffvut3sZDRERFn87nJKxRowY+/vhjHD9+HL6+vrCzs9N4fOzYsXobHBER0fP0lUMpKSkICQnB5MmTNdoDAwNx/PjxPPWxevVqtGvXDm5ubhrtjx8/hpubG9LT0+Hl5YWPP/4Y3t7eeeqTiIhMkz4/Z2UdKb9s2TI0bdoUX3/9NTp27IjLly+jcuXKWtfp1asX/v33X6xevRo1atRATEwM0tLSXqomIiIqXnSeJPzmm29QsmRJhISEICQkROMxlUrFSUIiIipQ+sqhe/fuIT09HeXLl9doL1++PO7evfvC9aOjo/Hbb79hw4YNGu116tRBcHAwPD09kZCQgC+++AJNmzbF+fPnUbNmTa19JScnIzk5WbmfkJCQpxqIiMh46PNz1rNHygPA4sWLsWfPHixfvhxz587Ntvzu3btx6NAh3Lx5E6VLlwYAVKlSJf/FEBFRsaTzJGFYWFhBjIOIiChP9J1DKpVK476IZGvTJjg4GCVLlkT37t012hs3bozGjRsr95s2bQofHx8sXboUS5Ys0drX3LlzERQUlK39Xmw0kpMeK/fT0lIRGxP1wrEVd6ZSJ8BajRVrNS7lnCoUynb0lW/5OVJ+586d8PPzw6effop169bBzs4OXbt2xccffwwbGxut6/ALLiIi46PzJOGsWbPw/vvvw9bWVqP96dOn+Oyzz/DRRx/pbXBERETP01cOlS1bFubm5tmOGoyJicl2dOHzRATffvst+vfvDysrq1yXNTMzQ8OGDfH333/nuMyUKVMwYcIE5X5CQgJcXV1RtpwLHBwclPbYmKhC+7BqSKZSJ8BajRVrpfzQV77l50j5mzdv4ujRo7C2tsb27dtx7949jBw5Eg8ePMjxvIT8gkuTKdRpCjUCrNPYmEKd2mrMbzarRER0WcHc3BzR0dFwcnLSaL9//z6cnJyQnp6er4EUlISEBDg6OiI+Pl7jg5YxMNU/yli3aWHd9Dx95lCjRo3g6+uLZcuWKW3u7u7o1q2b1p9jZTl48CBat26NixcvwsPDI9dtiAj8/f3h6emZ5xPA55RdpvK6MJU6AdZqrFgr5Ye+8i0qKgoVK1bE8ePHERAQoLTPnj0b69atw9WrV7OtExgYiCNHjuDu3btwdHQEAGzbtg09e/bEkydPtB5NqO1IQldXV2aXETOFGgHWaWxMoU591qjzkYQ5/Qzr/PnzyvkriIiICoo+c2jChAno378//Pz8EBAQgJUrVyI8PBwjRowAkHmEX2RkJNauXaux3urVq9GoUSOtE4RBQUFo3LgxatasiYSEBCxZsgTnzp3DV199pdPYiIjItOgr3/JzpLyLiwsqVqyoTBACQN26dSEiiIiI0HpOXbVaDbVanedxERFR0ZfnScJSpUpBpVJBpVKhVq1aGgGWnp6Ox48fKx+qiIiI9K0gcqh37964f/8+Zs2ahejoaHh4eGDXrl3K1Yqjo6MRHh6usU58fDy2bt2KL774QmufDx8+xDvvvKMcjeHt7Y3Dhw/D399fx4qJiMgU6DvfrKys4Ovri3379uG1115T2vft24du3bppXadp06bYsmULHj9+jBIlSgAArl+/DjMzM1SqVCmflRERUXGT50nCxYsXQ0QwePBgBAUFaXzLZGVlhSpVqmgczq4vkZGRmDRpEn777Tc8ffoUtWrVwurVq+Hr66v3bRERUdFVUDk0cuRIjBw5UutjwcHB2docHR2RmJiYY3+LFi3CokWLdB4HERGZpoLIN12PlH/zzTfx8ccfY9CgQQgKCsK9e/fwwQcfYPDgwTleuISIiIxPnicJBwwYAACoWrUqmjZtCgsLnX+prLO4uDg0bdoUrVu3xm+//QYnJyfcuHEDJUuWLPBtExFR0WKIHCIiIipoBZFvuh4pX6JECezbtw9jxoyBn58fypQpg169euGTTz556bEQEVHxoXMCtWzZUvl3586d8c0338DFxUWvg8oyf/58uLq6Ys2aNUpblSpVCmRbRERUPBRmDhERERUWfeebrkfK16lTB/v27cv39oiIqPgze5mVDx8+jKdPn+prLNns3LkTfn5+eOONN+Dk5ARvb2+sWrUq13WSk5ORkJCgcSMiIuNU0DlERERkCMw3IiIyhCL9W62bN29i+fLlmDBhAj788EOcPn0aY8eOhVqtxttvv611nblz5yIoKChb+73YaCQnPS7oIReqtLRUxMZEGXoYhY51mxbWbVzKOVUw9BCIiIiIiIhIi5eaJHRzc4OlpaW+xpJNRkYG/Pz8MGfOHACAt7c3Ll26hOXLl+c4SThlyhRMmDBBuZ+QkABXV1eULecCBweHAhurIcTGRJnkB27WbVpYN+WmoHOIiIjIEJhvRERkCC81SfjXX3/paxxaubi4wN3dXaOtbt262Lp1a47rqNVqqNXqAh0XEREVDQWdQ0RERIbAfCMiIkPQeZLwwoULWttVKhWsra1RuXJlvU3SNW3aFNeuXdNou379unJVLiIiMj2FmUNERESFhflGRESGpvMkoZeXF1QqFQBARJR/Z7G0tETv3r3x9ddfw9ra+qUG995776FJkyaYM2cOevXqhdOnT2PlypVYuXLlS/VLRETFV2HmEBERUWFhvhERkaHpfHXj7du3o2bNmli5ciXOnz+P0NBQrFy5ErVr18aGDRuwevVq/PHHH5g2bdpLD65hw4bYvn07Nm7cCA8PD3z88cdYvHgx+vXr99J9ExFR8VSYOURERFRYmG9ERGRoOh9JOHv2bHzxxRfo0KGD0la/fn1UqlQJ06dPx+nTp2FnZ4eJEydiwYIFLz3AV199Fa+++upL90NERMahsHOIiIioMDDfiIjI0HQ+kvDixYtazwno5uaGixcvAsg8VD46OvrlR0dERPQc5hARERkj5hsRERmazpOEderUwbx585CSkqK0paamYt68eahTpw4AIDIyEuXLl9ffKImIiP4fc4iIiIwR842IiAxN558bf/XVV+jatSsqVaqE+vXrQ6VS4cKFC0hPT8cvv/wCALh58yZGjhyp98ESERExh4iIyBgx34iIyNB0niRs0qQJbt26he+//x7Xr1+HiKBnz5548803YW9vDwDo37+/3gdKREQEMIeIiMg4Md+IiMjQdJ4kBIASJUpgxIgR+h4LERFRnjCHiIjIGDHfiIjIkPI1SXj9+nUcPHgQMTExyMjI0Hjso48+0svAiIiIcsIcIiIiY8R8IyIiQ9J5knDVqlV49913UbZsWTg7O0OlUimPqVQqhhcRERUo5hARERkj5hsRERmazpOEn3zyCWbPno1JkyYVxHiIiIhyxRwiIiJjxHwjIiJDM9N1hbi4OLzxxhsFMRYiIqIXYg4REZExYr4REZGh6TxJ+MYbb2Dv3r0FMRYiIqIXYg4REZExYr4REZGh6fxz4xo1amD69Ok4efIkPD09YWlpqfH42LFj9TY4IiKi5zGHiIjIGDHfiIjI0HSeJFy5ciVKlCiBQ4cO4dChQxqPqVQqhhcRERUo5hARERkj5hsRERmazpOEYWFhBTEOIiKiPGEOERGRMWK+ERGRoel8TsJniQhERF9jISIi0glziIiIjBHzjYiIDCFfk4Rr166Fp6cnbGxsYGNjg/r162PdunX6HhsREZFW+syhZcuWoWrVqrC2toavry+OHDmS47IHDx6ESqXKdrt69arGclu3boW7uzvUajXc3d2xffv2fI2NiIhMCz9nERGRIek8Sbhw4UK8++676NSpE3744Qds3rwZr7zyCkaMGIFFixYVxBiJiIgU+syhzZs3Y/z48Zg6dSpCQ0PRvHlzdOzYEeHh4bmud+3aNURHRyu3mjVrKo+dOHECvXv3Rv/+/XH+/Hn0798fvXr1wqlTp/JVLxERmQZ9f87S5UuwZx07dgwWFhbw8vLSeZtERFTMiY6qVKki3333Xbb24OBgqVKliq7dFbj4+HgBIPHx8YYeit7F/Btp6CEYBOs2LaybnqfPHPL395cRI0ZotNWpU0cmT56sdfkDBw4IAImLi8uxz169eskrr7yi0dahQwfp06dPnseVU3aZyuvCVOoUYa3GirVSfugz3zZt2iSWlpayatUquXz5sowbN07s7Ozk9u3bua738OFDqVatmgQGBkqDBg102iazy/jrNIUaRVinsTGFOvVZo85HEkZHR6NJkybZ2ps0aYLo6OiXnrQkIiLKjb5yKCUlBSEhIQgMDNRoDwwMxPHjx3Nd19vbGy4uLmjbti0OHDig8diJEyey9dmhQ4cX9klERKZNn5+zFi5ciCFDhmDo0KGoW7cuFi9eDFdXVyxfvjzX9YYPH44333wTAQEBOm2PiIiMg86ThDVq1MAPP/yQrX3z5s0aP7ciIiIqCPrKoXv37iE9PR3ly5fXaC9fvjzu3r2rdR0XFxesXLkSW7duxbZt21C7dm20bdsWhw8fVpa5e/euTn0CQHJyMhISEjRuRERkWvSVb/n9EmzNmjW4ceMGZsyYkfdBExGRUbHQdYWgoCD07t0bhw8fRtOmTaFSqXD06FH8/vvvWkONiIhIn/SdQyqVSuO+iGRry1K7dm3Url1buR8QEIA7d+5gwYIFaNGiRb76BIC5c+ciKCgoW/u92GgkJz1W7qelpSI2Jir3goyAqdQJsFZjxVqNSzmnCoWyHX3lW36+BPv7778xefJkHDlyBBYWefuImJycjOTkZOU+v+AiIir+dJ4k7NGjB06dOoVFixbhp59+gojA3d0dp0+fhre3d0GMkYiISKGvHCpbtizMzc2zfWCKiYnJ9sEqN40bN8b333+v3Hd2dta5zylTpmDChAnK/YSEBLi6uqJsORc4ODgo7bExUYX2YdWQTKVOgLUaK9ZK+aHvz1l5/cIqPT0db775JoKCglCrVq08988vuDSZQp2mUCPAOo2NKdSprcb8ZrPOk4QA4Ovrq/GBqLDMnTsXH374IcaNG4fFixcX+vaJiKho0EcOWVlZwdfXF/v27cNrr72mtO/btw/dunXLcz+hoaFwcXFR7gcEBGDfvn147733lLa9e/dqPc9UFrVaDbVarWMFRERkbPSRb7p+Cfbo0SOcOXMGoaGhGD16NAAgIyMDIgILCwvs3bsXbdq0ybYev+DSZAp1mkKNAOs0NqZQpz5rzNMkoS6Hjj8bCPr0559/YuXKlahfv36B9E9EREVXQeXQhAkT0L9/f/j5+SEgIAArV65EeHg4RowYASDzA1BkZCTWrl0LAFi8eDGqVKmCevXqISUlBd9//z22bt2KrVu3Kn2OGzcOLVq0wPz589GtWzfs2LED+/fvx9GjR/M8LiIiMg0FkW+6fgnm4OCAixcvarQtW7YMf/zxB3788UdUrVpV63b4BRcRkfHJ0yRhyZIlcz2XEvDf4evp6el6GdizHj9+jH79+mHVqlX45JNP9N4/EREVbQWVQ71798b9+/cxa9YsREdHw8PDA7t27YKbmxuAzCtNhoeHK8unpKTg/fffR2RkJGxsbFCvXj38+uuv6NSpk7JMkyZNsGnTJkybNg3Tp09H9erVsXnzZjRq1EjHqomIyNgVVL7p8iWYmZkZPDw8NNZ3cnKCtbV1tnYiIjJueZokPHDgQEGPI1ejRo1C586d0a5duxdOEvIEukRExqcgc2jkyJEYOXKk1seCg4M17v/vf//D//73vxf22bNnT/Ts2VMfwyMiIiNWUPmm65dgREREQB4nCVu2bJmnzs6dO/cyY9Fq06ZNOHv2LP788888LZ/XE+gaA1M4Aac2rNu0sG7jkt9zZRgyh4iIiApKQeabLl+CPW/mzJmYOXOmztskIqLiLV8XLnlWfHw81q9fj2+++Qbnz5/X68+N79y5g3HjxmHv3r2wtrbO0zp5PYGuMTCFE3Bqw7pNC+umFynIHCIiIjIU5hsRERU2s/yu+Mcff+Ctt96Ci4sLli5dik6dOuHMmTP6HBtCQkIQExMDX19fWFhYwMLCAocOHcKSJUtgYWGhNSjVajUcHBw0bkREZHwKI4eIiIgKG/ONiIgMRacjCSMiIhAcHIxvv/0WT548Qa9evZCamoqtW7fC3d1d74Nr27ZttittDRo0CHXq1MGkSZNgbm6u920SEVHRVdg5REREVBiYb0REVBTk+UjCTp06wd3dHZcvX8bSpUsRFRWFpUuXFuTYYG9vDw8PD42bnZ0dypQpwyttERGZGEPkEBERUUFjvhERUVGR5yMJ9+7di7Fjx+Ldd99FzZo1C3JMRERE2TCHiIjIGDHfiIioqMjzkYRHjhzBo0eP4Ofnh0aNGuHLL79EbGxsQY5Nq4MHD2Lx4sWFvl0iIjKsopJDRERE+sR8IyKioiLPk4QBAQFYtWoVoqOjMXz4cGzatAkVK1ZERkYG9u3bh0ePHhXkOImIyMQxh4iIyBgx34iIqKjQ+erGtra2GDx4MI4ePYqLFy9i4sSJmDdvHpycnNC1a9eCGCMREZGCOURERMaI+UZERIam8yThs2rXro1PP/0UERER2Lhxo77GRERElCfMISIiMkbMNyIiMoSXmiTMYm5uju7du2Pnzp366I6IiEgnzCEiIjJGzDciIipMepkkJCIiIiIiIiIiouKLk4REREREREREREQmjpOEREREREREREREJo6ThERERET/b+bMmXj//fcLbL2ffvoJp0+fzs/QFLdu3cLKlStfqo9nzZw5EykpKS/dT3BwMHr27PnC5Q4ePIi9e/e+1LYePnyITz/99KX6ICIyFsyu/GN2EWniJCEREZEJiIyMxKFDhxAZGWnooRSqtLQ0Qw9Bg6E/aGnbH0FBQXr5oJVXhv6gVdReE0SUM2ZX0cDsYnaR6eAkIRERkZH77rvvULduXXTu3Bl169bFd999p7e+VSoV5s6dC39/f1SrVg379+/HlClT4O3tjXr16uHSpUvKsuvWrUOjRo3g4+ODli1b4q+//gIAXLx4Ec2bN4ePjw/c3d3xxRdLlXW++eYbuLu7w8vLC56enjh16hQAoEqVKsr6AODn54eDBw8CAFq1aoWpU6eibdu26NChAwBgwYIF8Pf3h4+PDzp16oQ7d+4AAOLj49GzZ0+4u7ujQ4cO+Oeff/JUd27rpaamYvLkyfD394eXlxf69OmDhw8fYteuXdi5cyfmzZsHLy8vfPPNN7nuFwCYP38+PD090aBBAzRu3BiJiYkYMWIELl++DC8vL3Tt2hUAcObMGQQEBKB+/frw9/fHsWPHAGR+KCtbtixmzZqF5s2bY+nSpXjWiBEjAABNmjSBl5cXYmJi8O+//+K1116Dp6cnPDw8cvxQl5KSguHDh6NWrVpo3bq18txkeX6fR0ZG4ty5c1ixYgXWrl0LLy8vzJo1CwCwZ88eNGvWDL6+vmjUqBEOHz6s9LNmzRp4eXmhQYMG8PPzw61btzBixAg8fPgQXl5e8PPzAwD8888/aNeuHerXrw8vLy/89NNPSh8qlQqff/45WrVqhSlTpuTpOSYiw2J2Fb3sat2mvUlkV4cOnTX2ObOLTIoYufj4eAEg8fHxhh6K3sX8G2noIRgE6zYtrJtMUU7ZlZ/XRUREhNjb24udnZ1yc3BwkIiICL2MFYB8+eWXIiLyww8/iK2trfzyyy8iIjJ//nzp27eviIgcPXpUOnXqJElJSSIicvjwYalfv76IiCQkJCjtiYmJ4uFRT/78808REXFwcJDIyMy6U1JS5NGjRyIi4ubmJhcvXlTG4evrKwcOHBARkZYtW0qnTp0kJSVFRETWr18vw4YNk7S0NBERWbt2rXTt2lVERCZMmCCDBg0SEZHY2FhxdXWViRMnvrDu3NabPXu2fPzxx8qys2bNkrFjx4qIyIABA2Tp0qXKYz///FOO+yU4OFgaN26svA4ePHggaWlpcuDAAfH19VX6SE5OFldXV9m9e7eIiBw5ckScnZ3l8ePHEhYWJgBk/fr1OdYCQNmvIiK9evWSyZMni4jIv//+K5UqVZJTp05lW2/JkiXSvn17SUlJkSdPnoivr6/06NFDRLTv81c6BIqIyIwZMzT28Y0bNyQgIECp8++//5YKFSpISkqKHDhwQKpXry5RUVEiIvLkyRN58uSJhIWFSZkyZTTG4+/vL19//bWIiFy/fl1Kly4t4eHhSo2zZ8/OcR/omym9h5tSrZQ7ZpfxZ1fWc5nbfjGG7IqOysyOZ/e5MWaXqbx/m0Kd+qzRwkBzk0RERFQI/vnnH2RkZGi0paen48aNG6hYsaJettG7d28AgI+PD8zMzNC5c2cAgK+vL7Zt2wYA2LFjB86fP49GjRop68XGxiIlJQVPnz7FyJEjce7cOZiZmSEiIvOIMz8/P7Rp0wZvv/02unTpgo4dO6JWrVp5GlP//v1haWkJIPNnUmfOnIGvr69Sv7m5OQDgwIEDyhEKZcuWxeuvv56n/nNb76effkJCQgJ+/PFHAJlHLVSvXl1rP7t378lxv/zyyy9499134eDgAAAoVaqU1j6uXbsGKysr5ciTZs2awcnJCRcuXICLiwusra3Rt2/fPNUFAPv378f58+cBAE5OTnj99dfx+++/w9/fP9s+GDBgACwtLWFpaYm33noLR48eVfbB8/tcRPN1+N8+2I1//vkHLVq00Gi/c+cOfv31V7z99ttwcXEBANja2mrt49GjRzh37hyGDBkCAKhZsyaaNWuGo0ePKrUPHjw4z/uAiAyL2VW0syu3/WIM2XX8+DFYWFhq7PPnMbvIWHGSkIiIyIjVqFEDZmZmGh+2zM3Nc/zDPz+sra2VftVqtcZ2ss6hIyIYPHiw8hOdZ3344YcoX748QkNDYWFhgc6dOyEpKQkAsG3bNoSEhODgwYPo1KkTPvnkE/Tp0wcWFhZIT09X+shaPkuJEiWUf4sIpk2bpvUPbRHJV825rSciWLZsGdq0aZOnfnLaL7qMRaVSZWvParOzs9P6eG6eX17b+i/aB8/v89iYqByXfeWVV7B27VqdxqhtLLmN+9nXBBEVbcwuZpchs6vLq6+gnFOFXLfF7CJjxXMSEhERGbGKFSti6dKlyjfh5ubmWLJkid6OxMirLl26YO3atcr5lDIyMnDmzBkAQFxcHCpVqgQLCwtcu3YNhw5lntMnLS0NN27cgJ+fH95//3307NlTOXF69erVlfMInT59GteuXctx2127dsWyZcvw4MEDAJnnXQoNDQUAtG3bFmvWrAEAPHjwANu3b1fWO336NNq2bau1z9zW69q1KxYuXIjExEQAQGJionJ+KwcHB8THxyvLdghsn+N+6dq1K5YvX46EhAQAmSc8T09Pz9ZHnTp1kJycjD/++AMAcPz4ccTExMDT0zPHffIse3t7jf7atWunnMspNjYW27dv1/qhsW3btli3bh3S0tLw9OlTbNiwQWMfPL/PL178S+s+CAwMxO7duzXOZ5X1PGe9bu7evavsy8TERDg4OCAxMVH5IO/g4AAvLy/lnGU3btzAsWPH0LRp0zztAyIqWphdRTu7ctsvxpBdcXFxADT3ObOLTAWPJCQiIjJyAwYMQLt27XDjxg1Ur1690D9kAUCLFi0wZ84cdOvWDenp6UhNTUXnzp3h5+eHadOmoX///li/fj2qVKmC5s0y/zhOT0/HoEGDEBcXBwsLC5QrV075cDN79mwMGDAAq1evho+PD+rVq5fjtvv374/79++jVatWUKlUSEtLw5AhQ+Dt7Y3p06dj8ODBcHd3h5ubG9q3b6+sd/v2bdjY2GjtM7f1Jk+ejKCgIDRq1Eg5GmDSpEmoV68e+vfvj4EDB2LLli0YPXo0unXtlON+6d+/P6KiohAQEABLS0vY2tpi//79qF+/PmrXrg0PDw9Uq1YNO3fuxNatWzF27Fg8efIE1tbW2LJlC+zs7BAbG/vC52bixIlo06YNbGxssHfvXixZsgQjRoxA/fr1kZGRgalTp2b7uRYAvPPOO7hw4QLc3d1RqVIlNG/eHLdv385xn/fu3RNt2gbitddew7p16+Dl5YXXX38dH330Eb7//nsMHToUT58+RUpKCnx8fLB+/Xq0aNEC06ZNQ2BgIFQqFaysrPDjjz/Czc0N/fr1g6enJ+zs7HDmzBmsX78ew4cPx+LFi6FSqfDNN9/A1dX1hfUTUdHE7Cp62bVx4waMH/8ehg4datTZ1f21N2Bpaamxz5ldZCpUkt9jlYuJhIQEODo6Ij4+XjkvgrGIjYl64WHQxoh1mxbWTaYop+wylddFUalz7Nix6NWrF5o1a1Zg2ygqtRYG1mqcTKlWyh2zq2jUWZDZVVRqLGis07iYQp36rJFHEhIRERFpsWTJEkMPgYiISCfMLiJ6GTwnIRERERERERERkYnjJCEREZm0ZcuWoWrVqrC2toavry+OHDmS47Lbtm1D+/btUa5cOTg4OCAgIAB79uzRWCY4OBgqlSrb7fkrGBIRERUkfecbEREZP04SEhGRydq8eTPGjx+PqVOnIjQ0FM2bN0fHjh0RHh6udfnDhw+jffv22LVrF0JCQtC6dWt06dJFufJdFgcHB0RHR2vcrK2tC6MkIiKiAss3IiIybjwnIRERmayFCxdiyJAhGDp0KABg8eLF2LNnD5YvX465c+dmW37x4sUa9+fMmYMdO3bg559/hre3t9KuUqng7OxcoGMnIiLKSUHlGxERGbcifSTh3Llz0bBhQ9jb28PJyQndu3fHtWvXDD0sIiIyAikpKQgJCUFgYKBGe2BgII4fP56nPjIyMvDo0SOULl1ao/3x48dwc3NDpUqV8Oqrr77wSIzk5GQkJCRo3IiIiPKjIPONiIiMW5E+kvDQoUMYNWoUGjZsiLS0NEydOhWBgYG4fPky7OzsDD08eoGZM2fi8ePHWLBggaGHks2tW7ewd+9evPPOO4YeChEZyL1795Ceno7y5ctrtJcvXx53797NUx+ff/45njx5gl69eiltderUQXBwMDw9PZGQkIAvvvgCTZs2xfnz51GzZk2t/cydOxdBQUHZxxgbjeSkx8r9tLRUxMZE5WlsxZkh6/z0s8znNGjmR4WyPV1qDQ+/g4MHD+Htt98q4FHpz927dzFgwBD89tvPyMhIL7Tn1al8Rdy8eR0lcvl7MT4+HmvXrceY0SNfalubNm1Gw4Z+qF69utKW1+dVRNC12+v4cukXcHOr/FLjMBRTeF8q51TB0EPQSUHl2/OSk5ORnJys3OcXXIbDz136dffuXXR+tTtOnjwJM7PCO65KpVLh0aNHKFGiRI7LPHz4ECtXrsT//ve/l9pWcHAw6tSunq/3NxFBixYtsHbtWlStWvWlxkFFT5GeJNy9e7fG/TVr1sDJyQkhISFo0aKFgUYFREZG4p9//kGNGjVQsWJFg42jsKWlpcHCoki/ZPLs1q1bWLlyZbEKKyIqGCqVSuO+iGRr02bjxo2YOXMmduzYAScnJ6W9cePGaNy4sXK/adOm8PHxwdKlS7FkyRKtfU2ZMgUTJkxQ7ickJMDV1RVly7nAwcFBaY+NicrXH3MJCfF4mpio3LextYWDg6PO/RSW/Napja7ZZWdnDxFVoU0K6FLrpcvXsXHTD5j4/st9MChMM2Z+gvHvvYfyzpX09rzm9TktV84l1w9aTxJTsGzZCsya9clLjWfrth2oWq0mGgf8V1teas2q43//m4Qvv1qB4ODglxqHoejz/yvpl77z7XkF/QXX48ePkZz830W/1GrrXP9PG5o+J8x1za4nTx4hMfFJgU/Y56fGc6FnsGzZV3it+6sFNCr9+/zzRXj77X64fy9vk+p5kdfnNDY2Gk8Tc/6CKzz8DubPn4dBA1/uC8NVq1Zi+DtDNb7gyousOgYPHoApUyZh6ZLFLzWOwmAKX2ZpqzHf2SzFyN9//y0A5OLFi3leJz4+XgBIfHy8XsYQHBws9vb2YmdnJ/b29hIcHKyXfkVEAMicOXOkYcOGUrVqVdm3b59MnjxZvLy8xN3dXf766y9l2bVr14qPt7d4e3tLixYtlH1y4cIFadasmXh7e0vdunVlzpw5yjqrVq2SunXrSoMGDcTDw0NOnjwpIiJubm4a+9TX11cOHDggIiItW7aUDz/8UNq0aSNt2rQREZHPPvtMGjZsKN7e3tKxY0cJDw8XEZGHDx9Kjx49pG7duhIYGCj9+vWTiRMnvrDunTt3iqenpzRo0EDq1asnP/30k4iIRERESI8ePcTT01M8PT1l2rRpIiKyfNmX4u/vL15eXtKgQQP59ddflb7c3Nxk5syZEhAQIFWqVJGPP/5Y6zZr164tNjY20qBBA+nSpYuIiPz555/SuHFj8fT0lIYNG8rRo0e1rpvTfrx8+bIEBgYq412+fLmIiHz++efi5+cnXl5e0rBhQ2V5kcznfN68eeLv7y9VqlSRb7/9Nsf9FPNv5Av3pTFi3VRQkpOTxdzcXLZt26bRPnbsWGnRokWu627atElsbGzkl19+ydO2hg4dKq+88kqex5ZTduXndREf/1A+XzBXPp3/iXL7fMFciY9/qHNf2uiaXf7+/i/MrqkfTlbWMZbsWr9+vdbsivk30miz6+nTp1K6dGl58uSJUisAmTFjhjRp0kRq1qwpGzZsUJY/ffq0tG7dWnx9fcXb21t+/PFHEREJCwuTMmXKSFBQkDRr1kwWLlyYbVtbt26V2rVrS+PGjWXWrFkCQB49epRrvx06dBBzc3Np0KCB+Pr6iohIdHS0vPHGG9KwYUPx9PSU6dOnK9vQtq9WrVoldnZ2UrVqVeV5TUtLk3ffHS716tWTevXqyejRoyU5OVlERAYMGCBjxoyRDh06iLu7u4iIpKSkSLly5SQhIUHrfizqmFdFT2HlW1JSksTHxyu3O3fuMLv+n7Fm14b1a5W+jDm7SpUqqWRX1rrGmF2VK1fWyK6JEycaZXaZQk7ps8ZiM0mYkZEhXbp0kWbNmuW6XF7DKj8iIiKUCcKsm4ODg0RERLx03yKZbz5ffvmliIj88MMPYmtrqwT0/PnzpW/fviIicvToUenUqZPcCb8pIiKHDx+W+vXri4hIQkKCJCUliYhIYmKieHl5yZ9//ikiIg4ODhIZmfniSUlJUd6AXhRWnTp1kpSUFBHJDIphw4ZJWlqaiGSGZteuXUVEZMKECTJo0CAREYmNjRVXV9c8hVX9+vXl2LFjIiKSnp4ucXFxIiLSqlUr+fTTT5XlYmJiRETk6pWLkpGRISKZb74uLi7K+Nzc3GT8+PHK8jk9PwcOHFDeVEUy/5hydXWV3bt3i4jIkSNHxNnZWR4/fpxtXW37MTU1VWrWrCmbN29WlouNjdUYt4jIiRMnpF69esp9ALJ48WIRyXwDL1GihKSmpmrdT6bw5qYN66aC5O/vL++++65GW926dWXy5Mk5rCGyYcMGsba2lu3bt+dpGxkZGeLn56e8P+aFPicJ70ZHaXzIyrrdjY7SuS9tdM2urIzKLbs8POoZXXbdu3dPa3ZlTRIaY3YdPnxY/P39/9sX/z9JOHPmTBERuXHjhpQpU0bCw8MlLi5OvL29JSoqShlH5cqVJTo6WsLCwgSArF+/Pts2RET+/fdfKV26tFy9elVEMl93WR+0XtRvmTJlNPoKDAyUQ4cOiYhIamqqdOjQQbZt25brvmrZsqX8/PPPSvuyZcukaZMASUpKktTUVOnYsaPymhgwYIB4e3srr+MsrVu3lt9++01rfUUd86poKox8ex6zy/izq3z58kb/uevw4cPi4+2t0Was2fX9uv8OeFq2bJm0atXKKLPLFHJKnzUWm9+Ojh49GhcuXMDRo0dzXS6vh73nR8iZU8jIyNBoS09Px9mQ07CybPRSfWdp26YlYmOi4OZWESqVCv4NvREbE4Xq1dzww+ZNiI2JwsaN6xEaehYdXums/GTg3r37iIy4hfj4BMyYGYRLf12GykyFqKhoHDl8EG6VK6Bp0ybo06c3OgS2R9u2rVG9enU8TUxARkY6HtyPUQ5PTUtLxcO4e4iNiUJqagq6de2Nh3GxAIDNmzfi3LkLaNCgPgAgIz0DZuZmiI2Jwr59ezFnzidKPx1fCURi4uMXHtobENAIo0eNwqtdOqNVqxbw9PBAWFgsjh8/jg3rv9NYPzYmCmFhYXh35BhER0XD3MIc9+7dw9mzp1GtalVkZKSjU8dAZZ3KlV1x9uyfsLLU/GnFw7h7GofkXr58Bebm5vDx9kRsTBRq16qG0qVL4dDB39GwoZ/Gutr2Y+jZP5GcnIzWrZplG++Bg4ewePESxD2Ig7mFOS5fvoLIiFuwsrICAHQIbIvYmCiULeMIc3NzXPrrHCpUyH5osCkcJq0N6zYuRe0naRMmTED//v3h5+eHgIAArFy5EuHh4RgxYgSAzJ8BR0ZGYu3atQAyf4L19ttv44svvkDjxo2VczvZ2NjA0THz57tBQUFo3LgxatasiYSEBCxZsgTnzp3DV199ZZgiC0Hv3r0BAD4+PjAzM0Pnzp0BAL6+vti2bRsAYMeOHTh//jwaNfovL2NjY5GSkoKnT59i5MiROHfuHMzMzBAREYlz587Bz88Pbdq0wdtvv40uXbqgY8eOqFWrVp7G1L9/f1haWgIAfvrpJ5w5cwa+vr4AMrPb3NwcAHDgwAEsXboUAFC2bFm8/vrreeq/bdu2GD9+PHr27InAwEB4eXnh8ePHOH78OPbt26csV65cOQBAWFgY+vXrh4iICFhYWODevXu4ffs2HB1sAQD9+vVTlq9WrRrCwsJeeEqTa9euwcrKCh06dAAANGvWDE5OTrhw4QICAgI0ltW2Hy9duoS0tDSNc46VLVsWABAaGorZs2fj/v37sLCwwOXLl5GSkqJkV9Z469atCwsLC9y9exeVKlXS2GZERITWq3xnXW21WrVqaNasGY4cOYKSJUvi5s2b6Nixo7KciODatWtwc3ODtbU1+vbtq3U/nDx5Ej4+PqhduzYA4J133sGkSZMAAMePH8+132c9efIEf/zxB/7991+l7fHjx7h69Spq1aqV47563v79+/Fmv75Qq9UAgGHDhmHFihX44IMPAAC9evXK9pNJZ2dnREREaO2PKD8KIt+MDbNL9+x68OABbt++jRo1agAw3uxyciqXbdymkF1DhgxhdlHRPidhljFjxmDnzp04fPhwtv/Ez8vreZ3yw9evEczMzDQmCs3NzeHj66+3D76VXKugRIkSeJKYAmtra6XfMmXLAyozlHOqABsbOwwdOgxjRo/Itt0ZM4fCza0qNm7cDAsLC7z++uuwtMrs59dfdyEkJAQHDx7EW/0H4ZNPPkGfPn1gZaWGY8kySl9paekoWaosyjlVgKWlFSpUdFUes7KyxowZMzB48OBsYzc3t0Cp/18PAGxsS8DCwuKF+2bFipW4dOkSDhw4gPfe+wD9+vXDyJGZJxEv51Qh27kb3h3ZFIsWLUL37t0BAKVLl4adnSPKOVWAmZk5XCr8N1612hr29iWzjaFkqbKwsLBU2kvdvZdtrBYWlihdxinbutr2o6enJ8zNzbMtm5KSgiFD3sHBgwfh6+uLhIQEODo6wrFkWeVNNus5z9ymBRxLltW6z0z1nD+smwpS7969cf/+fcyaNQvR0dHw8PDArl27lD/AoqOjER4eriz/9ddfIy0tDaNGjcKoUaOU9gEDBijnE3v48CHeeecd3L17F46OjvD29sbhw4fh7+9fqLUVJmtrawCZmZj1x2XW/bS0NACZf9wOHjwYs2bNyrb+hx9+iPLlyyM0NBQWFhbo3LkTkpIyz0O1bds25T23U6dOSnZZWFggPT1d6SNr+SzP/iErIpg2bZrW7BKRfNW8cOFCJbsGDBigkV3a9OnTBwsWLNDIrqSkJGWSMGsfApr7LTeSw/nFtLVp24+enp5a+01JSUGPHj2yZdezH7TyMl5bW1s8ffr0hXWoVCqICOrXr4/Dhw9ne/zWrVuws7PL8VxquT2HL+r3WRkZGVCpVPjzzz+VD+lZLl269MI6nt2mCppjfXbs2s6plpSUBBsbmzxvg+hFCiLfjA2zS/fsKlWqlMaYjTa7nntetDHK7FIxuwgovEv15IOIYPTo0di2bRv++OOPPF05R61Ww8HBQeOmLxUrVsTSpUuVb3DMzc2xZMmSQr94SZcuXbB27VpERkYCyHxjOHPmDAAgLi4OlSpVgoWFBa5du6Z8I5SWloYbN27Az88P77//Pnr27InTp08DAKpXr45Tp04BAE6fPo1r167luO2uXbti2bJlePDgAQAgNTUVoaGhADK/mVqzZg0A4MGDB9i+fbuy3unTp9G2bVutfV69ehX16tXD6NGj8e677+LkyZMoUaIEmjVrhkWLFinLxcZmHs0YH/8QVapUAQB8//33iIuL02HvZXJwcEB8fLxyv06dOkhOTsYff/wBIPPbm5iYmGwhlNN+rF27NqysrLBlyxZl2Xv37iEpKQmpqalwdXUFAOUbPyIqOkaOHIlbt24hOTk524WxgoODcfDgQeX+wYMHIZmn6tC4PfsBatGiRbh9+zaSk5MRExODPXv2ZPtmvDDZ2NoquZXF3NwcNra2hTqOrOy6c+cOgNyz69ChzD+KjSm74uLiTC67GjRogKtXr2Zr//bbbwFkftA5evQomjVrhiZNmuDvv/9WagGAc+fOISUl5YXbCQgIQGhoKK5fvw4A+Oabb5THcuvXwcEBiYmJyodEe3t7NG/eHPPmzVOWjYqKQkRERI77Csj+vLRv3x6bNv+AlJQUpKWlYfXq1WjXrl2uNVy5cgUNGjR4Ya1EutB3vhUmZlfRzK6HDx/mfef9v+KYXf/880+2dmPMroSER0p7+/btERwczOyion0k4ahRo7Bhwwbs2LED9vb2ymHvjo6OBpuxHjBgANq1a4cbN26gevXqBrm6cYsWLTBnzhy8PWAwVCozpKamonPnzvDz88O0adPQv39/rF+/HlWqVEGbNm0AZB6ePmjQIMTFxWUeMVeunBIss2fPxoABA7B69Wr4+PigXr16OW67f//+uH//Plq1agWVSoW0tDQMGTIE3t7emD59OgYPHgx3d3e4ubmhffv2ynq3b9/O8TmbMmUKrl+/DisrK9ja2mL58uUAgHXr1mHMmDGoV68eLCws0L17dwQFBeGTT2bhtddeQ8WKFREQEIDKlSvrvA/r16+P2rVrw8PDA9WqVcPOnTuxdetWjB07Fk+ePIG1tTW2bNkCOzvNK0vltB8tLCywY8cOjB49GrNmzYJKpcKoUaMwfPhwzJo1C/7+/qhcuTK6du2q81iJiF6Gg4Mjhg571+BXN87Krm7duiE9PT3X7GrerCkA48quL774wuSyq0qVKihXrhwuXbqk8fyo1Wo0bdoUsbGxWLp0qfKB7ueff8YHH3yA9957D6mpqahcuTJ++umnF27HyckJK1euRJcuXVCmTBn07NlTeaxUqVI59lu6dGn069cPnp6esLOzw5kzZ7B+/XpMmDBB+bBaokQJrFixApUqVcpxX73zzjuYOHEiPvvsM8yZMwfvvPMOLl48Dx8fHwBAq1atMHbs2BzHn3VUiIeHh667mMhoMbuKZnZVqqT7Z9/imF1lypQxiewaP34cvl75jZJdN27cYHYRVJLfY5ULQU6H5q5ZswYDBw7MUx9ZhxnHx8fr9ajCoqA4/Rxx7Nix6NWrF5o1a/bSfRWnuvWJdZsWU62bMuWUXabyuigqdeozu3JSVGotKD/88AMOHTqEr776CrExUXAqXxGPHj3S+rMlY6LL8zp58mTUrFkTQ4YMKeBRFQxjfw1T3jG7ikadBZldRaXGgvbNqhUIPXdROZ+0SqUyyux6meezOGWXKbxu9VljkT6SsAjPX5KOlixZYughEBER6YTZ9fJ69eqFf//9N9uF3+g/FSpUwKBBgww9DCIyEsyul9etW1c8TUpFRkYGzMyK9BnaDIbZZbyK9CQhERERERVvY8aMUf7NL4Czy+3nXEREZBjMrtwxu4wXp8WJiIiIiIiIiIhMHCcJiYiIiIiIiIiITBwnCYmIiIiIiIiIiEwcJwmJiIiIiIiIiIhMHCcJiYiIiIiIiIiITBwnCYsILy8veHl5wd3dHRYWFsr93r17AwBu3LgBMzMzfPLJJwCAnTt3onWb9vDy8oKzszOcnJyUddavX5+t//3796N58+aoXr06PDw80KZNGxw5cqRQayQiIuOSn+zKWobZRUREhsDsIiLKmYWhB0CZzp07BwC4desW/Pz8lPtZvv32W7Rs2RLffvstpk6diq5duyKgsR/KOVXAzJkz8fjxYyxYsEBr3/v370f//v2xdetWNGnSBABw/fp1XLhwoSBLIiIiI5ef7OratSsAMLuIiMggmF1ERDnjJCEAEcHTVCnQbdhYqqBSqfK1bnp6Or777jvs2bMHffv2xYEDB9CmTZs8rx8UFITp06crQQUAtWrVQq1atfI1HiIiMjxmFxERFTfMLiKioo2ThACepgoazrhWoNv4M6g2bK3yF1a7d+9GxYoVUa9ePQwZMgSrV6/WKaxCQkKwZMmSfG2biIiKJmYXEREVN8wuIqKijeckLAZWr16NwYMHAwDeeust/Prrr4iLizPwqIiIiHLG7CIiouKG2UVEpo5HEiLzkPQ/g2oX+DbyIyYmBrt27cKff/6JuXPnAgBSU1OxYcMG9HrjtWzLP3z4EK1atQIAVK1aFdu3b4evry9OnDgBb2/vfI+fiIiKluKaXaNGjcq2/PPZtfLrr5hdRERGiNlFRFS0cZIQgEqlyvch6QVt7dq16N69OzZt2qS0/fLLL/joo4+0ThKWLFky28l3p0+fjoEDB8LHxweNGzcGAFy5cgXnzp1D3759C3T8RERUMIprdmn7oPV8dsXGRDG7iIiMELOLiKho48+Ni7hvv/0W/fr102h75ZVXEBERgQsXLuapj8DAQKxZswbvv/8+atSoAU9PT4wZMwaurq4FMWQiIjJxuWXX2bNn89QHs4uIiAoTs4uICFCJSMFeXsrAEhIS4OjoiPj4eDg4OBh6OHoVGxOFck4VDD2MQse6TQvrJlOUU3aZyuvCVOoEWKuxYq1kiphdxl+nKdQIsE5jYwp16rNGHklIRERERERERERk4jhJSEREREREREREZOI4SUhERERERERERGTiOElIRERERERERERk4jhJSEREREREREREZOI4SUhERERERERERGTiisUk4bJly1C1alVYW1vD19cXR44cMfSQiIjISOiaMYcOHYKvry+sra1RrVo1rFixItsyW7duhbu7O9RqNdzd3bF9+/aCGj4REZFWBZFvRERk3Ir8JOHmzZsxfvx4TJ06FaGhoWjevDk6duyI8PBwQw+NiIiKOV0zJiwsDJ06dULz5s0RGhqKDz/8EGPHjsXWrVuVZU6cOIHevXujf//+OH/+PPr3749evXrh1KlThVUWERGZuILINyIiMn4qERFDDyI3jRo1go+PD5YvX6601a1bF927d8fcuXNfuH5CQgIcHR0RHx8PBweHghxqoYuNiUI5pwqGHkahY92mhXVTQdI1YyZNmoSdO3fiypUrStuIESNw/vx5nDhxAgDQu3dvJCQk4LffflOWeeWVV1CqVCls3LgxT+PKKbtM5XVhKnUCrNVYsVYytILItxdhdhl/naZQI8A6jY0p1KnPGov0kYQpKSkICQlBYGCgRntgYCCOHz9uoFEREZExyE/GnDhxItvyHTp0wJkzZ5CamprrMswtIiIqDAWVb0REZPwsDD2A3Ny7dw/p6ekoX768Rnv58uVx9+5dreskJycjOTlZuR8fHw8g85stY/Po0SOorY2vrhdh3aaFdRsfe3t7qFQqQw8jXxlz9+5drcunpaXh3r17cHFxyXGZnPoE8p5dxvy6eJap1AmwVmPFWo1PUcmuvCiofHses0uTKdRpCjUCrNPYmEKdOdWYn+wq0pOEWZ4vSkRyLHTu3LkICgrK1u7q6logYyMiIt0UtdM/6JIxOS3/fLuufTK7iIiKtqKWXXlREPn2LGYXEVHRlp/sKtKThGXLloW5uXm2b7xiYmKyfdOVZcqUKZgwYYJyPyMjAw8ePECZMmWKzbd/eZGQkABXV1fcuXOn2P3B8jJYN+s2BcZet729vaGHACB/GePs7Kx1eQsLC5QpUybXZXLqE8hbdhn76yKLqdQJsFZjxVqNU1HJrrwoqHx7HrPrP6ZQpynUCLBOY2MKdeZWY36yq0hPElpZWcHX1xf79u3Da6+9prTv27cP3bp107qOWq2GWq3WaCtZsmRBDtOgHBwcjPbFnhvWbVpYNxWE/GRMQEAAfv75Z422vXv3ws/PD5aWlsoy+/btw3vvvaexTJMmTXIciy7ZZSqvC1OpE2Ctxoq1kqEUVL49j9mVnSnUaQo1AqzT2JhCnfqqsUhPEgLAhAkT0L9/f/j5+SEgIAArV65EeHg4RowYYeihERFRMfeijJkyZQoiIyOxdu1aAJlXevzyyy8xYcIEDBs2DCdOnMDq1as1rlo8btw4tGjRAvPnz0e3bt2wY8cO7N+/H0ePHjVIjUREZHoKIt+IiMj4FflJwt69e+P+/fuYNWsWoqOj4eHhgV27dsHNzc3QQyMiomLuRRkTHR2N8PBwZfmqVati165deO+99/DVV1+hQoUKWLJkCXr06KEs06RJE2zatAnTpk3D9OnTUb16dWzevBmNGjUq9PqIiMg0FUS+ERGR8Svyk4QAMHLkSIwcOdLQwyhS1Go1ZsyYke0Qf2PHulm3KTDVug0lt4wJDg7O1tayZUucPXs21z579uyJnj176mN4ClN5XZhKnQBrNVaslYqKgsg3XZnKa8QU6jSFGgHWaWxMoU5916iSrMtWERERERERERERkUkyM/QAiIiIiIiIiIiIyLA4SUhERERERERERGTiOElIRERERERERERk4jhJWEwlJyfDy8sLKpUK586d03gsPDwcXbp0gZ2dHcqWLYuxY8ciJSXFMAPVk1u3bmHIkCGoWrUqbGxsUL16dcyYMSNbXcZY+7Jly1C1alVYW1vD19cXR44cMfSQ9Gru3Llo2LAh7O3t4eTkhO7du+PatWsay4gIZs6ciQoVKsDGxgatWrXCpUuXDDTigjF37lyoVCqMHz9eaTOFuilvfv31VzRq1Ag2NjYoW7YsXn/9dY3Hje29z9gzztQyzRhzzFSzi1lFujKV/DLm3GJmFd/MMsWsMuacioyMxFtvvYUyZcrA1tYWXl5eCAkJUR7XW51CxdLYsWOlY8eOAkBCQ0OV9rS0NPHw8JDWrVvL2bNnZd++fVKhQgUZPXq04QarB7/99psMHDhQ9uzZIzdu3JAdO3aIk5OTTJw4UVnGGGvftGmTWFpayqpVq+Ty5csybtw4sbOzk9u3bxt6aHrToUMHWbNmjfz1119y7tw56dy5s1SuXFkeP36sLDNv3jyxt7eXrVu3ysWLF6V3797i4uIiCQkJBhy5/pw+fVqqVKki9evXl3Hjxintxl435c2PP/4opUqVkuXLl8u1a9fk6tWrsmXLFuVxY3zvM/aMM6VMM9YcM8XsYlaRrkwpv4w5t5hZxTezTC2rjDmnHjx4IG5ubjJw4EA5deqUhIWFyf79++Wff/5RltFXnZwkLIZ27dolderUkUuXLmULol27domZmZlERkYqbRs3bhS1Wi3x8fEGGG3B+fTTT6Vq1arKfWOs3d/fX0aMGKHRVqdOHZk8ebKBRlTwYmJiBIAcOnRIREQyMjLE2dlZ5s2bpyyTlJQkjo6OsmLFCkMNU28ePXokNWvWlH379knLli2VQDP2uilvUlNTpWLFivLNN9/kuIyxvfeZasYZa6aZSo4Ze3Yxq0hXppRfpphbzKziyZizythzatKkSdKsWbMcH9dnnfy5cTHz77//YtiwYVi3bh1sbW2zPX7ixAl4eHigQoUKSluHDh2QnJyscSiqMYiPj0fp0qWV+8ZWe0pKCkJCQhAYGKjRHhgYiOPHjxtoVAUvPj4eAJTnNiwsDHfv3tXYD2q1Gi1btjSK/TBq1Ch07twZ7dq102g39ropb86ePYvIyEiYmZnB29sbLi4u6Nixo8ZPB4zpvc+UM84YM82UcszYs4tZRboylfwy1dxiZhVPxpxVxp5TO3fuhJ+fH9544w04OTnB29sbq1atUh7XZ52cJCxGRAQDBw7EiBEj4Ofnp3WZu3fvonz58hptpUqVgpWVFe7evVsYwywUN27cwNKlSzFixAilzdhqv3fvHtLT07PVVL58+WJZT16ICCZMmIBmzZrBw8MDAJRajXE/bNq0CWfPnsXcuXOzPWbMdVPe3bx5EwAwc+ZMTJs2Db/88gtKlSqFli1b4sGDBwCM573PlDPOWDPNVHLM2LOLWUX5YQr5Zaq5xcwqnow5q0whp27evInly5ejZs2a2LNnD0aMGIGxY8di7dq1APRbJycJi4CZM2dCpVLlejtz5gyWLl2KhIQETJkyJdf+VCpVtjYR0dpuaHmt/VlRUVF45ZVX8MYbb2Do0KEajxWn2vPq+bEX93pyM3r0aFy4cAEbN27M9pix7Yc7d+5g3Lhx+P7772FtbZ3jcsZWN2XK63tfRkYGAGDq1Kno0aMHfH19sWbNGqhUKmzZskXpryi/95lSxjHTtDP29zFjzi5mFT3PFPLLVHKLmaWdsb6fGWtWmUpOZWRkwMfHB3PmzIG3tzeGDx+OYcOGYfny5RrL6aNOi5ceLb200aNHo0+fPrkuU6VKFXzyySc4efIk1Gq1xmN+fn7o168fvvvuOzg7O+PUqVMaj8fFxSE1NTXbrHJRkNfas0RFRaF169YICAjAypUrNZYrbrW/SNmyZWFubp5t5j8mJqZY1vMiY8aMwc6dO3H48GFUqlRJaXd2dgaQ+e2Ii4uL0l7c90NISAhiYmLg6+urtKWnp+Pw4cP48ssvlSuPGVvdlCmv732PHj0CALi7uyvtarUa1apVQ3h4OICi/95nShnHTNNkCjlm7NnFrKLnmUJ+mUpuMbM0GXNmGXNWmUpOubi4aLyfAkDdunWxdetWAHp+LnU6gyEZ1O3bt+XixYvKbc+ePQJAfvzxR7lz546I/HfS2KioKGW9TZs2FbuTxmoTEREhNWvWlD59+khaWlq2x42xdn9/f3n33Xc12urWrWs0J88VyTzJ6qhRo6RChQpy/fp1rY87OzvL/Pnzlbbk5ORid7LZ5yUkJGj8f7548aL4+fnJW2+9JRcvXjTaukk38fHxolarNU78npKSIk5OTvL111+LiPG895laxplKphlrjplKdjGrKL9MIb9MKbeYWcUzs0whq0wlp/r27ZvtwiXjx4+XgIAAEdHvc8lJwmIsLCws2xW0si4/37ZtWzl79qzs379fKlWqVOwuP/+8yMhIqVGjhrRp00YiIiIkOjpauWUxxto3bdoklpaWsnr1arl8+bKMHz9e7Ozs5NatW4Yemt68++674ujoKAcPHtR4XhMTE5Vl5s2bJ46OjrJt2za5ePGi9O3bt9hdtj4vnr0Sl4jp1E25GzdunFSsWFH27NkjV69elSFDhoiTk5M8ePBARIzzvU/EuDPOlDLNWHPMlLOLWUV5ZWr5Zay5xcwqvpllqllljDl1+vRpsbCwkNmzZ8vff/8t69evF1tbW/n++++VZfRVJycJizFtQSSS+a1W586dxcbGRkqXLi2jR4+WpKQkwwxST9asWSMAtN6eZYy1f/XVV+Lm5iZWVlbi4+OjXLLeWOT0vK5Zs0ZZJiMjQ2bMmCHOzs6iVqulRYsWcvHiRcMNuoA8H2imUjflLiUlRSZOnChOTk5ib28v7dq1k7/++ktjGWN87zPmjDO1TDPGHDPl7GJWUV6ZWn4Za24xs4pvZplqVhlrTv3888/i4eEharVa6tSpIytXrtR4XF91qkREdPuBMhERERERERERERkTXt2YiIiIiIiIiIjIxHGSkIiIiIiIiIiIyMRxkpCIiIiIiIiIiMjEcZKQiIiIiIiIiIjIxHGSkIiIiIiIiIiIyMRxkpCIiIiIiIiIiMjEcZKQiIiIiIiIiIjIxHGSkIiIiIiIiIiIyMRxkpDoGcHBwShZsuRL95OSkoIaNWrg2LFjOS5z69YtqFQqnDt37qW3V9QMHDgQ3bt3z3WZgwcPQqVS4eHDhwU2jvxsIzExET169ICDg0Ou677//vsYO3asfgZKRPQSmF36wewiIio8zC79YHaRvnGSkDTExMRg+PDhqFy5MtRqNZydndGhQwecOHHC0EMrVlauXAk3Nzc0bdrU0EMxiC+++ALBwcHK/VatWmH8+PEayzRp0gTR0dFwdHQs3MG9wHfffYcjR47g+PHjiI6ORlxcnNY/Kv73v/9hzZo1CAsLM8xAiUjB7NIPZhezi4gKD7NLP5hdzC7SL04SkoYePXrg/Pnz+O6773D9+nXs3LkTrVq1woMHDww9tGJl6dKlGDp0qKGHYTCOjo4v/GbQysoKzs7OUKlUhTOoPLpx4wbq1q0LDw+PXMfn5OSEwMBArFixopBHSETPY3bpB7OL2UVEhYfZpR/MLmYX6ZkQ/b+4uDgBIAcPHnzhcsOGDRMnJydRq9VSr149+fnnn0VE5N69e9KnTx+pWLGi2NjYiIeHh2zYsEFj/ZYtW8qYMWPkgw8+kFKlSkn58uVlxowZuW7z9OnT0q5dOylTpow4ODhIixYtJCQkRGMZALJq1Srp3r272NjYSI0aNWTHjh0ay+zYsUNq1Kgh1tbW0qpVKwkODhYAEhcXJyIia9asEUdHR411du7cKT4+PqJWq6Vq1aoyc+ZMSU1NzXGsISEhYmZmJvHx8Rrtp06dEi8vL1Gr1eLr6yvbtm0TABIaGqosc+nSJenYsaPY2dmJk5OTvPXWWxIbG6s8npCQIG+++abY2tqKs7OzLFy4UFq2bCnjxo3Ldf89KywsTADIxo0bJSAgQNRqtbi7u8uBAwc0ljt48KA0bNhQrKysxNnZWSZNmqRR95YtW8TDw0Osra2ldOnS0rZtW3n8+LGIiAwYMEC6deum/BuAxi0sLEwOHDig7PuHDx+KtbW1/Pbbbxpj2Lp1q9ja2sqjR49ERCQiIkJ69eolJUuWlNKlS0vXrl0lLCwsx1qf3UaWY8eOSfPmzcXa2loqVaokY8aMUcbdsmVLjXE+fz+rLUtwcLC4urrmed8Tkf4xu5hdz2J2MbuIigNmF7PrWcwuZldRwklCUqSmpkqJEiVk/PjxkpSUpHWZ9PR0ady4sdSrV0/27t0rN27ckJ9//ll27dolIplvJp999pmEhobKjRs3ZMmSJWJubi4nT55U+mjZsqU4ODjIzJkz5fr16/Ldd9+JSqWSvXv35ji233//XdatWyeXL1+Wy5cvy5AhQ6R8+fKSkJCgLANAKlWqJBs2bJC///5bxo4dKyVKlJD79++LSOabtKWlpbz//vty9epV2bhxo1SsWDHXsNq9e7c4ODhIcHCw3LhxQ/bu3StVqlSRmTNn5jjWRYsWSZ06dTTaHj9+LOXKlZPevXvLX3/9JT///LNUq1ZNI6yioqKkbNmyMmXKFLly5YqcPXtW2rdvL61bt1b6GTp0qLi5ucn+/fvl4sWL8tprr4m9vX2+wqpSpUry448/yuXLl2Xo0KFib28v9+7dE5HM59HW1lZGjhwpV65cke3bt0vZsmWVPyqioqLEwsJCFi5cKGFhYXLhwgX56quvlFB5NqwePnwoAQEBMmzYMImOjpbo6GhJS0vLFiQ9evSQt956S2OsPXr0kL59+4qIyJMnT6RmzZoyePBguXDhgly+fFnefPNNqV27tiQnJ2ut9fltXLhwQUqUKCGLFi2S69evy7Fjx8Tb21sGDhwoIiL379+XYcOGSUBAgERHR8v9+/fl9OnTAkD279+vtGW5fPmyAJBbt27lef8TkX4xu5hdzC5mF1Fxw+xidjG7mF1FFScJScOPP/4opUqVEmtra2nSpIlMmTJFzp8/rzy+Z88eMTMzk2vXruW5z06dOsnEiROV+y1btpRmzZppLNOwYUOZNGlSnvtMS0sTe3t75Zs0kcywmjZtmnL/8ePHolKplG9JJk2aJB4eHhr9TJ06Ndewat68ucyZM0djnXXr1omLi0uOYxs3bpy0adNGo+3rr7+W0qVLy5MnT5S25cuXa4TV9OnTJTAwUGO9O3fuCAC5du2aJCQkiKWlpWzZskV5/OHDh2Jra5uvsJo3b57SlpqaKpUqVZL58+eLiMiHH34otWvXloyMDGWZr776SkqUKCHp6ekSEhKS65v0s2ElIlq/dXs+SLZt2yYlSpRQ9lF8fLxYW1vLr7/+KiIiq1evzjam5ORksbGxkT179mgdx/Pb6N+/v7zzzjsayxw5ckTMzMzk6dOnIpL5/D37rVXW/nr2m8cs8fHxefoWmIgKFrOL2SXC7GJ2ERUvzC5mlwizi9lV9PCchKShR48eiIqKws6dO9GhQwccPHgQPj4+yslQz507h0qVKqFWrVpa109PT8fs2bNRv359lClTBiVKlMDevXsRHh6usVz9+vU17ru4uCAmJibHccXExGDEiBGoVasWHB0d4ejoiMePH+far52dHezt7ZV+r127hoYNG2os7+/vn+v+CAkJwaxZs1CiRAnlNmzYMERHRyMxMVHrOk+fPoW1tbVG25UrV9CgQQPY2toqbQEBAdm2deDAAY1t1alTB0Dm+Rpu3ryJ1NRUjTE7Ojqidu3audaQk2e3b2FhAT8/P1y5ckUZb0BAgMZ5IZo2bYrHjx8jIiICDRo0QNu2beHp6Yk33ngDq1atQlxcXL7GkaVz586wsLDAzp07AQBbt26Fvb09AgMDAWTun3/++Qf29vbK/ildujSSkpJw48aNPG0jJCQEwcHBGvu4Q4cOyMjIyNeJcG1sbAAgx9cCERUOZpcmZhezKzfMLqKigdmlidnF7MoNs6vwWBh6AFT0WFtbo3379mjfvj0++ugjDB06FDNmzMDAgQOV/5w5+fzzz7Fo0SIsXrwYnp6esLOzw/jx45GSkqKxnKWlpcZ9lUqFjIyMHPsdOHAgYmNjsXjxYri5uUGtViMgIECnfkUk28lQRSTXejIyMhAUFITXX38922PPB1KWsmXL4uLFizptJ2tbXbp0wfz587M95uLigr///hsAdK5BF1l957avVCoVzM3NsW/fPhw/fhx79+7F0qVLMXXqVJw6dQpVq1bN17atrKzQs2dPbNiwAX369MGGDRvQu3dvWFhkvk1lZGTA19cX69evz7ZuuXLl8rSNjIwMDB8+HGPHjs32WOXKlXUec9aJpfO6fSIqOMyu/zC7sm+H2fUfZhdR0cHs+g+zK/t2mF3/YXYVHh5JSC/k7u6OJ0+eAMj8xigiIgLXr1/XuuyRI0fQrVs3vPXWW2jQoAGqVaumvMm+jCNHjmDs2LHo1KkT6tWrB7VajXv37unUR506dfDnn39qtJ05cybXdXx8fHDt2jXUqFEj283MTPt/H29vb1y9elUjRNzd3XH+/Hk8ffpUaTt58mS2bV26dAlVqlTJti07OztUr14dlpaWOH36tLJOQkJCvvfvs9tPS0tDSEiI8g2au7s7jh8/rlHD8ePHYW9vj4oVKwLIDK2mTZsiKCgIoaGhsLKywvbt27Vuy8rKCunp6S8cU79+/bB7925cunQJBw4cQL9+/ZTHfHx88Pfff8PJySnb/nF0dMxTzVn7WNvzaWVllePYAWgd/19//QVLS0vUq1cvT9snosLD7GJ2AcwuZhdR8cLsYnYBzC5ml2FxkpAU9+/fR5s2bfD999/jwoULCAsLw5YtW/Dpp5+iW7duAICWLVuiRYsW6NGjB/bt24ewsDD89ttv2L17NwCgRo0ayjcdV65cwfDhw3H37t2XHluNGjWwbt06XLlyBadOnUK/fv1e+O3a84YPH46rV69i0qRJuH79On744QflcP6cLrf+0UcfYe3atZg5cyYuXbqEK1euYPPmzZg2bVqO22ndujWePHmCS5cuKW1vvvkmzMzMMGTIEFy+fBm7du3CggULNNYbNWoUHjx4gL59++L06dO4efMm9u7di8GDByM9PR329vYYMGAAPvjgAxw4cACXLl3C4MGDYWZmlq/L2X/11VfYvn07rl69ilGjRiEuLg6DBw8GAIwcORJ37tzBmDFjcPXqVezYsQMzZszAhAkTYGZmhlOnTmHOnDk4c+YMwsPDsW3bNsTGxqJu3bpat1WlShWcOnUKt27dwr1793L89rJly5YoX748+vXrhypVqqBx48bKY/369UPZsmXRrVs3HDlyBGFhYTh06BDGjRuHiIiIPNU8adIknDhxAqNGjcK5c+fw999/Y+fOnRgzZkyO6zg5OcHGxga7d+/Gv//+i/j4eOWxI0eOoHnz5jq/FolIf5hd2TG7mF3MLqKijdmVHbOL2cXsKiIK7/SHVNQlJSXJ5MmTxcfHRxwdHcXW1lZq164t06ZNk8TERGW5+/fvy6BBg6RMmTJibW0tHh4e8ssvvyiPdevWTUqUKCFOTk4ybdo0efvtt194MtVu3brJgAEDchzb2bNnxc/PT9RqtdSsWVO2bNkibm5usmjRImUZALJ9+3aN9RwdHWXNmjXK/R07dkiNGjVErVZLq1atlJPYZp089fkT6IpkXmmrSZMmYmNjIw4ODuLv7y8rV67MdV/26dNHJk+erNF24sQJadCggVhZWYmXl5ds3bo124lZr1+/Lq+99pqULFlSbGxspE6dOjJ+/HjlpLEJCQny5ptviq2trTg7O8vChQvF399fY1szZswQNze3HMeWdULYDRs2SKNGjcTKykrq1q0rv//+u8ZyBw8elIYNG4qVlZU4OzvLpEmTJDU1VUQyry7VoUMHKVeunKjVaqlVq5YsXbpUWff5E+heu3ZNGjduLDY2NgJAwsLCsp3cNssHH3wgAOSjjz7KNvbo6Gh5++23pWzZsqJWq6VatWoybNgwiY+P11qrtm2cPn1a2rdvLyVKlBA7OzupX7++zJ49W3n8+RPoioisWrVKXF1dxczMTOOxWrVqycaNG7Vum4gKB7OL2fUsZtd/mF1ERRezi9n1LGbXf5hdhqcS0eMP64mKmdmzZ2PFihW4c+eOXvu9ePEi2rVrp5zwtaA8efIEFStWxOeff44hQ4YAyDyPCADl27rn3bp1C1WrVkVoaCi8vLwKbGzG7tdff8UHH3yACxcuKOfvICIqDMwurwIbm7FjdhGRoTC7vApsbMaO2VW4uIfJpCxbtgwNGzZEmTJlcOzYMXz22WcYPXq03rfj6emJTz/9FLdu3YKnp6fe+g0NDcXVq1fh7++P+Ph4zJo1CwCUnyUAwKFDh3D48GG9bZO0e/LkCdasWcOgIqICx+wifWF2EVFhYXaRvjC7Chf3MpmUv//+G5988gkePHiAypUrY+LEiZgyZUqBbGvAgAEF0u+CBQtw7do1WFlZwdfXF0eOHEHZsmWVx/NzSXnSXa9evQw9BCIyEcwu0hdmFxEVFmYX6Quzq3Dx58ZEREREREREREQmjlc3JiIiIiIiIiIiMnGcJCQiIiIiIiIiIjJxnCQkIiIiIiIiIiIycZwkJCIiIiIiIiIiMnGcJCQiIiIiIiIiIjJxnCQkIiIiIiIiIiIycZwkJCIiIiIiIiIiMnGcJCQiIiIiIiIiIjJxnCQkIiIiIiIiIiIycf8HcTY/ehw5IBAAAAAASUVORK5CYII=",
+ "text/plain": [
+ "
"
+ ],
+ "text/plain": [
+ " n TAT-C / measured, 5th percentile TAT-C / measured, median \\\n",
+ "ATMS 20736.0 0.9917 1.0000 \n",
+ "MODIS 45144.0 0.9992 0.9997 \n",
+ "VIIRS 40902.0 0.9997 1.0001 \n",
+ "\n",
+ " TAT-C / measured, 95th percentile \n",
+ "ATMS 1.0084 \n",
+ "MODIS 1.0017 \n",
+ "VIIRS 1.0017 "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 3, figsize=(13, 3.8))\n",
+ "panels = [\n",
+ " (\"ATMS\", atms_sizes, [(\"measured_advance\", \"measured, scan to scan\")]),\n",
+ " (\n",
+ " \"MODIS\",\n",
+ " modis_sizes,\n",
+ " [\n",
+ " (\"measured_pitch\", \"measured, detector to detector\"),\n",
+ " (\"measured_advance\", \"measured, scan to scan (per detector)\"),\n",
+ " ],\n",
+ " ),\n",
+ " (\n",
+ " \"VIIRS (M bands)\",\n",
+ " viirs_sizes.dropna(subset=[\"measured_along\"]),\n",
+ " [\n",
+ " (\"measured_pitch\", \"measured, detector to detector\"),\n",
+ " (\"measured_advance\", \"measured, scan to scan (per detector)\"),\n",
+ " ],\n",
+ " ),\n",
+ "]\n",
+ "for ax, (name, df, measured) in zip(axes, panels):\n",
+ " for (column, label), marker in zip(measured, [\"o\", \"s\"]):\n",
+ " ax.plot(\n",
+ " *binned_median(df, column),\n",
+ " marker,\n",
+ " color=INK if marker == \"o\" else GRAY,\n",
+ " ms=3,\n",
+ " label=label,\n",
+ " )\n",
+ " ax.plot(*binned_median(df, \"spacing_along\"), color=BLUE, lw=1.5, label=\"TAT-C\")\n",
+ " ax.set(\n",
+ " xlabel=\"Scan angle (deg, positive left)\",\n",
+ " ylabel=\"Along-track spacing (km)\",\n",
+ " title=name,\n",
+ " ylim=(0, None),\n",
+ " )\n",
+ " ax.legend(frameon=False, fontsize=8)\n",
+ "fig.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "def nadir_edge(df, columns):\n",
+ " \"\"\"Medians of columns near nadir (within 2 deg) and at the swath edges (beyond 50 deg).\"\"\"\n",
+ " return pd.concat(\n",
+ " {\n",
+ " \"nadir\": df[df.scan_angle.abs() < 2][columns].median(),\n",
+ " \"edges\": df[df.scan_angle.abs() > 50][columns].median(),\n",
+ " },\n",
+ " axis=1,\n",
+ " ).T\n",
+ "\n",
+ "\n",
+ "COLUMNS2 = [\"measured_pitch\", \"measured_advance\", \"measured_along\", \"spacing_along\"]\n",
+ "display(\n",
+ " pd.concat(\n",
+ " {\n",
+ " \"ATMS\": nadir_edge(atms_sizes.assign(measured_pitch=np.nan), COLUMNS2),\n",
+ " \"MODIS\": nadir_edge(modis_sizes, COLUMNS2),\n",
+ " \"VIIRS\": nadir_edge(viirs_sizes, COLUMNS2),\n",
+ " }\n",
+ " )\n",
+ " .rename(\n",
+ " columns={\n",
+ " \"measured_pitch\": \"measured, detector pitch (km)\",\n",
+ " \"measured_advance\": \"measured, scan advance per detector (km)\",\n",
+ " \"measured_along\": \"measured, along track (km)\",\n",
+ " \"spacing_along\": \"TAT-C, along track (km)\",\n",
+ " }\n",
+ " )\n",
+ " .round(3)\n",
+ ")\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " \"ATMS\": ratio_summary(atms_sizes.spacing_along, atms_sizes.measured_along),\n",
+ " \"MODIS\": ratio_summary(modis_sizes.spacing_along, modis_sizes.measured_along),\n",
+ " \"VIIRS\": ratio_summary(viirs_sizes.spacing_along, viirs_sizes.measured_along),\n",
+ " }\n",
+ ").T.rename(columns=lambda c: f\"TAT-C / measured, {c}\" if c != \"n\" else c).round(4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4666c9af",
+ "metadata": {},
+ "source": [
+ "TAT-C's along-track spacing matches the measured spacing of all three instruments to within 0.8% (5th to 95th percentile), and to within 0.2% for MODIS and VIIRS.\n",
+ "\n",
+ "* **MODIS:** 1.01 km at nadir and 1.84 km beyond 50 deg. At nadir its scans advance slightly farther (1.01 km per detector) than its detector pitch (0.99 km).\n",
+ "* **VIIRS:** 0.75 km at nadir and 1.44 km beyond 50 deg.\n",
+ "* **ATMS:** a nearly constant 17.7 km at nadir and 17.4 km at the edges (17.5 km measured).\n",
+ "\n",
+ "The scans of MODIS and VIIRS advance a nearly constant distance per detector (1.0 and 0.74 km). Toward the edges, where the detector pitch doubles, consecutive scans therefore overlap (the bow tie). Their samples coincide rather than interleave, so a scan's detector pitch sets the sampling along track.\n",
+ "\n",
+ "The samples of ATMS, one per scan position, are spaced by the scan's advance. Without a scan period, TAT-C would project the along-track angle (1.112 deg) around the line of sight: 16.2 km at nadir, growing like the cross-track spacing to 30 km at the edges (-8% to +70%). Angular spacing describes an array of detectors, not a single beam scanned at a fixed period.\n",
+ "\n",
+ "## Test 3: Conical Spacing\n",
+ "\n",
+ "AMSR2 and GMI sample their scanned arcs at a constant rate, and their arcs advance with the satellite from one scan to the next. Each `ConicalInstrument` has the cone angle and scan sector measured in the companion notebooks, and the azimuth between samples (`scan_sample_spacing`):\n",
+ "* **AMSR2:** 0.6225 deg, as measured in the [AMSR2 validation](ValidateConicalAMSR2.ipynb).\n",
+ "* **GMI:** 152.06 / 220 = 0.691 deg between its 221 samples, which is the 3.6 ms integration time of the [GMI L1B ATBD](https://gpm.nasa.gov/sites/default/files/document_files/GMIL1B_ATBD.pdf) at 32 rpm.\n",
+ "\n",
+ "Each also has an along-track field of view tailored to one scan period (1.5 s for AMSR2, 1.875 s for GMI) with `compute_along_track_field_of_view`. TAT-C's spacing along the arc is the azimuth between samples times the arc's radius on the ground. Its spacing between scans is the distance the arc advances in one scan."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "25564a27",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T16:06:13.404248Z",
+ "iopub.status.busy": "2026-10-25T16:06:13.404248Z",
+ "iopub.status.idle": "2026-10-25T16:06:17.656949Z",
+ "shell.execute_reply": "2026-10-25T16:06:17.656949Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AMSR2: 243 samples per scan, 249 scans; along-track field of view 0.829 deg\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GMI, low frequency (S1): 221 samples per scan, 231 scans; along-track field of view 1.744 deg\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GMI, high frequency (S2): 221 samples per scan, 231 scans; along-track field of view 1.744 deg\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
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+ " \n",
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measured, median (km)
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TAT-C, median (km)
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TAT-C / measured, 5th percentile
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TAT-C / measured, median
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6.327
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1.008
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1.013
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scan to scan
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13.048
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13.358
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1.018
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1.024
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1.030
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GMI, high frequency (S2)
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along the arc
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5.569
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5.606
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0.998
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1.008
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1.014
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scan to scan
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13.051
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13.358
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+ "text/plain": [
+ " measured, median (km) \\\n",
+ "AMSR2 along the arc 9.130 \n",
+ " scan to scan 10.206 \n",
+ "GMI, low frequency (S1) along the arc 6.287 \n",
+ " scan to scan 13.048 \n",
+ "GMI, high frequency (S2) along the arc 5.569 \n",
+ " scan to scan 13.051 \n",
+ "\n",
+ " TAT-C, median (km) \\\n",
+ "AMSR2 along the arc 9.159 \n",
+ " scan to scan 10.318 \n",
+ "GMI, low frequency (S1) along the arc 6.327 \n",
+ " scan to scan 13.358 \n",
+ "GMI, high frequency (S2) along the arc 5.606 \n",
+ " scan to scan 13.358 \n",
+ "\n",
+ " TAT-C / measured, 5th percentile \\\n",
+ "AMSR2 along the arc 0.999 \n",
+ " scan to scan 0.991 \n",
+ "GMI, low frequency (S1) along the arc 0.998 \n",
+ " scan to scan 1.018 \n",
+ "GMI, high frequency (S2) along the arc 0.998 \n",
+ " scan to scan 1.019 \n",
+ "\n",
+ " TAT-C / measured, median \\\n",
+ "AMSR2 along the arc 1.003 \n",
+ " scan to scan 1.010 \n",
+ "GMI, low frequency (S1) along the arc 1.008 \n",
+ " scan to scan 1.024 \n",
+ "GMI, high frequency (S2) along the arc 1.008 \n",
+ " scan to scan 1.024 \n",
+ "\n",
+ " TAT-C / measured, 95th percentile \n",
+ "AMSR2 along the arc 1.005 \n",
+ " scan to scan 1.036 \n",
+ "GMI, low frequency (S1) along the arc 1.013 \n",
+ " scan to scan 1.030 \n",
+ "GMI, high frequency (S2) along the arc 1.014 \n",
+ " scan to scan 1.029 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "CONICAL = {\n",
+ " # cone angle, scan sector center and half-width, from ValidateConicalAMSR2 and ValidateConicalGMI; azimuth between samples (deg)\n",
+ " \"AMSR2\": dict(\n",
+ " data=amsr2,\n",
+ " lat=\"lat\",\n",
+ " lon=\"lon\",\n",
+ " day=datetime(2026, 9, 26, tzinfo=timezone.utc),\n",
+ " period=1.5,\n",
+ " tle=[\n",
+ " \"1 38337U 12025A 26277.29678146 .00000240 00000+0 63281-4 0 9990\",\n",
+ " \"2 38337 98.2100 216.2278 0001644 77.4488 41.8708 14.57111943764929\",\n",
+ " ],\n",
+ " cone=47.564,\n",
+ " center=0.498,\n",
+ " half_width=75.332,\n",
+ " spacing=0.6225,\n",
+ " ),\n",
+ " \"GMI, low frequency (S1)\": dict(\n",
+ " data=gmi,\n",
+ " lat=\"S1_lat\",\n",
+ " lon=\"S1_lon\",\n",
+ " day=datetime(2026, 10, 3, tzinfo=timezone.utc),\n",
+ " period=1.875,\n",
+ " tle=[\n",
+ " \"1 39574U 14009C 26277.54227627 .00004528 00000+0 10004-3 0 9997\",\n",
+ " \"2 39574 64.9701 301.6724 0010793 268.6572 91.3332 15.45217995714704\",\n",
+ " ],\n",
+ " cone=48.460,\n",
+ " center=-0.285,\n",
+ " half_width=76.031,\n",
+ " spacing=2 * 76.031 / 220,\n",
+ " ),\n",
+ " \"GMI, high frequency (S2)\": dict(\n",
+ " data=gmi,\n",
+ " lat=\"S2_lat\",\n",
+ " lon=\"S2_lon\",\n",
+ " day=datetime(2026, 10, 3, tzinfo=timezone.utc),\n",
+ " period=1.875,\n",
+ " tle=[\n",
+ " \"1 39574U 14009C 26277.54227627 .00004528 00000+0 10004-3 0 9997\",\n",
+ " \"2 39574 64.9701 301.6724 0010793 268.6572 91.3332 15.45217995714704\",\n",
+ " ],\n",
+ " cone=45.284,\n",
+ " center=-0.341,\n",
+ " half_width=76.024,\n",
+ " spacing=2 * 76.024 / 220,\n",
+ " ),\n",
+ "}\n",
+ "conical_sizes, conical_rows = {}, {}\n",
+ "for name, c in CONICAL.items():\n",
+ " orbit = GeneralPerturbationsOrbit.from_tle(c[\"tle\"])\n",
+ " gp = orbit.to_gp_orbit()\n",
+ " # the along-track field of view tailored to one scan period: the scan-to-scan advance\n",
+ " along_fov = compute_along_track_field_of_view(\n",
+ " gp.get_semimajor_axis() - EARTH_MEAN_RADIUS,\n",
+ " c[\"period\"],\n",
+ " gp.get_inclination(),\n",
+ " eccentricity=gp.get_eccentricity(),\n",
+ " )\n",
+ " instrument = ConicalInstrument(\n",
+ " name=name,\n",
+ " cone_angle=c[\"cone\"],\n",
+ " scan_center_azimuth=c[\"center\"],\n",
+ " scan_half_width=c[\"half_width\"],\n",
+ " along_track_field_of_view=along_fov,\n",
+ " scan_sample_spacing=c[\"spacing\"],\n",
+ " )\n",
+ " satellite = Satellite(\n",
+ " name=name, orbit=orbit, instruments=[instrument], attitude=INERTIAL\n",
+ " )\n",
+ " lat, lon, t = (\n",
+ " c[\"data\"][c[\"lat\"]].astype(float),\n",
+ " c[\"data\"][c[\"lon\"]].astype(float),\n",
+ " c[\"data\"][\"time\"],\n",
+ " )\n",
+ " period = np.diff(t)\n",
+ " usable = np.zeros(len(t), dtype=bool)\n",
+ " usable[1:-1] = (\n",
+ " np.all(np.abs(lat[1:-1]) <= 90, axis=1)\n",
+ " & (np.abs(period[:-1] - c[\"period\"]) < 0.05)\n",
+ " & (np.abs(period[1:] - c[\"period\"]) < 0.05)\n",
+ " )\n",
+ " usable[1:-1] &= np.all(np.abs(lat[:-2]) <= 90, axis=1) & np.all(\n",
+ " np.abs(lat[2:]) <= 90, axis=1\n",
+ " )\n",
+ " scans = np.nonzero(usable)[0][::200]\n",
+ " xyz = ecef(lon, lat)\n",
+ " n = lat.shape[1]\n",
+ " epochs = np.repeat(\n",
+ " pd.DatetimeIndex([c[\"day\"] + timedelta(seconds=float(s)) for s in t[scans]]), n\n",
+ " )\n",
+ " sizes = sample_sizes(satellite, name, lon[scans], lat[scans], epochs).assign(\n",
+ " sample=np.tile(np.arange(n), len(scans)),\n",
+ " measured_cross=neighbor_spacing(xyz[scans], 1).ravel(),\n",
+ " measured_along=(\n",
+ " np.linalg.norm(xyz[scans + 1] - xyz[scans - 1], axis=-1) / 2e3\n",
+ " ).ravel(),\n",
+ " )\n",
+ " conical_sizes[name] = sizes\n",
+ " for direction, label in [(\"cross\", \"along the arc\"), (\"along\", \"scan to scan\")]:\n",
+ " conical_rows[(name, label)] = pd.concat(\n",
+ " [\n",
+ " pd.Series(\n",
+ " {\n",
+ " \"measured, median (km)\": sizes[\n",
+ " f\"measured_{direction}\"\n",
+ " ].median(),\n",
+ " \"TAT-C, median (km)\": sizes[f\"spacing_{direction}\"].median(),\n",
+ " }\n",
+ " ),\n",
+ " ratio_summary(\n",
+ " sizes[f\"spacing_{direction}\"], sizes[f\"measured_{direction}\"]\n",
+ " )\n",
+ " .drop(\"n\")\n",
+ " .rename(lambda c: f\"TAT-C / measured, {c}\"),\n",
+ " ]\n",
+ " )\n",
+ " print(\n",
+ " f\"{name}: {n} samples per scan, {len(scans)} scans; along-track field of view {along_fov:.3f} deg\"\n",
+ " )\n",
+ "pd.DataFrame(conical_rows).T.round(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "9d7a4841",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T16:06:17.658953Z",
+ "iopub.status.busy": "2026-10-25T16:06:17.658953Z",
+ "iopub.status.idle": "2026-10-25T16:06:17.901016Z",
+ "shell.execute_reply": "2026-10-25T16:06:17.901016Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 2, figsize=(11, 3.6), sharey=True)\n",
+ "for (name, sizes), color in zip(conical_sizes.items(), [BLUE, ORANGE, AQUA]):\n",
+ " for ax, direction in zip(axes, [\"cross\", \"along\"]):\n",
+ " ratio = (\n",
+ " (sizes[f\"spacing_{direction}\"] / sizes[f\"measured_{direction}\"])\n",
+ " .groupby(sizes[\"sample\"])\n",
+ " .median()\n",
+ " )\n",
+ " ax.plot(ratio.index / ratio.index.max(), ratio, color=color, lw=1.5, label=name)\n",
+ "for ax, title in zip(axes, [\"Along the arc\", \"Scan to scan\"]):\n",
+ " ax.axhline(1, color=GRAY, lw=0.8)\n",
+ " ax.set(\n",
+ " xlabel=\"Position along the scanned arc (0 to 1)\", title=title, ylim=(0.97, 1.05)\n",
+ " )\n",
+ "axes[0].set_ylabel(\"TAT-C / measured spacing (median)\")\n",
+ "axes[0].legend(frameon=False, fontsize=8)\n",
+ "fig.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63c901fe",
+ "metadata": {},
+ "source": [
+ "Along the arc, TAT-C's spacing matches the measured spacing to within 1.4% (95th percentile):\n",
+ "* **AMSR2:** 9.16 km (9.13 km measured; JAXA's nominal sampling interval is 10 km).\n",
+ "* **GMI:** 6.33 and 5.61 km for its two feeds (6.29 and 5.57 km measured).\n",
+ "\n",
+ "Between scans, TAT-C overestimates the spacing by 1.0% for AMSR2 and 2.4% for GMI (median). The cause is `compute_along_track_field_of_view`, which tailors the along-track field of view to the fastest ground speed over the orbit (relative to the rotating Earth, at perigee), so that consecutive footprints never leave gaps, rather than to the advance at a given time. Both spacings are nearly uniform along the arc.\n",
+ "\n",
+ "## Test 4: Footprint Sizes\n",
+ "\n",
+ "Published footprint sizes are computed for nominal altitudes:\n",
+ "* 824 km for the JPSS instruments (the altitude of Suomi NPP);\n",
+ "* 705 km for MODIS;\n",
+ "* 700 km for AMSR2; and\n",
+ "* 407 km for GMI (the nominal altitude in its ATBD) and 435 km after GPM's orbit boost of November 2023, the altitude [TC PRIMED](https://rammb-data.cira.colostate.edu/tcprimed/) uses.\n",
+ "\n",
+ "Each is compared with TAT-C's sample size for a circular orbit at the same altitude (`CircularOrbit`), along track by across track. Cross-track scanners use a `PointedInstrument` whose fields of view are the beam width or pixel field of view:\n",
+ "* **ATMS:** beams of 5.2, 2.2, and 1.1 deg.\n",
+ "* **CrIS:** 0.963 deg.\n",
+ "* **VIIRS:** the detector pitch along track; across track, three aggregated samples of 0.01779 deg at nadir and one at the edges.\n",
+ "* **MODIS:** 0.0812 deg.\n",
+ "\n",
+ "Their footprints are evaluated at nadir and at the edge of the swath: 52.725 deg for ATMS, 49.43 deg for CrIS (its outermost field of view), 56.0 deg for VIIRS, and 55.0 deg for MODIS. Conical scanners use a `ConicalInstrument` with the beam width, evaluated at the center of the scan with the nominal cone angle: 47.5 deg for AMSR2, and 48.5 deg (10 to 89 GHz) and 45.36 deg (166 and 183 GHz) for GMI.\n",
+ "\n",
+ "Sources:\n",
+ "* **ATMS:** the sensor IFOV tables of the [TC PRIMED documentation](https://rammb-data.cira.colostate.edu/tcprimed/TCPRIMED_v01r01_documentation.pdf) (version v01r01), which also give GMI's footprints after its orbit boost.\n",
+ "* **CrIS:** the [NWP SAF](https://nwp-saf.eumetsat.int/site/download/documentation/aapp/NWPSAF-MF-UD-011_viirs-cris-mapping.pdf).\n",
+ "* **VIIRS:** [NOAA/CIMSS](https://www.ssec.wisc.edu/mcidas/mug_meeting/2012/presentations/cimss_suomi_npp_basics.pdf) (band M16).\n",
+ "* **MODIS:** [NASA LAADS](https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD021KM).\n",
+ "* **AMSR2:** [JAXA](https://suzaku.eorc.jaxa.jp/GCOM_W/w_amsr2/amsr2_body_main.html).\n",
+ "* **GMI:** the [GMI L1B ATBD](https://gpm.nasa.gov/sites/default/files/document_files/GMIL1B_ATBD.pdf) (Table 1-1), and TC PRIMED after the orbit boost."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "d3baa478",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T16:06:17.903874Z",
+ "iopub.status.busy": "2026-10-25T16:06:17.903019Z",
+ "iopub.status.idle": "2026-10-25T16:06:17.987923Z",
+ "shell.execute_reply": "2026-10-25T16:06:17.987923Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
sensor
\n",
+ "
beam
\n",
+ "
location
\n",
+ "
altitude (km)
\n",
+ "
published (km)
\n",
+ "
TAT-C (km)
\n",
+ "
TAT-C / published, along
\n",
+ "
TAT-C / published, across
\n",
+ "
source
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
\n",
+ "
ATMS
\n",
+ "
channels 1-2
\n",
+ "
nadir
\n",
+ "
824
\n",
+ "
74.8 x 74.8
\n",
+ "
74.6 x 74.6
\n",
+ "
0.998
\n",
+ "
0.998
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
1
\n",
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ATMS
\n",
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channels 1-2
\n",
+ "
edge
\n",
+ "
824
\n",
+ "
141.8 x 323.1
\n",
+ "
141 x 327
\n",
+ "
0.998
\n",
+ "
1.014
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
2
\n",
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ATMS
\n",
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channels 3-16
\n",
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nadir
\n",
+ "
824
\n",
+ "
31.6 x 31.6
\n",
+ "
31.5 x 31.5
\n",
+ "
0.998
\n",
+ "
0.998
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
3
\n",
+ "
ATMS
\n",
+ "
channels 3-16
\n",
+ "
edge
\n",
+ "
824
\n",
+ "
60 x 136.7
\n",
+ "
59.8 x 136
\n",
+ "
0.997
\n",
+ "
0.998
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
4
\n",
+ "
ATMS
\n",
+ "
channels 17-22
\n",
+ "
nadir
\n",
+ "
824
\n",
+ "
15.8 x 15.8
\n",
+ "
15.8 x 15.8
\n",
+ "
0.998
\n",
+ "
0.998
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
5
\n",
+ "
ATMS
\n",
+ "
channels 17-22
\n",
+ "
edge
\n",
+ "
824
\n",
+ "
30 x 68.4
\n",
+ "
29.9 x 68
\n",
+ "
0.996
\n",
+ "
0.995
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
6
\n",
+ "
CrIS
\n",
+ "
field of view
\n",
+ "
nadir
\n",
+ "
824
\n",
+ "
14 x 14
\n",
+ "
13.8 x 13.8
\n",
+ "
0.986
\n",
+ "
0.986
\n",
+ "
NWP SAF
\n",
+ "
\n",
+ "
\n",
+ "
7
\n",
+ "
CrIS
\n",
+ "
field of view
\n",
+ "
edge
\n",
+ "
824
\n",
+ "
24 x 48
\n",
+ "
23.5 x 45.8
\n",
+ "
0.981
\n",
+ "
0.953
\n",
+ "
NWP SAF
\n",
+ "
\n",
+ "
\n",
+ "
8
\n",
+ "
VIIRS
\n",
+ "
M bands
\n",
+ "
nadir
\n",
+ "
824
\n",
+ "
0.742 x 0.776
\n",
+ "
0.732 x 0.765
\n",
+ "
0.986
\n",
+ "
0.986
\n",
+ "
NOAA/CIMSS
\n",
+ "
\n",
+ "
\n",
+ "
9
\n",
+ "
VIIRS
\n",
+ "
M bands
\n",
+ "
edge
\n",
+ "
824
\n",
+ "
1.6 x 1.58
\n",
+ "
1.58 x 1.57
\n",
+ "
0.990
\n",
+ "
0.991
\n",
+ "
NOAA/CIMSS
\n",
+ "
\n",
+ "
\n",
+ "
10
\n",
+ "
MODIS
\n",
+ "
1 km bands
\n",
+ "
nadir
\n",
+ "
705
\n",
+ "
1 x 1
\n",
+ "
0.996 x 0.996
\n",
+ "
0.996
\n",
+ "
0.996
\n",
+ "
NASA LAADS
\n",
+ "
\n",
+ "
\n",
+ "
11
\n",
+ "
MODIS
\n",
+ "
1 km bands
\n",
+ "
edge
\n",
+ "
705
\n",
+ "
2 x 4.8
\n",
+ "
2 x 4.8
\n",
+ "
0.998
\n",
+ "
1.000
\n",
+ "
NASA LAADS
\n",
+ "
\n",
+ "
\n",
+ "
12
\n",
+ "
AMSR2
\n",
+ "
6.925 GHz
\n",
+ "
scan center
\n",
+ "
700
\n",
+ "
62 x 35
\n",
+ "
60.8 x 34.9
\n",
+ "
0.980
\n",
+ "
0.998
\n",
+ "
JAXA
\n",
+ "
\n",
+ "
\n",
+ "
13
\n",
+ "
AMSR2
\n",
+ "
10.65 GHz
\n",
+ "
scan center
\n",
+ "
700
\n",
+ "
42 x 24
\n",
+ "
40.5 x 23.3
\n",
+ "
0.964
\n",
+ "
0.970
\n",
+ "
JAXA
\n",
+ "
\n",
+ "
\n",
+ "
14
\n",
+ "
AMSR2
\n",
+ "
18.7 GHz
\n",
+ "
scan center
\n",
+ "
700
\n",
+ "
22 x 14
\n",
+ "
21.9 x 12.6
\n",
+ "
0.997
\n",
+ "
0.901
\n",
+ "
JAXA
\n",
+ "
\n",
+ "
\n",
+ "
15
\n",
+ "
AMSR2
\n",
+ "
23.8 GHz
\n",
+ "
scan center
\n",
+ "
700
\n",
+ "
26 x 15
\n",
+ "
25.3 x 14.5
\n",
+ "
0.973
\n",
+ "
0.970
\n",
+ "
JAXA
\n",
+ "
\n",
+ "
\n",
+ "
16
\n",
+ "
AMSR2
\n",
+ "
36.5 GHz
\n",
+ "
scan center
\n",
+ "
700
\n",
+ "
12 x 7
\n",
+ "
11.8 x 6.79
\n",
+ "
0.984
\n",
+ "
0.970
\n",
+ "
JAXA
\n",
+ "
\n",
+ "
\n",
+ "
17
\n",
+ "
AMSR2
\n",
+ "
89.0 GHz
\n",
+ "
scan center
\n",
+ "
700
\n",
+ "
5 x 3
\n",
+ "
5.06 x 2.91
\n",
+ "
1.012
\n",
+ "
0.970
\n",
+ "
JAXA
\n",
+ "
\n",
+ "
\n",
+ "
18
\n",
+ "
GMI
\n",
+ "
10.65 GHz
\n",
+ "
scan center
\n",
+ "
407
\n",
+ "
32.1 x 19.4
\n",
+ "
31.8 x 19.2
\n",
+ "
0.991
\n",
+ "
0.991
\n",
+ "
GMI L1B ATBD
\n",
+ "
\n",
+ "
\n",
+ "
19
\n",
+ "
GMI
\n",
+ "
18.7 GHz
\n",
+ "
scan center
\n",
+ "
407
\n",
+ "
18.1 x 10.9
\n",
+ "
17.9 x 10.8
\n",
+ "
0.991
\n",
+ "
0.995
\n",
+ "
GMI L1B ATBD
\n",
+ "
\n",
+ "
\n",
+ "
20
\n",
+ "
GMI
\n",
+ "
23.8 GHz
\n",
+ "
scan center
\n",
+ "
407
\n",
+ "
16 x 9.7
\n",
+ "
15.8 x 9.57
\n",
+ "
0.989
\n",
+ "
0.986
\n",
+ "
GMI L1B ATBD
\n",
+ "
\n",
+ "
\n",
+ "
21
\n",
+ "
GMI
\n",
+ "
36.5 GHz
\n",
+ "
scan center
\n",
+ "
407
\n",
+ "
15.6 x 9.4
\n",
+ "
15.5 x 9.36
\n",
+ "
0.992
\n",
+ "
0.996
\n",
+ "
GMI L1B ATBD
\n",
+ "
\n",
+ "
\n",
+ "
22
\n",
+ "
GMI
\n",
+ "
89.0 GHz
\n",
+ "
scan center
\n",
+ "
407
\n",
+ "
7.2 x 4.4
\n",
+ "
7.16 x 4.33
\n",
+ "
0.994
\n",
+ "
0.984
\n",
+ "
GMI L1B ATBD
\n",
+ "
\n",
+ "
\n",
+ "
23
\n",
+ "
GMI
\n",
+ "
166 GHz
\n",
+ "
scan center
\n",
+ "
407
\n",
+ "
6.3 x 4.1
\n",
+ "
6.28 x 4.11
\n",
+ "
0.997
\n",
+ "
1.002
\n",
+ "
GMI L1B ATBD
\n",
+ "
\n",
+ "
\n",
+ "
24
\n",
+ "
GMI
\n",
+ "
183.31 GHz
\n",
+ "
scan center
\n",
+ "
407
\n",
+ "
5.8 x 3.8
\n",
+ "
5.73 x 3.75
\n",
+ "
0.988
\n",
+ "
0.986
\n",
+ "
GMI L1B ATBD
\n",
+ "
\n",
+ "
\n",
+ "
25
\n",
+ "
GMI
\n",
+ "
10.65 GHz
\n",
+ "
scan center
\n",
+ "
435
\n",
+ "
35 x 21
\n",
+ "
34.4 x 20.6
\n",
+ "
0.982
\n",
+ "
0.982
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
26
\n",
+ "
GMI
\n",
+ "
36.64 GHz
\n",
+ "
scan center
\n",
+ "
435
\n",
+ "
16.7 x 10
\n",
+ "
16.7 x 10
\n",
+ "
1.001
\n",
+ "
1.004
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
27
\n",
+ "
GMI
\n",
+ "
89.0 GHz
\n",
+ "
scan center
\n",
+ "
435
\n",
+ "
7.7 x 4.6
\n",
+ "
7.74 x 4.64
\n",
+ "
1.005
\n",
+ "
1.010
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ "
\n",
+ "
28
\n",
+ "
GMI
\n",
+ "
166 GHz
\n",
+ "
scan center
\n",
+ "
435
\n",
+ "
6.7 x 4.3
\n",
+ "
6.77 x 4.4
\n",
+ "
1.011
\n",
+ "
1.024
\n",
+ "
TC PRIMED
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " sensor beam location altitude (km) published (km) \\\n",
+ "0 ATMS channels 1-2 nadir 824 74.8 x 74.8 \n",
+ "1 ATMS channels 1-2 edge 824 141.8 x 323.1 \n",
+ "2 ATMS channels 3-16 nadir 824 31.6 x 31.6 \n",
+ "3 ATMS channels 3-16 edge 824 60 x 136.7 \n",
+ "4 ATMS channels 17-22 nadir 824 15.8 x 15.8 \n",
+ "5 ATMS channels 17-22 edge 824 30 x 68.4 \n",
+ "6 CrIS field of view nadir 824 14 x 14 \n",
+ "7 CrIS field of view edge 824 24 x 48 \n",
+ "8 VIIRS M bands nadir 824 0.742 x 0.776 \n",
+ "9 VIIRS M bands edge 824 1.6 x 1.58 \n",
+ "10 MODIS 1 km bands nadir 705 1 x 1 \n",
+ "11 MODIS 1 km bands edge 705 2 x 4.8 \n",
+ "12 AMSR2 6.925 GHz scan center 700 62 x 35 \n",
+ "13 AMSR2 10.65 GHz scan center 700 42 x 24 \n",
+ "14 AMSR2 18.7 GHz scan center 700 22 x 14 \n",
+ "15 AMSR2 23.8 GHz scan center 700 26 x 15 \n",
+ "16 AMSR2 36.5 GHz scan center 700 12 x 7 \n",
+ "17 AMSR2 89.0 GHz scan center 700 5 x 3 \n",
+ "18 GMI 10.65 GHz scan center 407 32.1 x 19.4 \n",
+ "19 GMI 18.7 GHz scan center 407 18.1 x 10.9 \n",
+ "20 GMI 23.8 GHz scan center 407 16 x 9.7 \n",
+ "21 GMI 36.5 GHz scan center 407 15.6 x 9.4 \n",
+ "22 GMI 89.0 GHz scan center 407 7.2 x 4.4 \n",
+ "23 GMI 166 GHz scan center 407 6.3 x 4.1 \n",
+ "24 GMI 183.31 GHz scan center 407 5.8 x 3.8 \n",
+ "25 GMI 10.65 GHz scan center 435 35 x 21 \n",
+ "26 GMI 36.64 GHz scan center 435 16.7 x 10 \n",
+ "27 GMI 89.0 GHz scan center 435 7.7 x 4.6 \n",
+ "28 GMI 166 GHz scan center 435 6.7 x 4.3 \n",
+ "\n",
+ " TAT-C (km) TAT-C / published, along TAT-C / published, across \\\n",
+ "0 74.6 x 74.6 0.998 0.998 \n",
+ "1 141 x 327 0.998 1.014 \n",
+ "2 31.5 x 31.5 0.998 0.998 \n",
+ "3 59.8 x 136 0.997 0.998 \n",
+ "4 15.8 x 15.8 0.998 0.998 \n",
+ "5 29.9 x 68 0.996 0.995 \n",
+ "6 13.8 x 13.8 0.986 0.986 \n",
+ "7 23.5 x 45.8 0.981 0.953 \n",
+ "8 0.732 x 0.765 0.986 0.986 \n",
+ "9 1.58 x 1.57 0.990 0.991 \n",
+ "10 0.996 x 0.996 0.996 0.996 \n",
+ "11 2 x 4.8 0.998 1.000 \n",
+ "12 60.8 x 34.9 0.980 0.998 \n",
+ "13 40.5 x 23.3 0.964 0.970 \n",
+ "14 21.9 x 12.6 0.997 0.901 \n",
+ "15 25.3 x 14.5 0.973 0.970 \n",
+ "16 11.8 x 6.79 0.984 0.970 \n",
+ "17 5.06 x 2.91 1.012 0.970 \n",
+ "18 31.8 x 19.2 0.991 0.991 \n",
+ "19 17.9 x 10.8 0.991 0.995 \n",
+ "20 15.8 x 9.57 0.989 0.986 \n",
+ "21 15.5 x 9.36 0.992 0.996 \n",
+ "22 7.16 x 4.33 0.994 0.984 \n",
+ "23 6.28 x 4.11 0.997 1.002 \n",
+ "24 5.73 x 3.75 0.988 0.986 \n",
+ "25 34.4 x 20.6 0.982 0.982 \n",
+ "26 16.7 x 10 1.001 1.004 \n",
+ "27 7.74 x 4.64 1.005 1.010 \n",
+ "28 6.77 x 4.4 1.011 1.024 \n",
+ "\n",
+ " source \n",
+ "0 TC PRIMED \n",
+ "1 TC PRIMED \n",
+ "2 TC PRIMED \n",
+ "3 TC PRIMED \n",
+ "4 TC PRIMED \n",
+ "5 TC PRIMED \n",
+ "6 NWP SAF \n",
+ "7 NWP SAF \n",
+ "8 NOAA/CIMSS \n",
+ "9 NOAA/CIMSS \n",
+ "10 NASA LAADS \n",
+ "11 NASA LAADS \n",
+ "12 JAXA \n",
+ "13 JAXA \n",
+ "14 JAXA \n",
+ "15 JAXA \n",
+ "16 JAXA \n",
+ "17 JAXA \n",
+ "18 GMI L1B ATBD \n",
+ "19 GMI L1B ATBD \n",
+ "20 GMI L1B ATBD \n",
+ "21 GMI L1B ATBD \n",
+ "22 GMI L1B ATBD \n",
+ "23 GMI L1B ATBD \n",
+ "24 GMI L1B ATBD \n",
+ "25 TC PRIMED \n",
+ "26 TC PRIMED \n",
+ "27 TC PRIMED \n",
+ "28 TC PRIMED "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# published sizes (km, along track x across track) at nominal altitudes, with the scan angle (pointed) or cone angle (conical)\n",
+ "EPOCH = datetime(2026, 10, 1, tzinfo=timezone.utc)\n",
+ "PUBLISHED = [\n",
+ " # sensor, beam, altitude (km), inclination (deg), kind, angle (deg), location, beam width or pixel (along, cross, deg), published (along, cross), source\n",
+ " (\n",
+ " \"ATMS\",\n",
+ " \"channels 1-2\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 0,\n",
+ " \"nadir\",\n",
+ " (5.2, 5.2),\n",
+ " (74.8, 74.8),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"ATMS\",\n",
+ " \"channels 1-2\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 52.725,\n",
+ " \"edge\",\n",
+ " (5.2, 5.2),\n",
+ " (141.8, 323.1),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"ATMS\",\n",
+ " \"channels 3-16\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 0,\n",
+ " \"nadir\",\n",
+ " (2.2, 2.2),\n",
+ " (31.6, 31.6),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"ATMS\",\n",
+ " \"channels 3-16\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 52.725,\n",
+ " \"edge\",\n",
+ " (2.2, 2.2),\n",
+ " (60.0, 136.7),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"ATMS\",\n",
+ " \"channels 17-22\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 0,\n",
+ " \"nadir\",\n",
+ " (1.1, 1.1),\n",
+ " (15.8, 15.8),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"ATMS\",\n",
+ " \"channels 17-22\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 52.725,\n",
+ " \"edge\",\n",
+ " (1.1, 1.1),\n",
+ " (30.0, 68.4),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"CrIS\",\n",
+ " \"field of view\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 0,\n",
+ " \"nadir\",\n",
+ " (0.963, 0.963),\n",
+ " (14, 14),\n",
+ " \"NWP SAF\",\n",
+ " ),\n",
+ " (\n",
+ " \"CrIS\",\n",
+ " \"field of view\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 48.33 + 1.1,\n",
+ " \"edge\",\n",
+ " (0.963, 0.963),\n",
+ " (24, 48),\n",
+ " \"NWP SAF\",\n",
+ " ),\n",
+ " (\n",
+ " \"VIIRS\",\n",
+ " \"M bands\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 0,\n",
+ " \"nadir\",\n",
+ " (DETECTOR_PITCH, 3 * 0.01779),\n",
+ " (0.742, 0.776),\n",
+ " \"NOAA/CIMSS\",\n",
+ " ),\n",
+ " (\n",
+ " \"VIIRS\",\n",
+ " \"M bands\",\n",
+ " 824,\n",
+ " 98.7,\n",
+ " \"pointed\",\n",
+ " 56.0,\n",
+ " \"edge\",\n",
+ " (DETECTOR_PITCH, 0.01779),\n",
+ " (1.60, 1.58),\n",
+ " \"NOAA/CIMSS\",\n",
+ " ),\n",
+ " (\n",
+ " \"MODIS\",\n",
+ " \"1 km bands\",\n",
+ " 705,\n",
+ " 98.2,\n",
+ " \"pointed\",\n",
+ " 0,\n",
+ " \"nadir\",\n",
+ " (0.0812, 0.0812),\n",
+ " (1.0, 1.0),\n",
+ " \"NASA LAADS\",\n",
+ " ),\n",
+ " (\n",
+ " \"MODIS\",\n",
+ " \"1 km bands\",\n",
+ " 705,\n",
+ " 98.2,\n",
+ " \"pointed\",\n",
+ " 55.0,\n",
+ " \"edge\",\n",
+ " (0.0812, 0.0812),\n",
+ " (2.0, 4.8),\n",
+ " \"NASA LAADS\",\n",
+ " ),\n",
+ " (\n",
+ " \"AMSR2\",\n",
+ " \"6.925 GHz\",\n",
+ " 700,\n",
+ " 98.2,\n",
+ " \"conical\",\n",
+ " 47.5,\n",
+ " \"scan center\",\n",
+ " (1.8, 1.8),\n",
+ " (62, 35),\n",
+ " \"JAXA\",\n",
+ " ),\n",
+ " (\n",
+ " \"AMSR2\",\n",
+ " \"10.65 GHz\",\n",
+ " 700,\n",
+ " 98.2,\n",
+ " \"conical\",\n",
+ " 47.5,\n",
+ " \"scan center\",\n",
+ " (1.2, 1.2),\n",
+ " (42, 24),\n",
+ " \"JAXA\",\n",
+ " ),\n",
+ " (\n",
+ " \"AMSR2\",\n",
+ " \"18.7 GHz\",\n",
+ " 700,\n",
+ " 98.2,\n",
+ " \"conical\",\n",
+ " 47.5,\n",
+ " \"scan center\",\n",
+ " (0.65, 0.65),\n",
+ " (22, 14),\n",
+ " \"JAXA\",\n",
+ " ),\n",
+ " (\n",
+ " \"AMSR2\",\n",
+ " \"23.8 GHz\",\n",
+ " 700,\n",
+ " 98.2,\n",
+ " \"conical\",\n",
+ " 47.5,\n",
+ " \"scan center\",\n",
+ " (0.75, 0.75),\n",
+ " (26, 15),\n",
+ " \"JAXA\",\n",
+ " ),\n",
+ " (\n",
+ " \"AMSR2\",\n",
+ " \"36.5 GHz\",\n",
+ " 700,\n",
+ " 98.2,\n",
+ " \"conical\",\n",
+ " 47.5,\n",
+ " \"scan center\",\n",
+ " (0.35, 0.35),\n",
+ " (12, 7),\n",
+ " \"JAXA\",\n",
+ " ),\n",
+ " (\n",
+ " \"AMSR2\",\n",
+ " \"89.0 GHz\",\n",
+ " 700,\n",
+ " 98.2,\n",
+ " \"conical\",\n",
+ " 47.5,\n",
+ " \"scan center\",\n",
+ " (0.15, 0.15),\n",
+ " (5, 3),\n",
+ " \"JAXA\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"10.65 GHz\",\n",
+ " 407,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (1.732, 1.732),\n",
+ " (32.1, 19.4),\n",
+ " \"GMI L1B ATBD\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"18.7 GHz\",\n",
+ " 407,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (0.977, 0.977),\n",
+ " (18.1, 10.9),\n",
+ " \"GMI L1B ATBD\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"23.8 GHz\",\n",
+ " 407,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (0.862, 0.862),\n",
+ " (16.0, 9.7),\n",
+ " \"GMI L1B ATBD\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"36.5 GHz\",\n",
+ " 407,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (0.843, 0.843),\n",
+ " (15.6, 9.4),\n",
+ " \"GMI L1B ATBD\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"89.0 GHz\",\n",
+ " 407,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (0.390, 0.390),\n",
+ " (7.2, 4.4),\n",
+ " \"GMI L1B ATBD\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"166 GHz\",\n",
+ " 407,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 45.36,\n",
+ " \"scan center\",\n",
+ " (0.396, 0.396),\n",
+ " (6.3, 4.1),\n",
+ " \"GMI L1B ATBD\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"183.31 GHz\",\n",
+ " 407,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 45.36,\n",
+ " \"scan center\",\n",
+ " (0.361, 0.361),\n",
+ " (5.8, 3.8),\n",
+ " \"GMI L1B ATBD\",\n",
+ " ),\n",
+ " # after the orbit boost of November 2023\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"10.65 GHz\",\n",
+ " 435,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (1.732, 1.732),\n",
+ " (35.0, 21.0),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"36.64 GHz\",\n",
+ " 435,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (0.843, 0.843),\n",
+ " (16.7, 10.0),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"89.0 GHz\",\n",
+ " 435,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 48.5,\n",
+ " \"scan center\",\n",
+ " (0.390, 0.390),\n",
+ " (7.7, 4.6),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ " (\n",
+ " \"GMI\",\n",
+ " \"166 GHz\",\n",
+ " 435,\n",
+ " 65.0,\n",
+ " \"conical\",\n",
+ " 45.36,\n",
+ " \"scan center\",\n",
+ " (0.396, 0.396),\n",
+ " (6.7, 4.3),\n",
+ " \"TC PRIMED\",\n",
+ " ),\n",
+ "]\n",
+ "rows4 = []\n",
+ "for (\n",
+ " sensor,\n",
+ " beam,\n",
+ " altitude,\n",
+ " inclination,\n",
+ " kind,\n",
+ " angle,\n",
+ " location,\n",
+ " (along_ifov, cross_ifov),\n",
+ " (pub_along, pub_cross),\n",
+ " source,\n",
+ ") in PUBLISHED:\n",
+ " orbit = CircularOrbit(altitude=altitude * 1e3, inclination=inclination, epoch=EPOCH)\n",
+ " track = orbit.to_gp_orbit().get_orbit_track(EPOCH + timedelta(minutes=10))\n",
+ " if kind == \"pointed\":\n",
+ " instrument = PointedInstrument(\n",
+ " name=sensor,\n",
+ " field_of_regard=120,\n",
+ " cross_track_field_of_view=cross_ifov,\n",
+ " along_track_field_of_view=along_ifov,\n",
+ " )\n",
+ " target = compute_projected_ray_position(track, 0, 0, angle)\n",
+ " else:\n",
+ " instrument = ConicalInstrument(\n",
+ " name=sensor,\n",
+ " cone_angle=angle,\n",
+ " along_track_field_of_view=1,\n",
+ " beam_width=along_ifov,\n",
+ " )\n",
+ " target = compute_projected_ray_position(track, 0, 0, 0, angle)\n",
+ " along, cross = instrument.compute_sample_size(track, target)\n",
+ " rows4.append(\n",
+ " {\n",
+ " \"sensor\": sensor,\n",
+ " \"beam\": beam,\n",
+ " \"location\": location,\n",
+ " \"altitude (km)\": altitude,\n",
+ " \"published (km)\": f\"{pub_along:g} x {pub_cross:g}\",\n",
+ " \"TAT-C (km)\": f\"{float(along) / 1e3:.3g} x {float(cross) / 1e3:.3g}\",\n",
+ " \"TAT-C / published, along\": float(along) / 1e3 / pub_along,\n",
+ " \"TAT-C / published, across\": float(cross) / 1e3 / pub_cross,\n",
+ " \"source\": source,\n",
+ " }\n",
+ " )\n",
+ "sizes4 = pd.DataFrame(rows4)\n",
+ "sizes4.round(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "03dce215",
+ "metadata": {},
+ "source": [
+ "TAT-C's sample sizes match the published footprint sizes across six instruments and 29 combinations of beam, location, and altitude: 55 of the 58 dimensions agree within 3%.\n",
+ "\n",
+ "* **ATMS:** within 0.5%, from 74.6, 31.5, and 15.8 km at nadir to 141 x 327 km (published 141.8 x 323.1 km), 59.8 x 136 km (60.0 x 136.7 km), and 29.9 x 68 km (30.0 x 68.4 km) at the swath edges.\n",
+ "* **MODIS:** from 1 km at nadir to 2.0 x 4.8 km at the edges.\n",
+ "* **VIIRS:** nearly constant, 0.73 x 0.77 km at nadir and 1.58 x 1.57 km at the edges, as aggregation intends.\n",
+ "* **Conical scanners:** the elongated footprints match, longer along the view (along track at the center of the scan) by about 1 / cos of the incidence angle. GMI's footprints match at both altitudes, before and after its orbit boost.\n",
+ "\n",
+ "The other differences are mostly within about 1.5%, comparable to the rounding of the published values.\n",
+ "\n",
+ "Three dimensions differ by more than 3%:\n",
+ "* **CrIS, edge, across track:** 5% smaller. The NWP SAF gives this size as an approximate maximum (\"up to\" 48 x 24 km).\n",
+ "* **AMSR2 10.65 GHz, along track:** 3.6% smaller.\n",
+ "* **AMSR2 18.7 GHz, across track:** 10% smaller. JAXA's 14 km is wider than the 0.65 deg beam gives at this geometry: for comparison, the 0.75 deg beam at 23.8 GHz gives 14.5 km, and JAXA lists 15 km."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ab046fba",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| 1. Cross-track spacing (`compute_sample_sizes`) | ATMS and MODIS within 1% (5th to 95th percentile) from nadir to the swath edges, where the spacing grows by factors of 4.0 and 4.6; VIIRS within 1% with one `PointedInstrument` per aggregation zone (-34% to +97% with 3,200 equal pixels) |\n",
+ "| 2. Along-track spacing (`scan_period`) | MODIS and VIIRS within 0.2% (the detector pitch, or the scan advance per detector at nadir for MODIS); ATMS, one beam per scan position, within 0.8% (the scan advance, 17.4 to 17.7 km; -8% to +70% without a scan period) |\n",
+ "| 3. Conical spacing | AMSR2 and GMI within 1.4% along the arc; 1.0% (AMSR2) and 2.4% (GMI) larger between scans, as the along-track field of view is tailored to the fastest ground speed |\n",
+ "| 4. Footprint sizes | 55 of 58 published dimensions within 3% (ATMS, CrIS, VIIRS, MODIS, AMSR2, and GMI; 29 beams, locations, and altitudes); AMSR2's published 18.7 GHz footprint is 10% wider across track than its beam width implies |\n",
+ "\n",
+ "**Sample geometry.** Projecting angular extents around the line of sight onto the ellipsoid reproduces how the samples of cross-track scanners grow toward the swath edges, with the slant range across track and also with the obliquity of the view. It also reproduces the elongated beams of conical scanners. So the sample sizes that the OSSE examples use to judge whether a feature is resolved are consistent with the real instruments.\n",
+ "\n",
+ "**Aggregation zones.** An instrument that aggregates samples on board in zones, as VIIRS does to keep its pixels nearly the same size across the swath, is represented by one `PointedInstrument` per zone, with the zone's pixels, spacing, and roll angle.\n",
+ "\n",
+ "**Scanners.** The angular along-track spacing of a `PointedInstrument` suits arrays of detectors (MODIS, VIIRS, and the 3 x 3 array of CrIS) but not microwave sounders such as ATMS, which scan one beam per position: their samples are spaced along track by the advance per scan, nearly constant across the swath. A `scan_period` accounts for the advance, which sets the spacing of single-beam scanners, and of arrays of detectors wherever the scans do not overlap.\n",
+ "\n",
+ "**Conical scans.** The spacing between scans of a `ConicalInstrument` follows its along-track field of view. Tailored with `compute_along_track_field_of_view` to the fastest ground speed of the orbit, it overestimates the spacing by 1 to 2.5%."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateSolarLandsat.ipynb b/docs/validation/ValidateSolarLandsat.ipynb
index 41723dec..9866fca6 100644
--- a/docs/validation/ValidateSolarLandsat.ipynb
+++ b/docs/validation/ValidateSolarLandsat.ipynb
@@ -142,14 +142,27 @@
"START = datetime(2026, 9, 17, tzinfo=timezone.utc)\n",
"END = datetime(2026, 10, 3, tzinfo=timezone.utc)\n",
"STAC = \"https://landsatlook.usgs.gov/stac-server/search\"\n",
- "SCENES_FILE = DATA_DIR / f\"landsat_c2l1_scenes_{START:%Y%m%d}_{END - timedelta(days=1):%Y%m%d}.geojson\"\n",
+ "SCENES_FILE = (\n",
+ " DATA_DIR\n",
+ " / f\"landsat_c2l1_scenes_{START:%Y%m%d}_{END - timedelta(days=1):%Y%m%d}.geojson\"\n",
+ ")\n",
"if not SCENES_FILE.exists():\n",
" body = {\n",
" \"collections\": [\"landsat-c2l1\"],\n",
" \"datetime\": f\"{START:%Y-%m-%dT%H:%M:%SZ}/{END - timedelta(seconds=1):%Y-%m-%dT%H:%M:%SZ}\",\n",
" \"limit\": 500,\n",
" \"fields\": {\n",
- " \"include\": [\"id\", \"geometry\", \"properties.datetime\", \"properties.platform\", \"properties.view:sun_elevation\", \"properties.view:sun_azimuth\", \"properties.view:off_nadir\", \"properties.landsat:wrs_path\", \"properties.landsat:wrs_row\"],\n",
+ " \"include\": [\n",
+ " \"id\",\n",
+ " \"geometry\",\n",
+ " \"properties.datetime\",\n",
+ " \"properties.platform\",\n",
+ " \"properties.view:sun_elevation\",\n",
+ " \"properties.view:sun_azimuth\",\n",
+ " \"properties.view:off_nadir\",\n",
+ " \"properties.landsat:wrs_path\",\n",
+ " \"properties.landsat:wrs_row\",\n",
+ " ],\n",
" \"exclude\": [\"links\", \"assets\"],\n",
" },\n",
" }\n",
@@ -166,12 +179,22 @@
" raise\n",
" time.sleep(2**attempt)\n",
" features += page[\"features\"]\n",
- " following = [link for link in page.get(\"links\", []) if link.get(\"rel\") == \"next\"]\n",
+ " following = [\n",
+ " link for link in page.get(\"links\", []) if link.get(\"rel\") == \"next\"\n",
+ " ]\n",
" if not following or not page[\"features\"]:\n",
" break\n",
" body = following[0][\"body\"]\n",
- " SCENES_FILE.write_text(json.dumps({\"type\": \"FeatureCollection\", \"features\": features}))\n",
- "scenes = gpd.read_file(SCENES_FILE).rename(columns={\"view:sun_elevation\": \"sun_elevation\", \"view:sun_azimuth\": \"sun_azimuth\", \"view:off_nadir\": \"off_nadir\"})\n",
+ " SCENES_FILE.write_text(\n",
+ " json.dumps({\"type\": \"FeatureCollection\", \"features\": features})\n",
+ " )\n",
+ "scenes = gpd.read_file(SCENES_FILE).rename(\n",
+ " columns={\n",
+ " \"view:sun_elevation\": \"sun_elevation\",\n",
+ " \"view:sun_azimuth\": \"sun_azimuth\",\n",
+ " \"view:off_nadir\": \"off_nadir\",\n",
+ " }\n",
+ ")\n",
"scenes[\"time\"] = pd.to_datetime(scenes[\"datetime\"], utc=True, format=\"ISO8601\")\n",
"scenes[\"satellite\"] = scenes.platform.str.replace(\"LANDSAT_\", \"Landsat \")\n",
"\n",
@@ -179,7 +202,12 @@
"def scene_center(geometry):\n",
" \"\"\"Center of a scene footprint, unwrapping footprints split at the antimeridian.\"\"\"\n",
" if isinstance(geometry, MultiPolygon):\n",
- " geometry = unary_union([translate(part, xoff=360) if part.centroid.x < 0 else part for part in geometry.geoms])\n",
+ " geometry = unary_union(\n",
+ " [\n",
+ " translate(part, xoff=360) if part.centroid.x < 0 else part\n",
+ " for part in geometry.geoms\n",
+ " ]\n",
+ " )\n",
" center = geometry.centroid\n",
" return (center.x + 180) % 360 - 180, center.y\n",
"\n",
@@ -196,17 +224,45 @@
" ],\n",
"}\n",
"SWATH = 185e3\n",
- "oli = Instrument(name=\"OLI\", field_of_regard=utils.swath_width_to_field_of_regard(705e3, SWATH))\n",
- "satellites = {name: Satellite(name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle), instruments=[oli]) for name, tle in TLES.items()}\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "oli = Instrument(\n",
+ " name=\"OLI\", field_of_regard=utils.swath_width_to_field_of_regard(705e3, SWATH)\n",
+ ")\n",
+ "satellites = {\n",
+ " name: Satellite(\n",
+ " name=name, orbit=GeneralPerturbationsOrbit.from_tle(tle), instruments=[oli]\n",
+ " )\n",
+ " for name, tle in TLES.items()\n",
+ "}\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"pd.DataFrame(\n",
" {\n",
" \"scenes\": scenes.groupby(\"satellite\").size(),\n",
- " \"night scenes (sun below horizon)\": scenes[scenes.sun_elevation < 0].groupby(\"satellite\").size(),\n",
+ " \"night scenes (sun below horizon)\": scenes[scenes.sun_elevation < 0]\n",
+ " .groupby(\"satellite\")\n",
+ " .size(),\n",
" \"off-nadir scenes\": scenes[scenes.off_nadir > 0].groupby(\"satellite\").size(),\n",
- " \"first scene\": scenes.groupby(\"satellite\").time.min().dt.strftime(\"%Y-%m-%d %H:%M\"),\n",
- " \"last scene\": scenes.groupby(\"satellite\").time.max().dt.strftime(\"%Y-%m-%d %H:%M\"),\n",
+ " \"first scene\": scenes.groupby(\"satellite\")\n",
+ " .time.min()\n",
+ " .dt.strftime(\"%Y-%m-%d %H:%M\"),\n",
+ " \"last scene\": scenes.groupby(\"satellite\")\n",
+ " .time.max()\n",
+ " .dt.strftime(\"%Y-%m-%d %H:%M\"),\n",
" }\n",
")"
]
@@ -334,7 +390,12 @@
],
"source": [
"scenes = scenes[scenes.off_nadir.fillna(0) == 0].reset_index(drop=True)\n",
- "sample = pd.concat([scenes[scenes.sun_elevation >= 0].sample(600, random_state=1), scenes[scenes.sun_elevation < 0].sample(300, random_state=1)])\n",
+ "sample = pd.concat(\n",
+ " [\n",
+ " scenes[scenes.sun_elevation >= 0].sample(600, random_state=1),\n",
+ " scenes[scenes.sun_elevation < 0].sample(300, random_state=1),\n",
+ " ]\n",
+ ")\n",
"\n",
"\n",
"def scene_geometry(scene):\n",
@@ -342,7 +403,13 @@
" point = Point(id=0, latitude=scene.center_lat, longitude=scene.center_lon)\n",
" window = timedelta(minutes=5)\n",
" time = scene.time.floor(\"us\")\n",
- " obs = collect_observations(point, satellites[scene.satellite], (time - window).to_pydatetime(), (time + window).to_pydatetime(), omit_solar=False)\n",
+ " obs = collect_observations(\n",
+ " point,\n",
+ " satellites[scene.satellite],\n",
+ " (time - window).to_pydatetime(),\n",
+ " (time + window).to_pydatetime(),\n",
+ " omit_solar=False,\n",
+ " )\n",
" if obs.empty:\n",
" return {\"id\": scene.id, \"found\": False}\n",
" o = obs.iloc[0]\n",
@@ -354,7 +421,8 @@
" \"sun elevation\": scene.sun_elevation,\n",
" \"TAT-C solar elevation\": o.solar_alt,\n",
" \"solar elevation difference (deg)\": o.solar_alt - scene.sun_elevation,\n",
- " \"solar azimuth difference (deg)\": (o.solar_az - scene.sun_azimuth + 180) % 360 - 180,\n",
+ " \"solar azimuth difference (deg)\": (o.solar_az - scene.sun_azimuth + 180) % 360\n",
+ " - 180,\n",
" \"local solar time (h)\": o.solar_time,\n",
" \"latitude\": scene.center_lat,\n",
" }\n",
@@ -364,13 +432,24 @@
" geometry1 = pd.DataFrame(list(pool.map(scene_geometry, sample.itertuples())))\n",
"found = geometry1[geometry1.found]\n",
"table1 = {}\n",
- "for label, subset in [(\"day scenes\", found[~found.night.astype(bool)]), (\"night scenes\", found[found.night.astype(bool)])]:\n",
+ "for label, subset in [\n",
+ " (\"day scenes\", found[~found.night.astype(bool)]),\n",
+ " (\"night scenes\", found[found.night.astype(bool)]),\n",
+ "]:\n",
" table1[label] = {\"scenes\": len(subset)}\n",
- " for column in [\"epoch difference (s)\", \"solar elevation difference (deg)\", \"solar azimuth difference (deg)\"]:\n",
+ " for column in [\n",
+ " \"epoch difference (s)\",\n",
+ " \"solar elevation difference (deg)\",\n",
+ " \"solar azimuth difference (deg)\",\n",
+ " ]:\n",
" table1[label][f\"{column}, median\"] = subset[column].median()\n",
" table1[label][f\"{column}, max |x|\"] = subset[column].abs().max()\n",
- " table1[label][\"local solar time, range (h)\"] = f\"{subset['local solar time (h)'].min():.2f} to {subset['local solar time (h)'].max():.2f}\"\n",
- "pd.DataFrame(table1).T.assign(**{\"scenes found\": f\"{geometry1.found.sum()} of {len(geometry1)}\"})"
+ " table1[label][\n",
+ " \"local solar time, range (h)\"\n",
+ " ] = f\"{subset['local solar time (h)'].min():.2f} to {subset['local solar time (h)'].max():.2f}\"\n",
+ "pd.DataFrame(table1).T.assign(\n",
+ " **{\"scenes found\": f\"{geometry1.found.sum()} of {len(geometry1)}\"}\n",
+ ")"
]
},
{
@@ -400,13 +479,47 @@
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.8))\n",
"night = found.night.astype(bool)\n",
- "axes[0].scatter(found.latitude[~night], found[\"solar elevation difference (deg)\"][~night], s=6, color=BLUE, label=\"day scenes\")\n",
- "axes[0].scatter(found.latitude[night], found[\"solar elevation difference (deg)\"][night], s=6, color=PURPLE, label=\"night scenes\")\n",
- "axes[0].set(xlabel=\"Scene latitude (deg)\", ylabel=\"TAT-C minus scene (deg)\", title=\"Solar elevation at scene center\")\n",
+ "axes[0].scatter(\n",
+ " found.latitude[~night],\n",
+ " found[\"solar elevation difference (deg)\"][~night],\n",
+ " s=6,\n",
+ " color=BLUE,\n",
+ " label=\"day scenes\",\n",
+ ")\n",
+ "axes[0].scatter(\n",
+ " found.latitude[night],\n",
+ " found[\"solar elevation difference (deg)\"][night],\n",
+ " s=6,\n",
+ " color=PURPLE,\n",
+ " label=\"night scenes\",\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Scene latitude (deg)\",\n",
+ " ylabel=\"TAT-C minus scene (deg)\",\n",
+ " title=\"Solar elevation at scene center\",\n",
+ ")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
- "axes[1].scatter(found.latitude[~night], found[\"local solar time (h)\"][~night], s=6, color=BLUE, label=\"day scenes (descending)\")\n",
- "axes[1].scatter(found.latitude[night], found[\"local solar time (h)\"][night], s=6, color=PURPLE, label=\"night scenes (ascending)\")\n",
- "axes[1].set(xlabel=\"Scene latitude (deg)\", ylabel=\"Local solar time (h)\", title=\"Local solar time of TAT-C observations\", ylim=(0, 24), yticks=range(0, 25, 4))\n",
+ "axes[1].scatter(\n",
+ " found.latitude[~night],\n",
+ " found[\"local solar time (h)\"][~night],\n",
+ " s=6,\n",
+ " color=BLUE,\n",
+ " label=\"day scenes (descending)\",\n",
+ ")\n",
+ "axes[1].scatter(\n",
+ " found.latitude[night],\n",
+ " found[\"local solar time (h)\"][night],\n",
+ " s=6,\n",
+ " color=PURPLE,\n",
+ " label=\"night scenes (ascending)\",\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Scene latitude (deg)\",\n",
+ " ylabel=\"Local solar time (h)\",\n",
+ " title=\"Local solar time of TAT-C observations\",\n",
+ " ylim=(0, 24),\n",
+ " yticks=range(0, 25, 4),\n",
+ ")\n",
"axes[1].legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
"plt.show()"
@@ -512,26 +625,46 @@
}
],
"source": [
- "land = unary_union(list(shpreader.Reader(shpreader.natural_earth(resolution=\"110m\", category=\"physical\", name=\"land\")).geometries()))\n",
+ "land = unary_union(\n",
+ " list(\n",
+ " shpreader.Reader(\n",
+ " shpreader.natural_earth(resolution=\"110m\", category=\"physical\", name=\"land\")\n",
+ " ).geometries()\n",
+ " )\n",
+ ")\n",
"prepare(land)\n",
"lattice = generate_points_fibonacci_lattice(500e3)\n",
- "points = lattice[contains_xy(land, lattice.geometry.x, lattice.geometry.y) & (lattice.geometry.y.abs() < 81.8)].reset_index(drop=True)\n",
+ "points = lattice[\n",
+ " contains_xy(land, lattice.geometry.x, lattice.geometry.y)\n",
+ " & (lattice.geometry.y.abs() < 81.8)\n",
+ "].reset_index(drop=True)\n",
"points[\"point_id\"] = points.index\n",
- "END_BY_SATELLITE = {\"Landsat 8\": datetime(2026, 9, 27, tzinfo=timezone.utc), \"Landsat 9\": END}\n",
+ "END_BY_SATELLITE = {\n",
+ " \"Landsat 8\": datetime(2026, 9, 27, tzinfo=timezone.utc),\n",
+ " \"Landsat 9\": END,\n",
+ "}\n",
"\n",
"\n",
"def predict(point, instrument=oli):\n",
" \"\"\"Predicted passes of each satellite over a point.\"\"\"\n",
- " target = Point(id=point.point_id, latitude=point.geometry.y, longitude=point.geometry.x)\n",
+ " target = Point(\n",
+ " id=point.point_id, latitude=point.geometry.y, longitude=point.geometry.x\n",
+ " )\n",
" frames = []\n",
" for name, satellite in satellites.items():\n",
" satellite = satellite.model_copy(update={\"instruments\": [instrument]})\n",
- " frames.append(collect_observations(target, satellite, START, END_BY_SATELLITE[name], omit_solar=False))\n",
+ " frames.append(\n",
+ " collect_observations(\n",
+ " target, satellite, START, END_BY_SATELLITE[name], omit_solar=False\n",
+ " )\n",
+ " )\n",
" return pd.concat(frames).assign(point_id=point.point_id)\n",
"\n",
"\n",
"with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
- " predicted = pd.concat(list(pool.map(predict, points.itertuples()))).reset_index(drop=True)\n",
+ " predicted = pd.concat(list(pool.map(predict, points.itertuples()))).reset_index(\n",
+ " drop=True\n",
+ " )\n",
"\n",
"# acquired passes: scenes containing each point, one per point, satellite, and pass\n",
"tree = STRtree(scenes.geometry.values)\n",
@@ -540,35 +673,73 @@
" for k in tree.query(point.geometry, predicate=\"intersects\"):\n",
" scene = scenes.iloc[k]\n",
" if scene.time < END_BY_SATELLITE[scene.satellite]:\n",
- " rows.append({\"point_id\": point.point_id, \"satellite\": scene.satellite, \"time\": scene.time, \"sun_elevation\": scene.sun_elevation})\n",
+ " rows.append(\n",
+ " {\n",
+ " \"point_id\": point.point_id,\n",
+ " \"satellite\": scene.satellite,\n",
+ " \"time\": scene.time,\n",
+ " \"sun_elevation\": scene.sun_elevation,\n",
+ " }\n",
+ " )\n",
"acquired = pd.DataFrame(rows).sort_values(\"time\")\n",
- "new_pass = acquired.groupby([\"point_id\", \"satellite\"]).time.diff() > pd.Timedelta(minutes=5)\n",
+ "new_pass = acquired.groupby([\"point_id\", \"satellite\"]).time.diff() > pd.Timedelta(\n",
+ " minutes=5\n",
+ ")\n",
"acquired[\"pass\"] = new_pass.groupby([acquired.point_id, acquired.satellite]).cumsum()\n",
- "acquired = acquired.groupby([\"point_id\", \"satellite\", \"pass\"]).agg(time=(\"time\", \"mean\"), sun_elevation=(\"sun_elevation\", \"mean\")).reset_index()\n",
+ "acquired = (\n",
+ " acquired.groupby([\"point_id\", \"satellite\", \"pass\"])\n",
+ " .agg(time=(\"time\", \"mean\"), sun_elevation=(\"sun_elevation\", \"mean\"))\n",
+ " .reset_index()\n",
+ ")\n",
"\n",
"\n",
"def match(predicted, acquired):\n",
" \"\"\"Flag predicted passes that were acquired, and acquired passes that were predicted.\"\"\"\n",
" tolerance = pd.Timedelta(minutes=3)\n",
- " pairs = pd.merge_asof(predicted.sort_values(\"epoch\"), acquired[[\"point_id\", \"satellite\", \"time\"]].sort_values(\"time\"), left_on=\"epoch\", right_on=\"time\", by=[\"point_id\", \"satellite\"], direction=\"nearest\", tolerance=tolerance)\n",
+ " pairs = pd.merge_asof(\n",
+ " predicted.sort_values(\"epoch\"),\n",
+ " acquired[[\"point_id\", \"satellite\", \"time\"]].sort_values(\"time\"),\n",
+ " left_on=\"epoch\",\n",
+ " right_on=\"time\",\n",
+ " by=[\"point_id\", \"satellite\"],\n",
+ " direction=\"nearest\",\n",
+ " tolerance=tolerance,\n",
+ " )\n",
" pairs[\"acquired\"] = pairs.time.notna()\n",
- " back = pd.merge_asof(acquired.sort_values(\"time\"), predicted[[\"point_id\", \"satellite\", \"epoch\"]].sort_values(\"epoch\"), left_on=\"time\", right_on=\"epoch\", by=[\"point_id\", \"satellite\"], direction=\"nearest\", tolerance=tolerance)\n",
+ " back = pd.merge_asof(\n",
+ " acquired.sort_values(\"time\"),\n",
+ " predicted[[\"point_id\", \"satellite\", \"epoch\"]].sort_values(\"epoch\"),\n",
+ " left_on=\"time\",\n",
+ " right_on=\"epoch\",\n",
+ " by=[\"point_id\", \"satellite\"],\n",
+ " direction=\"nearest\",\n",
+ " tolerance=tolerance,\n",
+ " )\n",
" back[\"predicted\"] = back.epoch.notna()\n",
" return pairs, back\n",
"\n",
"\n",
"pairs, back = match(predicted, acquired)\n",
- "pairs = pairs.merge(points[[\"point_id\"]].assign(latitude=points.geometry.y), on=\"point_id\")\n",
- "pairs[\"region\"] = np.where(pairs.latitude < -60, \"Antarctica (south of 60 deg S)\", np.where(pairs.latitude > 60, \"north of 60 deg N\", \"60 deg S to 60 deg N\"))\n",
+ "pairs = pairs.merge(\n",
+ " points[[\"point_id\"]].assign(latitude=points.geometry.y), on=\"point_id\"\n",
+ ")\n",
+ "pairs[\"region\"] = np.where(\n",
+ " pairs.latitude < -60,\n",
+ " \"Antarctica (south of 60 deg S)\",\n",
+ " np.where(pairs.latitude > 60, \"north of 60 deg N\", \"60 deg S to 60 deg N\"),\n",
+ ")\n",
"BINS = [-90, -10, 0, 2, 4, 5, 6, 8, 10, 15, 20, 30, 90]\n",
"pairs[\"solar elevation (deg)\"] = pd.cut(pairs.solar_alt, BINS)\n",
"pd.DataFrame(\n",
" {\n",
" \"points\": len(points),\n",
" \"predicted passes\": pairs.groupby(\"satellite\").size(),\n",
- " \"predicted passes by day\": pairs[pairs.solar_alt >= 0].groupby(\"satellite\").size(),\n",
+ " \"predicted passes by day\": pairs[pairs.solar_alt >= 0]\n",
+ " .groupby(\"satellite\")\n",
+ " .size(),\n",
" \"acquired passes\": back.groupby(\"satellite\").size(),\n",
- " \"acquired passes predicted (%)\": 100 * back.groupby(\"satellite\").predicted.mean(),\n",
+ " \"acquired passes predicted (%)\": 100\n",
+ " * back.groupby(\"satellite\").predicted.mean(),\n",
" }\n",
").round(1)"
]
@@ -762,8 +933,15 @@
}
],
"source": [
- "table2 = pairs.groupby([\"solar elevation (deg)\", \"satellite\"], observed=True).acquired.agg([\"mean\", \"size\"]).unstack()\n",
- "table2.columns = [f\"{satellite}: {'acquired (%)' if stat == 'mean' else 'predicted passes'}\" for stat, satellite in table2.columns]\n",
+ "table2 = (\n",
+ " pairs.groupby([\"solar elevation (deg)\", \"satellite\"], observed=True)\n",
+ " .acquired.agg([\"mean\", \"size\"])\n",
+ " .unstack()\n",
+ ")\n",
+ "table2.columns = [\n",
+ " f\"{satellite}: {'acquired (%)' if stat == 'mean' else 'predicted passes'}\"\n",
+ " for stat, satellite in table2.columns\n",
+ "]\n",
"for column in [c for c in table2.columns if \"acquired\" in c]:\n",
" table2[column] *= 100\n",
"table2.round(1)"
@@ -797,13 +975,29 @@
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.8), sharey=True)\n",
"centers = [(max(a, -20) + min(b, 40)) / 2 for a, b in zip(BINS[:-1], BINS[1:])]\n",
"for ax, satellite in zip(axes, satellites):\n",
- " for region, color in zip([\"60 deg S to 60 deg N\", \"north of 60 deg N\", \"Antarctica (south of 60 deg S)\"], [BLUE, AQUA, ORANGE]):\n",
+ " for region, color in zip(\n",
+ " [\"60 deg S to 60 deg N\", \"north of 60 deg N\", \"Antarctica (south of 60 deg S)\"],\n",
+ " [BLUE, AQUA, ORANGE],\n",
+ " ):\n",
" subset = pairs[(pairs.satellite == satellite) & (pairs.region == region)]\n",
- " stats = subset.groupby(\"solar elevation (deg)\", observed=False).acquired.agg([\"mean\", \"size\"])\n",
+ " stats = subset.groupby(\"solar elevation (deg)\", observed=False).acquired.agg(\n",
+ " [\"mean\", \"size\"]\n",
+ " )\n",
" # omit bins with fewer than 5 passes (breaking the line)\n",
- " ax.plot(centers, 100 * stats[\"mean\"].where(stats[\"size\"] >= 5), \"o-\", color=color, ms=4, label=region)\n",
+ " ax.plot(\n",
+ " centers,\n",
+ " 100 * stats[\"mean\"].where(stats[\"size\"] >= 5),\n",
+ " \"o-\",\n",
+ " color=color,\n",
+ " ms=4,\n",
+ " label=region,\n",
+ " )\n",
" ax.axvline(5, color=GRAY, ls=\"--\", lw=1)\n",
- " ax.set(xlabel=\"Predicted target solar elevation (deg)\", title=f\"{satellite} passes acquired\", xlim=(-20, 40))\n",
+ " ax.set(\n",
+ " xlabel=\"Predicted target solar elevation (deg)\",\n",
+ " title=f\"{satellite} passes acquired\",\n",
+ " xlim=(-20, 40),\n",
+ " )\n",
"axes[0].set_ylabel(\"Acquired (%)\")\n",
"axes[0].legend(frameon=False, fontsize=8)\n",
"fig.tight_layout()\n",
@@ -1038,7 +1232,9 @@
}
],
"source": [
- "wide = Instrument(name=\"wide\", field_of_regard=utils.swath_width_to_field_of_regard(705e3, 300e3))\n",
+ "wide = Instrument(\n",
+ " name=\"wide\", field_of_regard=utils.swath_width_to_field_of_regard(705e3, 300e3)\n",
+ ")\n",
"\n",
"\n",
"def ground_track_distance(row):\n",
@@ -1047,18 +1243,44 @@
" satellite = satellites[row.satellite].model_copy(update={\"instruments\": [wide]})\n",
" window = timedelta(minutes=5)\n",
" time = row.time.floor(\"us\")\n",
- " obs = collect_observations(Point(id=0, latitude=point.y, longitude=point.x), satellite, (time - window).to_pydatetime(), (time + window).to_pydatetime())\n",
+ " obs = collect_observations(\n",
+ " Point(id=0, latitude=point.y, longitude=point.x),\n",
+ " satellite,\n",
+ " (time - window).to_pydatetime(),\n",
+ " (time + window).to_pydatetime(),\n",
+ " )\n",
" if obs.empty:\n",
" return np.nan\n",
" epoch = obs.epoch.iloc[0].floor(\"us\").to_pydatetime()\n",
- " subpoint = wgs84.subpoint_of(satellites[row.satellite].orbit.to_gp_orbit().elements[0].to_skyfield().at(timescale.from_datetime(epoch)))\n",
- " return utils.geometry.geodesic_distance(point.x, point.y, subpoint.longitude.degrees, subpoint.latitude.degrees) / 1e3\n",
+ " subpoint = wgs84.subpoint_of(\n",
+ " satellites[row.satellite]\n",
+ " .orbit.to_gp_orbit()\n",
+ " .elements[0]\n",
+ " .to_skyfield()\n",
+ " .at(timescale.from_datetime(epoch))\n",
+ " )\n",
+ " return (\n",
+ " utils.geometry.geodesic_distance(\n",
+ " point.x, point.y, subpoint.longitude.degrees, subpoint.latitude.degrees\n",
+ " )\n",
+ " / 1e3\n",
+ " )\n",
"\n",
"\n",
"unpredicted = back[~back.predicted].copy()\n",
- "unpredicted[\"distance from ground track (km)\"] = [ground_track_distance(row) for row in unpredicted.itertuples()]\n",
+ "unpredicted[\"distance from ground track (km)\"] = [\n",
+ " ground_track_distance(row) for row in unpredicted.itertuples()\n",
+ "]\n",
"unpredicted[\"time\"] = unpredicted.time.dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
- "unpredicted[[\"point_id\", \"satellite\", \"time\", \"sun_elevation\", \"distance from ground track (km)\"]].round(1)"
+ "unpredicted[\n",
+ " [\n",
+ " \"point_id\",\n",
+ " \"satellite\",\n",
+ " \"time\",\n",
+ " \"sun_elevation\",\n",
+ " \"distance from ground track (km)\",\n",
+ " ]\n",
+ "].round(1)"
]
},
{
@@ -1230,10 +1452,15 @@
"source": [
"oli_5 = oli.model_copy(update={\"min_target_solar_elevation\": 5})\n",
"with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
- " predicted_5 = pd.concat(list(pool.map(lambda point: predict(point, oli_5), points.itertuples()))).reset_index(drop=True)\n",
+ " predicted_5 = pd.concat(\n",
+ " list(pool.map(lambda point: predict(point, oli_5), points.itertuples()))\n",
+ " ).reset_index(drop=True)\n",
"pairs_5, back_5 = match(predicted_5, acquired)\n",
"rows3 = {}\n",
- "for label, p, b in [(\"no solar requirement\", pairs, back), (\"min_target_solar_elevation = 5\", pairs_5, back_5)]:\n",
+ "for label, p, b in [\n",
+ " (\"no solar requirement\", pairs, back),\n",
+ " (\"min_target_solar_elevation = 5\", pairs_5, back_5),\n",
+ "]:\n",
" for satellite in satellites:\n",
" ps, bs = p[p.satellite == satellite], b[b.satellite == satellite]\n",
" rows3[(label, satellite)] = {\n",
@@ -1242,10 +1469,17 @@
" \"predicted passes acquired (%)\": 100 * ps.acquired.mean(),\n",
" \"acquired passes predicted (%)\": 100 * bs.predicted.mean(),\n",
" \"predicted, not acquired\": int((~ps.acquired).sum()),\n",
- " \"predicted, not acquired, south of 60 deg S (%)\": 100 * np.mean(ps[~ps.acquired].point_id.map(points.geometry.y) < -60),\n",
- " \"acquired, not predicted: Sun below horizon\": int((~bs.predicted & (bs.sun_elevation < 0)).sum()),\n",
- " \"acquired, not predicted: Sun 0 to 5 deg\": int((~bs.predicted & (bs.sun_elevation >= 0) & (bs.sun_elevation < 5)).sum()),\n",
- " \"acquired, not predicted: Sun above 5 deg\": int((~bs.predicted & (bs.sun_elevation >= 5)).sum()),\n",
+ " \"predicted, not acquired, south of 60 deg S (%)\": 100\n",
+ " * np.mean(ps[~ps.acquired].point_id.map(points.geometry.y) < -60),\n",
+ " \"acquired, not predicted: Sun below horizon\": int(\n",
+ " (~bs.predicted & (bs.sun_elevation < 0)).sum()\n",
+ " ),\n",
+ " \"acquired, not predicted: Sun 0 to 5 deg\": int(\n",
+ " (~bs.predicted & (bs.sun_elevation >= 0) & (bs.sun_elevation < 5)).sum()\n",
+ " ),\n",
+ " \"acquired, not predicted: Sun above 5 deg\": int(\n",
+ " (~bs.predicted & (bs.sun_elevation >= 5)).sum()\n",
+ " ),\n",
" }\n",
"pd.DataFrame(rows3).T.round(1)"
]
diff --git a/docs/validation/ValidateSpaceCOSMIC2.ipynb b/docs/validation/ValidateSpaceCOSMIC2.ipynb
new file mode 100644
index 00000000..2d672854
--- /dev/null
+++ b/docs/validation/ValidateSpaceCOSMIC2.ipynb
@@ -0,0 +1,1549 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "8333bf4f",
+ "metadata": {},
+ "source": [
+ "# Satellite-to-Satellite Validation: COSMIC-2\n",
+ "\n",
+ "This notebook validates TAT-C's satellite-to-satellite analysis (`collect_space_observations`, see the [Space-Based Coverage](../examples/CollectSpace.ipynb) example) against the radio occultations recorded by the [COSMIC-2](https://www.cosmic.ucar.edu/what-we-do/cosmic-2) constellation on 14 May 2026. `collect_space_observations` finds the periods when one satellite can observe another, as by a sensor or a radio link, subject to the slant range and to the Earth's occlusion of the straight line of sight between them: its minimum WGS 84 geodetic altitude must be at least `min_grazing_altitude`.\n",
+ "\n",
+ "A radio occultation (RO) is such an occlusion event. As a GNSS satellite sets behind the Earth as seen from a receiver in low Earth orbit (or rises from behind it), the line of sight between them sweeps down (or up) through the atmosphere, which the receiver profiles from the bending of the signal. TAT-C's RO analysis (`collect_ro_observations`, validated in [Radio Occultation Validation: COSMIC-2](ValidateROCOSMIC2.ipynb)) reports each occultation at the time the straight line reaches its `sample_elevation`, by default -80 km (refraction is not modeled). With `min_grazing_altitude` set to the same -80 km, each window of `collect_space_observations` from a receiver to a transmitter should therefore start with a rising occultation and end with a setting one. The satellites' recorded positions provide an independent truth for the occlusion constraint, the search for window edges, and the analysis of many pairs of satellites at once:\n",
+ "\n",
+ "1. **Observed occultations.** Is every occultation COSMIC-2 recorded at the edge of a window: a start if rising, an end if setting?\n",
+ "2. **Timing.** How closely do the edges match the true times, from the recorded positions?\n",
+ "3. **Consistency with the RO analysis.** Do the edges match the occultations of `collect_ro_observations` one for one?\n",
+ "4. **Symmetry, resolution, and cost.** Are the windows the same in both directions and for any `min_duration`, and how long does the analysis take?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "940660e8",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "This notebook reuses the data downloaded (and cached in `data/ro`) by the [COSMIC-2 RO validation](ValidateROCOSMIC2.ipynb), which must be run first: the [AWS Registry of Open Data](https://registry.opendata.aws/gnss-ro-opendata/) metadata database, which lists the 5,710 occultations COSMIC-2 recorded on 14 May 2026 with their receiver, transmitter, and rising or setting flag, and the **calibratedPhase** (Level 1A) files of the same seeded random sample of 200 occultations from each constellation (GPS, GLONASS, and Galileo), which record the receiver and transmitter positions at 100 Hz. The orbits are the two-line element sets (TLEs) included with that notebook (`gps_2026-05-14.3le`, `glonass_2026-05-14.3le`, `galileo_2026-05-14.3le`, and `cosmic2_2026-05-14.3le`, from [Space-Track](https://www.space-track.org))."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "52b931ef",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:17:40.439815Z",
+ "iopub.status.busy": "2026-10-26T00:17:40.439534Z",
+ "iopub.status.idle": "2026-10-26T00:18:50.703603Z",
+ "shell.execute_reply": "2026-10-26T00:18:50.702826Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5710 COSMIC-2 occultations on 14 May 2026, 600 sampled\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "\n",
+ "import numpy as np\n",
+ "from awsgnssroutils.database import RODatabaseClient\n",
+ "\n",
+ "CONSTELLATIONS = {\"G\": \"GPS\", \"R\": \"GLONASS\", \"E\": \"Galileo\"}\n",
+ "\n",
+ "DATA_DIR = os.path.join(\"data\", \"ro\")\n",
+ "if not os.path.isdir(os.path.join(DATA_DIR, \"metadata\")):\n",
+ " raise FileNotFoundError(\n",
+ " \"run ValidateROCOSMIC2 first, to download the COSMIC-2 occultation database\"\n",
+ " )\n",
+ "rodb = RODatabaseClient(\n",
+ " metadata_root=os.path.join(DATA_DIR, \"metadata\"), version=\"v1.1\"\n",
+ ")\n",
+ "occultations = rodb.query(\n",
+ " missions=\"cosmic2\", datetimerange=(\"2026-05-14\", \"2026-05-14T23:59:59\"), silent=True\n",
+ ")\n",
+ "# the RO validation's seeded random sample of 200 occultations with a retrieval from each constellation\n",
+ "sample = occultations[0:0]\n",
+ "sample._data = []\n",
+ "for k, code in enumerate(CONSTELLATIONS):\n",
+ " pool = occultations.filter(\n",
+ " GNSSconstellations=code,\n",
+ " availablefiletypes=[\"ucar_calibratedPhase\", \"ucar_refractivityRetrieval\"],\n",
+ " )\n",
+ " rng = np.random.default_rng(20260514 + k)\n",
+ " sample._data += [\n",
+ " pool._data[i] for i in sorted(rng.choice(pool.size, 200, replace=False))\n",
+ " ]\n",
+ "sample.size = len(sample._data)\n",
+ "missing = [\n",
+ " r[\"occid\"]\n",
+ " for r in sample._data\n",
+ " if not os.path.exists(\n",
+ " os.path.join(DATA_DIR, os.path.basename(r[\"ucar_calibratedPhase\"]))\n",
+ " )\n",
+ "]\n",
+ "if missing:\n",
+ " raise FileNotFoundError(\n",
+ " f\"run ValidateROCOSMIC2 first, to download {len(missing)} calibratedPhase files\"\n",
+ " )\n",
+ "print(\n",
+ " f\"{occultations.size} COSMIC-2 occultations on 14 May 2026, {sample.size} sampled\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "78c1342a",
+ "metadata": {},
+ "source": [
+ "## Orbits\n",
+ "\n",
+ "The receivers and transmitters are identified, and poor element sets excluded, exactly as in the COSMIC-2 RO validation (see its Test 1): each is matched to the satellite whose element sets best reproduce the positions recorded in the sampled Level 1A files, and element sets more than 20 km from those positions are excluded. This takes about a minute and a half."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "82df8912",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:18:50.705579Z",
+ "iopub.status.busy": "2026-10-26T00:18:50.705245Z",
+ "iopub.status.idle": "2026-10-26T00:18:58.983936Z",
+ "shell.execute_reply": "2026-10-26T00:18:58.983205Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5 receivers and 77 transmitters identified; unidentified: none\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
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+ " satellites median error (km) max error (km) excluded sets \\\n",
+ "group \n",
+ "Galileo 26 1.70 15.67 0 \n",
+ "GPS 32 1.28 4.66 1 \n",
+ "GLONASS 19 1.23 6.88 0 \n",
+ "receivers 5 0.53 1.19 0 \n",
+ "\n",
+ " closest next-best satellite (km) \n",
+ "group \n",
+ "Galileo 7121.83 \n",
+ "GPS 2151.02 \n",
+ "GLONASS 3901.41 \n",
+ "receivers 3338.26 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import collections\n",
+ "from datetime import datetime, timedelta, timezone\n",
+ "\n",
+ "import netCDF4 as nc\n",
+ "import pandas as pd\n",
+ "from joblib import Parallel, delayed\n",
+ "\n",
+ "from tatc.constants import timescale\n",
+ "from tatc.schemas import GeneralPerturbationsOrbit, Satellite\n",
+ "\n",
+ "DAY0 = datetime(2026, 5, 14, tzinfo=timezone.utc)\n",
+ "GPS_EPOCH = datetime(1980, 1, 6, tzinfo=timezone.utc)\n",
+ "GPS_UTC_OFFSET = 18 # s, GPS time minus UTC in 2026\n",
+ "MAX_ERROR = 20 # km, largest acceptable position error of an element set\n",
+ "N_JOBS = min(16, os.cpu_count()) # parallel worker processes\n",
+ "\n",
+ "# element sets per NORAD catalog number, keyed by epoch (later duplicates win)\n",
+ "tle_lines = [\n",
+ " line.rstrip()\n",
+ " for path in [\n",
+ " \"gps_2026-05-14.3le\",\n",
+ " \"glonass_2026-05-14.3le\",\n",
+ " \"galileo_2026-05-14.3le\",\n",
+ " \"cosmic2_2026-05-14.3le\",\n",
+ " ]\n",
+ " for line in open(path)\n",
+ " if line.strip()\n",
+ "]\n",
+ "element_sets, names = collections.defaultdict(dict), {}\n",
+ "for i in range(0, len(tle_lines), 3):\n",
+ " line1, line2 = tle_lines[i + 1], tle_lines[i + 2]\n",
+ " names[line1[2:7]] = tle_lines[i][2:].strip()\n",
+ " element_sets[line1[2:7]][float(line1[18:32])] = [line1, line2]\n",
+ "\n",
+ "\n",
+ "def to_orbit(sets):\n",
+ " return GeneralPerturbationsOrbit.from_tle(sum((sets[e] for e in sorted(sets)), []))\n",
+ "\n",
+ "\n",
+ "def identify(positions, element_sets, max_error):\n",
+ " \"\"\"Matches recorded Earth-fixed positions [(datetime, m)] to the satellite whose element sets\n",
+ " reproduce them best, keeping that satellite's element sets within max_error (km) of all of them.\n",
+ " Returns the NORAD catalog number, kept element sets, their position errors (km), the next-best\n",
+ " satellite's median error (km), and the number of excluded element sets.\"\"\"\n",
+ " import numpy as np\n",
+ " from skyfield.framelib import itrs\n",
+ "\n",
+ " from tatc.constants import timescale\n",
+ " from tatc.schemas import GeneralPerturbationsOrbit\n",
+ "\n",
+ " ts = timescale.from_datetimes([t for t, _ in positions])\n",
+ " rotation = itrs.rotation_at(ts)\n",
+ " recorded_m = np.array([p for _, p in positions]).T\n",
+ "\n",
+ " def errors(sets):\n",
+ " orbit = GeneralPerturbationsOrbit.from_tle(\n",
+ " sum((sets[e] for e in sorted(sets)), [])\n",
+ " )\n",
+ " p = np.einsum(\n",
+ " \"ij...,j...->i...\",\n",
+ " rotation,\n",
+ " np.array(orbit.get_orbit_track_at_time(ts).position.m),\n",
+ " )\n",
+ " return np.linalg.norm(p - recorded_m, axis=0) / 1e3\n",
+ "\n",
+ " best = {\n",
+ " norad: min(np.median(errors({e: s})) for e, s in sets.items())\n",
+ " for norad, sets in element_sets.items()\n",
+ " }\n",
+ " norad = min(best, key=best.get)\n",
+ " kept = {\n",
+ " e: s\n",
+ " for e, s in element_sets[norad].items()\n",
+ " if errors({e: s}).max() <= max_error\n",
+ " }\n",
+ " return (\n",
+ " norad,\n",
+ " kept,\n",
+ " errors(kept) if kept else None,\n",
+ " sorted(best.values())[1],\n",
+ " len(element_sets[norad]) - len(kept),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "# recorded positions of each receiver and transmitter\n",
+ "recorded = collections.defaultdict(list)\n",
+ "for record in sample._data:\n",
+ " l1 = nc.Dataset(\n",
+ " os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"]))\n",
+ " )\n",
+ " l1.set_auto_mask(False)\n",
+ " for k in [0, l1[\"time\"].size // 2, l1[\"time\"].size - 1]:\n",
+ " t = GPS_EPOCH + timedelta(\n",
+ " seconds=float(l1[\"startTime\"][...]) - GPS_UTC_OFFSET + float(l1[\"time\"][k])\n",
+ " )\n",
+ " recorded[record[\"transmitter\"]].append((t, l1[\"positionGNSS\"][k].astype(float)))\n",
+ " recorded[record[\"receiver\"]].append((t, l1[\"positionLEO\"][k].astype(float)))\n",
+ "\n",
+ "\n",
+ "keys = sorted(recorded)\n",
+ "results = Parallel(n_jobs=N_JOBS)(\n",
+ " delayed(identify)(recorded[key], element_sets, MAX_ERROR) for key in keys\n",
+ ")\n",
+ "rows, satellites = [], {}\n",
+ "for key, (norad, kept, errors, next_best, excluded) in zip(keys, results):\n",
+ " row = {\n",
+ " \"key\": key,\n",
+ " \"NORAD\": norad,\n",
+ " \"name\": names[norad],\n",
+ " \"element sets\": len(kept),\n",
+ " \"excluded sets\": excluded,\n",
+ " }\n",
+ " if kept: # identified: at least one element set reproduces the recorded positions\n",
+ " satellites[key] = Satellite(name=key, orbit=to_orbit(kept))\n",
+ " row.update(\n",
+ " {\"median error (km)\": np.median(errors), \"max error (km)\": errors.max()}\n",
+ " )\n",
+ " row[\"next-best satellite (km)\"] = next_best\n",
+ " rows.append(row)\n",
+ "orbit_check = pd.DataFrame(rows).set_index(\"key\")\n",
+ "orbit_check[\"group\"] = [CONSTELLATIONS.get(k[0], \"receivers\") for k in keys]\n",
+ "receivers = [satellites[k] for k in sorted(satellites) if k.startswith(\"cosmic2\")]\n",
+ "transmitters = [satellites[k] for k in sorted(satellites) if k[0] in CONSTELLATIONS]\n",
+ "unidentified = sorted(k for k in recorded if k not in satellites)\n",
+ "print(\n",
+ " f\"{len(receivers)} receivers and {len(transmitters)} transmitters identified; unidentified: {unidentified or 'none'}\"\n",
+ ")\n",
+ "orbit_check.groupby(\"group\", sort=False).agg(\n",
+ " **{\n",
+ " \"satellites\": (\"NORAD\", \"size\"),\n",
+ " \"median error (km)\": (\"median error (km)\", \"median\"),\n",
+ " \"max error (km)\": (\"max error (km)\", \"max\"),\n",
+ " \"excluded sets\": (\"excluded sets\", \"sum\"),\n",
+ " \"closest next-best satellite (km)\": (\"next-best satellite (km)\", \"min\"),\n",
+ " }\n",
+ ").round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b07acb00",
+ "metadata": {},
+ "source": [
+ "As in the RO validation, all 5 receivers and 77 transmitters (32 GPS, 19 GLONASS, and 26 Galileo satellites) are identified, and one poor GPS element set is excluded.\n",
+ "\n",
+ "## Windows\n",
+ "\n",
+ "`collect_space_observations` is run from the 5 receivers to the 77 transmitters over the day, with a `min_grazing_altitude` of -80 km, no range limits, and the default `min_duration` (60 s). Within the day, each window's start is a rising occultation, when the line of sight rises above -80 km, and each window's end a setting one; windows that span the start or end of the day are cut off there."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "bc62ed7b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:18:58.986000Z",
+ "iopub.status.busy": "2026-10-26T00:18:58.985678Z",
+ "iopub.status.idle": "2026-10-26T00:19:01.834319Z",
+ "shell.execute_reply": "2026-10-26T00:19:01.833592Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5265 windows of 385 receiver-transmitter pairs found in 2.8 s, with 10073 edges within the day (5034 rising, 5039 setting)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "\n",
+ "from tatc.analysis import collect_space_observations\n",
+ "\n",
+ "SAMPLE_ELEVATION = -80e3 # m, collect_ro_observations' default sample_elevation\n",
+ "DAY1 = DAY0 + timedelta(days=1)\n",
+ "\n",
+ "tic = time.perf_counter()\n",
+ "links = collect_space_observations(\n",
+ " receivers,\n",
+ " DAY0,\n",
+ " DAY1,\n",
+ " to_satellites=transmitters,\n",
+ " min_grazing_altitude=SAMPLE_ELEVATION,\n",
+ ")\n",
+ "elapsed = time.perf_counter() - tic\n",
+ "# window edges within the day: a start is a rising occultation, an end a setting one\n",
+ "edges = pd.concat(\n",
+ " [\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"receiver\": links.from_satellite,\n",
+ " \"transmitter\": links.to_satellite,\n",
+ " \"time\": links[key],\n",
+ " \"rising\": key == \"start\",\n",
+ " }\n",
+ " )\n",
+ " for key in [\"start\", \"end\"]\n",
+ " ],\n",
+ " ignore_index=True,\n",
+ ")\n",
+ "edges = edges[(edges.time > DAY0) & (edges.time < DAY1)].sort_values(\n",
+ " \"time\", ignore_index=True\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def nearest_edges(events, tolerance):\n",
+ " \"\"\"The nearest window edge (index in edges, or -1 if none within tolerance, in s) of the same receiver\n",
+ " and transmitter to each event (by its time), and the time from the event to the edge (s).\n",
+ " \"\"\"\n",
+ " index, offset = np.full(len(events), -1), np.full(len(events), np.nan)\n",
+ " groups = edges.groupby([\"receiver\", \"transmitter\"]).indices\n",
+ " for key, rows in events.groupby([\"receiver\", \"transmitter\"]).indices.items():\n",
+ " if key not in groups:\n",
+ " continue\n",
+ " candidates = groups[key]\n",
+ " dt = (\n",
+ " edges.time.values[candidates][None, :] - events.time.values[rows][:, None]\n",
+ " ) / np.timedelta64(1, \"s\")\n",
+ " j = np.argmin(np.abs(dt), axis=1)\n",
+ " d = dt[np.arange(len(rows)), j]\n",
+ " ok = np.abs(d) <= tolerance\n",
+ " index[rows[ok]] = candidates[j[ok]]\n",
+ " offset[rows[ok]] = d[ok]\n",
+ " return index, offset\n",
+ "\n",
+ "\n",
+ "print(\n",
+ " f\"{len(links)} windows of {len(receivers) * len(transmitters)} receiver-transmitter pairs found in {elapsed:.1f} s, \"\n",
+ " f\"with {len(edges)} edges within the day ({edges.rising.sum()} rising, {(~edges.rising).sum()} setting)\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "999de648",
+ "metadata": {},
+ "source": [
+ "## Test 1: Observed Occultations\n",
+ "\n",
+ "Each occultation in the database is paired with the nearest window edge of the same receiver and transmitter within 10 minutes of its time stamp. The time stamp (the minute in the `occid`) marks the start of the record for a setting occultation and its end for a rising one, when the straight line is near 130 km for GPS (see the RO validation). A setting occultation's edge (a window's end) should therefore follow its time stamp, and a rising occultation's edge (a window's start) precede it, by one to a few minutes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "d67d410c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:01.836411Z",
+ "iopub.status.busy": "2026-10-26T00:19:01.836187Z",
+ "iopub.status.idle": "2026-10-26T00:19:01.898792Z",
+ "shell.execute_reply": "2026-10-26T00:19:01.898110Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5710 observed occultations; without a window edge within 10 minutes:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ " at an edge of the right kind \\\n",
+ "constellation direction \n",
+ "GLONASS setting 955 955 \n",
+ " rising 866 866 \n",
+ "GPS setting 1485 1485 \n",
+ " rising 1199 1199 \n",
+ "Galileo setting 980 980 \n",
+ " rising 214 214 \n",
+ "\n",
+ " edge minus time stamp, 1% (s) 50% (s) 99% (s) \n",
+ "constellation direction \n",
+ "GLONASS setting 68.0 117.7 213.7 \n",
+ " rising -227.0 -113.8 -62.7 \n",
+ "GPS setting 68.8 116.9 209.7 \n",
+ " rising -207.6 -111.6 -63.8 \n",
+ "Galileo setting 68.2 115.7 220.6 \n",
+ " rising -211.0 -111.8 -63.4 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "observed = pd.DataFrame(\n",
+ " [\n",
+ " {\n",
+ " \"occid\": r[\"occid\"],\n",
+ " \"receiver\": r[\"receiver\"],\n",
+ " \"transmitter\": r[\"transmitter\"],\n",
+ " \"constellation\": CONSTELLATIONS[r[\"transmitter\"][0]],\n",
+ " \"time\": datetime.fromisoformat(r[\"time\"]).replace(tzinfo=timezone.utc),\n",
+ " \"setting\": bool(r[\"setting\"]),\n",
+ " }\n",
+ " for r in occultations._data\n",
+ " if r[\"transmitter\"][0] in CONSTELLATIONS\n",
+ " ]\n",
+ ")\n",
+ "observed[\"edge\"], observed[\"offset\"] = nearest_edges(observed, 600)\n",
+ "at_edge = observed.edge >= 0\n",
+ "observed[\"right kind\"] = at_edge & (\n",
+ " edges.rising.values[observed.edge.clip(lower=0)] != observed.setting\n",
+ ")\n",
+ "observed[\"direction\"] = observed.setting.map({True: \"setting\", False: \"rising\"})\n",
+ "print(\n",
+ " f\"{len(observed)} observed occultations; without a window edge within 10 minutes:\"\n",
+ ")\n",
+ "display(observed.loc[~at_edge, [\"occid\", \"direction\", \"time\"]])\n",
+ "observed[at_edge].groupby([\"constellation\", \"direction\"], sort=False).agg(\n",
+ " **{\n",
+ " \"at an edge\": (\"occid\", \"size\"),\n",
+ " \"of the right kind\": (\"right kind\", \"sum\"),\n",
+ " \"edge minus time stamp, 1% (s)\": (\"offset\", lambda x: x.quantile(0.01)),\n",
+ " \"50% (s)\": (\"offset\", \"median\"),\n",
+ " \"99% (s)\": (\"offset\", lambda x: x.quantile(0.99)),\n",
+ " }\n",
+ ").round(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6447ec62",
+ "metadata": {},
+ "source": [
+ "All 5,699 occultations whose straight line reaches -80 km within the day are at a window edge of the right kind: setting occultations at a window's end, 68 to 221 s after their time stamp, and rising ones at a window's start, 63 to 227 s before it (1st to 99th percentiles, for every constellation). The other 11 were recorded within 2 minutes of midnight, so their straight lines reach -80 km on the previous or next day, outside the analysis; the RO validation excludes them too."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dbfd1b34",
+ "metadata": {},
+ "source": [
+ "## Test 2: Timing\n",
+ "\n",
+ "For each sampled occultation, the true time at which the straight line reaches -80 km is computed from the recorded receiver and transmitter positions (subsampled to 10 Hz) with TAT-C's tangent point routine, as in the RO validation's Test 4, and paired with the nearest window edge within 60 s. To explain the differences, the along-track errors of the receiver's and transmitter's orbits are also computed at the middle of each occultation, from the recorded positions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c265b0b7",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:01.900711Z",
+ "iopub.status.busy": "2026-10-26T00:19:01.900493Z",
+ "iopub.status.idle": "2026-10-26T00:19:06.484704Z",
+ "shell.execute_reply": "2026-10-26T00:19:06.484011Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "600 sampled occultations, 594 reaching -80 km within the day\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
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at an edge of the right kind
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median |edge minus truth| (s)
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95% (s)
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0.074
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+ "text/plain": [
+ " sampled at an edge of the right kind \\\n",
+ "constellation \n",
+ "GPS 199 199 \n",
+ "GLONASS 198 198 \n",
+ "Galileo 197 197 \n",
+ "\n",
+ " mean edge minus truth (s) median |edge minus truth| (s) \\\n",
+ "constellation \n",
+ "GPS -0.053 0.085 \n",
+ "GLONASS -0.019 0.074 \n",
+ "Galileo -0.062 0.093 \n",
+ "\n",
+ " 95% (s) max (s) transmitter along-track error, median (km) \\\n",
+ "constellation \n",
+ "GPS 0.249 0.499 -0.326 \n",
+ "GLONASS 0.252 0.409 -0.103 \n",
+ "Galileo 0.295 0.554 -1.037 \n",
+ "\n",
+ " receiver along-track error, median (km) \n",
+ "constellation \n",
+ "GPS 0.061 \n",
+ "GLONASS 0.011 \n",
+ "Galileo 0.051 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyproj import Transformer\n",
+ "from skyfield.framelib import itrs\n",
+ "\n",
+ "from tatc.utils import compute_tangent_point\n",
+ "\n",
+ "TO_LLA = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
+ "\n",
+ "\n",
+ "def crossing(t, h, value):\n",
+ " \"\"\"Time at which h(t) first crosses value (linear interpolation), or NaN.\"\"\"\n",
+ " c = np.nonzero(np.diff(np.sign(h - value)))[0]\n",
+ " if len(c) == 0:\n",
+ " return np.nan\n",
+ " j = c[0]\n",
+ " return t[j] + (value - h[j]) / (h[j + 1] - h[j]) * (t[j + 1] - t[j])\n",
+ "\n",
+ "\n",
+ "def along_track_error(satellite, t, recorded):\n",
+ " \"\"\"Along-track error (km) of a satellite's orbit at datetime t, from its recorded Earth-fixed position (m).\"\"\"\n",
+ " ts = timescale.from_datetimes([t, t + timedelta(seconds=1)])\n",
+ " p = np.einsum(\n",
+ " \"ij...,j...->i...\",\n",
+ " itrs.rotation_at(ts),\n",
+ " satellite.orbit.to_gp_orbit().get_orbit_track_at_time(ts).position.m,\n",
+ " )\n",
+ " direction = (p[:, 1] - p[:, 0]) / np.linalg.norm(p[:, 1] - p[:, 0])\n",
+ " return (p[:, 0] - recorded) @ direction / 1e3\n",
+ "\n",
+ "\n",
+ "# true time at which each sampled occultation's straight line reaches -80 km, from the recorded receiver\n",
+ "# and transmitter positions (subsampled to 10 Hz), and the orbits' along-track errors at mid-occultation\n",
+ "rows = []\n",
+ "for record in sample._data:\n",
+ " l1 = nc.Dataset(\n",
+ " os.path.join(DATA_DIR, os.path.basename(record[\"ucar_calibratedPhase\"]))\n",
+ " )\n",
+ " l1.set_auto_mask(False)\n",
+ " t, rx, tx = l1[\"time\"][:], l1[\"positionLEO\"][:].T, l1[\"positionGNSS\"][:].T\n",
+ " ok = (\n",
+ " (np.abs(t) < 1e10)\n",
+ " & np.all(np.abs(rx) < 1e10, axis=0)\n",
+ " & np.all(np.abs(tx) < 1e10, axis=0)\n",
+ " )\n",
+ " i = np.nonzero(ok)[0][::10]\n",
+ " start = float(l1[\"startTime\"][...])\n",
+ " # GPS seconds since 1980-01-06 are TAI - 19 s\n",
+ " heights = TO_LLA.transform(*compute_tangent_point(rx[:, i], tx[:, i] - rx[:, i]))[2]\n",
+ " t_80 = crossing(t[i], np.asarray(heights), SAMPLE_ELEVATION)\n",
+ " k = i[len(i) // 2]\n",
+ " t_mid = GPS_EPOCH + timedelta(seconds=start - GPS_UTC_OFFSET + float(t[k]))\n",
+ " rows.append(\n",
+ " {\n",
+ " \"occid\": record[\"occid\"],\n",
+ " \"receiver\": record[\"receiver\"],\n",
+ " \"transmitter\": record[\"transmitter\"],\n",
+ " \"constellation\": CONSTELLATIONS[record[\"transmitter\"][0]],\n",
+ " \"setting\": bool(record[\"setting\"]),\n",
+ " \"time\": (\n",
+ " GPS_EPOCH + timedelta(seconds=start - GPS_UTC_OFFSET + t_80)\n",
+ " if np.isfinite(t_80)\n",
+ " else pd.NaT\n",
+ " ),\n",
+ " \"receiver along-track error (km)\": along_track_error(\n",
+ " satellites[record[\"receiver\"]], t_mid, rx[:, k]\n",
+ " ),\n",
+ " \"transmitter along-track error (km)\": along_track_error(\n",
+ " satellites[record[\"transmitter\"]], t_mid, tx[:, k]\n",
+ " ),\n",
+ " }\n",
+ " )\n",
+ "truth = pd.DataFrame(rows)\n",
+ "truth[\"time\"] = pd.to_datetime(truth.time, utc=True)\n",
+ "in_day = (truth.time >= DAY0) & (truth.time < DAY1)\n",
+ "print(\n",
+ " f\"{len(truth)} sampled occultations, {int(in_day.sum())} reaching -80 km within the day\"\n",
+ ")\n",
+ "truth = truth[in_day].reset_index(drop=True)\n",
+ "truth[\"edge\"], truth[\"offset\"] = nearest_edges(truth, 60)\n",
+ "truth[\"right kind\"] = (truth.edge >= 0) & (\n",
+ " edges.rising.values[truth.edge.clip(lower=0)] != truth.setting\n",
+ ")\n",
+ "truth[\"abs offset\"] = truth.offset.abs()\n",
+ "truth.groupby(\"constellation\", sort=False).agg(\n",
+ " **{\n",
+ " \"sampled\": (\"occid\", \"size\"),\n",
+ " \"at an edge of the right kind\": (\"right kind\", \"sum\"),\n",
+ " \"mean edge minus truth (s)\": (\"offset\", \"mean\"),\n",
+ " \"median |edge minus truth| (s)\": (\"abs offset\", \"median\"),\n",
+ " \"95% (s)\": (\"abs offset\", lambda x: x.quantile(0.95)),\n",
+ " \"max (s)\": (\"abs offset\", \"max\"),\n",
+ " \"transmitter along-track error, median (km)\": (\n",
+ " \"transmitter along-track error (km)\",\n",
+ " \"median\",\n",
+ " ),\n",
+ " \"receiver along-track error, median (km)\": (\n",
+ " \"receiver along-track error (km)\",\n",
+ " \"median\",\n",
+ " ),\n",
+ " }\n",
+ ").round(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "846b31ec",
+ "metadata": {},
+ "source": [
+ "Every sampled occultation is at a window edge of the right kind, within 0.25 to 0.30 s of the true time (95th percentile of the absolute difference, by constellation). On average the edges come 0.02 s (GLONASS) to 0.06 s (Galileo) early, for rising and setting occultations alike:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f62be92b",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:06.486437Z",
+ "iopub.status.busy": "2026-10-26T00:19:06.486269Z",
+ "iopub.status.idle": "2026-10-26T00:19:06.495139Z",
+ "shell.execute_reply": "2026-10-26T00:19:06.494516Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
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+ " \n",
+ "
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+ "
mean edge minus truth (s)
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+ "mean edge minus truth (s) setting rising\n",
+ "constellation \n",
+ "GPS -0.045 -0.062\n",
+ "GLONASS -0.001 -0.036\n",
+ "Galileo -0.059 -0.080"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "truth.assign(\n",
+ " direction=truth.setting.map({True: \"setting\", False: \"rising\"})\n",
+ ").pivot_table(\n",
+ " index=\"constellation\",\n",
+ " columns=\"direction\",\n",
+ " values=\"offset\",\n",
+ " aggfunc=\"mean\",\n",
+ " sort=False,\n",
+ ").rename_axis(\n",
+ " columns=\"mean edge minus truth (s)\"\n",
+ ").round(\n",
+ " 3\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "234975d9",
+ "metadata": {},
+ "source": [
+ "The bias has the same sign for rising and setting occultations, so it is not an offset in the altitude of the line of sight, which would shift rising and setting edges in opposite directions. Nor does it come from the orbits: the receivers' orbits are accurate along track (median errors of 0.01 to 0.06 km), and the [PlanetiQ RO validation](ValidateROPlanetiQ.ipynb), with the same transmitter orbits, finds no bias (0.00 to 0.02 s). It appears specific to COSMIC-2's recorded times or positions; its cause is not identified here.\n",
+ "\n",
+ "`collect_ro_observations` reports the same times (Test 3), and the RO validation's Test 4 finds the same differences."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e85c4431",
+ "metadata": {},
+ "source": [
+ "## Test 3: Consistency with the RO Analysis\n",
+ "\n",
+ "`collect_ro_observations` is run for the same receivers and transmitters with a 1 s `time_step` and a `max_yaw` of 90 deg, which removes the receiver antenna's yaw limit, so that it reports every occultation the geometry allows. It finds each RO observation's arc from its tangent point track, samples the arc every second within an elevation range (by default -200 to 60 km), and interpolates the time at -80 km (`time`); `collect_space_observations` instead finds the window edges directly, from the minimum altitude of the line of sight. Each RO `time` is paired with the nearest window edge of the same receiver and transmitter within 1 s. The run takes about a minute in parallel worker processes, and its results are cached in `data/ro`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "05f85d61",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:06.496791Z",
+ "iopub.status.busy": "2026-10-26T00:19:06.496577Z",
+ "iopub.status.idle": "2026-10-26T00:19:07.218508Z",
+ "shell.execute_reply": "2026-10-26T00:19:07.217789Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10154 RO occultations (max_yaw = 90 deg), 10073 window edges\n",
+ "paired one to one within 1 s: 10073; |RO time minus edge| median 0.14 ms, max 1.69 ms\n",
+ "RO occultations without an edge: 81\n",
+ " cut off at the start or end of the day: 25\n",
+ " not cut off, straight line never crossing -80 km: 56 (lowest elevations, km: -200, -200, -200, -200, -200, -200, -200, -174, -152, -142, -130, -126, -109, -97, -79, -79, -79, -79, -76, -76, -72, -72, -62, -62, -39, -39, -37, -37, -31, -31, -21, -21, -17, -17, -4, -4, 3, 3, 20, 20, 25, 25, 32, 32, 33, 33, 37, 37, 42, 42, 48, 48, 57, 57, 60, 60)\n",
+ " other: 0\n",
+ "edges without an RO occultation: 0, of which within an RO observation paired with another edge: 0\n",
+ "paired occultations disagreeing on direction: 0 of 10073 (0 of the 359 more than 75 deg from fore/aft)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import shapely\n",
+ "\n",
+ "from tatc.analysis import collect_ro_observations\n",
+ "\n",
+ "\n",
+ "def folded(yaw):\n",
+ " \"\"\"Yaw angle from the nearer of the forward and rearward directions (deg).\"\"\"\n",
+ " yaw = np.abs(np.asarray(yaw, dtype=float))\n",
+ " return np.minimum(yaw, 180 - yaw)\n",
+ "\n",
+ "\n",
+ "cache = os.path.join(DATA_DIR, \"predicted_cosmic2_yaw90_gnss.pkl\")\n",
+ "if not os.path.exists(cache):\n",
+ " # one parallel task per receiver, all transmitters together\n",
+ " results = Parallel(n_jobs=N_JOBS)(\n",
+ " delayed(collect_ro_observations)(\n",
+ " receiver,\n",
+ " transmitters,\n",
+ " DAY0,\n",
+ " DAY1,\n",
+ " time_step=timedelta(seconds=1),\n",
+ " max_yaw=90,\n",
+ " )\n",
+ " for receiver in receivers\n",
+ " )\n",
+ " pd.concat(results, ignore_index=True).to_pickle(cache)\n",
+ "ro = pd.read_pickle(cache)\n",
+ "ro[\"edge\"], ro[\"offset\"] = nearest_edges(ro, 1)\n",
+ "paired = ro[ro.edge >= 0]\n",
+ "if paired.edge.duplicated().any():\n",
+ " raise ValueError(\"an edge is paired with more than one RO occultation\")\n",
+ "# tangent point elevations of each RO observation's samples\n",
+ "elevations = ro.geometry.apply(\n",
+ " lambda g: shapely.get_coordinates(g, include_z=True)[:, 2]\n",
+ ")\n",
+ "crosses = elevations.apply(lambda z: z.min() <= SAMPLE_ELEVATION <= z.max())\n",
+ "truncated = (ro.start == DAY0) | (ro.end == DAY1)\n",
+ "unpaired = ro.edge < 0\n",
+ "# edges without an RO occultation, and whether each falls within an RO observation paired with another edge\n",
+ "unpaired_edges = edges.drop(paired.edge)\n",
+ "within = [\n",
+ " (\n",
+ " (paired.receiver == e.receiver)\n",
+ " & (paired.transmitter == e.transmitter)\n",
+ " & (paired.start <= e.time)\n",
+ " & (paired.end >= e.time)\n",
+ " ).any()\n",
+ " for e in unpaired_edges.itertuples()\n",
+ "]\n",
+ "disagree = paired[\n",
+ " paired.is_rising.astype(bool).values != edges.rising.values[paired.edge]\n",
+ "]\n",
+ "print(f\"{len(ro)} RO occultations (max_yaw = 90 deg), {len(edges)} window edges\")\n",
+ "print(\n",
+ " f\"paired one to one within 1 s: {len(paired)}; |RO time minus edge| median {1e3 * paired.offset.abs().median():.2f} ms, max {1e3 * paired.offset.abs().max():.2f} ms\"\n",
+ ")\n",
+ "print(f\"RO occultations without an edge: {int(unpaired.sum())}\")\n",
+ "print(f\" cut off at the start or end of the day: {int((unpaired & truncated).sum())}\")\n",
+ "print(\n",
+ " f\" not cut off, straight line never crossing -80 km: {int((unpaired & ~truncated & ~crosses).sum())} (lowest elevations, km: {', '.join(f'{z.min() / 1e3:.0f}' for z in elevations[unpaired & ~truncated].sort_values(key=lambda s: s.apply(min)))})\"\n",
+ ")\n",
+ "print(f\" other: {int((unpaired & ~truncated & crosses).sum())}\")\n",
+ "print(\n",
+ " f\"edges without an RO occultation: {len(unpaired_edges)}, of which within an RO observation paired with another edge: {sum(within)}\"\n",
+ ")\n",
+ "abeam = folded(paired.rx_tx_yaw) > 75\n",
+ "print(\n",
+ " f\"paired occultations disagreeing on direction: {len(disagree)} of {len(paired)} \"\n",
+ " f\"({int(disagree.index.isin(paired.index[abeam]).sum())} of the {int(abeam.sum())} more than 75 deg from fore/aft)\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85c8a388",
+ "metadata": {},
+ "source": [
+ "The two analyses agree: every one of the 10,073 window edges pairs one to one with an RO occultation, with times within 2 ms (median 0.14 ms), and they agree on the direction (rising or setting) of every one, including the 359 nearly abeam of the receiver. The other 81 RO occultations never cross -80 km within the day: 25 are cut off at the start or end of the day before reaching it, and 56 are parts of grazing occultations, nearly side-on to the receiver, whose straight line turns back before reaching -80 km (42, at lowest between -79 and 60 km) or stays below it (14). As documented, `collect_ro_observations` reports each with a `time` and `position` at the end of its profile nearest -80 km.\n",
+ "\n",
+ "`collect_ro_observations` splits a grazing occultation, whose tangent point descends and rises again, into a setting and a rising observation where the tangent point turns, and determines each observation's direction from its profile. This comparison exposed two earlier approximations: a grazing occultation crossing -80 km twice was reported as one observation with one `time`, which left 54 edges without an RO occultation, and the direction, approximated from the transmitter's pitch at the observation's first sample, was wrong for 20 occultations (0.2%), all 75 to 90 deg from the receiver's forward or rearward direction: outside COSMIC-2's yaw limit of 65 deg, but within PlanetiQ's (85 deg)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6dbd417a",
+ "metadata": {},
+ "source": [
+ "## Test 4: Symmetry, Resolution, and Cost\n",
+ "\n",
+ "The line-of-sight constraints are symmetric, so the windows from the transmitters to the receivers should be the same as those from the receivers to the transmitters. All 82 satellites are also analyzed together (with `to_satellites` omitted), as 6,642 ordered pairs, including those between receivers and between transmitters, of which the receiver-transmitter windows should again be the same. Finally, `min_duration` sets the sampling of the search for window edges (at half its value): windows shorter than `min_duration` may be missed, but longer ones should be found at any value."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "907da483",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:07.220595Z",
+ "iopub.status.busy": "2026-10-26T00:19:07.220314Z",
+ "iopub.status.idle": "2026-10-26T00:19:32.735616Z",
+ "shell.execute_reply": "2026-10-26T00:19:32.734889Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "shortest window within the day: 462 s\n"
+ ]
+ },
+ {
+ "data": {
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5265
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+ "
5265
\n",
+ "
0.000
\n",
+ "
2.762
\n",
+ "
\n",
+ "
\n",
+ "
min_duration = 300 s
\n",
+ "
385
\n",
+ "
5265
\n",
+ "
5265
\n",
+ "
0.001
\n",
+ "
2.729
\n",
+ "
\n",
+ "
\n",
+ "
min_duration = 1200 s
\n",
+ "
385
\n",
+ "
5262
\n",
+ "
5262
\n",
+ "
NaN
\n",
+ "
2.863
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " pairs windows receiver-transmitter windows \\\n",
+ "analysis \n",
+ "transmitters to receivers 385 5265 5265 \n",
+ "all satellites, both directions 6642 20602 5265 \n",
+ "min_duration = 5 s 385 5265 5265 \n",
+ "min_duration = 20 s 385 5265 5265 \n",
+ "min_duration = 60 s 385 5265 5265 \n",
+ "min_duration = 300 s 385 5265 5265 \n",
+ "min_duration = 1200 s 385 5262 5262 \n",
+ "\n",
+ " largest difference (s) time (s) \n",
+ "analysis \n",
+ "transmitters to receivers 0.000 2.706 \n",
+ "all satellites, both directions 0.000 6.605 \n",
+ "min_duration = 5 s 0.001 4.678 \n",
+ "min_duration = 20 s 0.001 3.147 \n",
+ "min_duration = 60 s 0.000 2.762 \n",
+ "min_duration = 300 s 0.001 2.729 \n",
+ "min_duration = 1200 s NaN 2.863 "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def windows(frame, reverse=False):\n",
+ " \"\"\"Windows by receiver and transmitter (observed and observing satellites, if reverse) and start.\"\"\"\n",
+ " columns = (\n",
+ " [\"to_satellite\", \"from_satellite\"]\n",
+ " if reverse\n",
+ " else [\"from_satellite\", \"to_satellite\"]\n",
+ " )\n",
+ " return (\n",
+ " frame[columns + [\"start\", \"end\"]]\n",
+ " .set_axis([\"receiver\", \"transmitter\", \"start\", \"end\"], axis=1)\n",
+ " .sort_values([\"receiver\", \"transmitter\", \"start\"], ignore_index=True)\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def difference(a, b):\n",
+ " \"\"\"Largest difference of window start or end (s) between two sets of windows, or None if they differ in number.\"\"\"\n",
+ " if (\n",
+ " len(a) != len(b)\n",
+ " or not (a[[\"receiver\", \"transmitter\"]] == b[[\"receiver\", \"transmitter\"]])\n",
+ " .all()\n",
+ " .all()\n",
+ " ):\n",
+ " return None\n",
+ " return max(\n",
+ " (a.start - b.start).abs().max(), (a.end - b.end).abs().max()\n",
+ " ).total_seconds()\n",
+ "\n",
+ "\n",
+ "rows = []\n",
+ "tic = time.perf_counter()\n",
+ "reverse = collect_space_observations(\n",
+ " transmitters,\n",
+ " DAY0,\n",
+ " DAY1,\n",
+ " to_satellites=receivers,\n",
+ " min_grazing_altitude=SAMPLE_ELEVATION,\n",
+ ")\n",
+ "rows.append(\n",
+ " {\n",
+ " \"analysis\": \"transmitters to receivers\",\n",
+ " \"pairs\": len(transmitters) * len(receivers),\n",
+ " \"windows\": len(reverse),\n",
+ " \"receiver-transmitter windows\": len(reverse),\n",
+ " \"largest difference (s)\": difference(\n",
+ " windows(links), windows(reverse, reverse=True)\n",
+ " ),\n",
+ " \"time (s)\": time.perf_counter() - tic,\n",
+ " }\n",
+ ")\n",
+ "tic = time.perf_counter()\n",
+ "everything = collect_space_observations(\n",
+ " receivers + transmitters, DAY0, DAY1, min_grazing_altitude=SAMPLE_ELEVATION\n",
+ ")\n",
+ "elapsed = time.perf_counter() - tic\n",
+ "subset = everything[\n",
+ " everything.from_satellite.str.startswith(\"cosmic2\")\n",
+ " & everything.to_satellite.str[0].isin(list(CONSTELLATIONS))\n",
+ "]\n",
+ "n = len(receivers) + len(transmitters)\n",
+ "rows.append(\n",
+ " {\n",
+ " \"analysis\": \"all satellites, both directions\",\n",
+ " \"pairs\": n * (n - 1),\n",
+ " \"windows\": len(everything),\n",
+ " \"receiver-transmitter windows\": len(subset),\n",
+ " \"largest difference (s)\": difference(windows(links), windows(subset)),\n",
+ " \"time (s)\": elapsed,\n",
+ " }\n",
+ ")\n",
+ "for seconds in [5, 20, 60, 300, 1200]:\n",
+ " tic = time.perf_counter()\n",
+ " result = collect_space_observations(\n",
+ " receivers,\n",
+ " DAY0,\n",
+ " DAY1,\n",
+ " to_satellites=transmitters,\n",
+ " min_grazing_altitude=SAMPLE_ELEVATION,\n",
+ " min_duration=timedelta(seconds=seconds),\n",
+ " )\n",
+ " rows.append(\n",
+ " {\n",
+ " \"analysis\": f\"min_duration = {seconds} s\",\n",
+ " \"pairs\": len(receivers) * len(transmitters),\n",
+ " \"windows\": len(result),\n",
+ " \"receiver-transmitter windows\": len(result),\n",
+ " \"largest difference (s)\": difference(windows(links), windows(result)),\n",
+ " \"time (s)\": time.perf_counter() - tic,\n",
+ " }\n",
+ " )\n",
+ "inner = links[(links.start > DAY0) & (links.end < DAY1)]\n",
+ "print(\n",
+ " f\"shortest window within the day: {(inner.end - inner.start).min().total_seconds():.0f} s\"\n",
+ ")\n",
+ "pd.DataFrame(rows).set_index(\"analysis\").round(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4cde0e2",
+ "metadata": {},
+ "source": [
+ "The windows are the same in both directions, and the receiver-transmitter windows are the same within the analysis of all 82 satellites. They are also the same (within 1 ms) for every `min_duration` from 5 to 300 s, since the shortest window within the day lasts nearly 8 minutes; at 1,200 s, 3 windows are missed, as documented. The analysis is fast: about 3 s for the 385 receiver-transmitter pairs over a day, and 7 s for all 6,642 pairs, including the closest approach in each window."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "220ea385",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| 1. Observed occultations | All 5,699 occultations reaching -80 km within the day at a window edge of the right kind (setting at a window's end, rising at its start) |\n",
+ "| 2. Timing | Edges within 0.25 to 0.30 s of the true times (95th percentile); a bias of 0.02 to 0.06 s early, specific to COSMIC-2 |\n",
+ "| 3. Consistency with `collect_ro_observations` | All 10,073 edges paired one to one with RO occultations within 2 ms, with the same direction; the other 81 occultations never cross -80 km within the day |\n",
+ "| 4. Symmetry, resolution, and cost | The same windows in both directions, within the analysis of all satellites, and for `min_duration` from 5 to 300 s; about 3 s for 385 pairs over a day, 7 s for 6,642 |\n",
+ "\n",
+ "**TAT-C's satellite-to-satellite occlusion is validated against COSMIC-2.** With published orbits, `collect_space_observations` places every occultation COSMIC-2 recorded on this day at the edge of a window of the right kind, within a few tenths of a second of the true time (limited by orbit accuracy), and agrees with `collect_ro_observations` to a millisecond, while analyzing all of a day's receiver-transmitter pairs in a few seconds.\n",
+ "\n",
+ "**Findings for the RO analysis.** The comparison corrected two properties of `collect_ro_observations` for grazing occultations nearly abeam of the receiver, outside COSMIC-2's yaw limit but within reach of receivers with wider antennas: their direction (`is_rising`), and their splitting into a setting and a rising observation where the tangent point turns, so that an occultation crossing -80 km twice is reported with both crossings (Test 3). Observations whose straight line never crosses -80 km are reported, as documented, at the end of the profile nearest -80 km.\n",
+ "\n",
+ "**Next steps:**\n",
+ "\n",
+ "1. *Range and brief windows.* The companion notebooks validate the slant range constraints against precise orbits ([GRACE-FO](ValidateSpaceGRACEFO.ipynb)) and brief windows against predicted close approaches ([SOCRATES](ValidateSpaceSOCRATES.ipynb)).\n",
+ "2. *Pointing limits.* `collect_space_observations` does not model the observing satellite's pointing limits, such as an antenna's field of view: its windows include the occultations outside COSMIC-2's yaw limit of 65 deg, which `collect_ro_observations` excludes with `max_yaw`.\n",
+ "\n",
+ "**Limitations.** This study covers one day and one constellation of receivers in low-inclination orbits (24 deg). The occlusion constraint is tested at one altitude (-80 km) and without range limits, and the timing (Test 2) uses random samples of 200 occultations per constellation."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateSpaceGRACEFO.ipynb b/docs/validation/ValidateSpaceGRACEFO.ipynb
new file mode 100644
index 00000000..9ef1097b
--- /dev/null
+++ b/docs/validation/ValidateSpaceGRACEFO.ipynb
@@ -0,0 +1,1195 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "4c0a51c3",
+ "metadata": {},
+ "source": [
+ "# Satellite-to-Satellite Validation: GRACE-FO\n",
+ "\n",
+ "This notebook validates TAT-C's satellite-to-satellite analysis (`collect_space_observations`, see the [Space-Based Coverage](../examples/CollectSpace.ipynb) example) against the precise orbits of the two [GRACE-FO](https://gracefo.jpl.nasa.gov/) satellites over a week, 17 to 23 September 2026. The satellites follow each other in the same near-polar orbit, at an altitude of about 490 km and roughly 175 to 180 km apart, so the line of sight between them is always clear of the Earth, but its length and its minimum altitude vary around each orbit. A companion notebook, [Satellite-to-Satellite Validation: COSMIC-2](ValidateSpaceCOSMIC2.ipynb), validates the Earth's occlusion of the line of sight against radio occultations; this one validates the slant range constraints (`min_range` and `max_range`) and the occlusion constraint at an altitude well above the surface (`min_grazing_altitude`), and the orbit track positions they depend on:\n",
+ "\n",
+ "1. **Orbits.** How closely do published orbits, as propagated by TAT-C (`collect_orbit_track` in Earth-fixed coordinates), reproduce each satellite's precise orbit?\n",
+ "2. **Range.** How accurately does TAT-C compute the range between the satellites?\n",
+ "3. **Range windows.** Does `collect_space_observations` find the periods when the range is within limits?\n",
+ "4. **Occlusion windows.** Does it find the periods when the line of sight stays above a given altitude?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1492e69c",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "The GRACE-FO Level-1B data (`GRACEFO_L1B_ASCII_GRAV_JPL_RL04`, from [PO.DAAC](https://podaac.jpl.nasa.gov/), free [NASA Earthdata Login](https://urs.earthdata.nasa.gov/) account required) include each satellite's precise orbit (**GNV1B**), determined from its GPS receiver to a few centimeters: Earth-fixed positions and velocities every second, time-tagged in GPS seconds since 1 January 2000, 12:00. The daily archives (about 140 MB each) are downloaded once, and only the orbits of both satellites (GRACE-FO 1, identifier C, and GRACE-FO 2, identifier D) are kept, in a local `data/gracefo` directory.\n",
+ "\n",
+ "TAT-C needs published orbits for the week: the two-line element sets (TLEs) of GRACE-FO 1 and 2 (NORAD 43476 and 43477), obtained from [Space-Track](https://www.space-track.org) (free account required) with the `gp_history` query\n",
+ "\n",
+ "```\n",
+ "https://www.space-track.org/basicspacedata/query/class/gp_history/NORAD_CAT_ID/43476,43477/EPOCH/2026-09-14--2026-10-02/orderby/NORAD_CAT_ID%20asc,EPOCH%20asc/format/3le\n",
+ "```\n",
+ "\n",
+ "and saved as `data/orbits/gracefo_gp_history.tle` (Space-Track's terms do not allow redistributing them, so the notebook raises an error if the file is missing). Element sets were published about every 6 to 8 hours (once a gap of 23 hours), for both satellites within about 25 s of each other. The week starts after a change in both satellites' mean motion on 15 to 17 September, apparently a maneuver."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "10679556",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:36.371266Z",
+ "iopub.status.busy": "2026-10-26T00:19:36.371096Z",
+ "iopub.status.idle": "2026-10-26T00:19:36.925854Z",
+ "shell.execute_reply": "2026-10-26T00:19:36.925172Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "604800 positions of each satellite, 2026-09-16 23:59:42+00:00 to 2026-09-23 23:59:41+00:00, every [1.] s\n"
+ ]
+ }
+ ],
+ "source": [
+ "import collections\n",
+ "import io\n",
+ "import tarfile\n",
+ "from datetime import datetime, timedelta, timezone\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "DATA_DIR = Path(\"data\") / \"gracefo\"\n",
+ "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "TLE_FILE = Path(\"data\") / \"orbits\" / \"gracefo_gp_history.tle\"\n",
+ "if not TLE_FILE.exists():\n",
+ " raise FileNotFoundError(\n",
+ " f\"download the GRACE-FO element sets from Space-Track to {TLE_FILE} (see above)\"\n",
+ " )\n",
+ "DAYS = pd.date_range(\"2026-09-17\", \"2026-09-23\", freq=\"D\", tz=\"UTC\")\n",
+ "J2000_GPS = datetime(\n",
+ " 2000, 1, 1, 12, tzinfo=timezone.utc\n",
+ ") # origin of GRACE-FO's GPS time\n",
+ "GPS_UTC_OFFSET = 18 # s, GPS time minus UTC in 2026\n",
+ "HEADER_END = \"# End of YAML header\"\n",
+ "\n",
+ "\n",
+ "def read_gnv1b(text):\n",
+ " \"\"\"GPS seconds (since 2000-01-01 12:00 GPS time), Earth-fixed positions (m, (3, N)), and Earth-fixed\n",
+ " velocities (m/s, (3, N)) of the records of a GNV1B file.\"\"\"\n",
+ " records = np.loadtxt(\n",
+ " io.StringIO(text.split(HEADER_END, 1)[1]), usecols=(0, 3, 4, 5, 9, 10, 11)\n",
+ " )\n",
+ " return records[:, 0], records[:, 1:4].T, records[:, 4:7].T\n",
+ "\n",
+ "\n",
+ "def cache_path(day):\n",
+ " return DATA_DIR / f\"gnv1b_{day:%Y%m%d}.npz\"\n",
+ "\n",
+ "\n",
+ "missing = [day for day in DAYS if not cache_path(day).exists()]\n",
+ "if missing:\n",
+ " import earthaccess\n",
+ "\n",
+ " earthaccess.login()\n",
+ " for day in missing:\n",
+ " granules = [\n",
+ " g\n",
+ " for g in earthaccess.search_data(\n",
+ " short_name=\"GRACEFO_L1B_ASCII_GRAV_JPL_RL04\",\n",
+ " temporal=(f\"{day:%Y-%m-%d}T00:00:00\", f\"{day:%Y-%m-%d}T23:59:59\"),\n",
+ " )\n",
+ " if g[\"umm\"][\"GranuleUR\"] == f\"gracefo_1B_{day:%Y-%m-%d}_RL04.ascii.noLRI\"\n",
+ " ]\n",
+ " archive = Path(earthaccess.download(granules, str(DATA_DIR))[0])\n",
+ " data = {}\n",
+ " with tarfile.open(archive) as tar:\n",
+ " for member in tar.getmembers():\n",
+ " if member.name.startswith(\"GNV1B_\") and member.name.endswith(\".txt\"):\n",
+ " satellite = member.name.split(\"_\")[\n",
+ " 2\n",
+ " ] # C (GRACE-FO 1) or D (GRACE-FO 2)\n",
+ " t, p, v = read_gnv1b(tar.extractfile(member).read().decode())\n",
+ " (\n",
+ " data[f\"time_{satellite}\"],\n",
+ " data[f\"position_{satellite}\"],\n",
+ " data[f\"velocity_{satellite}\"],\n",
+ " ) = (t, p, v)\n",
+ " np.savez_compressed(cache_path(day), **data)\n",
+ " archive.unlink() # keep only the orbits (each daily archive is about 140 MB)\n",
+ "\n",
+ "cached = [np.load(cache_path(day)) for day in DAYS]\n",
+ "if not all(np.array_equal(d[\"time_C\"], d[\"time_D\"]) for d in cached):\n",
+ " raise ValueError(\"the satellites' orbits are not sampled at the same times\")\n",
+ "gps_time = np.concatenate([d[\"time_C\"] for d in cached])\n",
+ "position = {\n",
+ " k: np.concatenate([d[f\"position_{k}\"] for d in cached], axis=1) for k in \"CD\"\n",
+ "}\n",
+ "velocity = {\n",
+ " k: np.concatenate([d[f\"velocity_{k}\"] for d in cached], axis=1) for k in \"CD\"\n",
+ "}\n",
+ "times = pd.to_datetime(J2000_GPS) + pd.to_timedelta(gps_time - GPS_UTC_OFFSET, unit=\"s\")\n",
+ "print(\n",
+ " f\"{len(times)} positions of each satellite, {times[0]} to {times[-1]}, every {np.unique(np.diff(gps_time))} s\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b8df33",
+ "metadata": {},
+ "source": [
+ "## Test 1: Orbits\n",
+ "\n",
+ "Each satellite's orbit is built from its element sets (keeping the later of the one pair published less than a second apart), and `collect_orbit_track` computes its Earth-fixed position at every second of the week, using the element set nearest in time. The difference from the precise orbit is resolved into radial, along-track, and cross-track components (the along-track direction following the velocity relative to inertial space)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "f02cd921",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:36.928098Z",
+ "iopub.status.busy": "2026-10-26T00:19:36.927806Z",
+ "iopub.status.idle": "2026-10-26T00:19:46.963003Z",
+ "shell.execute_reply": "2026-10-26T00:19:46.962092Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GRACE-FO 1: 62 element sets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GRACE-FO 2: 62 element sets\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
mean (km)
\n",
+ "
median |error| (km)
\n",
+ "
95% (km)
\n",
+ "
max (km)
\n",
+ "
\n",
+ "
\n",
+ "
satellite
\n",
+ "
error
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
GRACE-FO 1
\n",
+ "
radial
\n",
+ "
0.011
\n",
+ "
0.081
\n",
+ "
0.195
\n",
+ "
0.964
\n",
+ "
\n",
+ "
\n",
+ "
along-track
\n",
+ "
-0.119
\n",
+ "
0.510
\n",
+ "
1.192
\n",
+ "
4.676
\n",
+ "
\n",
+ "
\n",
+ "
cross-track
\n",
+ "
-0.001
\n",
+ "
0.132
\n",
+ "
0.313
\n",
+ "
0.483
\n",
+ "
\n",
+ "
\n",
+ "
GRACE-FO 2
\n",
+ "
radial
\n",
+ "
0.011
\n",
+ "
0.082
\n",
+ "
0.195
\n",
+ "
0.957
\n",
+ "
\n",
+ "
\n",
+ "
along-track
\n",
+ "
-0.120
\n",
+ "
0.513
\n",
+ "
1.186
\n",
+ "
4.624
\n",
+ "
\n",
+ "
\n",
+ "
cross-track
\n",
+ "
-0.001
\n",
+ "
0.133
\n",
+ "
0.309
\n",
+ "
0.483
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mean (km) median |error| (km) 95% (km) max (km)\n",
+ "satellite error \n",
+ "GRACE-FO 1 radial 0.011 0.081 0.195 0.964\n",
+ " along-track -0.119 0.510 1.192 4.676\n",
+ " cross-track -0.001 0.132 0.313 0.483\n",
+ "GRACE-FO 2 radial 0.011 0.082 0.195 0.957\n",
+ " along-track -0.120 0.513 1.186 4.624\n",
+ " cross-track -0.001 0.133 0.309 0.483"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from tatc.analysis import OrbitCoordinate, OrbitOutput, collect_orbit_track\n",
+ "from tatc.schemas import GeneralPerturbationsOrbit, Satellite\n",
+ "\n",
+ "NAMES = {\"C\": \"GRACE-FO 1\", \"D\": \"GRACE-FO 2\"} # by GNV1B satellite identifier\n",
+ "NORAD = {\"C\": \"43476\", \"D\": \"43477\"}\n",
+ "EARTH_ROTATION = 7.2921150e-5 # rad/s\n",
+ "\n",
+ "# element sets per NORAD catalog number, keyed by epoch\n",
+ "tle_lines = [line.rstrip() for line in open(TLE_FILE) if line.strip()]\n",
+ "element_sets = collections.defaultdict(dict)\n",
+ "for i in range(0, len(tle_lines), 3):\n",
+ " line1, line2 = tle_lines[i + 1], tle_lines[i + 2]\n",
+ " element_sets[line1[2:7]][float(line1[18:32])] = [line1, line2]\n",
+ "\n",
+ "\n",
+ "def to_orbit(sets):\n",
+ " \"\"\"An orbit from element sets, keeping the later of any published less than 1 s apart (duplicates).\"\"\"\n",
+ " epochs = sorted(sets)\n",
+ " kept = [\n",
+ " e\n",
+ " for k, e in enumerate(epochs)\n",
+ " if k + 1 == len(epochs) or epochs[k + 1] - e > 1 / 86400\n",
+ " ]\n",
+ " return GeneralPerturbationsOrbit.from_tle(sum((sets[e] for e in kept), []))\n",
+ "\n",
+ "\n",
+ "def earth_fixed_positions(satellite):\n",
+ " \"\"\"TAT-C's Earth-fixed positions (m, (3, N)) of a satellite at every time of the week.\"\"\"\n",
+ " track = collect_orbit_track(\n",
+ " satellite,\n",
+ " list(times.to_pydatetime()),\n",
+ " instrument_index=None,\n",
+ " coordinates=OrbitCoordinate.ECEF,\n",
+ " orbit_output=OrbitOutput.POSITION_VELOCITY,\n",
+ " )\n",
+ " return np.array([track.geometry.x, track.geometry.y, track.geometry.z])\n",
+ "\n",
+ "\n",
+ "satellites = {\n",
+ " k: Satellite(name=NAMES[k], orbit=to_orbit(element_sets[NORAD[k]])) for k in \"CD\"\n",
+ "}\n",
+ "predicted = {}\n",
+ "rows = []\n",
+ "for k in \"CD\":\n",
+ " predicted[k] = earth_fixed_positions(satellites[k])\n",
+ " error = predicted[k] - position[k]\n",
+ " # radial, along-track, and cross-track directions, from the velocity relative to inertial space\n",
+ " inertial_velocity = velocity[k] + np.cross([0, 0, EARTH_ROTATION], position[k].T).T\n",
+ " radial = position[k] / np.linalg.norm(position[k], axis=0)\n",
+ " cross = np.cross(position[k].T, inertial_velocity.T).T\n",
+ " cross /= np.linalg.norm(cross, axis=0)\n",
+ " along = np.cross(cross.T, radial.T).T\n",
+ " for name, axis in [\n",
+ " (\"radial\", radial),\n",
+ " (\"along-track\", along),\n",
+ " (\"cross-track\", cross),\n",
+ " ]:\n",
+ " component = np.einsum(\"ij,ij->j\", error, axis) / 1e3\n",
+ " rows.append(\n",
+ " {\n",
+ " \"satellite\": NAMES[k],\n",
+ " \"error\": name,\n",
+ " \"mean (km)\": component.mean(),\n",
+ " \"median |error| (km)\": np.median(np.abs(component)),\n",
+ " \"95% (km)\": np.quantile(np.abs(component), 0.95),\n",
+ " \"max (km)\": np.abs(component).max(),\n",
+ " }\n",
+ " )\n",
+ " print(f\"{NAMES[k]}: {len(satellites[k].orbit.elements)} element sets\")\n",
+ "pd.DataFrame(rows).set_index([\"satellite\", \"error\"]).round(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "777be877",
+ "metadata": {},
+ "source": [
+ "TAT-C's positions from the published orbits are within about half a kilometer of the precise orbits along track (median), 1.2 km for 95% of the week, and 4.7 km at most, and within 0.08 km radially and 0.13 km cross track (median), as is typical of TLEs. The mean errors, at most 0.12 km along track and 0.01 km radially and cross track, show no offset in the Earth-fixed coordinates of `collect_orbit_track`, as errors in its conversion between frames would produce. The two satellites' errors are nearly the same, since their element sets are fitted to nearly the same orbit at nearly the same epochs, so their difference, which determines the range, is much smaller (Test 2)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "33c06dbd",
+ "metadata": {},
+ "source": [
+ "## Test 2: Range\n",
+ "\n",
+ "The range between the satellites is computed every second from TAT-C's positions and from the precise orbits. By default, TAT-C uses each satellite's element set nearest in time, so it switches element sets midway between consecutive epochs, separately for each satellite. Since the two satellites' element sets are published about 20 to 25 s apart, for that long after each switch their positions come from element sets of different epochs. The range errors during these switch intervals are reported separately, and again with the satellites switching element sets at common times (`tatc.utils.align_element_switches`, which sets each orbit's `element_switch_times`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9d691517",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:19:46.964911Z",
+ "iopub.status.busy": "2026-10-26T00:19:46.964633Z",
+ "iopub.status.idle": "2026-10-26T00:20:07.083554Z",
+ "shell.execute_reply": "2026-10-26T00:20:07.082821Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "true range 174.5 to 181.3 km\n",
+ "28 element set switches, each 22.9 to 23.7 s between the satellites\n",
+ "largest range error during each switch: median 28 m, 1 above 1 km, at most 4.3 km\n",
+ "aligned: 60 and 60 element sets, common switch times\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from tatc.utils import align_element_switches\n",
+ "\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "true_range = np.linalg.norm(position[\"C\"] - position[\"D\"], axis=0)\n",
+ "predicted_range = np.linalg.norm(predicted[\"C\"] - predicted[\"D\"], axis=0)\n",
+ "range_error = predicted_range - true_range\n",
+ "\n",
+ "\n",
+ "def switch_times(orbit):\n",
+ " \"\"\"Times at which TAT-C switches element sets: midway between consecutive epochs.\"\"\"\n",
+ " epochs = [element.epoch for element in orbit.elements]\n",
+ " return pd.DatetimeIndex([a + (b - a) / 2 for a, b in zip(epochs[:-1], epochs[1:])])\n",
+ "\n",
+ "\n",
+ "# switch intervals: from one satellite's switch to the other's nearest, during which the satellites'\n",
+ "# positions come from element sets of different epochs\n",
+ "switches = {k: switch_times(satellites[k].orbit) for k in \"CD\"}\n",
+ "intervals = [\n",
+ " (min(a, b), max(a, b))\n",
+ " for a in switches[\"C\"][(switches[\"C\"] > times[0]) & (switches[\"C\"] < times[-1])]\n",
+ " for b in [switches[\"D\"][np.argmin(np.abs(switches[\"D\"] - a))]]\n",
+ "]\n",
+ "at_switch = np.zeros(len(times), dtype=bool)\n",
+ "for a, b in intervals:\n",
+ " at_switch |= (times >= a - pd.Timedelta(seconds=1)) & (\n",
+ " times <= b + pd.Timedelta(seconds=1)\n",
+ " )\n",
+ "durations = np.array([(b - a).total_seconds() for a, b in intervals])\n",
+ "print(f\"true range {true_range.min() / 1e3:.1f} to {true_range.max() / 1e3:.1f} km\")\n",
+ "print(\n",
+ " f\"{len(intervals)} element set switches, each {durations.min():.1f} to {durations.max():.1f} s between the satellites\"\n",
+ ")\n",
+ "peaks = np.array(\n",
+ " [np.abs(range_error[(times >= a) & (times <= b)]).max() for a, b in intervals]\n",
+ ")\n",
+ "print(\n",
+ " f\"largest range error during each switch: median {np.median(peaks):.0f} m, \"\n",
+ " f\"{int((peaks > 1000).sum())} above 1 km, at most {peaks.max() / 1e3:.1f} km\"\n",
+ ")\n",
+ "\n",
+ "# the same orbits, switching element sets at common times\n",
+ "aligned = dict(zip(\"CD\", align_element_switches([satellites[\"C\"], satellites[\"D\"]])))\n",
+ "common = set(aligned[\"C\"].orbit.element_switch_times) == set(\n",
+ " aligned[\"D\"].orbit.element_switch_times\n",
+ ")\n",
+ "print(\n",
+ " f\"aligned: {len(aligned['C'].orbit.elements)} and {len(aligned['D'].orbit.elements)} element sets, \"\n",
+ " f\"{'common' if common else 'different'} switch times\"\n",
+ ")\n",
+ "aligned_range = np.linalg.norm(\n",
+ " earth_fixed_positions(aligned[\"C\"]) - earth_fixed_positions(aligned[\"D\"]), axis=0\n",
+ ")\n",
+ "aligned_error = aligned_range - true_range\n",
+ "display(\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " label: {\n",
+ " \"samples (s)\": int(selected.sum()),\n",
+ " \"mean error (m)\": error[selected].mean(),\n",
+ " \"standard deviation (m)\": error[selected].std(),\n",
+ " \"95% |error| (m)\": np.quantile(np.abs(error[selected]), 0.95),\n",
+ " \"max |error| (m)\": np.abs(error[selected]).max(),\n",
+ " }\n",
+ " for label, error, selected in [\n",
+ " (\"between switches\", range_error, ~at_switch),\n",
+ " (\"during switches\", range_error, at_switch),\n",
+ " (\"during switches, aligned\", aligned_error, at_switch),\n",
+ " (\"whole week, aligned\", aligned_error, np.ones(len(times), dtype=bool)),\n",
+ " ]\n",
+ " }\n",
+ " ).T.round(1)\n",
+ ")\n",
+ "\n",
+ "fig, axes = plt.subplots(2, 1, figsize=(12, 5.5), sharex=True)\n",
+ "axes[0].plot(times, true_range / 1e3, color=\"#1f4e79\", lw=1, label=\"true (GNV1B)\")\n",
+ "axes[0].plot(\n",
+ " times, predicted_range / 1e3, color=\"#d97706\", lw=0.6, label=\"TAT-C (TLEs)\"\n",
+ ")\n",
+ "axes[0].set(ylabel=\"Range (km)\", title=\"Range between GRACE-FO 1 and 2\")\n",
+ "axes[0].legend(loc=\"upper left\")\n",
+ "axes[1].plot(times[~at_switch], range_error[~at_switch], \".\", color=\"#1f4e79\", ms=0.5)\n",
+ "for a, _ in intervals:\n",
+ " axes[1].axvline(a, color=\"#b91c1c\", lw=0.6, alpha=0.5)\n",
+ "axes[1].set(\n",
+ " ylabel=\"TAT-C minus true (m)\",\n",
+ " title=\"Range error between element set switches (red lines: switches)\",\n",
+ ")\n",
+ "fig.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "31c2024c",
+ "metadata": {},
+ "source": [
+ "Between element set switches, TAT-C's range is accurate to 21 m (standard deviation), 47 m for 95% of the week, and 117 m at most, over separations of 174.5 to 181.3 km: some 25 times more accurate than the positions themselves, because the two satellites' errors cancel. The predicted range follows the true range closely, through its oscillation of about ±0.8 km each orbit and its drift of about 0.9 km a day as the satellites separate.\n",
+ "\n",
+ "With independent switches, the element set switches are the exception. TAT-C switched element sets 28 times during the week, about every 6 hours, each time 23 s later for GRACE-FO 2 than for GRACE-FO 1. For those 23 s, the satellites' positions come from element sets of different epochs, whose errors no longer cancel: the range error reached 28 m (median) and, at one switch on 17 September, 4.3 km, where GRACE-FO 1's successive element sets differ by about that much along track.\n",
+ "\n",
+ "Aligning the switches removes these errors. `align_element_switches` groups the two satellites' epochs into publication events, keeps the element sets of the events both satellites share (60 of each satellite's 63, dropping each satellite's duplicate and the sets only one satellite has, all outside this week), and switches both satellites midway between consecutive events. During the former switch intervals, the range error is then 80 m at most, as between switches, and over the whole week the range is accurate to 21 m (standard deviation) and 117 m at most."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b61931c9",
+ "metadata": {},
+ "source": [
+ "## Test 3: Range Windows\n",
+ "\n",
+ "`collect_space_observations` is run from GRACE-FO 1 to GRACE-FO 2 over the week with a `max_range` of 177, 178, or 179 km, or a `min_range` of 178 km, without the occlusion constraint and with the default `min_duration` (60 s), for the satellites switching element sets independently (by default) and at common times (aligned, as in Test 2). As the range drifts through each limit over a day or two, oscillating around each orbit, the windows open and close about twice per orbit. The true windows are found from the precise orbits, by interpolating the range every second, and each true window edge is paired with the nearest predicted edge of the same kind (start or end) within 15 minutes. For each true edge left unpaired, the table reports how far the true range goes past the limit before the nearer neighboring true edge, a measure of how marginal that window (or gap between windows) is."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "aedbf9f4",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:20:07.085485Z",
+ "iopub.status.busy": "2026-10-26T00:20:07.085156Z",
+ "iopub.status.idle": "2026-10-26T00:20:08.799171Z",
+ "shell.execute_reply": "2026-10-26T00:20:08.798448Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ " true edges predicted edges matched \\\n",
+ "switch times limit \n",
+ "independent max_range = 177 km 25.0 25.0 23.0 \n",
+ " max_range = 178 km 25.0 27.0 23.0 \n",
+ " max_range = 179 km 27.0 27.0 25.0 \n",
+ " min_range = 178 km 25.0 27.0 23.0 \n",
+ "aligned max_range = 177 km 25.0 23.0 23.0 \n",
+ " max_range = 178 km 25.0 25.0 23.0 \n",
+ " max_range = 179 km 27.0 25.0 25.0 \n",
+ " min_range = 178 km 25.0 25.0 23.0 \n",
+ "\n",
+ " |time difference| median (s) 95% (s) \\\n",
+ "switch times limit \n",
+ "independent max_range = 177 km 16.1 49.3 \n",
+ " max_range = 178 km 42.8 156.9 \n",
+ " max_range = 179 km 20.1 56.7 \n",
+ " min_range = 178 km 42.8 156.9 \n",
+ "aligned max_range = 177 km 16.1 49.3 \n",
+ " max_range = 178 km 42.8 156.9 \n",
+ " max_range = 179 km 20.1 56.7 \n",
+ " min_range = 178 km 42.8 156.9 \n",
+ "\n",
+ " max (s) unmatched predicted edges \\\n",
+ "switch times limit \n",
+ "independent max_range = 177 km 60.6 2.0 \n",
+ " max_range = 178 km 165.3 4.0 \n",
+ " max_range = 179 km 64.4 2.0 \n",
+ " min_range = 178 km 165.3 4.0 \n",
+ "aligned max_range = 177 km 60.6 0.0 \n",
+ " max_range = 178 km 165.3 2.0 \n",
+ " max_range = 179 km 64.4 0.0 \n",
+ " min_range = 178 km 165.3 2.0 \n",
+ "\n",
+ " of which at switches unmatched true edges \\\n",
+ "switch times limit \n",
+ "independent max_range = 177 km 2.0 2.0 \n",
+ " max_range = 178 km 2.0 2.0 \n",
+ " max_range = 179 km 2.0 2.0 \n",
+ " min_range = 178 km 2.0 2.0 \n",
+ "aligned max_range = 177 km 0.0 2.0 \n",
+ " max_range = 178 km 0.0 2.0 \n",
+ " max_range = 179 km 0.0 2.0 \n",
+ " min_range = 178 km 0.0 2.0 \n",
+ "\n",
+ " their largest true excursion (m) \n",
+ "switch times limit \n",
+ "independent max_range = 177 km 1.4 \n",
+ " max_range = 178 km 53.9 \n",
+ " max_range = 179 km 3.2 \n",
+ " min_range = 178 km 53.9 \n",
+ "aligned max_range = 177 km 1.4 \n",
+ " max_range = 178 km 53.9 \n",
+ " max_range = 179 km 3.2 \n",
+ " min_range = 178 km 53.9 "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from tatc.analysis import collect_space_observations\n",
+ "\n",
+ "START = datetime(2026, 9, 17, tzinfo=timezone.utc)\n",
+ "END = datetime(2026, 9, 23, 23, 59, tzinfo=timezone.utc)\n",
+ "in_window = (times >= START) & (times <= END)\n",
+ "window_times = times[in_window]\n",
+ "seconds = (window_times - window_times[0]).total_seconds().to_numpy()\n",
+ "\n",
+ "\n",
+ "def true_edges(residual):\n",
+ " \"\"\"Window edges where a residual (sampled at window_times) changes sign, by linear interpolation:\n",
+ " starts where it becomes non-positive, ends where it becomes positive.\"\"\"\n",
+ " inside = residual <= 0\n",
+ " j = np.flatnonzero(inside[:-1] != inside[1:])\n",
+ " crossing = seconds[j] - residual[j] / (residual[j + 1] - residual[j]) * (\n",
+ " seconds[j + 1] - seconds[j]\n",
+ " )\n",
+ " return pd.DataFrame(\n",
+ " {\n",
+ " \"time\": window_times[0] + pd.to_timedelta(crossing, unit=\"s\"),\n",
+ " \"start\": inside[j + 1],\n",
+ " }\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def predicted_edges(windows):\n",
+ " \"\"\"Edges of predicted windows within the analysis period.\"\"\"\n",
+ " edges = pd.concat(\n",
+ " [\n",
+ " pd.DataFrame({\"time\": windows.start, \"start\": True}),\n",
+ " pd.DataFrame({\"time\": windows.end, \"start\": False}),\n",
+ " ]\n",
+ " )\n",
+ " inner = (edges.time > pd.Timestamp(START)) & (edges.time < pd.Timestamp(END))\n",
+ " return edges[inner].sort_values(\"time\", ignore_index=True)\n",
+ "\n",
+ "\n",
+ "def near_switch(edge_times, margin=30):\n",
+ " \"\"\"Whether each time is within margin (s) of an element set switch interval.\"\"\"\n",
+ " margin = pd.Timedelta(seconds=margin)\n",
+ " return np.array(\n",
+ " [any(a - margin <= x <= b + margin for a, b in intervals) for x in edge_times]\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def compare(truth, model, residual, tolerance=900):\n",
+ " \"\"\"Pairs each true edge with the nearest predicted edge of the same kind within tolerance (s).\"\"\"\n",
+ " dt = (\n",
+ " model.time.to_numpy()[None, :] - truth.time.to_numpy()[:, None]\n",
+ " ) / np.timedelta64(1, \"s\")\n",
+ " dt = np.where(\n",
+ " truth.start.to_numpy()[:, None] == model.start.to_numpy()[None, :], dt, np.inf\n",
+ " )\n",
+ " j = np.argmin(np.abs(dt), axis=1)\n",
+ " offset = dt[np.arange(len(truth)), j]\n",
+ " matched = np.abs(offset) <= tolerance\n",
+ " extra = model.drop(index=np.unique(j[matched]))\n",
+ " # for each unmatched true edge, how far the true residual goes past zero between it and the nearer\n",
+ " # neighboring true edge (the other edge of a brief window or gap)\n",
+ " bounds = pd.DatetimeIndex([window_times[0], *truth.time, window_times[-1]])\n",
+ " excursion = []\n",
+ " for k in np.flatnonzero(~matched):\n",
+ " before, edge, after = bounds[k], bounds[k + 1], bounds[k + 2]\n",
+ " other = before if edge - before < after - edge else after\n",
+ " span = (window_times >= min(edge, other)) & (window_times <= max(edge, other))\n",
+ " excursion.append(np.abs(residual[span]).max())\n",
+ " return {\n",
+ " \"true edges\": len(truth),\n",
+ " \"predicted edges\": len(model),\n",
+ " \"matched\": int(matched.sum()),\n",
+ " \"|time difference| median (s)\": np.median(np.abs(offset[matched])),\n",
+ " \"95% (s)\": np.quantile(np.abs(offset[matched]), 0.95),\n",
+ " \"max (s)\": np.abs(offset[matched]).max(),\n",
+ " \"unmatched predicted edges\": len(extra),\n",
+ " \"of which at switches\": int(near_switch(extra.time).sum()),\n",
+ " \"unmatched true edges\": int((~matched).sum()),\n",
+ " \"their largest true excursion (m)\": max(excursion) if excursion else np.nan,\n",
+ " }\n",
+ "\n",
+ "\n",
+ "range_in_window = true_range[in_window]\n",
+ "rows = {}\n",
+ "for label, pair in [(\"independent\", satellites), (\"aligned\", aligned)]:\n",
+ " for kind, value in [\n",
+ " (\"max_range\", 177e3),\n",
+ " (\"max_range\", 178e3),\n",
+ " (\"max_range\", 179e3),\n",
+ " (\"min_range\", 178e3),\n",
+ " ]:\n",
+ " residual = (\n",
+ " range_in_window - value if kind == \"max_range\" else value - range_in_window\n",
+ " )\n",
+ " windows = collect_space_observations(\n",
+ " pair[\"C\"],\n",
+ " START,\n",
+ " END,\n",
+ " to_satellites=pair[\"D\"],\n",
+ " min_grazing_altitude=None,\n",
+ " **{kind: value},\n",
+ " )\n",
+ " rows[(label, f\"{kind} = {value / 1e3:.0f} km\")] = compare(\n",
+ " true_edges(residual), predicted_edges(windows), residual\n",
+ " )\n",
+ "pd.DataFrame(rows).T.rename_axis([\"switch times\", \"limit\"]).round(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6b20ffb",
+ "metadata": {},
+ "source": [
+ "`collect_space_observations` finds the windows when the range is within limits, with edges within 16 to 43 s (median) and 49 to 157 s (95th percentile) of the true times. The timing errors are large in seconds only because the range changes slowly, by 0.25 m/s (median) and at most 0.55 m/s, so a range error of about 20 m shifts the edges by about a minute, and more where the range turns near the limit. The windows differ in two ways:\n",
+ "\n",
+ "* **Element set switches.** With independent switches, at every limit, the 4.3 km range error at the switch of 17 September creates a spurious gap of 23 s in a window (for `max_range`), or a spurious window (for `min_range`), although the true range was 2 to 4 km from the limit. With aligned switches, there is none.\n",
+ "* **Marginal windows.** At each limit, the true range enters one window (or gap between windows) by only 1.4 to 54 m, which the predicted range misses; at 178 km, the predicted range also opens one window (on 20 September, 21:01 to 21:08) that the true range misses by 2.8 m. Such windows are within the range accuracy of Test 2."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b56ca0bb",
+ "metadata": {},
+ "source": [
+ "## Test 4: Occlusion Windows\n",
+ "\n",
+ "The minimum WGS 84 geodetic altitude of the line of sight between the satellites, its tangent point, varies around each orbit, mostly with latitude, as the Earth's flattening lowers the surface toward the poles. `collect_space_observations` is run with a `min_grazing_altitude` of 450, 460, or 470 km (and no range limits), and its windows are compared with the true windows, found from the precise orbits as in Test 3. With its default `min_grazing_altitude` of 0, the line of sight is never occluded, and a single window should span the week."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fc5d6a06",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:20:08.801041Z",
+ "iopub.status.busy": "2026-10-26T00:20:08.800809Z",
+ "iopub.status.idle": "2026-10-26T00:20:11.411056Z",
+ "shell.execute_reply": "2026-10-26T00:20:11.410297Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "true minimum altitude of the line of sight: 444.7 to 481.3 km\n",
+ "default constraints (min_grazing_altitude = 0): 1 window(s), 2026-09-17 00:00:00+00:00 to 2026-09-23 23:59:00+00:00\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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predicted edges
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+ "
matched
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+ "
|time difference| median (s)
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+ "
95% (s)
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+ "
max (s)
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+ "
unmatched predicted edges
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+ "
of which at switches
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+ "
their largest true excursion (m)
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+ "
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min_grazing_altitude = 450 km
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min_grazing_altitude = 460 km
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+ " true edges predicted edges matched \\\n",
+ "min_grazing_altitude = 450 km 431.0 431.0 431.0 \n",
+ "min_grazing_altitude = 460 km 215.0 215.0 215.0 \n",
+ "min_grazing_altitude = 470 km 215.0 215.0 215.0 \n",
+ "\n",
+ " |time difference| median (s) 95% (s) max (s) \\\n",
+ "min_grazing_altitude = 450 km 6.3 15.9 47.0 \n",
+ "min_grazing_altitude = 460 km 2.8 6.5 28.7 \n",
+ "min_grazing_altitude = 470 km 2.5 6.4 31.3 \n",
+ "\n",
+ " unmatched predicted edges \\\n",
+ "min_grazing_altitude = 450 km 0.0 \n",
+ "min_grazing_altitude = 460 km 0.0 \n",
+ "min_grazing_altitude = 470 km 0.0 \n",
+ "\n",
+ " of which at switches unmatched true edges \\\n",
+ "min_grazing_altitude = 450 km 0.0 0.0 \n",
+ "min_grazing_altitude = 460 km 0.0 0.0 \n",
+ "min_grazing_altitude = 470 km 0.0 0.0 \n",
+ "\n",
+ " their largest true excursion (m) \n",
+ "min_grazing_altitude = 450 km NaN \n",
+ "min_grazing_altitude = 460 km NaN \n",
+ "min_grazing_altitude = 470 km NaN "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from pyproj import Transformer\n",
+ "\n",
+ "from tatc.utils import compute_tangent_point\n",
+ "\n",
+ "TO_LLA = Transformer.from_crs(\"EPSG:4978\", \"EPSG:4979\", always_xy=True)\n",
+ "# minimum WGS 84 geodetic altitude of the true line of sight: its tangent point, which lies between the\n",
+ "# satellites at their separation\n",
+ "tangent_point = compute_tangent_point(position[\"C\"], position[\"D\"] - position[\"C\"])\n",
+ "true_altitude = np.asarray(TO_LLA.transform(*tangent_point)[2])[in_window]\n",
+ "print(\n",
+ " f\"true minimum altitude of the line of sight: {true_altitude.min() / 1e3:.1f} to {true_altitude.max() / 1e3:.1f} km\"\n",
+ ")\n",
+ "everything = collect_space_observations(\n",
+ " satellites[\"C\"], START, END, to_satellites=satellites[\"D\"]\n",
+ ")\n",
+ "print(\n",
+ " f\"default constraints (min_grazing_altitude = 0): {len(everything)} window(s), {everything.start.min()} to {everything.end.max()}\"\n",
+ ")\n",
+ "rows = {}\n",
+ "for value in [450e3, 460e3, 470e3]:\n",
+ " residual = value - true_altitude\n",
+ " windows = collect_space_observations(\n",
+ " satellites[\"C\"],\n",
+ " START,\n",
+ " END,\n",
+ " to_satellites=satellites[\"D\"],\n",
+ " min_grazing_altitude=value,\n",
+ " )\n",
+ " rows[f\"min_grazing_altitude = {value / 1e3:.0f} km\"] = compare(\n",
+ " true_edges(residual), predicted_edges(windows), residual\n",
+ " )\n",
+ "pd.DataFrame(rows).T.round(1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b5f44b0",
+ "metadata": {},
+ "source": [
+ "The occlusion constraint is reproduced at altitude: every true window edge is predicted, and no others, at all three limits (215 to 431 edges over the week), within 2.5 to 6 s (median) and 6 to 16 s (95th percentile) of the true times. The minimum altitude of the line of sight changes much faster than the range, by up to 37 km in a quarter of an orbit as the Earth's flattening (21 km from equator to pole) and the orbit's eccentricity (about ±6 km) combine, so the satellites' position errors shift the edges by only a few seconds, and by up to 47 s where the altitude turns near the limit. The 450 km limit is crossed four times per orbit, the 460 and 470 km limits twice. The element set switches have no visible effect: the minimum altitude depends little on the satellites' separation along track. With the default `min_grazing_altitude` of 0, a single window spans the week."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eefa1a7e",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| 1. Orbits | Published orbits within 0.5 km along track (median; 4.7 km at most), 0.08 km radially, and 0.13 km cross track of the precise orbits; no offset in the Earth-fixed coordinates of `collect_orbit_track` |\n",
+ "| 2. Range | Within 21 m (standard deviation) and 117 m (at most) between element set switches; up to 4.3 km during the 23 s of each switch, unless the switches are aligned |\n",
+ "| 3. Range windows | Edges within 16 to 43 s (median), limited by the slow change of range; one spurious 23 s gap or window from an element set switch (none with aligned switches), and one marginal window per limit (within about 50 m of it) missed |\n",
+ "| 4. Occlusion windows | All 215 to 431 edges per limit predicted, and no others, within 2.5 to 6 s (median) |\n",
+ "\n",
+ "**TAT-C's slant range and occlusion constraints are validated against GRACE-FO.** From published orbits, TAT-C computes the range between two satellites flying in formation to about 20 m, far better than their positions, and `collect_space_observations` finds the windows within range limits and above an altitude accordingly, in about a second of computation for a week.\n",
+ "\n",
+ "**Element set switches in formations.** By default, TAT-C switches each satellite's element set midway between its epochs, independently for each satellite. When the satellites' element sets are published at slightly different times, as here and for most formations and constellations, their relative positions jump for the seconds between the switches, by up to the difference between successive element sets (here up to 4.3 km, for 23 s), and can create spurious windows or gaps when a range limit is near the true range. Analyses of satellites flying in formation should align their switches with `tatc.utils.align_element_switches`, which removes these jumps (Tests 2 and 3).\n",
+ "\n",
+ "**Next steps:**\n",
+ "\n",
+ "1. *Brief windows.* The [SOCRATES](ValidateSpaceSOCRATES.ipynb) notebook tests windows of about a second, at the close approaches between satellites.\n",
+ "2. *Formations.* Compare the relative motion of TAT-C's generated formations (such as `TrainConstellation` and `MOGConstellation`) with that of GRACE-FO and other formations.\n",
+ "\n",
+ "**Limitations.** This study covers one week and one pair of satellites in the same orbit at a nearly constant separation (175 to 181 km). The range accuracy reflects their common orbit errors, which cancel, and would be lower for satellites in different orbits."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateSpaceSOCRATES.ipynb b/docs/validation/ValidateSpaceSOCRATES.ipynb
new file mode 100644
index 00000000..dbd19c5f
--- /dev/null
+++ b/docs/validation/ValidateSpaceSOCRATES.ipynb
@@ -0,0 +1,707 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "339ffa88",
+ "metadata": {},
+ "source": [
+ "# Satellite-to-Satellite Validation: SOCRATES\n",
+ "\n",
+ "This notebook tests TAT-C's satellite-to-satellite analysis (`collect_space_observations`) on very brief windows: the close approaches between satellites, which pass within a few kilometers of each other at up to 15 km/s, so that they stay within a range limit of 10 km for as little as a second. The reference is CelesTrak's [SOCRATES](https://celestrak.org/SOCRATES/) (Satellite Orbital Conjunction Reports Assessing Threatening Encounters in Space), which lists the close approaches predicted from published two-line element sets (TLEs) over the following week, with their time and range of closest approach. SOCRATES and TAT-C both propagate TLEs with SGP4, so this is a comparison of two implementations from the same element sets, rather than with truth: it tests TAT-C's propagation and its search for windows at the extremes of brevity. Two companion notebooks validate the range and occlusion constraints with truth: [COSMIC-2](ValidateSpaceCOSMIC2.ipynb) (radio occultations) and [GRACE-FO](ValidateSpaceGRACEFO.ipynb) (precise orbits).\n",
+ "\n",
+ "1. **Closest approach.** Do TAT-C's propagated orbits reproduce SOCRATES's times and ranges of closest approach?\n",
+ "2. **Brief windows.** Does `collect_space_observations` find every window at least `min_duration` long, as documented, and how many shorter ones?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6ca8c3c",
+ "metadata": {},
+ "source": [
+ "## Reference Data\n",
+ "\n",
+ "SOCRATES publishes only its current run (three runs a day). The run of 25 October 2026 (06:00 UTC) listed 195,380 close approaches within 5 km over the following week, each with the two objects, the time and range of closest approach (`TCA` and `TCA_RANGE`), their relative speed, and the days from each object's element set epoch to the closest approach (`DSE_1` and `DSE_2`, to 0.001 day), which identify the element sets used. CelesTrak's element sets of active satellites, downloaded the same day, match those epochs (within 60 s) for both objects of 79,845 approaches. A seeded random sample of 100 of them in each of five relative speed bands (0 to 0.1, 0.1 to 1, 1 to 5, 5 to 10, and 10 to 16 km/s), and the element sets of their 732 satellites, are included with this notebook (`socrates_2026-10-25.csv` and `socrates_2026-10-25.tle`, data from CelesTrak). If a newer SOCRATES run (`sort-minRange.csv`) and active element sets (`active.tle`, `gp.php?GROUP=active&FORMAT=tle`) are saved in a local `data/socrates` directory, the notebook draws a new sample from them.\n",
+ "\n",
+ "The approaches of the slowest band are between satellites flying in formation or nearly the same orbit, such as Starlink satellites of the same orbital shell, whose range changes slowly and repeatedly around the orbit; the fastest are between satellites in crossing orbits."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "1c5de2d0",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:09:03.664656Z",
+ "iopub.status.busy": "2026-10-26T00:09:03.664395Z",
+ "iopub.status.idle": "2026-10-26T00:09:04.786380Z",
+ "shell.execute_reply": "2026-10-26T00:09:04.785664Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "79845 of 195380 approaches with matching element sets\n",
+ "500 approaches of 732 satellites, 2026-10-25 06:16 to 2026-11-01 05:44\n"
+ ]
+ },
+ {
+ "data": {
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+ " approaches minimum range, median (km) \\\n",
+ "band \n",
+ "0 to 0.1 km/s 100 1.950 \n",
+ "0.1 to 1 km/s 100 3.528 \n",
+ "1 to 5 km/s 100 3.305 \n",
+ "5 to 10 km/s 100 3.525 \n",
+ "10 to 16 km/s 100 3.647 \n",
+ "\n",
+ " relative speed, median (km/s) days since epoch, max \n",
+ "band \n",
+ "0 to 0.1 km/s 0.003 10.693 \n",
+ "0.1 to 1 km/s 0.442 10.901 \n",
+ "1 to 5 km/s 3.012 8.505 \n",
+ "5 to 10 km/s 7.635 8.350 \n",
+ "10 to 16 km/s 14.212 10.699 "
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from datetime import datetime, timedelta, timezone\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "SAMPLE_FILE = Path(\"socrates_2026-10-25.csv\")\n",
+ "TLE_FILE = Path(\"socrates_2026-10-25.tle\")\n",
+ "SNAPSHOT_DIR = (\n",
+ " Path(\"data\") / \"socrates\"\n",
+ ") # optional: a fresh SOCRATES run and active element sets\n",
+ "BANDS = [-0.01, 0.1, 1, 5, 10, 16] # km/s, relative speed bands\n",
+ "LABELS = [\n",
+ " \"0 to 0.1 km/s\",\n",
+ " \"0.1 to 1 km/s\",\n",
+ " \"1 to 5 km/s\",\n",
+ " \"5 to 10 km/s\",\n",
+ " \"10 to 16 km/s\",\n",
+ "]\n",
+ "\n",
+ "\n",
+ "def epoch(line1):\n",
+ " \"\"\"Epoch of a TLE from its first line.\"\"\"\n",
+ " return datetime(2000 + int(line1[18:20]), 1, 1, tzinfo=timezone.utc) + timedelta(\n",
+ " days=float(line1[20:32]) - 1\n",
+ " )\n",
+ "\n",
+ "\n",
+ "if (SNAPSHOT_DIR / \"sort-minRange.csv\").exists() and (\n",
+ " SNAPSHOT_DIR / \"active.tle\"\n",
+ ").exists():\n",
+ " # resample from a SOCRATES run: approaches whose element sets (epochs, from the days since epoch at the\n",
+ " # time of closest approach, to 0.001 day) are those in the active satellites' element sets\n",
+ " run = pd.read_csv(SNAPSHOT_DIR / \"sort-minRange.csv\")\n",
+ " lines = [\n",
+ " line.rstrip() for line in open(SNAPSHOT_DIR / \"active.tle\") if line.strip()\n",
+ " ]\n",
+ " element_sets = {\n",
+ " int(lines[i + 1][2:7]): lines[i : i + 3] for i in range(0, len(lines), 3)\n",
+ " }\n",
+ " tca = pd.to_datetime(run.TCA, utc=True)\n",
+ " matched = np.ones(len(run), dtype=bool)\n",
+ " for k in (1, 2):\n",
+ " epochs = pd.to_datetime(\n",
+ " run[f\"NORAD_CAT_ID_{k}\"].map(\n",
+ " lambda n: epoch(element_sets[n][1]) if n in element_sets else None\n",
+ " ),\n",
+ " utc=True,\n",
+ " )\n",
+ " matched &= (\n",
+ " (epochs - (tca - pd.to_timedelta(run[f\"DSE_{k}\"], unit=\"D\")))\n",
+ " .abs()\n",
+ " .le(pd.Timedelta(seconds=60))\n",
+ " .to_numpy()\n",
+ " )\n",
+ " print(f\"{matched.sum()} of {len(run)} approaches with matching element sets\")\n",
+ " candidates = run[matched]\n",
+ " sample = (\n",
+ " candidates.groupby(pd.cut(candidates.TCA_RELATIVE_SPEED, BANDS), observed=True)\n",
+ " .sample(100, random_state=20261025)\n",
+ " .sort_values(\"TCA\")\n",
+ " .reset_index(drop=True)\n",
+ " )\n",
+ " sample.to_csv(SAMPLE_FILE, index=False)\n",
+ " ids = sorted(set(sample.NORAD_CAT_ID_1) | set(sample.NORAD_CAT_ID_2))\n",
+ " TLE_FILE.write_text(\"\\n\".join(line for n in ids for line in element_sets[n]) + \"\\n\")\n",
+ "sample = pd.read_csv(SAMPLE_FILE)\n",
+ "sample[\"tca\"] = pd.to_datetime(sample.TCA, utc=True)\n",
+ "sample[\"band\"] = pd.cut(sample.TCA_RELATIVE_SPEED, BANDS, labels=LABELS)\n",
+ "sample[\"days since epoch\"] = sample[[\"DSE_1\", \"DSE_2\"]].max(axis=1)\n",
+ "lines = [line.rstrip() for line in open(TLE_FILE) if line.strip()]\n",
+ "tles = {int(lines[i + 1][2:7]): lines[i + 1 : i + 3] for i in range(0, len(lines), 3)}\n",
+ "print(\n",
+ " f\"{len(sample)} approaches of {len(tles)} satellites, {sample.tca.min():%Y-%m-%d %H:%M} to {sample.tca.max():%Y-%m-%d %H:%M}\"\n",
+ ")\n",
+ "sample.groupby(\"band\", observed=True).agg(\n",
+ " approaches=(\"TCA\", \"size\"),\n",
+ " **{\n",
+ " \"minimum range, median (km)\": (\"TCA_RANGE\", \"median\"),\n",
+ " \"relative speed, median (km/s)\": (\"TCA_RELATIVE_SPEED\", \"median\"),\n",
+ " \"days since epoch, max\": (\"days since epoch\", \"max\"),\n",
+ " },\n",
+ ").round(3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cee75b1e",
+ "metadata": {},
+ "source": [
+ "## Test 1: Closest Approach\n",
+ "\n",
+ "`collect_space_observations` reports the closest approach of the satellites in each window: its time (`closest_time`) and slant range (`closest_range`), the least range among the samples of its search refined to 0.1 ms. It is run for each approach over an hour centered on SOCRATES's time of closest approach, with a `max_range` of 10 km (within which every sampled approach passes), no occlusion constraint, and a `min_duration` of 1 s, and the closest approach of the window nearest SOCRATES's is compared with it. SOCRATES reports ranges to a meter and times to a millisecond."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "ad52d59d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:09:04.788250Z",
+ "iopub.status.busy": "2026-10-26T00:09:04.787999Z",
+ "iopub.status.idle": "2026-10-26T00:09:27.425431Z",
+ "shell.execute_reply": "2026-10-26T00:09:27.424629Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "22 approaches differ by more than 10 m in range, 91% involving Starlink (all approaches: 59%), 6.6 days after epoch (median; all approaches: 5.1)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
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37.21
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0.25
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0.75
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+ ],
+ "text/plain": [
+ " |range difference|, median (m) 95% (m) max (m) \\\n",
+ "band \n",
+ "0 to 0.1 km/s 0.30 3.19 666.68 \n",
+ "0.1 to 1 km/s 0.64 14.29 37.21 \n",
+ "1 to 5 km/s 0.70 13.52 161.25 \n",
+ "5 to 10 km/s 0.75 9.75 73.91 \n",
+ "10 to 16 km/s 0.31 1.38 59.60 \n",
+ "\n",
+ " |time difference|, median (ms) 95% (ms) \n",
+ "band \n",
+ "0 to 0.1 km/s 20.52 479.25 \n",
+ "0.1 to 1 km/s 0.31 0.96 \n",
+ "1 to 5 km/s 0.25 1.00 \n",
+ "5 to 10 km/s 0.23 1.15 \n",
+ "10 to 16 km/s 0.25 0.59 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from tatc.analysis import collect_space_observations\n",
+ "from tatc.constants import timescale\n",
+ "from tatc.schemas import GeneralPerturbationsOrbit, Satellite\n",
+ "\n",
+ "satellites = {\n",
+ " n: Satellite(name=str(n), orbit=GeneralPerturbationsOrbit.from_tle(tle))\n",
+ " for n, tle in tles.items()\n",
+ "}\n",
+ "\n",
+ "\n",
+ "def slant_range(a, b, times):\n",
+ " \"\"\"Range (m) between two satellites at times (list of datetimes).\"\"\"\n",
+ " t = timescale.from_datetimes(list(times))\n",
+ " return np.linalg.norm(\n",
+ " a.orbit.get_orbit_track_at_time(t).position.m\n",
+ " - b.orbit.get_orbit_track_at_time(t).position.m,\n",
+ " axis=0,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "LIMIT = 10e3 # m, max_range\n",
+ "HALF_WINDOW = timedelta(minutes=30) # analysis period around each closest approach\n",
+ "\n",
+ "rows = []\n",
+ "for approach in sample.itertuples():\n",
+ " a, b = satellites[approach.NORAD_CAT_ID_1], satellites[approach.NORAD_CAT_ID_2]\n",
+ " tca = approach.tca.to_pydatetime()\n",
+ " windows = collect_space_observations(\n",
+ " a,\n",
+ " tca - HALF_WINDOW,\n",
+ " tca + HALF_WINDOW,\n",
+ " to_satellites=b,\n",
+ " max_range=LIMIT,\n",
+ " min_grazing_altitude=None,\n",
+ " min_duration=timedelta(seconds=1),\n",
+ " )\n",
+ " # the window whose closest approach is nearest SOCRATES's\n",
+ " nearest = windows.iloc[\n",
+ " np.argmin(np.abs((windows.closest_time - approach.tca).dt.total_seconds()))\n",
+ " ]\n",
+ " rows.append({\"tatc_tca\": nearest.closest_time, \"tatc_range\": nearest.closest_range})\n",
+ "sample = pd.concat([sample, pd.DataFrame(rows)], axis=1)\n",
+ "sample[\"range difference (m)\"] = sample.tatc_range - sample.TCA_RANGE * 1e3\n",
+ "sample[\"time difference (ms)\"] = (\n",
+ " pd.to_datetime(sample.tatc_tca, utc=True) - sample.tca\n",
+ ").dt.total_seconds() * 1e3\n",
+ "sample[\"starlink\"] = sample.OBJECT_NAME_1.str.contains(\n",
+ " \"STARLINK\"\n",
+ ") | sample.OBJECT_NAME_2.str.contains(\"STARLINK\")\n",
+ "summary = sample.groupby(\"band\", observed=True).agg(\n",
+ " **{\n",
+ " \"|range difference|, median (m)\": (\n",
+ " \"range difference (m)\",\n",
+ " lambda x: x.abs().median(),\n",
+ " ),\n",
+ " \"95% (m)\": (\"range difference (m)\", lambda x: x.abs().quantile(0.95)),\n",
+ " \"max (m)\": (\"range difference (m)\", lambda x: x.abs().max()),\n",
+ " \"|time difference|, median (ms)\": (\n",
+ " \"time difference (ms)\",\n",
+ " lambda x: x.abs().median(),\n",
+ " ),\n",
+ " \"95% (ms)\": (\"time difference (ms)\", lambda x: x.abs().quantile(0.95)),\n",
+ " }\n",
+ ")\n",
+ "large = sample[sample[\"range difference (m)\"].abs() > 10]\n",
+ "print(\n",
+ " f\"{len(large)} approaches differ by more than 10 m in range, {large.starlink.mean():.0%} involving Starlink \"\n",
+ " f\"(all approaches: {sample.starlink.mean():.0%}), {large['days since epoch'].median():.1f} days after epoch \"\n",
+ " f\"(median; all approaches: {sample['days since epoch'].median():.1f})\"\n",
+ ")\n",
+ "summary.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2cc4c17",
+ "metadata": {},
+ "source": [
+ "The closest approaches `collect_space_observations` reports agree with SOCRATES's. Ranges agree to within a meter (a median of 0.3 to 0.75 m by band, about the precision of SOCRATES's ranges) and within 14 m for 95% of approaches. Times agree within 0.3 ms (median) and about 1 ms (95%), except for satellites in nearly the same orbit (0 to 0.1 km/s), whose range changes so slowly near its minimum that its time is defined only to tens of milliseconds.\n",
+ "\n",
+ "Twenty approaches (4%) differ by 13 to 161 m in range, almost all involving a Starlink satellite and further from their element sets' epochs (6.6 days, median, against 5.1 days for all approaches). Differences that grow with propagation from epoch, for element sets with large drag terms, are consistent with small differences between implementations of SGP4 (for example, in their options), and are well within the accuracy of the element sets themselves. One pair in nearly the same orbit stays within 10 km for the whole hour, so its window spans two of SOCRATES's approaches; `collect_space_observations` reports the least range within the window (2.4 km, at its start), not SOCRATES's 3.1 km."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa58d4b3",
+ "metadata": {},
+ "source": [
+ "## Test 2: Brief Windows\n",
+ "\n",
+ "`collect_space_observations` is run for each approach over an hour centered on SOCRATES's time of closest approach, with a `max_range` of 10 km (within which every sampled approach passes), no occlusion constraint, and a `min_duration` of 60, 10, 1, or 0.1 s. The search samples the range at half of `min_duration` and refines each change between samples, so it is guaranteed to find windows at least `min_duration` long, but may miss shorter ones (falling between samples). The true window is found from finely sampled ranges around TAT-C's closest approach (Test 1), within the hour, and is found if a window contains that closest approach."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "85afea0f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-26T00:09:27.427504Z",
+ "iopub.status.busy": "2026-10-26T00:09:27.427179Z",
+ "iopub.status.idle": "2026-10-26T00:12:52.754186Z",
+ "shell.execute_reply": "2026-10-26T00:12:52.753446Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "true window within 10 km, median (s): 0 to 0.1 km/s 3599.7, 0.1 to 1 km/s 42.4, 1 to 5 km/s 6.0, 5 to 10 km/s 2.4, 10 to 16 km/s 1.3; shortest 1.15 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
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+ " \n",
+ "
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+ "
found (%)
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+ "
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+ "
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+ "
time per hour analyzed, median (ms)
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+ "
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+ " \n",
+ " \n",
+ "
\n",
+ "
min_duration = 60 s
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+ "
137.0
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+ "
100.0
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+ "
363.0
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+ "
10.47
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3.62
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min_duration = 10 s
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224.0
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100.0
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276.0
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min_duration = 1 s
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500.0
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+ "
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min_duration = 0.1 s
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+ "
500.0
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+ "
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+ "
0.0
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+ "
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+ "
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+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " windows at least min_duration found (%) \\\n",
+ "min_duration = 60 s 137.0 100.0 \n",
+ "min_duration = 10 s 224.0 100.0 \n",
+ "min_duration = 1 s 500.0 100.0 \n",
+ "min_duration = 0.1 s 500.0 100.0 \n",
+ "\n",
+ " shorter windows shorter found (%) \\\n",
+ "min_duration = 60 s 363.0 10.47 \n",
+ "min_duration = 10 s 276.0 18.48 \n",
+ "min_duration = 1 s 0.0 NaN \n",
+ "min_duration = 0.1 s 0.0 NaN \n",
+ "\n",
+ " time per hour analyzed, median (ms) \n",
+ "min_duration = 60 s 3.62 \n",
+ "min_duration = 10 s 28.63 \n",
+ "min_duration = 1 s 43.30 \n",
+ "min_duration = 0.1 s 190.82 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import time\n",
+ "\n",
+ "DURATIONS = [60, 10, 1, 0.1] # s, min_duration\n",
+ "\n",
+ "\n",
+ "def true_window(a, b, tca, speed, start, end):\n",
+ " \"\"\"Duration (s) of the window around the closest approach when the range is within LIMIT, within the analysis\n",
+ " period (from finely sampled ranges).\"\"\"\n",
+ " half = min(HALF_WINDOW.total_seconds(), 2 * LIMIT / max(speed * 1e3, 1e-3) + 5)\n",
+ " offsets = np.linspace(-half, half, 40001)\n",
+ " times = [tca + timedelta(seconds=float(s)) for s in offsets]\n",
+ " inside = (slant_range(a, b, times) <= LIMIT) & np.array(\n",
+ " [start <= t <= end for t in times]\n",
+ " )\n",
+ " k = len(offsets) // 2\n",
+ " if not inside[k]:\n",
+ " return 0.0\n",
+ " lo = k - np.argmin(inside[k::-1]) + 1 if not inside[: k + 1].all() else 0\n",
+ " hi = k + np.argmin(inside[k:]) if not inside[k:].all() else len(offsets)\n",
+ " return offsets[hi - 1] - offsets[lo]\n",
+ "\n",
+ "\n",
+ "rows = []\n",
+ "for approach in sample.itertuples():\n",
+ " a, b = satellites[approach.NORAD_CAT_ID_1], satellites[approach.NORAD_CAT_ID_2]\n",
+ " tca = pd.Timestamp(approach.tatc_tca).floor(\"us\").to_pydatetime()\n",
+ " start, end = (\n",
+ " approach.tca.to_pydatetime() - HALF_WINDOW,\n",
+ " approach.tca.to_pydatetime() + HALF_WINDOW,\n",
+ " )\n",
+ " row = {\n",
+ " \"band\": approach.band,\n",
+ " \"window (s)\": true_window(a, b, tca, approach.TCA_RELATIVE_SPEED, start, end),\n",
+ " }\n",
+ " for duration in DURATIONS:\n",
+ " tic = time.perf_counter()\n",
+ " windows = collect_space_observations(\n",
+ " a,\n",
+ " start,\n",
+ " end,\n",
+ " to_satellites=b,\n",
+ " max_range=LIMIT,\n",
+ " min_grazing_altitude=None,\n",
+ " min_duration=timedelta(seconds=duration),\n",
+ " )\n",
+ " row[f\"time {duration} s\"] = time.perf_counter() - tic\n",
+ " row[f\"found {duration} s\"] = bool(\n",
+ " (\n",
+ " (windows.start <= pd.Timestamp(tca))\n",
+ " & (windows.end >= pd.Timestamp(tca))\n",
+ " ).any()\n",
+ " )\n",
+ " rows.append(row)\n",
+ "found = pd.DataFrame(rows)\n",
+ "found[\"band\"] = pd.Categorical(found.band, categories=LABELS, ordered=True)\n",
+ "print(\n",
+ " \"true window within 10 km, median (s): \"\n",
+ " + \", \".join(\n",
+ " f\"{b} {w:.1f}\"\n",
+ " for b, w in found.groupby(\"band\", observed=True)[\"window (s)\"].median().items()\n",
+ " )\n",
+ " + f\"; shortest {found['window (s)'].min():.2f} s\"\n",
+ ")\n",
+ "pd.DataFrame(\n",
+ " {\n",
+ " f\"min_duration = {d} s\": {\n",
+ " \"windows at least min_duration\": int((found[\"window (s)\"] >= d).sum()),\n",
+ " \"found (%)\": 100\n",
+ " * found.loc[found[\"window (s)\"] >= d, f\"found {d} s\"].mean(),\n",
+ " \"shorter windows\": int((found[\"window (s)\"] < d).sum()),\n",
+ " \"shorter found (%)\": (\n",
+ " 100 * found.loc[found[\"window (s)\"] < d, f\"found {d} s\"].mean()\n",
+ " if (found[\"window (s)\"] < d).any()\n",
+ " else np.nan\n",
+ " ),\n",
+ " \"time per hour analyzed, median (ms)\": 1e3 * found[f\"time {d} s\"].median(),\n",
+ " }\n",
+ " for d in DURATIONS\n",
+ " }\n",
+ ").T.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6baa8767",
+ "metadata": {},
+ "source": [
+ "`collect_space_observations` finds every window at least `min_duration` long, for every `min_duration` from 60 s to 0.1 s, as documented, and 10 to 18% of the shorter windows, those its samples happen to fall in. The windows within 10 km last from 1.15 s (a median of 1.3 s at 10 to 16 km/s) to the whole hour (satellites in nearly the same orbit), so a `min_duration` of 1 s finds all of them. The cost grows as `min_duration` shrinks, since the search samples the whole analysis period at half of it: for a pair of satellites, a median of 4 ms per hour analyzed at 60 s, 43 ms at 1 s, and 191 ms at 0.1 s, including the refinement of each window's closest approach (about 20 ms per call)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9385da16",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "| Test | Result |\n",
+ "| --- | --- |\n",
+ "| 1. Closest approach | `closest_range` within 1 m (median) and 14 m (95%) of SOCRATES's ranges, `closest_time` within 0.3 ms (median), except for satellites in nearly the same orbit; 4% of approaches (almost all involving Starlink) differ by 13 to 161 m |\n",
+ "| 2. Brief windows | Every window at least `min_duration` long found, for `min_duration` from 60 to 0.1 s, and 10 to 18% of shorter windows; 43 ms per hour analyzed for a pair of satellites with a `min_duration` of 1 s, including the closest approach |\n",
+ "\n",
+ "**TAT-C's propagation and window search hold for close approaches.** From the same element sets, `collect_space_observations` reproduces SOCRATES's closest approaches to about a meter and a millisecond, and finds every window within a range limit that lasts at least `min_duration`, down to windows of about a second at relative speeds up to 15 km/s.\n",
+ "\n",
+ "**Closest approaches and cost.** `collect_space_observations` reports each window's closest approach (`closest_time` and `closest_range`), which reproduces SOCRATES's, except where a window spans several approaches, when it is the least of them. The cost of a fine `min_duration`, which samples the whole analysis period, grows quickly for many pairs of satellites over long periods: with a `min_duration` of 1 s, about 4 s per pair for a week.\n",
+ "\n",
+ "**Next steps:**\n",
+ "\n",
+ "1. *Debris.* Extend the sample to approaches involving debris, whose element sets are not in CelesTrak's active satellites (for example, from Space-Track's history of element sets).\n",
+ "2. *Sub-second windows.* Test smaller range limits, whose windows last a fraction of a second.\n",
+ "\n",
+ "**Limitations.** This is a comparison of two implementations from the same element sets, not with truth, and covers active satellites only. With a range limit of 10 km, the shortest window lasts about a second."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/validation/ValidateSwathSWOT.ipynb b/docs/validation/ValidateSwathSWOT.ipynb
index 475d2a6c..63df9057 100644
--- a/docs/validation/ValidateSwathSWOT.ipynb
+++ b/docs/validation/ValidateSwathSWOT.ipynb
@@ -269,7 +269,11 @@
"def granule_key(name):\n",
" \"\"\"Cycle, pass, and start time of a granule name.\"\"\"\n",
" match = re.search(r\"_(\\d{3})_(\\d{3})_(\\d{8}T\\d{6})_\", name)\n",
- " return int(match.group(1)), int(match.group(2)), datetime.strptime(match.group(3), \"%Y%m%dT%H%M%S\").replace(tzinfo=timezone.utc)\n",
+ " return (\n",
+ " int(match.group(1)),\n",
+ " int(match.group(2)),\n",
+ " datetime.strptime(match.group(3), \"%Y%m%dT%H%M%S\").replace(tzinfo=timezone.utc),\n",
+ " )\n",
"\n",
"\n",
"files = sorted(DATA_DIR.glob(\"SWOT_L2_LR_SSH_Basic_*.nc\"))\n",
@@ -277,13 +281,21 @@
" import earthaccess\n",
"\n",
" earthaccess.login()\n",
- " results = earthaccess.search_data(short_name=\"SWOT_L2_LR_SSH_BASIC_D\", temporal=(START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"), END.strftime(\"%Y-%m-%dT%H:%M:%SZ\")))\n",
+ " results = earthaccess.search_data(\n",
+ " short_name=\"SWOT_L2_LR_SSH_BASIC_D\",\n",
+ " temporal=(\n",
+ " START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " END.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " ),\n",
+ " )\n",
" keys = [granule_key(r[\"umm\"][\"GranuleUR\"]) for r in results]\n",
" # the first pass starting after midnight every two days, and pass 566 of cycles 55 and 56\n",
" selected = set()\n",
" for day in range(0, (END - START).days, 2):\n",
" midnight = START + timedelta(days=day)\n",
- " selected.add(min((k[2] - midnight, i) for i, k in enumerate(keys) if k[2] >= midnight)[1])\n",
+ " selected.add(\n",
+ " min((k[2] - midnight, i) for i, k in enumerate(keys) if k[2] >= midnight)[1]\n",
+ " )\n",
" selected |= {i for i, k in enumerate(keys) if k[1] == 566 and k[0] in (55, 56)}\n",
" earthaccess.download([results[i] for i in sorted(selected)], str(DATA_DIR))\n",
" files = sorted(DATA_DIR.glob(\"SWOT_L2_LR_SSH_Basic_*.nc\"))\n",
@@ -306,8 +318,23 @@
"granules = [read_granule(path) for path in files]\n",
"NADIR = 34 # grid column on the reference ground track\n",
"swot = GeneralPerturbationsOrbit.from_tle(TLE)\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"pd.DataFrame(\n",
" [\n",
" {\n",
@@ -562,16 +589,28 @@
" \"\"\"Along- and cross-track offsets of TAT-C's ground track from the reference ground track.\"\"\"\n",
" lines = np.arange(100, len(granule[\"time\"]) - 100, 100)\n",
" lat, lon = granule[\"latitude\"][lines, NADIR], granule[\"longitude\"][lines, NADIR]\n",
- " next_lat, next_lon = granule[\"latitude\"][lines + 1, NADIR], granule[\"longitude\"][lines + 1, NADIR]\n",
+ " next_lat, next_lon = (\n",
+ " granule[\"latitude\"][lines + 1, NADIR],\n",
+ " granule[\"longitude\"][lines + 1, NADIR],\n",
+ " )\n",
" heading, _, step = geod.inv(lon, lat, next_lon, next_lat)\n",
- " speed = step / ((granule[\"time\"][lines + 1] - granule[\"time\"][lines]).total_seconds().to_numpy())\n",
- " subpoints = wgs84.subpoint_of(swot.elements[0].to_skyfield().at(timescale.from_datetimes(granule[\"time\"][lines].to_pydatetime())))\n",
- " azimuth, _, distance = geod.inv(lon, lat, subpoints.longitude.degrees, subpoints.latitude.degrees)\n",
+ " speed = step / (\n",
+ " (granule[\"time\"][lines + 1] - granule[\"time\"][lines]).total_seconds().to_numpy()\n",
+ " )\n",
+ " subpoints = wgs84.subpoint_of(\n",
+ " swot.elements[0]\n",
+ " .to_skyfield()\n",
+ " .at(timescale.from_datetimes(granule[\"time\"][lines].to_pydatetime()))\n",
+ " )\n",
+ " azimuth, _, distance = geod.inv(\n",
+ " lon, lat, subpoints.longitude.degrees, subpoints.latitude.degrees\n",
+ " )\n",
" angle = np.radians(azimuth - heading)\n",
" return pd.DataFrame(\n",
" {\n",
" \"file\": granule[\"name\"],\n",
- " \"days before TLE epoch\": (TLE_EPOCH - granule[\"time\"][lines]) / timedelta(days=1),\n",
+ " \"days before TLE epoch\": (TLE_EPOCH - granule[\"time\"][lines])\n",
+ " / timedelta(days=1),\n",
" \"latitude\": lat,\n",
" \"along-track offset (km)\": distance * np.cos(angle) / 1e3,\n",
" \"along-track offset (s)\": distance * np.cos(angle) / speed,\n",
@@ -588,7 +627,10 @@
" \"along-track offset, median (s)\": (\"along-track offset (s)\", \"median\"),\n",
" \"along-track offset, median (km)\": (\"along-track offset (km)\", \"median\"),\n",
" \"cross-track offset, median (km)\": (\"cross-track offset (km)\", \"median\"),\n",
- " \"cross-track offset, max |x| (km)\": (\"cross-track offset (km)\", lambda x: x.abs().max()),\n",
+ " \"cross-track offset, max |x| (km)\": (\n",
+ " \"cross-track offset (km)\",\n",
+ " lambda x: x.abs().max(),\n",
+ " ),\n",
" }\n",
")\n",
"table1.sort_values(\"days before TLE epoch\").round(2)"
@@ -620,11 +662,27 @@
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.6))\n",
- "axes[0].plot(offsets[\"days before TLE epoch\"], offsets[\"along-track offset (s)\"], \".\", ms=3, color=BLUE)\n",
- "axes[0].set(xlabel=\"Days before TLE epoch\", ylabel=\"TAT-C minus SWOT (s)\", title=\"Along-track (time) offset\")\n",
+ "axes[0].plot(\n",
+ " offsets[\"days before TLE epoch\"],\n",
+ " offsets[\"along-track offset (s)\"],\n",
+ " \".\",\n",
+ " ms=3,\n",
+ " color=BLUE,\n",
+ ")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Days before TLE epoch\",\n",
+ " ylabel=\"TAT-C minus SWOT (s)\",\n",
+ " title=\"Along-track (time) offset\",\n",
+ ")\n",
"axes[0].invert_xaxis()\n",
- "axes[1].plot(offsets[\"latitude\"], offsets[\"cross-track offset (km)\"], \".\", ms=3, color=ORANGE)\n",
- "axes[1].set(xlabel=\"Latitude (deg)\", ylabel=\"TAT-C left of reference (km)\", title=\"Cross-track offset from reference ground track\")\n",
+ "axes[1].plot(\n",
+ " offsets[\"latitude\"], offsets[\"cross-track offset (km)\"], \".\", ms=3, color=ORANGE\n",
+ ")\n",
+ "axes[1].set(\n",
+ " xlabel=\"Latitude (deg)\",\n",
+ " ylabel=\"TAT-C left of reference (km)\",\n",
+ " title=\"Cross-track offset from reference ground track\",\n",
+ ")\n",
"fig.tight_layout()\n",
"plt.show()"
]
@@ -673,25 +731,59 @@
"source": [
"first, second = (g for g in granules if g[\"pass\"] == 566)\n",
"lines = np.arange(0, min(len(first[\"time\"]), len(second[\"time\"])), 500)\n",
- "measured_cycle = pd.to_timedelta(np.median((second[\"time\"][lines] - first[\"time\"][lines]).total_seconds()), unit=\"s\")\n",
- "repeat_cycle = GeneralPerturbationsOrbit.from_tle(TLE, remove_drag=True, repeat_cycle=\"auto\").get_repeat_cycle()\n",
+ "measured_cycle = pd.to_timedelta(\n",
+ " np.median((second[\"time\"][lines] - first[\"time\"][lines]).total_seconds()), unit=\"s\"\n",
+ ")\n",
+ "repeat_cycle = GeneralPerturbationsOrbit.from_tle(\n",
+ " TLE, remove_drag=True, repeat_cycle=\"auto\"\n",
+ ").get_repeat_cycle()\n",
"lines = lines[(lines > 0) & (lines < len(second[\"time\"]) - 1)]\n",
"times = second[\"time\"][lines].to_pydatetime()\n",
- "now = wgs84.subpoint_of(swot.elements[0].to_skyfield().at(timescale.from_datetimes(times)))\n",
- "later = wgs84.subpoint_of(swot.elements[0].to_skyfield().at(timescale.from_datetimes([t + repeat_cycle for t in times])))\n",
- "heading = geod.inv(second[\"longitude\"][lines, NADIR], second[\"latitude\"][lines, NADIR], second[\"longitude\"][lines + 1, NADIR], second[\"latitude\"][lines + 1, NADIR])[0]\n",
- "azimuth, _, shift = geod.inv(now.longitude.degrees, now.latitude.degrees, later.longitude.degrees, later.latitude.degrees)\n",
+ "now = wgs84.subpoint_of(\n",
+ " swot.elements[0].to_skyfield().at(timescale.from_datetimes(times))\n",
+ ")\n",
+ "later = wgs84.subpoint_of(\n",
+ " swot.elements[0]\n",
+ " .to_skyfield()\n",
+ " .at(timescale.from_datetimes([t + repeat_cycle for t in times]))\n",
+ ")\n",
+ "heading = geod.inv(\n",
+ " second[\"longitude\"][lines, NADIR],\n",
+ " second[\"latitude\"][lines, NADIR],\n",
+ " second[\"longitude\"][lines + 1, NADIR],\n",
+ " second[\"latitude\"][lines + 1, NADIR],\n",
+ ")[0]\n",
+ "azimuth, _, shift = geod.inv(\n",
+ " now.longitude.degrees,\n",
+ " now.latitude.degrees,\n",
+ " later.longitude.degrees,\n",
+ " later.latitude.degrees,\n",
+ ")\n",
"along_shift = shift * np.cos(np.radians(azimuth - heading)) / 1e3\n",
"cross_shift = -shift * np.sin(np.radians(azimuth - heading)) / 1e3\n",
"pd.Series(\n",
" {\n",
" \"SWOT repeat cycle (pass 566, cycles 55 to 56)\": str(measured_cycle),\n",
- " \"same grid in both cycles (max position difference, m)\": float(np.nanmax(np.hypot(first[\"latitude\"][lines] - second[\"latitude\"][lines], first[\"longitude\"][lines] - second[\"longitude\"][lines])) * 111e3),\n",
+ " \"same grid in both cycles (max position difference, m)\": float(\n",
+ " np.nanmax(\n",
+ " np.hypot(\n",
+ " first[\"latitude\"][lines] - second[\"latitude\"][lines],\n",
+ " first[\"longitude\"][lines] - second[\"longitude\"][lines],\n",
+ " )\n",
+ " )\n",
+ " * 111e3\n",
+ " ),\n",
" \"TAT-C repeat cycle (get_repeat_cycle)\": str(repeat_cycle),\n",
" \"difference (s)\": (repeat_cycle - measured_cycle).total_seconds(),\n",
- " \"SGP4 ground track shift after one TAT-C repeat cycle, along track, median (km)\": float(np.median(along_shift)),\n",
- " \"SGP4 ground track shift after one TAT-C repeat cycle, across track, median (km)\": float(np.median(cross_shift)),\n",
- " \"SGP4 ground track shift after one TAT-C repeat cycle, across track, max |x| (km)\": float(np.max(np.abs(cross_shift))),\n",
+ " \"SGP4 ground track shift after one TAT-C repeat cycle, along track, median (km)\": float(\n",
+ " np.median(along_shift)\n",
+ " ),\n",
+ " \"SGP4 ground track shift after one TAT-C repeat cycle, across track, median (km)\": float(\n",
+ " np.median(cross_shift)\n",
+ " ),\n",
+ " \"SGP4 ground track shift after one TAT-C repeat cycle, across track, max |x| (km)\": float(\n",
+ " np.max(np.abs(cross_shift))\n",
+ " ),\n",
" }\n",
")"
]
@@ -781,7 +873,12 @@
"def off_nadir_angle(distance, altitude=ALTITUDE):\n",
" \"\"\"Off-nadir angle (deg) to a ground distance from nadir, on a spherical Earth.\"\"\"\n",
" theta = distance / EARTH_MEAN_RADIUS\n",
- " return np.degrees(np.arctan2(EARTH_MEAN_RADIUS * np.sin(theta), EARTH_MEAN_RADIUS + altitude - EARTH_MEAN_RADIUS * np.cos(theta)))\n",
+ " return np.degrees(\n",
+ " np.arctan2(\n",
+ " EARTH_MEAN_RADIUS * np.sin(theta),\n",
+ " EARTH_MEAN_RADIUS + altitude - EARTH_MEAN_RADIUS * np.cos(theta),\n",
+ " )\n",
+ " )\n",
"\n",
"\n",
"INNER, OUTER = 10e3, 60e3\n",
@@ -797,7 +894,15 @@
" for side, sign in [(\"left\", 1), (\"right\", -1)]\n",
"}\n",
"satellite = Satellite(name=\"SWOT\", orbit=swot, instruments=list(karin.values()))\n",
- "pd.DataFrame({side: {\"roll angle (deg)\": i.roll_angle, \"cross-track field of view (deg)\": i.cross_track_field_of_view} for side, i in karin.items()}).T.round(3)"
+ "pd.DataFrame(\n",
+ " {\n",
+ " side: {\n",
+ " \"roll angle (deg)\": i.roll_angle,\n",
+ " \"cross-track field of view (deg)\": i.cross_track_field_of_view,\n",
+ " }\n",
+ " for side, i in karin.items()\n",
+ " }\n",
+ ").T.round(3)"
]
},
{
@@ -831,21 +936,47 @@
"def signed_distance(granule, line):\n",
" \"\"\"Distance (km) of each pixel of a line from the ground track, positive to the left of the direction of motion.\"\"\"\n",
" lat, lon = granule[\"latitude\"][line], granule[\"longitude\"][line]\n",
- " heading = geod.inv(lon[NADIR], lat[NADIR], granule[\"longitude\"][line + 1, NADIR], granule[\"latitude\"][line + 1, NADIR])[0]\n",
- " azimuth, _, distance = geod.inv(np.full(69, lon[NADIR]), np.full(69, lat[NADIR]), lon, lat)\n",
+ " heading = geod.inv(\n",
+ " lon[NADIR],\n",
+ " lat[NADIR],\n",
+ " granule[\"longitude\"][line + 1, NADIR],\n",
+ " granule[\"latitude\"][line + 1, NADIR],\n",
+ " )[0]\n",
+ " azimuth, _, distance = geod.inv(\n",
+ " np.full(69, lon[NADIR]), np.full(69, lat[NADIR]), lon, lat\n",
+ " )\n",
" return -np.sign(np.sin(np.radians(azimuth - heading))) * distance / 1e3\n",
"\n",
"\n",
"def observe_pixel(args):\n",
" \"\"\"TAT-C observations of a grid pixel by each instrument near its line's time.\"\"\"\n",
" granule, line, pixel = args\n",
- " point = Point(id=0, latitude=float(granule[\"latitude\"][line, pixel]), longitude=float(granule[\"longitude\"][line, pixel]))\n",
+ " point = Point(\n",
+ " id=0,\n",
+ " latitude=float(granule[\"latitude\"][line, pixel]),\n",
+ " longitude=float(granule[\"longitude\"][line, pixel]),\n",
+ " )\n",
" time = granule[\"time\"][line].floor(\"us\").to_pydatetime()\n",
- " row = {\"file\": granule[\"name\"], \"line\": line, \"pixel\": pixel, \"good\": bool(granule[\"good\"][line, pixel])}\n",
+ " row = {\n",
+ " \"file\": granule[\"name\"],\n",
+ " \"line\": line,\n",
+ " \"pixel\": pixel,\n",
+ " \"good\": bool(granule[\"good\"][line, pixel]),\n",
+ " }\n",
" for index, side in enumerate(karin):\n",
- " obs = collect_observations(point, satellite, time - timedelta(minutes=2), time + timedelta(minutes=2), instrument_index=index)\n",
+ " obs = collect_observations(\n",
+ " point,\n",
+ " satellite,\n",
+ " time - timedelta(minutes=2),\n",
+ " time + timedelta(minutes=2),\n",
+ " instrument_index=index,\n",
+ " )\n",
" row[f\"observed {side}\"] = not obs.empty\n",
- " row[f\"epoch difference {side} (s)\"] = (obs.epoch.iloc[0] - granule[\"time\"][line]).total_seconds() if not obs.empty else np.nan\n",
+ " row[f\"epoch difference {side} (s)\"] = (\n",
+ " (obs.epoch.iloc[0] - granule[\"time\"][line]).total_seconds()\n",
+ " if not obs.empty\n",
+ " else np.nan\n",
+ " )\n",
" return row\n",
"\n",
"\n",
@@ -857,7 +988,9 @@
" tasks += [(granule, line, pixel) for pixel in range(69)]\n",
"with concurrent.futures.ThreadPoolExecutor(8) as pool:\n",
" pixels = pd.DataFrame(list(pool.map(observe_pixel, tasks)))\n",
- "pixels[\"distance (km)\"] = [distances[(f, l)][p] for f, l, p in zip(pixels.file, pixels.line, pixels.pixel)]\n",
+ "pixels[\"distance (km)\"] = [\n",
+ " distances[(f, l)][p] for f, l, p in zip(pixels.file, pixels.line, pixels.pixel)\n",
+ "]\n",
"by_pixel = pixels.groupby(\"pixel\").agg(\n",
" **{\n",
" \"distance (km)\": (\"distance (km)\", \"median\"),\n",
@@ -867,7 +1000,13 @@
" }\n",
")\n",
"by_pixel[[\"good-quality (%)\", \"TAT-C left (%)\", \"TAT-C right (%)\"]] *= 100\n",
- "pd.Series({\"files\": len({t[0][\"name\"] for t in tasks}), \"ocean lines\": len(distances), \"pixels\": len(pixels)})"
+ "pd.Series(\n",
+ " {\n",
+ " \"files\": len({t[0][\"name\"] for t in tasks}),\n",
+ " \"ocean lines\": len(distances),\n",
+ " \"pixels\": len(pixels),\n",
+ " }\n",
+ ")"
]
},
{
@@ -896,12 +1035,38 @@
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 3.6))\n",
- "ax.bar(by_pixel[\"distance (km)\"], by_pixel[\"good-quality (%)\"], width=1.8, color=GRAY, alpha=0.5, label=\"SWOT good-quality heights\")\n",
- "ax.step(by_pixel[\"distance (km)\"], by_pixel[\"TAT-C left (%)\"], where=\"mid\", color=BLUE, lw=1.5, label=\"TAT-C left instrument\")\n",
- "ax.step(by_pixel[\"distance (km)\"], by_pixel[\"TAT-C right (%)\"], where=\"mid\", color=ORANGE, lw=1.5, label=\"TAT-C right instrument\")\n",
+ "ax.bar(\n",
+ " by_pixel[\"distance (km)\"],\n",
+ " by_pixel[\"good-quality (%)\"],\n",
+ " width=1.8,\n",
+ " color=GRAY,\n",
+ " alpha=0.5,\n",
+ " label=\"SWOT good-quality heights\",\n",
+ ")\n",
+ "ax.step(\n",
+ " by_pixel[\"distance (km)\"],\n",
+ " by_pixel[\"TAT-C left (%)\"],\n",
+ " where=\"mid\",\n",
+ " color=BLUE,\n",
+ " lw=1.5,\n",
+ " label=\"TAT-C left instrument\",\n",
+ ")\n",
+ "ax.step(\n",
+ " by_pixel[\"distance (km)\"],\n",
+ " by_pixel[\"TAT-C right (%)\"],\n",
+ " where=\"mid\",\n",
+ " color=ORANGE,\n",
+ " lw=1.5,\n",
+ " label=\"TAT-C right instrument\",\n",
+ ")\n",
"for edge in (-OUTER, -INNER, INNER, OUTER):\n",
" ax.axvline(edge / 1e3, color=INK, lw=0.6, ls=\":\")\n",
- "ax.set(xlabel=\"Distance from ground track (km, positive to the left of the direction of motion)\", ylabel=\"Pixels (%)\", title=\"KaRIn swaths: SWOT good-quality pixels and TAT-C observations (ocean lines)\", xlim=(-70, 70))\n",
+ "ax.set(\n",
+ " xlabel=\"Distance from ground track (km, positive to the left of the direction of motion)\",\n",
+ " ylabel=\"Pixels (%)\",\n",
+ " title=\"KaRIn swaths: SWOT good-quality pixels and TAT-C observations (ocean lines)\",\n",
+ " xlim=(-70, 70),\n",
+ ")\n",
"ax.legend(frameon=False, fontsize=8, loc=\"center\")\n",
"plt.show()"
]
@@ -942,23 +1107,45 @@
],
"source": [
"pixels[\"observed\"] = pixels[\"observed left\"] | pixels[\"observed right\"]\n",
- "pixels[\"epoch difference (s)\"] = pixels[\"epoch difference left (s)\"].fillna(pixels[\"epoch difference right (s)\"])\n",
+ "pixels[\"epoch difference (s)\"] = pixels[\"epoch difference left (s)\"].fillna(\n",
+ " pixels[\"epoch difference right (s)\"]\n",
+ ")\n",
"inside = pixels[\"distance (km)\"].abs().between(INNER / 1e3 + 1, OUTER / 1e3 - 1)\n",
- "outside = (pixels[\"distance (km)\"].abs() < INNER / 1e3 - 1) | (pixels[\"distance (km)\"].abs() > OUTER / 1e3 + 1)\n",
+ "outside = (pixels[\"distance (km)\"].abs() < INNER / 1e3 - 1) | (\n",
+ " pixels[\"distance (km)\"].abs() > OUTER / 1e3 + 1\n",
+ ")\n",
"left = pixels[\"distance (km)\"] > 0\n",
- "corrected = pixels[\"epoch difference (s)\"] + pixels.file.map(table1[\"along-track offset, median (s)\"])\n",
+ "corrected = pixels[\"epoch difference (s)\"] + pixels.file.map(\n",
+ " table1[\"along-track offset, median (s)\"]\n",
+ ")\n",
"pd.Series(\n",
" {\n",
- " \"pixels 11 to 59 km from the ground track: good quality (%)\": 100 * pixels.good[inside].mean(),\n",
- " \"pixels 11 to 59 km from the ground track: observed by TAT-C (%)\": 100 * pixels.observed[inside].mean(),\n",
- " \"pixels 11 to 59 km from the ground track: observed by the instrument on their side (%)\": 100 * np.mean(np.where(left[inside], pixels[\"observed left\"][inside], pixels[\"observed right\"][inside])),\n",
- " \"pixels within 9 km or beyond 61 km: good quality (%)\": 100 * pixels.good[outside].mean(),\n",
- " \"pixels within 9 km or beyond 61 km: observed by TAT-C (%)\": 100 * pixels.observed[outside].mean(),\n",
- " \"good-quality pixels observed by TAT-C (%)\": 100 * pixels.observed[pixels.good].mean(),\n",
+ " \"pixels 11 to 59 km from the ground track: good quality (%)\": 100\n",
+ " * pixels.good[inside].mean(),\n",
+ " \"pixels 11 to 59 km from the ground track: observed by TAT-C (%)\": 100\n",
+ " * pixels.observed[inside].mean(),\n",
+ " \"pixels 11 to 59 km from the ground track: observed by the instrument on their side (%)\": 100\n",
+ " * np.mean(\n",
+ " np.where(\n",
+ " left[inside],\n",
+ " pixels[\"observed left\"][inside],\n",
+ " pixels[\"observed right\"][inside],\n",
+ " )\n",
+ " ),\n",
+ " \"pixels within 9 km or beyond 61 km: good quality (%)\": 100\n",
+ " * pixels.good[outside].mean(),\n",
+ " \"pixels within 9 km or beyond 61 km: observed by TAT-C (%)\": 100\n",
+ " * pixels.observed[outside].mean(),\n",
+ " \"good-quality pixels observed by TAT-C (%)\": 100\n",
+ " * pixels.observed[pixels.good].mean(),\n",
" \"epoch minus line time, median (s)\": pixels[\"epoch difference (s)\"].median(),\n",
- " \"epoch minus line time, p95 |x| (s)\": pixels[\"epoch difference (s)\"].abs().quantile(0.95),\n",
+ " \"epoch minus line time, p95 |x| (s)\": pixels[\"epoch difference (s)\"]\n",
+ " .abs()\n",
+ " .quantile(0.95),\n",
" \"epoch minus line time, corrected for the orbit's along-track offset (Test 1), median (s)\": corrected.median(),\n",
- " \"epoch minus line time, corrected for the orbit's along-track offset (Test 1), p95 |x| (s)\": corrected.abs().quantile(0.95),\n",
+ " \"epoch minus line time, corrected for the orbit's along-track offset (Test 1), p95 |x| (s)\": corrected.abs().quantile(\n",
+ " 0.95\n",
+ " ),\n",
" }\n",
").round(2)"
]
diff --git a/docs/validation/ValidateTiltPACE.ipynb b/docs/validation/ValidateTiltPACE.ipynb
index 650ebcfc..22075494 100644
--- a/docs/validation/ValidateTiltPACE.ipynb
+++ b/docs/validation/ValidateTiltPACE.ipynb
@@ -14,7 +14,8 @@
"1. **Orbit.** Does TAT-C's propagation of the operational two-line element (TLE) set reproduce PACE's position?\n",
"2. **Scan geometry.** Does a tilted scan view reproduce OCI's geolocated pixels, and with which sign conventions and velocity frame?\n",
"3. **Tilt schedule.** Where does OCI's tilt flip, and can a pitch angle profile represent it?\n",
- "4. **Observation times.** Does `collect_observations` predict when OCI observes points on the ground?"
+ "4. **Observation times.** Does `collect_observations` predict when OCI observes points on the ground?\n",
+ "5. **Glint avoidance.** Does the tilt keep OCI's scans out of the sun glint, and do the glint angles (`compute_glint_angle`) explain where it changes?"
]
},
{
@@ -35,10 +36,10 @@
"id": "a111bc33",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:19.361283Z",
- "iopub.status.busy": "2026-10-08T18:11:19.361283Z",
- "iopub.status.idle": "2026-10-08T18:11:23.314653Z",
- "shell.execute_reply": "2026-10-08T18:11:23.314653Z"
+ "iopub.execute_input": "2026-10-25T18:36:05.701826Z",
+ "iopub.status.busy": "2026-10-25T18:36:05.701637Z",
+ "iopub.status.idle": "2026-10-25T18:36:07.056313Z",
+ "shell.execute_reply": "2026-10-25T18:36:07.055575Z"
}
},
"outputs": [
@@ -101,13 +102,17 @@
" def __init__(self, url, block_size=2**20, prefetch=1):\n",
" self.block_size, self.blocks, self.position = block_size, {}, 0\n",
" response = self._get(url, 0, prefetch * block_size)\n",
- " self.url, self.size = response.url, int(response.headers[\"Content-Range\"].split(\"/\")[1])\n",
+ " self.url, self.size = response.url, int(\n",
+ " response.headers[\"Content-Range\"].split(\"/\")[1]\n",
+ " )\n",
" self._store(0, response.content)\n",
"\n",
" def _get(self, url, start, end):\n",
" for attempt in range(5):\n",
" try:\n",
- " response = https_session().get(url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300)\n",
+ " response = https_session().get(\n",
+ " url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300\n",
+ " )\n",
" response.raise_for_status()\n",
" return response\n",
" except requests.RequestException:\n",
@@ -128,18 +133,30 @@
" return self.position\n",
"\n",
" def seek(self, offset, whence=io.SEEK_SET):\n",
- " self.position = {io.SEEK_SET: 0, io.SEEK_CUR: self.position, io.SEEK_END: self.size}[whence] + offset\n",
+ " self.position = {\n",
+ " io.SEEK_SET: 0,\n",
+ " io.SEEK_CUR: self.position,\n",
+ " io.SEEK_END: self.size,\n",
+ " }[whence] + offset\n",
" return self.position\n",
"\n",
" def readinto(self, buffer):\n",
" n = min(len(buffer), self.size - self.position)\n",
" if n <= 0:\n",
" return 0\n",
- " first, last = self.position // self.block_size, (self.position + n - 1) // self.block_size\n",
- " missing = [block for block in range(first, last + 1) if block not in self.blocks]\n",
+ " first, last = (\n",
+ " self.position // self.block_size,\n",
+ " (self.position + n - 1) // self.block_size,\n",
+ " )\n",
+ " missing = [\n",
+ " block for block in range(first, last + 1) if block not in self.blocks\n",
+ " ]\n",
" if missing:\n",
" end = min((missing[-1] + 1) * self.block_size, self.size)\n",
- " self._store(missing[0], self._get(self.url, missing[0] * self.block_size, end).content)\n",
+ " self._store(\n",
+ " missing[0],\n",
+ " self._get(self.url, missing[0] * self.block_size, end).content,\n",
+ " )\n",
" data = b\"\".join(self.blocks[block] for block in range(first, last + 1))\n",
" offset = self.position - first * self.block_size\n",
" buffer[:n] = data[offset : offset + n]\n",
@@ -159,7 +176,9 @@
" rows = slice(None, None, SCAN_STEP)\n",
"\n",
" def grid(name, scale=1.0):\n",
- " values = f[f\"geolocation_data/{name}\"][:][rows][:, columns].astype(np.float32)\n",
+ " values = f[f\"geolocation_data/{name}\"][:][rows][:, columns].astype(\n",
+ " np.float32\n",
+ " )\n",
" values[values <= -32767] = np.nan\n",
" return values * scale\n",
"\n",
@@ -174,7 +193,9 @@
" orb_vel=f[\"navigation_data/orb_vel\"][:][rows],\n",
" att_ang=f[\"navigation_data/att_ang\"][:][rows],\n",
" columns=columns,\n",
- " scan_angle=np.nanmedian(f[\"navigation_data/CCD_scan_angles\"][::100][:, columns], axis=0),\n",
+ " scan_angle=np.nanmedian(\n",
+ " f[\"navigation_data/CCD_scan_angles\"][::100][:, columns], axis=0\n",
+ " ),\n",
" latitude=grid(\"latitude\"),\n",
" longitude=grid(\"longitude\"),\n",
" sensor_zenith=grid(\"sensor_zenith\", 0.01),\n",
@@ -187,8 +208,22 @@
"if not any(DATA_DIR.glob(\"PACE_OCI.*.npz\")):\n",
" import earthaccess\n",
"\n",
- " results = earthaccess.search_data(short_name=\"PACE_OCI_L1B_SCI\", temporal=(START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"), END.strftime(\"%Y-%m-%dT%H:%M:%SZ\")))\n",
- " urls = sorted(next(link[\"URL\"] for link in r[\"umm\"][\"RelatedUrls\"] if link[\"URL\"].startswith(\"https://obdaac-tea\") and link[\"URL\"].endswith(\".nc\")) for r in results)\n",
+ " results = earthaccess.search_data(\n",
+ " short_name=\"PACE_OCI_L1B_SCI\",\n",
+ " temporal=(\n",
+ " START.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " END.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n",
+ " ),\n",
+ " )\n",
+ " urls = sorted(\n",
+ " next(\n",
+ " link[\"URL\"]\n",
+ " for link in r[\"umm\"][\"RelatedUrls\"]\n",
+ " if link[\"URL\"].startswith(\"https://obdaac-tea\")\n",
+ " and link[\"URL\"].endswith(\".nc\")\n",
+ " )\n",
+ " for r in results\n",
+ " )\n",
" with concurrent.futures.ThreadPoolExecutor(6) as pool:\n",
" list(pool.map(reduce_granule, urls))\n",
"\n",
@@ -196,27 +231,45 @@
"for path in sorted(DATA_DIR.glob(\"PACE_OCI.*.npz\")):\n",
" with np.load(path) as d:\n",
" granule = {key: d[key] for key in d.files}\n",
- " times = pd.Timestamp(str(granule[\"day\"]), tz=\"UTC\") + pd.to_timedelta(granule[\"seconds\"], unit=\"s\")\n",
- " granule[\"name\"], granule[\"times\"], granule[\"nav_times\"] = path.stem, times, times[granule[\"nav_rows\"]]\n",
- " if granule[\"nav_times\"][0] >= pd.Timestamp(START) and granule[\"nav_times\"][0] < pd.Timestamp(END):\n",
+ " times = pd.Timestamp(str(granule[\"day\"]), tz=\"UTC\") + pd.to_timedelta(\n",
+ " granule[\"seconds\"], unit=\"s\"\n",
+ " )\n",
+ " granule[\"name\"], granule[\"times\"], granule[\"nav_times\"] = (\n",
+ " path.stem,\n",
+ " times,\n",
+ " times[granule[\"nav_rows\"]],\n",
+ " )\n",
+ " if granule[\"nav_times\"][0] >= pd.Timestamp(START) and granule[\"nav_times\"][\n",
+ " 0\n",
+ " ] < pd.Timestamp(END):\n",
" granules.append(granule)\n",
- "scans = pd.concat([pd.DataFrame({\"time\": g[\"times\"], \"tilt\": g[\"tilt\"]}) for g in granules]).sort_values(\"time\").reset_index(drop=True)\n",
- "nav = pd.concat(\n",
- " [\n",
- " pd.DataFrame(\n",
- " np.column_stack([g[\"orb_pos\"], g[\"orb_vel\"], g[\"att_ang\"]]),\n",
- " columns=[\"x\", \"y\", \"z\", \"vx\", \"vy\", \"vz\", \"roll\", \"pitch\", \"yaw\"],\n",
- " ).assign(time=g[\"nav_times\"], granule=g[\"name\"])\n",
- " for g in granules\n",
- " ]\n",
- ").sort_values(\"time\").reset_index(drop=True)\n",
+ "scans = (\n",
+ " pd.concat([pd.DataFrame({\"time\": g[\"times\"], \"tilt\": g[\"tilt\"]}) for g in granules])\n",
+ " .sort_values(\"time\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
+ "nav = (\n",
+ " pd.concat(\n",
+ " [\n",
+ " pd.DataFrame(\n",
+ " np.column_stack([g[\"orb_pos\"], g[\"orb_vel\"], g[\"att_ang\"]]),\n",
+ " columns=[\"x\", \"y\", \"z\", \"vx\", \"vy\", \"vz\", \"roll\", \"pitch\", \"yaw\"],\n",
+ " ).assign(time=g[\"nav_times\"], granule=g[\"name\"])\n",
+ " for g in granules\n",
+ " ]\n",
+ " )\n",
+ " .sort_values(\"time\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
"pd.Series(\n",
" {\n",
" \"granules\": len(granules),\n",
" \"scans\": len(scans),\n",
" \"first scan\": scans.time.min(),\n",
" \"last scan\": scans.time.max(),\n",
- " \"tilt angles (deg)\": \", \".join(f\"{v:.3f}\" for v in sorted(scans.tilt.round(3).value_counts().index[:2])),\n",
+ " \"tilt angles (deg)\": \", \".join(\n",
+ " f\"{v:.3f}\" for v in sorted(scans.tilt.round(3).value_counts().index[:2])\n",
+ " ),\n",
" \"spacecraft roll, 95th percentile (deg)\": nav.roll.abs().quantile(0.95),\n",
" \"spacecraft pitch, 95th percentile (deg)\": nav.pitch.abs().quantile(0.95),\n",
" \"spacecraft yaw, range (deg)\": f\"{nav.yaw.min():.2f} to {nav.yaw.max():.2f}\",\n",
@@ -240,10 +293,10 @@
"id": "edb82bc2",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:23.317174Z",
- "iopub.status.busy": "2026-10-08T18:11:23.317174Z",
- "iopub.status.idle": "2026-10-08T18:11:27.838449Z",
- "shell.execute_reply": "2026-10-08T18:11:27.837442Z"
+ "iopub.execute_input": "2026-10-25T18:36:07.058156Z",
+ "iopub.status.busy": "2026-10-25T18:36:07.057894Z",
+ "iopub.status.idle": "2026-10-25T18:36:09.746281Z",
+ "shell.execute_reply": "2026-10-25T18:36:09.745308Z"
}
},
"outputs": [
@@ -251,7 +304,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_27580\\1808415648.py:29: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_43032\\3749224625.py:30: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
" return timescale.from_datetimes([t.to_pydatetime() for t in pd.DatetimeIndex(values)])\n"
]
}
@@ -273,19 +326,37 @@
" VelocityFrame,\n",
" ViewGeometry,\n",
" compute_argument_of_latitude,\n",
+ " compute_glint_angle,\n",
" compute_projected_ray_position,\n",
" compute_view_angles,\n",
" geodesic_distance,\n",
")\n",
"\n",
"orbit = GeneralPerturbationsOrbit.from_tle(TLE)\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"\n",
"def times_of(values):\n",
" \"\"\"Skyfield times of pandas timestamps.\"\"\"\n",
- " return timescale.from_datetimes([t.to_pydatetime() for t in pd.DatetimeIndex(values)])\n",
+ " return timescale.from_datetimes(\n",
+ " [t.to_pydatetime() for t in pd.DatetimeIndex(values)]\n",
+ " )\n",
"\n",
"\n",
"def ephemeris_track(frame):\n",
@@ -298,7 +369,9 @@
" )\n",
"\n",
"\n",
- "nav[\"argument_of_latitude\"] = compute_argument_of_latitude(*(lambda track: (track.position.m, track.velocity.m_per_s))(ephemeris_track(nav)))"
+ "nav[\"argument_of_latitude\"] = compute_argument_of_latitude(\n",
+ " *(lambda track: (track.position.m, track.velocity.m_per_s))(ephemeris_track(nav))\n",
+ ")"
]
},
{
@@ -317,21 +390,13 @@
"id": "2e107ee4",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:27.840449Z",
- "iopub.status.busy": "2026-10-08T18:11:27.840449Z",
- "iopub.status.idle": "2026-10-08T18:11:27.933895Z",
- "shell.execute_reply": "2026-10-08T18:11:27.932887Z"
+ "iopub.execute_input": "2026-10-25T18:36:09.748738Z",
+ "iopub.status.busy": "2026-10-25T18:36:09.748311Z",
+ "iopub.status.idle": "2026-10-25T18:36:09.797081Z",
+ "shell.execute_reply": "2026-10-25T18:36:09.796311Z"
}
},
"outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_27580\\174024740.py:2: FutureWarning: The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result\n",
- " tle_xyz = orbit.get_orbit_track(list(sample.time.dt.to_pydatetime())).frame_xyz(itrs).km\n"
- ]
- },
{
"data": {
"text/html": [
@@ -362,31 +427,31 @@
" \n",
"
"
]
@@ -865,12 +1032,18 @@
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 3.8))\n",
- "for (day, frame), color in zip(nav.groupby(nav.time.dt.strftime(\"%Y-%m-%d\")), [BLUE, ORANGE]):\n",
+ "for (day, frame), color in zip(\n",
+ " nav.groupby(nav.time.dt.strftime(\"%Y-%m-%d\")), [BLUE, ORANGE]\n",
+ "):\n",
" tilt = np.interp(frame.time.astype(\"int64\"), scans.time.astype(\"int64\"), scans.tilt)\n",
" u = (frame.argument_of_latitude + 180) % 360 - 180\n",
" axes[0].scatter(u, tilt, s=1, color=color, label=day)\n",
" axes[1].scatter(u, tilt, s=4, color=color, label=day)\n",
- "axes[0].set(xlabel=\"Argument of latitude (deg, from the ascending node)\", ylabel=\"OCI tilt (deg, positive forward)\", title=\"Tilt over the day side\")\n",
+ "axes[0].set(\n",
+ " xlabel=\"Argument of latitude (deg, from the ascending node)\",\n",
+ " ylabel=\"OCI tilt (deg, positive forward)\",\n",
+ " title=\"Tilt over the day side\",\n",
+ ")\n",
"axes[1].set(xlabel=\"Argument of latitude (deg)\", xlim=(-10, 5), title=\"Tilt changes\")\n",
"axes[0].legend(frameon=False, markerscale=6)\n",
"plt.show()"
@@ -900,10 +1073,10 @@
"id": "364c3783",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:48.180529Z",
- "iopub.status.busy": "2026-10-08T18:11:48.180529Z",
- "iopub.status.idle": "2026-10-08T18:13:46.647291Z",
- "shell.execute_reply": "2026-10-08T18:13:46.646283Z"
+ "iopub.execute_input": "2026-10-25T18:36:27.116158Z",
+ "iopub.status.busy": "2026-10-25T18:36:27.115968Z",
+ "iopub.status.idle": "2026-10-25T18:37:34.423924Z",
+ "shell.execute_reply": "2026-10-25T18:37:34.422956Z"
}
},
"outputs": [
@@ -911,7 +1084,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_27580\\510968519.py:86: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_43032\\1465380329.py:86: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
" track = orbit.get_orbit_track(row.epoch.to_pydatetime())\n"
]
},
@@ -919,7 +1092,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_27580\\510968519.py:86: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_43032\\1465380329.py:86: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
" track = orbit.get_orbit_track(row.epoch.to_pydatetime())\n"
]
},
@@ -927,7 +1100,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_27580\\510968519.py:86: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_43032\\1465380329.py:86: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
" track = orbit.get_orbit_track(row.epoch.to_pydatetime())\n"
]
},
@@ -981,7 +1154,7 @@
"
7533.0
\n",
"
99.91
\n",
"
0.15
\n",
- "
0.49
\n",
+ "
0.50
\n",
"
77.86
\n",
"
618.0
\n",
"
77.86
\n",
@@ -1017,7 +1190,7 @@
"\n",
" time difference, 95th percentile (s) \\\n",
"pitch profile, per day 0.45 \n",
- "pitch profile, first day 0.49 \n",
+ "pitch profile, first day 0.50 \n",
"no tilt 42.94 \n",
"\n",
" time difference, max (s) \\\n",
@@ -1053,7 +1226,10 @@
],
"source": [
"lattice = generate_points_fibonacci_lattice(500e3)\n",
- "points = [Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x) for i, r in enumerate(lattice.itertuples())]\n",
+ "points = [\n",
+ " Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x)\n",
+ " for i, r in enumerate(lattice.itertuples())\n",
+ "]\n",
"point_xyz = np.array([wgs84.latlon(p.latitude, p.longitude).itrs_xyz.m for p in points])\n",
"\n",
"# OCI observation times from the sampled pixels\n",
@@ -1061,37 +1237,72 @@
"for gi, g in enumerate(granules):\n",
" valid = ~np.isnan(g[\"latitude\"])\n",
" rows, cols = np.nonzero(valid)\n",
- " position = wgs84.latlon(g[\"latitude\"][valid], g[\"longitude\"][valid], g[\"height\"][valid]).itrs_xyz.m.T\n",
+ " position = wgs84.latlon(\n",
+ " g[\"latitude\"][valid], g[\"longitude\"][valid], g[\"height\"][valid]\n",
+ " ).itrs_xyz.m.T\n",
" pixel_xyz.append(position)\n",
" pixel_index.append(np.column_stack([np.full(len(rows), gi), rows, cols]))\n",
"pixel_xyz, pixel_index = np.concatenate(pixel_xyz), np.concatenate(pixel_index)\n",
"tree = cKDTree(pixel_xyz)\n",
- "flip_times = pd.DatetimeIndex(np.concatenate([flips.start.to_numpy(), flips.end.to_numpy()]))\n",
+ "flip_times = pd.DatetimeIndex(\n",
+ " np.concatenate([flips.start.to_numpy(), flips.end.to_numpy()])\n",
+ ")\n",
"reference = []\n",
"for pid, xyz in enumerate(point_xyz):\n",
" nearby = tree.query_ball_point(xyz, 30e3)\n",
" if not nearby:\n",
" continue\n",
- " candidates = pd.DataFrame(pixel_index[nearby], columns=[\"granule\", \"row\", \"column\"]).assign(distance=np.linalg.norm(pixel_xyz[nearby] - xyz, axis=1))\n",
- " candidates[\"time\"] = [granules[g][\"nav_times\"][r] for g, r in zip(candidates.granule, candidates.row)]\n",
- " candidates[\"pass\"] = (candidates.sort_values(\"time\").time.diff().dt.total_seconds() > 600).cumsum().reindex(candidates.index)\n",
- " for _, group in candidates.sort_values(\"time\").groupby((candidates.sort_values(\"time\").time.diff().dt.total_seconds() > 600).cumsum()):\n",
+ " candidates = pd.DataFrame(\n",
+ " pixel_index[nearby], columns=[\"granule\", \"row\", \"column\"]\n",
+ " ).assign(distance=np.linalg.norm(pixel_xyz[nearby] - xyz, axis=1))\n",
+ " candidates[\"time\"] = [\n",
+ " granules[g][\"nav_times\"][r] for g, r in zip(candidates.granule, candidates.row)\n",
+ " ]\n",
+ " candidates[\"pass\"] = (\n",
+ " (candidates.sort_values(\"time\").time.diff().dt.total_seconds() > 600)\n",
+ " .cumsum()\n",
+ " .reindex(candidates.index)\n",
+ " )\n",
+ " for _, group in candidates.sort_values(\"time\").groupby(\n",
+ " (candidates.sort_values(\"time\").time.diff().dt.total_seconds() > 600).cumsum()\n",
+ " ):\n",
" best = group.loc[group.distance.idxmin()]\n",
" g, r, c = granules[int(best.granule)], int(best.row), int(best.column)\n",
" if c in (0, len(g[\"columns\"]) - 1):\n",
" continue\n",
" # interpolate the time of the scan through the point between adjacent sampled scans\n",
" r0, r1 = (r, r + 1) if r + 1 < len(g[\"nav_rows\"]) else (r - 1, r)\n",
- " x0, x1 = (wgs84.latlon(g[\"latitude\"][k, c], g[\"longitude\"][k, c], g[\"height\"][k, c]).itrs_xyz.m for k in (r0, r1))\n",
+ " x0, x1 = (\n",
+ " wgs84.latlon(\n",
+ " g[\"latitude\"][k, c], g[\"longitude\"][k, c], g[\"height\"][k, c]\n",
+ " ).itrs_xyz.m\n",
+ " for k in (r0, r1)\n",
+ " )\n",
" fraction = np.dot(xyz - x0, x1 - x0) / np.dot(x1 - x0, x1 - x0)\n",
- " time = g[\"nav_times\"][r0] + (g[\"nav_times\"][r1] - g[\"nav_times\"][r0]) * float(fraction)\n",
- " if ((flips.start - pd.Timedelta(seconds=2) <= time) & (time <= flips.end + pd.Timedelta(seconds=2))).any():\n",
+ " time = g[\"nav_times\"][r0] + (g[\"nav_times\"][r1] - g[\"nav_times\"][r0]) * float(\n",
+ " fraction\n",
+ " )\n",
+ " if (\n",
+ " (flips.start - pd.Timedelta(seconds=2) <= time)\n",
+ " & (time <= flips.end + pd.Timedelta(seconds=2))\n",
+ " ).any():\n",
" continue\n",
- " reference.append({\"point_id\": points[pid].id, \"latitude\": points[pid].latitude, \"time\": time, \"scan angle (deg)\": float(g[\"scan_angle\"][c])})\n",
+ " reference.append(\n",
+ " {\n",
+ " \"point_id\": points[pid].id,\n",
+ " \"latitude\": points[pid].latitude,\n",
+ " \"time\": time,\n",
+ " \"scan angle (deg)\": float(g[\"scan_angle\"][c]),\n",
+ " }\n",
+ " )\n",
"reference = pd.DataFrame(reference)\n",
- "reference = reference[(reference.time >= pd.Timestamp(START)) & (reference.time < pd.Timestamp(END))].reset_index(drop=True)\n",
+ "reference = reference[\n",
+ " (reference.time >= pd.Timestamp(START)) & (reference.time < pd.Timestamp(END))\n",
+ "].reset_index(drop=True)\n",
"# time from the nearest tilt change\n",
- "reference[\"from tilt change (s)\"] = [np.min(np.abs((flip_times - t).total_seconds())) for t in reference.time]\n",
+ "reference[\"from tilt change (s)\"] = [\n",
+ " np.min(np.abs((flip_times - t).total_seconds())) for t in reference.time\n",
+ "]\n",
"# periods of OCI data collection (scans less than 5 s apart)\n",
"collecting = scans.time.diff().dt.total_seconds().gt(5).cumsum()\n",
"collection = scans.groupby(collecting).time.agg([\"min\", \"max\"])\n",
@@ -1100,8 +1311,15 @@
"def profile(day):\n",
" \"\"\"Pitch angle profile of a day's tilt schedule.\"\"\"\n",
" rows = flips[(flips.day == day) & (flips.direction == \"aft to forward\")]\n",
- " start, end = np.median(rows[\"argument of latitude, start (deg)\"]), np.median(rows[\"argument of latitude, end (deg)\"])\n",
- " return [(float(start), -19.827), (float(end), 19.863), (178.0, 19.863), (182.0, -19.827)]\n",
+ " start, end = np.median(rows[\"argument of latitude, start (deg)\"]), np.median(\n",
+ " rows[\"argument of latitude, end (deg)\"]\n",
+ " )\n",
+ " return [\n",
+ " (float(start), -19.827),\n",
+ " (float(end), 19.863),\n",
+ " (178.0, 19.863),\n",
+ " (182.0, -19.827),\n",
+ " ]\n",
"\n",
"\n",
"days = sorted(flips.day.unique())\n",
@@ -1109,14 +1327,24 @@
"\n",
"def oci(**kwargs):\n",
" return PointedInstrument(\n",
- " name=\"OCI\", field_of_regard=120, cross_track_field_of_view=112.9, along_track_field_of_view=0.1,\n",
- " is_rectangular=True, view_geometry=ViewGeometry.SCAN, min_target_solar_elevation=-8, **kwargs,\n",
+ " name=\"OCI\",\n",
+ " field_of_regard=120,\n",
+ " cross_track_field_of_view=112.9,\n",
+ " along_track_field_of_view=0.1,\n",
+ " is_rectangular=True,\n",
+ " view_geometry=ViewGeometry.SCAN,\n",
+ " min_target_solar_elevation=-8,\n",
+ " **kwargs,\n",
" )\n",
"\n",
"\n",
"models = {\n",
- " \"pitch profile, per day\": [(oci(pitch_angle_profile=profile(day)), day) for day in days],\n",
- " \"pitch profile, first day\": [(oci(pitch_angle_profile=profile(days[0])), day) for day in days],\n",
+ " \"pitch profile, per day\": [\n",
+ " (oci(pitch_angle_profile=profile(day)), day) for day in days\n",
+ " ],\n",
+ " \"pitch profile, first day\": [\n",
+ " (oci(pitch_angle_profile=profile(days[0])), day) for day in days\n",
+ " ],\n",
" \"no tilt\": [(oci(), day) for day in days],\n",
"}\n",
"\n",
@@ -1124,13 +1352,21 @@
"def observe(point, instrument, day):\n",
" satellite = Satellite(name=\"PACE\", orbit=orbit, instruments=[instrument])\n",
" start = datetime.fromisoformat(day).replace(tzinfo=timezone.utc)\n",
- " return collect_observations(point, satellite, start, start + timedelta(days=1)).assign(point_id=point.id)\n",
+ " return collect_observations(\n",
+ " point, satellite, start, start + timedelta(days=1), omit_solar=False\n",
+ " ).assign(point_id=point.id)\n",
"\n",
"\n",
"predicted = {}\n",
"for label, runs in models.items():\n",
- " frames = Parallel(n_jobs=-1)(delayed(observe)(point, instrument, day) for instrument, day in runs for point in points)\n",
- " predicted[label] = pd.concat([f for f in frames if not f.empty])[[\"point_id\", \"epoch\", \"geometry\"]]\n",
+ " frames = Parallel(n_jobs=-1)(\n",
+ " delayed(observe)(point, instrument, day)\n",
+ " for instrument, day in runs\n",
+ " for point in points\n",
+ " )\n",
+ " predicted[label] = pd.concat([f for f in frames if not f.empty])[\n",
+ " [\"point_id\", \"epoch\", \"geometry\", \"glint_angle\"]\n",
+ " ]\n",
"\n",
"\n",
"def scan_angle(frame, instrument_by_day):\n",
@@ -1139,16 +1375,25 @@
" for row in frame.itertuples():\n",
" track = orbit.get_orbit_track(row.epoch.to_pydatetime())\n",
" instrument = instrument_by_day[row.epoch.strftime(\"%Y-%m-%d\")]\n",
- " roll, _ = compute_view_angles(track, wgs84.latlon(row.geometry.y, row.geometry.x), tilt_angle=instrument.get_pitch_angle(track))\n",
+ " roll, _ = compute_view_angles(\n",
+ " track,\n",
+ " wgs84.latlon(row.geometry.y, row.geometry.x),\n",
+ " tilt_angle=instrument.get_pitch_angle(track),\n",
+ " )\n",
" angles.append(-float(roll))\n",
" return angles\n",
"\n",
+ "\n",
"rows4, matched4, found4 = {}, {}, {}\n",
"for label, frame in predicted.items():\n",
" merged = pd.merge_asof(\n",
" reference.sort_values(\"time\"),\n",
" frame.rename(columns={\"epoch\": \"predicted\"}).sort_values(\"predicted\"),\n",
- " left_on=\"time\", right_on=\"predicted\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=3),\n",
+ " left_on=\"time\",\n",
+ " right_on=\"predicted\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=3),\n",
" )\n",
" merged[\"difference (s)\"] = (merged.predicted - merged.time).dt.total_seconds()\n",
" matched4[label] = merged\n",
@@ -1157,22 +1402,40 @@
" inside = np.zeros(len(frame), bool)\n",
" for start, end in collection.itertuples(index=False):\n",
" inside |= (frame.epoch >= start).to_numpy() & (frame.epoch <= end).to_numpy()\n",
- " candidates = frame[inside & np.array([np.min(np.abs((flip_times - t).total_seconds())) > 30 for t in frame.epoch])].copy()\n",
- " candidates[\"scan angle (deg)\"] = scan_angle(candidates, dict(zip(days, [instrument for instrument, _ in models[label]])))\n",
+ " candidates = frame[\n",
+ " inside\n",
+ " & np.array(\n",
+ " [np.min(np.abs((flip_times - t).total_seconds())) > 30 for t in frame.epoch]\n",
+ " )\n",
+ " ].copy()\n",
+ " candidates[\"scan angle (deg)\"] = scan_angle(\n",
+ " candidates, dict(zip(days, [instrument for instrument, _ in models[label]]))\n",
+ " )\n",
" candidates = candidates[candidates[\"scan angle (deg)\"].abs() < 55.5]\n",
" found = pd.merge_asof(\n",
- " candidates.sort_values(\"epoch\"), reference[[\"point_id\", \"time\"]].sort_values(\"time\"),\n",
- " left_on=\"epoch\", right_on=\"time\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=3),\n",
+ " candidates.sort_values(\"epoch\"),\n",
+ " reference[[\"point_id\", \"time\"]].sort_values(\"time\"),\n",
+ " left_on=\"epoch\",\n",
+ " right_on=\"time\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=3),\n",
" )\n",
" found4[label] = found\n",
" rows4[label] = {\n",
" \"OCI observations\": len(merged),\n",
" \"matched (%)\": 100 * merged.predicted.notna().mean(),\n",
" \"time difference, median (s)\": merged[\"difference (s)\"].abs().median(),\n",
- " \"time difference, 95th percentile (s)\": merged[\"difference (s)\"].abs().quantile(0.95),\n",
+ " \"time difference, 95th percentile (s)\": merged[\"difference (s)\"]\n",
+ " .abs()\n",
+ " .quantile(0.95),\n",
" \"time difference, max (s)\": merged[\"difference (s)\"].abs().max(),\n",
" \"within 2 min of a tilt change: observations\": int(near.sum()),\n",
- " \"within 2 min of a tilt change: time difference, max (s)\": merged.loc[near, \"difference (s)\"].abs().max(),\n",
+ " \"within 2 min of a tilt change: time difference, max (s)\": merged.loc[\n",
+ " near, \"difference (s)\"\n",
+ " ]\n",
+ " .abs()\n",
+ " .max(),\n",
" \"TAT-C observations in OCI's collection and swath\": len(found),\n",
" \"found in OCI data (%)\": 100 * found.time.notna().mean(),\n",
" }\n",
@@ -1185,10 +1448,10 @@
"id": "baedbc76",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:13:46.651291Z",
- "iopub.status.busy": "2026-10-08T18:13:46.651291Z",
- "iopub.status.idle": "2026-10-08T18:13:46.704248Z",
- "shell.execute_reply": "2026-10-08T18:13:46.703240Z"
+ "iopub.execute_input": "2026-10-25T18:37:34.425829Z",
+ "iopub.status.busy": "2026-10-25T18:37:34.425582Z",
+ "iopub.status.idle": "2026-10-25T18:37:34.442400Z",
+ "shell.execute_reply": "2026-10-25T18:37:34.441491Z"
}
},
"outputs": [
@@ -1286,8 +1549,12 @@
"source": [
"found = found4[\"pitch profile, per day\"]\n",
"found[\"day\"] = found.epoch.dt.strftime(\"%Y-%m-%d\")\n",
- "found[\"scan angle bin (deg)\"] = pd.cut(found[\"scan angle (deg)\"].abs(), [0, 20, 40, 50, 55.5])\n",
- "found.groupby([\"day\", \"scan angle bin (deg)\"], observed=True).time.agg(predicted=\"size\", found=lambda x: 100 * x.notna().mean()).rename(columns={\"found\": \"found in OCI data (%)\"}).round(2).unstack(0)"
+ "found[\"scan angle bin (deg)\"] = pd.cut(\n",
+ " found[\"scan angle (deg)\"].abs(), [0, 20, 40, 50, 55.5]\n",
+ ")\n",
+ "found.groupby([\"day\", \"scan angle bin (deg)\"], observed=True).time.agg(\n",
+ " predicted=\"size\", found=lambda x: 100 * x.notna().mean()\n",
+ ").rename(columns={\"found\": \"found in OCI data (%)\"}).round(2).unstack(0)"
]
},
{
@@ -1296,16 +1563,16 @@
"id": "8710dde7",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:13:46.706259Z",
- "iopub.status.busy": "2026-10-08T18:13:46.706259Z",
- "iopub.status.idle": "2026-10-08T18:13:46.903313Z",
- "shell.execute_reply": "2026-10-08T18:13:46.902301Z"
+ "iopub.execute_input": "2026-10-25T18:37:34.444081Z",
+ "iopub.status.busy": "2026-10-25T18:37:34.443905Z",
+ "iopub.status.idle": "2026-10-25T18:37:34.601139Z",
+ "shell.execute_reply": "2026-10-25T18:37:34.600278Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"
"
]
@@ -1317,8 +1584,19 @@
"source": [
"fig, ax = plt.subplots(figsize=(12, 3.8))\n",
"for (label, merged), color in zip(matched4.items(), [BLUE, PURPLE, ORANGE]):\n",
- " ax.scatter(merged.latitude, merged[\"difference (s)\"], s=3, color=color, label=label, alpha=0.6)\n",
- "ax.set(xlabel=\"Point latitude (deg)\", ylabel=\"TAT-C minus OCI observation time (s)\", title=\"Observation time differences\")\n",
+ " ax.scatter(\n",
+ " merged.latitude,\n",
+ " merged[\"difference (s)\"],\n",
+ " s=3,\n",
+ " color=color,\n",
+ " label=label,\n",
+ " alpha=0.6,\n",
+ " )\n",
+ "ax.set(\n",
+ " xlabel=\"Point latitude (deg)\",\n",
+ " ylabel=\"TAT-C minus OCI observation time (s)\",\n",
+ " title=\"Observation time differences\",\n",
+ ")\n",
"ax.legend(frameon=False, markerscale=4)\n",
"plt.show()"
]
@@ -1330,6 +1608,608 @@
"source": [
"With each day's pitch angle profile, `collect_observations` reproduces OCI's observations: all 7,533 OCI observations of the points are matched, within 0.15 s (median) and 0.95 s (maximum), including the 618 within 2 minutes of a tilt change (within 0.63 s), and all 7,062 predicted observations within OCI's data collection and swath are found in its data. Without the tilt, the predicted observations are 38 s late (in the northern part of the orbit) or early (in the southern part), the time the satellite takes to travel the 260 km between the nadir and the tilted scan line, and 9% of OCI's observations are missed, as the untilted scan reaches less far across track (56.5 deg from nadir at its edges, instead of 58.8 deg for the tilted scan). With the first day's profile on both days, the observations between the two days' tilt changes, about 700 km of ground track per orbit on the second day, are predicted with the wrong tilt, up to 78 s from OCI's.\n",
"\n",
+ "## Test 5: Glint Avoidance\n",
+ "\n",
+ "The glint angle at a point is the angle between the direction toward the satellite and the direction of the Sun's specular reflection at the point (`compute_glint_angle`); it is zero at the center of the sun glint (the specular point, `compute_specular_point`), and ocean color observations are contaminated by glint at angles below about 20 to 30 deg. The glint angles at OCI's sampled pixels (every 100th scan and every 16th pixel, excluding tilt changes) are computed from the spacecraft's reported positions and compared with those at the pixels of the same scans cast by TAT-C (as in Test 2) with no tilt and with the opposite tilt. For each scan, the minimum glint angle across the scan measures how close it comes to the glint."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "86b3a263",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-25T18:37:34.602985Z",
+ "iopub.status.busy": "2026-10-25T18:37:34.602805Z",
+ "iopub.status.idle": "2026-10-25T18:38:17.818511Z",
+ "shell.execute_reply": "2026-10-25T18:38:17.817525Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_43032\\3749224625.py:30: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+ " return timescale.from_datetimes([t.to_pydatetime() for t in pd.DatetimeIndex(values)])\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4868 day-side scans\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
matched: TAT-C minus OCI glint angle, median (deg)
\n",
+ "
matched: TAT-C minus OCI glint angle, max (deg)
\n",
+ "
TAT-C observations in OCI's collection and swath
\n",
+ "
glint angle, lowest (deg)
\n",
+ "
glint angle below 20 deg (%)
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
pitch profile, per day
\n",
+ "
20.79
\n",
+ "
0.05
\n",
+ "
0.26
\n",
+ "
7062.0
\n",
+ "
23.00
\n",
+ "
0.00
\n",
+ "
\n",
+ "
\n",
+ "
pitch profile, first day
\n",
+ "
20.79
\n",
+ "
0.05
\n",
+ "
19.59
\n",
+ "
7152.0
\n",
+ "
3.40
\n",
+ "
0.18
\n",
+ "
\n",
+ "
\n",
+ "
no tilt
\n",
+ "
20.79
\n",
+ "
13.98
\n",
+ "
21.68
\n",
+ "
6404.0
\n",
+ "
1.92
\n",
+ "
3.00
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " OCI observations: glint angle, lowest (deg) \\\n",
+ "pitch profile, per day 20.79 \n",
+ "pitch profile, first day 20.79 \n",
+ "no tilt 20.79 \n",
+ "\n",
+ " matched: TAT-C minus OCI glint angle, median (deg) \\\n",
+ "pitch profile, per day 0.05 \n",
+ "pitch profile, first day 0.05 \n",
+ "no tilt 13.98 \n",
+ "\n",
+ " matched: TAT-C minus OCI glint angle, max (deg) \\\n",
+ "pitch profile, per day 0.26 \n",
+ "pitch profile, first day 19.59 \n",
+ "no tilt 21.68 \n",
+ "\n",
+ " TAT-C observations in OCI's collection and swath \\\n",
+ "pitch profile, per day 7062.0 \n",
+ "pitch profile, first day 7152.0 \n",
+ "no tilt 6404.0 \n",
+ "\n",
+ " glint angle, lowest (deg) \\\n",
+ "pitch profile, per day 23.00 \n",
+ "pitch profile, first day 3.40 \n",
+ "no tilt 1.92 \n",
+ "\n",
+ " glint angle below 20 deg (%) \n",
+ "pitch profile, per day 0.00 \n",
+ "pitch profile, first day 0.18 \n",
+ "no tilt 3.00 "
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def oci_glint_angle(frame):\n",
+ " \"\"\"Glint angles (deg) at the points of OCI's observations, at OCI's observation times (TLE orbit).\"\"\"\n",
+ " point = {p.id: p for p in points}\n",
+ " targets = wgs84.latlon(\n",
+ " [point[i].latitude for i in frame.point_id],\n",
+ " [point[i].longitude for i in frame.point_id],\n",
+ " )\n",
+ " return compute_glint_angle(\n",
+ " orbit.get_orbit_track(list(frame.time.dt.to_pydatetime())), targets\n",
+ " )\n",
+ "\n",
+ "\n",
+ "oci_glint = oci_glint_angle(reference.sort_values(\"time\"))\n",
+ "rows5c = {}\n",
+ "for label in predicted:\n",
+ " merged, found = matched4[label], found4[label]\n",
+ " difference = merged.glint_angle - oci_glint\n",
+ " rows5c[label] = {\n",
+ " \"OCI observations: glint angle, lowest (deg)\": oci_glint.min(),\n",
+ " \"matched: TAT-C minus OCI glint angle, median (deg)\": difference.abs().median(),\n",
+ " \"matched: TAT-C minus OCI glint angle, max (deg)\": difference.abs().max(),\n",
+ " \"TAT-C observations in OCI's collection and swath\": len(found),\n",
+ " \"glint angle, lowest (deg)\": found.glint_angle.min(),\n",
+ " \"glint angle below 20 deg (%)\": 100 * (found.glint_angle < 20).mean(),\n",
+ " }\n",
+ "pd.DataFrame(rows5c).T.round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b526ad5",
+ "metadata": {},
+ "source": [
+ "OCI's tilt keeps its scans out of the sun glint: no sampled pixel has a glint angle below 20 deg (the lowest is 20.3 deg), and 1.3% are below 30 deg. Without the tilt, the scans would cross the glint (down to 0.4 deg), with 5.2% of the day-side pixels below 20 deg; with the opposite tilt (forward in the south, aft in the north), 11.2% would be. On the ascending day side, the aft tilt keeps away from the glint south of the subsolar region, and the forward tilt north of it. The two tilts come equally close to the glint (23.6 deg) at an argument of latitude of -0.8 deg on 4 October and -1.2 deg on 5 October, about 3.5 deg north of the subsolar latitude (-4.5 and -4.9 deg). On 4 October, OCI's tilt change starts within 0.9 deg of this balance, and its scans come no closer than 22.6 deg to the glint. On 5 October, it starts 5.3 deg earlier (at -6.5 deg), so that the forward tilt comes within 20.3 deg of the glint just after the change. The glint angles thus explain where the tilt changes to within the steps by which the schedule moves between days, but not the steps themselves, so the schedule is still given as a pitch angle profile per day (Test 3). The scans during a tilt change (27.5 s per orbit, excluded here) sweep through the untilted geometry, and so through the glint.\n",
+ "\n",
+ "With each day's pitch angle profile, the glint angles that `collect_observations` reports for its observations (`glint_angle`, which requires `omit_solar=False`) match those of OCI's observations (at OCI's times) within 0.05 deg (median) and 0.26 deg (maximum), and none of its observations within OCI's collection and swath is within 20 deg of the glint (the lowest is 23.0 deg, as is 20.8 deg for OCI's). With the first day's profile, 0.18% are, observed during its tilt changes, which on the second day fall away from OCI's. Without the tilt, 3.0% are, down to 1.9 deg. A minimum glint angle at the target (`min_target_glint_angle=20`) would exclude these observations, but it removes them rather than moving them away from the glint, as OCI's tilt does: a glint angle requirement complements a pitch angle profile rather than replacing it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "51dd0a51",
+ "metadata": {},
+ "source": [
"## Summary\n",
"\n",
"| Test | Result |\n",
@@ -1338,12 +2218,13 @@
"| 2. Scan geometry (`ViewGeometry.SCAN`, pitch tilting the scan plane) | pixels within 0.8 km near nadir and 9 km at the swath edges (median), with the Earth-fixed velocity frame; a constant along-track angle would misplace the edges by 210 km, and the inertial frame by 63 km |\n",
"| 3. Tilt schedule (`pitch_angle_profile`) | one change per orbit on the day side, over 27.5 s, at the same argument of latitude within 0.06 deg each day; the schedule moves between days with the subsolar latitude |\n",
"| 4. Observation times (`collect_observations`) | with a per-day pitch profile, all OCI observations matched, within 0.15 s (median) and 0.95 s (maximum); 38 s off without the tilt; up to 78 s off with the previous day's profile |\n",
+ "| 5. Glint avoidance (`compute_glint_angle`) | with the tilt, no pixel within 20 deg of the glint (5.2% without the tilt, 11.2% with the opposite tilt); the tilts are balanced 3.5 deg north of the subsolar latitude, within 0.9 deg of OCI's tilt change on one day and 5.3 deg on the next; observation glint angles within 0.26 deg |\n",
"\n",
"**Tilted scanners.** A `PointedInstrument` in scan geometry with a pitch angle represents a cross-track scanner whose scan plane tilts fore or aft, such as OCI (and SeaWiFS before it), and a pitch angle profile represents its tilt changes around the orbit.\n",
"\n",
"**Changes to TAT-C.** This validation added a pitch angle profile (`PointedInstrument.pitch_angle_profile`, as a function of argument of latitude, like `roll_angle_profile`) and changed scan geometry so that the pitch angle tilts the scan plane (as `tilt_angle` in `compute_projected_ray_position` and `compute_view_angles`); previously, scan geometry applied the pitch angle after the scan angle, sweeping a cone that misplaced the edges of a tilted scan by 210 km.\n",
"\n",
- "**Limitations.** The tilt schedule changes between days, so analyses longer than a day need a profile per day (or a profile fitted to the period, with errors where the schedule moved). OCI's collection window (from pole to pole on the ascending day side, as observed) is approximated here by a minimum solar elevation of the target."
+ "**Limitations.** The tilt schedule changes between days, in steps that the glint angles do not explain (Test 5), so analyses longer than a day need a profile per day (or a profile fitted to the period, with errors where the schedule moved). OCI's collection window (from pole to pole on the ascending day side, as observed) is approximated here by a minimum solar elevation of the target."
]
}
],
@@ -1363,7 +2244,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.8"
+ "version": "3.14.9"
}
},
"nbformat": 4,
diff --git a/docs/validation/ValidateYawFlipGPM.ipynb b/docs/validation/ValidateYawFlipGPM.ipynb
index 14e8f14a..d290730e 100644
--- a/docs/validation/ValidateYawFlipGPM.ipynb
+++ b/docs/validation/ValidateYawFlipGPM.ipynb
@@ -41,10 +41,10 @@
"id": "bf6575b6",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:20.496788Z",
- "iopub.status.busy": "2026-10-08T18:11:20.496788Z",
- "iopub.status.idle": "2026-10-08T18:11:30.499392Z",
- "shell.execute_reply": "2026-10-08T18:11:30.498382Z"
+ "iopub.execute_input": "2026-10-25T05:22:15.629320Z",
+ "iopub.status.busy": "2026-10-25T05:22:15.629320Z",
+ "iopub.status.idle": "2026-10-25T05:22:25.003626Z",
+ "shell.execute_reply": "2026-10-25T05:22:25.003626Z"
}
},
"outputs": [
@@ -104,13 +104,17 @@
" def __init__(self, url, block_size=2**20, prefetch=1):\n",
" self.block_size, self.blocks, self.position = block_size, {}, 0\n",
" response = self._get(url, 0, prefetch * block_size)\n",
- " self.url, self.size = response.url, int(response.headers[\"Content-Range\"].split(\"/\")[1])\n",
+ " self.url, self.size = response.url, int(\n",
+ " response.headers[\"Content-Range\"].split(\"/\")[1]\n",
+ " )\n",
" self._store(0, response.content)\n",
"\n",
" def _get(self, url, start, end):\n",
" for attempt in range(5):\n",
" try:\n",
- " response = https_session().get(url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300)\n",
+ " response = https_session().get(\n",
+ " url, headers={\"Range\": f\"bytes={start}-{end - 1}\"}, timeout=300\n",
+ " )\n",
" response.raise_for_status()\n",
" return response\n",
" except requests.RequestException:\n",
@@ -131,18 +135,30 @@
" return self.position\n",
"\n",
" def seek(self, offset, whence=io.SEEK_SET):\n",
- " self.position = {io.SEEK_SET: 0, io.SEEK_CUR: self.position, io.SEEK_END: self.size}[whence] + offset\n",
+ " self.position = {\n",
+ " io.SEEK_SET: 0,\n",
+ " io.SEEK_CUR: self.position,\n",
+ " io.SEEK_END: self.size,\n",
+ " }[whence] + offset\n",
" return self.position\n",
"\n",
" def readinto(self, buffer):\n",
" n = min(len(buffer), self.size - self.position)\n",
" if n <= 0:\n",
" return 0\n",
- " first, last = self.position // self.block_size, (self.position + n - 1) // self.block_size\n",
- " missing = [block for block in range(first, last + 1) if block not in self.blocks]\n",
+ " first, last = (\n",
+ " self.position // self.block_size,\n",
+ " (self.position + n - 1) // self.block_size,\n",
+ " )\n",
+ " missing = [\n",
+ " block for block in range(first, last + 1) if block not in self.blocks\n",
+ " ]\n",
" if missing:\n",
" end = min((missing[-1] + 1) * self.block_size, self.size)\n",
- " self._store(missing[0], self._get(self.url, missing[0] * self.block_size, end).content)\n",
+ " self._store(\n",
+ " missing[0],\n",
+ " self._get(self.url, missing[0] * self.block_size, end).content,\n",
+ " )\n",
" data = b\"\".join(self.blocks[block] for block in range(first, last + 1))\n",
" offset = self.position - first * self.block_size\n",
" buffer[:n] = data[offset : offset + n]\n",
@@ -152,7 +168,16 @@
"\n",
"def scan_seconds(group):\n",
" \"\"\"Scan times as seconds since 2000-01-01 (UTC calendar fields).\"\"\"\n",
- " t = pd.to_datetime({\"year\": group[\"Year\"][:], \"month\": group[\"Month\"][:], \"day\": group[\"DayOfMonth\"][:], \"hour\": group[\"Hour\"][:], \"minute\": group[\"Minute\"][:], \"second\": group[\"Second\"][:]}) + pd.to_timedelta(group[\"MilliSecond\"][:], unit=\"ms\")\n",
+ " t = pd.to_datetime(\n",
+ " {\n",
+ " \"year\": group[\"Year\"][:],\n",
+ " \"month\": group[\"Month\"][:],\n",
+ " \"day\": group[\"DayOfMonth\"][:],\n",
+ " \"hour\": group[\"Hour\"][:],\n",
+ " \"minute\": group[\"Minute\"][:],\n",
+ " \"second\": group[\"Second\"][:],\n",
+ " }\n",
+ " ) + pd.to_timedelta(group[\"MilliSecond\"][:], unit=\"ms\")\n",
" return ((t - pd.Timestamp(\"2000-01-01\")).dt.total_seconds()).to_numpy()\n",
"\n",
"\n",
@@ -164,10 +189,17 @@
" g, k = f[\"FS\"], slice(None, None, 5)\n",
" np.savez_compressed(\n",
" path,\n",
- " seconds=scan_seconds(g[\"ScanTime\"])[k], sc_pos=g[\"navigation/scPos\"][:][k], sc_vel=g[\"navigation/scVel\"][:][k],\n",
- " yaw=g[\"navigation/scAttYawGeod\"][:][k], roll=g[\"navigation/scAttRollGeod\"][:][k], pitch=g[\"navigation/scAttPitchGeod\"][:][k],\n",
- " orientation=g[\"scanStatus/SCorientation\"][:][k], latitude=g[\"Latitude\"][:][k], longitude=g[\"Longitude\"][:][k],\n",
- " zenith=g[\"PRE/localZenithAngle\"][:][k], elevation=g[\"PRE/elevation\"][:][k],\n",
+ " seconds=scan_seconds(g[\"ScanTime\"])[k],\n",
+ " sc_pos=g[\"navigation/scPos\"][:][k],\n",
+ " sc_vel=g[\"navigation/scVel\"][:][k],\n",
+ " yaw=g[\"navigation/scAttYawGeod\"][:][k],\n",
+ " roll=g[\"navigation/scAttRollGeod\"][:][k],\n",
+ " pitch=g[\"navigation/scAttPitchGeod\"][:][k],\n",
+ " orientation=g[\"scanStatus/SCorientation\"][:][k],\n",
+ " latitude=g[\"Latitude\"][:][k],\n",
+ " longitude=g[\"Longitude\"][:][k],\n",
+ " zenith=g[\"PRE/localZenithAngle\"][:][k],\n",
+ " elevation=g[\"PRE/elevation\"][:][k],\n",
" )\n",
" return path\n",
"\n",
@@ -180,9 +212,14 @@
" g, k = f[\"S1\"], slice(None, None, 3)\n",
" np.savez_compressed(\n",
" path,\n",
- " seconds=scan_seconds(g[\"ScanTime\"])[k], orientation=g[\"SCstatus/SCorientation\"][:][k],\n",
- " sc_latitude=g[\"SCstatus/SClatitude\"][:][k], sc_longitude=g[\"SCstatus/SClongitude\"][:][k], sc_altitude=g[\"SCstatus/SCaltitude\"][:][k],\n",
- " latitude=g[\"Latitude\"][:][k], longitude=g[\"Longitude\"][:][k], incidence=g[\"incidenceAngle\"][:][k][..., 0],\n",
+ " seconds=scan_seconds(g[\"ScanTime\"])[k],\n",
+ " orientation=g[\"SCstatus/SCorientation\"][:][k],\n",
+ " sc_latitude=g[\"SCstatus/SClatitude\"][:][k],\n",
+ " sc_longitude=g[\"SCstatus/SClongitude\"][:][k],\n",
+ " sc_altitude=g[\"SCstatus/SCaltitude\"][:][k],\n",
+ " latitude=g[\"Latitude\"][:][k],\n",
+ " longitude=g[\"Longitude\"][:][k],\n",
+ " incidence=g[\"incidenceAngle\"][:][k][..., 0],\n",
" )\n",
" return path\n",
"\n",
@@ -193,7 +230,17 @@
" jobs = []\n",
" for short_name, reducer in ((\"GPM_2AKu\", reduce_ku), (\"GPM_1CGPMGMI\", reduce_gmi)):\n",
" results = earthaccess.search_data(short_name=short_name, temporal=(START, END))\n",
- " jobs += [(reducer, next(link[\"URL\"] for link in r[\"umm\"][\"RelatedUrls\"] if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".nc\"))) for r in results]\n",
+ " jobs += [\n",
+ " (\n",
+ " reducer,\n",
+ " next(\n",
+ " link[\"URL\"]\n",
+ " for link in r[\"umm\"][\"RelatedUrls\"]\n",
+ " if link[\"URL\"].startswith(\"https\") and link[\"URL\"].endswith(\".nc\")\n",
+ " ),\n",
+ " )\n",
+ " for r in results\n",
+ " ]\n",
" with concurrent.futures.ThreadPoolExecutor(6) as pool:\n",
" list(pool.map(lambda job: job[0](job[1]), jobs))\n",
"\n",
@@ -210,16 +257,31 @@
"\n",
"\n",
"ku, gmi = load(\"2A.GPM.Ku.*.npz\"), load(\"1C.GPM.GMI.*.npz\")\n",
- "orientation = pd.concat([pd.DataFrame({\"time\": r[\"times\"], \"orientation\": r[\"orientation\"]}) for r in ku.values()]).sort_values(\"time\").reset_index(drop=True)\n",
+ "orientation = (\n",
+ " pd.concat(\n",
+ " [\n",
+ " pd.DataFrame({\"time\": r[\"times\"], \"orientation\": r[\"orientation\"]})\n",
+ " for r in ku.values()\n",
+ " ]\n",
+ " )\n",
+ " .sort_values(\"time\")\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
"missing = int((~orientation.orientation.isin([0, 180])).sum())\n",
"orientation = orientation[orientation.orientation.isin([0, 180])].reset_index(drop=True)\n",
"changes = orientation[orientation.orientation.diff().fillna(0) != 0]\n",
"FLIP = changes.time.iloc[0] if len(changes) else None\n",
"pd.Series(\n",
" {\n",
- " \"radar orbits\": len(ku), \"GMI orbits\": len(gmi),\n",
- " \"first scan\": orientation.time.min(), \"last scan\": orientation.time.max(),\n",
- " \"orientations\": \", \".join(f\"{o} deg ({n} scans)\" for o, n in orientation.orientation.value_counts().sort_index().items()) + f\"; {missing} sampled scans without orientation\",\n",
+ " \"radar orbits\": len(ku),\n",
+ " \"GMI orbits\": len(gmi),\n",
+ " \"first scan\": orientation.time.min(),\n",
+ " \"last scan\": orientation.time.max(),\n",
+ " \"orientations\": \", \".join(\n",
+ " f\"{o} deg ({n} scans)\"\n",
+ " for o, n in orientation.orientation.value_counts().sort_index().items()\n",
+ " )\n",
+ " + f\"; {missing} sampled scans without orientation\",\n",
" \"orientation change\": FLIP,\n",
" }\n",
")"
@@ -239,10 +301,10 @@
"id": "c3bf8bef",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:30.502906Z",
- "iopub.status.busy": "2026-10-08T18:11:30.501898Z",
- "iopub.status.idle": "2026-10-08T18:11:40.774527Z",
- "shell.execute_reply": "2026-10-08T18:11:40.774018Z"
+ "iopub.execute_input": "2026-10-25T05:22:25.005662Z",
+ "iopub.status.busy": "2026-10-25T05:22:25.005662Z",
+ "iopub.status.idle": "2026-10-25T05:22:26.982616Z",
+ "shell.execute_reply": "2026-10-25T05:22:26.982616Z"
}
},
"outputs": [],
@@ -258,21 +320,57 @@
"from tatc.analysis import collect_observations\n",
"from tatc.constants import timescale\n",
"from tatc.generation import generate_points_fibonacci_lattice\n",
- "from tatc.schemas import ConicalInstrument, GeneralPerturbationsOrbit, Point, PointedInstrument, Satellite\n",
- "from tatc.utils import VelocityFrame, ViewGeometry, compute_cone_and_azimuth, compute_view_angles, geodesic_distance\n",
+ "from tatc.schemas import (\n",
+ " Attitude,\n",
+ " AttitudeSchedule,\n",
+ " ConicalInstrument,\n",
+ " GeneralPerturbationsOrbit,\n",
+ " Point,\n",
+ " PointedInstrument,\n",
+ " Satellite,\n",
+ ")\n",
+ "from tatc.utils import (\n",
+ " VelocityFrame,\n",
+ " ViewGeometry,\n",
+ " compute_cone_and_azimuth,\n",
+ " compute_view_angles,\n",
+ " geodesic_distance,\n",
+ ")\n",
"\n",
"orbit = GeneralPerturbationsOrbit.from_tle(TLE)\n",
- "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = \"#2a78d6\", \"#eb6834\", \"#1baf7a\", \"#8a8984\", \"#0b0b0b\", \"#8e5bd8\"\n",
- "plt.rcParams.update({\"axes.spines.top\": False, \"axes.spines.right\": False, \"axes.grid\": True, \"grid.color\": \"#e6e5e0\", \"grid.linewidth\": 0.6})\n",
+ "BLUE, ORANGE, AQUA, GRAY, INK, PURPLE = (\n",
+ " \"#2a78d6\",\n",
+ " \"#eb6834\",\n",
+ " \"#1baf7a\",\n",
+ " \"#8a8984\",\n",
+ " \"#0b0b0b\",\n",
+ " \"#8e5bd8\",\n",
+ ")\n",
+ "plt.rcParams.update(\n",
+ " {\n",
+ " \"axes.spines.top\": False,\n",
+ " \"axes.spines.right\": False,\n",
+ " \"axes.grid\": True,\n",
+ " \"grid.color\": \"#e6e5e0\",\n",
+ " \"grid.linewidth\": 0.6,\n",
+ " }\n",
+ ")\n",
"\n",
"\n",
"def times_of(values):\n",
- " return timescale.from_datetimes([t.to_pydatetime() for t in pd.DatetimeIndex(values)])\n",
+ " return timescale.from_datetimes(\n",
+ " [t.to_pydatetime() for t in pd.DatetimeIndex(values)]\n",
+ " )\n",
"\n",
"\n",
"def ephemeris_track(record, k):\n",
" \"\"\"GPM's reported (Earth-fixed) position and velocity at radar scans k, as an inertial orbit track.\"\"\"\n",
- " return Geocentric.from_time_and_frame_vectors(times_of(record[\"times\"][k]), itrs, Distance(m=record[\"sc_pos\"][k].T.astype(float)), Velocity(km_per_s=record[\"sc_vel\"][k].T.astype(float) / 1e3))"
+ " return Geocentric.from_time_and_frame_vectors(\n",
+ " times_of(record[\"times\"][k]),\n",
+ " itrs,\n",
+ " Distance(m=record[\"sc_pos\"][k].T.astype(float)),\n",
+ " Velocity(km_per_s=record[\"sc_vel\"][k].T.astype(float) / 1e3),\n",
+ " )"
]
},
{
@@ -291,10 +389,10 @@
"id": "c3c59a8d",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:40.778046Z",
- "iopub.status.busy": "2026-10-08T18:11:40.778046Z",
- "iopub.status.idle": "2026-10-08T18:11:45.942646Z",
- "shell.execute_reply": "2026-10-08T18:11:45.942646Z"
+ "iopub.execute_input": "2026-10-25T05:22:26.985623Z",
+ "iopub.status.busy": "2026-10-25T05:22:26.985623Z",
+ "iopub.status.idle": "2026-10-25T05:22:29.072953Z",
+ "shell.execute_reply": "2026-10-25T05:22:29.072953Z"
}
},
"outputs": [
@@ -302,7 +400,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_5676\\741476047.py:28: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_12408\\741476047.py:28: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
" xyz = orbit.get_orbit_track([time.to_pydatetime() + timedelta(seconds=float(s)) for s in seconds]).frame_xyz(itrs).m\n"
]
},
@@ -440,44 +538,98 @@
"records = []\n",
"for record in ku.values():\n",
" k = np.arange(0, len(record[\"seconds\"]), 50)\n",
- " reported, velocity = record[\"sc_pos\"][k].T.astype(float), record[\"sc_vel\"][k].T.astype(float)\n",
- " tle_xyz = orbit.get_orbit_track(list(record[\"times\"][k].to_pydatetime())).frame_xyz(itrs).m\n",
+ " reported, velocity = record[\"sc_pos\"][k].T.astype(float), record[\"sc_vel\"][\n",
+ " k\n",
+ " ].T.astype(float)\n",
+ " tle_xyz = (\n",
+ " orbit.get_orbit_track(list(record[\"times\"][k].to_pydatetime()))\n",
+ " .frame_xyz(itrs)\n",
+ " .m\n",
+ " )\n",
" radial = reported / np.linalg.norm(reported, axis=0)\n",
" cross = np.cross(reported, velocity, axis=0)\n",
" cross /= np.linalg.norm(cross, axis=0)\n",
" along = np.cross(cross, radial, axis=0)\n",
" difference = (tle_xyz - reported) / 1e3\n",
- " records.append(pd.DataFrame({\"time\": record[\"times\"][k], \"along-track (km)\": np.sum(difference * along, axis=0), \"cross-track (km)\": np.sum(difference * cross, axis=0), \"radial (km)\": np.sum(difference * radial, axis=0)}))\n",
+ " records.append(\n",
+ " pd.DataFrame(\n",
+ " {\n",
+ " \"time\": record[\"times\"][k],\n",
+ " \"along-track (km)\": np.sum(difference * along, axis=0),\n",
+ " \"cross-track (km)\": np.sum(difference * cross, axis=0),\n",
+ " \"radial (km)\": np.sum(difference * radial, axis=0),\n",
+ " }\n",
+ " )\n",
+ " )\n",
"errors = pd.concat(records)\n",
"errors[\"day\"] = errors.time.dt.strftime(\"%m-%d\")\n",
- "errors[\"days before TLE epoch\"] = ((pd.Timestamp(orbit.get_epoch()) - errors.time).dt.total_seconds() / 86400).round(0)\n",
+ "errors[\"days before TLE epoch\"] = (\n",
+ " (pd.Timestamp(orbit.get_epoch()) - errors.time).dt.total_seconds() / 86400\n",
+ ").round(0)\n",
"nodes = []\n",
"for record in ku.values():\n",
" z = record[\"sc_pos\"][:, 2]\n",
" for i in np.nonzero((z[:-1] < 0) & (z[1:] >= 0))[0]:\n",
" f = -z[i] / (z[i + 1] - z[i])\n",
" time = record[\"times\"][i] + (record[\"times\"][i + 1] - record[\"times\"][i]) * f\n",
- " x, y = (record[\"sc_pos\"][i, j] + f * (record[\"sc_pos\"][i + 1, j] - record[\"sc_pos\"][i, j]) for j in (0, 1))\n",
- " nodes.append({\"time\": time, \"local time (h)\": ((time - time.normalize()).total_seconds() / 3600 + np.degrees(np.arctan2(y, x)) / 15) % 24})\n",
+ " x, y = (\n",
+ " record[\"sc_pos\"][i, j]\n",
+ " + f * (record[\"sc_pos\"][i + 1, j] - record[\"sc_pos\"][i, j])\n",
+ " for j in (0, 1)\n",
+ " )\n",
+ " nodes.append(\n",
+ " {\n",
+ " \"time\": time,\n",
+ " \"local time (h)\": (\n",
+ " (time - time.normalize()).total_seconds() / 3600\n",
+ " + np.degrees(np.arctan2(y, x)) / 15\n",
+ " )\n",
+ " % 24,\n",
+ " }\n",
+ " )\n",
"nodes = pd.DataFrame(nodes)\n",
"\n",
"\n",
"def tle_node_local_time(time):\n",
" seconds = np.arange(-3000, 3001, 10)\n",
- " xyz = orbit.get_orbit_track([time.to_pydatetime() + timedelta(seconds=float(s)) for s in seconds]).frame_xyz(itrs).m\n",
+ " xyz = (\n",
+ " orbit.get_orbit_track(\n",
+ " [time.to_pydatetime() + timedelta(seconds=float(s)) for s in seconds]\n",
+ " )\n",
+ " .frame_xyz(itrs)\n",
+ " .m\n",
+ " )\n",
" k = np.nonzero((xyz[2, :-1] < 0) & (xyz[2, 1:] >= 0))[0]\n",
" k = k[np.argmin(np.abs(seconds[k]))]\n",
" f = -xyz[2, k] / (xyz[2, k + 1] - xyz[2, k])\n",
" crossing = time + pd.Timedelta(seconds=float(seconds[k] + 10 * f))\n",
- " lon = np.degrees(np.arctan2(xyz[1, k] + f * (xyz[1, k + 1] - xyz[1, k]), xyz[0, k] + f * (xyz[0, k + 1] - xyz[0, k])))\n",
+ " lon = np.degrees(\n",
+ " np.arctan2(\n",
+ " xyz[1, k] + f * (xyz[1, k + 1] - xyz[1, k]),\n",
+ " xyz[0, k] + f * (xyz[0, k + 1] - xyz[0, k]),\n",
+ " )\n",
+ " )\n",
" return ((crossing - crossing.normalize()).total_seconds() / 3600 + lon / 15) % 24\n",
"\n",
"\n",
"nodes[\"TAT-C local time (h)\"] = [tle_node_local_time(t) for t in nodes.time]\n",
- "nodes[\"TAT-C minus reported (min)\"] = 60 * ((nodes[\"TAT-C local time (h)\"] - nodes[\"local time (h)\"] + 12) % 24 - 12)\n",
- "rate = 60 * np.polyfit((nodes.time - nodes.time.iloc[0]).dt.total_seconds() / 86400, np.unwrap(nodes[\"local time (h)\"] * np.pi / 12) * 12 / np.pi, 1)[0]\n",
- "print(f\"ascending node local time: {nodes['local time (h)'].iloc[0]:.2f} h on {nodes.time.iloc[0]:%d %b} to {nodes['local time (h)'].iloc[-1]:.2f} h on {nodes.time.iloc[-1]:%d %b} ({rate:.1f} min/day); TAT-C minus reported within {nodes['TAT-C minus reported (min)'].abs().max():.2f} min\")\n",
- "errors.groupby(\"days before TLE epoch\")[[\"along-track (km)\", \"cross-track (km)\", \"radial (km)\"]].median().round(2).T"
+ "nodes[\"TAT-C minus reported (min)\"] = 60 * (\n",
+ " (nodes[\"TAT-C local time (h)\"] - nodes[\"local time (h)\"] + 12) % 24 - 12\n",
+ ")\n",
+ "rate = (\n",
+ " 60\n",
+ " * np.polyfit(\n",
+ " (nodes.time - nodes.time.iloc[0]).dt.total_seconds() / 86400,\n",
+ " np.unwrap(nodes[\"local time (h)\"] * np.pi / 12) * 12 / np.pi,\n",
+ " 1,\n",
+ " )[0]\n",
+ ")\n",
+ "print(\n",
+ " f\"ascending node local time: {nodes['local time (h)'].iloc[0]:.2f} h on {nodes.time.iloc[0]:%d %b} to {nodes['local time (h)'].iloc[-1]:.2f} h on {nodes.time.iloc[-1]:%d %b} ({rate:.1f} min/day); TAT-C minus reported within {nodes['TAT-C minus reported (min)'].abs().max():.2f} min\"\n",
+ ")\n",
+ "errors.groupby(\"days before TLE epoch\")[\n",
+ " [\"along-track (km)\", \"cross-track (km)\", \"radial (km)\"]\n",
+ "].median().round(2).T"
]
},
{
@@ -500,10 +652,10 @@
"id": "a72efb9d",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:45.945322Z",
- "iopub.status.busy": "2026-10-08T18:11:45.945322Z",
- "iopub.status.idle": "2026-10-08T18:11:53.113263Z",
- "shell.execute_reply": "2026-10-08T18:11:53.113263Z"
+ "iopub.execute_input": "2026-10-25T05:22:29.074956Z",
+ "iopub.status.busy": "2026-10-25T05:22:29.074956Z",
+ "iopub.status.idle": "2026-10-25T05:22:37.749703Z",
+ "shell.execute_reply": "2026-10-25T05:22:37.749703Z"
}
},
"outputs": [
@@ -647,12 +799,45 @@
" for s in range(10, len(record[\"seconds\"]), step):\n",
" track = ephemeris_track(record, [s])[0]\n",
" for frame in VelocityFrame:\n",
- " angles = np.array([compute_view_angles(track, wgs84.latlon(float(record[\"latitude\"][s, j]), float(record[\"longitude\"][s, j])), velocity_frame=frame) for j in range(49)], dtype=float)\n",
- " rows.append({\"orientation\": int(record[\"orientation\"][s]), \"frame\": frame.value, \"beam 1 roll (deg)\": angles[0, 0], \"beam 25 roll (deg)\": angles[24, 0], \"beam 49 roll (deg)\": angles[48, 0], \"pitch, mean (deg)\": angles[:, 1].mean(), \"pitch trend (deg per deg)\": np.polyfit(angles[:, 0], angles[:, 1], 1)[0]})\n",
+ " angles = np.array(\n",
+ " [\n",
+ " compute_view_angles(\n",
+ " track,\n",
+ " wgs84.latlon(\n",
+ " float(record[\"latitude\"][s, j]),\n",
+ " float(record[\"longitude\"][s, j]),\n",
+ " ),\n",
+ " velocity_frame=frame,\n",
+ " )\n",
+ " for j in range(49)\n",
+ " ],\n",
+ " dtype=float,\n",
+ " )\n",
+ " rows.append(\n",
+ " {\n",
+ " \"orientation\": int(record[\"orientation\"][s]),\n",
+ " \"frame\": frame.value,\n",
+ " \"beam 1 roll (deg)\": angles[0, 0],\n",
+ " \"beam 25 roll (deg)\": angles[24, 0],\n",
+ " \"beam 49 roll (deg)\": angles[48, 0],\n",
+ " \"pitch, mean (deg)\": angles[:, 1].mean(),\n",
+ " \"pitch trend (deg per deg)\": np.polyfit(\n",
+ " angles[:, 0], angles[:, 1], 1\n",
+ " )[0],\n",
+ " }\n",
+ " )\n",
" return rows\n",
"\n",
"\n",
- "beams = pd.DataFrame([row for rows in Parallel(n_jobs=-1)(delayed(beam_angles)(r) for r in list(ku.values())[::6]) for row in rows])\n",
+ "beams = pd.DataFrame(\n",
+ " [\n",
+ " row\n",
+ " for rows in Parallel(n_jobs=-1)(\n",
+ " delayed(beam_angles)(r) for r in list(ku.values())[::6]\n",
+ " )\n",
+ " for row in rows\n",
+ " ]\n",
+ ")\n",
"beams = beams[beams.orientation.isin([0, 180])]\n",
"beams.groupby([\"orientation\", \"frame\"]).agg([\"median\", \"std\"]).round(4).T"
]
@@ -664,7 +849,9 @@
"source": [
"The radar's 49 beam positions span -17.05 to +16.95 deg of roll (0.708 deg apart), with the first beam on the right of the direction of motion when GPM flies backward and on the left when it flies forward: the yaw flip reverses the order of the beams. The beams also point 0.15 deg along track toward the spacecraft's aft axis, so behind the satellite when it flies forward and ahead of it when it flies backward. The scans are perpendicular to the inertial velocity (GPM is not yaw-steered): in the inertial frame, the along-track angle of the beams varies across the scan by the same amount at all times (0.017 deg per degree of roll, from the scan's duration), whereas in the Earth-fixed frame its variation changes around the orbit (standard deviation 0.04 deg per degree).\n",
"\n",
- "A radar `PointedInstrument` is defined for each orientation from these values: 49 pixels across track spanning 34.7 deg (49 beams of 0.708 deg), the orientation's pitch angle (0.15 deg forward or aft), the inertial velocity frame, and scan geometry. TAT-C's cross-track pixel index 0 is on the right of the direction of motion, so beam 1 is pixel 0 in one orientation and pixel 48 in the other. The positions of beams 1, 25, and 49 are compared with the reported ones (using the reported orbit), with each orientation's instrument and pixel order, and with the first orientation's throughout."
+ "A radar `PointedInstrument` is defined for each orientation from these values: 49 pixels across track spanning 34.7 deg (49 beams of 0.708 deg), the orientation's pitch angle (0.15 deg forward or aft), the inertial velocity frame, and scan geometry. TAT-C's cross-track pixel index 0 is on the right of the direction of motion, so beam 1 is pixel 0 in one orientation and pixel 48 in the other. The positions of beams 1, 25, and 49 are compared with the reported ones (using the reported orbit), with each orientation's instrument and pixel order, and with the first orientation's throughout.\n",
+ "\n",
+ "With an attitude schedule, GPM is instead a single `Satellite` whose body yaws with its orientation (`Attitude(yaw_angle=180)` while it flies backward, and 0 deg after the flip, in the inertial velocity frame), and the radar is defined once, relative to the body: with the forward orientation's pitch angle and pixel order. The yaw then reverses the pixel order and the sign of the along-track offset while GPM flies backward."
]
},
{
@@ -673,13 +860,21 @@
"id": "d8de734b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:53.116777Z",
- "iopub.status.busy": "2026-10-08T18:11:53.116777Z",
- "iopub.status.idle": "2026-10-08T18:11:54.096614Z",
- "shell.execute_reply": "2026-10-08T18:11:54.096614Z"
+ "iopub.execute_input": "2026-10-25T05:22:37.751709Z",
+ "iopub.status.busy": "2026-10-25T05:22:37.751709Z",
+ "iopub.status.idle": "2026-10-25T05:22:39.310454Z",
+ "shell.execute_reply": "2026-10-25T05:22:39.309450Z"
}
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\pgrogan\\AppData\\Local\\Temp\\ipykernel_12408\\2622748566.py:13: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+ " [orientation.time.iloc[0].to_pydatetime(), FLIP.to_pydatetime()],\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
@@ -723,6 +918,19 @@
" \n",
" \n",
"
\n",
+ "
attitude schedule
\n",
+ "
0
\n",
+ "
3.22
\n",
+ "
0.59
\n",
+ "
1.37
\n",
+ "
\n",
+ "
\n",
+ "
180
\n",
+ "
1.42
\n",
+ "
0.59
\n",
+ "
3.18
\n",
+ "
\n",
+ "
\n",
"
first orientation's instrument
\n",
"
0
\n",
"
270.67
\n",
@@ -755,6 +963,8 @@
"text/plain": [
"beam 1 25 49\n",
"model orientation \n",
+ "attitude schedule 0 3.22 0.59 1.37\n",
+ " 180 1.42 0.59 3.18\n",
"first orientation's instrument 0 270.67 1.94 269.74\n",
" 180 1.42 0.59 3.18\n",
"orientation's instrument 0 3.22 0.59 1.37\n",
@@ -767,13 +977,54 @@
}
],
"source": [
- "inertial = beams[beams.frame == \"inertial\"].groupby(\"orientation\").median(numeric_only=True)\n",
+ "inertial = (\n",
+ " beams[beams.frame == \"inertial\"].groupby(\"orientation\").median(numeric_only=True)\n",
+ ")\n",
"radar = {\n",
- " o: PointedInstrument(name=\"Ku\", field_of_regard=36, cross_track_field_of_view=49 * 0.708, along_track_field_of_view=0.7, pitch_angle=round(float(inertial.loc[o, \"pitch, mean (deg)\"]), 2), cross_track_pixels=49, is_rectangular=True, view_geometry=ViewGeometry.SCAN, velocity_frame=VelocityFrame.INERTIAL)\n",
+ " o: PointedInstrument(\n",
+ " name=\"Ku\",\n",
+ " field_of_regard=36,\n",
+ " cross_track_field_of_view=49 * 0.708,\n",
+ " along_track_field_of_view=0.7,\n",
+ " pitch_angle=round(float(inertial.loc[o, \"pitch, mean (deg)\"]), 2),\n",
+ " cross_track_pixels=49,\n",
+ " is_rectangular=True,\n",
+ " view_geometry=ViewGeometry.SCAN,\n",
+ " velocity_frame=VelocityFrame.INERTIAL,\n",
+ " )\n",
" for o in (0, 180)\n",
"}\n",
"first_orientation = int(orientation.orientation.iloc[0])\n",
- "beam1_pixel = {o: (0 if inertial.loc[o, \"beam 1 roll (deg)\"] < 0 else 48) for o in (0, 180)}\n",
+ "beam1_pixel = {\n",
+ " o: (0 if inertial.loc[o, \"beam 1 roll (deg)\"] < 0 else 48) for o in (0, 180)\n",
+ "}\n",
+ "\n",
+ "# with an attitude schedule, GPM is one satellite whose body yaws with its orientation (180 deg: flying backward),\n",
+ "# and the radar is defined once, relative to the body (as in the forward orientation, 0 deg)\n",
+ "orientations = [\n",
+ " int(orientation[orientation.time < FLIP].orientation.mode()[0]),\n",
+ " int(orientation[orientation.time >= FLIP].orientation.mode()[0]),\n",
+ "]\n",
+ "schedule = AttitudeSchedule.from_attitudes(\n",
+ " [orientation.time.iloc[0].to_pydatetime(), FLIP.to_pydatetime()],\n",
+ " [\n",
+ " Attitude(yaw_angle=o, velocity_frame=VelocityFrame.INERTIAL)\n",
+ " for o in orientations\n",
+ " ],\n",
+ ")\n",
+ "body_radar = PointedInstrument(\n",
+ " name=\"Ku\",\n",
+ " field_of_regard=36,\n",
+ " cross_track_field_of_view=49 * 0.708,\n",
+ " along_track_field_of_view=0.7,\n",
+ " pitch_angle=radar[0].pitch_angle,\n",
+ " cross_track_pixels=49,\n",
+ " is_rectangular=True,\n",
+ " view_geometry=ViewGeometry.SCAN,\n",
+ ")\n",
+ "scheduled_radar = Satellite(\n",
+ " name=\"GPM\", orbit=orbit, instruments=[body_radar], attitude=schedule\n",
+ ").get_instrument(0)\n",
"\n",
"\n",
"def beam_errors(record, step=40):\n",
@@ -784,17 +1035,55 @@
" if o not in (0, 180):\n",
" continue\n",
" for beam in (1, 25, 49):\n",
- " lat, lon = float(record[\"latitude\"][s, beam - 1]), float(record[\"longitude\"][s, beam - 1])\n",
- " for label, model in ((\"orientation's instrument\", o), (\"first orientation's instrument\", first_orientation)):\n",
- " pixel = (beam - 1) if beam1_pixel[model] == 0 else (49 - beam)\n",
- " position = radar[model].compute_projected_pixel_position(track, pixel, 0)\n",
- " rows.append({\"orientation\": o, \"beam\": beam, \"model\": label, \"distance (km)\": geodesic_distance(lon, lat, position.longitude.degrees, position.latitude.degrees) / 1e3})\n",
+ " lat, lon = float(record[\"latitude\"][s, beam - 1]), float(\n",
+ " record[\"longitude\"][s, beam - 1]\n",
+ " )\n",
+ " for label, instrument, order in (\n",
+ " (\"orientation's instrument\", radar[o], o),\n",
+ " (\n",
+ " \"first orientation's instrument\",\n",
+ " radar[first_orientation],\n",
+ " first_orientation,\n",
+ " ),\n",
+ " (\"attitude schedule\", scheduled_radar, 0),\n",
+ " ):\n",
+ " pixel = (beam - 1) if beam1_pixel[order] == 0 else (49 - beam)\n",
+ " position = instrument.compute_projected_pixel_position(track, pixel, 0)\n",
+ " rows.append(\n",
+ " {\n",
+ " \"orientation\": o,\n",
+ " \"beam\": beam,\n",
+ " \"model\": label,\n",
+ " \"distance (km)\": geodesic_distance(\n",
+ " lon,\n",
+ " lat,\n",
+ " position.longitude.degrees,\n",
+ " position.latitude.degrees,\n",
+ " )\n",
+ " / 1e3,\n",
+ " }\n",
+ " )\n",
" return rows\n",
"\n",
"\n",
- "beam_table = pd.DataFrame([row for rows in Parallel(n_jobs=-1)(delayed(beam_errors)(r) for r in list(ku.values())[::3]) for row in rows])\n",
- "print(\"beam 1 is TAT-C pixel\", beam1_pixel, \"; pitch angles (deg):\", {o: r.pitch_angle for o, r in radar.items()})\n",
- "beam_table.groupby([\"model\", \"orientation\", \"beam\"])[\"distance (km)\"].median().unstack(\"beam\").round(2)"
+ "beam_table = pd.DataFrame(\n",
+ " [\n",
+ " row\n",
+ " for rows in Parallel(n_jobs=-1)(\n",
+ " delayed(beam_errors)(r) for r in list(ku.values())[::3]\n",
+ " )\n",
+ " for row in rows\n",
+ " ]\n",
+ ")\n",
+ "print(\n",
+ " \"beam 1 is TAT-C pixel\",\n",
+ " beam1_pixel,\n",
+ " \"; pitch angles (deg):\",\n",
+ " {o: r.pitch_angle for o, r in radar.items()},\n",
+ ")\n",
+ "beam_table.groupby([\"model\", \"orientation\", \"beam\"])[\"distance (km)\"].median().unstack(\n",
+ " \"beam\"\n",
+ ").round(2)"
]
},
{
@@ -802,7 +1091,7 @@
"id": "e99a318c",
"metadata": {},
"source": [
- "With each orientation's instrument and pixel order, the beam positions are within 0.6 km (median) at nadir and 1.4 to 3.2 km at the swath edges (largest for the beam scanned first). With the first orientation's instrument after the flip, the beams at the edges are 270 km away, on the other side of the swath; nadir positions are unaffected. Since the swath is symmetric, the radar's coverage does not depend on the orientation, but its pixel order does.\n",
+ "With each orientation's instrument and pixel order, the beam positions are within 0.6 km (median) at nadir and 1.4 to 3.2 km at the swath edges (largest for the beam scanned first). With the first orientation's instrument after the flip, the beams at the edges are 270 km away, on the other side of the swath; nadir positions are unaffected. Since the swath is symmetric, the radar's coverage does not depend on the orientation, but its pixel order does. The radar defined once relative to the body, with the attitude schedule, gives the same beam positions as each orientation's instrument, in both orientations.\n",
"\n",
"## Test 3: GMI Scan Geometry\n",
"\n",
@@ -815,10 +1104,10 @@
"id": "abc89e41",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:11:54.099364Z",
- "iopub.status.busy": "2026-10-08T18:11:54.097622Z",
- "iopub.status.idle": "2026-10-08T18:15:17.227284Z",
- "shell.execute_reply": "2026-10-08T18:15:17.227284Z"
+ "iopub.execute_input": "2026-10-25T05:22:39.311740Z",
+ "iopub.status.busy": "2026-10-25T05:22:39.311740Z",
+ "iopub.status.idle": "2026-10-25T05:25:36.381245Z",
+ "shell.execute_reply": "2026-10-25T05:25:36.380739Z"
}
},
"outputs": [
@@ -901,17 +1190,52 @@
" i = np.searchsorted(k[\"seconds\"], g[\"seconds\"][s])\n",
" if i == 0 or i >= len(k[\"seconds\"]):\n",
" continue\n",
- " f = (g[\"seconds\"][s] - k[\"seconds\"][i - 1]) / (k[\"seconds\"][i] - k[\"seconds\"][i - 1])\n",
+ " f = (g[\"seconds\"][s] - k[\"seconds\"][i - 1]) / (\n",
+ " k[\"seconds\"][i] - k[\"seconds\"][i - 1]\n",
+ " )\n",
" pos = (1 - f) * k[\"sc_pos\"][i - 1] + f * k[\"sc_pos\"][i]\n",
" vel = (1 - f) * k[\"sc_vel\"][i - 1] + f * k[\"sc_vel\"][i]\n",
- " track = Geocentric.from_time_and_frame_vectors(times_of([g[\"times\"][s]])[0], itrs, Distance(m=pos.astype(float)), Velocity(km_per_s=vel.astype(float) / 1e3))\n",
- " angles = np.array([compute_cone_and_azimuth(track, wgs84.latlon(float(g[\"latitude\"][s, j]), float(g[\"longitude\"][s, j])), VelocityFrame.INERTIAL) for j in range(0, 221, 10)], dtype=float)\n",
+ " track = Geocentric.from_time_and_frame_vectors(\n",
+ " times_of([g[\"times\"][s]])[0],\n",
+ " itrs,\n",
+ " Distance(m=pos.astype(float)),\n",
+ " Velocity(km_per_s=vel.astype(float) / 1e3),\n",
+ " )\n",
+ " angles = np.array(\n",
+ " [\n",
+ " compute_cone_and_azimuth(\n",
+ " track,\n",
+ " wgs84.latlon(\n",
+ " float(g[\"latitude\"][s, j]), float(g[\"longitude\"][s, j])\n",
+ " ),\n",
+ " VelocityFrame.INERTIAL,\n",
+ " )\n",
+ " for j in range(0, 221, 10)\n",
+ " ],\n",
+ " dtype=float,\n",
+ " )\n",
" azimuth = np.degrees(np.unwrap(np.radians(angles[:, 1])))\n",
- " rows.append({\"orientation\": int(g[\"orientation\"][s]), \"cone angle (deg)\": np.median(angles[:, 0]), \"first pixel azimuth (deg)\": azimuth[0], \"last pixel azimuth (deg)\": azimuth[-1], \"sector center (deg)\": (((azimuth[0] + azimuth[-1]) / 2 + 180) % 360) - 180, \"sector half width (deg)\": abs(azimuth[-1] - azimuth[0]) / 2})\n",
+ " rows.append(\n",
+ " {\n",
+ " \"orientation\": int(g[\"orientation\"][s]),\n",
+ " \"cone angle (deg)\": np.median(angles[:, 0]),\n",
+ " \"first pixel azimuth (deg)\": azimuth[0],\n",
+ " \"last pixel azimuth (deg)\": azimuth[-1],\n",
+ " \"sector center (deg)\": (((azimuth[0] + azimuth[-1]) / 2 + 180) % 360)\n",
+ " - 180,\n",
+ " \"sector half width (deg)\": abs(azimuth[-1] - azimuth[0]) / 2,\n",
+ " }\n",
+ " )\n",
" return rows\n",
"\n",
"\n",
- "gmi_table = pd.DataFrame([row for rows in Parallel(n_jobs=-1)(delayed(gmi_angles)(n) for n in list(gmi)[::4]) for row in rows])\n",
+ "gmi_table = pd.DataFrame(\n",
+ " [\n",
+ " row\n",
+ " for rows in Parallel(n_jobs=-1)(delayed(gmi_angles)(n) for n in list(gmi)[::4])\n",
+ " for row in rows\n",
+ " ]\n",
+ ")\n",
"gmi_table = gmi_table[gmi_table.orientation.isin([0, 180])]\n",
"sector = gmi_table.groupby(\"orientation\").median()\n",
"sector.round(2).T"
@@ -922,13 +1246,13 @@
"id": "378a942a",
"metadata": {},
"source": [
- "GMI's cone angle is 48.3 to 48.6 deg in both orientations, and its scan sector spans about 76 deg on either side of the spacecraft's forward axis: centered at -0.3 deg (ahead of the satellite) when GPM flies forward and at -179.7 deg (behind it) when it flies backward. A `ConicalInstrument` represents each orientation with its scan center azimuth; the yaw flip changes the azimuth by 180 deg.\n",
+ "GMI's cone angle is 48.3 to 48.6 deg in both orientations, and its scan sector spans about 76 deg on either side of the spacecraft's forward axis: centered at -0.3 deg (ahead of the satellite) when GPM flies forward and at -179.7 deg (behind it) when it flies backward. A `ConicalInstrument` represents each orientation with its scan center azimuth; the yaw flip changes the azimuth by 180 deg. With the attitude schedule, GMI is instead defined once, relative to the body, with the forward orientation's scan sector, which the body's yaw turns behind the satellite while GPM flies backward.\n",
"\n",
"## Test 4: Observation Times\n",
"\n",
"The points of interest are those of a Fibonacci lattice with a typical spacing of 500 km, within 65 deg of the equator. For each point and pass, the radar's and GMI's observation times are the times of their scans through the point, interpolated between sampled scans from the nearest sampled pixel (within 15 km for the radar and 30 km for GMI, excluding the outermost pixels), over the three days before and after the yaw flip.\n",
"\n",
- "`collect_observations` predicts the observations of the radar `PointedInstrument` of each orientation (analyzed separately before and after the flip) and of a GMI `ConicalInstrument` (with the cone angle and scan sector of Test 3) with the TLE orbit. Two GMI models are compared: with the scan sector of each orientation (analyzed separately before and after the flip), and with the forward sector throughout. Since the TLE's along-track error shifts all predicted times of a pass equally (Test 1), GMI's observation time is also compared relative to the radar's on the same pass."
+ "`collect_observations` predicts the observations of the radar `PointedInstrument` of each orientation (analyzed separately before and after the flip) and of a GMI `ConicalInstrument` (with the cone angle and scan sector of Test 3) with the TLE orbit. Two GMI models are compared: with the scan sector of each orientation (analyzed separately before and after the flip), and with the forward sector throughout. With the attitude schedule, the radar and GMI of the single satellite (Tests 2 and 3) are each analyzed once, across the flip. Since the TLE's along-track error shifts all predicted times of a pass equally (Test 1), GMI's observation time is also compared relative to the radar's on the same pass."
]
},
{
@@ -937,10 +1261,10 @@
"id": "d3df78aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:15:17.229299Z",
- "iopub.status.busy": "2026-10-08T18:15:17.229299Z",
- "iopub.status.idle": "2026-10-08T18:16:25.884176Z",
- "shell.execute_reply": "2026-10-08T18:16:25.884176Z"
+ "iopub.execute_input": "2026-10-25T05:25:36.383255Z",
+ "iopub.status.busy": "2026-10-25T05:25:36.383255Z",
+ "iopub.status.idle": "2026-10-25T05:26:56.926586Z",
+ "shell.execute_reply": "2026-10-25T05:26:56.925580Z"
}
},
"outputs": [
@@ -977,15 +1301,30 @@
"
radar
\n",
"
before flip
\n",
"
2521
\n",
- "
96.231654
\n",
- "
13.595436
\n",
+ "
96.429988
\n",
+ "
13.585023
\n",
"
9.5 to 18.6
\n",
"
\n",
"
\n",
"
after flip
\n",
"
2539
\n",
- "
96.770382
\n",
- "
5.420916
\n",
+ "
96.809768
\n",
+ "
5.420904
\n",
+ "
2.4 to 8.7
\n",
+ "
\n",
+ "
\n",
+ "
radar, attitude schedule
\n",
+ "
before flip
\n",
+ "
2521
\n",
+ "
96.429988
\n",
+ "
13.585023
\n",
+ "
9.5 to 18.6
\n",
+ "
\n",
+ "
\n",
+ "
after flip
\n",
+ "
2539
\n",
+ "
96.809768
\n",
+ "
5.420904
\n",
"
2.4 to 8.7
\n",
"
\n",
"
\n",
@@ -999,23 +1338,38 @@
"
\n",
"
after flip
\n",
"
9163
\n",
- "
95.307214
\n",
- "
5.886651
\n",
+ "
95.427262
\n",
+ "
5.884612
\n",
"
2.5 to 9.6
\n",
"
\n",
"
\n",
"
GMI, forward sector
\n",
"
before flip
\n",
"
9158
\n",
- "
95.217296
\n",
- "
-117.309244
\n",
- "
-137.8 to -44.1
\n",
+ "
95.315571
\n",
+ "
-117.318934
\n",
+ "
-137.8 to -44.2
\n",
+ "
\n",
+ "
\n",
+ "
after flip
\n",
+ "
9163
\n",
+ "
95.427262
\n",
+ "
5.884612
\n",
+ "
2.5 to 9.6
\n",
+ "
\n",
+ "
\n",
+ "
GMI, attitude schedule
\n",
+ "
before flip
\n",
+ "
9158
\n",
+ "
95.413846
\n",
+ "
14.021261
\n",
+ "
9.5 to 19.5
\n",
"
\n",
"
\n",
"
after flip
\n",
"
9163
\n",
- "
95.307214
\n",
- "
5.886651
\n",
+ "
95.427262
\n",
+ "
5.884612
\n",
"
2.5 to 9.6
\n",
"
\n",
" \n",
@@ -1024,27 +1378,39 @@
],
"text/plain": [
" observations matched (%) \\\n",
- "radar before flip 2521 96.231654 \n",
- " after flip 2539 96.770382 \n",
+ "radar before flip 2521 96.429988 \n",
+ " after flip 2539 96.809768 \n",
+ "radar, attitude schedule before flip 2521 96.429988 \n",
+ " after flip 2539 96.809768 \n",
"GMI, sector by orientation before flip 9158 95.370168 \n",
- " after flip 9163 95.307214 \n",
- "GMI, forward sector before flip 9158 95.217296 \n",
- " after flip 9163 95.307214 \n",
+ " after flip 9163 95.427262 \n",
+ "GMI, forward sector before flip 9158 95.315571 \n",
+ " after flip 9163 95.427262 \n",
+ "GMI, attitude schedule before flip 9158 95.413846 \n",
+ " after flip 9163 95.427262 \n",
"\n",
" time difference, median (s) \\\n",
- "radar before flip 13.595436 \n",
- " after flip 5.420916 \n",
+ "radar before flip 13.585023 \n",
+ " after flip 5.420904 \n",
+ "radar, attitude schedule before flip 13.585023 \n",
+ " after flip 5.420904 \n",
"GMI, sector by orientation before flip 14.028344 \n",
- " after flip 5.886651 \n",
- "GMI, forward sector before flip -117.309244 \n",
- " after flip 5.886651 \n",
+ " after flip 5.884612 \n",
+ "GMI, forward sector before flip -117.318934 \n",
+ " after flip 5.884612 \n",
+ "GMI, attitude schedule before flip 14.021261 \n",
+ " after flip 5.884612 \n",
"\n",
" time difference, 5th to 95th percentile (s) \n",
"radar before flip 9.5 to 18.6 \n",
" after flip 2.4 to 8.7 \n",
+ "radar, attitude schedule before flip 9.5 to 18.6 \n",
+ " after flip 2.4 to 8.7 \n",
"GMI, sector by orientation before flip 9.5 to 19.5 \n",
" after flip 2.5 to 9.6 \n",
- "GMI, forward sector before flip -137.8 to -44.1 \n",
+ "GMI, forward sector before flip -137.8 to -44.2 \n",
+ " after flip 2.5 to 9.6 \n",
+ "GMI, attitude schedule before flip 9.5 to 19.5 \n",
" after flip 2.5 to 9.6 "
]
},
@@ -1056,7 +1422,11 @@
"source": [
"WINDOW = (FLIP - pd.Timedelta(days=3), FLIP + pd.Timedelta(days=3))\n",
"lattice = generate_points_fibonacci_lattice(500e3)\n",
- "points = [Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x) for i, r in enumerate(lattice.itertuples()) if abs(r.geometry.y) < 65]\n",
+ "points = [\n",
+ " Point(id=i, latitude=r.geometry.y, longitude=r.geometry.x)\n",
+ " for i, r in enumerate(lattice.itertuples())\n",
+ " if abs(r.geometry.y) < 65\n",
+ "]\n",
"\n",
"\n",
"def reference_times(records, radius, sample):\n",
@@ -1064,10 +1434,18 @@
" for number, r in records.items():\n",
" if r[\"times\"][-1] < WINDOW[0] or r[\"times\"][0] > WINDOW[1]:\n",
" continue\n",
- " rows, cols = np.nonzero(np.isfinite(r[\"latitude\"]) & (np.abs(r[\"latitude\"]) <= 90))\n",
- " cols_ok = (cols > 0) & (cols < r[\"latitude\"].shape[1] - 1) & (cols % sample == 0)\n",
+ " rows, cols = np.nonzero(\n",
+ " np.isfinite(r[\"latitude\"]) & (np.abs(r[\"latitude\"]) <= 90)\n",
+ " )\n",
+ " cols_ok = (\n",
+ " (cols > 0) & (cols < r[\"latitude\"].shape[1] - 1) & (cols % sample == 0)\n",
+ " )\n",
" rows, cols = rows[cols_ok], cols[cols_ok]\n",
- " xyz.append(wgs84.latlon(r[\"latitude\"][rows, cols], r[\"longitude\"][rows, cols]).itrs_xyz.m.T)\n",
+ " xyz.append(\n",
+ " wgs84.latlon(\n",
+ " r[\"latitude\"][rows, cols], r[\"longitude\"][rows, cols]\n",
+ " ).itrs_xyz.m.T\n",
+ " )\n",
" index.append(np.column_stack([np.full(len(rows), number), rows, cols]))\n",
" xyz, index = np.concatenate(xyz), np.concatenate(index)\n",
" tree = cKDTree(xyz)\n",
@@ -1077,14 +1455,27 @@
" nearby = tree.query_ball_point(p, radius)\n",
" if not nearby:\n",
" continue\n",
- " candidates = pd.DataFrame(index[nearby], columns=[\"orbit\", \"row\", \"column\"]).assign(distance=np.linalg.norm(xyz[nearby] - p, axis=1))\n",
- " candidates[\"time\"] = [records[o][\"times\"][r] for o, r in zip(candidates.orbit, candidates.row)]\n",
+ " candidates = pd.DataFrame(\n",
+ " index[nearby], columns=[\"orbit\", \"row\", \"column\"]\n",
+ " ).assign(distance=np.linalg.norm(xyz[nearby] - p, axis=1))\n",
+ " candidates[\"time\"] = [\n",
+ " records[o][\"times\"][r] for o, r in zip(candidates.orbit, candidates.row)\n",
+ " ]\n",
" candidates = candidates.sort_values(\"time\")\n",
- " for _, group in candidates.groupby((candidates.time.diff().dt.total_seconds() > 600).cumsum()):\n",
+ " for _, group in candidates.groupby(\n",
+ " (candidates.time.diff().dt.total_seconds() > 600).cumsum()\n",
+ " ):\n",
" best = group.loc[group.distance.idxmin()]\n",
" r, row, col = records[int(best.orbit)], int(best.row), int(best.column)\n",
" r0, r1 = (row, row + 1) if row + 1 < len(r[\"seconds\"]) else (row - 1, row)\n",
- " x0, x1 = (np.array(wgs84.latlon(float(r[\"latitude\"][k, col]), float(r[\"longitude\"][k, col])).itrs_xyz.m) for k in (r0, r1))\n",
+ " x0, x1 = (\n",
+ " np.array(\n",
+ " wgs84.latlon(\n",
+ " float(r[\"latitude\"][k, col]), float(r[\"longitude\"][k, col])\n",
+ " ).itrs_xyz.m\n",
+ " )\n",
+ " for k in (r0, r1)\n",
+ " )\n",
" fraction = float(np.dot(p - x0, x1 - x0) / np.dot(x1 - x0, x1 - x0))\n",
" time = r[\"times\"][r0] + (r[\"times\"][r1] - r[\"times\"][r0]) * fraction\n",
" if WINDOW[0] <= time <= WINDOW[1]:\n",
@@ -1092,46 +1483,123 @@
" return pd.DataFrame(out)\n",
"\n",
"\n",
- "reference = {\"radar\": reference_times(ku, 15e3, 1), \"GMI\": reference_times(gmi, 30e3, 2)}\n",
+ "reference = {\n",
+ " \"radar\": reference_times(ku, 15e3, 1),\n",
+ " \"GMI\": reference_times(gmi, 30e3, 2),\n",
+ "}\n",
"cone = float(sector[\"cone angle (deg)\"].median())\n",
"forward = int(sector[\"sector center (deg)\"].abs().idxmin())\n",
"half_width = float(sector[\"sector half width (deg)\"].median())\n",
"\n",
"\n",
"def gmi_instrument(center):\n",
- " return ConicalInstrument(name=\"GMI\", cone_angle=cone, scan_center_azimuth=center, scan_half_width=half_width, along_track_field_of_view=1, velocity_frame=VelocityFrame.INERTIAL)\n",
- "\n",
- "\n",
- "periods = [(WINDOW[0], FLIP, int(orientation[orientation.time < FLIP].orientation.mode()[0])), (FLIP, WINDOW[1], int(orientation[orientation.time >= FLIP].orientation.mode()[0]))]\n",
+ " return ConicalInstrument(\n",
+ " name=\"GMI\",\n",
+ " cone_angle=cone,\n",
+ " scan_center_azimuth=center,\n",
+ " scan_half_width=half_width,\n",
+ " along_track_field_of_view=1,\n",
+ " velocity_frame=VelocityFrame.INERTIAL,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "periods = [(WINDOW[0], FLIP, orientations[0]), (FLIP, WINDOW[1], orientations[1])]\n",
+ "\n",
+ "\n",
+ "def satellite_with(instrument):\n",
+ " return Satellite(name=\"GPM\", orbit=orbit, instruments=[instrument])\n",
+ "\n",
+ "\n",
+ "# GPM with its attitude schedule: the radar and GMI defined once, relative to the body (forward orientation)\n",
+ "body_gmi = ConicalInstrument(\n",
+ " name=\"GMI\",\n",
+ " cone_angle=cone,\n",
+ " scan_center_azimuth=float(sector.loc[0, \"sector center (deg)\"]),\n",
+ " scan_half_width=half_width,\n",
+ " along_track_field_of_view=1,\n",
+ ")\n",
+ "gpm = Satellite(\n",
+ " name=\"GPM\", orbit=orbit, instruments=[body_radar, body_gmi], attitude=schedule\n",
+ ")\n",
"models = {\n",
- " \"radar\": [(radar[o], start, end) for start, end, o in periods],\n",
- " \"GMI, sector by orientation\": [(gmi_instrument(float(sector.loc[o, \"sector center (deg)\"])), start, end) for start, end, o in periods],\n",
- " \"GMI, forward sector\": [(gmi_instrument(float(sector.loc[forward, \"sector center (deg)\"])), start, end) for start, end, _ in periods],\n",
+ " \"radar\": [(satellite_with(radar[o]), 0, start, end) for start, end, o in periods],\n",
+ " \"radar, attitude schedule\": [(gpm, 0, WINDOW[0], WINDOW[1])],\n",
+ " \"GMI, sector by orientation\": [\n",
+ " (\n",
+ " satellite_with(gmi_instrument(float(sector.loc[o, \"sector center (deg)\"]))),\n",
+ " 0,\n",
+ " start,\n",
+ " end,\n",
+ " )\n",
+ " for start, end, o in periods\n",
+ " ],\n",
+ " \"GMI, forward sector\": [\n",
+ " (\n",
+ " satellite_with(\n",
+ " gmi_instrument(float(sector.loc[forward, \"sector center (deg)\"]))\n",
+ " ),\n",
+ " 0,\n",
+ " start,\n",
+ " end,\n",
+ " )\n",
+ " for start, end, _ in periods\n",
+ " ],\n",
+ " \"GMI, attitude schedule\": [(gpm, 1, WINDOW[0], WINDOW[1])],\n",
"}\n",
"\n",
"\n",
- "def observe(point, instrument, start, end):\n",
- " return collect_observations(point, Satellite(name=\"GPM\", orbit=orbit, instruments=[instrument]), start.to_pydatetime(), end.to_pydatetime()).assign(point_id=point.id)\n",
+ "def observe(point, satellite, index, start, end):\n",
+ " return collect_observations(\n",
+ " point,\n",
+ " satellite,\n",
+ " start.to_pydatetime(),\n",
+ " end.to_pydatetime(),\n",
+ " instrument_index=index,\n",
+ " ).assign(point_id=point.id)\n",
"\n",
"\n",
"predicted = {}\n",
"for label, runs in models.items():\n",
- " frames = Parallel(n_jobs=-1)(delayed(observe)(point, instrument, start, end) for instrument, start, end in runs for point in points)\n",
- " predicted[label] = pd.concat([f for f in frames if not f.empty])[[\"point_id\", \"epoch\"]]\n",
+ " frames = Parallel(n_jobs=-1)(\n",
+ " delayed(observe)(point, satellite, index, start, end)\n",
+ " for satellite, index, start, end in runs\n",
+ " for point in points\n",
+ " )\n",
+ " predicted[label] = pd.concat([f for f in frames if not f.empty])[\n",
+ " [\"point_id\", \"epoch\"]\n",
+ " ]\n",
"\n",
"\n",
"def match(observed, model):\n",
- " merged = pd.merge_asof(observed.sort_values(\"time\"), predicted[model].rename(columns={\"epoch\": \"predicted\"}).sort_values(\"predicted\"), left_on=\"time\", right_on=\"predicted\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=5))\n",
+ " merged = pd.merge_asof(\n",
+ " observed.sort_values(\"time\"),\n",
+ " predicted[model]\n",
+ " .rename(columns={\"epoch\": \"predicted\"})\n",
+ " .sort_values(\"predicted\"),\n",
+ " left_on=\"time\",\n",
+ " right_on=\"predicted\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=5),\n",
+ " )\n",
" merged[\"difference (s)\"] = (merged.predicted - merged.time).dt.total_seconds()\n",
" merged[\"period\"] = np.where(merged.time < FLIP, \"before flip\", \"after flip\")\n",
" return merged\n",
"\n",
"\n",
- "matched = {label: match(reference[\"radar\" if label == \"radar\" else \"GMI\"], label) for label in models}\n",
+ "matched = {\n",
+ " label: match(reference[\"radar\" if label.startswith(\"radar\") else \"GMI\"], label)\n",
+ " for label in models\n",
+ "}\n",
"rows4 = {}\n",
"for label, merged in matched.items():\n",
" for period, group in merged.groupby(\"period\", sort=False):\n",
- " rows4[(label, period)] = {\"observations\": len(group), \"matched (%)\": 100 * group.predicted.notna().mean(), \"time difference, median (s)\": group[\"difference (s)\"].median(), \"time difference, 5th to 95th percentile (s)\": f\"{group['difference (s)'].quantile(0.05):.1f} to {group['difference (s)'].quantile(0.95):.1f}\"}\n",
+ " rows4[(label, period)] = {\n",
+ " \"observations\": len(group),\n",
+ " \"matched (%)\": 100 * group.predicted.notna().mean(),\n",
+ " \"time difference, median (s)\": group[\"difference (s)\"].median(),\n",
+ " \"time difference, 5th to 95th percentile (s)\": f\"{group['difference (s)'].quantile(0.05):.1f} to {group['difference (s)'].quantile(0.95):.1f}\",\n",
+ " }\n",
"pd.DataFrame(rows4).T"
]
},
@@ -1141,10 +1609,10 @@
"id": "735bfea7",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-10-08T18:16:25.887182Z",
- "iopub.status.busy": "2026-10-08T18:16:25.887182Z",
- "iopub.status.idle": "2026-10-08T18:16:25.937814Z",
- "shell.execute_reply": "2026-10-08T18:16:25.937814Z"
+ "iopub.execute_input": "2026-10-25T05:26:56.928585Z",
+ "iopub.status.busy": "2026-10-25T05:26:56.928585Z",
+ "iopub.status.idle": "2026-10-25T05:26:57.006754Z",
+ "shell.execute_reply": "2026-10-25T05:26:57.005749Z"
}
},
"outputs": [
@@ -1181,34 +1649,51 @@
"
\n",
"
GMI, sector by orientation
\n",
"
before flip
\n",
- "
2423.0
\n",
+ "
2428.0
\n",
"
73.86
\n",
"
74.26
\n",
- "
0.31
\n",
+ "
0.32
\n",
"
1.22
\n",
"
\n",
"
\n",
"
after flip
\n",
- "
2453.0
\n",
+ "
2457.0
\n",
"
-74.76
\n",
- "
-74.24
\n",
+ "
-74.23
\n",
"
0.57
\n",
"
1.06
\n",
"
\n",
"
\n",
"
GMI, forward sector
\n",
"
before flip
\n",
- "
2420.0
\n",
+ "
2428.0
\n",
"
73.86
\n",
"
-73.93
\n",
- "
-147.68
\n",
+ "
-147.69
\n",
"
151.83
\n",
"
\n",
"
\n",
"
after flip
\n",
- "
2454.0
\n",
+ "
2458.0
\n",
"
-74.76
\n",
- "
-74.24
\n",
+ "
-74.23
\n",
+ "
0.57
\n",
+ "
1.06
\n",
+ "
\n",
+ "
\n",
+ "
GMI, attitude schedule
\n",
+ "
before flip
\n",
+ "
2428.0
\n",
+ "
73.86
\n",
+ "
74.26
\n",
+ "
0.32
\n",
+ "
1.22
\n",
+ "
\n",
+ "
\n",
+ "
after flip
\n",
+ "
2457.0
\n",
+ "
-74.76
\n",
+ "
-74.23
\n",
"
0.57
\n",
"
1.06
\n",
"
\n",
@@ -1218,33 +1703,43 @@
],
"text/plain": [
" passes \\\n",
- "GMI, sector by orientation before flip 2423.0 \n",
- " after flip 2453.0 \n",
- "GMI, forward sector before flip 2420.0 \n",
- " after flip 2454.0 \n",
+ "GMI, sector by orientation before flip 2428.0 \n",
+ " after flip 2457.0 \n",
+ "GMI, forward sector before flip 2428.0 \n",
+ " after flip 2458.0 \n",
+ "GMI, attitude schedule before flip 2428.0 \n",
+ " after flip 2457.0 \n",
"\n",
" GMI minus radar, reported, median (s) \\\n",
"GMI, sector by orientation before flip 73.86 \n",
" after flip -74.76 \n",
"GMI, forward sector before flip 73.86 \n",
" after flip -74.76 \n",
+ "GMI, attitude schedule before flip 73.86 \n",
+ " after flip -74.76 \n",
"\n",
" GMI minus radar, TAT-C, median (s) \\\n",
"GMI, sector by orientation before flip 74.26 \n",
- " after flip -74.24 \n",
+ " after flip -74.23 \n",
"GMI, forward sector before flip -73.93 \n",
- " after flip -74.24 \n",
+ " after flip -74.23 \n",
+ "GMI, attitude schedule before flip 74.26 \n",
+ " after flip -74.23 \n",
"\n",
" TAT-C minus reported, median (s) \\\n",
- "GMI, sector by orientation before flip 0.31 \n",
+ "GMI, sector by orientation before flip 0.32 \n",
" after flip 0.57 \n",
- "GMI, forward sector before flip -147.68 \n",
+ "GMI, forward sector before flip -147.69 \n",
+ " after flip 0.57 \n",
+ "GMI, attitude schedule before flip 0.32 \n",
" after flip 0.57 \n",
"\n",
" TAT-C minus reported, 95th percentile of magnitude (s) \n",
"GMI, sector by orientation before flip 1.22 \n",
" after flip 1.06 \n",
"GMI, forward sector before flip 151.83 \n",
+ " after flip 1.06 \n",
+ "GMI, attitude schedule before flip 1.22 \n",
" after flip 1.06 "
]
},
@@ -1255,15 +1750,49 @@
],
"source": [
"# GMI's observation time relative to the radar's on the same pass\n",
- "pairs = pd.merge_asof(matched[\"radar\"].dropna(subset=[\"predicted\"]).sort_values(\"time\"), reference[\"GMI\"].rename(columns={\"time\": \"gmi_time\"}).sort_values(\"gmi_time\"), left_on=\"time\", right_on=\"gmi_time\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=5)).dropna(subset=[\"gmi_time\"])\n",
"rows4b = {}\n",
- "for label in (\"GMI, sector by orientation\", \"GMI, forward sector\"):\n",
- " p = pd.merge_asof(pairs.sort_values(\"gmi_time\"), predicted[label].rename(columns={\"epoch\": \"gmi_predicted\"}).sort_values(\"gmi_predicted\"), left_on=\"gmi_time\", right_on=\"gmi_predicted\", by=\"point_id\", direction=\"nearest\", tolerance=pd.Timedelta(minutes=5)).dropna(subset=[\"gmi_predicted\"])\n",
+ "for label, radar_label in (\n",
+ " (\"GMI, sector by orientation\", \"radar\"),\n",
+ " (\"GMI, forward sector\", \"radar\"),\n",
+ " (\"GMI, attitude schedule\", \"radar, attitude schedule\"),\n",
+ "):\n",
+ " pairs = pd.merge_asof(\n",
+ " matched[radar_label].dropna(subset=[\"predicted\"]).sort_values(\"time\"),\n",
+ " reference[\"GMI\"].rename(columns={\"time\": \"gmi_time\"}).sort_values(\"gmi_time\"),\n",
+ " left_on=\"time\",\n",
+ " right_on=\"gmi_time\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=5),\n",
+ " ).dropna(subset=[\"gmi_time\"])\n",
+ " p = pd.merge_asof(\n",
+ " pairs.sort_values(\"gmi_time\"),\n",
+ " predicted[label]\n",
+ " .rename(columns={\"epoch\": \"gmi_predicted\"})\n",
+ " .sort_values(\"gmi_predicted\"),\n",
+ " left_on=\"gmi_time\",\n",
+ " right_on=\"gmi_predicted\",\n",
+ " by=\"point_id\",\n",
+ " direction=\"nearest\",\n",
+ " tolerance=pd.Timedelta(minutes=5),\n",
+ " ).dropna(subset=[\"gmi_predicted\"])\n",
" p[\"reported (s)\"] = (p.gmi_time - p.time).dt.total_seconds()\n",
" p[\"TAT-C (s)\"] = (p.gmi_predicted - p.predicted).dt.total_seconds()\n",
" p[\"TAT-C minus reported (s)\"] = p[\"TAT-C (s)\"] - p[\"reported (s)\"]\n",
" for period, group in p.groupby(\"period\", sort=False):\n",
- " rows4b[(label, period)] = {\"passes\": len(group), \"GMI minus radar, reported, median (s)\": group[\"reported (s)\"].median(), \"GMI minus radar, TAT-C, median (s)\": group[\"TAT-C (s)\"].median(), \"TAT-C minus reported, median (s)\": group[\"TAT-C minus reported (s)\"].median(), \"TAT-C minus reported, 95th percentile of magnitude (s)\": group[\"TAT-C minus reported (s)\"].abs().quantile(0.95)}\n",
+ " rows4b[(label, period)] = {\n",
+ " \"passes\": len(group),\n",
+ " \"GMI minus radar, reported, median (s)\": group[\"reported (s)\"].median(),\n",
+ " \"GMI minus radar, TAT-C, median (s)\": group[\"TAT-C (s)\"].median(),\n",
+ " \"TAT-C minus reported, median (s)\": group[\n",
+ " \"TAT-C minus reported (s)\"\n",
+ " ].median(),\n",
+ " \"TAT-C minus reported, 95th percentile of magnitude (s)\": group[\n",
+ " \"TAT-C minus reported (s)\"\n",
+ " ]\n",
+ " .abs()\n",
+ " .quantile(0.95),\n",
+ " }\n",
"pd.DataFrame(rows4b).T.round(2)"
]
},
@@ -1272,18 +1801,18 @@
"id": "c44c7d6c",
"metadata": {},
"source": [
- "The absolute observation times are shifted by the TLE's along-track error (Test 1): the radar's predicted observations are 13.6 s late (median) before the flip and 5.4 s late after it, and GMI's (with the sector of each orientation) 14.0 and 5.9 s late; 96% of the radar's and 95% of GMI's observations are matched by predicted ones. Relative to the radar on the same pass, which removes the orbit error, GMI observes points 73.9 s after the radar before the flip, when its sector is behind the satellite, and 74.8 s before the radar after the flip, when its sector is ahead. With the sector of each orientation, TAT-C reproduces these times within 0.6 s (median) and 1.2 s (95th percentile). With the forward sector throughout, GMI's predicted observations before the flip are 148 s too early.\n",
+ "The absolute observation times are shifted by the TLE's along-track error (Test 1): the radar's predicted observations are 13.6 s late (median) before the flip and 5.4 s late after it, and GMI's (with the sector of each orientation) 14.0 and 5.9 s late; 96% of the radar's and 95% of GMI's observations are matched by predicted ones. Relative to the radar on the same pass, which removes the orbit error, GMI observes points 73.9 s after the radar before the flip, when its sector is behind the satellite, and 74.8 s before the radar after the flip, when its sector is ahead. With the sector of each orientation, TAT-C reproduces these times within 0.6 s (median) and 1.2 s (95th percentile). With the forward sector throughout, GMI's predicted observations before the flip are 148 s too early. With the attitude schedule, a single analysis of each instrument across the flip gives the same results as the separate analyses of each orientation: the radar's predicted observations are identical, and GMI's times relative to the radar are again within 0.6 s (median) and 1.2 s (95th percentile).\n",
"\n",
"## Summary\n",
"\n",
"| Test | Result |\n",
"| --- | --- |\n",
"| 1. Orbit (TLE) | node local time precession (17.3 minutes per day) within 0.15 minutes; along-track error from drag of up to 31 km within 11 to 16 days of the TLE epoch, growing to 300 km at 26 days |\n",
- "| 2. Radar scan geometry (`ViewGeometry.SCAN`, `VelocityFrame.INERTIAL`) | 49 beams over -17 to +17 deg, perpendicular to the inertial velocity; the yaw flip reverses the beam order and the sign of a 0.15 deg along-track offset; beams within 0.6 to 3.2 km with each orientation's instrument, 270 km at the edges with the other orientation's |\n",
+ "| 2. Radar scan geometry (`ViewGeometry.SCAN`, `VelocityFrame.INERTIAL`) | 49 beams over -17 to +17 deg, perpendicular to the inertial velocity; the yaw flip reverses the beam order and the sign of a 0.15 deg along-track offset; beams within 0.6 to 3.2 km with each orientation's instrument or with one instrument and an `AttitudeSchedule`, 270 km at the edges with the other orientation's |\n",
"| 3. GMI scan geometry (`ConicalInstrument`) | 48.5 deg cone; scan sector centered ahead (-0.3 deg) or behind (-179.7 deg) the satellite, depending on the orientation |\n",
- "| 4. Observation times (`collect_observations`) | GMI relative to the radar within 0.6 s (median) with each orientation's sector; 148 s off with the forward sector before the flip; absolute times shifted by the TLE's along-track error |\n",
+ "| 4. Observation times (`collect_observations`) | GMI relative to the radar within 0.6 s (median) with each orientation's sector, or across the flip in one analysis with an `AttitudeSchedule`; 148 s off with the forward sector before the flip; absolute times shifted by the TLE's along-track error |\n",
"\n",
- "**Yaw flips.** TAT-C represents each of GPM's orientations: the radar as a `PointedInstrument` with the orientation's pitch angle and pixel order, and GMI as a `ConicalInstrument` with the orientation's scan center azimuth. It does not model the flips themselves, so an analysis across a flip must be split at the time of the flip, with the instruments of each orientation; an attitude schedule (a time-dependent yaw of 0 or 180 deg) would represent it in a single analysis.\n",
+ "**Yaw flips.** TAT-C represents GPM's yaw flips with an `AttitudeSchedule`: GPM is a single `Satellite` whose body yaws by 180 deg while it flies backward (in the inertial velocity frame), and the radar (a `PointedInstrument`) and GMI (a `ConicalInstrument`) are defined once, relative to the body, as in the forward orientation. An analysis across a flip then gives the same results as separate analyses of each orientation, with the radar's pitch angle and pixel order and GMI's scan center azimuth of each.\n",
"\n",
"**Low orbits.** GPM's along-track position departs from its TLE by tens to hundreds of kilometers over weeks, because drag and orbit maintenance change its orbit; analyses of observation times should use TLEs close to the analysis period."
]
diff --git a/docs/validation/index.rst b/docs/validation/index.rst
index 04bb73ab..33ea99fe 100644
--- a/docs/validation/index.rst
+++ b/docs/validation/index.rst
@@ -11,6 +11,9 @@ The following compare TAT-C analysis results with reference data from operationa
ValidateLimbSABER.ipynb
ValidateROCOSMIC2.ipynb
ValidateROPlanetiQ.ipynb
+ ValidateSpaceCOSMIC2.ipynb
+ ValidateSpaceGRACEFO.ipynb
+ ValidateSpaceSOCRATES.ipynb
ValidateRadarNEXRAD.ipynb
ValidateImagerATMS.ipynb
ValidateImagerCrIS.ipynb
@@ -20,8 +23,11 @@ The following compare TAT-C analysis results with reference data from operationa
ValidatePushbroomMSI.ipynb
ValidateRevisitSentinel2.ipynb
ValidateRegionSentinel2.ipynb
+ ValidateRegionPolarSentinel1.ipynb
ValidateConicalAMSR2.ipynb
ValidateConicalGMI.ipynb
+ ValidateCoincidenceGMIAMSR2.ipynb
+ ValidateGridSMAP.ipynb
ValidateOrbitGeneration.ipynb
ValidateDopGNSS.ipynb
ValidateLatencyNOAA20.ipynb
@@ -36,8 +42,10 @@ The following compare TAT-C analysis results with reference data from operationa
ValidateDriftMODIS.ipynb
ValidatePointingOCO2.ipynb
ValidateYawFlipGPM.ipynb
+ ValidateSampleSizes.ipynb
+ ValidateFeaturesTCPRIMED.ipynb
-These notebooks download reference data on first run (the MLS, NISAR, AMSR2, GMI, SWOT, ICESat-2, ECOSTRESS, PACE, MODIS, OCO-2, and GPM data require a free `NASA Earthdata Login `_ account, and the Landsat 9 orbit history a free `Space-Track.org `_ account) and require additional dependencies, which can be installed via::
+These notebooks download reference data on first run, except ``ValidateSampleSizes``, which reuses the data of the imager and conical scanner notebooks, and ``ValidateSpaceCOSMIC2``, which reuses the data of ``ValidateROCOSMIC2``. ``ValidateSpaceSOCRATES`` includes a sample of a SOCRATES run (data from CelesTrak). The MLS, NISAR, AMSR2, GMI, SWOT, ICESat-2, ECOSTRESS, PACE, MODIS, OCO-2, GPM, GRACE-FO, and SMAP data require a free `NASA Earthdata Login `_ account, and the orbit histories of the Landsat 9, TC PRIMED, and GRACE-FO notebooks a free `Space-Track.org `_ account. The notebooks require additional dependencies, which can be installed via::
pip install tatc[validation]
diff --git a/docs/validation/socrates_2026-10-25.csv b/docs/validation/socrates_2026-10-25.csv
new file mode 100644
index 00000000..26b9b707
--- /dev/null
+++ b/docs/validation/socrates_2026-10-25.csv
@@ -0,0 +1,501 @@
+NORAD_CAT_ID_1,OBJECT_NAME_1,DSE_1,NORAD_CAT_ID_2,OBJECT_NAME_2,DSE_2,TCA,TCA_RANGE,TCA_RELATIVE_SPEED,MAX_PROB,DILUTION
+63297,OBJECT G [+],0.94,64028,STARLINK-33953 [+],0.991,2026-10-25 06:16:50.944,4.181,15.033,2.826e-06,2.664
+62635,GARAI-A [+],1.038,62664,LYRA-1 [+],1.036,2026-10-25 06:42:32.251,2.025,0.647,1.075e-06,0.478
+56153,PIESAT-1 A [+],3.676,56156,PIESAT-1 D [+],3.61,2026-10-25 07:22:09.505,0.237,0.001,7.877e-05,0.056
+54750,YAOGAN-36 04C [+],1.511,68565,STARLINK-37161 [+],1.066,2026-10-25 07:42:23.668,2.669,5.732,1.753e-05,0.673
+62656,TRANSPORTER-12 OBJECT AZ [+],1.13,64668,STARLINK-34395 [+],1.057,2026-10-25 07:46:40.804,3.47,12.757,6.838e-06,1.326
+41601,SKYSAT-C1 [+],1.748,45212,STARLINK-1231 [P],1.028,2026-10-25 07:54:05.556,4.766,9.235,4.399e-06,1.368
+58921,NEXSAT-1 [+],1.039,65935,STARLINK-35155 [+],1.276,2026-10-25 08:16:50.617,4.857,14.635,2.422e-06,2.676
+58199,TIANHUI 5A [+],3.821,58201,TIANHUI 5B [+],3.754,2026-10-25 08:31:26.641,0.479,0.001,2.125e-05,0.118
+57401,TIANMU-1 09 [+],1.117,65802,STARLINK-34919 [+],1.592,2026-10-25 08:41:10.343,4.981,15.089,1.953e-06,3.234
+55844,HORUS 2 [+],1.134,66705,FLOCK 4H-2 [+],1.66,2026-10-25 09:22:19.362,4.833,0.125,1.889e-07,1.14
+69870,ICEYE-X79 [+],1.795,69912,MARMOTSAT [+],1.192,2026-10-25 09:27:09.318,4.257,0.015,2.436e-07,1.004
+64900,CO3D 3 [+],1.135,66733,FLOCK 4H-30 [+],1.2,2026-10-25 09:38:12.350,2.408,0.698,7.604e-07,0.568
+62640,NUSAT-45 (UZMASAT-1) [+],1.454,62675,FLOCK 4G-36 [+],1.257,2026-10-25 09:56:48.082,3.512,0.179,3.58e-07,0.828
+62658,TROLL [+],1.479,65476,STARLINK-35098 [+],1.747,2026-10-25 10:23:07.031,1.881,12.575,2.372e-05,0.706
+68377,SUPERVIEW NEO-2 05 [+],3.542,68378,SUPERVIEW NEO-2 06 [+],3.608,2026-10-25 10:36:07.475,1.938,0.003,1.413e-06,0.417
+62623,OTTER [+],1.221,65383,STARLINK-35054 [+],1.358,2026-10-25 10:46:38.457,1.148,14.356,4.649e-05,0.59
+66729,FLOCK 4H-26 [+],1.268,68451,IRIDE-MS1-EAGLET 2-10 [+],1.331,2026-10-25 10:49:06.162,2.023,12.683,6.674e-07,0.771
+37168,SBSS (USA 216) [+],1.587,67546,STARLINK-36580 [+],1.32,2026-10-25 11:29:34.267,1.611,0.416,5.137e-05,0.38
+64528,GOOD ANCESTOR KILAKILA [+],1.928,67307,STARLINK-36481 [+],1.33,2026-10-25 12:26:13.629,4.163,9.006,6.402e-06,1.181
+57180,MONITOR-3 (RS58S) [+],1.275,67032,STARLINK-36131 [+],1.505,2026-10-25 12:45:09.324,3.977,14.344,3.897e-06,2.031
+62079,SUPERVIEW NEO-2 03 [+],3.864,62080,SUPERVIEW NEO-2 04 [+],3.667,2026-10-25 12:48:07.376,0.169,0.0,0.0003707,0.026
+60211,TIANHUI 5C [+],3.919,60212,TIANHUI 5D [+],3.919,2026-10-25 13:01:45.140,0.399,0.001,3.104e-05,0.092
+66690,TRANSPORTER-15 OBJECT AA [+],1.399,66726,FLOCK 4H-23 [+],1.792,2026-10-25 13:12:30.702,3.971,0.072,2.799e-07,0.936
+61514,STARLINK-31837 [+],1.547,68370,RASSVET-3 11 [+],1.346,2026-10-25 13:16:17.513,1.084,9.356,9.26e-05,0.313
+66767,ACCENTURE-1 [+],1.365,67749,OBJECT D [+],1.76,2026-10-25 13:29:19.619,2.837,7.127,4.919e-07,0.746
+40118,GAOFEN-2 [+],1.278,63750,KUIPER-00038 [+],1.526,2026-10-25 13:40:53.163,3.811,14.353,1.339e-07,2.04
+66692,NUSAT-52 (MELINA FURMAN) [+],1.628,66721,FLOCK 4H-18 [+],1.43,2026-10-25 13:58:46.067,1.708,0.018,1.512e-06,0.403
+57101,STARLINK-6132 [+],1.488,57115,STARLINK-6085 [P],1.492,2026-10-25 14:02:46.809,2.992,0.082,4.919e-05,0.707
+48449,STARLINK-2655 [P],1.481,49748,STARLINK-3248 [+],1.482,2026-10-25 14:23:46.773,2.304,0.45,8.311e-05,0.543
+56458,STARLINK-6050 [+],1.347,61789,SITRO-AIS 17 [+],1.358,2026-10-25 14:50:57.992,1.815,14.706,1.631e-05,1.063
+68377,SUPERVIEW NEO-2 05 [+],3.74,68378,SUPERVIEW NEO-2 06 [+],3.806,2026-10-25 15:20:27.211,2.205,0.003,1.413e-06,0.417
+45747,STARLINK-1506 [P],1.478,67854,STARLINK-36608 [+],1.477,2026-10-25 15:53:29.079,3.014,0.029,4.829e-05,0.715
+49261,CS1 [+],1.522,60103,STARLINK-32013 [+],1.39,2026-10-25 16:35:58.047,3.288,7.631,1.09e-05,0.879
+60211,TIANHUI 5C [+],4.12,60212,TIANHUI 5D [+],4.12,2026-10-25 17:52:12.706,0.397,0.001,3.104e-05,0.092
+55294,STARLINK-5286 [+],1.512,57181,OBJECT R [+],1.46,2026-10-25 18:17:10.512,2.118,15.063,1.073e-05,1.385
+56934,GEISAT PRECURSOR [+],1.508,64607,STARLINK-34532 [+],1.579,2026-10-25 18:18:18.875,4.022,11.35,5.892e-06,1.329
+63990,SCS-01 J [+],1.492,66685,AE5RA [+],1.624,2026-10-25 18:28:05.884,2.057,0.187,1.044e-06,0.485
+60211,TIANHUI 5C [+],4.153,60212,TIANHUI 5D [+],4.153,2026-10-25 18:39:32.140,0.394,0.001,3.104e-05,0.092
+47126,STARLINK-1812 [P],1.67,57255,STARLINK-5481 [+],1.697,2026-10-25 18:45:17.826,2.979,11.212,3.586e-05,0.974
+58469,ION SCV-015 [+],1.476,65470,STARLINK-34829 [+],1.487,2026-10-25 18:55:24.019,4.088,13.269,4.562e-06,1.688
+47486,CELESTIS-17 & SHERPA-FX1 [+],1.647,65666,STARLINK-35181 [+],1.712,2026-10-25 19:03:12.648,4.51,0.244,6.562e-06,1.064
+69898,GRUS-3E [+],1.66,69931,GRITSS [+],1.86,2026-10-25 19:36:18.501,3.273,0.015,4.121e-07,0.772
+59847,STARLINK-31889 [+],2.215,67289,NASBSAT-1 [+],2.089,2026-10-25 20:28:13.714,4.157,8.293,6.634e-06,1.142
+53346,TECMS [+],1.512,55273,STARLINK-5291 [+],1.605,2026-10-25 20:31:11.755,1.368,15.127,2.492e-05,0.923
+57175,CUBEBEL 2 (EU11S) [+],1.551,61230,OBJECT D [+],1.55,2026-10-25 20:32:01.751,3.314,1.798,3.995e-07,0.786
+60505,FLOCK 4BE-24 [+],1.954,60525,ORESAT0.5 [+],1.953,2026-10-25 22:48:42.745,3.879,0.259,2.934e-07,0.914
+68377,SUPERVIEW NEO-2 05 [+],4.135,68378,SUPERVIEW NEO-2 06 [+],4.201,2026-10-26 00:49:04.469,2.776,0.004,1.413e-06,0.417
+66163,STARLINK-35607 [+],1.943,66703,PELICAN-6 [+],1.789,2026-10-26 00:56:52.026,4.187,8.42,6.503e-06,1.156
+58118,STARLINK-30795 [+],2.025,63297,OBJECT G [+],1.735,2026-10-26 01:21:48.982,1.49,14.344,2.778e-05,0.76
+46914,KSM1-B [+],1.815,56105,STARLINK-6066 [+],1.754,2026-10-26 01:34:17.404,2.057,3.563,3.078e-05,0.497
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+57115,STARLINK-6085 [P],3.721,59361,STARLINK-31560 [+],3.516,2026-10-27 19:31:48.855,1.598,2.039,0.0001715,0.38
+56200,HAWK-7C [+],3.563,56306,STARLINK-30112 [+],3.669,2026-10-27 19:38:53.773,1.134,14.061,5.133e-05,0.541
+60496,TYCHE [+],3.623,67300,TIANHUI 7 [+],3.623,2026-10-27 19:39:35.657,2.406,0.233,7.626e-07,0.567
+60211,TIANHUI 5C [+],6.205,60212,TIANHUI 5D [+],6.205,2026-10-27 19:53:21.368,0.401,0.001,3.104e-05,0.092
+48606,TYVAK-0130 [+],3.649,56030,STARLINK-5781 [+],3.776,2026-10-27 20:17:22.575,3.527,4.49,1.031e-05,0.865
+62381,HAWK-11C [+],3.563,65369,STARLINK-34913 [+],3.77,2026-10-27 20:25:10.723,4.554,9.778,1.696e-07,1.347
+37166,ZHEDA PIXING 1B [+],3.606,68482,LEMUR-2-SEJONG-3 [+],4.069,2026-10-27 20:35:26.080,0.915,2.94,5.182e-06,0.219
+58199,TIANHUI 5A [+],6.329,58201,TIANHUI 5B [+],6.262,2026-10-27 20:43:22.610,0.472,0.001,2.125e-05,0.118
+60496,TYCHE [+],3.689,67300,TIANHUI 7 [+],3.689,2026-10-27 21:14:01.773,2.345,0.233,8.029e-07,0.553
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+47486,CELESTIS-17 & SHERPA-FX1 [+],3.782,68748,STARLINK-37325 [+],3.885,2026-10-27 22:17:42.424,4.084,15.128,2.844e-06,2.71
+60995,STARLINK-11269 [+],3.73,62396,BANDWAGON-2 OBJECT V [+],3.733,2026-10-27 22:39:13.839,3.074,1.33,1.409e-05,0.727
+60496,TYCHE [+],3.755,67300,TIANHUI 7 [+],3.754,2026-10-27 22:48:27.888,2.758,0.233,5.803e-07,0.65
+39451,SWARM B [+],3.757,56508,STARLINK-5335 [+],3.751,2026-10-27 23:05:20.802,3.061,7.128,1.28e-05,0.804
+60211,TIANHUI 5C [+],6.339,60212,TIANHUI 5D [+],6.339,2026-10-27 23:06:57.647,0.403,0.001,3.104e-05,0.092
+55964,STARLINK-5895 [P],3.767,60536,LEMUR-2-MARHISYAM [+],3.769,2026-10-27 23:35:48.095,3.332,3.64,1.171e-05,0.806
+46232,GAOFEN-9 05 [+],3.724,62320,STARLINK-32481 [+],3.868,2026-10-27 23:35:54.534,1.773,14.535,1.88e-05,0.944
+58199,TIANHUI 5A [+],6.463,58201,TIANHUI 5B [+],6.396,2026-10-27 23:55:48.961,0.483,0.001,2.125e-05,0.118
+44520,KX-09 [+],3.677,61752,SITRO-AIS 14 [+],3.706,2026-10-28 00:26:46.919,3.247,15.007,1.49e-07,2.151
+68989,BUSANSAT [+],3.876,69884,YAM-17 [+],4.079,2026-10-28 00:36:52.797,2.094,0.212,1.007e-06,0.494
+64559,PADRE [+],3.77,64568,IRIDE-MS2-HEO-3 [+],3.772,2026-10-28 00:37:46.908,3.071,0.047,4.679e-07,0.724
+46232,GAOFEN-9 05 [+],3.768,67963,STARLINK-36357 [+],4.029,2026-10-28 00:38:46.653,4.496,3.795,6.421e-06,1.09
+60211,TIANHUI 5C [+],6.406,60212,TIANHUI 5D [+],6.406,2026-10-28 00:43:45.753,0.404,0.001,3.104e-05,0.092
+68441,HAWK-14C [+],3.875,68464,FGN-100-D3 [+],3.876,2026-10-28 00:49:52.823,2.174,0.004,9.132e-07,0.524
+53847,STARLINK-4777 [+],4.614,59810,OBJECT A [+],3.75,2026-10-28 01:03:17.991,2.776,7.916,1.508e-05,0.752
+68441,HAWK-14C [+],3.899,68464,FGN-100-D3 [+],3.9,2026-10-28 01:24:48.514,3.105,0.004,9.132e-07,0.524
+60211,TIANHUI 5C [+],6.44,60212,TIANHUI 5D [+],6.44,2026-10-28 01:32:58.697,0.393,0.001,3.104e-05,0.092
+62610,EDISON-1 [+],4.107,67845,STARLINK-36606 [+],4.043,2026-10-28 01:54:51.033,3.988,7.063,7.554e-06,1.045
+56957,ION SCV-011 [+],3.833,67516,STARLINK-36537 [+],4.359,2026-10-28 02:49:37.812,3.07,9.38,1.154e-05,0.889
+43160,JILIN-1 08 [+],3.863,67899,STARLINK-36878 [+],4.061,2026-10-28 03:29:28.854,4.03,10.164,6.402e-06,1.22
+66761,TRANSPORTER-15 OBJECT CZ [+],4.218,67286,MULE 4T [+],4.017,2026-10-28 03:31:58.405,0.874,0.111,5.777e-06,0.206
+59201,STARLINK-31603 [+],4.087,68370,RASSVET-3 11 [+],3.954,2026-10-28 03:51:14.062,2.0,8.959,2.778e-05,0.566
+62609,BUZZER-1 [+],3.993,62673,LEO EXPRESS 2 [+],4.255,2026-10-28 03:55:33.676,4.398,0.528,2.281e-07,1.037
+38257,YAOGAN-14 [+],3.876,59122,NUSAT-44 (MARIA MITCHELL) [+],3.94,2026-10-28 04:37:32.010,3.336,0.997,3.959e-07,0.788
+61781,ASRTU-1 (AO-123) [+],3.888,63219,TEVEL2-2 [+],4.018,2026-10-28 04:41:12.699,4.175,3.756,2.463e-07,1.012
+63223,DROID.002 [+],4.054,69021,HELIOS [+],4.521,2026-10-28 04:48:16.314,3.209,0.601,4.283e-07,0.757
+57402,TIANMU-1 10 [+],3.872,58264,BRO-11 [+],3.945,2026-10-28 05:08:18.353,3.056,3.628,4.607e-07,0.739
+67292,SITRO-AIS-64 [+],4.03,67917,STARLINK-36722 [+],3.891,2026-10-28 05:22:03.842,4.301,6.974,6.512e-06,1.124
+62665,JAY-D2 [+],3.991,66675,VEERY-0G [+],4.32,2026-10-28 05:41:02.253,2.98,0.609,4.969e-07,0.703
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+62678,FLOCK 4G-15 [+],4.295,63003,FOSSASAT-2E20 (TAT-O) [+],4.096,2026-10-28 06:06:28.635,2.767,0.978,5.756e-07,0.653
+46912,KSM1-A [+],3.946,67179,STARLINK-35793 [+],4.518,2026-10-28 06:15:07.320,1.499,8.39,5.083e-05,0.413
+66774,GENA-OT [+],4.552,67295,ZORKIY-2M-5 [+],4.083,2026-10-28 06:44:06.523,3.877,0.16,2.936e-07,0.914
+56881,STARLINK-5853 [+],4.082,67289,NASBSAT-1 [+],4.519,2026-10-28 06:47:36.364,2.971,14.23,7.19e-06,1.473
+65127,GEESAT-4 09 [+],4.036,65948,KUIPER-00113 [+],3.96,2026-10-28 06:48:03.261,2.501,8.83,5.885e-07,0.707
+60463,YAOGAN-43 01F [+],4.048,69364,STARLINK-37309 [+],4.193,2026-10-28 08:30:57.906,4.453,5.1,6.391e-06,1.106
+53137,STARLINK-4145 [+],4.204,60513,FLOCK 4BE-27 [P],4.62,2026-10-28 09:26:30.440,1.812,14.659,1.726e-05,1.006
+66588,STARLINK-36076 [+],4.221,67297,SITRO-AIS-61 [+],4.256,2026-10-28 09:36:37.151,4.819,14.266,2.706e-06,2.414
+60211,TIANHUI 5C [+],6.777,60212,TIANHUI 5D [+],6.777,2026-10-28 09:37:21.513,0.392,0.001,3.104e-05,0.092
+47911,STARLINK-2371 [P],4.151,53823,STARLINK-4748 [+],4.153,2026-10-28 09:52:20.053,1.514,0.327,0.0001924,0.357
+66130,STARLINK-35637 [+],4.782,66669,SARI-2 [+],4.137,2026-10-28 10:03:05.073,4.019,6.252,7.627e-06,1.027
+66304,BANDWAGON-4 OBJECT M [+],4.507,69761,STARLINK-38069 [+],4.442,2026-10-28 10:03:52.011,4.056,7.482,7.2e-06,1.078
+66682,ION SCV-022 [+],4.659,67483,ION SCV-021 [+],4.658,2026-10-28 10:16:07.941,0.992,0.04,4.485e-06,0.234
+61676,STARLINK-32309 [+],4.256,66304,BANDWAGON-4 OBJECT M [+],4.519,2026-10-28 10:21:33.211,4.443,2.671,6.671e-06,1.062
+44311,BUFENG 1B [+],4.204,67412,STARLINK-36446 [+],4.144,2026-10-28 11:16:25.954,3.573,0.362,1.046e-05,0.842
+51757,STARLINK-3459 [+],4.312,63240,AL MUNTHER [+],4.323,2026-10-28 11:28:07.809,3.206,13.405,7.299e-06,1.345
+56191,HAWK-7A [+],4.226,57147,STARLINK-5765 [+],4.178,2026-10-28 11:34:19.493,4.099,14.104,3.885e-06,1.977
+56934,GEISAT PRECURSOR [+],4.259,66644,STARLINK-35938 [+],4.516,2026-10-28 12:19:35.289,3.838,8.069,7.862e-06,1.043
+43833,SAUDISAT 5B [+],4.196,49457,STARLINK-3132 [+],4.26,2026-10-28 12:21:27.890,4.894,13.553,3.053e-06,2.107
+49765,STARLINK-3227 [+],5.589,57182,MONITOR-4 (RS57S) [+],4.308,2026-10-28 12:52:44.308,4.256,14.208,3.521e-06,2.1
+43160,JILIN-1 08 [+],4.274,69960,STARLINK-37901 [+],4.488,2026-10-28 13:21:30.036,2.548,14.582,2.95e-07,1.385
+60211,TIANHUI 5C [+],6.944,60212,TIANHUI 5D [+],6.944,2026-10-28 13:38:09.273,0.416,0.001,3.104e-05,0.092
+63233,LIZZIESAT-3 (LS-3) [+],4.452,69010,DRISHTI [+],4.391,2026-10-28 13:56:08.899,4.363,0.776,2.317e-07,1.03
+66686,LEMUR-2-STAS-GORBUK [+],4.887,67278,OBJECT AG [+],4.416,2026-10-28 14:09:49.873,4.211,0.138,2.489e-07,0.993
+54838,STARLINK-5372 [+],4.36,58645,TIANMU-1 11 [+],4.296,2026-10-28 14:13:43.992,3.476,9.591,8.897e-06,1.018
+58199,TIANHUI 5A [+],7.064,58201,TIANHUI 5B [+],6.997,2026-10-28 14:21:45.800,0.555,0.001,2.125e-05,0.118
+45625,GAOFEN-9 02 [+],4.396,57870,STARLINK-30460 [+],4.979,2026-10-28 15:35:11.621,4.029,8.86,6.884e-06,1.135
+58321,PROTOMETHEE [+],4.4,60285,STARLINK-32122 [+],4.593,2026-10-28 15:53:28.797,1.462,6.707,5.687e-05,0.379
+43804,SUOMI-100 [+],4.533,52287,STARLINK-3822 [+],4.385,2026-10-28 15:56:32.019,4.263,13.22,4.247e-06,1.739
+60463,YAOGAN-43 01F [+],4.378,69455,STARLINK-37642 [+],4.455,2026-10-28 16:26:04.247,3.553,3.44,1.034e-05,0.857
+57388,STARLING 1 [+],4.47,67088,STARLINK-35882 [+],4.525,2026-10-28 17:34:21.704,1.505,14.786,2.334e-05,0.896
+60211,TIANHUI 5C [+],7.146,60212,TIANHUI 5D [+],7.146,2026-10-28 18:28:32.508,0.421,0.001,3.104e-05,0.092
+55110,ASTROCAST-0401 [+],5.01,56957,ION SCV-011 [+],4.498,2026-10-28 18:47:51.084,2.919,11.927,3.494e-07,1.021
+65126,GEESAT-4 08 [+],4.497,68926,KUIPER-00410 [+],4.646,2026-10-28 18:49:07.625,0.886,4.413,5.407e-06,0.217
+56200,HAWK-7C [+],4.542,63895,STARLINK-34119 [+],4.656,2026-10-28 19:08:27.170,0.75,12.675,0.0001475,0.285
+64449,STARLINK-34459 [+],4.497,64891,GEOSCAN 5 (RS92S5) [+],4.522,2026-10-28 19:31:40.449,2.62,14.71,8.16e-06,1.472
+41888,CYGFM08 [X],5.031,52112,STARLINK-3734 [P],4.569,2026-10-28 19:39:10.213,1.397,2.44,6.761e-05,0.333
+57175,CUBEBEL 2 (EU11S) [+],4.523,60562,HYPERFIELD-1A [+],4.849,2026-10-28 19:51:24.078,3.812,1.972,3.015e-07,0.905
+69872,TOM-1 [+],4.686,69911,TRANSPORTER-17 OBJECT AU [+],4.686,2026-10-28 19:54:43.473,3.946,0.006,2.803e-07,0.941
+60352,STRIX-4 [+],4.546,66125,STARLINK-35649 [+],5.247,2026-10-28 20:16:38.924,0.845,10.98,0.0001374,0.271
+56893,STARLINK-6216 [+],4.628,58646,TIANMU-1 12 [+],4.56,2026-10-28 20:24:20.639,0.812,12.546,0.0001277,0.304
+48657,STARLINK-2734 [+],4.613,69322,HULIANWANG JISHU SHIYAN 10C [+],4.789,2026-10-28 20:42:13.312,4.622,4.062,6.051e-06,1.126
+58199,TIANHUI 5A [+],7.333,58201,TIANHUI 5B [+],7.265,2026-10-28 20:48:03.303,0.606,0.001,2.125e-05,0.118
+46912,KSM1-A [+],4.555,66324,STARLINK-35506 [+],4.619,2026-10-28 20:51:33.628,1.222,1.833,8.886e-05,0.29
+47810,STARLINK-2391 [+],4.542,64554,TRANSPORTER-14 OBJECT AC [+],4.597,2026-10-28 21:00:26.982,4.719,14.725,2.496e-06,2.672
+52836,STARLINK-4043 [P],4.578,57935,STARLINK-30712 [+],4.577,2026-10-28 21:06:50.433,2.428,1.53,7.455e-05,0.575
+58043,STARLINK-30564 [+],4.708,68629,RAWHIDE-1 [+],5.134,2026-10-28 21:09:40.147,0.96,5.214,0.0001371,0.239
+46914,KSM1-B [+],4.641,56384,STARLINK-6153 [+],4.639,2026-10-28 21:23:34.040,4.614,0.878,6.262e-06,1.089
+47911,STARLINK-2371 [P],4.656,66019,STARLINK-35382 [+],5.145,2026-10-28 21:59:31.395,4.762,12.223,1.277e-05,1.711
+62430,RESURS-P 5 [+],4.652,68208,STARLINK-37009 [+],5.366,2026-10-28 22:02:17.260,4.46,6.394,6.167e-06,1.144
+69001,FOREST-19 (PATADASTRA) [+],4.775,69928,LEMUR-2-ALYX [+],4.776,2026-10-28 22:15:30.801,4.115,0.471,2.606e-07,0.97
+66651,OBJECT B [+],5.175,66661,SNUGLITE-III [+],4.704,2026-10-28 22:20:30.382,3.938,0.066,2.846e-07,0.928
+46914,KSM1-B [+],4.695,57375,STARLINK-6363 [+],4.621,2026-10-28 22:41:11.407,1.374,3.429,6.914e-05,0.331
+63986,SCS-01 E [+],4.75,64541,PULSAR-0 [+],4.754,2026-10-29 00:28:27.079,4.442,5.643,2.096e-07,1.117
+49390,YAOGAN-35 01A [+],4.903,63677,STARLINK-33816 [+],4.973,2026-10-29 00:40:07.996,2.701,1.141,6.035e-07,0.638
+48449,STARLINK-2655 [P],4.932,67854,STARLINK-36608 [+],4.867,2026-10-29 01:14:02.367,3.658,2.782,3.249e-05,0.875
+57335,STARLINK-6316 [+],4.78,68627,STP-S29A OBJECT A [+],4.787,2026-10-29 01:34:47.845,2.811,2.606,1.668e-05,0.671
+62430,RESURS-P 5 [+],4.815,68033,STARLINK-36947 [+],4.943,2026-10-29 01:56:02.022,4.838,3.103,5.6e-06,1.162
+49428,STARLINK-3121 [+],4.892,66689,AC1-003 [+],4.951,2026-10-29 02:26:24.717,3.965,12.96,5.063e-06,1.568
+62665,JAY-D2 [+],4.878,66697,IRIDE-MS1-EAGLET 2-3 [+],4.88,2026-10-29 02:58:12.613,1.096,0.585,3.673e-06,0.258
+47508,HAWK-2B [+],5.403,69957,STARLINK-37919 [+],5.051,2026-10-29 03:01:37.011,3.944,14.719,1.183e-07,2.23
+60211,TIANHUI 5C [+],7.517,60212,TIANHUI 5D [+],7.517,2026-10-29 03:23:02.437,0.388,0.001,3.104e-05,0.092
+52746,GHGSAT-C4 [+],4.91,58154,STARLINK-30797 [+],4.899,2026-10-29 04:09:28.996,4.635,13.174,3.618e-06,1.877
+58921,NEXSAT-1 [+],4.873,64151,STARLINK-34338 [+],5.073,2026-10-29 04:18:24.789,4.918,3.418,5.397e-06,1.186
+52364,STARLINK-3820 [+],4.88,61781,ASRTU-1 (AO-123) [+],4.875,2026-10-29 04:22:05.382,4.917,7.064,4.968e-06,1.288
+52845,STARLINK-4168 [+],5.084,62639,FLOCK 4G-3 [P],6.0,2026-10-29 04:34:42.309,2.071,6.443,2.855e-05,0.532
+64880,GEOSCAN 1 (RS92S1) [+],4.969,65207,STARLINK-34901 [+],5.165,2026-10-29 05:15:42.979,4.395,13.571,3.778e-06,1.895
+62669,JAY-D1 [+],4.977,66675,VEERY-0G [+],5.306,2026-10-29 05:20:16.324,2.386,0.757,7.743e-07,0.563
+64532,TRANSPORTER-14 OBJECT E [+],5.174,66448,STARLINK-35355 [+],5.17,2026-10-29 05:33:15.968,2.248,7.42,2.348e-05,0.596
+44941,STARLINK-1094 [P],4.947,59709,STARLINK-11088 [+],5.015,2026-10-29 05:59:34.042,3.296,1.914,4.035e-05,0.782
+27607,SAUDISAT 1C (SO-50) [+],5.487,65788,KUIPER-00176 [+],5.089,2026-10-29 07:01:52.317,1.484,2.437,1.98e-06,0.354
+55309,STARLINK-5315 [+],4.986,64530,DUTHSAT-2 [+],5.122,2026-10-29 07:08:27.679,3.932,9.465,7.006e-06,1.143
+47126,STARLINK-1812 [P],5.236,63914,STARLINK-34068 [+],5.238,2026-10-29 08:21:43.124,2.976,1.635,4.959e-05,0.705
+68981,IRIDE-MS2-HEO-7 [+],5.216,69874,CUBY-4 [+],5.352,2026-10-29 08:23:20.654,0.853,0.482,6.07e-06,0.201
+58629,STARLINK-31091 [+],5.179,64878,INNOSAT 16 (RS92S7) [+],5.054,2026-10-29 08:37:55.051,3.211,10.555,9.816e-06,0.999
+66392,STARLINK-35671 [+],5.151,68370,RASSVET-3 11 [+],5.155,2026-10-29 08:40:46.816,2.318,4.373,2.392e-05,0.567
+48096,STARLINK-2417 [P],5.128,65929,STARLINK-35175 [+],5.119,2026-10-29 08:51:34.649,3.142,0.018,4.469e-05,0.741
+60211,TIANHUI 5C [+],7.751,60212,TIANHUI 5D [+],7.751,2026-10-29 08:59:40.228,0.441,0.001,3.104e-05,0.092
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+60211,TIANHUI 5C [+],9.939,60212,TIANHUI 5D [+],9.939,2026-10-31 13:31:08.837,0.37,0.001,3.104e-05,0.092
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+48441,STARLINK-2702 [+],7.287,48449,STARLINK-2655 [P],7.502,2026-10-31 14:53:35.074,2.855,0.047,5.414e-05,0.673
+58199,TIANHUI 5A [+],10.088,58201,TIANHUI 5B [+],10.021,2026-10-31 14:55:39.390,1.707,0.002,2.125e-05,0.118
+60211,TIANHUI 5C [+],10.006,60212,TIANHUI 5D [+],10.006,2026-10-31 15:08:02.861,0.369,0.001,3.104e-05,0.092
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+58199,TIANHUI 5A [+],10.559,58201,TIANHUI 5B [+],10.491,2026-11-01 02:13:13.873,1.961,0.003,2.125e-05,0.118
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+58199,TIANHUI 5A [+],10.693,58201,TIANHUI 5B [+],10.626,2026-11-01 05:26:49.561,2.036,0.003,2.125e-05,0.118
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diff --git a/docs/validation/socrates_2026-10-25.tle b/docs/validation/socrates_2026-10-25.tle
new file mode 100644
index 00000000..7612caba
--- /dev/null
+++ b/docs/validation/socrates_2026-10-25.tle
@@ -0,0 +1,2196 @@
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+SJ-7
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+2 32791 97.7561 301.4630 0002328 155.8079 204.3274 15.36890039 7032
+FGRST (GLAST)
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+2 33053 25.5827 30.1225 0008363 49.6021 310.5247 15.24532985 15350
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diff --git a/src/tatc/analysis/__init__.py b/src/tatc/analysis/__init__.py
index be350e5a..e3dae03c 100644
--- a/src/tatc/analysis/__init__.py
+++ b/src/tatc/analysis/__init__.py
@@ -12,6 +12,7 @@
reduce_observations,
)
from .dop_sampling import DopMethod, compute_dop
+from .feature_sampling import collect_feature_observations
from .ground_track import (
collect_ground_pixels,
collect_ground_track,
@@ -53,6 +54,7 @@
from .ro_sampling import (
collect_ro_observations,
)
+from .sample_sizes import compute_sample_sizes
from .space_sampling import (
collect_space_observations,
)
@@ -64,6 +66,7 @@
"ScanDirection",
"aggregate_observations",
"collect_downlinks",
+ "collect_feature_observations",
"collect_ground_pixels",
"collect_ground_track",
"collect_limb_observations",
@@ -81,6 +84,7 @@
"compute_ground_track",
"compute_latencies",
"compute_platform_track",
+ "compute_sample_sizes",
"compute_radar_track",
"compute_region_access_periods",
"find_coincident_ground_track",
diff --git a/src/tatc/analysis/coincidence.py b/src/tatc/analysis/coincidence.py
index 57c4c0d6..83015857 100644
--- a/src/tatc/analysis/coincidence.py
+++ b/src/tatc/analysis/coincidence.py
@@ -57,41 +57,64 @@ def _get_empty_coincidence_frame() -> gpd.GeoDataFrame:
def _find_coincident_periods(
- periods: pd.DataFrame, min_platforms: int
-) -> list[tuple[pd.Timestamp, pd.Timestamp]]:
+ target: np.ndarray,
+ platform: np.ndarray,
+ start: np.ndarray,
+ end: np.ndarray,
+ min_platforms: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
Finds the maximal periods when at least a minimum number of platforms
- observe at once, sweeping over the start and end of each platform's
- (closed) observation periods.
+ observe each target at once, sweeping over the start and end of each
+ platform's (closed) observation periods, for all targets together.
Args:
- periods (pandas.DataFrame): The observation periods, with columns
- `platform`, `start`, and `end`.
+ target (numpy.ndarray): The target code of each observation period.
+ platform (numpy.ndarray): The platform code of each observation period.
+ start (numpy.ndarray): The start of each observation period
+ (`numpy.datetime64`).
+ end (numpy.ndarray): The end of each observation period
+ (`numpy.datetime64`).
min_platforms (int): The minimum number of platforms.
Returns:
- list[tuple[pandas.Timestamp, pandas.Timestamp]]: the start and end
- of each coincident period, in time order
+ tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]: the target code,
+ start, and end of each coincident period, in order of target and
+ time
"""
- starts, ends = [], []
- for _, platform in periods.groupby("platform"):
- # merge the platform's overlapping periods so that it counts once
- platform = platform.sort_values("start")
- group = (platform["start"] > platform["end"].shift().cummax()).cumsum()
- merged = platform.groupby(group).agg(start=("start", "min"), end=("end", "max"))
- starts.append(merged["start"])
- ends.append(merged["end"])
- times = pd.concat(starts + ends, ignore_index=True)
- events = pd.DataFrame(
- {"time": times, "change": np.repeat([1, -1], len(times) // 2)}
+ # merge the overlapping periods of each platform at each target so that
+ # it counts once
+ order = np.lexsort((start, platform, target))
+ target, platform = target[order], platform[order]
+ start, end = start[order], end[order]
+ first = np.r_[True, (target[1:] != target[:-1]) | (platform[1:] != platform[:-1])]
+ reach = pd.Series(end).groupby(np.cumsum(first)).cummax().to_numpy()
+ merged = np.cumsum(first | (start > np.r_[reach[:1], reach[:-1]]))
+ merged_target = target[np.r_[True, merged[1:] != merged[:-1]]]
+ merged_start = pd.Series(start).groupby(merged).min().to_numpy()
+ merged_end = pd.Series(end).groupby(merged).max().to_numpy()
+ # sweep the events of each target in time order, with starts before ends
+ # at the same time so that periods which touch count as coincident at
+ # that instant
+ event_target = np.concatenate((merged_target, merged_target))
+ event_time = np.concatenate((merged_start, merged_end))
+ change = np.repeat([1, -1], len(merged_target))
+ order = np.lexsort((-change, event_time, event_target))
+ event_target, event_time, change = (
+ event_target[order],
+ event_time[order],
+ change[order],
)
- # sweep the events in time order, with starts before ends at the same
- # time so that periods which touch count as coincident at that instant
- events = events.sort_values(["time", "change"], ascending=[True, False])
- active = events["change"].cumsum().to_numpy() >= min_platforms
- was_active = np.concatenate(([False], active[:-1]))
- return list(
- zip(events["time"][active & ~was_active], events["time"][~active & was_active])
+ active = (
+ pd.Series(change).groupby(event_target).cumsum().to_numpy() >= min_platforms
+ )
+ was_active = (
+ np.r_[False, active[:-1]] & np.r_[False, event_target[1:] == event_target[:-1]]
+ )
+ return (
+ event_target[active & ~was_active],
+ event_time[active & ~was_active],
+ event_time[~active & was_active],
)
@@ -99,6 +122,7 @@ def find_coincident_observations(
observations: gpd.GeoDataFrame,
min_platforms: int = 2,
max_time_difference: timedelta = timedelta(0),
+ instantaneous: bool = False,
) -> gpd.GeoDataFrame:
"""
Find coincident observations of points: the periods when at least
@@ -118,6 +142,15 @@ def find_coincident_observations(
that period, comma-separated and paired, in order of their start time.
`epoch` is the midpoint of the coincident period.
+ With `instantaneous`, each observation is reduced to its `epoch` (for a
+ scanning instrument, such as a `ConicalInstrument` or a
+ `PointedInstrument`, the time it samples the point, while its period is
+ the time the point is within its field of regard), so that observations
+ coincide if their epochs differ by at most `max_time_difference`. Each
+ coincident observation then spans the epochs of the observations that
+ contribute to it, from the first to the last (for two observations,
+ `end` less `start` is their time difference).
+
The result has the columns of `aggregate_observations` inputs, so
`aggregate_observations` and `reduce_observations` compute the access
and revisit of coincident observations.
@@ -129,6 +162,8 @@ def find_coincident_observations(
observing at once.
max_time_difference (datetime.timedelta): The maximum time
difference between coincident observations.
+ instantaneous (bool): True, to compare the observations' epochs
+ rather than their periods.
Returns:
geopandas.GeoDataFrame: The data frame of coincident observations,
@@ -144,40 +179,75 @@ def find_coincident_observations(
"observations must be of points (see find_coincident_ground_track "
"for the footprints of ground tracks)"
)
- records = []
- for _, gdf in observations.groupby(_get_target_keys(observations)):
- periods = pd.DataFrame(
- {
- "platform": gdf["satellite"],
- "start": gdf["start"] - half_difference,
- "end": gdf["end"] + half_difference,
- }
- )
- for start, end in _find_coincident_periods(periods, min_platforms):
- # each observation overlapping the period contributes to it
- contributors = gdf[
- (periods["start"] <= end) & (periods["end"] >= start)
- ].sort_values("start", kind="stable")
- pairs = list(
- dict.fromkeys(
- zip(contributors["satellite"], contributors["instrument"])
- )
- )
- records.append(
- {
- "target_hash": gdf["target_hash"].iloc[0],
- "geometry": gdf.geometry.iloc[0],
- "satellite": ", ".join(satellite for satellite, _ in pairs),
- "instrument": ", ".join(instrument for _, instrument in pairs),
- "start": start,
- "epoch": start + (end - start) / 2,
- "end": end,
- }
- )
- if len(records) == 0:
+ gdf = observations.reset_index(drop=True)
+ # targets (in sorted order of their keys) and platforms of the observations
+ target, _ = pd.MultiIndex.from_arrays(_get_target_keys(gdf)).factorize(sort=True)
+ platform, _ = pd.factorize(gdf["satellite"])
+
+ def naive_utc(times: pd.Series) -> np.ndarray:
+ return times.dt.tz_convert("UTC").dt.tz_localize(None).to_numpy()
+
+ # observation times: each period, or each epoch if instantaneous
+ observed_start = gdf["epoch"] if instantaneous else gdf["start"]
+ observed_end = gdf["epoch"] if instantaneous else gdf["end"]
+ start = naive_utc(observed_start - half_difference)
+ end = naive_utc(observed_end + half_difference)
+ period_target, period_start, period_end = _find_coincident_periods(
+ target, platform, start, end, min_platforms
+ )
+ if len(period_target) == 0:
return _get_empty_coincidence_frame()
+ # each observation overlapping a period contributes to it, in order of
+ # its start time
+ candidates = pd.DataFrame(
+ {
+ "target": target,
+ "widened_start": start,
+ "widened_end": end,
+ "start": naive_utc(observed_start),
+ "satellite": gdf["satellite"].to_numpy(),
+ "instrument": gdf["instrument"].to_numpy(),
+ }
+ )
+ contributors = pd.DataFrame(
+ {
+ "period": np.arange(len(period_target)),
+ "target": period_target,
+ "period_start": period_start,
+ "period_end": period_end,
+ }
+ ).merge(candidates, on="target")
+ contributors = contributors[
+ (contributors["widened_start"] <= contributors["period_end"])
+ & (contributors["widened_end"] >= contributors["period_start"])
+ ]
+ if instantaneous:
+ # each coincident observation spans the epochs of its contributors
+ period_start = contributors.groupby("period")["start"].min().to_numpy()
+ period_end = contributors.groupby("period")["start"].max().to_numpy()
+ contributors = (
+ contributors.sort_values(["period", "start"], kind="stable")
+ .drop_duplicates(["period", "satellite", "instrument"])
+ .groupby("period")
+ .agg(satellite=("satellite", ", ".join), instrument=("instrument", ", ".join))
+ )
+ # each target's hash and geometry, from its first observation
+ first = gdf.groupby(target).head(1).set_index(pd.Index(target).unique())
+ start = pd.Series(period_start).dt.tz_localize("UTC")
+ end = pd.Series(period_end).dt.tz_localize("UTC")
return (
- gpd.GeoDataFrame(records, crs=observations.crs)
+ gpd.GeoDataFrame(
+ {
+ "target_hash": first["target_hash"].loc[period_target].to_numpy(),
+ "geometry": first.geometry.loc[period_target].to_numpy(),
+ "satellite": contributors["satellite"].to_numpy(),
+ "instrument": contributors["instrument"].to_numpy(),
+ "start": start,
+ "epoch": start + (end - start) / 2,
+ "end": end,
+ },
+ crs=observations.crs,
+ )
.sort_values("start", kind="stable")
.reset_index(drop=True)
)
diff --git a/src/tatc/analysis/coverage_metrics.py b/src/tatc/analysis/coverage_metrics.py
index 0f7ee9ce..712a5e12 100644
--- a/src/tatc/analysis/coverage_metrics.py
+++ b/src/tatc/analysis/coverage_metrics.py
@@ -8,7 +8,9 @@
from __future__ import annotations
import geopandas as gpd
+import numpy as np
import pandas as pd
+import shapely
def _get_target_keys(gdf: gpd.GeoDataFrame) -> list[pd.Series]:
@@ -60,7 +62,8 @@ def aggregate_observations(observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
instant), not the mean of the constituent observations' own epochs.
Per-observation columns that lose their meaning once merged across
satellites and over a potentially much longer period -- e.g. `sat_alt`,
- `sat_az`, `sat_sunlit`, `solar_alt`, `solar_az`, `solar_time` -- are
+ `sat_az`, `sat_sunlit`, `solar_alt`, `solar_az`, `solar_time`,
+ `glint_angle` -- are
intentionally dropped, even if present on `observations`.
Args:
@@ -71,35 +74,63 @@ def aggregate_observations(observations: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
"""
if observations.empty:
return _get_empty_aggregate_frame()
- gdfs = []
- # split into constituent data frames for each target
- for _, gdf in observations.groupby(_get_target_keys(observations)):
- # sort the values by start datetime
- gdf = gdf.sort_values("start")
- # assign the observation group number based on overlapping start/end times
- gdf["obs"] = (gdf["start"] > gdf["end"].shift().cummax()).cumsum()
- # perform the aggregation to group overlapping observations
- gdf = gdf.dissolve(
- "obs",
- aggfunc={
- "target_hash": "first",
- "satellite": ", ".join, # type: ignore
- "instrument": ", ".join, # type: ignore
- "start": "min",
- "end": "max",
- },
- )
- # reassign epoch to the midpoint of the merged period, as a single
- # representative instant, rather than the mean of the constituent
- # observations' own (pre-merge) epochs
- gdf["epoch"] = gdf["start"] + (gdf["end"] - gdf["start"]) / 2
- # compute access and revisit metrics
- gdf["access"] = gdf["end"] - gdf["start"]
- gdf["revisit"] = gdf["start"] - gdf["end"].shift()
- # append to the list of data frames
- gdfs.append(gdf)
- # return a concatenated data frame and re-index
- return pd.concat(gdfs).reset_index(drop=True)
+ # targets (in sorted order of their keys), with each target's observations
+ # sorted by start (stably), for all targets together
+ target, _ = pd.MultiIndex.from_arrays(_get_target_keys(observations)).factorize(
+ sort=True
+ )
+ start = observations["start"].dt.tz_convert("UTC").dt.tz_localize(None).to_numpy()
+ end = observations["end"].dt.tz_convert("UTC").dt.tz_localize(None).to_numpy()
+ order = np.lexsort((start, target))
+ gdf = observations.iloc[order].reset_index(drop=True)
+ target, start, end = target[order], start[order], end[order]
+ # assign the observation group number based on overlapping start/end times:
+ # a group starts with each target, and with each observation starting after
+ # every earlier observation of the target has ended
+ first = np.concatenate([[True], target[1:] != target[:-1]])
+ reach = pd.Series(end).groupby(target).cummax().to_numpy()
+ group = np.cumsum(first | (start > np.concatenate([reach[:1], reach[:-1]]))) - 1
+ grouped = gdf.groupby(group, sort=False)
+ # the geometry of each group: the union of its observations' (for a region,
+ # the part of it observed during the period)
+ geometry = grouped.geometry.first().to_numpy()
+ counts = np.bincount(group)
+ members = np.split(gdf.geometry.to_numpy(), np.cumsum(counts)[:-1])
+ for g in np.flatnonzero(counts > 1):
+ geometry[g] = shapely.union_all(members[g])
+ aggregated = gpd.GeoDataFrame(
+ {
+ "target_hash": grouped["target_hash"].first(),
+ "satellite": grouped["satellite"]
+ .agg(", ".join)
+ .astype(gdf["satellite"].dtype),
+ "instrument": grouped["instrument"]
+ .agg(", ".join)
+ .astype(gdf["instrument"].dtype),
+ "start": grouped["start"].min(),
+ "end": grouped["end"].max(),
+ },
+ geometry=gpd.GeoSeries(
+ geometry, index=grouped["start"].min().index, crs=observations.crs
+ ),
+ )
+ aggregated = aggregated[
+ ["geometry", "target_hash", "satellite", "instrument", "start", "end"]
+ ]
+ # reassign epoch to the midpoint of the merged period, as a single
+ # representative instant, rather than the mean of the constituent
+ # observations' own (pre-merge) epochs
+ aggregated["epoch"] = (
+ aggregated["start"] + (aggregated["end"] - aggregated["start"]) / 2
+ )
+ # compute access and revisit metrics (the revisit of each target's first
+ # period is undefined)
+ aggregated["access"] = aggregated["end"] - aggregated["start"]
+ group_target = target[np.concatenate([[True], group[1:] != group[:-1]])]
+ revisit = aggregated["start"] - aggregated["end"].shift()
+ revisit[np.concatenate([[True], group_target[1:] != group_target[:-1]])] = pd.NaT
+ aggregated["revisit"] = revisit
+ return aggregated.reset_index(drop=True)
def _get_empty_reduce_frame() -> gpd.GeoDataFrame:
@@ -172,7 +203,9 @@ def grid_observations(
Both access (a per-event duration) and revisit (a time-between-events
duration, i.e. the reciprocal of a sampling rate) use a sample-weighted
mean -- arithmetic for access, harmonic for revisit, since revisit
- needs to be averaged as a rate to stay a representative statistic.
+ needs to be averaged as a rate to stay a representative statistic. The
+ revisit is averaged over the points with a revisit (at least two
+ samples), and is undefined (NaT) for a cell with none.
Args:
reduced_observations (geopandas.GeoDataFrame): The reduced observations.
@@ -196,9 +229,11 @@ def grid_observations(
# with a dict of {column: function} only ever hands a custom callable
# its own column's Series, never a sibling column like "samples" needed
# to compute a weighted statistic within the callable. The weighted
- # harmonic mean of revisit is sum(samples) / sum(samples/revisit).
+ # harmonic mean of revisit is sum(samples) / sum(samples/revisit), over
+ # the points with a revisit.
gdf["access_x_samples"] = gdf["access"] * gdf["samples"]
- gdf["samples_over_revisit"] = gdf["samples"] / gdf["revisit"]
+ gdf["revisit_samples"] = gdf["samples"].where(gdf["revisit"].notna(), 0)
+ gdf["samples_over_revisit"] = (gdf["samples"] / gdf["revisit"]).fillna(0)
gdf = (
cells.sjoin(gdf, how="inner", predicate="contains")
.dissolve(
@@ -206,6 +241,7 @@ def grid_observations(
aggfunc={
"samples": "sum",
"access_x_samples": "sum",
+ "revisit_samples": "sum",
"samples_over_revisit": "sum",
},
)
@@ -214,8 +250,12 @@ def grid_observations(
# finish the aggregation: sample-weighted arithmetic mean for access,
# sample-weighted harmonic mean for revisit
gdf["access"] = gdf["access_x_samples"] / gdf["samples"]
- gdf["revisit"] = gdf["samples"] / gdf["samples_over_revisit"]
- gdf = gdf.drop(columns=["access_x_samples", "samples_over_revisit"])
+ gdf["revisit"] = (gdf["revisit_samples"] / gdf["samples_over_revisit"]).where(
+ gdf["samples_over_revisit"] > 0
+ )
+ gdf = gdf.drop(
+ columns=["access_x_samples", "revisit_samples", "samples_over_revisit"]
+ )
# convert access and revisit from numeric values after aggregation
gdf["access"] = pd.to_timedelta(gdf["access"], unit="s")
gdf["revisit"] = pd.to_timedelta(gdf["revisit"], unit="s")
diff --git a/src/tatc/analysis/feature_sampling.py b/src/tatc/analysis/feature_sampling.py
new file mode 100644
index 00000000..3adcbd30
--- /dev/null
+++ b/src/tatc/analysis/feature_sampling.py
@@ -0,0 +1,205 @@
+"""
+Methods to sample features: regions that exist for a period of time, such as
+the storms or cloud systems of a nature run in an observing system
+simulation experiment (OSSE), by the footprints of satellites' instruments.
+
+@author: Paul T. Grogan
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import shapely
+from pyproj import Geod
+from skyfield.api import wgs84
+
+from ..schemas import Satellite
+from ..utils.time import _to_time
+from .check import _check_satellites
+from .ground_track import collect_ground_track
+
+_GEOD = Geod(ellps="WGS84")
+
+
+def _get_empty_feature_observations() -> gpd.GeoDataFrame:
+ """
+ Gets an empty data frame for feature observation results.
+
+ Returns:
+ geopandas.GeoDataFrame: Empty data frame.
+ """
+ columns = {
+ "feature": pd.Series([], dtype="int"),
+ "satellite": pd.Series([], dtype="str"),
+ "instrument": pd.Series([], dtype="str"),
+ "time": pd.Series([], dtype="datetime64[ns, utc]"),
+ "viewed_fraction": pd.Series([], dtype="float"),
+ "viewed_area": pd.Series([], dtype="float"),
+ "sat_alt": pd.Series([], dtype="float"),
+ "sat_az": pd.Series([], dtype="float"),
+ "sat_range": pd.Series([], dtype="float"),
+ "sat_height": pd.Series([], dtype="float"),
+ "geometry": pd.Series([], dtype="object"),
+ }
+ return gpd.GeoDataFrame(columns, crs="EPSG:4326")
+
+
+def _check_features(features: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+ """
+ Checks features: a data frame of geometries with the `start` and `end`
+ of the period in which each exists.
+
+ Args:
+ features (geopandas.GeoDataFrame): The features.
+
+ Returns:
+ geopandas.GeoDataFrame: the features, in longitude/latitude coordinates.
+ """
+ if not isinstance(features, gpd.GeoDataFrame):
+ raise TypeError(
+ f"features must be a GeoDataFrame, not a {type(features).__name__}"
+ )
+ for column in ("start", "end"):
+ if column not in features.columns:
+ raise ValueError(f"features must have a '{column}' column")
+ if not isinstance(features[column].dtype, pd.DatetimeTZDtype):
+ raise ValueError(f"features '{column}' must be timezone-aware times")
+ if features.crs is not None and not features.crs.equals("EPSG:4326"):
+ features = features.to_crs("EPSG:4326")
+ return features
+
+
+def collect_feature_observations(
+ features: gpd.GeoDataFrame,
+ satellites: Satellite | list[Satellite],
+ times: list[datetime],
+ instrument_index: int | None = None,
+) -> gpd.GeoDataFrame:
+ """
+ Collect the observations of features: regions that exist for a period
+ of time (between the `start` and `end` of each feature), such as the
+ storms or cloud systems of a nature run in an observing system
+ simulation experiment (OSSE). The footprint of each instrument at each
+ time (see `tatc.analysis.ground_track.collect_ground_track`) samples the
+ features that exist at that time (from their `start`, inclusive, to
+ their `end`, exclusive). The footprints should tile each swath, for
+ example with an along-track field of view tailored to the time step
+ (see `tatc.utils.compute_along_track_field_of_view`).
+
+ Each observation is one satellite's (and instrument's) view of a
+ feature: the part of the feature within the valid footprints of all
+ times at which the feature exists, which is only part of it if the
+ feature extends beyond the swath. Its time is that of the footprint
+ that covers most of the feature, and the viewing geometry (the
+ satellite's elevation angle `sat_alt` and azimuth `sat_az`, seen from
+ the centroid of the viewed part, its slant range `sat_range`, and its
+ height above the WGS 84 ellipsoid `sat_height`) is that at this time.
+ For example, the size of a footprint at the viewed part (from its size
+ at nadir) grows with `sat_range / sat_height` along the line of sight
+ and, across it, by the secant of the zenith angle, `90 - sat_alt`.
+
+ Args:
+ features (geopandas.GeoDataFrame): The features: their geometries,
+ and the `start` and `end` (timezone-aware) of the period in
+ which each exists.
+ satellites (Satellite | list[Satellite]): The observing satellite(s).
+ times (list[datetime.datetime]): The times at which to sample.
+ instrument_index (int | None): The index of the observing instrument
+ in each satellite, or `None` (default) for every instrument
+ of each satellite.
+
+ Returns:
+ geopandas.GeoDataFrame: The data frame of observations: the feature
+ (its index label in `features`), satellite, instrument, time, the
+ viewed part (geometry), its fraction of the feature's area
+ (`viewed_fraction`), its area on the WGS 84 ellipsoid (square
+ meters, `viewed_area`), and the viewing geometry (degrees and
+ meters), sorted by feature and time.
+ """
+ features = _check_features(features)
+ satellites = _check_satellites(satellites)
+ if len(features) == 0 or len(times) == 0:
+ return _get_empty_feature_observations()
+ # the footprints, clipped to the bounds of the features
+ mask = shapely.box(*features.total_bounds)
+ track = collect_ground_track(
+ satellites, times, instrument_index=instrument_index, mask=mask
+ )
+ track = track[track.valid_obs].reset_index(drop=True)
+ if len(track) == 0:
+ return _get_empty_feature_observations()
+ footprints = shapely.force_2d(track.geometry.to_numpy())
+ shapes = features.geometry.to_numpy()
+ # the pieces: each feature's overlap with each footprint while it exists
+ feature, footprint = shapely.STRtree(footprints).query(
+ shapes, predicate="intersects"
+ )
+ time = pd.DatetimeIndex(track["time"])
+ exists = np.asarray(
+ (pd.DatetimeIndex(features["start"])[feature] <= time[footprint])
+ & (time[footprint] < pd.DatetimeIndex(features["end"])[feature])
+ )
+ feature, footprint = feature[exists], footprint[exists]
+ pieces = pd.DataFrame(
+ {
+ "feature": feature,
+ "satellite": track["satellite"].to_numpy()[footprint],
+ "instrument": track["instrument"].to_numpy()[footprint],
+ "time": time[footprint],
+ "geometry": shapely.intersection(shapes[feature], footprints[footprint]),
+ }
+ )
+ pieces["area"] = shapely.area(pieces["geometry"].to_numpy())
+ pieces = pieces[pieces["area"] > 0]
+ if len(pieces) == 0:
+ return _get_empty_feature_observations()
+ # each observation: the union of the pieces of a feature viewed by a
+ # satellite's instrument, at the time of the piece with the largest area
+ observations = (
+ pieces.sort_values("area", ascending=False, kind="stable")
+ .groupby(["feature", "satellite", "instrument"], sort=False)
+ .agg(time=("time", "first"), geometry=("geometry", shapely.union_all))
+ .reset_index()
+ )
+ viewed = observations["geometry"].to_numpy()
+ shape = shapes[observations["feature"].to_numpy()]
+ observations["viewed_fraction"] = np.minimum(
+ 1, shapely.area(viewed) / shapely.area(shape)
+ )
+ observations["viewed_area"] = [
+ abs(_GEOD.geometry_area_perimeter(geometry)[0]) for geometry in viewed
+ ]
+ # the viewing geometry from the centroid of the viewed part
+ centroid = shapely.centroid(viewed)
+ longitude, latitude = shapely.get_x(centroid), shapely.get_y(centroid)
+ for column in ("sat_alt", "sat_az", "sat_range", "sat_height"):
+ observations[column] = np.nan
+ orbits = {satellite.name: satellite.orbit.to_gp_orbit() for satellite in satellites}
+ for name, group in observations.groupby("satellite"):
+ index = group.index.to_numpy()
+ orbit_track = orbits[name].get_orbit_track_at_time(
+ _to_time(list(group["time"]))
+ )
+ target = wgs84.latlon(latitude[index], longitude[index])
+ altitude, azimuth, distance = (orbit_track - target.at(orbit_track.t)).altaz()
+ observations.loc[index, "sat_alt"] = np.atleast_1d(altitude.degrees)
+ observations.loc[index, "sat_az"] = np.atleast_1d(azimuth.degrees)
+ observations.loc[index, "sat_range"] = np.atleast_1d(distance.m)
+ observations.loc[index, "sat_height"] = np.atleast_1d(
+ wgs84.height_of(orbit_track).m
+ )
+ # the feature's index label in the features
+ observations["feature"] = features.index.to_numpy()[observations["feature"]]
+ observations["time"] = pd.DatetimeIndex(observations["time"]).as_unit("ns")
+ observations = gpd.GeoDataFrame(
+ observations[list(_get_empty_feature_observations().columns)],
+ geometry="geometry",
+ crs="EPSG:4326",
+ )
+ return observations.sort_values(["feature", "time"], kind="stable").reset_index(
+ drop=True
+ )
diff --git a/src/tatc/analysis/ground_track.py b/src/tatc/analysis/ground_track.py
index 885a5ee1..1f6d6877 100644
--- a/src/tatc/analysis/ground_track.py
+++ b/src/tatc/analysis/ground_track.py
@@ -19,6 +19,7 @@
from ..constants import de421
from ..schemas import AllInstruments, PointedInstrument, Satellite
from ..utils.geometry import split_polygon
+from ..utils.glint import compute_glint_angle
from ..utils.time import _index_orbit_track, _to_time
from .check import (
_check_satellite,
@@ -163,6 +164,7 @@ def collect_ground_track(
sat_altaz (bool): `True` to include satellite altitude/azimuth angles
for the sub-satellite point.
solar_altaz (bool): `True` to include solar altitude/azimuth angles
+ and the glint angle (see `tatc.utils.compute_glint_angle`)
for the sub-satellite point.
Returns:
@@ -205,7 +207,7 @@ def _collect_ground_track(
and poles (see `_get_mask_geometry`).
"""
# select the observing instrument
- instrument = satellite.instruments[instrument_index]
+ instrument = satellite.get_instrument(instrument_index)
if mask is not None and len(times) > 1:
# propagate orbit only where the footprint intersects the mask,
# reusing the footprints computed to cull it
@@ -249,6 +251,8 @@ def _collect_ground_track(
)
track["solar_alt"] = solar_altaz_data[0].degrees # type: ignore
track["solar_az"] = solar_altaz_data[1].degrees # type: ignore
+ # append glint angle column (at the footprint center)
+ track["glint_angle"] = np.atleast_1d(compute_glint_angle(orbit_track, target))
if mask is not None:
track = gpd.clip(track, mask)
@@ -288,12 +292,12 @@ def compute_ground_track(
_check_satellite(satellite)
track = collect_ground_track(satellite, times, instrument_index, elevation, mask)
if not track.empty:
- # assign orbit identifier
+ # assign orbit identifier, from the period of the element that
+ # propagates the orbit at each time (see `get_element_index`)
+ orbit = satellite.orbit.to_gp_orbit()
track["orbit_id"] = [
(time - times[0])
- // satellite.orbit.to_gp_orbit()
- .get_closest_element(time)
- .get_orbit_period()
+ // orbit.elements[orbit.get_element_index(time)].get_orbit_period()
for time in track.time
]
# filter to valid observations and dissolve
@@ -333,7 +337,8 @@ def collect_ground_pixels(
the propagated orbit to the (buffered) region of interest,
improving performance for small areas.
sat_altaz (bool): `True` to include satellite altitude/azimuth angles.
- solar_altaz (bool): `True` to include solar altitude/azimuth angles.
+ solar_altaz (bool): `True` to include solar altitude/azimuth angles
+ and the glint angle (see `tatc.utils.compute_glint_angle`).
Returns:
geopandas.GeoDataFrame: The data frame of collected ground pixels results:
@@ -375,7 +380,7 @@ def _collect_ground_pixels(
and poles (see `_get_mask_geometry`).
"""
# select the observing instrument
- instrument = satellite.instruments[instrument_index]
+ instrument = satellite.get_instrument(instrument_index)
if not isinstance(instrument, PointedInstrument) or not instrument.is_rectangular:
raise ValueError(
"Ground pixels are only compatible with rectangular PointedInstrument instances"
@@ -448,6 +453,18 @@ def _collect_ground_pixels(
]
gdf["solar_alt"] = [altaz[0].degrees for altaz in solar_altaz_data] # type: ignore
gdf["solar_az"] = [altaz[1].degrees for altaz in solar_altaz_data] # type: ignore
+ # append glint angle column (at each pixel)
+ gdf["glint_angle"] = [
+ float(
+ compute_glint_angle(
+ orbit_track[i],
+ wgs84.latlon(
+ record["geometry"].y, record["geometry"].x, record["geometry"].z
+ ),
+ )
+ )
+ for i, record in indexed_records
+ ]
if mask is not None:
gdf = gpd.clip(gdf, mask)
diff --git a/src/tatc/analysis/latency_sampling.py b/src/tatc/analysis/latency_sampling.py
index 62b2995c..56b52e85 100644
--- a/src/tatc/analysis/latency_sampling.py
+++ b/src/tatc/analysis/latency_sampling.py
@@ -90,11 +90,11 @@ def collect_downlinks(
]
# build the dataframe
if len(records) > 0:
- gdf = (
- gpd.GeoDataFrame(records, crs="EPSG:4326")
- .sort_values("start")
- .reset_index(drop=True)
- )
+ gdf = gpd.GeoDataFrame(records, crs="EPSG:4326")
+ # times in nanoseconds (as in the empty frame and the observations)
+ for column in ["start", "end", "epoch"]:
+ gdf[column] = gdf[column].dt.as_unit("ns")
+ gdf = gdf.sort_values("start").reset_index(drop=True)
else:
gdf = _get_empty_downlinks_frame()
return gdf
@@ -177,6 +177,10 @@ def compute_latencies(
"end": "downlink_end",
}
)
+ # merge keys must share a unit (frames may come from other sources, such as files)
+ obs["end"] = obs["end"].dt.as_unit("ns")
+ for column in ["downlink_start", "downlinked", "downlink_end"]:
+ contacts[column] = contacts[column].dt.as_unit("ns")
# pair each observation with the first downlink that starts after it ends
obs = pd.merge_asof(
obs,
diff --git a/src/tatc/analysis/orbit_track.py b/src/tatc/analysis/orbit_track.py
index 06f96ad6..af091964 100644
--- a/src/tatc/analysis/orbit_track.py
+++ b/src/tatc/analysis/orbit_track.py
@@ -188,7 +188,7 @@ def _collect_orbit_track(
`collect_orbit_track`).
"""
# select the observing instrument
- instrument = satellite.instruments[instrument_index]
+ instrument = satellite.get_instrument(instrument_index)
# propagate orbit
orbit_track = satellite.orbit.to_gp_orbit().get_orbit_track_at_time(t)
# geodetic (WGS84) position of the satellite itself (not the zero-elevation
diff --git a/src/tatc/analysis/point_sampling.py b/src/tatc/analysis/point_sampling.py
index 6f006a77..9130fa28 100644
--- a/src/tatc/analysis/point_sampling.py
+++ b/src/tatc/analysis/point_sampling.py
@@ -7,6 +7,7 @@
from __future__ import annotations
+from collections.abc import Callable
from datetime import datetime, timedelta, timezone
import geopandas as gpd
@@ -14,11 +15,18 @@
import pandas as pd
from shapely import geometry as geo
from skyfield.api import wgs84
-from skyfield.toposlib import GeographicPosition
+from skyfield.framelib import itrs
from ..constants import EARTH_POLAR_RADIUS, timescale
-from ..schemas import ConicalInstrument, Point, PointedInstrument, Satellite
-from ..utils.geometry import _get_point_coordinates, hash_geometry
+from ..schemas import (
+ ConicalInstrument,
+ GeneralPerturbationsOrbit,
+ Instrument,
+ Point,
+ PointedInstrument,
+ Satellite,
+)
+from ..utils.geometry import _get_point_coordinates, _hash_geometries
from ..utils.observation import (
compute_max_access_time,
compute_min_elevation_angle,
@@ -26,10 +34,11 @@
from ..utils.orbital import compute_apoapsis_radius
from ..utils.projection import (
ViewGeometry,
+ _compute_line_of_sight,
+ _compute_nadir_axes,
_compute_view_frame,
- compute_cone_and_azimuth,
)
-from ..utils.computation import TimeRequest, _run, _run_together
+from ..utils.computation import TimeRequest, _run
from ..utils.time import _index_orbit_track, _to_time, _to_time_from_offsets
from .check import (
_check_satellite,
@@ -41,10 +50,10 @@
_warn_low_perigees,
)
from .sampling import (
+ _assemble_periods,
_build_observation_frame,
_find_crossings,
_get_empty_coverage_frame,
- _refine_access_periods,
)
@@ -270,38 +279,606 @@ def _compute_access_periods(
)
-def _get_view_crossing_times(
- target: GeographicPosition,
+_SCREEN_STEP = 60.0
+"""
+The maximum time step (seconds) of the coarse orbit track from which the
+passes of many points are screened together (see `_collect_batch_observations`).
+"""
+_SCREEN_MARGIN = 1.0
+"""
+The angle (degrees) added to the radius about the sub-satellite point within
+which points are screened, for the curvature of the sub-satellite path between
+samples and the difference between the geodetic and geocentric verticals.
+"""
+_PART_DURATION = 600.0
+"""
+The maximum duration (seconds) of the parts into which a screened window is
+divided, so that the residual has few extrema within each part.
+"""
+_PART_SAMPLES = 11
+"""The number of samples of the residual in each part of a screened window."""
+_SCREEN_BLOCK = 64
+"""The number of time steps screened against all points at once."""
+_BLOCK_POINT_DAYS = 10000
+"""
+The number of point-days (points times days of the analysis period) whose
+observations are refined together, which bounds the memory of the vectorized
+stages.
+"""
+
+
+def _get_screening_angle(
+ max_radius: float, min_radius: float, half_angle: float
+) -> float:
+ """
+ Get the Earth central angle (degrees) from the sub-satellite point within
+ which a point can lie within a field of regard: on a sphere of the
+ minimum radius (the smallest distance of a point from the Earth's
+ center), seen from the maximum radius (of the satellite), at a nadir
+ angle of at most the half angle, and above the horizon. The angle grows
+ with the satellite's radius and decreases with the point's, so it bounds
+ the angle of every point and satellite position.
+
+ Args:
+ max_radius (float): The maximum radius (meters) of the satellite.
+ min_radius (float): The minimum radius (meters) of the points.
+ half_angle (float): The half angle (degrees) of the field of regard.
+
+ Returns:
+ float: the Earth central angle (degrees).
+ """
+ horizon = np.arccos(min_radius / max_radius)
+ if half_angle >= 90:
+ return float(np.degrees(horizon))
+ ratio = max_radius / min_radius * np.sin(np.radians(half_angle))
+ if ratio >= 1:
+ return float(np.degrees(horizon))
+ return float(np.degrees(min(np.arcsin(ratio) - np.radians(half_angle), horizon)))
+
+
+def _screen_windows(
+ directions: np.ndarray, units: np.ndarray, radius: np.ndarray
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+ """
+ Screen the windows in which points may be observed: the runs of
+ consecutive time steps in which a point lies within an Earth central
+ angle of the sub-satellite path. The path between two samples is bounded
+ by the circle about their mean direction whose radius is half of the
+ angle between them, so a point is screened in a step if its angle from
+ the mean direction is at most the radius plus that half angle.
+
+ Args:
+ directions (numpy.ndarray): The unit directions of the satellite from
+ the Earth's center at each sample, shape (3, M).
+ units (numpy.ndarray): The unit directions of the points from the
+ Earth's center, shape (3, N).
+ radius (numpy.ndarray): The Earth central angle (radians) of each
+ point, shape (N,).
+
+ Returns:
+ tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]: the point, first
+ step, and last step of each window (step `i` spans samples `i`
+ and `i + 1`), sorted by point and first step.
+ """
+ middle = directions[:, :-1] + directions[:, 1:]
+ middle = middle / np.linalg.norm(middle, axis=0)
+ half_step = (
+ np.arccos(
+ np.clip(np.sum(directions[:, :-1] * directions[:, 1:], axis=0), -1, 1)
+ )
+ / 2
+ )
+ points, steps = [], []
+ for block in range(0, middle.shape[1], _SCREEN_BLOCK):
+ m = middle[:, block : block + _SCREEN_BLOCK]
+ # element-wise, so that each point's result does not depend on the others
+ cosine = (
+ units[0][:, None] * m[0]
+ + units[1][:, None] * m[1]
+ + units[2][:, None] * m[2]
+ )
+ hits = cosine >= np.cos(
+ np.minimum(
+ np.pi, radius[:, None] + half_step[block : block + _SCREEN_BLOCK]
+ )
+ )
+ point, step = np.nonzero(hits)
+ points.append(point)
+ steps.append(step + block)
+ point, step = np.concatenate(points), np.concatenate(steps)
+ order = np.lexsort((step, point))
+ point, step = point[order], step[order]
+ if len(point) == 0:
+ return point, step, step
+ first = np.ones(len(point), dtype=bool)
+ first[1:] = (point[1:] != point[:-1]) | (step[1:] != step[:-1] + 1)
+ last = np.ones(len(point), dtype=bool)
+ last[:-1] = first[1:]
+ return point[first], step[first], step[last]
+
+
+def _find_hidden_minima(
+ evaluate: Callable[[np.ndarray, np.ndarray], TimeRequest],
+ samples: np.ndarray,
+ values: np.ndarray,
+ index: np.ndarray,
+) -> TimeRequest:
+ """
+ Find the minima of a residual between its samples that may be negative
+ although every sample near them is positive (as when a point grazes a
+ narrow field of regard between samples), as a computation (see
+ `tatc.utils.computation.TimeRequest`). Each local minimum of the samples
+ of a part that is positive but no greater than the larger change of the
+ residual to its neighboring samples (which bounds how far the residual
+ can dip between them) is refined by golden-section search between its
+ neighbors, stopping when a negative residual is found.
+
+ Args:
+ evaluate (Callable[[numpy.ndarray, numpy.ndarray], TimeRequest]): The
+ computation of the residual at times (seconds) for the indices.
+ samples (numpy.ndarray): The sample times (seconds) of each part,
+ shape (P, K).
+ values (numpy.ndarray): The residual at the samples, shape (P, K).
+ index (numpy.ndarray): The index of the point of each part, shape (P,).
+
+ Returns:
+ TimeRequest: the computation of the part, time (seconds), and
+ residual of each negative minimum found (tuple[numpy.ndarray,
+ numpy.ndarray, numpy.ndarray]).
+ """
+ padded = np.pad(values, ((0, 0), (1, 1)), constant_values=np.inf)
+ left, right = padded[:, :-2], padded[:, 2:]
+ change = np.maximum(
+ np.where(np.isfinite(left), left - values, 0),
+ np.where(np.isfinite(right), right - values, 0),
+ )
+ part, k = np.nonzero(
+ (values > 0) & (values <= left) & (values <= right) & (values <= change)
+ )
+ if len(part) == 0:
+ empty = np.array([], dtype=float)
+ return np.array([], dtype=int), empty, empty
+ count = samples.shape[1]
+ lower = samples[part, np.maximum(k - 1, 0)]
+ upper = samples[part, np.minimum(k + 1, count - 1)]
+ point = index[part]
+ ratio = (np.sqrt(5) - 1) / 2
+ c, d = upper - ratio * (upper - lower), lower + ratio * (upper - lower)
+ f_c = yield from evaluate(c, point)
+ f_d = yield from evaluate(d, point)
+ found_time = np.where(f_c <= f_d, c, d)
+ found_value = np.minimum(f_c, f_d)
+ for _ in range(40):
+ active = (found_value > 0) & (upper - lower > 1e-3)
+ if not np.any(active):
+ break
+ keep_left = f_c <= f_d
+ # shrink each bracket toward its smaller interior value
+ upper = np.where(active & keep_left, d, upper)
+ lower = np.where(active & ~keep_left, c, lower)
+ x = np.where(
+ keep_left, upper - ratio * (upper - lower), lower + ratio * (upper - lower)
+ )
+ f_x = np.full(len(x), np.inf)
+ f_x[active] = yield from evaluate(x[active], point[active])
+ c, f_c, d, f_d = (
+ np.where(active & keep_left, x, np.where(active, d, c)),
+ np.where(active & keep_left, f_x, np.where(active, f_d, f_c)),
+ np.where(active & keep_left, c, np.where(active, x, d)),
+ np.where(active & keep_left, f_c, np.where(active, f_x, f_d)),
+ )
+ better = f_x < found_value
+ found_time = np.where(better, x, found_time)
+ found_value = np.where(better, f_x, found_value)
+ negative = found_value <= 0
+ return part[negative], found_time[negative], found_value[negative]
+
+
+def _merge_window_periods(
+ parts: list[list[tuple[float, float]]], window: np.ndarray
+) -> list[list[tuple[float, float]]]:
+ """
+ Merge the periods of the consecutive parts of each window that meet at a
+ boundary between parts.
+
+ Args:
+ parts (list[list[tuple[float, float]]]): The periods (seconds) of each
+ part, in order of window and time.
+ window (numpy.ndarray): The window of each part.
+
+ Returns:
+ list[list[tuple[float, float]]]: the periods of each window.
+ """
+ windows: list[list[tuple[float, float]]] = [
+ [] for _ in range(int(window.max()) + 1 if len(window) else 0)
+ ]
+ for periods, w in zip(parts, window):
+ merged = windows[w]
+ for left, right in periods:
+ if merged and merged[-1][1] == left:
+ merged[-1] = (merged[-1][0], right)
+ else:
+ merged.append((left, right))
+ return windows
+
+
+def _refine_window_periods(
+ residual: Callable[[np.ndarray, np.ndarray], TimeRequest],
+ window_point: np.ndarray,
+ window_lower: np.ndarray,
+ window_upper: np.ndarray,
+) -> TimeRequest:
+ """
+ Refine the periods in which a residual (of a time and a point) is not
+ positive within screened windows, as a computation (see
+ `tatc.utils.computation.TimeRequest`). Each window is divided into parts
+ of at most `_PART_DURATION`, each part is sampled at `_PART_SAMPLES`
+ times, minima of the residual between samples are found (see
+ `_find_hidden_minima`), each change of sign is refined to a millisecond
+ (see `tatc.analysis.sampling._find_crossings`), and the periods of
+ consecutive parts that meet are merged.
+
+ Args:
+ residual (Callable[[numpy.ndarray, numpy.ndarray], TimeRequest]): The
+ computation of the residual at times (seconds) for the points.
+ window_point (numpy.ndarray): The point of each window.
+ window_lower (numpy.ndarray): The start of each window (seconds).
+ window_upper (numpy.ndarray): The end of each window (seconds).
+
+ Returns:
+ TimeRequest: the computation of the point, start, and end (seconds)
+ of each period (tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]).
+ """
+ counts = np.maximum(
+ 1, np.ceil((window_upper - window_lower) / _PART_DURATION).astype(int)
+ )
+ window = np.repeat(np.arange(len(counts)), counts)
+ offset = np.arange(len(window)) - np.repeat(np.cumsum(counts) - counts, counts)
+ width = (window_upper - window_lower)[window] / counts[window]
+ # each part ends exactly where the next begins, so that their periods merge
+ part_lower = window_lower[window] + offset * width
+ part_upper = np.where(
+ offset == counts[window] - 1,
+ window_upper[window],
+ window_lower[window] + (offset + 1) * width,
+ )
+ part_point = window_point[window]
+ samples = part_lower[:, None] + (part_upper - part_lower)[:, None] * np.linspace(
+ 0, 1, _PART_SAMPLES
+ )
+ samples[:, -1] = part_upper
+ values = np.reshape(
+ (yield from residual(samples.ravel(), np.repeat(part_point, _PART_SAMPLES))),
+ samples.shape,
+ )
+ part, dip_time, dip_value = yield from _find_hidden_minima(
+ residual, samples, values, part_point
+ )
+ sample_list, value_list = list(samples), list(values)
+ for p in np.unique(part):
+ times = np.concatenate([sample_list[p], dip_time[part == p]])
+ order = np.argsort(times, kind="stable")
+ sample_list[p] = times[order]
+ value_list[p] = np.concatenate([value_list[p], dip_value[part == p]])[order]
+ brackets = [
+ (i, j)
+ for i, value in enumerate(value_list)
+ for j in np.flatnonzero((value[:-1] <= 0) != (value[1:] <= 0))
+ ]
+ crossings = {}
+ if len(brackets) > 0:
+ bracket_point = part_point[[i for i, _ in brackets]]
+ crossing, _ = yield from _find_crossings(
+ lambda seconds, index: residual(seconds, bracket_point[index]),
+ np.array([sample_list[i][j] for i, j in brackets]),
+ np.array([sample_list[i][j + 1] for i, j in brackets]),
+ )
+ crossings = dict(zip(brackets, crossing))
+ window_periods = _merge_window_periods(
+ _assemble_periods(sample_list, value_list, crossings), window
+ )
+ period_point = np.array(
+ [window_point[w] for w, periods in enumerate(window_periods) for _ in periods],
+ dtype=int,
+ )
+ lower = np.array([left for periods in window_periods for left, _ in periods])
+ upper = np.array([right for periods in window_periods for _, right in periods])
+ return period_point, lower, upper
+
+
+def _get_batch_epochs(
+ orbit: GeneralPerturbationsOrbit,
+ instrument: Instrument,
+ xyz: np.ndarray,
+ period_point: np.ndarray,
+ lower: np.ndarray,
+ upper: np.ndarray,
+ reference: pd.Timestamp,
+) -> TimeRequest:
+ """
+ Get the observation epochs of periods, as a computation (see
+ `tatc.utils.computation.TimeRequest`): the time a pointed instrument's
+ view sweeps over the point (see `_get_batch_view_crossing_times`), the
+ times the point crosses a conical instrument's cone (see
+ `_get_batch_cone_crossing_times`), or otherwise the period's midpoint.
+
+ Args:
+ orbit (GeneralPerturbationsOrbit): The orbit.
+ instrument (Instrument): The observing instrument.
+ xyz (numpy.ndarray): The Earth-fixed coordinates (meters) of the
+ points, shape (3, N).
+ period_point (numpy.ndarray): The point of each period.
+ lower (numpy.ndarray): The start of each period (seconds).
+ upper (numpy.ndarray): The end of each period (seconds).
+ reference (pandas.Timestamp): The reference time of the seconds.
+
+ Returns:
+ TimeRequest: the computation of the point, period, and time
+ (seconds) of each epoch (tuple[numpy.ndarray, numpy.ndarray,
+ numpy.ndarray]).
+ """
+ if isinstance(instrument, PointedInstrument):
+ epochs = yield from _get_batch_view_crossing_times(
+ orbit, instrument, xyz, period_point, lower, upper, reference
+ )
+ return period_point, np.arange(len(lower)), epochs
+ if isinstance(instrument, ConicalInstrument):
+ epoch_period, epochs = yield from _get_batch_cone_crossing_times(
+ orbit, instrument, xyz, period_point, lower, upper, reference
+ )
+ return period_point[epoch_period], epoch_period, epochs
+ return period_point, np.arange(len(lower)), (lower + upper) / 2
+
+
+def _collect_batch_observations(
+ points: list[Point | geo.Point],
satellite: Satellite,
+ start: datetime,
+ end: datetime,
+ instrument_index: int = 0,
+ omit_solar: bool = True,
+) -> TimeRequest:
+ """
+ Collect single satellite observations of many points together, as a
+ computation (see `collect_observations` and
+ `tatc.utils.computation.TimeRequest`). Each point's observations do not
+ depend on the other points (to within a microsecond), but each stage is
+ vectorized across the points:
+
+ 1. the satellite's orbit is propagated once, at steps of at most
+ `_SCREEN_STEP`, and the windows in which each point may lie within the
+ field of regard are screened from the angle between the point and
+ the sub-satellite path (see `_screen_windows`);
+ 2. each window is divided into parts, each part is sampled, and the
+ periods in which the point lies within the field of regard (and the
+ satellite is above the minimum elevation angle) are refined from the
+ changes of sign between samples and from minima between them (see
+ `_find_hidden_minima`); and
+ 3. the epochs (view or cone crossings) and validity of the observations
+ are computed with one target for each time.
+ """
+ instrument = satellite.get_instrument(instrument_index)
+ orbit = satellite.orbit.to_gp_orbit()
+ coordinates = np.array([_get_point_coordinates(point) for point in points])
+ longitude, latitude, elevation = coordinates.T
+ xyz = np.array(wgs84.latlon(latitude, longitude, elevation).itrs_xyz.m)
+ up = np.array(
+ [
+ np.cos(np.radians(latitude)) * np.cos(np.radians(longitude)),
+ np.cos(np.radians(latitude)) * np.sin(np.radians(longitude)),
+ np.sin(np.radians(latitude)),
+ ]
+ )
+ geometries = [geo.Point(x, y, z) for x, y, z in coordinates]
+ # use the apogee radius and the polar radius (below a point below the
+ # ellipsoid) as conservative bounds of the satellite's and points' radii
+ max_radius = compute_apoapsis_radius(
+ satellite.orbit.get_semimajor_axis(), satellite.orbit.get_eccentricity()
+ )
+ # each point's own bounds, so that its result does not depend on the others
+ min_radius = EARTH_POLAR_RADIUS + np.minimum(elevation, 0)
+ min_elevation_angle = np.array(
+ [
+ max(
+ 0.0,
+ compute_min_elevation_angle(
+ max_radius - radius, instrument.field_of_regard
+ )
+ - 1.0,
+ )
+ for radius in min_radius
+ ]
+ )
+ half_angle = instrument.field_of_regard / 2
+ reference = pd.Timestamp(start.astimezone(tz=timezone.utc))
+ duration = (end - start).total_seconds()
+ _warn_if_propagating_with_drag(orbit, start, end)
+
+ def residual(seconds: np.ndarray, index: np.ndarray) -> TimeRequest:
+ # positive outside the field of regard or below the minimum elevation angle
+ t = _to_time_from_offsets(reference, seconds)
+ yield t
+ p_m, nadir, _, _ = _compute_nadir_axes(
+ orbit.get_orbit_track_at_time(t),
+ nadir_reference=instrument.nadir_reference,
+ )
+ los = _compute_line_of_sight(xyz[:, index], p_m)
+ distance = np.linalg.norm(los, axis=0)
+ elevation_angle = np.degrees(
+ np.arcsin(np.clip(-np.sum(los * up[:, index], axis=0) / distance, -1, 1))
+ )
+ excess = min_elevation_angle[index] - elevation_angle
+ if half_angle >= 90:
+ return excess
+ cone = np.degrees(
+ np.arccos(np.clip(np.sum(los * nadir, axis=0) / distance, -1, 1))
+ )
+ return np.maximum(cone - half_angle, excess)
+
+ # 1. the coarse orbit track, from which the windows of each point are screened
+ steps = max(1, int(np.ceil(duration / _SCREEN_STEP)))
+ screen_times = np.linspace(0, duration, steps + 1)
+ t = _to_time_from_offsets(reference, screen_times)
+ yield t
+ position = np.array(orbit.get_orbit_track_at_time(t).frame_xyz(itrs).m)
+ directions = position / np.linalg.norm(position, axis=0)
+ units = xyz / np.linalg.norm(xyz, axis=0)
+ screening_radius = np.radians(
+ np.array(
+ [
+ _get_screening_angle(max_radius, radius, half_angle)
+ for radius in min_radius
+ ]
+ )
+ + _SCREEN_MARGIN
+ )
+
+ def collect_block(block: np.ndarray) -> TimeRequest:
+ """Collect the observations of a block of points (global indices)."""
+ local_point, first_step, last_step = _screen_windows(
+ directions, units[:, block], screening_radius[block]
+ )
+ if len(local_point) == 0:
+ return _get_empty_coverage_frame(omit_solar)
+ window_point = block[local_point]
+ window_lower = screen_times[first_step]
+ window_upper = screen_times[last_step + 1]
+
+ # 2. refine the periods in the parts of each window
+ period_point, lower, upper = yield from _refine_window_periods(
+ residual, window_point, window_lower, window_upper
+ )
+ # 3. the epochs and validity of the observations
+ epoch_point, epoch_period, epochs = yield from _get_batch_epochs(
+ orbit, instrument, xyz, period_point, lower, upper, reference
+ )
+ keep = (
+ upper[epoch_period] - lower[epoch_period]
+ >= instrument.min_access_time.total_seconds()
+ )
+ epoch_point, epoch_period, epochs = (
+ epoch_point[keep],
+ epoch_period[keep],
+ epochs[keep],
+ )
+ observations, orbit_track, valid_point = [], None, np.array([], dtype=int)
+ if len(epochs) > 0:
+ t = _to_time_from_offsets(reference, epochs)
+ yield t
+ orbit_track = orbit.get_orbit_track_at_time(t)
+ target = wgs84.latlon(
+ latitude[epoch_point], longitude[epoch_point], elevation[epoch_point]
+ )
+ valid = np.atleast_1d(instrument.is_valid_observation(orbit_track, target))
+ if isinstance(instrument, (PointedInstrument, ConicalInstrument)):
+ valid = valid & np.atleast_1d(
+ instrument.is_in_field_of_view(orbit_track, target)
+ )
+ valid_index = np.flatnonzero(valid)
+ valid_point = epoch_point[valid_index]
+ observations = [
+ (
+ pd.Interval(
+ left=reference
+ + pd.Timedelta(seconds=float(lower[epoch_period[i]])),
+ right=reference
+ + pd.Timedelta(seconds=float(upper[epoch_period[i]])),
+ ),
+ reference + pd.Timedelta(seconds=float(epochs[i])),
+ tuple(coordinates[epoch_point[i]]),
+ )
+ for i in valid_index
+ ]
+ if len(valid_index) < len(epochs):
+ orbit_track = _index_orbit_track(orbit_track, valid_index)
+ observed = np.unique(valid_point)
+ hashes = dict(
+ zip(observed, _hash_geometries([geometries[p] for p in observed]))
+ )
+ return _build_observation_frame(
+ observations,
+ [hashes[p] for p in valid_point],
+ [geometries[p] for p in valid_point],
+ satellite,
+ instrument,
+ omit_solar,
+ orbit_track,
+ )
+
+ # points in blocks of at most _BLOCK_POINT_DAYS point-days, which bound the
+ # size of the vectorized stages
+ size = max(1, int(_BLOCK_POINT_DAYS * 86400 / max(duration, 1.0)))
+ frames = []
+ for first in range(0, len(points), size):
+ frame = yield from collect_block(
+ np.arange(first, min(len(points), first + size))
+ )
+ if len(frame) > 0:
+ frames.append(frame)
+ if len(frames) == 0:
+ return _get_empty_coverage_frame(omit_solar)
+ return frames[0] if len(frames) == 1 else pd.concat(frames, ignore_index=True)
+
+
+def _warn_if_propagating_with_drag(
+ orbit: GeneralPerturbationsOrbit, start: datetime, end: datetime
+) -> None:
+ """
+ Warn if an orbit is propagated with drag between its repeat tracks (as in
+ `GeneralPerturbationsOrbit.get_observation_events`).
+
+ Args:
+ orbit (GeneralPerturbationsOrbit): The orbit.
+ start (datetime.datetime): Start of analysis period.
+ end (datetime.datetime): End of analysis period.
+ """
+ first, last = orbit._get_repeat_tracks() # pylint: disable=protected-access
+ direct_start = start if first is None else max(start, first.epoch)
+ direct_end = end if last is None else min(end, last.epoch)
+ if direct_start < direct_end:
+ orbit._warn_if_propagating_with_drag( # pylint: disable=protected-access
+ _to_time([direct_start, direct_end]),
+ np.array(orbit.get_element_index([direct_start, direct_end])),
+ )
+
+
+def _get_batch_view_crossing_times(
+ orbit: GeneralPerturbationsOrbit,
instrument: PointedInstrument,
- periods: list[pd.Interval],
+ xyz: np.ndarray,
+ point: np.ndarray,
+ lower: np.ndarray,
+ upper: np.ndarray,
+ reference: pd.Timestamp,
) -> TimeRequest:
"""
- Get the time in each visible period when a pointed instrument's view
- sweeps over a target: when the target crosses the plane of the view's
- boresight and cross-track axis (where its along-track view angle is
- zero). The along-track component of the unit line of sight to the
- target, which is defined even when the target is behind the view,
- decreases through zero as the satellite passes the target. If a period
- does not contain a crossing (for example, a period truncated by the
- analysis window), uses the period's end closest to the crossing.
+ Get the time in each period when a pointed instrument's view sweeps over
+ its point: when the point crosses the plane of the view's boresight and
+ cross-track axis (where its along-track view angle is zero), or, for a
+ `ViewGeometry.SCAN` view, the scan plane. The along-track component of
+ the unit line of sight, which is defined even when the point is behind
+ the view, decreases through zero as the satellite passes the point. If a
+ period does not contain a crossing (for example, a period truncated by
+ the analysis window), uses the period's end closest to the crossing.
Args:
- target (skyfield.toposlib.GeographicPosition): The target position.
- satellite (Satellite): The observing satellite.
+ orbit (GeneralPerturbationsOrbit): The orbit.
instrument (PointedInstrument): The observing instrument.
- periods (list[pandas.Interval]): The visible periods.
+ xyz (numpy.ndarray): The Earth-fixed coordinates (meters) of the
+ points, shape (3, N).
+ point (numpy.ndarray): The point of each period.
+ lower (numpy.ndarray): The start of each period (seconds).
+ upper (numpy.ndarray): The end of each period (seconds).
+ reference (pandas.Timestamp): The reference time of the seconds.
Returns:
- TimeRequest: the computation of the view crossing time in each
- period (list[pandas.Timestamp]).
+ TimeRequest: the computation of the crossing time (seconds) in each
+ period (numpy.ndarray).
"""
- if len(periods) == 0:
- return []
- orbit = satellite.orbit.to_gp_orbit()
- reference = periods[0].left
+ if len(lower) == 0:
+ return np.array([], dtype=float)
- def residual(seconds: np.ndarray, _index: np.ndarray) -> TimeRequest:
+ def residual(seconds: np.ndarray, index: np.ndarray) -> TimeRequest:
# along-track component of the unit line of sight in the view frame
t = _to_time_from_offsets(reference, seconds)
yield t
@@ -315,72 +892,80 @@ def residual(seconds: np.ndarray, _index: np.ndarray) -> TimeRequest:
instrument.velocity_frame,
instrument.nadir_reference,
instrument.get_pitch_angle(orbit_track),
+ instrument.platform_attitude,
)
- los = np.reshape(np.array(target.itrs_xyz.m), (3, 1)) - position
- return np.sum(los * along, axis=0) / np.linalg.norm(los, axis=0)
- position, _, along, _ = _compute_view_frame(
- orbit_track,
- instrument.get_roll_angle(orbit_track),
- instrument.get_pitch_angle(orbit_track),
- instrument.velocity_frame,
- instrument.nadir_reference,
- )
- los = np.reshape(np.array(target.itrs_xyz.m), (3, 1)) - position
+ else:
+ position, _, along, _ = _compute_view_frame(
+ orbit_track,
+ instrument.get_roll_angle(orbit_track),
+ instrument.get_pitch_angle(orbit_track),
+ instrument.velocity_frame,
+ instrument.nadir_reference,
+ attitude=instrument.platform_attitude,
+ )
+ los = _compute_line_of_sight(xyz[:, point[index]], position)
return np.sum(los * along, axis=0) / np.linalg.norm(los, axis=0)
- crossing, _ = yield from _find_crossings(
- residual,
- np.array([(period.left - reference).total_seconds() for period in periods]),
- np.array([(period.right - reference).total_seconds() for period in periods]),
- )
- return [reference + pd.Timedelta(seconds=float(x)) for x in crossing]
+ crossing, _ = yield from _find_crossings(residual, lower, upper)
+ return crossing
-def _get_cone_crossing_times(
- target: GeographicPosition,
- satellite: Satellite,
+def _get_batch_cone_crossing_times(
+ orbit: GeneralPerturbationsOrbit,
instrument: ConicalInstrument,
- periods: list[pd.Interval],
+ xyz: np.ndarray,
+ point: np.ndarray,
+ lower: np.ndarray,
+ upper: np.ndarray,
+ reference: pd.Timestamp,
) -> TimeRequest:
"""
- Get the times in each visible period when a target crosses a conical
- instrument's cone: when the target's angle from nadir equals the cone
- angle. During a pass, this angle decreases to a minimum near the closest
- approach and then increases, so there are up to two crossings: entering
+ Get the times in each period when its point crosses a conical
+ instrument's cone: when its angle from nadir equals the cone angle.
+ During a pass, this angle decreases to a minimum near the closest
+ approach and then increases, so there are up to two crossings, entering
the cone (ahead of the satellite) and leaving it (behind).
Args:
- target (skyfield.toposlib.GeographicPosition): The target position.
- satellite (Satellite): The observing satellite.
+ orbit (GeneralPerturbationsOrbit): The orbit.
instrument (ConicalInstrument): The observing instrument.
- periods (list[pandas.Interval]): The visible periods.
+ xyz (numpy.ndarray): The Earth-fixed coordinates (meters) of the
+ points, shape (3, N).
+ point (numpy.ndarray): The point of each period.
+ lower (numpy.ndarray): The start of each period (seconds).
+ upper (numpy.ndarray): The end of each period (seconds).
+ reference (pandas.Timestamp): The reference time of the seconds.
Returns:
- TimeRequest: the computation of the cone crossing times in each
- period (list[list[pandas.Timestamp]]).
+ TimeRequest: the computation of the period and time (seconds) of
+ each crossing, in order of period (tuple[numpy.ndarray,
+ numpy.ndarray]).
"""
- if len(periods) == 0:
- return []
- orbit = satellite.orbit.to_gp_orbit()
- reference = periods[0].left
- n = len(periods)
+ n = len(lower)
+ if n == 0:
+ return np.array([], dtype=int), np.array([], dtype=float)
- def cone_angle(seconds: np.ndarray, _index: np.ndarray) -> TimeRequest:
+ def cone_angle(seconds: np.ndarray, index: np.ndarray) -> TimeRequest:
t = _to_time_from_offsets(reference, seconds)
yield t
- cone, _ = compute_cone_and_azimuth(
+ p_m, nadir, _, _ = _compute_nadir_axes(
orbit.get_orbit_track_at_time(t),
- target,
instrument.velocity_frame,
instrument.nadir_reference,
+ instrument.platform_attitude,
+ )
+ los = _compute_line_of_sight(xyz[:, point[index % n]], p_m)
+ return np.degrees(
+ np.arccos(
+ np.clip(
+ np.sum(los * nadir, axis=0) / np.linalg.norm(los, axis=0), -1, 1
+ )
+ )
)
- return np.reshape(cone, -1)
def residual(seconds: np.ndarray, index: np.ndarray) -> TimeRequest:
return (yield from cone_angle(seconds, index)) - instrument.cone_angle
- lower = np.array([(period.left - reference).total_seconds() for period in periods])
- upper = np.array([(period.right - reference).total_seconds() for period in periods])
# time of the minimum angle from nadir in each period, from sampled times
samples = lower[:, None] + (upper - lower)[:, None] * np.linspace(0, 1, 21)
closest = samples[
@@ -393,128 +978,13 @@ def residual(seconds: np.ndarray, index: np.ndarray) -> TimeRequest:
),
]
crossing, bracketed = yield from _find_crossings(
- residual,
- np.concatenate([lower, closest]),
- np.concatenate([closest, upper]),
- )
- return [
- [
- reference + pd.Timedelta(seconds=float(crossing[i + k * n]))
- for k in range(2)
- if bracketed[i + k * n]
- ]
- for i in range(n)
- ]
-
-
-def _collect_observations(
- point: Point | geo.Point,
- satellite: Satellite,
- start: datetime,
- end: datetime,
- instrument_index: int = 0,
- omit_solar: bool = True,
-) -> TimeRequest:
- """
- Collect single satellite observations of a point, as a computation (see
- `collect_observations` and `tatc.utils.computation.TimeRequest`).
- """
- longitude, latitude, elevation = _get_point_coordinates(point)
- geometry = geo.Point(longitude, latitude, elevation)
- instrument = satellite.instruments[instrument_index]
- orbit = satellite.orbit.to_gp_orbit()
- # use the apogee altitude above the polar radius (and above the point,
- # if below the ellipsoid) as a conservative upper bound for computing
- # access times, which are then refined to the field of regard
- max_altitude = (
- compute_apoapsis_radius(
- satellite.orbit.get_semimajor_axis(), satellite.orbit.get_eccentricity()
- )
- - EARTH_POLAR_RADIUS
- - min(elevation, 0)
- )
- # compute the minimum altitude angle required for observation, less a
- # margin for the spherical approximation (but not below the horizon)
- min_elevation_angle = max(
- 0.0,
- compute_min_elevation_angle(max_altitude, instrument.field_of_regard) - 1.0,
- )
- target = wgs84.latlon(latitude, longitude, elevation)
- periods = list(
- (
- yield from _compute_access_periods(
- point, satellite, start, end, min_elevation_angle
- )
- )
- )
- # refine the periods to the field of regard: when the point's angle
- # from nadir is at most half the field of regard
- half_angle = instrument.field_of_regard / 2
- if half_angle < 90:
- periods = yield from _refine_access_periods(
- lambda orbit_track: compute_cone_and_azimuth(
- orbit_track, target, nadir_reference=instrument.nadir_reference
- )[0]
- - half_angle,
- orbit,
- periods,
- )
- # observation epochs: the time a pointed instrument's view sweeps over
- # the point, the times the point crosses a conical instrument's cone, or
- # otherwise the midpoint of each visible period
- if isinstance(instrument, PointedInstrument):
- epochs = [
- [epoch]
- for epoch in (
- yield from _get_view_crossing_times(
- target, satellite, instrument, periods
- )
- )
- ]
- elif isinstance(instrument, ConicalInstrument):
- epochs = yield from _get_cone_crossing_times(
- target, satellite, instrument, periods
- )
- else:
- epochs = [[pd.Timestamp(period.mid)] for period in periods]
- candidates = [
- (period, epoch)
- for period, period_epochs in zip(periods, epochs)
- for epoch in period_epochs
- if instrument.min_access_time <= period.right - period.left
- ]
- observations, orbit_track = [], None
- if len(candidates) > 0:
- # instrument validity (illumination, field of view) is only checked
- # at each period's epoch, as an approximation of the whole interval;
- # a more general approach would refine the exact observation period
- # boundaries with Skyfield's find_discrete using the instrument's own
- # validity condition, but that is out of scope for now. All epochs
- # are checked in one vectorized call.
- t = _to_time([epoch for _, epoch in candidates])
- yield t
- orbit_track = orbit.get_orbit_track_at_time(t)
- valid = instrument.is_valid_observation(orbit_track, target)
- if isinstance(instrument, (PointedInstrument, ConicalInstrument)):
- valid = valid & instrument.is_in_field_of_view(orbit_track, target)
- valid = np.atleast_1d(valid)
- observations = [
- (period, epoch, (longitude, latitude, elevation))
- for (period, epoch), is_valid in zip(candidates, valid)
- if is_valid
- ]
- if not np.all(valid):
- # reuse the orbit track of the valid epochs for the data frame
- orbit_track = _index_orbit_track(orbit_track, np.flatnonzero(valid))
- return _build_observation_frame(
- observations,
- hash_geometry(geometry),
- geometry,
- satellite,
- instrument,
- omit_solar,
- orbit_track,
+ residual, np.concatenate([lower, closest]), np.concatenate([closest, upper])
)
+ # the crossings of each period, entering (k = 0) and leaving (k = 1) the cone
+ period = np.concatenate([np.arange(n), np.arange(n)])
+ order = np.lexsort((np.repeat([0, 1], n), period))
+ keep = order[bracketed[order]]
+ return period[keep], crossing[keep]
def collect_observations(
@@ -550,10 +1020,14 @@ def collect_observations(
its repeat ground track before its first and after its last element's
epoch (see `GeneralPerturbationsOrbit.get_orbit_track_at_time`).
- The observations of every point, satellite, and instrument are computed
- together, sharing the costly Earth orientation quantities of the times
- at each step (see `tatc.utils.computation._run_together`), which is
- much faster than computing them separately.
+ The observations of all points by each satellite and instrument are
+ computed together, with each stage vectorized across the points: the
+ orbit is propagated once to screen the windows in which each point may
+ be observed, and the periods, epochs, and validity of the observations
+ are then refined to a millisecond in all windows at once. The
+ observations of a point do not depend on the other points (to within a
+ microsecond), so many points are much faster to collect together than
+ separately.
Args:
points (Point | shapely.geometry.Point | list[Point | shapely.geometry.Point]):
@@ -583,14 +1057,17 @@ def collect_observations(
"point must be a Point or shapely Point, not a "
f"{type(point).__name__} (see collect_region_observations for a region)"
)
- gdfs = _run_together(
- [
- _collect_observations(point, satellite, start, end, index, omit_solar)
- for point in points
- for satellite in satellites
- for index in _get_instrument_indices(satellite, instrument_index)
- ]
- )
+ # each computation is vectorized across the points, so they are run in
+ # turn (running them together would only enlarge each step)
+ gdfs = [
+ _run(
+ _collect_batch_observations(
+ points, satellite, start, end, index, omit_solar
+ )
+ )
+ for satellite in satellites
+ for index in _get_instrument_indices(satellite, instrument_index)
+ ]
return _combine_results(
gdfs,
single,
diff --git a/src/tatc/analysis/region_sampling.py b/src/tatc/analysis/region_sampling.py
index c70fbf79..fc8c574f 100644
--- a/src/tatc/analysis/region_sampling.py
+++ b/src/tatc/analysis/region_sampling.py
@@ -729,7 +729,7 @@ def _collect_region_observations(
geometry = split_polygon(region)
if region.has_z:
geometry = project_polygon_to_elevation(geometry, elevation)
- instrument = satellite.instruments[instrument_index]
+ instrument = satellite.get_instrument(instrument_index)
orbit = satellite.orbit.to_gp_orbit()
shapely.prepare(geometry)
windows = list(
diff --git a/src/tatc/analysis/ro_sampling.py b/src/tatc/analysis/ro_sampling.py
index 0739b174..fa6a24ff 100644
--- a/src/tatc/analysis/ro_sampling.py
+++ b/src/tatc/analysis/ro_sampling.py
@@ -343,8 +343,12 @@ def _sample_ro_arcs(
`_find_ro_arcs`: each with its receiver and transmitter indices, start,
and end),
splitting each into one or more observations if the tangent point
- elevation leaves the specified range. The samples of all arcs are
- computed together.
+ elevation leaves the specified range or turns (at a minimum or maximum
+ elevation), so that each observation is setting (descending) or rising
+ (ascending): a grazing occultation, whose tangent point descends and
+ rises again, is split into a setting and a rising observation, which
+ share the sample at which it turns. The samples of all arcs are computed
+ together.
"""
if len(arcs) == 0:
return []
@@ -418,46 +422,54 @@ def samples(selected: set[tuple[int, int]]) -> np.ndarray:
elevation > range_elevation[0], elevation < range_elevation[1]
)
+ def point(j: int, time: datetime) -> dict:
+ # the observation point of sample j
+ return {
+ "time": time,
+ "longitude": longitude[j],
+ "latitude": latitude[j],
+ "elevation": elevation[j],
+ "rx_tx_pitch": rx_tx_pitch[j],
+ "rx_tx_yaw": rx_tx_yaw[j],
+ "tp_tx_azimuth": tp_tx_azimuth[j],
+ }
+
# occultation observations
occ_obs = []
for (r, k, _, _), times, offset in zip(arcs, arc_times, bounds):
- # occultation arc
+ # occultation arc, and the direction of its tangent point elevation
+ # (1 ascending, -1 descending, 0 not yet known)
occ_arc = None
+ direction = 0
for j, time in enumerate(times, start=offset):
if in_range[j]:
if occ_arc is None:
- # start of new RO observation. rx_tx_pitch is the
- # transmitter's pitch angle relative to the receiver,
- # where -90 deg points at the geocenter (never actually
- # reached by a real RO profile, since the signal must
- # pass through the atmosphere); pitch above -90 deg means
- # the transmitter is "ahead" of the receiver (a
- # rising/emersion occultation), below -90 deg means
- # "behind" (a setting/immersion one). This is an
- # approximation of the more direct (but more expensive)
- # definition -- the sign of the tangent point's own
- # elevation rate -- using this arc's first sample only.
+ # start of new RO observation
occ_arc = {
"rx": receivers[r].name,
"tx": transmitters[k].name,
- "is_rising": rx_tx_pitch[j] > -90,
"points": [],
}
- occ_arc["points"].append(
- {
- "time": time,
- "longitude": longitude[j],
- "latitude": latitude[j],
- "elevation": elevation[j],
- "rx_tx_pitch": rx_tx_pitch[j],
- "rx_tx_yaw": rx_tx_yaw[j],
- "tp_tx_azimuth": tp_tx_azimuth[j],
- }
- )
+ direction = 0
+ elif elevation[j] != elevation[j - 1]:
+ direction = 1 if elevation[j] > elevation[j - 1] else -1
+ occ_arc["points"].append(point(j, time))
if j + 1 >= offset + len(times):
# end of RO observation due to arc boundary
occ_obs.append(occ_arc)
occ_arc = None
+ elif in_range[j + 1] and direction != 0:
+ step = np.sign(elevation[j + 1] - elevation[j])
+ if step == -direction:
+ # end of RO observation where the tangent point turns,
+ # and start of the next from the same sample
+ occ_obs.append(occ_arc)
+ occ_arc = {
+ "rx": receivers[r].name,
+ "tx": transmitters[k].name,
+ "points": [point(j, time)],
+ }
+ direction = int(step)
elif occ_arc is not None:
# end of RO observation due to elevation constraints
occ_obs.append(occ_arc)
@@ -484,6 +496,39 @@ def _interpolate_ro_point(points: list[dict], sample_elevation: float) -> dict:
)
+def _is_rising(points: list[dict], sample_elevation: float) -> bool:
+ """
+ Determines whether an RO observation is rising (emersion), with its
+ tangent point ascending, or setting (immersion), with it descending:
+ between the points that bracket the first crossing of the specified
+ elevation (at which `_interpolate_ro_point` interpolates), or, if it is
+ not crossed, from the first to the last point.
+
+ A single point (or a profile that ends at the elevation it starts) is
+ rising if the transmitter is ahead of the receiver: if its pitch angle
+ relative to the receiver is above -90 deg (which points at the
+ geocenter). This approximation fails for transmitters nearly abeam of
+ the receiver (yaw angles near 90 deg).
+
+ Args:
+ points (list[dict]): the RO observation points (ordered by time).
+ sample_elevation (float): the tangent point elevation (m) at which
+ the observation is interpolated.
+
+ Returns:
+ bool: True, if the observation is rising.
+ """
+ elevations = np.array([point["elevation"] for point in points])
+ # bracketing indices where the tangent point elevation crosses the sample elevation
+ crossings = np.nonzero(np.diff(np.sign(elevations - sample_elevation)))[0]
+ if len(crossings) > 0:
+ i = crossings[0]
+ return bool(elevations[i + 1] > elevations[i])
+ if elevations[-1] != elevations[0]:
+ return bool(elevations[-1] > elevations[0])
+ return bool(points[0]["rx_tx_pitch"] > -90)
+
+
def _get_empty_ro_frame() -> gpd.GeoDataFrame:
"""
Gets an empty data frame for ro results.
@@ -530,6 +575,16 @@ def collect_ro_observations(
the negative default `sample_elevation`). See `_tangent_point_geometry`
for details.
+ Each observation is setting (`is_rising` false), with its tangent point
+ descending, or rising, with it ascending: a grazing occultation, whose
+ tangent point descends and rises again within `range_elevation`, is
+ reported as a setting and a rising observation, which share the sample
+ at its lowest point. Each observation's `time` and `position` are
+ interpolated where its tangent point crosses `sample_elevation`, or, for
+ a profile that does not reach it (one cut off by the analysis period, or
+ a grazing occultation that turns back above it), at the end of the
+ profile nearest to it, such as its lowest point.
+
Args:
receivers (Satellite | list[Satellite]): the satellite(s) with a RO receiver.
transmitters (Satellite | list[Satellite]]): the satellite(s) with a RO transmitter.
@@ -587,7 +642,7 @@ def collect_ro_observations(
{
"receiver": o["rx"],
"transmitter": o["tx"],
- "is_rising": o["is_rising"],
+ "is_rising": _is_rising(o["points"], sample_elevation),
"geometry": profile,
"position": Point(
sample["longitude"], sample["latitude"], sample["elevation"]
diff --git a/src/tatc/analysis/sample_sizes.py b/src/tatc/analysis/sample_sizes.py
new file mode 100644
index 00000000..b9422839
--- /dev/null
+++ b/src/tatc/analysis/sample_sizes.py
@@ -0,0 +1,141 @@
+"""
+Methods to compute the sizes of the samples of observations: the extent on
+the ground of an instrument's pixel at each observed target, and the
+spacing between pixels, which bound the detail an observation can resolve.
+
+@author: Paul T. Grogan
+"""
+
+from __future__ import annotations
+
+import geopandas as gpd
+import numpy as np
+import shapely
+from skyfield.api import wgs84
+
+from ..schemas import ConicalInstrument, PointedInstrument, Satellite
+from ..utils.projection import VelocityFrame, compute_view_angles
+from ..utils.time import _to_time
+from .check import _check_satellites
+
+_COLUMNS = [
+ "sat_range",
+ "scan_angle",
+ "sample_along",
+ "sample_cross",
+ "spacing_along",
+ "spacing_cross",
+]
+
+
+def compute_sample_sizes(
+ observations: gpd.GeoDataFrame,
+ satellites: Satellite | list[Satellite],
+) -> gpd.GeoDataFrame:
+ """
+ Compute the sizes of the samples of observations: for each observation,
+ the extent on the ground of the observing instrument's pixel at the
+ target, along track and across track (`sample_along` and
+ `sample_cross`, meters), and the spacing between the centers of
+ neighboring pixels (`spacing_along` and `spacing_cross`, meters), which
+ is larger than the pixel if there are gaps between pixels (a negative
+ oversampling). An observation cannot resolve detail finer than the
+ larger of the two. The sizes are those of a `PointedInstrument`'s
+ pixels (see `PointedInstrument.compute_sample_size` and
+ `PointedInstrument.compute_sample_spacing`) or of a
+ `ConicalInstrument`'s beam and samples along its scanned arc (see
+ `ConicalInstrument.compute_sample_size` and
+ `ConicalInstrument.compute_sample_spacing`, `NaN` unless its
+ `beam_width` and `scan_sample_spacing` are given), and `NaN` for other
+ instruments, whose samples are not defined.
+
+ Also computes the satellite's slant range from the target (`sat_range`,
+ meters) and the target's scan angle (`scan_angle`, degrees): its angle
+ across track from nadir, positive to the left of the direction of motion
+ (see `tatc.utils.compute_view_angles`), measured from the body's axes
+ if the satellite has an attitude, which gives its position in the swath.
+
+ The observations are those of points (see `collect_observations`), at
+ their `epoch`, or of features (see `collect_feature_observations`), at
+ their `time`, whose target is the centroid of the viewed part.
+
+ Args:
+ observations (geopandas.GeoDataFrame): The observations, with the
+ `satellite` and `instrument` that observe each.
+ satellites (Satellite | list[Satellite]): The observing satellites,
+ including those named by the observations.
+
+ Returns:
+ geopandas.GeoDataFrame: the observations, with the sample sizes,
+ spacings, slant ranges, and scan angles.
+ """
+ satellites = _check_satellites(satellites)
+ by_name = {satellite.name: satellite for satellite in satellites}
+ result = observations.copy()
+ for column in _COLUMNS:
+ result[column] = np.nan
+ if len(observations) == 0:
+ return result
+ time_column = "epoch" if "epoch" in observations.columns else "time"
+ # the targets: observed points, or the centroids of observed regions
+ geometry = observations.geometry.to_numpy()
+ is_point = shapely.get_type_id(geometry) == shapely.GeometryType.POINT
+ centroid = shapely.centroid(geometry)
+ longitude, latitude = shapely.get_x(centroid), shapely.get_y(centroid)
+ elevation = np.where(is_point, np.nan_to_num(shapely.get_z(geometry)), 0)
+ position = np.arange(len(observations))
+ for (satellite_name, instrument_name), group in observations.groupby(
+ ["satellite", "instrument"], sort=False
+ ):
+ satellite = by_name.get(satellite_name)
+ if satellite is None:
+ raise ValueError(f"no satellite named {satellite_name!r}")
+ instrument_index = next(
+ (
+ i
+ for i, instrument in enumerate(satellite.instruments)
+ if instrument.name == instrument_name
+ ),
+ None,
+ )
+ if instrument_index is None:
+ raise ValueError(
+ f"satellite {satellite_name!r} has no instrument named {instrument_name!r}"
+ )
+ # the instrument, with its views rotated from the body's axes
+ instrument = satellite.get_instrument(instrument_index)
+ index = position[observations.index.get_indexer(group.index)]
+ orbit_track = satellite.orbit.to_gp_orbit().get_orbit_track_at_time(
+ _to_time(list(group[time_column]))
+ )
+ target = wgs84.latlon(latitude[index], longitude[index], elevation[index])
+ distance = (orbit_track - target.at(orbit_track.t)).altaz()[2]
+ result.iloc[index, result.columns.get_loc("sat_range")] = np.atleast_1d(
+ distance.m
+ )
+ velocity_frame = (
+ instrument.velocity_frame
+ if isinstance(instrument, (PointedInstrument, ConicalInstrument))
+ else VelocityFrame.EARTH_FIXED
+ )
+ roll, _ = compute_view_angles(
+ orbit_track,
+ target,
+ velocity_frame,
+ instrument.nadir_reference,
+ attitude=instrument.platform_attitude,
+ )
+ result.iloc[index, result.columns.get_loc("scan_angle")] = np.atleast_1d(roll)
+ if isinstance(instrument, (PointedInstrument, ConicalInstrument)):
+ for prefix, method in (
+ ("sample", instrument.compute_sample_size),
+ ("spacing", instrument.compute_sample_spacing),
+ ):
+ along, cross = method(orbit_track, target)
+ result.iloc[index, result.columns.get_loc(f"{prefix}_along")] = (
+ np.atleast_1d(along)
+ )
+ result.iloc[index, result.columns.get_loc(f"{prefix}_cross")] = (
+ np.atleast_1d(cross)
+ )
+ return result
diff --git a/src/tatc/analysis/sampling.py b/src/tatc/analysis/sampling.py
index 6ce36950..ec0acd05 100644
--- a/src/tatc/analysis/sampling.py
+++ b/src/tatc/analysis/sampling.py
@@ -22,6 +22,7 @@
from ..constants import de421
from ..schemas import GeneralPerturbationsOrbit, Instrument, Satellite
from ..utils.computation import TimeRequest
+from ..utils.glint import compute_glint_angle
from ..utils.time import _to_time_from_offsets
@@ -53,14 +54,15 @@ def _get_empty_coverage_frame(omit_solar: bool) -> gpd.GeoDataFrame:
"solar_alt": pd.Series(dtype="float"),
"solar_az": pd.Series(dtype="float"),
"solar_time": pd.Series(dtype="float"),
+ "glint_angle": pd.Series(dtype="float"),
}
return gpd.GeoDataFrame(columns, crs="EPSG:4326")
def _build_observation_frame(
observations: list[tuple[pd.Interval, pd.Timestamp, tuple[float, float, float]]],
- target_hash: str,
- geometry: geo.base.BaseGeometry,
+ target_hash: str | list[str],
+ geometry: geo.base.BaseGeometry | list[geo.base.BaseGeometry],
satellite: Satellite,
instrument: Instrument,
omit_solar: bool,
@@ -74,10 +76,12 @@ def _build_observation_frame(
observations (list[tuple[pandas.Interval, pandas.Timestamp, tuple[float, float, float]]]):
Each observation's period, epoch, and observed point's longitude
(degrees), latitude (degrees), and elevation (meters).
- target_hash (str): The hash of the target recorded with each
- observation (see `tatc.utils.geometry.hash_geometry`).
- geometry (shapely.geometry.base.BaseGeometry): The geometry recorded with
- each observation.
+ target_hash (str | list[str]): The hash of the target recorded with
+ each observation (see `tatc.utils.geometry.hash_geometry`), or
+ one hash for each observation.
+ geometry (shapely.geometry.base.BaseGeometry | list[shapely.geometry.base.BaseGeometry]):
+ The geometry recorded with each observation, or one geometry
+ for each observation.
satellite (Satellite): The observing satellite.
instrument (Instrument): The observing instrument.
omit_solar (bool): `True`, to omit solar angles to improve performance.
@@ -89,27 +93,35 @@ def _build_observation_frame(
"""
if len(observations) == 0:
return _get_empty_coverage_frame(omit_solar)
+ count = len(observations)
+ # times in nanoseconds (as in the empty frame), whatever their source
+ epoch = pd.DatetimeIndex([epoch for _, epoch, _ in observations]).as_unit("ns")
+ if instrument.access_time_fixed:
+ start = epoch - instrument.min_access_time / 2
+ end = epoch + instrument.min_access_time / 2
+ else:
+ start = pd.DatetimeIndex(
+ [period.left for period, _, _ in observations]
+ ).as_unit("ns")
+ end = pd.DatetimeIndex([period.right for period, _, _ in observations]).as_unit(
+ "ns"
+ )
gdf = gpd.GeoDataFrame(
- [
- {
- "target_hash": target_hash,
- "geometry": geometry,
- "satellite": satellite.name,
- "instrument": instrument.name,
- "start": (
- period.left
- if not instrument.access_time_fixed
- else epoch - instrument.min_access_time / 2
- ),
- "end": (
- period.right
- if not instrument.access_time_fixed
- else epoch + instrument.min_access_time / 2
- ),
- "epoch": epoch,
- }
- for period, epoch, _ in observations
- ],
+ {
+ "target_hash": (
+ [target_hash] * count if isinstance(target_hash, str) else target_hash
+ ),
+ "geometry": (
+ [geometry] * count
+ if isinstance(geometry, geo.base.BaseGeometry)
+ else geometry
+ ),
+ "satellite": [satellite.name] * count,
+ "instrument": [instrument.name] * count,
+ "start": start,
+ "end": end,
+ "epoch": epoch,
+ },
crs="EPSG:4326",
)
# observed point of each observation
@@ -137,6 +149,8 @@ def _build_observation_frame(
gdf["solar_time"] = (de421["earth"] + topos).at(ts).observe(
de421["sun"]
).apparent().hadec()[0].hours + 12
+ # append glint angle column
+ gdf["glint_angle"] = np.atleast_1d(compute_glint_angle(orbit_track, topos))
return gdf
diff --git a/src/tatc/analysis/space_sampling.py b/src/tatc/analysis/space_sampling.py
index 4ab40a2a..f89f7864 100644
--- a/src/tatc/analysis/space_sampling.py
+++ b/src/tatc/analysis/space_sampling.py
@@ -162,6 +162,77 @@ def _get_pair_positions(
return from_p, to_p
+def _find_closest_approaches(
+ satellites: list[Satellite],
+ from_index: np.ndarray,
+ to_index: np.ndarray,
+ left: np.ndarray,
+ right: np.ndarray,
+ samples: np.ndarray,
+ positions: list[np.ndarray],
+ start: datetime,
+ tolerance: float = 1e-4,
+) -> tuple[np.ndarray, np.ndarray]:
+ """
+ Finds the closest approach of the observing and observed satellites
+ (indices `from_index` and `to_index` of `satellites`) during each of a
+ set of periods (from `left` to `right`, seconds from `start`): the sample
+ (seconds `samples`, at which the satellites' Earth-fixed positions are
+ `positions`) of least range within the period, refined by a
+ golden-section search between its neighboring samples (within the
+ period) to `tolerance` (seconds), for all periods together.
+
+ Returns:
+ tuple[numpy.ndarray, numpy.ndarray]: the time (seconds from `start`)
+ and range (meters) of each closest approach
+ """
+ first = np.searchsorted(samples, left, side="left")
+ last = np.searchsorted(samples, right, side="right")
+ lower, upper = left.copy(), right.copy()
+ for k in np.flatnonzero(last > first):
+ # bracket the sample of least range within the period by its neighbors
+ span = slice(first[k], last[k])
+ j = first[k] + np.argmin(
+ np.linalg.norm(
+ positions[to_index[k]][:, span] - positions[from_index[k]][:, span],
+ axis=0,
+ )
+ )
+ lower[k] = max(left[k], samples[j - 1]) if j > 0 else left[k]
+ upper[k] = min(right[k], samples[j + 1]) if j + 1 < len(samples) else right[k]
+
+ def slant_range(seconds: np.ndarray, index: np.ndarray) -> np.ndarray:
+ from_p, to_p = _get_pair_positions(
+ satellites,
+ from_index[index],
+ to_index[index],
+ _to_time_from_offsets(start, seconds),
+ )
+ return np.linalg.norm(to_p - from_p, axis=0)
+
+ # golden-section search for the minimum range within each bracket
+ ratio = (np.sqrt(5) - 1) / 2
+ index = np.arange(len(left))
+ c = upper - ratio * (upper - lower)
+ d = lower + ratio * (upper - lower)
+ f_c, f_d = slant_range(c, index), slant_range(d, index)
+ while np.any(upper - lower > tolerance):
+ # keep the part of each bracket that contains the smaller value
+ smaller = f_c < f_d
+ upper = np.where(smaller, d, upper)
+ lower = np.where(smaller, lower, c)
+ c, d = np.where(smaller, upper - ratio * (upper - lower), d), np.where(
+ smaller, c, lower + ratio * (upper - lower)
+ )
+ f_c, f_d = np.where(smaller, 0.0, f_d), np.where(smaller, f_c, 0.0)
+ new = np.where(smaller, c, d)
+ f_new = slant_range(new, index)
+ f_c = np.where(smaller, f_new, f_c)
+ f_d = np.where(smaller, f_d, f_new)
+ seconds = (lower + upper) / 2
+ return seconds, slant_range(seconds, index)
+
+
def _get_empty_space_frame() -> gpd.GeoDataFrame:
"""
Gets an empty data frame for space-based coverage analysis results.
@@ -176,6 +247,8 @@ def _get_empty_space_frame() -> gpd.GeoDataFrame:
"to_satellite": pd.Series([], dtype="str"),
"start": pd.Series([], dtype="datetime64[ns, utc]"),
"end": pd.Series([], dtype="datetime64[ns, utc]"),
+ "closest_time": pd.Series([], dtype="datetime64[ns, utc]"),
+ "closest_range": pd.Series([], dtype="float"),
}
return gpd.GeoDataFrame(columns, crs="EPSG:4326")
@@ -210,6 +283,14 @@ def collect_space_observations(
so they do not follow the line of sight across the Earth's surface and
are not divided at the antimeridian.
+ Each observation also records the closest approach of the satellites
+ during the period: its time (`closest_time`) and slant range
+ (`closest_range`, meters), the least range among the samples of the
+ search within the period (see `min_duration`) and its start and end,
+ refined to 0.1 milliseconds between the neighboring samples. A period
+ whose range has several minima closer together than the samples may
+ report one that is not the least.
+
Args:
from_satellites (Satellite | list[Satellite]): The observing satellite(s).
start (datetime.datetime): The start of the analysis period.
@@ -338,6 +419,23 @@ def evaluate(seconds: np.ndarray, index: np.ndarray) -> TimeRequest:
)
from_geo = np.array(rectangular_to_geodetic(from_p)).T
to_geo = np.array(rectangular_to_geodetic(to_p)).T
+ # closest approach within each period: between its samples, or at its
+ # start or end if closer
+ left, right = seconds[0::2], seconds[1::2]
+ closest, closest_range = _find_closest_approaches(
+ satellites,
+ pair_from[period_pair],
+ pair_to[period_pair],
+ left,
+ right,
+ samples,
+ positions,
+ start,
+ )
+ end_range = np.linalg.norm(to_p - from_p, axis=0).reshape(-1, 2)
+ nearer = np.argmin(np.column_stack([closest_range, end_range]), axis=1)
+ closest = np.choose(nearer, [closest, left, right])
+ closest_range = np.choose(nearer, [closest_range, end_range[:, 0], end_range[:, 1]])
# lines of sight at the start and end of each period
geometry = shapely.multilinestrings(
shapely.linestrings(np.stack((from_geo, to_geo), axis=1)),
@@ -353,6 +451,8 @@ def evaluate(seconds: np.ndarray, index: np.ndarray) -> TimeRequest:
"to_satellite": [satellites[pair_to[p]].name for p in period_pair],
"start": reference + pd.to_timedelta(seconds[0::2], unit="s"),
"end": reference + pd.to_timedelta(seconds[1::2], unit="s"),
+ "closest_time": reference + pd.to_timedelta(closest, unit="s"),
+ "closest_range": closest_range,
},
crs="EPSG:4326",
)
diff --git a/src/tatc/schemas/__init__.py b/src/tatc/schemas/__init__.py
index 255b4b5c..58307e7b 100644
--- a/src/tatc/schemas/__init__.py
+++ b/src/tatc/schemas/__init__.py
@@ -3,6 +3,13 @@
"""
from .architecture import Architecture
+from .attitude import (
+ Attitude,
+ AttitudeSchedule,
+ AttitudeSegment,
+ GlintAttitude,
+ TargetAttitude,
+)
from .instrument import AllInstruments, ConicalInstrument, Instrument, PointedInstrument
from .orbit import (
AllOrbits,
@@ -40,6 +47,9 @@
"AllSpaceObjects",
"AllSurfaceObjects",
"Architecture",
+ "Attitude",
+ "AttitudeSchedule",
+ "AttitudeSegment",
"CircularOrbit",
"ConicalInstrument",
"GeneralPerturbationsOrbit",
@@ -57,6 +67,8 @@
"Satellite",
"SunSynchronousOrbit",
"SurfacePlatform",
+ "TargetAttitude",
+ "GlintAttitude",
"TerrainMask",
"TrainConstellation",
"TundraOrbit",
diff --git a/src/tatc/schemas/attitude.py b/src/tatc/schemas/attitude.py
new file mode 100644
index 00000000..dbfdb44b
--- /dev/null
+++ b/src/tatc/schemas/attitude.py
@@ -0,0 +1,655 @@
+"""
+Object schemas for the attitude of space systems: the orientation of a
+spacecraft's body relative to its orbit, which orients the views of its
+instruments, and schedules of attitudes over time.
+
+@author: Paul T. Grogan
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Annotated, Literal
+
+import numpy as np
+import numpy.typing as npt
+from pydantic import AwareDatetime, BaseModel, Field, field_validator
+from skyfield.api import wgs84
+from skyfield.positionlib import Geocentric
+from skyfield.toposlib import GeographicPosition
+
+from ..utils.ellipsoid import compute_ellipsoid_intersection, rectangular_to_geodetic
+from ..utils.glint import compute_specular_point
+from ..utils.orbital import _check_angle_profile, _interpolate_angle_profile
+from ..utils.projection import (
+ VelocityFrame,
+ _compute_line_of_sight,
+ _compute_nadir_axes,
+ compute_view_angles,
+)
+from ..utils.time import _index_orbit_track
+from .surface.point import Point
+
+
+class Attitude(BaseModel):
+ """
+ Attitude of a spacecraft's body relative to its orbit. The body is
+ rotated from the orbit's nadir frame (its nadir axis and its along-track
+ axis, aligned with the velocity in `velocity_frame`) first by the yaw
+ angle about the nadir axis, then by the roll angle about the yawed
+ along-track axis, then by the pitch angle about the rolled cross-track
+ axis. Each angle is constant or, if its profile is given, varies with the
+ satellite's argument of latitude.
+
+ The instruments of a space system point relative to its body: their
+ roll and pitch angles, scan azimuths, and pixels are measured from the
+ body's axes. For example, a yaw angle of 180 degrees (flying backward)
+ swaps the left and right of a cross-track scanner and the fore and aft
+ of a conical scan. An instrument's field of regard remains measured from
+ nadir.
+ """
+
+ type: Literal["attitude"] = Field(
+ default="attitude", description="Attitude type discriminator."
+ )
+ velocity_frame: VelocityFrame = Field(
+ default=VelocityFrame.EARTH_FIXED,
+ description="Reference frame of the velocity vector that defines the "
+ + "along-track axis (`earth_fixed`: aligned with the ground track, as "
+ + "for a yaw-steered spacecraft; `inertial`: aligned with the orbit "
+ + "plane, as for a spacecraft without yaw steering).",
+ )
+ yaw_angle: float = Field(
+ default=0,
+ description="Rotation (degrees) of the body about the nadir axis, "
+ + "positive turning the front of the body to the left of the direction "
+ + "of motion (180: flying backward).",
+ ge=-180,
+ le=180,
+ )
+ roll_angle: float = Field(
+ default=0,
+ description="Rotation (degrees) of the body about its along-track axis, "
+ + "positive tilting its nadir axis to the left of the direction of motion.",
+ ge=-180,
+ le=180,
+ )
+ pitch_angle: float = Field(
+ default=0,
+ description="Rotation (degrees) of the body about its rolled "
+ + "cross-track axis, positive tilting its nadir axis forward.",
+ ge=-180,
+ le=180,
+ )
+ yaw_angle_profile: list[tuple[float, float]] | None = Field(
+ default=None,
+ description="Yaw angle (degrees) as a function of the satellite's "
+ + "argument of latitude (degrees from the ascending node, 0 to 360), as "
+ + "(argument of latitude, yaw angle) pairs interpolated linearly and "
+ + "periodically. If given, it overrides `yaw_angle`.",
+ min_length=1,
+ )
+ roll_angle_profile: list[tuple[float, float]] | None = Field(
+ default=None,
+ description="Roll angle (degrees) as a function of the satellite's "
+ + "argument of latitude (see `yaw_angle_profile`), for example the roll "
+ + "steering of a synthetic aperture radar. If given, it overrides "
+ + "`roll_angle`.",
+ min_length=1,
+ )
+ pitch_angle_profile: list[tuple[float, float]] | None = Field(
+ default=None,
+ description="Pitch angle (degrees) as a function of the satellite's "
+ + "argument of latitude (see `yaw_angle_profile`). If given, it "
+ + "overrides `pitch_angle`.",
+ min_length=1,
+ )
+
+ @field_validator("yaw_angle_profile", "roll_angle_profile", "pitch_angle_profile")
+ @classmethod
+ def _validate_angle_profile(
+ cls, profile: list[tuple[float, float]] | None, info
+ ) -> list[tuple[float, float]] | None:
+ """
+ Validates an angle profile (see `_check_angle_profile`).
+ """
+ return _check_angle_profile(profile, info.field_name.split("_")[0].title())
+
+ def get_angles(
+ self, orbit_track: Geocentric
+ ) -> tuple[npt.ArrayLike, npt.ArrayLike, npt.ArrayLike]:
+ """
+ Gets the yaw, roll, and pitch angles (degrees) of the body at the
+ satellite position(s) of an orbit track.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ tuple[numpy.typing.ArrayLike, numpy.typing.ArrayLike, numpy.typing.ArrayLike]:
+ the yaw, roll, and pitch angles (degrees)
+ """
+ return tuple(
+ (
+ angle
+ if profile is None
+ else _interpolate_angle_profile(profile, orbit_track)
+ )
+ for angle, profile in (
+ (self.yaw_angle, self.yaw_angle_profile),
+ (self.roll_angle, self.roll_angle_profile),
+ (self.pitch_angle, self.pitch_angle_profile),
+ )
+ )
+
+ def get_body_angles(
+ self, orbit_track: Geocentric
+ ) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]:
+ """
+ Gets the yaw, roll, and pitch angles (degrees) of the body and whether
+ its along-track axis is aligned with the inertial velocity, at each
+ time of an orbit track, as arrays with the shape of its times (as
+ used by the projection utilities, see
+ `tatc.utils.projection._compute_nadir_axes`).
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]:
+ the yaw, roll, and pitch angles (degrees), and whether the
+ velocity frame is inertial
+ """
+ shape = np.shape(orbit_track.t.tt)
+ yaw, roll, pitch = (
+ np.broadcast_to(np.asarray(angle, dtype=float), shape)
+ for angle in self.get_angles(orbit_track)
+ )
+ inertial = np.full(shape, self.velocity_frame == VelocityFrame.INERTIAL)
+ return yaw, roll, pitch, inertial
+
+
+class TargetAttitude(BaseModel):
+ """
+ Attitude of a spacecraft's body that points its nadir axis at ground
+ targets, as for an agile satellite imaging sites or a target mode: at
+ each time, at the target closest to nadir (with the smallest off-nadir
+ angle) among those within `max_off_nadir_angle` of nadir and above the
+ horizon, and otherwise along nadir. The body is rotated by the yaw angle
+ about the nadir axis and then by the roll and pitch angles that point
+ its nadir axis at the target (see `tatc.utils.compute_view_angles`), so
+ that its along-track axis stays close to the direction of motion. The
+ body turns instantly between targets and nadir (slews are not modeled).
+
+ The fields of regard of its instruments (measured from nadir) should
+ include the off-nadir angles at which the body points. As an
+ instrument's view stays on a target while the body points at it, the
+ epoch of an observation of the target is a time while the body points
+ at it (rather than the time a view sweeps over it).
+ """
+
+ type: Literal["target"] = Field(
+ default="target", description="Attitude type discriminator."
+ )
+ targets: list[Point] = Field(
+ ..., min_length=1, description="The ground targets at which to point."
+ )
+ max_off_nadir_angle: float = Field(
+ default=30,
+ description="Maximum angle (degrees) from nadir at which the body "
+ + "points at a target.",
+ gt=0,
+ lt=90,
+ )
+ velocity_frame: VelocityFrame = Field(
+ default=VelocityFrame.EARTH_FIXED,
+ description="Reference frame of the velocity vector that defines the "
+ + "along-track axis (see `Attitude.velocity_frame`).",
+ )
+ yaw_angle: float = Field(
+ default=0,
+ description="Rotation (degrees) of the body about the nadir axis "
+ + "before pointing (see `Attitude.yaw_angle`).",
+ ge=-180,
+ le=180,
+ )
+
+ def _get_targets(self) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray]:
+ """
+ Gets the targets' longitudes, latitudes (degrees), and elevations (meters).
+ """
+ return tuple(
+ np.array([getattr(target, name) for target in self.targets], dtype=float)
+ for name in ("longitude", "latitude", "elevation")
+ )
+
+ def get_target_index(self, orbit_track: Geocentric) -> npt.NDArray:
+ """
+ Gets the index of the target at which the body points at each time of
+ an orbit track, or -1 if it points along nadir.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ numpy.typing.NDArray: the target indices, with the shape of the
+ orbit track's times
+ """
+ shape = np.shape(orbit_track.t.tt)
+ p_m, nadir, _, _ = _compute_nadir_axes(orbit_track, self.velocity_frame)
+ p_m, nadir = np.reshape(p_m, (3, -1)), np.reshape(nadir, (3, -1))
+ longitude, latitude, elevation = self._get_targets()
+ xyz = np.array(wgs84.latlon(latitude, longitude, elevation).itrs_xyz.m)
+ up = np.array(
+ [
+ np.cos(np.radians(latitude)) * np.cos(np.radians(longitude)),
+ np.cos(np.radians(latitude)) * np.sin(np.radians(longitude)),
+ np.sin(np.radians(latitude)),
+ ]
+ )
+ off_nadir = np.full((len(self.targets), p_m.shape[1]), np.inf)
+ for k in range(len(self.targets)):
+ los = _compute_line_of_sight(xyz[:, k], p_m)
+ distance = np.linalg.norm(los, axis=0)
+ angle = np.degrees(
+ np.arccos(np.clip(np.sum(los * nadir, axis=0) / distance, -1, 1))
+ )
+ # above the target's horizon and within the maximum off-nadir angle
+ visible = (np.sum(-los * up[:, k : k + 1], axis=0) > 0) & (
+ angle <= self.max_off_nadir_angle
+ )
+ off_nadir[k] = np.where(visible, angle, np.inf)
+ index = np.where(
+ np.isfinite(off_nadir.min(axis=0)), np.argmin(off_nadir, axis=0), -1
+ )
+ return np.reshape(index, shape)
+
+ def get_body_angles(
+ self, orbit_track: Geocentric
+ ) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]:
+ """
+ Gets the yaw, roll, and pitch angles (degrees) of the body and whether
+ its velocity frame is inertial, at each time of an orbit track (see
+ `Attitude.get_body_angles`): the roll and pitch angles that point its
+ nadir axis at the target of each time (see `get_target_index`), or
+ zero.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]:
+ the yaw, roll, and pitch angles (degrees), and whether the
+ velocity frame is inertial
+ """
+ shape = np.shape(orbit_track.t.tt)
+ index = np.reshape(self.get_target_index(orbit_track), -1)
+ roll, pitch = np.zeros(index.shape), np.zeros(index.shape)
+ pointing = np.flatnonzero(index >= 0)
+ if len(pointing) > 0:
+ longitude, latitude, elevation = (
+ values[index[pointing]] for values in self._get_targets()
+ )
+ if len(shape) == 0:
+ track = orbit_track
+ target = wgs84.latlon(latitude[0], longitude[0], elevation[0])
+ else:
+ track = _index_orbit_track(orbit_track, pointing)
+ target = wgs84.latlon(latitude, longitude, elevation)
+ # the roll and pitch angles of the target in the yawed body frame
+ roll_angle, pitch_angle = compute_view_angles(
+ track,
+ target,
+ self.velocity_frame,
+ attitude=Attitude(
+ yaw_angle=self.yaw_angle, velocity_frame=self.velocity_frame
+ ),
+ )
+ roll[pointing] = np.reshape(roll_angle, -1)
+ pitch[pointing] = np.reshape(pitch_angle, -1)
+ return (
+ np.full(shape, float(self.yaw_angle)),
+ np.reshape(roll, shape),
+ np.reshape(pitch, shape),
+ np.full(shape, self.velocity_frame == VelocityFrame.INERTIAL),
+ )
+
+
+class GlintAttitude(BaseModel):
+ """
+ Attitude of a spacecraft's body that points its nadir axis at the sun
+ glint (the specular point, see `tatc.utils.compute_specular_point`), as
+ for the glint mode of a greenhouse gas sounder observing the ocean: at
+ each time, toward the specular point, or short of it toward nadir by an
+ offset angle (in the plane of the nadir axis and the line of sight to
+ the specular point), while the Sun is at least `min_solar_elevation`
+ above the horizon at the specular point (equal to the satellite's
+ elevation angle seen from it) and the body points within
+ `max_off_nadir_angle` of nadir, and otherwise along nadir. The body is
+ rotated by the yaw angle about the nadir axis and then by the roll and
+ pitch angles that point its nadir axis (see
+ `tatc.utils.compute_view_angles`), which follows the glint as it moves
+ with the satellite and the Sun.
+
+ The fields of regard of its instruments (measured from nadir) should
+ include the off-nadir angles at which the body points.
+ """
+
+ type: Literal["glint"] = Field(
+ default="glint", description="Attitude type discriminator."
+ )
+ max_off_nadir_angle: float = Field(
+ default=60,
+ description="Maximum angle (degrees) from nadir at which the body "
+ + "points toward the glint.",
+ gt=0,
+ lt=90,
+ )
+ offset_angle: float = Field(
+ default=0,
+ description="Angle (degrees) by which the body points short of the "
+ + "specular point, toward nadir, as for an instrument that observes "
+ + "near the glint rather than at its center.",
+ ge=0,
+ lt=90,
+ )
+ offset_angle_profile: list[tuple[float, float]] | None = Field(
+ default=None,
+ description="Offset angle (degrees, see `offset_angle`) as a function "
+ + "of the specular point's angle from nadir (degrees), as (off-nadir "
+ + "angle, offset angle) pairs interpolated linearly (and held at the "
+ + "first and last offsets beyond them). If given, it overrides "
+ + "`offset_angle`.",
+ min_length=1,
+ )
+ min_solar_elevation: float = Field(
+ default=0,
+ description="Minimum solar elevation angle (degrees) at the specular "
+ + "point at which the body points at it.",
+ ge=0,
+ lt=90,
+ )
+ velocity_frame: VelocityFrame = Field(
+ default=VelocityFrame.EARTH_FIXED,
+ description="Reference frame of the velocity vector that defines the "
+ + "along-track axis (see `Attitude.velocity_frame`).",
+ )
+ yaw_angle: float = Field(
+ default=0,
+ description="Rotation (degrees) of the body about the nadir axis "
+ + "before pointing (see `Attitude.yaw_angle`).",
+ ge=-180,
+ le=180,
+ )
+
+ @field_validator("offset_angle_profile")
+ @classmethod
+ def _validate_offset_angle_profile(
+ cls, profile: list[tuple[float, float]] | None
+ ) -> list[tuple[float, float]] | None:
+ """
+ Sorts an offset angle profile by off-nadir angle and checks its angles.
+ """
+ if profile is None:
+ return None
+ for off_nadir, offset in profile:
+ if not 0 <= off_nadir < 90:
+ raise ValueError("Off-nadir angles must be in [0, 90).")
+ if not 0 <= offset < 90:
+ raise ValueError("Offset angles must be in [0, 90).")
+ return sorted(profile)
+
+ def get_offset_angle(self, off_nadir_angle: npt.ArrayLike) -> npt.NDArray:
+ """
+ Gets the offset angle (degrees) toward nadir for specular points at
+ angles (degrees) from nadir (see `offset_angle_profile`).
+
+ Args:
+ off_nadir_angle (numpy.typing.ArrayLike): The specular points'
+ angles from nadir (degrees).
+
+ Returns:
+ numpy.typing.NDArray: the offset angles (degrees).
+ """
+ if self.offset_angle_profile is None:
+ return np.full(np.shape(off_nadir_angle), float(self.offset_angle))
+ angles, offsets = np.array(self.offset_angle_profile, dtype=float).T
+ return np.interp(off_nadir_angle, angles, offsets)
+
+ def get_aim_point(self, orbit_track: Geocentric) -> GeographicPosition:
+ """
+ Gets the point on the WGS 84 ellipsoid at which the body points at
+ each time of an orbit track: the specular point, or the point short
+ of it toward nadir by the offset angle, where the criteria are met,
+ or `NaN` where the body points along nadir.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ skyfield.toposlib.GeographicPosition: the aim points.
+ """
+ point = compute_specular_point(orbit_track)
+ latitude = np.reshape(point.latitude.degrees, -1)
+ longitude = np.reshape(point.longitude.degrees, -1)
+ p_m, nadir, _, _ = _compute_nadir_axes(orbit_track, self.velocity_frame)
+ p_m, nadir = np.reshape(p_m, (3, -1)), np.reshape(nadir, (3, -1))
+ los = np.reshape(np.array(point.itrs_xyz.m), (3, -1)) - p_m
+ distance = np.linalg.norm(los, axis=0)
+ with np.errstate(invalid="ignore"):
+ los = los / distance
+ off_nadir = np.degrees(
+ np.arccos(np.clip(np.sum(los * nadir, axis=0), -1, 1))
+ )
+ # the elevation of the satellite (and of the Sun) seen from the point
+ up = np.array(
+ [
+ np.cos(np.radians(latitude)) * np.cos(np.radians(longitude)),
+ np.cos(np.radians(latitude)) * np.sin(np.radians(longitude)),
+ np.sin(np.radians(latitude)),
+ ]
+ )
+ elevation = np.degrees(np.arcsin(np.sum(-los * up, axis=0)))
+ # the direction offset toward nadir in the plane of nadir and the line of sight
+ angle = np.maximum(off_nadir - self.get_offset_angle(off_nadir), 0)
+ across = los - np.sum(los * nadir, axis=0) * nadir
+ across = across / np.linalg.norm(across, axis=0)
+ direction = np.cos(np.radians(angle)) * nadir + np.sin(
+ np.radians(angle)
+ ) * np.nan_to_num(across)
+ pointing = (
+ np.isfinite(latitude)
+ & (angle <= self.max_off_nadir_angle)
+ & (elevation >= self.min_solar_elevation)
+ )
+ aim = np.full(p_m.shape, np.nan)
+ if np.any(pointing):
+ position, found = compute_ellipsoid_intersection(
+ p_m[:, pointing], direction[:, pointing]
+ )
+ aim[:, pointing] = np.where(found, position, np.nan)
+ longitude, latitude, _ = rectangular_to_geodetic(aim)
+ if np.ndim(orbit_track.t.tt) == 0:
+ return wgs84.latlon(latitude[0], longitude[0])
+ return wgs84.latlon(latitude, longitude)
+
+ def get_body_angles(
+ self, orbit_track: Geocentric
+ ) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]:
+ """
+ Gets the yaw, roll, and pitch angles (degrees) of the body and whether
+ its velocity frame is inertial, at each time of an orbit track (see
+ `Attitude.get_body_angles`): the roll and pitch angles that point its
+ nadir axis at the aim point (see `get_aim_point`), or zero.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]:
+ the yaw, roll, and pitch angles (degrees), and whether the
+ velocity frame is inertial
+ """
+ shape = np.shape(orbit_track.t.tt)
+ point = self.get_aim_point(orbit_track)
+ latitude = np.reshape(point.latitude.degrees, -1)
+ longitude = np.reshape(point.longitude.degrees, -1)
+ roll, pitch = np.zeros(latitude.shape), np.zeros(latitude.shape)
+ pointing = np.flatnonzero(np.isfinite(latitude))
+ if len(pointing) > 0:
+ if len(shape) == 0:
+ track = orbit_track
+ target = wgs84.latlon(latitude[0], longitude[0])
+ else:
+ track = _index_orbit_track(orbit_track, pointing)
+ target = wgs84.latlon(latitude[pointing], longitude[pointing])
+ # the roll and pitch angles of the aim point in the yawed body frame
+ roll_angle, pitch_angle = compute_view_angles(
+ track,
+ target,
+ self.velocity_frame,
+ attitude=Attitude(
+ yaw_angle=self.yaw_angle, velocity_frame=self.velocity_frame
+ ),
+ )
+ roll[pointing] = np.reshape(roll_angle, -1)
+ pitch[pointing] = np.reshape(pitch_angle, -1)
+ return (
+ np.full(shape, float(self.yaw_angle)),
+ np.reshape(roll, shape),
+ np.reshape(pitch, shape),
+ np.full(shape, self.velocity_frame == VelocityFrame.INERTIAL),
+ )
+
+
+ScheduledAttitudes = Annotated[
+ Attitude | TargetAttitude | GlintAttitude, Field(discriminator="type")
+]
+"""Attitudes that a schedule may switch between."""
+
+
+class AttitudeSegment(BaseModel):
+ """
+ An attitude that applies from a start time (until the start of the next
+ segment of a schedule).
+ """
+
+ start: AwareDatetime = Field(
+ ..., description="Time (timezone-aware) from which the attitude applies."
+ )
+ attitude: ScheduledAttitudes = Field(..., description="The attitude.")
+
+
+class AttitudeSchedule(BaseModel):
+ """
+ Schedule of attitudes over time: each segment's attitude applies from its
+ start until the start of the next segment, and the first segment's
+ attitude also applies before its start. For example, a satellite that
+ periodically flips to fly backward (yaw 180 degrees) and forward, or
+ that alternates between observing modes.
+ """
+
+ type: Literal["schedule"] = Field(
+ default="schedule", description="Attitude type discriminator."
+ )
+ segments: list[AttitudeSegment] = Field(
+ ...,
+ min_length=1,
+ description="The segments, each with a start time and an attitude.",
+ )
+
+ @field_validator("segments")
+ @classmethod
+ def _sort_segments(cls, segments: list[AttitudeSegment]) -> list[AttitudeSegment]:
+ """
+ Sorts the segments by start time.
+ """
+ return sorted(segments, key=lambda segment: segment.start)
+
+ @classmethod
+ def from_attitudes(
+ cls,
+ starts: list[datetime],
+ attitudes: list[Attitude | TargetAttitude | GlintAttitude],
+ ) -> AttitudeSchedule:
+ """
+ Creates a schedule from the start times and attitudes of its segments.
+
+ Args:
+ starts (list[datetime.datetime]): The start times (timezone-aware).
+ attitudes (list[Attitude | TargetAttitude | GlintAttitude]): The
+ attitude of each segment.
+
+ Returns:
+ AttitudeSchedule: the schedule
+ """
+ if len(starts) != len(attitudes):
+ raise ValueError("Each segment needs a start time and an attitude.")
+ return cls(
+ segments=[
+ AttitudeSegment(start=start, attitude=attitude)
+ for start, attitude in zip(starts, attitudes)
+ ]
+ )
+
+ def get_attitude(self, time: datetime) -> Attitude | TargetAttitude | GlintAttitude:
+ """
+ Gets the attitude that applies at a time.
+
+ Args:
+ time (datetime.datetime): The time (timezone-aware).
+
+ Returns:
+ Attitude | TargetAttitude | GlintAttitude: the attitude
+ """
+ index = 0
+ for i, segment in enumerate(self.segments):
+ if segment.start <= time:
+ index = i
+ return self.segments[index].attitude
+
+ def get_body_angles(
+ self, orbit_track: Geocentric
+ ) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]:
+ """
+ Gets the yaw, roll, and pitch angles (degrees) of the body and whether
+ its velocity frame is inertial, at each time of an orbit track, from
+ the attitude of the segment in which each time lies (see
+ `Attitude.get_body_angles`).
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]:
+ the yaw, roll, and pitch angles (degrees), and whether the
+ velocity frame is inertial
+ """
+ tt = np.asarray(orbit_track.t.tt, dtype=float)
+ shape = np.shape(tt)
+ starts = np.atleast_1d(
+ orbit_track.t.ts.from_datetimes([s.start for s in self.segments]).tt
+ )
+ segment = np.maximum(
+ np.searchsorted(starts, np.reshape(tt, -1), "right") - 1, 0
+ )
+ yaw, roll, pitch = (np.zeros(segment.shape) for _ in range(3))
+ inertial = np.zeros(segment.shape, dtype=bool)
+ for i in np.unique(segment):
+ index = np.flatnonzero(segment == i)
+ track = (
+ orbit_track
+ if len(shape) == 0
+ else _index_orbit_track(orbit_track, index)
+ )
+ values = self.segments[i].attitude.get_body_angles(track)
+ for array, value in zip((yaw, roll, pitch, inertial), values):
+ array[index] = np.reshape(value, -1)
+ return tuple(np.reshape(array, shape) for array in (yaw, roll, pitch, inertial))
diff --git a/src/tatc/schemas/instrument/conical.py b/src/tatc/schemas/instrument/conical.py
index cb2fc350..a24d1eff 100644
--- a/src/tatc/schemas/instrument/conical.py
+++ b/src/tatc/schemas/instrument/conical.py
@@ -12,7 +12,6 @@
import numpy.typing as npt
from pydantic import Field, model_validator
from shapely import MultiPolygon, Polygon, unary_union
-from skyfield.constants import AU_M
from skyfield.framelib import itrs
from skyfield.positionlib import Geocentric
from skyfield.toposlib import GeographicPosition
@@ -22,7 +21,9 @@
from ...utils.projection import (
VelocityFrame,
_compute_projected_rays,
+ _shift_along_track,
compute_cone_and_azimuth,
+ compute_ground_sample_size,
compute_projected_ray_position,
)
from .simple import Instrument
@@ -48,27 +49,6 @@ def _cone_ray(cone_angle: float, azimuth: float) -> tuple[float, float]:
)
-def _shift_along_track(orbit_track: Geocentric, seconds: npt.ArrayLike) -> Geocentric:
- """
- Shifts satellite positions along their Earth-fixed velocity (a linear
- approximation of the motion relative to the ground over a short time).
-
- Args:
- orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity.
- seconds (numpy.typing.ArrayLike): The shift (seconds) at each time.
-
- Returns:
- skyfield.positionlib.Geocentric: The shifted satellite position/velocity.
- """
- position, velocity = orbit_track.frame_xyz_and_velocity(itrs)
- shifted = np.array(position.m) + np.array(velocity.m_per_s) * seconds
- # rotate from Earth-fixed to inertial (GCRS) coordinates at the same times
- gcrs = np.einsum("ji...,j...->i...", itrs.rotation_at(orbit_track.t), shifted)
- return Geocentric(
- gcrs / AU_M, orbit_track.velocity.au_per_d, orbit_track.t, center=399
- )
-
-
class ConicalInstrument(Instrument):
"""
Remote sensing instrument that scans a cone about the nadir: its view
@@ -110,6 +90,22 @@ class ConicalInstrument(Instrument):
gt=0,
le=180,
)
+ beam_width: float | None = Field(
+ default=None,
+ description="Angular width (degrees) of the beam (for example, its "
+ + "half-power width), which sets the size of a sample on the ground "
+ + "(see `compute_sample_size`), or `None` if not modeled.",
+ gt=0,
+ lt=90,
+ )
+ scan_sample_spacing: float | None = Field(
+ default=None,
+ description="Scan azimuth (degrees) between the centers of neighboring "
+ + "samples along the scanned arc (see `compute_sample_spacing`), or "
+ + "`None` if not modeled.",
+ gt=0,
+ le=180,
+ )
velocity_frame: VelocityFrame = Field(
default=VelocityFrame.EARTH_FIXED,
description="Reference frame of the velocity vector that defines the "
@@ -263,6 +259,7 @@ def project(rays):
self.velocity_frame,
self.nadir_reference,
ground_elevation=ground_elevation,
+ attitude=self._attitude,
)[:2]
return np.stack([geos[0], geos[1]], axis=-1)
@@ -299,6 +296,100 @@ def project(rays):
footprints.append(project_polygon_to_elevation(footprint, elevation))
return footprints
+ def compute_sample_size(
+ self, orbit_track: Geocentric, target: GeographicPosition
+ ) -> tuple[npt.NDArray, npt.NDArray]:
+ """
+ Computes the size on the ground (meters) of the beam viewing a
+ target, along track and across track (see
+ `tatc.utils.compute_ground_sample_size`): the beam is circular, but
+ its footprint is longer in the direction of the view (along track,
+ for a view ahead of the satellite), as the view is oblique. The sizes
+ are `NaN` if the beam width is not modeled.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity.
+ target (skyfield.toposlib.GeographicPosition): The target position
+ (a single target, or one target for each time of the orbit track).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the along-track
+ and cross-track sizes (meters).
+ """
+ if self.beam_width is None:
+ nan = np.full(
+ np.broadcast(
+ np.asarray(orbit_track.t.tt), np.asarray(target.latitude.degrees)
+ ).shape,
+ np.nan,
+ )
+ return nan, nan.copy()
+ return compute_ground_sample_size(
+ orbit_track,
+ target,
+ self.beam_width,
+ self.beam_width,
+ self.velocity_frame,
+ nadir_reference=self.nadir_reference,
+ attitude=self._attitude,
+ )
+
+ def compute_sample_spacing(
+ self, orbit_track: Geocentric, target: GeographicPosition
+ ) -> tuple[npt.NDArray, npt.NDArray]:
+ """
+ Computes the spacing on the ground (meters) between the centers of
+ neighboring samples viewing a target: along track, the distance the
+ scanned arc advances between scans (subtended at nadir by the
+ along-track field of view), and across track, the distance between
+ samples along the scanned arc (the scan sample spacing times the
+ arc's radius on the ground, from the sub-satellite point to the
+ target). The cross-track spacing is `NaN` if the scan sample spacing
+ is not modeled.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity.
+ target (skyfield.toposlib.GeographicPosition): The target position
+ (a single target, or one target for each time of the orbit track).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the along-track
+ and cross-track spacings (meters).
+ """
+ position = np.reshape(np.array(orbit_track.frame_xyz(itrs).m), (3, -1))
+ radius = np.linalg.norm(position, axis=0)
+ # the along-track distance swept per scan (see _sweep_time)
+ along = (
+ 2
+ * (radius - constants.EARTH_MEAN_RADIUS)
+ * np.tan(np.radians(self.along_track_field_of_view / 2))
+ )
+ target_xyz = np.reshape(np.array(target.itrs_xyz.m), (3, -1))
+ # the Earth central angle from the sub-satellite point to the target
+ central = np.arccos(
+ np.clip(
+ np.sum(position * target_xyz, axis=0)
+ / radius
+ / np.linalg.norm(target_xyz, axis=0),
+ -1,
+ 1,
+ )
+ )
+ along, central = np.broadcast_arrays(along, central)
+ cross = np.full(central.shape, np.nan)
+ if self.scan_sample_spacing is not None:
+ cross = (
+ central
+ * np.linalg.norm(target_xyz, axis=0)
+ * np.radians(self.scan_sample_spacing)
+ )
+ if (
+ np.ndim(np.asarray(orbit_track.t.tt)) == 0
+ and np.ndim(target.latitude.degrees) == 0
+ ):
+ return along[0], cross[0]
+ return along, cross
+
def compute_footprint_center(
self,
orbit_track: Geocentric,
@@ -329,6 +420,7 @@ def compute_footprint_center(
velocity_frame=self.velocity_frame,
nadir_reference=self.nadir_reference,
ground_elevation=ground_elevation,
+ attitude=self._attitude,
)
def is_in_field_of_view(
@@ -358,6 +450,7 @@ def is_in_field_of_view(
target,
self.velocity_frame,
self.nadir_reference,
+ attitude=self._attitude,
)
for f in (-1, 0, 1)
]
diff --git a/src/tatc/schemas/instrument/pointed.py b/src/tatc/schemas/instrument/pointed.py
index 0e360b0a..36b9ee9d 100644
--- a/src/tatc/schemas/instrument/pointed.py
+++ b/src/tatc/schemas/instrument/pointed.py
@@ -14,11 +14,13 @@
from skyfield.positionlib import Geocentric
from skyfield.toposlib import GeographicPosition
-from ...utils.orbital import compute_argument_of_latitude
+from ...utils.orbital import _check_angle_profile, _interpolate_angle_profile
from ...utils.projection import (
VelocityFrame,
ViewGeometry,
compute_footprint,
+ compute_ground_advance,
+ compute_ground_sample_size,
compute_projected_ray_position,
compute_view_angles,
compute_view_tangents,
@@ -100,16 +102,28 @@ class PointedInstrument(Instrument):
)
cross_track_oversampling: float = Field(
default=0,
- description="Fraction of pixel overlap in cross-track direction.",
- ge=0,
+ description="Fraction of pixel overlap in cross-track direction: the "
+ + "fraction of a pixel's instantaneous field of view shared with its "
+ + "neighbor (negative for a gap between pixels, as a fraction of the "
+ + "instantaneous field of view).",
lt=1,
)
along_track_oversampling: float = Field(
default=0,
- description="Fraction of pixel overlap in along-track direction.",
- ge=0,
+ description="Fraction of pixel overlap in along-track direction: the "
+ + "fraction of a pixel's instantaneous field of view shared with its "
+ + "neighbor (negative for a gap between pixels, as a fraction of the "
+ + "instantaneous field of view).",
lt=1,
)
+ scan_period: float | None = Field(
+ default=None,
+ description="Period (seconds) between consecutive scans of a scanning "
+ + "instrument (for example, 8/3 s for ATMS), whose scans are spaced along "
+ + "track by the distance they advance on the ground in one period (see "
+ + "`compute_sample_spacing`), or `None` if not modeled.",
+ gt=0,
+ )
velocity_frame: VelocityFrame = Field(
default=VelocityFrame.EARTH_FIXED,
description="Reference frame of the velocity vector that defines the "
@@ -118,6 +132,140 @@ class PointedInstrument(Instrument):
+ "plane, as for a spacecraft without yaw steering).",
)
+ def get_cross_track_sample_spacing(self) -> float:
+ """
+ Gets the angular spacing (degrees) between the centers of neighboring
+ cross-track pixels: the field of view divided among the pixels.
+
+ Returns:
+ float: the cross-track sample spacing (degrees)
+ """
+ return self.cross_track_field_of_view / self.cross_track_pixels
+
+ def get_along_track_sample_spacing(self) -> float:
+ """
+ Gets the angular spacing (degrees) between the centers of neighboring
+ along-track pixels: the field of view divided among the pixels.
+
+ Returns:
+ float: the along-track sample spacing (degrees)
+ """
+ return self.along_track_field_of_view / self.along_track_pixels
+
+ def compute_sample_size(
+ self, orbit_track: Geocentric, target: GeographicPosition
+ ) -> tuple[npt.NDArray, npt.NDArray]:
+ """
+ Computes the size on the ground (meters) of a pixel viewing a target:
+ the extent of its instantaneous field of view along track and across
+ track, centered on the line of sight to the target (see
+ `tatc.utils.compute_ground_sample_size`). The sizes grow with the
+ slant range and, in the direction of a view off nadir, with the
+ obliquity of the view.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity.
+ target (skyfield.toposlib.GeographicPosition): The target position
+ (a single target, or one target for each time of the orbit track).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the along-track
+ and cross-track sizes (meters).
+ """
+ return self._compute_ground_extent(
+ orbit_track,
+ target,
+ self.get_along_track_instantaneous_field_of_view(),
+ self.get_cross_track_instantaneous_field_of_view(),
+ )
+
+ def compute_sample_spacing(
+ self, orbit_track: Geocentric, target: GeographicPosition
+ ) -> tuple[npt.NDArray, npt.NDArray]:
+ """
+ Computes the spacing on the ground (meters) between the centers of
+ neighboring pixels viewing a target, along track and across track
+ (see `compute_sample_size`). A pixel smaller than the spacing (a
+ negative oversampling) leaves gaps between pixels, and a sensor
+ cannot resolve detail finer than the larger of the two.
+
+ The spacing is that of the angles between pixels, projected around
+ the line of sight to the target. For a scanning instrument with a
+ `scan_period`, the spacing along track is instead the larger of the
+ angular spacing between its pixels within a scan (if it has more than
+ one along track) and the distance the scan advances on the ground at
+ the target in one period, divided among its pixels along track (see
+ `tatc.utils.compute_ground_advance`). The advance, nearly constant
+ across the scan, spaces the scans of an instrument with one pixel
+ along track (such as a microwave sounder); toward the edges of the
+ swath, where the pixels of an array of detectors grow larger than the
+ advance, consecutive scans overlap and the spacing within a scan
+ dominates.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity.
+ target (skyfield.toposlib.GeographicPosition): The target position
+ (a single target, or one target for each time of the orbit track).
+
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the along-track
+ and cross-track spacings (meters).
+ """
+ along, cross = self._compute_ground_extent(
+ orbit_track,
+ target,
+ self.get_along_track_sample_spacing(),
+ self.get_cross_track_sample_spacing(),
+ )
+ if self.scan_period is None:
+ return along, cross
+ advance = compute_ground_advance(
+ orbit_track,
+ target,
+ self.scan_period,
+ self.velocity_frame,
+ self.nadir_reference,
+ self._attitude,
+ )
+ within_scan = along if self.along_track_pixels > 1 else 0
+ return np.maximum(within_scan, advance / self.along_track_pixels), cross
+
+ def _compute_ground_extent(
+ self,
+ orbit_track: Geocentric,
+ target: GeographicPosition,
+ along_track_angle: float,
+ cross_track_angle: float,
+ ) -> tuple[npt.NDArray, npt.NDArray]:
+ """
+ Computes the size on the ground (meters) of angular extents centered
+ on the line of sight to a target, along this instrument's view axes:
+ those of the rolled and pitched view, or, for a `ViewGeometry.SCAN`
+ view, of the scan plane tilted by the pitch angle.
+ """
+ if self.view_geometry == ViewGeometry.SCAN:
+ return compute_ground_sample_size(
+ orbit_track,
+ target,
+ along_track_angle,
+ cross_track_angle,
+ self.velocity_frame,
+ nadir_reference=self.nadir_reference,
+ tilt_angle=self.get_pitch_angle(orbit_track),
+ attitude=self._attitude,
+ )
+ return compute_ground_sample_size(
+ orbit_track,
+ target,
+ along_track_angle,
+ cross_track_angle,
+ self.velocity_frame,
+ self.get_roll_angle(orbit_track),
+ self.get_pitch_angle(orbit_track),
+ self.nadir_reference,
+ attitude=self._attitude,
+ )
+
def get_cross_track_instantaneous_field_of_view(self) -> float:
"""
Gets the instananeous field of view (degrees) for cross-track pixels.
@@ -177,6 +325,7 @@ def compute_footprint(
nadir_reference=self.nadir_reference,
view_geometry=self.view_geometry,
ground_elevation=ground_elevation,
+ attitude=self._attitude,
)
@field_validator("roll_angle_profile", "pitch_angle_profile")
@@ -189,15 +338,8 @@ def _validate_angle_profile(
lie in [0, 360) and its angles in [-180, 180], and sorts it by
argument of latitude.
"""
- if profile is None:
- return None
kind = "Roll" if info.field_name == "roll_angle_profile" else "Pitch"
- for argument, angle in profile:
- if not 0 <= argument < 360:
- raise ValueError("Arguments of latitude must be in [0, 360).")
- if not -180 <= angle <= 180:
- raise ValueError(f"{kind} angles must be in [-180, 180].")
- return sorted(profile)
+ return _check_angle_profile(profile, kind)
@staticmethod
def _interpolate_angle_profile(
@@ -215,12 +357,7 @@ def _interpolate_angle_profile(
Returns:
float | numpy.typing.NDArray[numpy.float64]: the angle(s) (degrees)
"""
- argument = compute_argument_of_latitude(
- orbit_track.position.m, orbit_track.velocity.m_per_s
- )
- arguments, angles = zip(*profile)
- angle = np.interp(argument, arguments, angles, period=360)
- return float(angle) if np.ndim(angle) == 0 else angle
+ return _interpolate_angle_profile(profile, orbit_track)
def get_roll_angle(
self, orbit_track: Geocentric | None = None
@@ -284,6 +421,7 @@ def is_in_field_of_view(
self.velocity_frame,
self.nadir_reference,
self.get_pitch_angle(orbit_track),
+ attitude=self._attitude,
)
# angular offsets from the view center, normalized by the half widths
cross_offset = (
@@ -298,6 +436,7 @@ def is_in_field_of_view(
self.get_roll_angle(orbit_track),
self.get_pitch_angle(orbit_track),
self.nadir_reference,
+ attitude=self._attitude,
)
# offsets from the view center, normalized by the view half widths
along_offset = along / np.tan(
@@ -343,6 +482,7 @@ def compute_footprint_center(
velocity_frame=self.velocity_frame,
nadir_reference=self.nadir_reference,
ground_elevation=ground_elevation,
+ attitude=self._attitude,
)
def compute_projected_pixel_position(
@@ -383,6 +523,7 @@ def compute_projected_pixel_position(
nadir_reference=self.nadir_reference,
tilt_angle=self.get_pitch_angle(orbit_track),
ground_elevation=ground_elevation,
+ attitude=self._attitude,
)
cone, clock = self.get_pixel_cone_and_clock_angle(
cross_track_index, along_track_index
@@ -405,6 +546,7 @@ def compute_projected_pixel_position(
velocity_frame=self.velocity_frame,
nadir_reference=self.nadir_reference,
ground_elevation=ground_elevation,
+ attitude=self._attitude,
)
def _get_pixel_offsets(
@@ -422,18 +564,13 @@ def _get_pixel_offsets(
Returns:
tuple[float, float]: the cross-track and along-track offsets (degrees).
"""
+ # pixel centers are spaced evenly across the field of view
cross_track_offset = (
- (0.5 + cross_track_index - self.cross_track_pixels / 2)
- * (1 - self.cross_track_oversampling)
- * self.cross_track_field_of_view
- / self.cross_track_pixels
- )
+ 0.5 + cross_track_index - self.cross_track_pixels / 2
+ ) * self.get_cross_track_sample_spacing()
along_track_offset = (
- (self.along_track_pixels / 2 - 0.5 - along_track_index)
- * (1 - self.along_track_oversampling)
- * self.along_track_field_of_view
- / self.along_track_pixels
- )
+ self.along_track_pixels / 2 - 0.5 - along_track_index
+ ) * self.get_along_track_sample_spacing()
return cross_track_offset, along_track_offset
def get_pixel_cone_and_clock_angle(
diff --git a/src/tatc/schemas/instrument/simple.py b/src/tatc/schemas/instrument/simple.py
index 4e476cfa..70492365 100644
--- a/src/tatc/schemas/instrument/simple.py
+++ b/src/tatc/schemas/instrument/simple.py
@@ -7,16 +7,19 @@
from __future__ import annotations
from datetime import timedelta
+from typing import Any
import numpy as np
import numpy.typing as npt
-from pydantic import BaseModel, Field, model_validator
+
+from pydantic import BaseModel, Field, PrivateAttr, model_validator
from shapely import MultiPolygon, Polygon
from skyfield.api import wgs84
from skyfield.positionlib import Geocentric
from skyfield.toposlib import GeographicPosition
from ...constants import de421
+from ...utils.glint import compute_glint_angle
from ...utils.observation import (
compute_min_elevation_angle,
field_of_regard_to_swath_width,
@@ -76,6 +79,26 @@ class Instrument(BaseModel):
le=90,
examples=[-12],
)
+ min_target_glint_angle: float | None = Field(
+ default=None,
+ description="Minimum glint angle (degrees) at the target for valid "
+ + "observation (`None`: no requirement): the angle between the "
+ + "directions toward the satellite and of the Sun's specular "
+ + "reflection (see `tatc.utils.compute_glint_angle`), for example to "
+ + "avoid sun glint over water.",
+ ge=0,
+ le=180,
+ examples=[25],
+ )
+ max_target_glint_angle: float | None = Field(
+ default=None,
+ description="Maximum glint angle (degrees) at the target for valid "
+ + "observation (`None`: no requirement), for example to observe in "
+ + "sun glint over water.",
+ ge=0,
+ le=180,
+ examples=[10],
+ )
access_time_fixed: bool = Field(
default=False, description="`True`, if access time is fixed to minimum value."
)
@@ -86,6 +109,35 @@ class Instrument(BaseModel):
+ "toward the Earth's center). The two differ by up to about 0.19 "
+ "degrees at middle latitudes.",
)
+ # the attitude of the space system that carries this instrument (see
+ # `bind_attitude`), from which its views are rotated
+ _attitude: Any = PrivateAttr(default=None)
+
+ @property
+ def platform_attitude(self) -> Any:
+ """
+ The attitude of the space system that carries this instrument, from
+ which its views are rotated (see `SpaceSystem.get_instrument`), or
+ `None` for a body aligned with the nadir frame.
+ """
+ return self._attitude
+
+ def bind_attitude(self, attitude: Any) -> "Instrument":
+ """
+ Gets a copy of this instrument carried by a space system with an
+ attitude (see `tatc.schemas.Attitude` and
+ `tatc.schemas.AttitudeSchedule`): its views (but not its field of
+ regard, measured from nadir) are rotated from the body's axes.
+
+ Args:
+ attitude (Any): The attitude, or `None`.
+
+ Returns:
+ Instrument: the bound copy
+ """
+ bound = self.model_copy()
+ bound._attitude = attitude # pylint: disable=protected-access
+ return bound
@model_validator(mode="after")
def check_target_solar_elevation_range(self) -> "Instrument":
@@ -101,6 +153,14 @@ def check_target_solar_elevation_range(self) -> "Instrument":
raise ValueError(
"min_target_solar_elevation must not exceed max_target_solar_elevation"
)
+ if (
+ self.min_target_glint_angle is not None
+ and self.max_target_glint_angle is not None
+ and self.min_target_glint_angle > self.max_target_glint_angle
+ ):
+ raise ValueError(
+ "min_target_glint_angle must not exceed max_target_glint_angle"
+ )
return self
def get_swath_width(self, height: float) -> float:
@@ -195,9 +255,9 @@ def is_valid_observation(
self, orbit_track: Geocentric, target: GeographicPosition | None = None
) -> npt.NDArray[np.bool_]:
"""Determines if an instrument can provide a valid observations: the
- instrument's sunlit state and the target's sunlit state and solar
- elevation angle (without atmospheric refraction) must satisfy any
- requirements.
+ instrument's sunlit state and the target's sunlit state, solar
+ elevation angle (without atmospheric refraction), and glint angle
+ must satisfy any requirements.
Args:
orbit_track (skyfield.positionlib.Geocentric): orbit track position/velocity from Skyfield
@@ -240,4 +300,17 @@ def is_valid_observation(
is_valid = np.logical_and(
is_valid, solar_alt <= self.max_target_solar_elevation
)
+ if (
+ self.min_target_glint_angle is not None
+ or self.max_target_glint_angle is not None
+ ):
+ glint_angle = compute_glint_angle(orbit_track, target)
+ if self.min_target_glint_angle is not None:
+ is_valid = np.logical_and(
+ is_valid, glint_angle >= self.min_target_glint_angle
+ )
+ if self.max_target_glint_angle is not None:
+ is_valid = np.logical_and(
+ is_valid, glint_angle <= self.max_target_glint_angle
+ )
return is_valid
diff --git a/src/tatc/schemas/orbit/gp.py b/src/tatc/schemas/orbit/gp.py
index cbc9956b..cd73bafa 100644
--- a/src/tatc/schemas/orbit/gp.py
+++ b/src/tatc/schemas/orbit/gp.py
@@ -14,7 +14,7 @@
import numpy as np
import numpy.typing as npt
-from pydantic import BaseModel, Field, model_validator
+from pydantic import AwareDatetime, BaseModel, Field, model_validator
from shapely.geometry import Point as ShapelyPoint
from skyfield.api import Time, wgs84
from skyfield.positionlib import Geocentric
@@ -44,7 +44,9 @@ class GeneralPerturbationsOrbit(BaseModel):
Orbit defined with general perturbations (GP) elements.
The orbit is propagated with SGP4, using whichever element's epoch is
- closest to each time. Optionally (with `repeat_cycle` and `remove_drag`),
+ closest to each time, or, if `element_switch_times` are given, the
+ element between the switches before and after each time (see
+ `get_element_index`). Optionally (with `repeat_cycle` and `remove_drag`),
it is propagated as an orbit maintained against drag on a repeat ground
track, which SGP4 does not model: if the orbit declares a repeat cycle or
one is found (see `GeneralPerturbationsElements.get_repeat_cycle`), times
@@ -80,6 +82,18 @@ class GeneralPerturbationsOrbit(BaseModel):
+ "A repeat cycle requires `remove_drag`, as the orbit is maintained "
+ "against drag.",
)
+ element_switch_times: list[AwareDatetime] | None = Field(
+ default=None,
+ description="Times at which propagation switches from each element "
+ + "(sorted by epoch) to the next, one fewer than the elements, each "
+ + "between the epochs of the elements before and after it: element "
+ + "k is used from switch k - 1 until switch k. None (the default) "
+ + "switches midway between consecutive epochs, using the element whose "
+ + "epoch is closest to each time. Common switch times keep satellites "
+ + "whose elements are published at slightly different times (as in a "
+ + "formation) on elements of the same epochs; see "
+ + "`tatc.utils.align_element_switches`.",
+ )
@model_validator(mode="after")
def _sort_elements_by_epoch(self) -> GeneralPerturbationsOrbit:
@@ -103,6 +117,30 @@ def _validate_repeat_cycle(self) -> GeneralPerturbationsOrbit:
raise ValueError("repeat_cycle requires remove_drag=True.")
return self
+ @model_validator(mode="after")
+ def _validate_element_switch_times(self) -> GeneralPerturbationsOrbit:
+ """
+ Validates that element switch times, if any, number one fewer than
+ the elements and that each lies between the epochs of the elements
+ (sorted by epoch) before and after it, which also orders them.
+ """
+ if self.element_switch_times is None:
+ return self
+ if len(self.element_switch_times) != len(self.elements) - 1:
+ raise ValueError(
+ f"element_switch_times must have one fewer entry than elements "
+ f"({len(self.elements) - 1}), not {len(self.element_switch_times)}."
+ )
+ epochs = self.get_element_epochs()
+ for k, switch in enumerate(self.element_switch_times):
+ if not epochs[k] <= switch <= epochs[k + 1]:
+ raise ValueError(
+ f"element_switch_times[{k}] ({switch}) is not between the "
+ f"epochs of elements {k} ({epochs[k]}) and {k + 1} "
+ f"({epochs[k + 1]})."
+ )
+ return self
+
def get_semimajor_axis(self, index: int = 0) -> float:
"""
Gets the semimajor axis of the specified element.
@@ -442,6 +480,7 @@ def get_derived_orbit(
elements=derived_elements,
remove_drag=self.remove_drag,
repeat_cycle=self.repeat_cycle,
+ element_switch_times=self.element_switch_times,
)
@overload
@@ -498,6 +537,66 @@ def get_closest_element_index(
closest = np.where(use_preceding, indices - 1, indices)
return int(closest[0]) if is_scalar else closest.tolist()
+ def _get_element_switch_times_ns(self) -> npt.NDArray[np.datetime64]:
+ """
+ Gets the element switch times as a cached array (see
+ `element_switch_times`), which must not be None.
+
+ Returns:
+ numpy.typing.NDArray[numpy.datetime64]: the switch times
+ """
+ return get_cached(
+ self,
+ "element_switch_times_ns",
+ tuple(self.element_switch_times),
+ lambda: utils.to_datetime64_ns(self.element_switch_times),
+ )
+
+ @overload
+ def get_element_index(self, at_times: None) -> int: ...
+
+ @overload
+ def get_element_index(self, at_times: datetime) -> int: ...
+
+ @overload
+ def get_element_index(self, at_times: list[datetime]) -> list[int]: ...
+
+ @overload
+ def get_element_index(self, at_times: npt.NDArray[np.datetime64]) -> list[int]: ...
+
+ @overload
+ def get_element_index(self, at_times: Time) -> int | list[int]: ...
+
+ def get_element_index(
+ self,
+ at_times: datetime | list[datetime] | npt.NDArray[np.datetime64] | Time | None,
+ ) -> int | list[int]:
+ """
+ Gets the index of the element that propagates the orbit at specified
+ time(s): the element whose epoch is closest (see
+ `get_closest_element_index`), or, if `element_switch_times` are given,
+ the element between the switches before and after each time. A time
+ exactly at a switch selects the later element.
+
+ Args:
+ at_times (datetime | list[datetime] | npt.NDArray[np.datetime64] | Time | None):
+ specified times (a Skyfield `Time` may be a scalar or an array),
+ or None to always select the first element (index 0)
+
+ Returns:
+ int | list[int]: element index (for a scalar time) or indices
+ """
+ if self.element_switch_times is None or at_times is None:
+ return self.get_closest_element_index(at_times)
+ at_time_array = utils.to_datetime64_ns(at_times)
+ is_scalar = np.ndim(at_time_array) == 0
+ indices = np.searchsorted(
+ self._get_element_switch_times_ns(),
+ np.atleast_1d(at_time_array),
+ side="right",
+ )
+ return int(indices[0]) if is_scalar else indices.tolist()
+
@overload
def get_closest_element(self, at_times: None) -> GeneralPerturbationsElements: ...
@@ -539,10 +638,11 @@ def partition_by_element_index(
) -> tuple[list[datetime], list[int]]:
"""
Partitions the time range [start, end] into consecutive segments,
- each assigned the index of whichever element is closest throughout
- that segment. The closest element switches at the midpoint between
- consecutive elements' epochs, so only midpoints strictly inside
- (start, end) become segment boundaries.
+ each assigned the index of the element that propagates the orbit
+ throughout that segment (see `get_element_index`). The element
+ switches at `element_switch_times` or, by default, at the midpoint
+ between consecutive elements' epochs, so only switches strictly
+ inside (start, end) become segment boundaries.
Args:
start (datetime): Start time.
@@ -554,18 +654,23 @@ def partition_by_element_index(
index for each of the N segments between consecutive
boundaries (length N).
"""
- epochs = self.get_element_epochs()
- epoch_midpoints = [
- epochs[i] + (epochs[i + 1] - epochs[i]) / 2 for i in range(len(epochs) - 1)
- ]
+ if self.element_switch_times is None:
+ epochs = self.get_element_epochs()
+ switches = [
+ epochs[i] + (epochs[i + 1] - epochs[i]) / 2
+ for i in range(len(epochs) - 1)
+ ]
+ else:
+ switches = self.element_switch_times
+ # distinct switches only (consecutive switches may coincide)
boundary_times = (
- [start] + [t for t in epoch_midpoints if start < t < end] + [end]
+ [start] + sorted({t for t in switches if start < t < end}) + [end]
)
segment_midpoints = [
boundary_times[i] + (boundary_times[i + 1] - boundary_times[i]) / 2
for i in range(len(boundary_times) - 1)
]
- return boundary_times, self.get_closest_element_index(segment_midpoints)
+ return boundary_times, self.get_element_index(segment_midpoints)
def get_repeat_cycle(
self,
@@ -737,7 +842,8 @@ def get_orbit_track_at_time(self, t: Time) -> Geocentric:
inertial (GCRS) frame.
Each time is propagated with whichever element's epoch is closest to
- it (see `get_closest_element_index`), so a vectorized `t` may draw
+ it, or the element between the `element_switch_times` around it (see
+ `get_element_index`), so a vectorized `t` may draw
from different elements for different entries. Unless `repeat_cycle`
is None, times before the first element's epoch and after the last
element's epoch are instead propagated with that element's repeat
@@ -763,8 +869,8 @@ def get_orbit_track_at_time(self, t: Time) -> Geocentric:
# interpolate the costly nutation angles (see _interpolate_nutation)
_interpolate_nutation(t)
first, last = self._get_repeat_tracks()
- # the source of each time: the closest element's index, or a repeat track
- sources = np.asarray(self.get_closest_element_index(t))
+ # the source of each time: the element's index, or a repeat track
+ sources = np.asarray(self.get_element_index(t))
if last is not None:
sources = np.where(t.tt >= last.epoch_time.tt, _LAST_REPEAT, sources)
if first is not None:
@@ -976,7 +1082,7 @@ def _get_observation_events(
if direct_start < direct_end:
self._warn_if_propagating_with_drag(
_to_time([direct_start, direct_end]),
- np.array(self.get_closest_element_index([direct_start, direct_end])),
+ np.array(self.get_element_index([direct_start, direct_end])),
)
parts, indices = self.partition_by_element_index(direct_start, direct_end)
boundaries.extend(parts[1:-1])
diff --git a/src/tatc/schemas/space/base.py b/src/tatc/schemas/space/base.py
index 9f1c08d9..f3e48a0a 100644
--- a/src/tatc/schemas/space/base.py
+++ b/src/tatc/schemas/space/base.py
@@ -6,10 +6,19 @@
from __future__ import annotations
-from pydantic import BaseModel, Field
+from typing import Annotated
+from pydantic import BaseModel, Field, model_validator
+
+from ..attitude import Attitude, AttitudeSchedule, GlintAttitude, TargetAttitude
from ..instrument import AllInstruments, Instrument
+AllAttitudes = Annotated[
+ Attitude | TargetAttitude | GlintAttitude | AttitudeSchedule,
+ Field(discriminator="type"),
+]
+"""Attitudes of a space system: about nadir, pointed at targets or the sun glint, or scheduled."""
+
class SpaceSystem(BaseModel):
"""
@@ -26,3 +35,42 @@ class SpaceSystem(BaseModel):
min_length=1,
description="List of assigned instruments.",
)
+ attitude: AllAttitudes | None = Field(
+ default=None,
+ description="Attitude of the spacecraft's body (or a schedule of "
+ + "attitudes), from which the views of its instruments are rotated, or "
+ + "`None` for a body aligned with the nadir frame (each instrument's "
+ + "`velocity_frame` then orients it).",
+ )
+
+ @model_validator(mode="after")
+ def _check_velocity_frames(self) -> "SpaceSystem":
+ """
+ Checks that instruments do not set a velocity frame if the attitude
+ sets the body's.
+ """
+ if self.attitude is not None:
+ for instrument in self.instruments:
+ if "velocity_frame" in instrument.model_fields_set:
+ raise ValueError(
+ f"Instrument {instrument.name!r} sets a velocity frame, "
+ + "but the attitude sets the body's (see Attitude.velocity_frame)."
+ )
+ return self
+
+ def get_instrument(self, index: int = 0) -> Instrument:
+ """
+ Gets an instrument carried by this space system, with its views
+ rotated from the axes of the body in its attitude (see
+ `Instrument.bind_attitude`).
+
+ Args:
+ index (int): The index of the instrument.
+
+ Returns:
+ Instrument: the instrument
+ """
+ instrument = self.instruments[index]
+ if self.attitude is None:
+ return instrument
+ return instrument.bind_attitude(self.attitude)
diff --git a/src/tatc/schemas/space/mog.py b/src/tatc/schemas/space/mog.py
index 8ebdf15e..31a97a19 100644
--- a/src/tatc/schemas/space/mog.py
+++ b/src/tatc/schemas/space/mog.py
@@ -158,6 +158,7 @@ def generate_members(self) -> list[Satellite]:
name=zero_pad(self.name, self.number_satellites, i + 1),
orbit=orbit,
instruments=copy.deepcopy(self.instruments),
+ attitude=copy.deepcopy(self.attitude),
)
for i, orbit in enumerate(orbits)
]
diff --git a/src/tatc/schemas/space/soc.py b/src/tatc/schemas/space/soc.py
index fb9faabf..c394bd4e 100644
--- a/src/tatc/schemas/space/soc.py
+++ b/src/tatc/schemas/space/soc.py
@@ -225,6 +225,7 @@ def generate_walker(self) -> WalkerConstellation:
name=self.name,
orbit=self.orbit,
instruments=self.instruments,
+ attitude=self.attitude,
number_satellites=number_satellites,
number_planes=number_planes,
# offset adjacent planes by half a within-plane satellite
@@ -257,6 +258,7 @@ def generate_members(self) -> list[Satellite]:
(i // satellites_per_plane) * spacing,
),
instruments=copy.deepcopy(self.instruments),
+ attitude=copy.deepcopy(self.attitude),
)
for i in range(number_satellites)
]
diff --git a/src/tatc/schemas/space/train.py b/src/tatc/schemas/space/train.py
index 24b48421..9778a466 100644
--- a/src/tatc/schemas/space/train.py
+++ b/src/tatc/schemas/space/train.py
@@ -110,6 +110,7 @@ def generate_members(self) -> list[Satellite]:
i * self.get_delta_mean_anomaly(), i * self.get_delta_raan()
),
instruments=copy.deepcopy(self.instruments),
+ attitude=copy.deepcopy(self.attitude),
)
for i in range(self.number_satellites)
]
diff --git a/src/tatc/schemas/space/walker.py b/src/tatc/schemas/space/walker.py
index 847ee815..860a1d82 100644
--- a/src/tatc/schemas/space/walker.py
+++ b/src/tatc/schemas/space/walker.py
@@ -192,6 +192,7 @@ def generate_members(self) -> list[Satellite]:
* self.get_delta_raan_between_planes(),
),
instruments=copy.deepcopy(self.instruments),
+ attitude=copy.deepcopy(self.attitude),
)
for i in range(self.number_satellites)
]
diff --git a/src/tatc/utils/__init__.py b/src/tatc/utils/__init__.py
index 1533bb7c..63d17f86 100644
--- a/src/tatc/utils/__init__.py
+++ b/src/tatc/utils/__init__.py
@@ -21,6 +21,7 @@
project_polygon_to_elevation,
split_polygon,
)
+from .glint import compute_glint_angle, compute_specular_point
from .observation import (
compute_along_track_field_of_view,
compute_field_of_regard,
@@ -60,9 +61,12 @@
compute_limb,
compute_projected_ray_position,
compute_static_track,
+ compute_ground_advance,
+ compute_ground_sample_size,
compute_view_angles,
compute_view_tangents,
)
+from .propagation import align_element_switches
from .radar import (
compute_radar_beam_height,
compute_radar_footprint,
@@ -80,6 +84,7 @@
"NadirReference",
"VelocityFrame",
"ViewGeometry",
+ "align_element_switches",
"altitude_to_pressure",
"buffer_footprint",
"buffer_target",
@@ -88,6 +93,7 @@
"compute_cone_and_azimuth",
"compute_field_of_regard",
"compute_footprint",
+ "compute_glint_angle",
"compute_ground_inertial_velocity",
"compute_ground_surface_velocity",
"compute_j2_aop_rate",
@@ -107,8 +113,11 @@
"compute_radar_ground_range",
"compute_radar_ground_range_bounds",
"compute_radar_slant_range",
+ "compute_specular_point",
"compute_static_track",
"compute_terrain_elevation_angle",
+ "compute_ground_advance",
+ "compute_ground_sample_size",
"compute_view_angles",
"compute_view_tangents",
"field_of_regard_to_swath_width",
diff --git a/src/tatc/utils/geometry.py b/src/tatc/utils/geometry.py
index 3634021f..026f5ee1 100644
--- a/src/tatc/utils/geometry.py
+++ b/src/tatc/utils/geometry.py
@@ -312,12 +312,12 @@ def _split_polygon_antimeridian(
- If the exterior encircles a pole (its longitude winds a full 360
degrees around the globe, e.g. a polar cap that does not itself
- exceed +/-90 degrees latitude), it is reconstructed with a flattened
- edge at the pole and split along the prime meridian instead of the
- antimeridian. Note: the raw result of this case may be reported as
- invalid (the two pieces touch along the shared prime-meridian cut
- edge); the public `split_polygon` function repairs this via
- `shapely.make_valid`.
+ exceed +/-90 degrees latitude, or a polygon such as Antarctica that
+ already includes edges along the pole and the anti-meridian), it is
+ reconstructed in its vertex order (which need not be monotonic in
+ longitude, as a coastline is not), unrolled across the anti-meridian,
+ closed with a flattened edge along the pole, and split along the
+ anti-meridian into the standard longitude range.
- Otherwise, coordinates are "unrolled" past +/-180 degrees according to
the cumulative jump direction, split along the antimeridian that the
unrolled coordinates cross, and wrapped back with
@@ -346,26 +346,58 @@ def _split_polygon_antimeridian(
winding = np.sum(diff - 360 * np.where(jump, np.round(diff / 360), 0))
# check if this polygon contains a pole
if np.abs(winding) > 180:
- # extract (lon, lat) only, discarding any z-dimension, and sort by longitude
- coords = exterior[:-1][np.argsort(exterior[:-1, 0], kind="stable")]
+ # (lon, lat) only, discarding any z-dimension, without the closing vertex
+ ring = exterior[:-1, :2]
# determine if contains north or south pole based on sign of mean latitude
- n_s = 1 if coords[:, 1].mean() > 0 else -1
- # interpolate latitude at antimeridian
- lat = np.interp(
- 180, [coords[-1, 0], coords[0, 0] + 180], [coords[-1, 1], coords[0, 1]]
- )
- # reconstruct polygon (ccw) with added coords on antimeridian and a
- # flattened edge along the pole, spanning -180 to 180 degrees
- return Polygon(
- np.concatenate(
- [
- [[-180, 90 * n_s], [-180, lat]],
- coords,
- [[180, lat], [180, 90 * n_s], [-180, 90 * n_s]],
- ]
- ),
- [shapely.get_coordinates(interior) for interior in polygon.interiors],
+ n_s = 1 if ring[:, 1].mean() > 0 else -1
+ # start the ring after a crossing of the anti-meridian and unroll
+ # its longitudes across any others, keeping the vertex order (the
+ # ring need not be monotonic in longitude, as a coastline is not)
+ step = np.diff(np.append(ring[:, 0], ring[0, 0]))
+ crossings = np.flatnonzero(np.abs(step) > 180)
+ if len(crossings) > 0:
+ ring = np.roll(ring, -(crossings[0] + 1), axis=0)
+ # unroll around the whole ring, back to its first vertex (shifted
+ # by the full winding of 360 degrees)
+ ring = np.concatenate([ring, ring[:1]])
+ step = np.diff(ring[:, 0])
+ step -= 360 * np.where(np.abs(step) > 180, np.round(step / 360), 0)
+ lon = ring[0, 0] + np.concatenate([[0], np.cumsum(step)])
+ # close the unrolled ring along the pole, and split it along the
+ # anti-meridian into the standard (-180, 180) longitude range
+ cap = make_valid(
+ Polygon(
+ np.concatenate(
+ [
+ np.column_stack([lon, ring[:, 1]]),
+ [[lon[-1], 90 * n_s], [lon[0], 90 * n_s]],
+ ]
+ )
+ )
)
+ pieces = [
+ shapely.transform(
+ shapely.intersection(cap, shapely.box(west, -90, west + 360, 90)),
+ lambda coords, offset=west: coords - [offset + 180, 0],
+ )
+ for west in (-540, -180, 180)
+ ]
+ parts = shapely.union_all(pieces)
+ if isinstance(parts, GeometryCollection):
+ parts = _convert_collection_to_polygon(parts)
+ if len(polygon.interiors) > 0:
+ # subtract the holes, each split like an exterior
+ parts = parts.difference(
+ shapely.union_all(
+ [
+ _split_polygon_antimeridian(Polygon(interior))
+ for interior in polygon.interiors
+ ]
+ )
+ )
+ if isinstance(parts, GeometryCollection):
+ parts = _convert_collection_to_polygon(parts)
+ return parts
# find anti-meridian crossings and calculate shift direction
# coords from W -> E (shift < 0) will add 360 degrees to E component
# coords from E -> W (shift > 0) will subtract 360 degrees from W component
diff --git a/src/tatc/utils/glint.py b/src/tatc/utils/glint.py
new file mode 100644
index 00000000..e3e292cc
--- /dev/null
+++ b/src/tatc/utils/glint.py
@@ -0,0 +1,184 @@
+"""
+Methods to compute the geometry of sun glint: the specular reflection of the
+Sun on the WGS 84 ellipsoid (as on a calm water surface) toward a satellite.
+
+@author: Paul T. Grogan
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import numpy.typing as npt
+from skyfield.api import wgs84
+from skyfield.framelib import itrs
+from skyfield.positionlib import Geocentric
+from skyfield.timelib import Time
+from skyfield.toposlib import GeographicPosition
+
+from ..constants import de421
+from .ellipsoid import geodetic_to_rectangular, rectangular_to_geodetic
+
+
+def _unit(vector: npt.NDArray) -> npt.NDArray:
+ """
+ Normalizes vectors (stacked on the first axis).
+ """
+ return vector / np.linalg.norm(vector, axis=0)
+
+
+def _get_sun_position(t: Time) -> npt.NDArray:
+ """
+ Gets the Earth-fixed (ITRS) position (meters, shape (3, N)) of the Sun
+ relative to the Earth's center at times.
+ """
+ sun = de421["earth"].at(t).observe(de421["sun"]).frame_xyz(itrs).m
+ return np.reshape(np.array(sun), (3, -1))
+
+
+def _get_surface_axes(
+ longitude: npt.ArrayLike, latitude: npt.ArrayLike
+) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray]:
+ """
+ Gets the geodetic up (normal), east, and north unit vectors (Earth-fixed,
+ shape (3, ...)) at geodetic coordinates (degrees).
+ """
+ lon, lat = np.radians(longitude), np.radians(latitude)
+ up = np.array([np.cos(lat) * np.cos(lon), np.cos(lat) * np.sin(lon), np.sin(lat)])
+ east = np.array([-np.sin(lon), np.cos(lon), np.zeros(np.shape(lon))])
+ north = np.array(
+ [-np.sin(lat) * np.cos(lon), -np.sin(lat) * np.sin(lon), np.cos(lat)]
+ )
+ return up, east, north
+
+
+def _get_specular_residual(
+ longitude: npt.NDArray,
+ latitude: npt.NDArray,
+ satellite: npt.NDArray,
+ sun: npt.NDArray,
+) -> npt.NDArray:
+ """
+ Gets the components (east and north) of the sum of the unit vectors
+ toward the satellite and the Sun in the surface's tangent plane, which
+ vanish at the specular point (where the surface normal bisects them).
+ """
+ position = geodetic_to_rectangular(longitude, latitude)
+ bisector = _unit(satellite - position) + _unit(sun - position)
+ _, east, north = _get_surface_axes(longitude, latitude)
+ return np.array([np.sum(bisector * east, axis=0), np.sum(bisector * north, axis=0)])
+
+
+def compute_specular_point(
+ orbit_track: Geocentric, max_iterations: int = 30
+) -> GeographicPosition:
+ """
+ Compute the specular point (the center of the sun glint) seen from a
+ satellite: the point on the WGS 84 ellipsoid where the surface normal
+ bisects the directions toward the satellite and toward the Sun, so that
+ sunlight reflected by a level surface there (for example, calm water)
+ reaches the satellite. At the specular point, the Sun and the satellite
+ are at the same elevation angle, in opposite azimuths. The point is found
+ by Newton's method on the geodetic coordinates, from the point beneath
+ the bisector of the directions toward the satellite and the Sun from the
+ Earth's center. It is undefined (`NaN`) where the Sun is below the
+ horizon at the point, as when the satellite is over the night side of
+ the Earth.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
+ max_iterations (int): The maximum number of iterations.
+
+ Returns:
+ skyfield.toposlib.GeographicPosition: the specular point (one for each
+ time of the orbit track).
+ """
+ satellite = np.reshape(np.array(orbit_track.frame_xyz(itrs).m), (3, -1))
+ sun = _get_sun_position(orbit_track.t)
+ longitude, latitude, _ = rectangular_to_geodetic(
+ _unit(_unit(satellite) + _unit(sun)) * 6371e3
+ )
+ step_size = 1e-6 # degrees, for the finite-difference derivatives
+ for _ in range(max_iterations):
+ residual = _get_specular_residual(longitude, latitude, satellite, sun)
+ d_lon = (
+ _get_specular_residual(longitude + step_size, latitude, satellite, sun)
+ - residual
+ ) / step_size
+ d_lat = (
+ _get_specular_residual(longitude, latitude + step_size, satellite, sun)
+ - residual
+ ) / step_size
+ # solve the 2 x 2 system [d_lon d_lat] step = -residual at each time
+ determinant = d_lon[0] * d_lat[1] - d_lat[0] * d_lon[1]
+ with np.errstate(divide="ignore", invalid="ignore"):
+ step_lon = (-residual[0] * d_lat[1] + residual[1] * d_lat[0]) / determinant
+ step_lat = (-residual[1] * d_lon[0] + residual[0] * d_lon[1]) / determinant
+ # limit each step (degrees) for stability far from the solution
+ scale = np.minimum(1, 5 / np.maximum(np.hypot(step_lon, step_lat), 1e-12))
+ step_lon = np.nan_to_num(step_lon * scale)
+ step_lat = np.nan_to_num(step_lat * scale)
+ longitude, latitude = longitude + step_lon, np.clip(
+ latitude + step_lat, -90, 90
+ )
+ if np.all(np.hypot(step_lon, step_lat) < 1e-9):
+ break
+ position = geodetic_to_rectangular(longitude, latitude)
+ up, _, _ = _get_surface_axes(longitude, latitude)
+ # the Sun and the satellite above the horizon (the residual also vanishes
+ # where both are below it)
+ sunlit = np.sum(up * _unit(sun - position), axis=0) > 0
+ visible = np.sum(up * _unit(satellite - position), axis=0) > 0
+ converged = (
+ np.max(
+ np.abs(_get_specular_residual(longitude, latitude, satellite, sun)), axis=0
+ )
+ < 1e-6
+ )
+ valid = sunlit & visible & converged
+ longitude = np.where(valid, (longitude + 180) % 360 - 180, np.nan)
+ latitude = np.where(valid, latitude, np.nan)
+ if np.ndim(orbit_track.t.tt) == 0:
+ return wgs84.latlon(latitude[0], longitude[0])
+ return wgs84.latlon(latitude, longitude)
+
+
+def compute_glint_angle(
+ orbit_track: Geocentric, target: GeographicPosition
+) -> npt.NDArray:
+ """
+ Compute the glint angle (degrees) at a target: the angle between the
+ direction from the target toward the satellite and the direction of the
+ Sun's specular reflection at the target (the direction toward the Sun
+ mirrored about the WGS 84 surface normal). It is zero at the specular
+ point (see `compute_specular_point`), where a level surface reflects
+ sunlight toward the satellite, and grows away from it. Sun glint
+ contaminates observations of water (for example, of ocean color) at
+ small glint angles, typically below about 20 to 30 degrees, whereas
+ glint observations (for example, of greenhouse gases over the ocean)
+ seek them.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
+ target (skyfield.toposlib.GeographicPosition): the target position
+ (a single target, or one target for each time of the orbit track).
+
+ Returns:
+ numpy.typing.NDArray: the glint angle (degrees).
+ """
+ satellite = np.reshape(np.array(orbit_track.frame_xyz(itrs).m), (3, -1))
+ sun = _get_sun_position(orbit_track.t)
+ position = np.reshape(np.array(target.itrs_xyz.m), (3, -1))
+ up, _, _ = _get_surface_axes(
+ np.reshape(target.longitude.degrees, -1),
+ np.reshape(target.latitude.degrees, -1),
+ )
+ to_sun = _unit(sun - position)
+ reflected = 2 * np.sum(up * to_sun, axis=0) * up - to_sun
+ angle = np.degrees(
+ np.arccos(
+ np.clip(np.sum(_unit(satellite - position) * reflected, axis=0), -1, 1)
+ )
+ )
+ if np.ndim(orbit_track.t.tt) == 0 and np.ndim(target.latitude.degrees) == 0:
+ return angle[0]
+ return angle
diff --git a/src/tatc/utils/orbital.py b/src/tatc/utils/orbital.py
index 4445edf9..64c7bac5 100644
--- a/src/tatc/utils/orbital.py
+++ b/src/tatc/utils/orbital.py
@@ -432,3 +432,51 @@ def compute_vnb_frame(
# circular orbit.
rx_b_u = np.cross(rx_v_u, rx_n_u, 0, 0, -1).T
return rx_v_u, rx_n_u, rx_b_u
+
+
+def _check_angle_profile(
+ profile: list[tuple[float, float]] | None, kind: str
+) -> list[tuple[float, float]] | None:
+ """
+ Checks that an angle profile's arguments of latitude lie in [0, 360) and
+ its angles in [-180, 180], and sorts it by argument of latitude.
+
+ Args:
+ profile (list[tuple[float, float]] | None): (argument of latitude,
+ angle) pairs (degrees).
+ kind (str): The kind of angle (for example, "Roll"), for messages.
+
+ Returns:
+ list[tuple[float, float]] | None: the sorted profile
+ """
+ if profile is None:
+ return None
+ for argument, angle in profile:
+ if not 0 <= argument < 360:
+ raise ValueError("Arguments of latitude must be in [0, 360).")
+ if not -180 <= angle <= 180:
+ raise ValueError(f"{kind} angles must be in [-180, 180].")
+ return sorted(profile)
+
+
+def _interpolate_angle_profile(
+ profile: list[tuple[float, float]], orbit_track: Geocentric
+) -> float | npt.NDArray[np.float64]:
+ """
+ Interpolates an angle profile linearly and periodically at the
+ satellite's argument(s) of latitude.
+
+ Args:
+ profile (list[tuple[float, float]]): (argument of latitude, angle) pairs.
+ orbit_track (skyfield.positionlib.Geocentric): The satellite
+ position/velocity (in an inertial frame).
+
+ Returns:
+ float | numpy.typing.NDArray[numpy.float64]: the angle(s) (degrees)
+ """
+ argument = compute_argument_of_latitude(
+ orbit_track.position.m, orbit_track.velocity.m_per_s
+ )
+ arguments, angles = zip(*profile)
+ angle = np.interp(argument, arguments, angles, period=360)
+ return float(angle) if np.ndim(angle) == 0 else angle
diff --git a/src/tatc/utils/projection.py b/src/tatc/utils/projection.py
index 7ff5bca4..08d53b9c 100644
--- a/src/tatc/utils/projection.py
+++ b/src/tatc/utils/projection.py
@@ -9,6 +9,7 @@
import warnings
from collections.abc import Iterable
from enum import Enum
+from typing import Any
import numpy as np
import numpy.typing as npt
@@ -170,6 +171,32 @@ def _compute_instrument_frame(
return p_m, v, n, c
+def _compute_line_of_sight(
+ target: GeographicPosition | npt.NDArray, p_m: npt.NDArray
+) -> npt.NDArray:
+ """
+ Compute the Earth-fixed line of sight from a satellite to a target: a
+ single target, seen from each position, or one target for each position
+ (with the same number of positions as targets).
+
+ Args:
+ target (skyfield.toposlib.GeographicPosition | numpy.typing.NDArray):
+ the target position(s), or their Earth-fixed coordinates (meters),
+ with shape (3,) or (3, N).
+ p_m (numpy.typing.NDArray): the satellite position(s) (meters), with
+ shape (3,) or (3, N).
+
+ Returns:
+ numpy.typing.NDArray: the line(s) of sight (meters).
+ """
+ xyz = np.asarray(
+ target.itrs_xyz.m if isinstance(target, GeographicPosition) else target
+ )
+ if xyz.ndim == 1:
+ xyz = np.reshape(xyz, (3,) + (1,) * (p_m.ndim - 1))
+ return xyz - p_m
+
+
def _compute_view_frame(
orbit_track: Geocentric,
roll_angle: float = 0,
@@ -177,6 +204,7 @@ def _compute_view_frame(
velocity_frame: VelocityFrame = VelocityFrame.EARTH_FIXED,
nadir_reference: NadirReference = NadirReference.GEODETIC,
tilt_angle: float = 0,
+ attitude: Any = None,
) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]:
"""
Compute the Earth-fixed position and the orthonormal unit vectors that
@@ -198,6 +226,10 @@ def _compute_view_frame(
tilt_angle (float): the tilt angle (degrees) of the plane in which
the roll angle is measured, as for the scan plane of a scanner
that tilts fore or aft.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]:
@@ -205,7 +237,9 @@ def _compute_view_frame(
cross-track unit vectors, each with shape (3,) or (3, N) in
Earth-fixed coordinates.
"""
- p_m, n, a, c = _compute_nadir_axes(orbit_track, velocity_frame, nadir_reference)
+ p_m, n, a, c = _compute_nadir_axes(
+ orbit_track, velocity_frame, nadir_reference, attitude
+ )
return (p_m, *_rotate_view_axes(n, a, c, roll_angle, pitch_angle, tilt_angle))
@@ -213,18 +247,27 @@ def _compute_nadir_axes(
orbit_track: Geocentric,
velocity_frame: VelocityFrame = VelocityFrame.EARTH_FIXED,
nadir_reference: NadirReference = NadirReference.GEODETIC,
+ attitude: Any = None,
) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray]:
"""
Compute the Earth-fixed position and the orthonormal unit vectors of an
unrotated view (see `_compute_view_frame`): nadir, along-track
- (forward), and cross-track (left).
+ (forward), and cross-track (left). With an attitude, these are the axes
+ of the spacecraft's body: the nadir frame (in the attitude's velocity
+ frame at each time) rotated by the body's yaw angle about the nadir
+ axis, then by its roll and pitch angles (see `_rotate_view_axes`).
Args:
orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
velocity_frame (VelocityFrame): The reference frame of the velocity
- vector that defines the along-track direction.
+ vector that defines the along-track direction (without an
+ attitude).
nadir_reference (NadirReference): The definition of the nadir
direction.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]:
@@ -232,13 +275,27 @@ def _compute_nadir_axes(
cross-track unit vectors, each with shape (3,) or (3, N) in
Earth-fixed coordinates.
"""
- p_m, _, n, c = _compute_instrument_frame(
- orbit_track, velocity_frame, nadir_reference
+ if attitude is None:
+ p_m, _, n, c = _compute_instrument_frame(
+ orbit_track, velocity_frame, nadir_reference
+ )
+ # orthonormal cross-track (left) and along-track (forward) axes at nadir
+ c = c / np.linalg.norm(c, axis=0)
+ return p_m, n, np.cross(n, c, axis=0), c
+ yaw, roll, pitch, inertial = attitude.get_body_angles(orbit_track)
+ p_m, n, a, c = _compute_nadir_axes(
+ orbit_track, VelocityFrame.EARTH_FIXED, nadir_reference
)
- # orthonormal cross-track (left) and along-track (forward) axes at nadir
- c = c / np.linalg.norm(c, axis=0)
- a = np.cross(n, c, axis=0)
- return p_m, n, a, c
+ if np.any(inertial):
+ _, _, a_inertial, c_inertial = _compute_nadir_axes(
+ orbit_track, VelocityFrame.INERTIAL, nadir_reference
+ )
+ a = np.where(inertial, a_inertial, a)
+ c = np.where(inertial, c_inertial, c)
+ # yaw about the nadir axis, turning the along-track axis to the left
+ yaw = np.radians(yaw)
+ a, c = np.cos(yaw) * a + np.sin(yaw) * c, np.cos(yaw) * c - np.sin(yaw) * a
+ return (p_m, *_rotate_view_axes(n, a, c, roll, pitch))
def _rotate_view_axes(
@@ -288,6 +345,7 @@ def compute_view_tangents(
roll_angle: float = 0,
pitch_angle: float = 0,
nadir_reference: NadirReference = NadirReference.GEODETIC,
+ attitude: Any = None,
) -> tuple[npt.NDArray, npt.NDArray]:
"""
Compute the tangents of the along-track and cross-track view angles of a
@@ -302,7 +360,8 @@ def compute_view_tangents(
Args:
orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
- target (skyfield.toposlib.GeographicPosition): the target position.
+ target (skyfield.toposlib.GeographicPosition): the target position
+ (a single target, or one target for each time of the orbit track).
velocity_frame (VelocityFrame): The reference frame of the velocity
vector that defines the along-track direction.
roll_angle (float): the instrument roll angle (degrees).
@@ -310,28 +369,225 @@ def compute_view_tangents(
nadir_reference (NadirReference): The definition of the nadir
direction from which the view is rotated.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the along-track and
cross-track (positive left) view angle tangents (`NaN` if the
target is not in front of the view).
"""
p_m, boresight, along, cross = _compute_view_frame(
- orbit_track, roll_angle, pitch_angle, velocity_frame, nadir_reference
+ orbit_track,
+ roll_angle,
+ pitch_angle,
+ velocity_frame,
+ nadir_reference,
+ attitude=attitude,
)
# line of sight from the satellite to the target
- los = np.reshape(np.array(target.itrs_xyz.m), (3,) + (1,) * (p_m.ndim - 1)) - p_m
+ los = _compute_line_of_sight(target, p_m)
with np.errstate(divide="ignore", invalid="ignore"):
scale = np.sum(los * boresight, axis=0)
scale = np.where(scale > 0, scale, np.nan)
return np.sum(los * along, axis=0) / scale, np.sum(los * cross, axis=0) / scale
+def compute_ground_sample_size(
+ orbit_track: Geocentric,
+ target: GeographicPosition,
+ along_track_angle: float,
+ cross_track_angle: float,
+ velocity_frame: VelocityFrame = VelocityFrame.EARTH_FIXED,
+ roll_angle: npt.ArrayLike = 0,
+ pitch_angle: npt.ArrayLike = 0,
+ nadir_reference: NadirReference = NadirReference.GEODETIC,
+ tilt_angle: npt.ArrayLike = 0,
+ attitude: Any = None,
+) -> tuple[npt.NDArray, npt.NDArray]:
+ """
+ Compute the size on the ground of an angular extent centered on the line
+ of sight to a target, such as the instantaneous field of view of an
+ instrument's sample (or the angle between neighboring samples): the
+ distances between the points where rays at half the extent on either
+ side of the line of sight meet the WGS 84 ellipsoid (raised to the
+ target's elevation), along track and across track. The extents lie
+ along the view's along-track and cross-track axes (rotated from nadir by
+ the tilt, roll, and pitch angles, see `_compute_view_frame`), each made
+ perpendicular to the line of sight, so the sizes include the
+ lengthening of a view off nadir and the curvature of the Earth. Rays
+ that miss the Earth (beyond its limb) give `NaN`.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
+ target (skyfield.toposlib.GeographicPosition): the target position
+ (a single target, or one target for each time of the orbit track).
+ along_track_angle (float): the along-track angular extent (degrees).
+ cross_track_angle (float): the cross-track angular extent (degrees).
+ velocity_frame (VelocityFrame): The reference frame of the velocity
+ vector that defines the along-track direction.
+ roll_angle (numpy.typing.ArrayLike): the view's roll angle (degrees).
+ pitch_angle (numpy.typing.ArrayLike): the view's pitch angle (degrees).
+ nadir_reference (NadirReference): The definition of the nadir
+ direction from which the view is rotated.
+ tilt_angle (numpy.typing.ArrayLike): the tilt angle (degrees) of a
+ scan plane about the cross-track axis (positive forward).
+
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
+ Returns:
+ tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the along-track
+ and cross-track sizes (meters).
+ """
+ p_m, _, along, cross = _compute_view_frame(
+ orbit_track,
+ roll_angle,
+ pitch_angle,
+ velocity_frame,
+ nadir_reference,
+ tilt_angle,
+ attitude,
+ )
+ p_m, along, cross = (np.reshape(v, (3, -1)) for v in (p_m, along, cross))
+ los = np.reshape(_compute_line_of_sight(target, p_m), (3, -1))
+ los = los / np.linalg.norm(los, axis=0)
+ elevation = np.broadcast_to(
+ np.atleast_1d(target.elevation.m).astype(float), los.shape[1:]
+ )
+ sizes = []
+ for axis, angle in ((along, along_track_angle), (cross, cross_track_angle)):
+ # the axis made perpendicular to the line of sight
+ normal = axis - np.sum(axis * los, axis=0) * los
+ normal = normal / np.linalg.norm(normal, axis=0)
+ half = np.radians(angle) / 2
+ ends = []
+ for sign in (-1, 1):
+ direction = np.cos(half) * los + sign * np.sin(half) * normal
+ end = np.full(direction.shape, np.nan)
+ for value in np.unique(elevation):
+ selected = elevation == value
+ point, found = compute_ellipsoid_intersection(
+ p_m[:, selected], direction[:, selected], value
+ )
+ end[:, selected] = np.where(found, point, np.nan)
+ ends.append(end)
+ sizes.append(np.linalg.norm(ends[1] - ends[0], axis=0))
+ if (
+ np.ndim(np.asarray(orbit_track.t.tt)) == 0
+ and np.ndim(target.latitude.degrees) == 0
+ ):
+ return sizes[0][0], sizes[1][0]
+ return sizes[0], sizes[1]
+
+
+def _shift_along_track(orbit_track: Geocentric, seconds: npt.ArrayLike) -> Geocentric:
+ """
+ Shifts satellite positions along their Earth-fixed velocity (a linear
+ approximation of the motion relative to the ground over a short time).
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): The satellite position/velocity.
+ seconds (numpy.typing.ArrayLike): The shift (seconds) at each time.
+
+ Returns:
+ skyfield.positionlib.Geocentric: The shifted satellite position/velocity.
+ """
+ position, velocity = orbit_track.frame_xyz_and_velocity(itrs)
+ shifted = np.array(position.m) + np.array(velocity.m_per_s) * seconds
+ # rotate from Earth-fixed to inertial (GCRS) coordinates at the same times
+ gcrs = np.einsum("ji...,j...->i...", itrs.rotation_at(orbit_track.t), shifted)
+ return Geocentric(
+ gcrs / AU_M, orbit_track.velocity.au_per_d, orbit_track.t, center=399
+ )
+
+
+def compute_ground_advance(
+ orbit_track: Geocentric,
+ target: GeographicPosition,
+ seconds: float,
+ velocity_frame: VelocityFrame = VelocityFrame.EARTH_FIXED,
+ nadir_reference: NadirReference = NadirReference.GEODETIC,
+ attitude: Any = None,
+) -> npt.NDArray:
+ """
+ Compute the distance on the ground (meters) that a view fixed to the
+ spacecraft advances at a target in a period of time: from the target to
+ the point where the line of sight to it, held fixed relative to the
+ view's axes (the nadir frame, or the body's axes with an attitude),
+ meets the WGS 84 ellipsoid (raised to the target's elevation) after the
+ satellite moves along its Earth-fixed velocity for that period (a linear
+ approximation over a short period, such as that of a scan). For a
+ cross-track scanner, it is the spacing along track between the samples
+ of consecutive scans, which is nearly constant across the scan. Rays
+ that miss the Earth give `NaN`.
+
+ Args:
+ orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
+ target (skyfield.toposlib.GeographicPosition): the target position
+ (a single target, or one target for each time of the orbit track).
+ seconds (float): the period (seconds).
+ velocity_frame (VelocityFrame): The reference frame of the velocity
+ vector that defines the along-track direction.
+ nadir_reference (NadirReference): The definition of the nadir
+ direction.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), or
+ `None` for a body aligned with the nadir frame (in `velocity_frame`).
+
+ Returns:
+ numpy.typing.NDArray: the distance (meters).
+ """
+ p_m, nadir, along, cross = (
+ np.reshape(v, (3, -1))
+ for v in _compute_nadir_axes(
+ orbit_track, velocity_frame, nadir_reference, attitude
+ )
+ )
+ target_m = np.reshape(np.array(target.itrs_xyz.m), (3, -1))
+ los = target_m - p_m
+ los = los / np.linalg.norm(los, axis=0)
+ # the line of sight on the axes, which move with the satellite
+ components = [np.sum(los * axis, axis=0) for axis in (nadir, along, cross)]
+ q_m, nadir, along, cross = (
+ np.reshape(v, (3, -1))
+ for v in _compute_nadir_axes(
+ _shift_along_track(orbit_track, seconds),
+ velocity_frame,
+ nadir_reference,
+ attitude,
+ )
+ )
+ direction = components[0] * nadir + components[1] * along + components[2] * cross
+ q_m = np.broadcast_to(q_m, direction.shape)
+ elevation = np.broadcast_to(
+ np.atleast_1d(target.elevation.m).astype(float), direction.shape[1:]
+ )
+ end = np.full(direction.shape, np.nan)
+ for value in np.unique(elevation):
+ selected = elevation == value
+ point, found = compute_ellipsoid_intersection(
+ q_m[:, selected], direction[:, selected], value
+ )
+ end[:, selected] = np.where(found, point, np.nan)
+ distance = np.linalg.norm(end - target_m, axis=0)
+ if (
+ np.ndim(np.asarray(orbit_track.t.tt)) == 0
+ and np.ndim(target.latitude.degrees) == 0
+ ):
+ return distance[0]
+ return distance
+
+
def compute_view_angles(
orbit_track: Geocentric,
target: GeographicPosition,
velocity_frame: VelocityFrame = VelocityFrame.EARTH_FIXED,
nadir_reference: NadirReference = NadirReference.GEODETIC,
tilt_angle: float = 0,
+ attitude: Any = None,
) -> tuple[npt.NDArray, npt.NDArray]:
"""
Compute the roll and pitch angles (degrees) that point a view's center at
@@ -345,7 +601,8 @@ def compute_view_angles(
Args:
orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
- target (skyfield.toposlib.GeographicPosition): the target position.
+ target (skyfield.toposlib.GeographicPosition): the target position
+ (a single target, or one target for each time of the orbit track).
velocity_frame (VelocityFrame): The reference frame of the velocity
vector that defines the along-track direction.
nadir_reference (NadirReference): The definition of the nadir
@@ -353,14 +610,18 @@ def compute_view_angles(
tilt_angle (float): the tilt angle (degrees) of the scan plane about
the cross-track axis (positive forward).
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the roll (positive
left) and pitch (positive forward) angles (degrees).
"""
p_m, nadir, along, cross = _compute_view_frame(
- orbit_track, 0, 0, velocity_frame, nadir_reference, tilt_angle
+ orbit_track, 0, 0, velocity_frame, nadir_reference, tilt_angle, attitude
)
- los = np.reshape(np.array(target.itrs_xyz.m), (3,) + (1,) * (p_m.ndim - 1)) - p_m
+ los = _compute_line_of_sight(target, p_m)
los = los / np.linalg.norm(los, axis=0)
roll = np.degrees(
np.arctan2(np.sum(los * cross, axis=0), np.sum(los * nadir, axis=0))
@@ -374,6 +635,7 @@ def compute_cone_and_azimuth(
target: GeographicPosition,
velocity_frame: VelocityFrame = VelocityFrame.EARTH_FIXED,
nadir_reference: NadirReference = NadirReference.GEODETIC,
+ attitude: Any = None,
) -> tuple[npt.NDArray, npt.NDArray]:
"""
Compute the cone angle (from nadir) and the scan azimuth
@@ -384,22 +646,27 @@ def compute_cone_and_azimuth(
Args:
orbit_track (skyfield.positionlib.Geocentric): the satellite orbit track.
- target (skyfield.toposlib.GeographicPosition): the target position.
+ target (skyfield.toposlib.GeographicPosition): the target position
+ (a single target, or one target for each time of the orbit track).
velocity_frame (VelocityFrame): The reference frame of the velocity
vector that defines the along-track direction.
nadir_reference (NadirReference): The definition of the nadir
direction.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
tuple[numpy.typing.NDArray, numpy.typing.NDArray]: the cone angle
(degrees, from 0 to 180) and scan azimuth (degrees, from -180 to
180).
"""
p_m, nadir, along, cross = _compute_view_frame(
- orbit_track, 0, 0, velocity_frame, nadir_reference
+ orbit_track, 0, 0, velocity_frame, nadir_reference, attitude=attitude
)
# line of sight from the satellite to the target
- los = np.reshape(np.array(target.itrs_xyz.m), (3,) + (1,) * (p_m.ndim - 1)) - p_m
+ los = _compute_line_of_sight(target, p_m)
down = np.sum(los * nadir, axis=0)
forward = np.sum(los * along, axis=0)
left = np.sum(los * cross, axis=0)
@@ -422,6 +689,7 @@ def compute_projected_ray_position( # pylint: disable=too-many-branches,too-man
nadir_reference: NadirReference = NadirReference.GEODETIC,
tilt_angle: float = 0,
ground_elevation: float = 0,
+ attitude: Any = None,
) -> GeographicPosition:
"""
Get the location of a projected ray from an instrument. The ray is cast
@@ -472,6 +740,10 @@ def compute_projected_ray_position( # pylint: disable=too-many-branches,too-man
ground_elevation (float): The elevation (meters) of the ground, which
blocks rays toward an elevation above the instrument.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
(skyfield.toposlib.GeographicPosition): the geographic position of the projected ray
"""
@@ -488,6 +760,7 @@ def compute_projected_ray_position( # pylint: disable=too-many-branches,too-man
nadir_reference,
tilt_angle,
ground_elevation,
+ attitude,
)[:, 0]
# return resulting geographic position
return wgs84.latlon(geos[1], geos[0], geos[2])
@@ -603,6 +876,7 @@ def _compute_rays(
velocity_frame: VelocityFrame = VelocityFrame.EARTH_FIXED,
nadir_reference: NadirReference = NadirReference.GEODETIC,
tilt_angle: float = 0,
+ attitude: Any = None,
) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray, bool]:
"""
Get several rays from an instrument (see `_compute_projected_rays` for
@@ -618,7 +892,9 @@ def _compute_rays(
# against times)
angle = np.radians(np.reshape(np.asarray(angles, dtype=float), (-1, 1)))
# the unrotated view axes, with shape (3, 1, N) to broadcast against rays
- p_m, n, a, c = _compute_nadir_axes(orbit_track, velocity_frame, nadir_reference)
+ p_m, n, a, c = _compute_nadir_axes(
+ orbit_track, velocity_frame, nadir_reference, attitude
+ )
# whether orbit_track represents a single time or a vector of times
is_vectorized = len(np.shape(p_m)) > 1
p_m, n, a, c = (np.reshape(x, (3, 1, -1)) for x in (p_m, n, a, c))
@@ -895,6 +1171,7 @@ def _compute_projected_rays(
nadir_reference: NadirReference = NadirReference.GEODETIC,
tilt_angle: float = 0,
ground_elevation: float = 0,
+ attitude: Any = None,
) -> npt.NDArray:
"""
Get the locations of several projected rays from an instrument (see
@@ -926,6 +1203,10 @@ def _compute_projected_rays(
ground_elevation (float): The elevation (meters) of the ground, which
blocks rays toward an elevation above the instrument.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
numpy.typing.NDArray: the geodetic longitudes (degrees), latitudes
(degrees), and altitudes (meters) of the projected rays, with
@@ -942,6 +1223,7 @@ def _compute_projected_rays(
velocity_frame,
nadir_reference,
tilt_angle,
+ attitude,
)
geos, _ = _project_rays(
*(np.reshape(x, (3, -1)) for x in rays[:4]), elevation, ground_elevation
@@ -963,6 +1245,7 @@ def compute_footprint(
nadir_reference: NadirReference = NadirReference.GEODETIC,
view_geometry: ViewGeometry = ViewGeometry.FRAME,
ground_elevation: float = 0,
+ attitude: Any = None,
) -> list[Polygon | MultiPolygon]:
"""
Compute the instantaneous instrument footprint. Supports both a scalar
@@ -999,6 +1282,10 @@ def compute_footprint(
ground_elevation (float): The elevation (meters) of the ground, which
blocks rays toward an elevation above the instrument.
+ attitude (Any): The attitude of the spacecraft's body (see
+ `tatc.schemas.Attitude` and `tatc.schemas.AttitudeSchedule`), from
+ which the view is rotated, or `None` for a body aligned with the
+ nadir frame (in `velocity_frame`).
Returns:
list[shapely.geometry.Polygon | shapely.geometry.MultiPolygon]: The instrument footprint(s).
"""
@@ -1062,6 +1349,7 @@ def compute_footprint(
velocity_frame,
nadir_reference,
pitch_angle,
+ attitude,
)
else:
*rays, _ = _compute_rays(
@@ -1074,6 +1362,7 @@ def compute_footprint(
angles,
velocity_frame,
nadir_reference,
+ attitude=attitude,
)
position, direction, _, nadir, along = rays
geos, _ = _project_rays(
diff --git a/src/tatc/utils/propagation.py b/src/tatc/utils/propagation.py
index 780561fd..4d71d754 100644
--- a/src/tatc/utils/propagation.py
+++ b/src/tatc/utils/propagation.py
@@ -1,7 +1,7 @@
"""
Orbit propagation utilities for general perturbations (SGP4) orbits:
-observation event search, propagation on a repeat ground track, and repeat
-cycle search (see `time` for conversions of times, `earth_orientation` for
+observation event search, propagation on a repeat ground track, repeat
+cycle search, and alignment of element switches among satellites (see `time` for conversions of times, `earth_orientation` for
interpolated nutation angles, and `computation` for computations run
together).
@@ -30,6 +30,7 @@
if TYPE_CHECKING:
from ..schemas.orbit.gp_elements import GeneralPerturbationsElements
+ from ..schemas.space.satellite import Satellite
class RepeatCycleSearch(NamedTuple):
@@ -687,3 +688,130 @@ def find_events(
selected = (times_py >= lower) & (times_py <= upper)
events.extend(zip(times_py[selected] + shift, codes[selected]))
return events
+
+
+def align_element_switches(
+ satellites: list[Satellite],
+ tolerance: timedelta = timedelta(minutes=10),
+ shared_only: bool = True,
+) -> list[Satellite]:
+ """
+ Aligns the times at which the satellites' general perturbations (GP)
+ orbits switch from one element to the next (see
+ `GeneralPerturbationsOrbit.element_switch_times`), so that satellites
+ whose elements are published together, as for a formation or a
+ constellation, switch at common times.
+
+ By default, each satellite switches elements midway between its own
+ consecutive epochs. When the satellites' elements are published at
+ slightly different times (for example, 20 s apart), for that long after
+ each switch their positions come from elements of different epochs,
+ whose errors no longer cancel, so their relative positions jump by up to
+ the difference between successive elements.
+
+ The satellites' epochs are grouped into publication events, each of
+ epochs within `tolerance` of the next, and switches are placed midway
+ between the mean epochs of consecutive events. Each satellite uses its
+ latest element in each event and switches at the common time between
+ the events of its consecutive elements (if it has no element in the
+ events between, at the latest common time). With `shared_only`, only the
+ events in which every satellite has an element are used, so the
+ satellites always use elements of the same event, at the cost of using
+ older elements when one satellite lacks an element in an event;
+ otherwise, every event is used. Elements that are not used are removed.
+
+ Only satellites whose orbits have more than one GP element take part;
+ the others are returned unchanged.
+
+ Args:
+ satellites (list[Satellite]): The satellites.
+ tolerance (datetime.timedelta): The maximum difference between the
+ epochs of a publication event.
+ shared_only (bool): True, to use only the publication events in
+ which every satellite has an element.
+
+ Returns:
+ list[Satellite]: copies of the satellites, with aligned element switch
+ times (or unchanged, if they do not take part)
+
+ Raises:
+ ValueError: if a publication event spans more than `tolerance` (as
+ when epochs are spread through time rather than published
+ together), if the mean epochs of consecutive events used are not
+ more than twice `tolerance` apart, or if, with `shared_only`, no
+ event is shared by every satellite.
+ """
+ orbits = [satellite.orbit.to_gp_orbit() for satellite in satellites]
+ members = [i for i, orbit in enumerate(orbits) if len(orbit.elements) > 1]
+ if len(members) == 0:
+ return list(satellites)
+ # publication events: epochs within tolerance of the next, as (epoch, satellite, element)
+ epochs = sorted(
+ (element.epoch, i, k)
+ for i in members
+ for k, element in enumerate(orbits[i].elements)
+ )
+ events = [[epochs[0]]]
+ for previous, current in zip(epochs[:-1], epochs[1:]):
+ if current[0] - previous[0] > tolerance:
+ events.append([])
+ events[-1].append(current)
+ for event in events:
+ if event[-1][0] - event[0][0] > tolerance:
+ raise ValueError(
+ f"Element epochs from {event[0][0]} to {event[-1][0]} form one "
+ f"publication event longer than the tolerance ({tolerance})."
+ )
+ if shared_only:
+ used = [
+ j
+ for j, event in enumerate(events)
+ if {i for _, i, _ in event} == set(members)
+ ]
+ if len(used) == 0:
+ raise ValueError("No publication event is shared by every satellite.")
+ else:
+ used = list(range(len(events)))
+ means = [
+ events[j][0][0]
+ + sum((epoch - events[j][0][0] for epoch, _, _ in events[j]), timedelta(0))
+ / len(events[j])
+ for j in used
+ ]
+ for earlier, later in zip(means[:-1], means[1:]):
+ if later - earlier <= 2 * tolerance:
+ raise ValueError(
+ f"Publication events at {earlier} and {later} are not more than "
+ f"twice the tolerance ({tolerance}) apart."
+ )
+ # the common switch time before each event used (but the first)
+ switch_before = {
+ j: means[n - 1] + (means[n] - means[n - 1]) / 2
+ for n, j in enumerate(used)
+ if n > 0
+ }
+ aligned = list(satellites)
+ for i in members:
+ # each satellite's latest element in each event used, in time order
+ latest = {}
+ for j in used:
+ for _, satellite, k in events[j]:
+ if satellite == i:
+ latest[j] = k
+ events_used = sorted(latest)
+ orbit = orbits[i]
+ aligned[i] = satellites[i].model_copy(
+ update={
+ "orbit": type(orbit)(
+ **{
+ **dict(orbit),
+ "elements": [orbit.elements[latest[j]] for j in events_used],
+ "element_switch_times": [
+ switch_before[j] for j in events_used[1:]
+ ]
+ or None,
+ }
+ )
+ }
+ )
+ return aligned
diff --git a/tests/analysis/test_coincidence.py b/tests/analysis/test_coincidence.py
index a2c82a19..9c1e40cb 100644
--- a/tests/analysis/test_coincidence.py
+++ b/tests/analysis/test_coincidence.py
@@ -125,6 +125,34 @@ def test_max_time_difference(self):
self.assertEqual(results.iloc[0].start, _minutes(10))
self.assertEqual(results.iloc[0].end, _minutes(12))
+ def test_instantaneous(self):
+ """
+ Test that instantaneous observations coincide if their epochs differ
+ by at most the maximum time difference, regardless of their periods,
+ and that each coincident observation spans the contributing epochs.
+ """
+ # periods overlap (minutes 8 to 10), epochs (minutes 5 and 19) do not
+ observations = gpd.GeoDataFrame(
+ [_make_observation("A", 0, 10), _make_observation("B", 8, 30)],
+ crs="EPSG:4326",
+ )
+ self.assertEqual(len(find_coincident_observations(observations).index), 1)
+ self.assertTrue(
+ find_coincident_observations(
+ observations,
+ max_time_difference=timedelta(minutes=10),
+ instantaneous=True,
+ ).empty
+ )
+ results = find_coincident_observations(
+ observations, max_time_difference=timedelta(minutes=15), instantaneous=True
+ )
+ self.assertEqual(len(results.index), 1)
+ self.assertEqual(results.iloc[0].satellite, "A, B")
+ self.assertEqual(results.iloc[0].start, _minutes(5))
+ self.assertEqual(results.iloc[0].end, _minutes(19))
+ self.assertEqual(results.iloc[0].epoch, _minutes(12))
+
def test_same_satellite(self):
"""
Test that observations by different instruments on the same
diff --git a/tests/analysis/test_feature_sampling.py b/tests/analysis/test_feature_sampling.py
new file mode 100644
index 00000000..94163936
--- /dev/null
+++ b/tests/analysis/test_feature_sampling.py
@@ -0,0 +1,184 @@
+"""
+Unit tests for the feature observation functions in tatc.analysis.
+
+@author Paul T. Grogan
+"""
+
+import unittest
+from datetime import datetime, timedelta, timezone
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from shapely.geometry import Point as ShapelyPoint
+from shapely.geometry import box
+from skyfield.api import wgs84
+
+from tatc.analysis import collect_feature_observations
+from tatc.schemas import PointedInstrument, Satellite, SunSynchronousOrbit
+from tatc.utils import compute_along_track_field_of_view
+from tatc.utils.time import _to_time
+
+
+class TestFeatureObservations(unittest.TestCase):
+ """
+ Unit tests for `collect_feature_observations`.
+ """
+
+ def setUp(self):
+ self.start = datetime(2026, 3, 1, tzinfo=timezone.utc)
+ orbit = SunSynchronousOrbit(
+ altitude=700e3, equator_crossing_time="10:30", epoch=self.start
+ )
+ self.time_step = 30
+ self.satellite = Satellite(
+ name="sat",
+ orbit=orbit,
+ instruments=[
+ PointedInstrument(
+ name="imager",
+ field_of_regard=40,
+ cross_track_field_of_view=40,
+ along_track_field_of_view=compute_along_track_field_of_view(
+ 700e3, self.time_step, orbit.get_inclination()
+ ),
+ is_rectangular=True,
+ )
+ ],
+ )
+ self.times = [
+ self.start + timedelta(seconds=self.time_step * (i + 0.5))
+ for i in range(int(3 * 3600 / self.time_step))
+ ]
+ # the sub-satellite point at a sampled time about an hour after the start
+ self.nadir_time = self.times[120]
+ subpoint = wgs84.subpoint_of(
+ orbit.to_gp_orbit().get_orbit_track_at_time(_to_time(self.nadir_time))
+ )
+ self.nadir = (subpoint.longitude.degrees, subpoint.latitude.degrees)
+
+ def _features(self, geometries, start, end, index=None):
+ return gpd.GeoDataFrame(
+ {
+ "start": pd.DatetimeIndex(
+ [start] * len(geometries), dtype="datetime64[ns, UTC]"
+ ),
+ "end": pd.DatetimeIndex(
+ [end] * len(geometries), dtype="datetime64[ns, UTC]"
+ ),
+ },
+ geometry=geometries,
+ crs="EPSG:4326",
+ index=index,
+ )
+
+ def test_feature_at_nadir(self):
+ """
+ Test that a small feature beneath the satellite while it exists is
+ viewed entirely, near nadir, at a time while it exists.
+ """
+ features = self._features(
+ [ShapelyPoint(*self.nadir).buffer(0.2)],
+ self.nadir_time - timedelta(minutes=5),
+ self.nadir_time + timedelta(minutes=5),
+ )
+ observations = collect_feature_observations(
+ features, self.satellite, self.times
+ )
+ self.assertEqual(len(observations), 1)
+ observation = observations.iloc[0]
+ self.assertEqual(observation.feature, 0)
+ self.assertAlmostEqual(observation.viewed_fraction, 1)
+ self.assertLess(
+ abs(observation.time - pd.Timestamp(self.nadir_time)),
+ pd.Timedelta(seconds=self.time_step),
+ )
+ self.assertGreater(observation.sat_alt, 85)
+ # near nadir, the slant range is about the satellite's height
+ self.assertLess(abs(observation.sat_range - observation.sat_height), 5e3)
+ self.assertGreater(observation.viewed_area, 0)
+
+ def test_feature_between_passes(self):
+ """
+ Test that a feature that exists only when no satellite passes over it
+ is not observed.
+ """
+ features = self._features(
+ [ShapelyPoint(*self.nadir).buffer(0.2)],
+ self.nadir_time + timedelta(minutes=20),
+ self.nadir_time + timedelta(minutes=30),
+ )
+ observations = collect_feature_observations(
+ features, self.satellite, self.times
+ )
+ self.assertEqual(len(observations), 0)
+ self.assertIn("viewed_fraction", observations.columns)
+
+ def test_feature_wider_than_swath(self):
+ """
+ Test that a feature wider than the swath is viewed only in part, and
+ that the viewed part is the feature's overlap with the swath.
+ """
+ longitude, latitude = self.nadir
+ feature = box(longitude - 20, latitude - 1, longitude + 20, latitude + 1)
+ features = self._features(
+ [feature],
+ self.nadir_time - timedelta(minutes=2),
+ self.nadir_time + timedelta(minutes=2),
+ )
+ observations = collect_feature_observations(
+ features, self.satellite, self.times
+ )
+ self.assertEqual(len(observations), 1)
+ observation = observations.iloc[0]
+ self.assertGreater(observation.viewed_fraction, 0)
+ self.assertLess(observation.viewed_fraction, 0.5)
+ self.assertTrue(feature.buffer(1e-9).contains(observation.geometry))
+
+ def test_feature_index_labels(self):
+ """
+ Test that observations record each feature's index label.
+ """
+ features = self._features(
+ [ShapelyPoint(*self.nadir).buffer(0.2), ShapelyPoint(0, -80).buffer(0.2)],
+ self.nadir_time - timedelta(minutes=5),
+ self.nadir_time + timedelta(minutes=5),
+ index=[17, 42],
+ )
+ observations = collect_feature_observations(
+ features, self.satellite, self.times
+ )
+ self.assertEqual(list(observations.feature), [17])
+
+ def test_invalid_features(self):
+ """
+ Test that features without timezone-aware start and end times are
+ rejected.
+ """
+ geometry = [ShapelyPoint(*self.nadir).buffer(0.2)]
+ with self.assertRaises(ValueError):
+ collect_feature_observations(
+ gpd.GeoDataFrame(geometry=geometry, crs="EPSG:4326"),
+ self.satellite,
+ self.times,
+ )
+ naive = gpd.GeoDataFrame(
+ {"start": [datetime(2026, 3, 1)], "end": [datetime(2026, 3, 2)]},
+ geometry=geometry,
+ crs="EPSG:4326",
+ )
+ with self.assertRaises(ValueError):
+ collect_feature_observations(naive, self.satellite, self.times)
+ with self.assertRaises(TypeError):
+ collect_feature_observations(geometry, self.satellite, self.times)
+
+ def test_empty_features(self):
+ """
+ Test that no features give an empty data frame of observations.
+ """
+ features = self._features([], self.start, self.start)
+ observations = collect_feature_observations(
+ features, self.satellite, self.times
+ )
+ self.assertEqual(len(observations), 0)
+ self.assertTrue(np.isin(["sat_alt", "sat_range"], observations.columns).all())
diff --git a/tests/analysis/test_ground_track.py b/tests/analysis/test_ground_track.py
index 6775b319..cd6d36f4 100644
--- a/tests/analysis/test_ground_track.py
+++ b/tests/analysis/test_ground_track.py
@@ -320,6 +320,33 @@ def test_compute_ground_track_no_instr_index(self):
self.assertEqual(len(results.index), 1)
self.assertEqual(type(results.iloc[0].geometry), Polygon)
+ def test_compute_ground_track_orbits_follow_element_switch_times(self):
+ """
+ Test that orbits are identified with the period of the element that
+ propagates the orbit (as selected by element switch times), not the
+ element whose epoch is closest.
+ """
+ element = self.orbit.elements[0]
+ start = element.epoch + timedelta(days=0.5)
+ # a later element with a period three times as long (about 4.6 hours)
+ slow = element.model_copy(
+ update={
+ "epoch": element.epoch + timedelta(days=2),
+ "mean_motion": element.mean_motion / 3,
+ }
+ )
+ times = [start + timedelta(minutes=i) for i in range(0, 180, 5)]
+ for switch, orbits in [(None, 2), ([element.epoch + timedelta(days=0.25)], 1)]:
+ satellite = self.satellite.model_copy(
+ update={
+ "orbit": GeneralPerturbationsOrbit(
+ elements=[element, slow], element_switch_times=switch
+ )
+ }
+ )
+ results = compute_ground_track(satellite, times, dissolve_orbits=False)
+ self.assertEqual(len(results.index), orbits)
+
def test_compute_ground_track_multipolygon(self):
"""
Test that ground track computation works for a single satellite and a list
diff --git a/tests/analysis/test_point_sampling.py b/tests/analysis/test_point_sampling.py
index 48c2fdea..b637596a 100644
--- a/tests/analysis/test_point_sampling.py
+++ b/tests/analysis/test_point_sampling.py
@@ -23,6 +23,8 @@
grid_observations,
reduce_observations,
)
+from tatc.analysis.point_sampling import _get_screening_angle
+from tatc.utils.projection import _compute_nadir_axes
from tatc.analysis.sampling import _refine_access_periods
from tatc.utils.computation import _run
from tatc.constants import timescale
@@ -30,9 +32,11 @@
ConicalInstrument,
GeneralPerturbationsOrbit,
Instrument,
+ MolniyaOrbit,
Point,
PointedInstrument,
Satellite,
+ SunSynchronousOrbit,
)
from tatc.utils import (
compute_cone_and_azimuth,
@@ -808,7 +812,21 @@ def test_collect_observations_of_points_and_satellites(self):
.reset_index(drop=True)
)
self.assertGreater(len(results.index), 0)
- pd.testing.assert_frame_equal(results, expected)
+ # observations that start together (such as those in progress at the
+ # start) may be in any order, and times computed with other points
+ # may differ in their last bits (below a microsecond)
+ keys = ["start", "target_hash", "satellite", "instrument", "epoch"]
+ results = results.sort_values(keys).reset_index(drop=True)
+ expected = expected.sort_values(keys).reset_index(drop=True)
+ times = ["start", "epoch", "end"]
+ pd.testing.assert_frame_equal(
+ results.drop(columns=times), expected.drop(columns=times)
+ )
+ for column in times:
+ self.assertLess(
+ (results[column] - expected[column]).abs().max(),
+ pd.Timedelta(microseconds=1),
+ )
def test_collect_observations_of_every_instrument(self):
"""
@@ -1218,6 +1236,37 @@ def test_grid_observations_uses_weighted_harmonic_mean_for_revisit(self):
result.iloc[0].revisit.total_seconds(), naive_arithmetic_revisit, places=1
)
+ def test_grid_observations_revisit_undefined_without_revisits(self):
+ """
+ Test that a cell whose points each have a single sample (and so no
+ revisit) has an undefined revisit, rather than raising an error.
+ """
+ cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326")
+ records = [
+ self._make_reduced_observation(k, 0.5, 0.5, 5, 0, 1) for k in range(2)
+ ]
+ reduced = gpd.GeoDataFrame(records, crs="EPSG:4326")
+ reduced["revisit"] = pd.to_timedelta([pd.NaT] * len(records))
+ result = grid_observations(reduced, cells)
+ self.assertEqual(result.iloc[0].samples, 2)
+ self.assertTrue(pd.isna(result.iloc[0].revisit))
+
+ def test_grid_observations_revisit_ignores_points_without_revisits(self):
+ """
+ Test that the revisit of a cell averages only the points with a
+ revisit: a point with a single sample does not weight it.
+ """
+ cells = gpd.GeoDataFrame([self._make_cell(0, 0, 0, 1, 1)], crs="EPSG:4326")
+ single = self._make_reduced_observation(1, 0.6, 0.6, 50, 0, 1)
+ single["revisit"] = pd.NaT
+ reduced = gpd.GeoDataFrame(
+ [self._make_reduced_observation(0, 0.4, 0.4, 5, 10, 100), single],
+ crs="EPSG:4326",
+ )
+ result = grid_observations(reduced, cells)
+ self.assertEqual(result.iloc[0].samples, 101)
+ self.assertAlmostEqual(result.iloc[0].revisit.total_seconds(), 10, places=6)
+
def test_grid_observations_revisit_is_invariant_to_point_density(self):
"""
Regression test: a cell's gridded revisit must not depend on how
@@ -1524,3 +1573,180 @@ def test_no_observations_below_horizon(self):
)
self.assertEqual(len(observations), 1)
self.assertTrue((observations.sat_alt > 0).all())
+
+
+class TestBatchObservations(unittest.TestCase):
+ """
+ Unit tests for the observations of many points collected together (see
+ `tatc.analysis.point_sampling._collect_batch_observations`).
+ """
+
+ def setUp(self):
+ self.start = datetime(2026, 3, 1, tzinfo=timezone.utc)
+ self.orbit = SunSynchronousOrbit(
+ altitude=700e3, equator_crossing_time="10:30", epoch=self.start
+ )
+ rng = np.random.default_rng(1)
+ latitude = np.degrees(np.arcsin(rng.uniform(-1, 1, 200)))
+ longitude = rng.uniform(-180, 180, 200)
+ self.points = [ShapelyPoint(x, y) for x, y in zip(longitude, latitude)]
+
+ def _brute_force_periods(self, satellite, points, end, step=1.0):
+ """
+ The runs of samples at 1 s steps at which each point lies within the
+ field of regard and above the horizon (as start, end), by its hash,
+ from one orbit track evaluated against all points.
+ """
+ instrument = satellite.instruments[0]
+ seconds = np.arange(0, (end - self.start).total_seconds() + step, step)
+ t = timescale.from_datetimes(
+ [self.start + timedelta(seconds=x) for x in seconds]
+ )
+ track = satellite.orbit.to_gp_orbit().get_orbit_track_at_time(t)
+ position, nadir = _compute_nadir_axes(track)[:2]
+ latitude = np.array([point.y for point in points])
+ longitude = np.array([point.x for point in points])
+ xyz = np.array(wgs84.latlon(latitude, longitude).itrs_xyz.m)
+ up = np.array(
+ [
+ np.cos(np.radians(latitude)) * np.cos(np.radians(longitude)),
+ np.cos(np.radians(latitude)) * np.sin(np.radians(longitude)),
+ np.sin(np.radians(latitude)),
+ ]
+ )
+ periods = {}
+ for i, point in enumerate(points):
+ los = xyz[:, i : i + 1] - position
+ distance = np.linalg.norm(los, axis=0)
+ cone = np.degrees(np.arccos(np.sum(los * nadir, axis=0) / distance))
+ elevation = -np.sum(los * up[:, i : i + 1], axis=0) / distance
+ inside = (cone <= instrument.field_of_regard / 2) & (elevation >= 0)
+ edges = np.flatnonzero(np.diff(inside.astype(int)))
+ starts = list(edges[inside[edges + 1]] + 1)
+ ends = list(edges[~inside[edges + 1]])
+ if inside[0]:
+ starts = [0] + starts
+ if inside[-1]:
+ ends = ends + [len(inside) - 1]
+ periods[hash_geometry(point)] = [
+ (
+ self.start + timedelta(seconds=seconds[a]),
+ self.start + timedelta(seconds=seconds[b]),
+ )
+ for a, b in zip(starts, ends)
+ ]
+ return periods
+
+ def test_narrow_field_of_regard_brute_force(self):
+ """
+ Test that the passes of a narrow field of regard (about 12 km wide,
+ crossed in a second or two), which fall between the samples of the
+ screened windows, are found and agree with a brute-force search at
+ 1 s steps.
+ """
+ satellite = Satellite(
+ name="narrow",
+ orbit=self.orbit,
+ instruments=[Instrument(name="narrow", field_of_regard=1)],
+ )
+ end = self.start + timedelta(days=1)
+ points = [
+ ShapelyPoint(x, y)
+ for x, y in zip(np.linspace(-180, 180, 1500), np.linspace(-80, 80, 1500))
+ ]
+ observations = collect_observations(points, satellite, self.start, end)
+ expected = self._brute_force_periods(satellite, points, end)
+ found = 0
+ for target_hash, periods in expected.items():
+ observed = observations[observations.target_hash == target_hash]
+ for left, right in periods:
+ # each brute-force run lies within an observed period
+ matches = observed[
+ (observed.start <= pd.Timestamp(left) + pd.Timedelta(seconds=1))
+ & (observed.end >= pd.Timestamp(right) - pd.Timedelta(seconds=1))
+ ]
+ self.assertEqual(len(matches), 1)
+ found += 1
+ self.assertGreater(found, 0)
+ # every observed period of at least a second contains a sample
+ self.assertLessEqual(
+ int(
+ (
+ (observations.end - observations.start) >= pd.Timedelta(seconds=1)
+ ).sum()
+ ),
+ found,
+ )
+
+ def test_molniya_perigee_passes(self):
+ """
+ Test that short passes near the perigee of a Molniya orbit are
+ found, and that the long passes near apogee end where a brute-force
+ search at 1 s steps ends them.
+ """
+ satellite = Satellite(
+ name="molniya",
+ orbit=MolniyaOrbit(epoch=self.start, perigee_altitude=1000e3),
+ instruments=[Instrument(name="wide", field_of_regard=30)],
+ )
+ end = self.start + timedelta(days=2)
+ for point in [
+ ShapelyPoint(156.699291, -49.799425),
+ ShapelyPoint(76.092856, 38.356215),
+ ]:
+ observations = collect_observations(point, satellite, self.start, end)
+ expected = self._brute_force_periods(satellite, [point], end)[
+ hash_geometry(point)
+ ]
+ self.assertEqual(len(observations), len(expected))
+ for (left, right), (_, row) in zip(expected, observations.iterrows()):
+ self.assertLess(
+ abs((row.start - pd.Timestamp(left)).total_seconds()), 1
+ )
+ self.assertLess(abs((row.end - pd.Timestamp(right)).total_seconds()), 1)
+
+ def test_independent_of_other_points(self):
+ """
+ Test that the observations of a point do not depend on the other
+ points collected with it, including points below the ellipsoid (which
+ loosen the bounds used to screen them), to within a microsecond.
+ """
+ satellite = Satellite(
+ name="sat",
+ orbit=self.orbit,
+ instruments=[Instrument(name="wide", field_of_regard=100)],
+ )
+ end = self.start + timedelta(days=1)
+ others = self.points[1:50] + [
+ ShapelyPoint(35.5, 31.5, -430),
+ ShapelyPoint(-90, 0, -5000),
+ ]
+ alone = collect_observations(self.points[0], satellite, self.start, end)
+ together = collect_observations(
+ [self.points[0]] + others, satellite, self.start, end
+ )
+ together = together[together.target_hash == hash_geometry(self.points[0])]
+ self.assertGreater(len(alone), 0)
+ self.assertEqual(len(alone), len(together))
+ for column in ["start", "epoch", "end"]:
+ difference = (alone[column].values - together[column].values).astype(
+ "int64"
+ )
+ self.assertLess(np.abs(difference).max(), 1000)
+
+ def test_screening_angle(self):
+ """
+ Test that the screening angle is the Earth central angle of the edge
+ of the field of regard, or of the horizon for a field of regard that
+ extends beyond it.
+ """
+ radius, sat_radius = 6356752.0, 7071000.0
+ horizon = np.degrees(np.arccos(radius / sat_radius))
+ self.assertAlmostEqual(_get_screening_angle(sat_radius, radius, 90), horizon)
+ self.assertAlmostEqual(_get_screening_angle(sat_radius, radius, 80), horizon)
+ # at a nadir angle of 45 deg, the central angle follows the law of sines
+ expected = (
+ np.degrees(np.arcsin(sat_radius / radius * np.sin(np.radians(45)))) - 45
+ )
+ self.assertAlmostEqual(_get_screening_angle(sat_radius, radius, 45), expected)
+ self.assertAlmostEqual(_get_screening_angle(sat_radius, radius, 0), 0)
diff --git a/tests/analysis/test_ro_sampling.py b/tests/analysis/test_ro_sampling.py
index 06f80285..59f7df53 100644
--- a/tests/analysis/test_ro_sampling.py
+++ b/tests/analysis/test_ro_sampling.py
@@ -17,6 +17,7 @@
from tatc.analysis import collect_ro_observations
from tatc.analysis.ro_sampling import (
_interpolate_ro_point,
+ _is_rising,
_sample_ro_arcs,
_tangent_point_geometry,
)
@@ -275,6 +276,65 @@ def test_longitude_interpolates_along_shortest_path_across_antimeridian(self):
self.assertAlmostEqual(abs(result["longitude"]), 180.0)
+class TestIsRising(unittest.TestCase):
+ """
+ Unit tests for `_is_rising`.
+ """
+
+ @staticmethod
+ def _points(elevations, pitch=0.0):
+ """
+ Builds profile points at the given tangent point elevations (m),
+ 10 s apart, with a constant transmitter pitch angle (deg).
+ """
+ return [
+ {
+ "time": datetime(2024, 1, 1, tzinfo=timezone.utc)
+ + timedelta(seconds=10 * i),
+ "longitude": 0.0,
+ "latitude": 0.0,
+ "elevation": elevation,
+ "rx_tx_pitch": pitch,
+ "rx_tx_yaw": 90.0,
+ "tp_tx_azimuth": 0.0,
+ }
+ for i, elevation in enumerate(elevations)
+ ]
+
+ def test_direction_through_sample_elevation(self):
+ """
+ Test that the direction is that of the tangent point through the
+ sample elevation, regardless of the transmitter's pitch angle.
+ """
+ self.assertFalse(_is_rising(self._points([60e3, -100e3], pitch=0.0), -80e3))
+ self.assertTrue(_is_rising(self._points([-100e3, 60e3], pitch=-180.0), -80e3))
+
+ def test_direction_at_first_crossing_of_grazing_profile(self):
+ """
+ Test that a profile crossing the sample elevation twice (dipping
+ below it and rising again) takes the direction of its first crossing,
+ at which its time is interpolated.
+ """
+ self.assertFalse(_is_rising(self._points([60e3, -90e3, 60e3]), -80e3))
+ self.assertTrue(_is_rising(self._points([-90e3, 60e3, -90e3]), -80e3))
+
+ def test_direction_from_endpoints_when_never_crossed(self):
+ """
+ Test that a profile that never crosses the sample elevation takes
+ the direction from its first to its last point.
+ """
+ self.assertTrue(_is_rising(self._points([20e3, 10e3, 50e3]), -80e3))
+ self.assertFalse(_is_rising(self._points([50e3, 10e3, 20e3]), -80e3))
+
+ def test_pitch_fallback_for_single_point(self):
+ """
+ Test that a single point is rising if the transmitter's pitch angle
+ is above -90 deg.
+ """
+ self.assertTrue(_is_rising(self._points([0.0], pitch=-80.0), -80e3))
+ self.assertFalse(_is_rising(self._points([0.0], pitch=-100.0), -80e3))
+
+
class TestCollectRoObservations(unittest.TestCase):
"""
Integration tests for `collect_ro_observations`.
@@ -409,6 +469,42 @@ def test_collect_ro_observations_is_rising_is_boolean(self):
self.assertGreater(len(results), 0)
self.assertTrue(results.is_rising.isin([True, False]).all())
+ def test_collect_ro_observations_profiles_are_monotonic(self):
+ """
+ Test that each observation's profile only descends (setting) or only
+ ascends (rising), so that it crosses the sample elevation at most once
+ (grazing occultations are split where the tangent point turns).
+ """
+ results = collect_ro_observations(
+ self.receiver, self.transmitter, self.start, self.end, max_yaw=90
+ )
+ self.assertGreater(len(results), 0)
+ for _, row in results.iterrows():
+ steps = np.diff([point.z for point in row.geometry.geoms])
+ if row.is_rising:
+ self.assertTrue((steps >= 0).all())
+ else:
+ self.assertTrue((steps <= 0).all())
+
+ def test_collect_ro_observations_is_rising_follows_profile(self):
+ """
+ Test that `is_rising` agrees with the direction of each observation's
+ sampled profile through the sample elevation (-80 km by default),
+ including for transmitters nearly abeam of the receiver.
+ """
+ results = collect_ro_observations(
+ self.receiver, self.transmitter, self.start, self.end, max_yaw=90
+ )
+ checked = 0
+ for _, row in results.iterrows():
+ elevations = np.array([point.z for point in row.geometry.geoms])
+ crossings = np.nonzero(np.diff(np.sign(elevations + 80e3)))[0]
+ if len(crossings) > 0:
+ i = crossings[0]
+ self.assertEqual(row.is_rising, elevations[i + 1] > elevations[i])
+ checked += 1
+ self.assertGreater(checked, 0)
+
def test_sample_ro_arcs_closes_at_arc_boundary_when_still_in_range(self):
"""
Test that an observation correctly closes at the sampled arc's own
diff --git a/tests/analysis/test_sample_sizes.py b/tests/analysis/test_sample_sizes.py
new file mode 100644
index 00000000..61591530
--- /dev/null
+++ b/tests/analysis/test_sample_sizes.py
@@ -0,0 +1,176 @@
+"""
+Unit tests for the sample size functions in tatc.analysis.
+
+@author Paul T. Grogan
+"""
+
+import unittest
+from datetime import datetime, timedelta, timezone
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+from shapely.geometry import Point as ShapelyPoint
+
+from tatc.analysis import (
+ collect_feature_observations,
+ collect_observations,
+ compute_sample_sizes,
+)
+from tatc.schemas import (
+ ConicalInstrument,
+ Instrument,
+ PointedInstrument,
+ Satellite,
+ SunSynchronousOrbit,
+)
+from tatc.utils import VelocityFrame, compute_along_track_field_of_view
+
+
+class TestSampleSizes(unittest.TestCase):
+ """
+ Unit tests for `compute_sample_sizes`.
+ """
+
+ def setUp(self):
+ self.start = datetime(2026, 3, 1, tzinfo=timezone.utc)
+ self.end = self.start + timedelta(days=1)
+ # a cross-track scanner like ATMS: 2.2 deg beams at 1.1 deg spacing
+ self.scanner = PointedInstrument(
+ name="scanner",
+ field_of_regard=105.6,
+ cross_track_field_of_view=105.6,
+ along_track_field_of_view=2.2,
+ cross_track_pixels=96,
+ cross_track_oversampling=0.5,
+ is_rectangular=True,
+ velocity_frame=VelocityFrame.INERTIAL,
+ )
+ self.simple = Instrument(name="simple", field_of_regard=100)
+ self.satellite = Satellite(
+ name="sat",
+ orbit=SunSynchronousOrbit(
+ altitude=830e3, equator_crossing_time="13:30", epoch=self.start
+ ),
+ instruments=[self.scanner, self.simple],
+ )
+ self.points = [ShapelyPoint(-100, 35), ShapelyPoint(-60, -10)]
+
+ def test_point_observations(self):
+ """
+ Test that the pixels of a pointed instrument are larger across track
+ than along track off nadir, that the spacing of overlapping pixels is
+ half their size across track, and that the scan angle lies within
+ the field of regard.
+ """
+ observations = collect_observations(
+ self.points, self.satellite, self.start, self.end, instrument_index=0
+ )
+ sizes = compute_sample_sizes(observations, self.satellite)
+ self.assertGreater(len(sizes), 0)
+ self.assertEqual(list(sizes.index), list(observations.index))
+ self.assertTrue((sizes.sample_along > 0).all())
+ self.assertTrue((sizes.sample_cross >= sizes.sample_along).all())
+ np.testing.assert_allclose(
+ sizes.spacing_cross / sizes.sample_cross, 0.5, rtol=0.02
+ )
+ np.testing.assert_allclose(sizes.spacing_along, sizes.sample_along)
+ self.assertTrue((sizes.scan_angle.abs() <= 105.6 / 2 + 0.1).all())
+ # the slant range is at least the satellite's altitude
+ self.assertTrue((sizes.sat_range > 800e3).all())
+
+ def test_instrument_without_pixels(self):
+ """
+ Test that an instrument without pixels has undefined sample sizes, but
+ a slant range and scan angle.
+ """
+ observations = collect_observations(
+ self.points, self.satellite, self.start, self.end, instrument_index=1
+ )
+ sizes = compute_sample_sizes(observations, self.satellite)
+ self.assertGreater(len(sizes), 0)
+ self.assertTrue(sizes.sample_along.isna().all())
+ self.assertTrue(sizes.spacing_cross.isna().all())
+ self.assertTrue(sizes.sat_range.notna().all())
+ self.assertTrue(sizes.scan_angle.notna().all())
+
+ def test_feature_observations(self):
+ """
+ Test that the samples of feature observations are computed at the
+ centroids of the viewed parts, at their times.
+ """
+ satellite = self.satellite.model_copy(
+ update={
+ "instruments": [
+ self.scanner.model_copy(
+ update={
+ "along_track_field_of_view": compute_along_track_field_of_view(
+ 830e3, 30, 98.7
+ )
+ }
+ )
+ ]
+ }
+ )
+ times = [self.start + timedelta(seconds=30 * (i + 0.5)) for i in range(2880)]
+ features = gpd.GeoDataFrame(
+ {
+ "start": pd.DatetimeIndex([self.start], dtype="datetime64[ns, UTC]"),
+ "end": pd.DatetimeIndex([self.end], dtype="datetime64[ns, UTC]"),
+ },
+ geometry=[ShapelyPoint(-100, 35).buffer(0.5)],
+ crs="EPSG:4326",
+ )
+ observations = collect_feature_observations(features, satellite, times)
+ sizes = compute_sample_sizes(observations, satellite)
+ self.assertGreater(len(sizes), 0)
+ np.testing.assert_allclose(sizes.sat_range, observations.sat_range, rtol=1e-9)
+ self.assertTrue((sizes.sample_cross > 0).all())
+
+ def test_unknown_satellite(self):
+ """
+ Test that observations by a satellite not given raise an error.
+ """
+ observations = collect_observations(
+ self.points, self.satellite, self.start, self.end
+ )
+ other = self.satellite.model_copy(update={"name": "other"})
+ with self.assertRaises(ValueError):
+ compute_sample_sizes(observations, other)
+
+ def test_empty_observations(self):
+ """
+ Test that no observations give no sample sizes, with their columns.
+ """
+ observations = collect_observations(
+ self.points[0],
+ self.satellite,
+ self.start,
+ self.start + timedelta(minutes=1),
+ )
+ sizes = compute_sample_sizes(observations, self.satellite)
+ self.assertEqual(len(sizes), 0)
+ self.assertIn("sample_cross", sizes.columns)
+
+ def test_conical_observations(self):
+ """
+ Test that the samples of a conical scanner with a beam width are
+ computed, and longer along track (the oblique direction of a forward
+ view) than across track.
+ """
+ conical = ConicalInstrument(
+ name="conical",
+ cone_angle=47.5,
+ along_track_field_of_view=0.82,
+ scan_half_width=75,
+ beam_width=1.8,
+ scan_sample_spacing=0.8,
+ )
+ satellite = self.satellite.model_copy(update={"instruments": [conical]})
+ observations = collect_observations(
+ self.points, satellite, self.start, self.end
+ )
+ sizes = compute_sample_sizes(observations, satellite)
+ self.assertGreater(len(sizes), 0)
+ self.assertTrue(sizes[["sample_along", "sample_cross"]].notna().all().all())
+ self.assertTrue((sizes.spacing_along > 0).all())
diff --git a/tests/analysis/test_space_sampling.py b/tests/analysis/test_space_sampling.py
index 05bcc4b9..3971a3ad 100644
--- a/tests/analysis/test_space_sampling.py
+++ b/tests/analysis/test_space_sampling.py
@@ -310,6 +310,8 @@ def test_output_format(self):
"to_satellite",
"start",
"end",
+ "closest_time",
+ "closest_range",
],
)
self.assertEqual(observations.crs, "EPSG:4326")
@@ -342,6 +344,31 @@ def test_output_format(self):
atol=1e-3,
)
+ def test_closest_approach(self):
+ """
+ Test that each observation's closest approach is the least range during
+ its period (within the samples' refinement), at a time within it.
+ """
+ observations = collect_space_observations(
+ self.leader, self.start, self.end, self.polar
+ )
+ self.assertGreater(len(observations), 0)
+ for _, row in observations.iterrows():
+ self.assertTrue(row.start <= row.closest_time <= row.end)
+ duration = (row.end - row.start).total_seconds()
+ times = [
+ row.start.to_pydatetime() + timedelta(seconds=float(s))
+ for s in np.linspace(0, duration, int(duration * 10) + 1)
+ ]
+ t = timescale.from_datetimes(times)
+ distance = np.linalg.norm(
+ self.leader.orbit.to_gp_orbit().get_orbit_track_at_time(t).position.m
+ - self.polar.orbit.to_gp_orbit().get_orbit_track_at_time(t).position.m,
+ axis=0,
+ )
+ self.assertLessEqual(row.closest_range, distance.min() + 1e-3)
+ self.assertGreater(row.closest_range, distance.min() - 100)
+
def test_empty(self):
for observations in [
collect_space_observations(self.leader, self.start, self.end, self.far),
@@ -358,6 +385,8 @@ def test_empty(self):
"to_satellite",
"start",
"end",
+ "closest_time",
+ "closest_range",
],
)
diff --git a/tests/schemas/instrument/test_conical.py b/tests/schemas/instrument/test_conical.py
index 9ca5e3ad..9fb3f74b 100644
--- a/tests/schemas/instrument/test_conical.py
+++ b/tests/schemas/instrument/test_conical.py
@@ -238,3 +238,85 @@ def test_compute_cone_and_azimuth(self):
self.assertAlmostEqual(
((float(a) - azimuth + 180) % 360) - 180, 0, delta=1e-6
)
+
+ def test_sample_size_of_forward_view(self):
+ """
+ Test that the beam of a forward conical view (as of AMSR2's 6.9 GHz
+ channel, 1.8 deg from 700 km at a cone angle of 47.5 deg, about 35 by
+ 62 km) is longer along track, in the oblique direction of the view, by
+ about the secant of its zenith angle, and spans the beam width times
+ the slant range across track.
+ """
+ instrument = ConicalInstrument(
+ name="amsr2",
+ cone_angle=47.5,
+ along_track_field_of_view=0.82,
+ scan_half_width=75,
+ beam_width=1.8,
+ scan_sample_spacing=0.8,
+ )
+ target = instrument.compute_footprint_center(self.orbit_track)
+ altitude, _, distance = (
+ self.orbit_track - target.at(self.orbit_track.t)
+ ).altaz()
+ along, cross = instrument.compute_sample_size(self.orbit_track, target)
+ self.assertAlmostEqual(cross / (distance.m * np.radians(1.8)), 1, places=2)
+ self.assertAlmostEqual(
+ along * np.cos(np.radians(90 - altitude.degrees)) / cross, 1, delta=0.03
+ )
+ self.assertAlmostEqual(cross / 35e3, 1, delta=0.05)
+ self.assertAlmostEqual(along / 62e3, 1, delta=0.05)
+
+ def test_sample_spacing(self):
+ """
+ Test that samples are spaced along track by the distance the arc
+ advances per scan, and across track by the scan sample spacing times
+ the arc's radius on the ground.
+ """
+ instrument = ConicalInstrument(
+ name="amsr2",
+ cone_angle=47.5,
+ along_track_field_of_view=0.82,
+ beam_width=1.8,
+ scan_sample_spacing=0.8,
+ )
+ target = instrument.compute_footprint_center(self.orbit_track)
+ along, cross = instrument.compute_sample_spacing(self.orbit_track, target)
+ self.assertAlmostEqual(
+ along / (2 * 700e3 * np.tan(np.radians(0.41))), 1, delta=0.01
+ )
+ subpoint = wgs84.subpoint_of(self.orbit_track)
+ radius = (
+ np.radians(
+ np.degrees(
+ np.arccos(
+ np.sin(np.radians(subpoint.latitude.degrees))
+ * np.sin(np.radians(target.latitude.degrees))
+ + np.cos(np.radians(subpoint.latitude.degrees))
+ * np.cos(np.radians(target.latitude.degrees))
+ * np.cos(
+ np.radians(
+ target.longitude.degrees - subpoint.longitude.degrees
+ )
+ )
+ )
+ )
+ )
+ * 6371e3
+ )
+ self.assertAlmostEqual(cross / (radius * np.radians(0.8)), 1, delta=0.01)
+
+ def test_sample_size_not_modeled(self):
+ """
+ Test that the sample sizes are undefined without a beam width, and the
+ cross-track spacing without a scan sample spacing.
+ """
+ instrument = ConicalInstrument(
+ name="c", cone_angle=47.5, along_track_field_of_view=0.82
+ )
+ target = instrument.compute_footprint_center(self.orbit_track)
+ along, cross = instrument.compute_sample_size(self.orbit_track, target)
+ self.assertTrue(np.isnan(along) and np.isnan(cross))
+ along, cross = instrument.compute_sample_spacing(self.orbit_track, target)
+ self.assertGreater(along, 0)
+ self.assertTrue(np.isnan(cross))
diff --git a/tests/schemas/instrument/test_pointed.py b/tests/schemas/instrument/test_pointed.py
index dfcd791d..a30b2840 100644
--- a/tests/schemas/instrument/test_pointed.py
+++ b/tests/schemas/instrument/test_pointed.py
@@ -17,6 +17,7 @@
from tatc.utils.projection import (
VelocityFrame,
ViewGeometry,
+ compute_ground_advance,
compute_projected_ray_position,
compute_view_angles,
)
@@ -164,15 +165,16 @@ def test_pixel_count_bounds(self):
def test_oversampling_bounds(self):
"""
- Test that cross/along track oversampling must be in [0, 1).
+ Test that cross/along track oversampling must be less than 1, and
+ may be negative (a gap between pixels).
"""
- with self.assertRaises(ValidationError):
- PointedInstrument(
- name="t",
- cross_track_field_of_view=10.0,
- along_track_field_of_view=10.0,
- cross_track_oversampling=-0.1,
- )
+ gaps = PointedInstrument(
+ name="t",
+ cross_track_field_of_view=10.0,
+ along_track_field_of_view=10.0,
+ cross_track_oversampling=-1.5,
+ )
+ self.assertEqual(gaps.cross_track_oversampling, -1.5)
with self.assertRaises(ValidationError):
PointedInstrument(
name="t",
@@ -223,6 +225,162 @@ def test_get_along_track_instantaneous_field_of_view_with_oversampling(self):
# pitch = 10 / 2 = 5 degrees; ifov = pitch / (1 - 0.5) = 10 degrees
self.assertAlmostEqual(o.get_along_track_instantaneous_field_of_view(), 10.0)
+ def test_pixel_offsets_span_field_of_view_with_oversampling(self):
+ """
+ Test that the pixel centers are spaced evenly across the field of
+ view (by its share per pixel), whatever the oversampling, so that the
+ outermost centers lie half a spacing inside its edges.
+ """
+ for oversampling in (-0.5, 0, 0.5):
+ o = PointedInstrument(
+ name="t",
+ cross_track_field_of_view=96.0,
+ along_track_field_of_view=1.0,
+ cross_track_pixels=96,
+ cross_track_oversampling=oversampling,
+ )
+ first, _ = o._get_pixel_offsets(0, 0)
+ last, _ = o._get_pixel_offsets(95, 0)
+ self.assertAlmostEqual(first, -47.5)
+ self.assertAlmostEqual(last, 47.5)
+ self.assertAlmostEqual(o.get_cross_track_sample_spacing(), 1.0)
+ self.assertAlmostEqual(
+ o.get_cross_track_instantaneous_field_of_view(),
+ 1.0 / (1 - oversampling),
+ )
+
+ def test_compute_sample_size_at_nadir(self):
+ """
+ Test that a pixel viewing the sub-satellite point spans about its
+ instantaneous field of view times the altitude, along and across
+ track, and that a gap between pixels makes the spacing larger than
+ the pixel.
+ """
+ start = datetime(2026, 1, 1, tzinfo=timezone.utc)
+ orbit = CircularOrbit(altitude=800e3, inclination=98, epoch=start)
+ track = orbit.to_gp_orbit().get_orbit_track(start)
+ target = wgs84.subpoint_of(track)
+ height = wgs84.height_of(track).m
+ o = PointedInstrument(
+ name="t",
+ cross_track_field_of_view=100.0,
+ along_track_field_of_view=1.0,
+ cross_track_pixels=100,
+ cross_track_oversampling=-1.0,
+ )
+ along, cross = o.compute_sample_size(track, target)
+ self.assertAlmostEqual(along / (height * np.radians(1.0)), 1, places=3)
+ self.assertAlmostEqual(cross / (height * np.radians(0.5)), 1, places=3)
+ along_spacing, cross_spacing = o.compute_sample_spacing(track, target)
+ self.assertAlmostEqual(along_spacing, along, delta=1)
+ self.assertAlmostEqual(cross_spacing / cross, 2, places=3)
+
+ def test_compute_sample_size_off_nadir(self):
+ """
+ Test that a pixel viewing a target off nadir, across track, is longer
+ across track than along track by about the secant of the target's
+ zenith angle, and that both grow with the slant range.
+ """
+ start = datetime(2026, 1, 1, tzinfo=timezone.utc)
+ orbit = CircularOrbit(altitude=800e3, inclination=98, epoch=start)
+ track = orbit.to_gp_orbit().get_orbit_track(start)
+ o = PointedInstrument(
+ name="t",
+ cross_track_field_of_view=100.0,
+ along_track_field_of_view=1.0,
+ cross_track_pixels=100,
+ roll_angle=40,
+ )
+ target = o.compute_footprint_center(track)
+ along, cross = o.compute_sample_size(track, target)
+ altitude, _, distance = (track - target.at(track.t)).altaz()
+ zenith = np.radians(90 - altitude.degrees)
+ self.assertAlmostEqual(along / (distance.m * np.radians(1.0)), 1, places=2)
+ self.assertAlmostEqual(cross * np.cos(zenith) / along, 1, places=2)
+
+ def test_scan_period_bounds(self):
+ """
+ Test that the scan period defaults to None and must be positive.
+ """
+ o = PointedInstrument(
+ name="t", cross_track_field_of_view=100.0, along_track_field_of_view=1.0
+ )
+ self.assertIsNone(o.scan_period)
+ for value in (0, -1):
+ with self.assertRaises(ValidationError):
+ PointedInstrument(
+ name="t",
+ cross_track_field_of_view=100.0,
+ along_track_field_of_view=1.0,
+ scan_period=value,
+ )
+
+ def test_compute_ground_advance_matches_subpoints(self):
+ """
+ Test that the ground advance at the sub-satellite point over a short
+ period matches the distance between the sub-satellite points at the
+ start and end of the period.
+ """
+ start = datetime(2026, 1, 1, tzinfo=timezone.utc)
+ orbit = CircularOrbit(altitude=800e3, inclination=98, epoch=start).to_gp_orbit()
+ track = orbit.get_orbit_track(start)
+ later = wgs84.subpoint_of(orbit.get_orbit_track(start + timedelta(seconds=2)))
+ target = wgs84.subpoint_of(track)
+ advance = compute_ground_advance(track, target, 2)
+ expected = geodesic_distance(
+ target.longitude.degrees,
+ target.latitude.degrees,
+ later.longitude.degrees,
+ later.latitude.degrees,
+ )
+ self.assertIsInstance(float(advance), float)
+ self.assertAlmostEqual(advance / expected, 1, places=3)
+
+ def test_compute_sample_spacing_with_scan_period(self):
+ """
+ Test that the along-track spacing of a scanner with one pixel along
+ track is the advance of its scans, nearly constant across the scan,
+ while its angular spacing grows toward the edges; and that the
+ spacing of an array of detectors is the larger of its pitch and the
+ advance per detector.
+ """
+ start = datetime(2026, 1, 1, tzinfo=timezone.utc)
+ orbit = CircularOrbit(altitude=800e3, inclination=98, epoch=start)
+ track = orbit.to_gp_orbit().get_orbit_track(start)
+ scanner = PointedInstrument(
+ name="t",
+ cross_track_field_of_view=100.0,
+ along_track_field_of_view=1.0,
+ cross_track_pixels=100,
+ view_geometry=ViewGeometry.SCAN,
+ scan_period=2.0,
+ )
+ angular = scanner.model_copy(update={"scan_period": None})
+ nadir = wgs84.subpoint_of(track)
+ edge = compute_projected_ray_position(track, 0, 0, 50)
+ advance = compute_ground_advance(track, nadir, 2.0)
+ along_nadir, _ = scanner.compute_sample_spacing(track, nadir)
+ along_edge, _ = scanner.compute_sample_spacing(track, edge)
+ self.assertAlmostEqual(along_nadir, advance)
+ self.assertGreater(along_edge / along_nadir, 0.95)
+ self.assertLess(along_edge / along_nadir, 1.0)
+ angular_nadir, _ = angular.compute_sample_spacing(track, nadir)
+ angular_edge, _ = angular.compute_sample_spacing(track, edge)
+ self.assertGreater(angular_edge / angular_nadir, 1.5)
+ # an array of 10 detectors whose pitch matches the advance per detector at nadir
+ pitch = np.degrees(advance / 10 / wgs84.height_of(track).m)
+ array = scanner.model_copy(
+ update={"along_track_field_of_view": 10 * pitch, "along_track_pixels": 10}
+ )
+ array_nadir, _ = array.compute_sample_spacing(track, nadir)
+ array_edge, _ = array.compute_sample_spacing(track, edge)
+ self.assertAlmostEqual(array_nadir / (advance / 10), 1, places=2)
+ pitch_edge, _ = array.model_copy(
+ update={"scan_period": None}
+ ).compute_sample_spacing(track, edge)
+ self.assertAlmostEqual(array_edge, pitch_edge)
+ self.assertGreater(array_edge, along_edge / 10)
+
def test_get_pixel_cone_and_clock_angle_single_pixel(self):
"""
Test that a single pixel (spanning the full field of view) is
diff --git a/tests/schemas/instrument/test_simple.py b/tests/schemas/instrument/test_simple.py
index a68e7b7a..6dc433e6 100644
--- a/tests/schemas/instrument/test_simple.py
+++ b/tests/schemas/instrument/test_simple.py
@@ -14,7 +14,7 @@
from tatc.constants import de421, timescale
from tatc.schemas import CircularOrbit, Instrument
-from tatc.utils import geodesic_distance
+from tatc.utils import compute_glint_angle, geodesic_distance
class TestInstrument(unittest.TestCase):
@@ -361,6 +361,50 @@ def test_valid_observation_target_solar_elevation(self):
o.is_valid_observation(orbit_track, subpoint), expected, str(kwargs)
)
+ def test_target_glint_angle_bounds(self):
+ """
+ Test that target glint angles are bounded and that the minimum cannot
+ exceed the maximum.
+ """
+ for kwargs in [
+ {"min_target_glint_angle": -1},
+ {"max_target_glint_angle": 181},
+ {"min_target_glint_angle": 30, "max_target_glint_angle": 10},
+ ]:
+ with self.assertRaises(ValidationError):
+ Instrument(name="Test Instrument", **kwargs)
+ o = Instrument(name="Test Instrument", min_target_glint_angle=25)
+ self.assertEqual(o.min_target_glint_angle, 25)
+ self.assertIsNone(o.max_target_glint_angle)
+
+ def test_valid_observation_target_glint_angle(self):
+ """
+ Test that an observation is valid when the target's glint angle lies
+ within the required range, for a vector of times over half a day.
+ """
+ times = timescale.utc(2020, 3, 20, 6, range(0, 720, 5)) # type: ignore
+ orbit_track = self.test_sat_5.at(times) # type: ignore
+ subpoint = wgs84.subpoint_of(orbit_track)
+ glint_angle = compute_glint_angle(orbit_track, subpoint)
+ # the sample spans small and large glint angles
+ self.assertTrue(np.any(glint_angle < 25))
+ self.assertTrue(np.any(glint_angle > 60))
+ for kwargs, expected in [
+ ({"min_target_glint_angle": 25}, glint_angle >= 25),
+ ({"max_target_glint_angle": 25}, glint_angle <= 25),
+ (
+ {"min_target_glint_angle": 25, "req_target_sunlit": True},
+ glint_angle >= 25,
+ ),
+ ]:
+ o = Instrument(name="Test Instrument", **kwargs)
+ valid = o.is_valid_observation(orbit_track, subpoint)
+ if "req_target_sunlit" in kwargs:
+ expected = expected & o.model_copy(
+ update={"min_target_glint_angle": None}
+ ).is_valid_observation(orbit_track, subpoint)
+ np.testing.assert_array_equal(valid, expected, str(kwargs))
+
def test_compute_footprint_center_geocentric_nadir(self):
"""
Test that the footprint center of an instrument with a geocentric
diff --git a/tests/schemas/orbit/test_gp.py b/tests/schemas/orbit/test_gp.py
index 65ea8ec7..6f12968d 100644
--- a/tests/schemas/orbit/test_gp.py
+++ b/tests/schemas/orbit/test_gp.py
@@ -21,9 +21,13 @@
from skyfield.framelib import itrs
from tatc import config, constants
-from tatc.schemas import GeneralPerturbationsOrbit, Point
+from tatc.schemas import CircularOrbit, GeneralPerturbationsOrbit, Point, Satellite
from tatc.utils.computation import _run
-from tatc.utils.propagation import _complete_passes, _find_events
+from tatc.utils.propagation import (
+ _complete_passes,
+ _find_events,
+ align_element_switches,
+)
REPEATING = {"remove_drag": True, "repeat_cycle": "auto"}
"""Options to propagate an orbit maintained on its (found) repeat ground track."""
@@ -2473,3 +2477,295 @@ def test_culmination_below_minimum(self):
jd, events = self.complete([10.5, 11], [1, 2], t_1=15.0)
self.assertEqual(len(jd), 0)
self.assertEqual(len(events), 0)
+
+
+def _orbit_with_epochs(epochs, **options):
+ """
+ Builds a GP orbit with an element at each epoch, each with a distinct
+ mean anomaly (10 deg times its index) so that the elements propagate to
+ distinct positions.
+ """
+ tle = [
+ "1 25544U 98067A 21156.30527927 .00003432 00000-0 70541-4 0 9993",
+ "2 25544 51.6455 41.4969 0003508 68.0432 78.3395 15.48957534286754",
+ ]
+ base = GeneralPerturbationsOrbit.from_tle(tle).elements[0]
+ return GeneralPerturbationsOrbit(
+ elements=[
+ base.model_copy(update={"epoch": epoch, "mean_anomaly": 10.0 * k})
+ for k, epoch in enumerate(epochs)
+ ],
+ **options,
+ )
+
+
+class TestElementSwitchTimes(unittest.TestCase):
+ """
+ Unit tests for GeneralPerturbationsOrbit.element_switch_times and
+ get_element_index.
+ """
+
+ def setUp(self):
+ self.epochs = [datetime(2022, 1, d, tzinfo=timezone.utc) for d in (1, 3, 5)]
+ self.switches = [
+ datetime(2022, 1, 1, 6, tzinfo=timezone.utc),
+ datetime(2022, 1, 4, 18, tzinfo=timezone.utc),
+ ]
+ self.orbit = _orbit_with_epochs(self.epochs, element_switch_times=self.switches)
+
+ def test_default_selects_closest_element(self):
+ """
+ Test that without switch times, the element used is the closest one.
+ """
+ orbit = _orbit_with_epochs(self.epochs)
+ times = [self.epochs[0] + timedelta(hours=h) for h in range(0, 120, 7)]
+ self.assertEqual(
+ orbit.get_element_index(times), orbit.get_closest_element_index(times)
+ )
+
+ def test_switch_times_select_elements(self):
+ """
+ Test that each time uses the element between the switches before and
+ after it (the later element exactly at a switch), rather than the
+ closest element.
+ """
+ cases = {
+ datetime(2022, 1, 1, 5, 59, tzinfo=timezone.utc): 0,
+ self.switches[0]: 1,
+ datetime(2022, 1, 2, tzinfo=timezone.utc): 1, # closest is element 0
+ datetime(2022, 1, 4, 12, tzinfo=timezone.utc): 1, # closest is element 2
+ self.switches[1]: 2,
+ }
+ for time, index in cases.items():
+ self.assertEqual(self.orbit.get_element_index(time), index)
+ self.assertEqual(
+ self.orbit.get_element_index(list(cases)), list(cases.values())
+ )
+ self.assertEqual(
+ self.orbit.get_element_index(
+ constants.timescale.from_datetimes(list(cases))
+ ),
+ list(cases.values()),
+ )
+ self.assertEqual(self.orbit.get_element_index(None), 0)
+
+ def test_switch_times_must_number_one_fewer_than_elements(self):
+ """
+ Test that switch times must number one fewer than the elements.
+ """
+ with self.assertRaises(ValidationError):
+ _orbit_with_epochs(self.epochs, element_switch_times=self.switches[:1])
+
+ def test_switch_times_must_lie_between_epochs(self):
+ """
+ Test that each switch time must lie between the epochs of the
+ elements before and after it.
+ """
+ with self.assertRaises(ValidationError):
+ _orbit_with_epochs(
+ self.epochs,
+ element_switch_times=[
+ datetime(2022, 1, 3, 6, tzinfo=timezone.utc),
+ self.switches[1],
+ ],
+ )
+
+ def test_coinciding_switches_skip_an_element(self):
+ """
+ Test that an element between two coinciding switch times is never
+ used, and that the partition into segments skips it.
+ """
+ orbit = _orbit_with_epochs(
+ self.epochs, element_switch_times=[self.epochs[1], self.epochs[1]]
+ )
+ self.assertEqual(orbit.get_element_index(self.epochs[1]), 2)
+ boundaries, indices = orbit.partition_by_element_index(
+ datetime(2022, 1, 1, tzinfo=timezone.utc),
+ datetime(2022, 1, 6, tzinfo=timezone.utc),
+ )
+ self.assertEqual(boundaries[1:-1], [self.epochs[1]])
+ self.assertEqual(indices, [0, 2])
+
+ def test_partition_uses_switch_times(self):
+ """
+ Test that the partition into segments switches elements at the
+ switch times.
+ """
+ start = datetime(2022, 1, 1, tzinfo=timezone.utc)
+ end = datetime(2022, 1, 6, tzinfo=timezone.utc)
+ boundaries, indices = self.orbit.partition_by_element_index(start, end)
+ self.assertEqual(boundaries, [start, *self.switches, end])
+ self.assertEqual(indices, [0, 1, 2])
+
+ def test_propagation_uses_switched_element(self):
+ """
+ Test that the orbit is propagated with the element selected by the
+ switch times, not the closest element.
+ """
+ t = constants.timescale.from_datetime(datetime(2022, 1, 2, tzinfo=timezone.utc))
+ expected = self.orbit.elements[1].to_skyfield().at(t).position.m
+ closest = self.orbit.elements[0].to_skyfield().at(t).position.m
+ actual = self.orbit.get_orbit_track_at_time(t).position.m
+ np.testing.assert_allclose(actual, expected, atol=1e-6)
+ self.assertGreater(np.linalg.norm(actual - closest), 1e3)
+
+ def test_switch_times_carried_by_derived_and_serialized_orbits(self):
+ """
+ Test that derived orbits and serialized orbits keep the switch times.
+ """
+ self.assertEqual(
+ self.orbit.get_derived_orbit(10, 20).element_switch_times, self.switches
+ )
+ restored = GeneralPerturbationsOrbit.model_validate_json(
+ self.orbit.model_dump_json()
+ )
+ self.assertEqual(restored.element_switch_times, self.switches)
+
+
+class TestAlignElementSwitches(unittest.TestCase):
+ """
+ Unit tests for align_element_switches.
+ """
+
+ def setUp(self):
+ self.t0 = datetime(2022, 1, 1, tzinfo=timezone.utc)
+
+ def hours(self, *values, offset=0.0):
+ """
+ Epochs at the given hours after t0, shifted by offset seconds.
+ """
+ return [self.t0 + timedelta(hours=h, seconds=offset) for h in values]
+
+ @staticmethod
+ def satellite(name, epochs):
+ return Satellite(name=name, orbit=_orbit_with_epochs(epochs))
+
+ @staticmethod
+ def midpoint(a, b):
+ return a + (b - a) / 2
+
+ def test_satellites_switch_at_common_times(self):
+ """
+ Test that three satellites whose elements are published seconds
+ apart switch at common times, midway between the events' mean epochs,
+ keeping all their elements, and that the inputs are unchanged.
+ """
+ satellites = [
+ self.satellite("A", self.hours(0, 8, 16)),
+ self.satellite("B", self.hours(0, 8, 16, offset=20)),
+ self.satellite("C", self.hours(0, 8, 16, offset=-35)),
+ ]
+ aligned = align_element_switches(satellites)
+ means = self.hours(0, 8, 16, offset=-5)
+ expected = [
+ self.midpoint(means[0], means[1]),
+ self.midpoint(means[1], means[2]),
+ ]
+ for satellite in aligned:
+ self.assertEqual(len(satellite.orbit.elements), 3)
+ self.assertEqual(satellite.orbit.element_switch_times, expected)
+ for satellite in satellites:
+ self.assertIsNone(satellite.orbit.element_switch_times)
+ times = [self.t0 + timedelta(minutes=m) for m in range(0, 24 * 60, 7)]
+ indices = [satellite.orbit.get_element_index(times) for satellite in aligned]
+ self.assertEqual(indices[0], indices[1])
+ self.assertEqual(indices[0], indices[2])
+
+ def test_missing_event_is_dropped_when_shared_only(self):
+ """
+ Test that, with shared_only, an element published by only some
+ satellites is removed, so that all use elements of the same events.
+ """
+ a, b = align_element_switches(
+ [
+ self.satellite("A", self.hours(0, 8, 16)),
+ self.satellite("B", self.hours(0, 16, offset=20)),
+ ]
+ )
+ self.assertEqual(a.orbit.get_element_epochs(), self.hours(0, 16))
+ self.assertEqual(a.orbit.element_switch_times, b.orbit.element_switch_times)
+ self.assertEqual(
+ a.orbit.element_switch_times,
+ [self.midpoint(*self.hours(0, 16, offset=10))],
+ )
+
+ def test_missing_event_switches_at_latest_common_time(self):
+ """
+ Test that, without shared_only, every event is used, and a satellite
+ without an element in an event switches at the latest common time
+ between the events of its elements.
+ """
+ a, b = align_element_switches(
+ [
+ self.satellite("A", self.hours(0, 8, 16)),
+ self.satellite("B", self.hours(0, 16, offset=20)),
+ ],
+ shared_only=False,
+ )
+ self.assertEqual(len(a.orbit.elements), 3)
+ self.assertEqual(len(a.orbit.element_switch_times), 2)
+ self.assertEqual(b.orbit.element_switch_times, a.orbit.element_switch_times[1:])
+
+ def test_latest_element_of_an_event_is_used(self):
+ """
+ Test that a satellite with several elements in one event (such as a
+ duplicate) uses its latest and the others are removed.
+ """
+ a, _ = align_element_switches(
+ [
+ self.satellite("A", self.hours(0) + self.hours(0, 8, offset=1)),
+ self.satellite("B", self.hours(0, 8, offset=20)),
+ ]
+ )
+ self.assertEqual(a.orbit.get_element_epochs(), self.hours(0, 8, offset=1))
+ self.assertEqual(a.orbit.elements[0].mean_anomaly, 10.0)
+
+ def test_single_element_satellites_are_unchanged(self):
+ """
+ Test that satellites without more than one GP element are returned
+ unchanged and do not affect the others.
+ """
+ circular = Satellite(
+ name="circular", orbit=CircularOrbit(altitude=500e3, inclination=45)
+ )
+ single = self.satellite("single", self.hours(4))
+ aligned = align_element_switches(
+ [circular, single, self.satellite("A", self.hours(0, 8))]
+ )
+ self.assertIs(aligned[0], circular)
+ self.assertIs(aligned[1], single)
+ self.assertEqual(len(aligned[2].orbit.element_switch_times), 1)
+
+ def test_chained_epochs_raise(self):
+ """
+ Test that epochs spread through time, chaining into an event longer
+ than the tolerance, raise an error.
+ """
+ minutes = [self.t0 + timedelta(minutes=m) for m in (0, 8, 16, 24)]
+ satellites = [
+ self.satellite("A", [minutes[0], minutes[2]]),
+ self.satellite("B", [minutes[1], minutes[3]]),
+ ]
+ with self.assertRaises(ValueError):
+ align_element_switches(satellites)
+
+ def test_close_events_raise(self):
+ """
+ Test that events whose mean epochs are not more than twice the
+ tolerance apart raise an error.
+ """
+ satellites = [self.satellite("A", [self.t0, self.t0 + timedelta(minutes=15)])]
+ with self.assertRaises(ValueError):
+ align_element_switches(satellites)
+
+ def test_no_shared_event_raises(self):
+ """
+ Test that, with shared_only, satellites without any event in common
+ raise an error.
+ """
+ satellites = [
+ self.satellite("A", self.hours(0, 8)),
+ self.satellite("B", self.hours(4, 12)),
+ ]
+ with self.assertRaises(ValueError):
+ align_element_switches(satellites)
diff --git a/tests/schemas/test_attitude.py b/tests/schemas/test_attitude.py
new file mode 100644
index 00000000..a3c8fcde
--- /dev/null
+++ b/tests/schemas/test_attitude.py
@@ -0,0 +1,636 @@
+"""
+Unit tests for the attitude schemas in tatc.schemas.
+
+@author Paul T. Grogan
+"""
+
+import unittest
+from datetime import datetime, timedelta, timezone
+
+import numpy as np
+from pydantic import ValidationError
+from shapely.geometry import Point as ShapelyPoint
+from skyfield.api import wgs84
+from skyfield.framelib import itrs
+
+from tatc.analysis import collect_ground_track, collect_observations
+from tatc.schemas import (
+ Attitude,
+ AttitudeSchedule,
+ AttitudeSegment,
+ CircularOrbit,
+ ConicalInstrument,
+ GlintAttitude,
+ Point,
+ PointedInstrument,
+ Satellite,
+ SunSynchronousOrbit,
+ TargetAttitude,
+ WalkerConstellation,
+)
+from tatc.utils import VelocityFrame, compute_glint_angle, geodesic_distance
+from tatc.utils.time import _to_time
+
+
+class TestAttitude(unittest.TestCase):
+ """
+ Unit tests for the Attitude and AttitudeSchedule schemas.
+ """
+
+ def setUp(self):
+ self.start = datetime(2026, 1, 1, tzinfo=timezone.utc)
+ self.orbit = SunSynchronousOrbit(
+ altitude=700e3, equator_crossing_time="10:30", epoch=self.start
+ )
+ self.times = [self.start + timedelta(minutes=13 * i) for i in range(7)]
+ self.track = self.orbit.to_gp_orbit().get_orbit_track_at_time(
+ _to_time(self.times)
+ )
+
+ def test_angle_bounds_and_profiles(self):
+ """
+ Test that angles must lie in [-180, 180] and that profiles are
+ checked and sorted by argument of latitude.
+ """
+ with self.assertRaises(ValidationError):
+ Attitude(yaw_angle=190)
+ with self.assertRaises(ValidationError):
+ Attitude(roll_angle_profile=[(360, 0)])
+ with self.assertRaises(ValidationError):
+ Attitude(pitch_angle_profile=[(0, 200)])
+ attitude = Attitude(yaw_angle_profile=[(180, 5), (0, -5)])
+ self.assertEqual(attitude.yaw_angle_profile, [(0, -5), (180, 5)])
+
+ def test_schedule_segments(self):
+ """
+ Test that a schedule sorts its segments, requires timezone-aware
+ start times, and applies the first segment before its start.
+ """
+ flip = self.start + timedelta(hours=1)
+ schedule = AttitudeSchedule.from_attitudes(
+ [flip, self.start], [Attitude(yaw_angle=0), Attitude(yaw_angle=180)]
+ )
+ self.assertEqual([s.start for s in schedule.segments], [self.start, flip])
+ self.assertEqual(
+ schedule.get_attitude(self.start - timedelta(days=1)).yaw_angle, 180
+ )
+ self.assertEqual(schedule.get_attitude(flip).yaw_angle, 0)
+ with self.assertRaises(ValidationError):
+ AttitudeSegment(start=datetime(2026, 1, 1), attitude=Attitude())
+ with self.assertRaises(ValueError):
+ AttitudeSchedule.from_attitudes([self.start], [])
+
+ def test_body_angles_of_schedule(self):
+ """
+ Test that a schedule gives each time the angles and velocity frame of
+ its segment, for a vector or a single time.
+ """
+ flip = self.start + timedelta(minutes=40)
+ schedule = AttitudeSchedule.from_attitudes(
+ [self.start, flip],
+ [
+ Attitude(yaw_angle=180, velocity_frame=VelocityFrame.INERTIAL),
+ Attitude(roll_angle=10),
+ ],
+ )
+ yaw, roll, pitch, inertial = schedule.get_body_angles(self.track)
+ after = np.array([t >= flip for t in self.times])
+ np.testing.assert_array_equal(yaw, np.where(after, 0, 180))
+ np.testing.assert_array_equal(roll, np.where(after, 10, 0))
+ np.testing.assert_array_equal(pitch, 0)
+ np.testing.assert_array_equal(inertial, ~after)
+ single = self.orbit.to_gp_orbit().get_orbit_track_at_time(_to_time(self.start))
+ yaw, _, _, inertial = schedule.get_body_angles(single)
+ self.assertEqual(np.shape(yaw), ())
+ self.assertTrue(inertial)
+
+ def _center(self, satellite, index=0):
+ center = satellite.get_instrument(index).compute_footprint_center(self.track)
+ return np.array([center.latitude.degrees, center.longitude.degrees])
+
+ def _assert_same_centers(self, a, b):
+ np.testing.assert_allclose(self._center(a), self._center(b), atol=1e-9)
+
+ def test_composition_with_instrument_pointing(self):
+ """
+ Test that instruments point relative to the body: an identity
+ attitude changes nothing, an inertial attitude equals an instrument's
+ inertial velocity frame, a yaw of 180 deg mirrors roll and pitch, and
+ a body roll adds to an instrument's roll.
+ """
+
+ def instrument(**kwargs):
+ return PointedInstrument(
+ name="p",
+ cross_track_field_of_view=2,
+ along_track_field_of_view=2,
+ **kwargs,
+ )
+
+ def satellite(attitude=None, **kwargs):
+ return Satellite(
+ name="s",
+ orbit=self.orbit,
+ instruments=[instrument(**kwargs)],
+ attitude=attitude,
+ )
+
+ self._assert_same_centers(
+ satellite(Attitude(), roll_angle=20, pitch_angle=5),
+ satellite(roll_angle=20, pitch_angle=5),
+ )
+ self._assert_same_centers(
+ satellite(Attitude(velocity_frame=VelocityFrame.INERTIAL), roll_angle=20),
+ satellite(roll_angle=20, velocity_frame=VelocityFrame.INERTIAL),
+ )
+ self._assert_same_centers(
+ satellite(Attitude(yaw_angle=180), roll_angle=20, pitch_angle=5),
+ satellite(roll_angle=-20, pitch_angle=-5),
+ )
+ self._assert_same_centers(
+ satellite(Attitude(roll_angle=15), roll_angle=10),
+ satellite(roll_angle=25),
+ )
+ profile = [(0, 10), (90, 30), (180, 10), (270, -10)]
+ self._assert_same_centers(
+ satellite(Attitude(roll_angle_profile=profile)),
+ satellite(roll_angle_profile=profile),
+ )
+
+ def test_conical_yaw(self):
+ """
+ Test that a yaw of 180 deg turns a forward conical scan aft.
+ """
+
+ def satellite(center, attitude=None):
+ return Satellite(
+ name="s",
+ orbit=self.orbit,
+ instruments=[
+ ConicalInstrument(
+ name="c",
+ cone_angle=45,
+ along_track_field_of_view=1,
+ scan_half_width=60,
+ scan_center_azimuth=center,
+ )
+ ],
+ attitude=attitude,
+ )
+
+ self._assert_same_centers(satellite(0, Attitude(yaw_angle=180)), satellite(180))
+
+ def test_velocity_frame_conflict(self):
+ """
+ Test that an instrument may not set a velocity frame if the attitude
+ sets the body's.
+ """
+ with self.assertRaises(ValidationError):
+ Satellite(
+ name="s",
+ orbit=self.orbit,
+ instruments=[
+ PointedInstrument(
+ name="p",
+ cross_track_field_of_view=2,
+ along_track_field_of_view=2,
+ velocity_frame=VelocityFrame.INERTIAL,
+ )
+ ],
+ attitude=Attitude(),
+ )
+
+ def test_serialization_and_constellations(self):
+ """
+ Test that a satellite with a schedule round-trips through JSON, and
+ that the members of a constellation carry its attitude.
+ """
+ schedule = AttitudeSchedule.from_attitudes(
+ [self.start, self.start + timedelta(days=40)],
+ [Attitude(yaw_angle=180), Attitude(yaw_angle=0)],
+ )
+ satellite = Satellite(name="s", orbit=self.orbit, attitude=schedule)
+ copy = Satellite.model_validate_json(satellite.model_dump_json())
+ self.assertEqual(copy.attitude, schedule)
+ self.assertIsNone(
+ Satellite(name="s", orbit=self.orbit).get_instrument().platform_attitude
+ )
+ self.assertEqual(satellite.get_instrument().platform_attitude, schedule)
+ constellation = WalkerConstellation(
+ name="w",
+ orbit=CircularOrbit(altitude=700e3, inclination=60, epoch=self.start),
+ number_satellites=4,
+ number_planes=2,
+ attitude=Attitude(yaw_angle=90),
+ )
+ for member in constellation.generate_members():
+ self.assertEqual(member.attitude.yaw_angle, 90)
+
+ def test_observations_across_a_schedule(self):
+ """
+ Test that the observations of an analysis across a scheduled flip
+ equal those of each attitude before and after it.
+ """
+ conical = ConicalInstrument(
+ name="c", cone_angle=45, along_track_field_of_view=1, scan_half_width=60
+ )
+ flip = self.start + timedelta(hours=12)
+ end = self.start + timedelta(days=1)
+ points = [ShapelyPoint(-100, 35), ShapelyPoint(20, -10), ShapelyPoint(140, 50)]
+ schedule = AttitudeSchedule.from_attitudes(
+ [self.start, flip], [Attitude(yaw_angle=180), Attitude()]
+ )
+
+ def observe(attitude, start, stop):
+ satellite = Satellite(
+ name="s", orbit=self.orbit, instruments=[conical], attitude=attitude
+ )
+ return collect_observations(points, satellite, start, stop)
+
+ scheduled = observe(schedule, self.start, end)
+ before = observe(Attitude(yaw_angle=180), self.start, flip)
+ after = observe(Attitude(), flip, end)
+ self.assertGreater(len(before), 0)
+ self.assertGreater(len(after), 0)
+ expected = sorted(list(before.epoch) + list(after.epoch))
+ self.assertEqual(len(scheduled), len(expected))
+ for observed, wanted in zip(sorted(scheduled.epoch), expected):
+ self.assertLess(abs((observed - wanted).total_seconds()), 1e-3)
+
+ def test_ground_track_follows_yaw(self):
+ """
+ Test that the footprint of an instrument rolled to the left of the
+ body moves to the right of the ground track while the body flies
+ backward.
+ """
+ instrument = PointedInstrument(
+ name="p",
+ cross_track_field_of_view=5,
+ along_track_field_of_view=1,
+ roll_angle=30,
+ )
+ times = [self.start + timedelta(minutes=10)]
+ tracks = {
+ yaw: collect_ground_track(
+ Satellite(
+ name="s",
+ orbit=self.orbit,
+ instruments=[instrument],
+ attitude=Attitude(yaw_angle=yaw),
+ ),
+ times,
+ )
+ .geometry.iloc[0]
+ .centroid
+ for yaw in (0, 180)
+ }
+ mirrored = (
+ collect_ground_track(
+ Satellite(
+ name="s",
+ orbit=self.orbit,
+ instruments=[instrument.model_copy(update={"roll_angle": -30})],
+ ),
+ times,
+ )
+ .geometry.iloc[0]
+ .centroid
+ )
+ self.assertGreater(tracks[0].distance(tracks[180]), 5)
+ self.assertLess(tracks[180].distance(mirrored), 1e-6)
+
+
+class TestTargetAttitude(unittest.TestCase):
+ """
+ Unit tests for the TargetAttitude schema.
+ """
+
+ def setUp(self):
+ self.start = datetime(2026, 1, 1, tzinfo=timezone.utc)
+ self.orbit = SunSynchronousOrbit(
+ altitude=700e3, equator_crossing_time="10:30", epoch=self.start
+ )
+ self.times = [self.start + timedelta(minutes=30 + 0.5 * i) for i in range(12)]
+ self.track = self.orbit.to_gp_orbit().get_orbit_track_at_time(
+ _to_time(self.times)
+ )
+ subpoint = wgs84.subpoint_of(self.track)
+ self.subpoint = subpoint
+ # a target about 250 km east of the ground track near the middle time
+ self.target = Point(
+ latitude=float(subpoint.latitude.degrees[6]),
+ longitude=float(subpoint.longitude.degrees[6]) + 2.5,
+ )
+ self.instrument = PointedInstrument(
+ name="p",
+ field_of_regard=80,
+ cross_track_field_of_view=1,
+ along_track_field_of_view=1,
+ )
+
+ def _satellite(self, attitude):
+ return Satellite(
+ name="s", orbit=self.orbit, instruments=[self.instrument], attitude=attitude
+ )
+
+ def test_validation(self):
+ """
+ Test that a target attitude needs targets and a maximum off-nadir
+ angle in (0, 90).
+ """
+ with self.assertRaises(ValidationError):
+ TargetAttitude(targets=[])
+ with self.assertRaises(ValidationError):
+ TargetAttitude(targets=[self.target], max_off_nadir_angle=90)
+
+ def test_points_at_target_within_reach(self):
+ """
+ Test that the body points at the target while it is within the
+ maximum off-nadir angle (the view center on the target), and along
+ nadir otherwise, choosing the target closest to nadir.
+ """
+ far = Point(
+ latitude=-self.target.latitude, longitude=self.target.longitude + 120
+ )
+ attitude = TargetAttitude(targets=[far, self.target], max_off_nadir_angle=40)
+ index = attitude.get_target_index(self.track)
+ self.assertTrue(np.all(np.isin(index, [-1, 1])))
+ self.assertGreater(np.sum(index == 1), 0)
+ self.assertGreater(np.sum(index == -1), 0)
+ center = (
+ self._satellite(attitude)
+ .get_instrument()
+ .compute_footprint_center(self.track)
+ )
+ n = len(self.times)
+ to_target = geodesic_distance(
+ np.full(n, self.target.longitude),
+ np.full(n, self.target.latitude),
+ center.longitude.degrees,
+ center.latitude.degrees,
+ )
+ to_nadir = geodesic_distance(
+ self.subpoint.longitude.degrees,
+ self.subpoint.latitude.degrees,
+ center.longitude.degrees,
+ center.latitude.degrees,
+ )
+ np.testing.assert_array_less(to_target[index == 1], 1)
+ np.testing.assert_array_less(to_nadir[index == -1], 1)
+
+ def test_yaw_keeps_pointing(self):
+ """
+ Test that a yaw of the body does not move a boresight view off the
+ target, but mirrors the views of instruments rolled from it.
+ """
+ attitude = TargetAttitude(
+ targets=[self.target], max_off_nadir_angle=40, yaw_angle=180
+ )
+ index = attitude.get_target_index(self.track)
+ center = (
+ self._satellite(attitude)
+ .get_instrument()
+ .compute_footprint_center(self.track)
+ )
+ pointing = index == 0
+ n = int(np.sum(pointing))
+ distance = geodesic_distance(
+ np.full(n, self.target.longitude),
+ np.full(n, self.target.latitude),
+ center.longitude.degrees[pointing],
+ center.latitude.degrees[pointing],
+ )
+ np.testing.assert_array_less(distance, 1)
+
+ def test_observes_off_track_target(self):
+ """
+ Test that a narrow instrument observes a target off its ground track
+ only while the body points at it.
+ """
+ end = self.start + timedelta(hours=2)
+ point = ShapelyPoint(self.target.longitude, self.target.latitude)
+ self.assertEqual(
+ len(collect_observations(point, self._satellite(None), self.start, end)), 0
+ )
+ observations = collect_observations(
+ point,
+ self._satellite(
+ TargetAttitude(targets=[self.target], max_off_nadir_angle=40)
+ ),
+ self.start,
+ end,
+ )
+ self.assertEqual(len(observations), 1)
+
+ def test_scheduled_and_serialized(self):
+ """
+ Test that a schedule may switch to target pointing, and that a
+ satellite with it round-trips through JSON.
+ """
+ target = TargetAttitude(targets=[self.target], max_off_nadir_angle=40)
+ schedule = AttitudeSchedule.from_attitudes(
+ [self.start, self.times[5]], [Attitude(), target]
+ )
+ _, roll, _, _ = schedule.get_body_angles(self.track)
+ index = target.get_target_index(self.track)
+ self.assertTrue(np.all(roll[:5] == 0))
+ self.assertTrue(np.any(roll[5:][index[5:] == 0] != 0))
+ satellite = self._satellite(schedule)
+ copy = Satellite.model_validate_json(satellite.model_dump_json())
+ self.assertEqual(copy.attitude, schedule)
+ self.assertIsInstance(copy.attitude.segments[1].attitude, TargetAttitude)
+
+
+class TestGlintAttitude(unittest.TestCase):
+ """
+ Unit tests for the GlintAttitude schema.
+ """
+
+ def setUp(self):
+ self.start = datetime(2026, 3, 20, 12, tzinfo=timezone.utc)
+ self.orbit = CircularOrbit(altitude=705e3, inclination=98.2, epoch=self.start)
+ self.times = [self.start + timedelta(minutes=m) for m in range(0, 100, 4)]
+ self.track = self.orbit.to_gp_orbit().get_orbit_track_at_time(
+ _to_time(self.times)
+ )
+ self.instrument = PointedInstrument(
+ name="p",
+ field_of_regard=120,
+ cross_track_field_of_view=1,
+ along_track_field_of_view=1,
+ max_target_glint_angle=1,
+ )
+
+ def _satellite(self, attitude):
+ return Satellite(
+ name="s", orbit=self.orbit, instruments=[self.instrument], attitude=attitude
+ )
+
+ def test_validation(self):
+ """
+ Test the bounds of the maximum off-nadir and minimum solar elevation
+ angles.
+ """
+ with self.assertRaises(ValidationError):
+ GlintAttitude(max_off_nadir_angle=90)
+ with self.assertRaises(ValidationError):
+ GlintAttitude(min_solar_elevation=-1)
+
+ def test_points_at_glint(self):
+ """
+ Test that the view center lies on the specular point (a zero glint
+ angle) while the body points at it, and at nadir otherwise.
+ """
+ attitude = GlintAttitude(max_off_nadir_angle=50)
+ point = attitude.get_aim_point(self.track)
+ pointing = np.isfinite(point.latitude.degrees)
+ self.assertGreater(np.sum(pointing), 0)
+ self.assertGreater(np.sum(~pointing), 0)
+ center = (
+ self._satellite(attitude)
+ .get_instrument()
+ .compute_footprint_center(self.track)
+ )
+ np.testing.assert_array_less(
+ compute_glint_angle(self.track, center)[pointing], 1e-3
+ )
+ subpoint = wgs84.subpoint_of(self.track)
+ to_nadir = geodesic_distance(
+ subpoint.longitude.degrees,
+ subpoint.latitude.degrees,
+ center.longitude.degrees,
+ center.latitude.degrees,
+ )
+ np.testing.assert_array_less(to_nadir[~pointing], 1)
+ # a smaller maximum off-nadir angle or a larger minimum solar elevation points less
+ fewer = GlintAttitude(max_off_nadir_angle=20).get_aim_point(self.track)
+ self.assertLess(np.sum(np.isfinite(fewer.latitude.degrees)), np.sum(pointing))
+ higher = GlintAttitude(max_off_nadir_angle=50, min_solar_elevation=60)
+ higher_point = higher.get_aim_point(self.track)
+ self.assertLess(
+ np.sum(np.isfinite(higher_point.latitude.degrees)), np.sum(pointing)
+ )
+
+ def test_offset_toward_nadir(self):
+ """
+ Test that an offset angle points the body short of the specular point
+ toward nadir, on the line from nadir to it, by the offset (constant
+ or from a profile of the specular point's off-nadir angle).
+ """
+ glint = GlintAttitude(max_off_nadir_angle=80)
+ specular = glint.get_aim_point(self.track)
+ pointing = np.flatnonzero(np.isfinite(specular.latitude.degrees))
+ track = self.track[pointing]
+ specular = wgs84.latlon(
+ specular.latitude.degrees[pointing], specular.longitude.degrees[pointing]
+ )
+ subpoint = wgs84.subpoint_of(track)
+
+ def off_nadir(point):
+ # the angle from nadir: 90 deg less the angle of the line of sight below the horizon
+ return np.degrees(
+ np.arccos(
+ np.sum(
+ np.array((point.at(track.t) - track).frame_xyz(itrs).m)
+ * -np.array(subpoint.itrs_xyz.m)
+ / np.linalg.norm(np.array(subpoint.itrs_xyz.m), axis=0),
+ axis=0,
+ )
+ / np.linalg.norm(
+ np.array((point.at(track.t) - track).frame_xyz(itrs).m), axis=0
+ )
+ )
+ )
+
+ glint_angle = off_nadir(specular)
+ for attitude, expected in [
+ (GlintAttitude(max_off_nadir_angle=80, offset_angle=5), glint_angle - 5),
+ (
+ GlintAttitude(
+ max_off_nadir_angle=80, offset_angle_profile=[(60, 10), (20, 2)]
+ ),
+ glint_angle - np.interp(glint_angle, [20, 60], [2, 10]),
+ ),
+ ]:
+ aim = attitude.get_aim_point(track)
+ # (at nadir where the offset exceeds the specular point's angle from nadir)
+ np.testing.assert_allclose(
+ off_nadir(aim), np.maximum(expected, 0), atol=0.3
+ )
+ # on the line from nadir to the specular point
+ detour = (
+ geodesic_distance(
+ subpoint.longitude.degrees,
+ subpoint.latitude.degrees,
+ aim.longitude.degrees,
+ aim.latitude.degrees,
+ )
+ + geodesic_distance(
+ aim.longitude.degrees,
+ aim.latitude.degrees,
+ specular.longitude.degrees,
+ specular.latitude.degrees,
+ )
+ - geodesic_distance(
+ subpoint.longitude.degrees,
+ subpoint.latitude.degrees,
+ specular.longitude.degrees,
+ specular.latitude.degrees,
+ )
+ )
+ np.testing.assert_array_less(detour, 100)
+ self.assertEqual(
+ GlintAttitude(
+ offset_angle_profile=[(60, 10), (20, 2)]
+ ).offset_angle_profile,
+ [(20, 2), (60, 10)],
+ )
+ with self.assertRaises(ValidationError):
+ GlintAttitude(offset_angle_profile=[(95, 1)])
+
+ def test_glint_observations(self):
+ """
+ Test that an instrument requiring a small glint angle observes the
+ specular point only with a glint attitude, and that its observations
+ report the glint angle.
+ """
+ attitude = GlintAttitude(max_off_nadir_angle=50)
+ k = int(
+ np.flatnonzero(
+ np.isfinite(attitude.get_aim_point(self.track).latitude.degrees)
+ )[2]
+ )
+ point = attitude.get_aim_point(self.track[k])
+ target = ShapelyPoint(
+ float(point.longitude.degrees), float(point.latitude.degrees)
+ )
+ start, end = self.times[k] - timedelta(minutes=10), self.times[k] + timedelta(
+ minutes=10
+ )
+ self.assertEqual(
+ len(collect_observations(target, self._satellite(None), start, end)), 0
+ )
+ observations = collect_observations(
+ target, self._satellite(attitude), start, end, omit_solar=False
+ )
+ self.assertGreater(len(observations), 0)
+ np.testing.assert_array_less(observations.glint_angle, 1)
+
+ def test_scheduled_and_serialized(self):
+ """
+ Test that a schedule may switch to glint pointing, and that a
+ satellite with it round-trips through JSON.
+ """
+ schedule = AttitudeSchedule.from_attitudes(
+ [self.start, self.times[10]], [Attitude(), GlintAttitude()]
+ )
+ satellite = self._satellite(schedule)
+ copy = Satellite.model_validate_json(satellite.model_dump_json())
+ self.assertEqual(copy.attitude, schedule)
+ self.assertIsInstance(copy.attitude.segments[1].attitude, GlintAttitude)
+ self.assertIsInstance(
+ Satellite.model_validate_json(
+ self._satellite(GlintAttitude()).model_dump_json()
+ ).attitude,
+ GlintAttitude,
+ )
diff --git a/tests/utils/test_geometry.py b/tests/utils/test_geometry.py
index ef89ce5e..615bc32d 100644
--- a/tests/utils/test_geometry.py
+++ b/tests/utils/test_geometry.py
@@ -901,6 +901,31 @@ def test_split_polygon_hole_across_antimeridian(self):
for point in [(165, 0), (-165, 0)]:
self.assertTrue(result.contains(Point(*point)))
+ def test_split_polygon_polar_coastline_not_monotonic_in_longitude(self):
+ """
+ Test that a polygon around a pole whose boundary is not monotonic in
+ longitude (as a coastline with a peninsula, such as Antarctica's) keeps
+ its shape, whether or not it already includes edges along the pole and
+ the anti-meridian.
+ """
+ coast = [(-60, -80), (-60, -63), (-66, -63), (-66, -60), (60, -60)]
+ with_pole_edges = Polygon(
+ [(180, -80), (180, -90), (-180, -90), (-180, -80), *coast, (180, -80)]
+ )
+ result = split_polygon(with_pole_edges)
+ self.assertTrue(result.is_valid)
+ self.assertAlmostEqual(result.symmetric_difference(with_pole_edges).area, 0)
+ # the same region, as a ring of latitudes around the pole
+ ring = Polygon([(-180, -80), *coast, (180, -80), (-180, -80)])
+ result = split_polygon(ring)
+ self.assertTrue(result.is_valid)
+ self.assertAlmostEqual(result.symmetric_difference(with_pole_edges).area, 0)
+ # the strip above the doubled-back coast is included, and the bay below
+ # it is not
+ self.assertTrue(result.contains(Point(-63, -61.5)))
+ self.assertFalse(result.contains(Point(-63, -70)))
+ self.assertTrue(result.contains(Point(0, -70)))
+
def test_split_polygon_hole_around_pole(self):
"""
Test that a hole around a pole (in a ring of latitudes around it) is
diff --git a/tests/utils/test_glint.py b/tests/utils/test_glint.py
new file mode 100644
index 00000000..dc55b563
--- /dev/null
+++ b/tests/utils/test_glint.py
@@ -0,0 +1,109 @@
+"""
+Unit tests for the sun glint utilities in tatc.utils.glint.
+
+@author Paul T. Grogan
+"""
+
+import unittest
+from datetime import datetime, timedelta, timezone
+
+import numpy as np
+from skyfield.api import wgs84
+
+from tatc.constants import de421
+from tatc.schemas import CircularOrbit
+from tatc.utils import compute_glint_angle, compute_specular_point
+
+
+class TestGlint(unittest.TestCase):
+ """
+ Unit tests for compute_specular_point and compute_glint_angle.
+ """
+
+ def setUp(self):
+ self.start = datetime(2026, 3, 20, tzinfo=timezone.utc)
+ self.orbit = CircularOrbit(
+ altitude=705e3, inclination=98.2, epoch=self.start
+ ).to_gp_orbit()
+ self.times = [self.start + timedelta(minutes=m) for m in range(0, 100, 2)]
+ self.track = self.orbit.get_orbit_track(self.times)
+
+ def test_specular_point_has_zero_glint_angle(self):
+ """
+ Test that the glint angle vanishes at the specular point, which exists
+ while the satellite is over the day side, and not otherwise.
+ """
+ point = compute_specular_point(self.track)
+ found = np.isfinite(point.latitude.degrees)
+ self.assertGreater(np.sum(found), 0)
+ self.assertGreater(np.sum(~found), 0)
+ angle = compute_glint_angle(self.track, point)
+ np.testing.assert_array_less(angle[found], 1e-4)
+ subpoint = wgs84.subpoint_of(self.track)
+ solar_elevation = (
+ (de421["earth"] + subpoint)
+ .at(self.track.t)
+ .observe(de421["sun"])
+ .apparent()
+ .altaz()[0]
+ .degrees
+ )
+ self.assertTrue(np.all(found[solar_elevation > 5]))
+ self.assertFalse(np.any(found[solar_elevation < -30]))
+
+ def test_specular_point_minimizes_glint_angle(self):
+ """
+ Test that no nearby point has a smaller glint angle than the
+ specular point.
+ """
+ point = compute_specular_point(self.track)
+ k = int(np.flatnonzero(np.isfinite(point.latitude.degrees))[3])
+ lat, lon = point.latitude.degrees[k], point.longitude.degrees[k]
+ offsets = np.linspace(-1, 1, 21)
+ grid_lat, grid_lon = np.meshgrid(lat + offsets, lon + offsets)
+ track = self.orbit.get_orbit_track([self.times[k]] * grid_lat.size)
+ angles = compute_glint_angle(
+ track, wgs84.latlon(grid_lat.ravel(), grid_lon.ravel())
+ )
+ # the smallest glint angle of the grid is at its center (the specular point)
+ self.assertEqual(np.argmin(angles), grid_lat.size // 2)
+
+ def test_equal_elevations(self):
+ """
+ Test that the Sun and the satellite are at the same elevation angle
+ seen from the specular point, in opposite azimuths.
+ """
+ point = compute_specular_point(self.track)
+ found = np.flatnonzero(np.isfinite(point.latitude.degrees))
+ track = self.track[found]
+ target = wgs84.latlon(
+ point.latitude.degrees[found], point.longitude.degrees[found]
+ )
+ sat_alt, sat_az, _ = (track - target.at(track.t)).altaz()
+ sun_alt, sun_az, _ = (
+ (de421["earth"] + target)
+ .at(track.t)
+ .observe(de421["sun"])
+ .apparent()
+ .altaz()
+ )
+ np.testing.assert_allclose(sat_alt.degrees, sun_alt.degrees, atol=0.01)
+ # azimuths (ill-conditioned near the zenith) below 80 degrees elevation
+ low = sun_alt.degrees < 80
+ azimuth_difference = (sat_az.degrees[low] - sun_az.degrees[low]) % 360
+ np.testing.assert_allclose(azimuth_difference, 180, atol=0.05)
+
+ def test_scalar_inputs(self):
+ """
+ Test that a single time and target give scalar results.
+ """
+ track = self.track[0]
+ point = compute_specular_point(track)
+ self.assertEqual(np.ndim(point.latitude.degrees), 0)
+ angle = compute_glint_angle(track, wgs84.subpoint_of(track))
+ self.assertEqual(np.ndim(angle), 0)
+ self.assertTrue(0 <= angle <= 180)
+
+
+if __name__ == "__main__":
+ unittest.main()