diff --git a/.gitignore b/.gitignore index 86d3a12..d290cdb 100644 --- a/.gitignore +++ b/.gitignore @@ -160,6 +160,9 @@ cython_debug/ #.idea/ gsolve.code-workspace docs/generated/* +docs/_build/* +docs/source/api/api/* +docs/zz/* .vscode/settings.json .vscode/launch.json /*.ipynb diff --git a/docs/source/Tutorial_terrain_correction.md b/docs/source/Tutorial_terrain_correction.md index ed69286..5934598 100644 --- a/docs/source/Tutorial_terrain_correction.md +++ b/docs/source/Tutorial_terrain_correction.md @@ -55,16 +55,24 @@ inner_zone_params = TerrainCorrectionParameters( water_density=1030.0, # in kg/m3 distance_mask_type="radial", dem_source=grid_dir / dem_file1, - clamp_elevation=True, + compute_topography=True, + compute_bathymetry=False, + site_height_field="dem_elevation", name="8m_dem", ) + outer_zone_params = TerrainCorrectionParameters( min_dist=2160.0, # in meters max_dist=21900.0, # in meters + terrain_density=2670.0, # in kg/m3 + water_density=1030.0, # in kg/m3 + distance_mask_type="radial", dem_source=grid_dir / dem_file2, + compute_topography=True, + compute_bathymetry=False, + site_height_field="dem_elevation", name="200m_dem", - clamp_elevation=False, ) ``` diff --git a/docs/source/api/core_utils.rst b/docs/source/api/core_utils.rst index 45aeb20..1532e44 100644 --- a/docs/source/api/core_utils.rst +++ b/docs/source/api/core_utils.rst @@ -18,6 +18,15 @@ Functions ========= .. currentmodule:: gsolve.core.utils +Type Checking +------------- +.. autosummary:: + :toctree: api/ + is_filepath_like + is_datetime_array + is_in_literal + + Data Conversion --------------- .. autosummary:: @@ -52,7 +61,7 @@ Survey Loop Handling loops_from_gaps Other Utilities -------------------- +--------------- .. autosummary:: :toctree: api/ diff --git a/docs/source/terrain_corrections.md b/docs/source/terrain_corrections.md index 8821c43..f8b8966 100644 --- a/docs/source/terrain_corrections.md +++ b/docs/source/terrain_corrections.md @@ -16,6 +16,9 @@ Terrain correction calculations in gSolve works by estimating the gravity effect The user imports the appropriate topography (or bathymetry) grid and observation location data. Terrain and water densities, and minimum and maximum calculation distances are also required to be supplied. +```{tip} gSolve can handle most grid formats readable by GDAL. Tiff is well tested and preferred. +``` + There is a choice of ```radial``` distance or ```rectangular``` distance. The algorithm trims the DEM to a box around the stations plus the ```max_dist```. This reduces the number of prisms needed to be created and reduces memory. @@ -40,11 +43,13 @@ To avoid duplicate calculations the following tips are useful. In ```TerrainCorrectionParameters``` there is an option ```compute_bathymetry``` or ```compute_topography```. For grids that contain both topography and bathymetry this can be used to set which part is computed. The parameter ```sea_level_elevation``` can be used to control the boundary between topography and bathymetry (default = 0). -The ```Sites``` object has a method ```sample_elevation()``` which can be used to extract the value of the DEM at the station location. This can be useful if the DEM values are preferred to the surveyed positions or if you want to convert station ellipsoidal heights to orthometric (assuming the DEM is in orthometric heights). A new column (```output_col=```) is created in the sites object for the interpolated DEM height which can be used in the computations through setting the ```site_height_field``` in the ```corrector.compute()```. +The ```Sites``` object has a method ```sample_elevation()``` which can be used to extract the value of the DEM at the station location. This can be useful if the DEM values are preferred to the surveyed positions or if you want to convert station ellipsoidal heights to orthometric (assuming the DEM is in orthometric heights). A new column (```output_col=```) is created in the sites object for the interpolated DEM height and is used by setting the ``site_height_field`` in the ```TerraincCorrectionParameters```. -gSolve will read most grid formats readable by GDAL. +```{tip} If you are manipulating grids outside of gsolve they must be returned to gsolve as xarray.DataArray objects not numpy objects. +``` -Densities are supplied in kg/m3. +```{tip} Densities are supplied in kg/m3. +``` ## Terrain correction calculations over land @@ -89,5 +94,3 @@ For calculation over the oceans the grids used at GNS are: * 2000m grid for terrain correction calculations between 21900 and 167000 m (derived from the resampled NIWA 250m dem) - -Note the source of the dems should be acknowledged in reports and papers. diff --git a/examples/scripts/terrain_correction_example.py b/examples/scripts/terrain_correction_example.py index ffd6f20..2fef443 100644 --- a/examples/scripts/terrain_correction_example.py +++ b/examples/scripts/terrain_correction_example.py @@ -16,7 +16,7 @@ # -*- coding: utf-8 -*- """ -Created on Fri Mar 3 17:54:24 2023 +Created on Fri Mar 3 17:54:24 2023. @author: craigm """ @@ -64,7 +64,9 @@ water_density=1030.0, # in kg/m3 distance_mask_type="radial", dem_source=grid_dir / dem_file1, + compute_topography=True, compute_bathymetry=False, + site_height_field="dem_elevation", name="8m_dem", ) @@ -75,16 +77,16 @@ water_density=1030.0, # in kg/m3 distance_mask_type="radial", dem_source=grid_dir / dem_file2, + compute_topography=True, compute_bathymetry=False, + site_height_field="dem_elevation", name="200m_dem", ) # Set up the Terrain corrector corrector = TerrainCorrector(params=[inner_zone_params, outer_zone_params]) # Calculate the corrections -results = corrector.compute( - points=sites, show_progress=True, site_height_field="dem_elevation" -) +results = corrector.compute(points=sites, show_progress=True) # Save results to file results.to_excel( diff --git a/examples/surveys/terrain_corrections/terrain_correction_results.xlsx b/examples/surveys/terrain_corrections/terrain_correction_results.xlsx index 3e4afb8..1138319 100644 Binary files a/examples/surveys/terrain_corrections/terrain_correction_results.xlsx and b/examples/surveys/terrain_corrections/terrain_correction_results.xlsx differ diff --git a/pyproject.toml b/pyproject.toml index d5ec770..5b7334b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,8 @@ classifiers = [ dependencies = [ "boule==0.6.*", "harmonica<0.8", - "pandas[excel,plot]<3", + "openpyxl>=3.1.5", + "pandas[plot]<3", "pydata-sphinx-theme>=0.16.1", "pygtide>=0.8.2", "pyhardisp>=0.2.1", @@ -41,7 +42,6 @@ dev = ["pytest>=9.0.3", "pytest-cov>=7.1.0", "pytest-datadir>=1.8.0", "ruff"] [project.urls] Repository = "https://github.com/GNS-Science/gsolve" -# Homepage = "https://github.com/GNS-Science/gsolve" Documentation = "https://gns-science.github.io/gsolve/" Issues = "https://github.com/GNS-Science/gsolve/issues" Changelog = "https://github.com/GNS-Science/gsolve/blob/main/CHANGELOG.md" @@ -116,14 +116,15 @@ ignore = ["ANN101", "ANN102", "ANN003", "D105", "DOC502", "DOC501"] preview = true [tool.ruff.lint.per-file-ignores] -"tests/**" = ["ANN001", "ANN201", "ANN202"] +"tests/**" = ["ANN001", "ANN201", "ANN202", "D"] +"examples/scripts/**" = ["D100"] [tool.ruff.lint.pydocstyle] convention = "numpy" [tool.burocrata] notice = ''' -# GSolve - gravity processing software. +# gSolve - gravity processing software. # Copyright (c) 2026 Earth Sciences New Zealand. # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by diff --git a/src/gsolve/core/_typing.py b/src/gsolve/core/_typing.py index 7ec3949..d521dba 100644 --- a/src/gsolve/core/_typing.py +++ b/src/gsolve/core/_typing.py @@ -30,7 +30,6 @@ from numpy.typing import ArrayLike, NDArray from pandas import DataFrame, DatetimeIndex, Index, Series, Timestamp from pandas.api.typing import NaTType -from pylab import ndarray # from pandas.api.typing.aliases import TimedeltaConvertibleTypes @@ -68,12 +67,12 @@ NDArray, NDArray, NDArray, NDArray, NDArray, float | np.float64 | None, NDArray ] -FilePath: TypeAlias = str | PathLike +type FilePath = str | PathLike # The following type aliases are copied/adapted from pandas to ensure # function parameters are compatible with pandas methods they are passed to -Renamer: TypeAlias = Union[Mapping[Any, Hashable], Callable[[Any], Hashable]] +Renamer: TypeAlias = Mapping[Any, Hashable] | Callable[[Any], Hashable] DateTimeConvertibleTypes: TypeAlias = Union[ @@ -92,7 +91,7 @@ int | float | str | datetime.date | np.datetime64 | pd.Timestamp ) -DatetimeArray: TypeAlias = list | tuple | ndarray | Series | Index | DatetimeIndex +type DatetimeArray = list | tuple | Series | Index | DatetimeIndex | np.ndarray DatetimeScalarOrArray: TypeAlias = DatetimeScalar | DatetimeArray TimedeltaScalar: TypeAlias = str | int | float | pd.Timedelta | datetime.timedelta @@ -107,7 +106,8 @@ ArrayOrCoords: TypeAlias = DatasetOrArray | Sequence[ArrayLike] Points2D: TypeAlias = tuple[FloatArray, FloatArray] Points3D: TypeAlias = tuple[FloatArray, FloatArray, FloatArray] -TCorrDistanceMaskType: TypeAlias = Literal["radial", "rectangular"] +Points3DTrue = tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]] +type TCorrDistanceMaskType = Literal["radial", "rectangular"] # protocols for select Gsolve classes @@ -115,6 +115,6 @@ class SitesLike(Protocol): data: pd.DataFrame - def get_points( - self, xcol: str, ycol: str, zcol: str - ) -> tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]]: ... + def get_points(self, xcol: str, ycol: str, zcol: str = "") -> Points3DTrue: ... + + def get_site_ids(self) -> NDArray[np.str_]: ... diff --git a/src/gsolve/core/excel_io.py b/src/gsolve/core/excel_io.py index 2319cb4..7b46695 100644 --- a/src/gsolve/core/excel_io.py +++ b/src/gsolve/core/excel_io.py @@ -226,6 +226,7 @@ def write_excel_worksheet( writer_kwargs: dict[str, Any] = { # "engine": "openpyxl", # "xlsxwriter", "openpyxl", "xlwt" + "engine": "openpyxl", "if_sheet_exists": if_sheet_exists, "mode": "w", } diff --git a/src/gsolve/core/utils.py b/src/gsolve/core/utils.py index 16fbfa0..6d57d41 100644 --- a/src/gsolve/core/utils.py +++ b/src/gsolve/core/utils.py @@ -22,7 +22,8 @@ import sys from collections.abc import Sequence -from typing import Any, Literal, overload +from typing import Any, Literal, overload, get_args, get_origin, Type, TypeAliasType +from os import PathLike import numpy as np import pandas as pd @@ -37,11 +38,16 @@ DatetimeScalar, StringArray, TimedeltaScalar, + FilePath, + Points3D, ) __all__ = [ "is_list_like", "is_dict_like", + "is_filepath_like", + "is_in_literal", + "is_datetime_array", "to_naive_utc_datetime", "check_duplicate_index", "normalize_field_names", @@ -61,6 +67,104 @@ ] +def is_filepath_like(obj: Any) -> bool: # noqa: ANN401 + """Test if object type is compatible with ``gsolve.core._typing.FilePath``. + + Returns + ------- + bool + """ + return isinstance(obj, FilePath.__value__) + + +def is_in_literal(value: Any, literal_type: TypeAliasType) -> bool: # noqa: ANN401 + """Test if value is in a Literal type. + + Parameters + ---------- + value : Any + The value to test. + literal_type : TypeAliasType + The type alias or literal type to test, + + Returns + ------- + bool + + Raises + ------ + TypeError + If ``literal_type`` is not a TypeAliasType that resolves to a Literal type. + """ + if not isinstance(literal_type, TypeAliasType): + raise TypeError(f"{literal_type} is not a TypeAliasType") + + if get_origin(literal_type.__value__) is Literal: + return value in get_args(literal_type.__value__) + else: + raise TypeError(f"{literal_type} is not a Literal type") + + +def is_datetime_array(v: Any) -> bool: # noqa: ANN401 + """Test if the input is a datetime-like array. + + Returns + ------- + bool + """ + return isinstance(v, DatetimeArray.__value__) + + +def is_points3d_like(v: Any) -> bool: # noqa: ANN401 + """Test if value is compatible with Points3D type. + + Note that is not possible to test the data type of contained arrays. + + Parameters + ---------- + v : + The object to test. + + Returns + ------- + bool + """ + if not is_list_like(v): + return False + if len(v) != 3: + return False + if not all([is_list_like(c) for c in v]): + return False + + return True + + +def to_points3D(v: Points3D) -> tuple[NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]]: # noqa: ANN401 + """Convert Points3d-like object to a bona-fide Points3D object. + + Parameters + ---------- + v : Points3D + Object to be transformed. + + Returns + ------- + (NDArray[np.float64], NDArray[np.float64], NDArray[np.float64]) + Tuple of 3 ndarray's with dtype=float64 + + """ + if not is_points3d_like(v): + raise TypeError( + "object is not Points3D-like so cannot be converted to a true Points3D" + ) + x, y, z = v + x = to_1d_ndarray(v[0]).astype(np.float64) + y = to_1d_ndarray(v[1], expected_size=x.size).astype(np.float64) + z = to_1d_ndarray(v[2], expected_size=x.size).astype(np.float64) + + return (x, y, z) + + @overload def to_naive_utc_datetime( t: DatetimeScalar, allow_nat: Literal[True], **kwargs @@ -126,23 +230,39 @@ def to_naive_utc_datetime( pandas.to_datetime The underlying function used to convert the input to a timestamp. """ + + def _nat_check[T](v: T) -> T: + if not allow_nat: + if isinstance(v, (pd.DatetimeIndex, pd.Series)): + if any(v.isna()): + raise ValueError( + "input contains values that resolve to NaT and allow_nat=False" + ) + elif v is pd.NaT: + raise ValueError("input resolves to NaT and allow_nat=False") + return v + # scalars if isinstance(t, (NaTType, NAType)): - rval = pd.NaT + return _nat_check(pd.NaT) elif isinstance(t, pd.Timestamp): - rval = t if t.tz is None else t.tz_convert("UTC").tz_localize(None) + return _nat_check(t if t.tz is None else t.tz_convert("UTC").tz_localize(None)) elif isinstance(t, pd.DatetimeIndex): - rval = t if t.tz is None else t.tz_convert("UTC").tz_localize(None) + return _nat_check(t if t.tz is None else t.tz_convert("UTC").tz_localize(None)) elif isinstance(t, pd.Series): ds = t if t.dtype == "datetime64[ns]" else pd.to_datetime(t, **kwargs) - rval = ds if ds.dt.tz is None else ds.dt.tz_convert("UTC").dt.tz_localize(None) + return _nat_check( + ds if ds.dt.tz is None else ds.dt.tz_convert("UTC").dt.tz_localize(None) + ) # arrays - elif isinstance(t, DatetimeArray): + elif is_datetime_array(t) or is_list_like(t): idx = pd.DatetimeIndex(pd.to_datetime(t, **kwargs)) - rval = idx if idx.tz is None else idx.tz_convert("UTC").tz_localize(None) + return _nat_check( + idx if idx.tz is None else idx.tz_convert("UTC").tz_localize(None) + ) else: return_scalar = False @@ -158,18 +278,11 @@ def to_naive_utc_datetime( "to Timestamp or DateTimeIndex" ) - rval = idx if idx.tz is None else idx.tz_convert("UTC").tz_localize(None) + rval = _nat_check( + idx if idx.tz is None else idx.tz_convert("UTC").tz_localize(None) + ) rval = rval[0] if return_scalar else rval - if not allow_nat: - if isinstance(rval, (pd.DatetimeIndex, pd.Series)): - if any(rval.isna()): - raise ValueError( - "input contains values that resolve to NaT and allow_nat=False" - ) - elif rval is pd.NaT: - raise ValueError("input resolves to NaT and allow_nat=False") - return rval @@ -264,6 +377,14 @@ def normalize_field_names(df: pd.DataFrame | pd.Series) -> pd.DataFrame | pd.Ser ) +@overload +def normalize_str(s: str | int | float | bool) -> str: ... + + +@overload +def normalize_str(s: None) -> None: ... + + def normalize_str(s: str | int | float | bool | None) -> str | None: """Convert ``s`` to str and format it to snake_case. @@ -749,14 +870,17 @@ def __init__( self.messages: list[str] = [] @property - def count(self) -> int: # noqa: D102 + def count(self) -> int: + """Count of messages added - i.e. the number of issues.""" return len(self.messages) - def __call__(self, msg: str) -> None: # noqa: D102 + def __call__(self, msg: str) -> None: + """Append a message and print if obj.show is True.""" self.messages.append(msg) self._display(msg) def _display(self, msg: str) -> None: + """Display a message if obj.show is True.""" if self.show: print(f"{self.prefix}: {msg}", file=sys.stderr) @@ -765,7 +889,8 @@ def print_msgs(self) -> None: for msg in self.messages: print(f"{self.prefix}: {msg}") - def final_msg(self) -> None: # noqa: D102 + def final_msg(self) -> None: + """Print closing summary message.""" if self.count > 0: self._display(f"{self.count} problem(s) encountered") diff --git a/src/gsolve/core/xr_accessor.py b/src/gsolve/core/xr_accessor.py index 675b095..83608e9 100644 --- a/src/gsolve/core/xr_accessor.py +++ b/src/gsolve/core/xr_accessor.py @@ -515,3 +515,19 @@ def cell_edges(self) -> tuple[np.ndarray, np.ndarray]: else: y_edges = np.linspace(y[0] - dy / 2, y[-1] + dy / 2, len(y) + 1) return x_edges, y_edges + + def is_valid_dem(self) -> bool: + """Test if DataArray is suitable to be used as a DEM. + + A DEM must be a 2-dimensional array of floating point values. + + Returns + ------- + bool + """ + return ( + self._obj.ndim == 2 + and self._obj.shape[0] > 0 + and self.obj.shape[1] > 0 + and np.issubdtype(self._obj.dtype, np.floating) + ) diff --git a/src/gsolve/reductions/terrain_corrections.py b/src/gsolve/reductions/terrain_corrections.py index c163c51..6441e80 100644 --- a/src/gsolve/reductions/terrain_corrections.py +++ b/src/gsolve/reductions/terrain_corrections.py @@ -15,13 +15,14 @@ # SPDX-License-Identifier: GPLv3 # Copyright (c) 2025 Earth Sciences New Zealand. +"""Functions and classes for computing gravity terrain corrections.""" +from gsolve.core.utils import is_filepath_like +from pandas.core.series import Series import dataclasses -import logging import warnings from collections.abc import Iterable, Sequence -from pathlib import Path -from typing import Any, Literal, Self +from typing import Any, Literal, Self, get_args import harmonica as hm import numpy as np @@ -31,20 +32,27 @@ from tqdm import tqdm as _tqdm from gsolve.core._typing import ( - DatasetOrArray, FilePath, FloatArray, IfSheetExists, IfWorkbookExists, - Points2D, Points3D, SitesLike, TCorrDistanceMaskType, ) from gsolve.core.data import DataFieldSpecification, GSolveParameters, GSolveTable from gsolve.core.excel_io import read_excel_worksheet, write_excel_worksheet -from gsolve.core.utils import is_list_like, prepare_writable_df, to_1d_ndarray -from gsolve.core.xr_accessor import TCorrMethods as _TCorrMethods +from gsolve.core.utils import ( + is_list_like, + prepare_writable_df, + to_1d_ndarray, + is_filepath_like, + is_in_literal, + is_points3d_like, + to_points3D, +) + +# from gsolve.core.xr_accessor import TCorrMethods as _TCorrMethods from gsolve.core.xr_methods import * __all__ = [ @@ -55,6 +63,11 @@ ] +def _is_dataarray(obj: Any) -> bool: # noqa: ANN401 + """Check if an object is an xarray DataArray.""" # noqa: DOC201 + return isinstance(obj, xr.DataArray) + + def calculate_terrain_correction( points: Points3D, dem: xr.DataArray, @@ -116,7 +129,7 @@ def calculate_terrain_correction( # ensure supplied density is a DataArray (if a Dataset was provided) use_supplied_density = density_dataset is not None if use_supplied_density: - if not isinstance(density_dataset, xr.DataArray): + if not _is_dataarray(density_dataset): raise TypeError( "density_dataset must be an xarray.DataArray not " f"'{type(density_dataset).__name__}'" @@ -399,7 +412,8 @@ def tcorr_harmonica_bathymetry( ) -@dataclasses.dataclass +# @dataclasses.dataclass(frozen=True) +@dataclasses.dataclass() class TerrainCorrectionParameters(GSolveParameters): """Class to store parameters for computing terrain corrections for a single "zone". @@ -449,9 +463,18 @@ class TerrainCorrectionParameters(GSolveParameters): ``sea_level_elevation``. Note that ``density_dataset_source`` inputs are converted to and stored as a string. compute_topography : bool, default is True - Compute gravity corrections due to topographic masses. + Compute gravity corrections due to topographic masses above ``sea_level_elevation``. compute_bathymetry : bool, default is True - Compute gravity corrections due to water bodies such as the ocean. + Compute gravity corrections due to water bodies below ``sea_level_elevation``. + site_height_field : str, default is "height_ellipsoidal" + Column in a ``GravitySites.data`` object containing site elevations/z coordinates. + site_easting_field : str, default is "easting" + Column in a ``GravitySites.data`` object containing site easting/x coordinates. + site_northing_field : str, default is "northing" + Column in a ``GravitySites.data`` object containing site northing/y coordinates. + method : {"harmonica"}, default is "harmonica" + Method to use for computing terrain corrections. Currently only "harmonica" is + supported. """ name: str @@ -461,36 +484,55 @@ class TerrainCorrectionParameters(GSolveParameters): water_density: float = 1030.0 sea_level_elevation: float = 0.0 distance_mask_type: TCorrDistanceMaskType = "radial" - dem_source: FilePath = "" - density_dataset_source: FilePath = "" + dem_source: FilePath | xr.DataArray = "" + density_dataset_source: FilePath | xr.DataArray = "" compute_topography: bool = True compute_bathymetry: bool = True - - def __setattr__(self, field_name: str, value: str | float | bool) -> None: # noqa: ANN401 - if field_name not in self.__dataclass_fields__: - return # silently ignore unknown attributes - - if field_name in ("dem_source", "density_dataset_source"): - try: - value = str(value) - except Exception: - raise ValueError( - f"could not convert {field_name} value to string: {value}" - ) - - elif field_name == "name": - value = str(value) - if not value: - raise ValueError("'name' attribute must be a non-zero length string") - - elif self.__dataclass_fields__[field_name].type in (float, str, bool): - value = self.__dataclass_fields__[field_name].type(value) - - super().__setattr__(field_name, value) + site_height_field: str = "height_ellipsoidal" + site_easting_field: str = "easting" + site_northing_field: str = "northing" + method: Literal["harmonica"] = "harmonica" def __post_init__(self) -> None: + self._normalize_fields() self._sanity_check() + def _normalize_fields(self) -> None: + if self.name is None: + raise ValueError("'name' attribute must be a non-zero length string") + + name = str(self.name) + if not name: + raise ValueError("'name' attribute must be a non-zero length string") + object.__setattr__(self, "name", name) + + object.__setattr__(self, "min_dist", float(self.min_dist)) + object.__setattr__(self, "max_dist", float(self.max_dist)) + object.__setattr__(self, "terrain_density", float(self.terrain_density)) + object.__setattr__(self, "water_density", float(self.water_density)) + object.__setattr__(self, "sea_level_elevation", float(self.sea_level_elevation)) + object.__setattr__(self, "distance_mask_type", str(self.distance_mask_type)) + object.__setattr__(self, "site_height_field", str(self.site_height_field)) + object.__setattr__(self, "site_easting_field", str(self.site_easting_field)) + object.__setattr__(self, "site_northing_field", str(self.site_northing_field)) + object.__setattr__(self, "compute_topography", bool(self.compute_topography)) + object.__setattr__(self, "compute_bathymetry", bool(self.compute_bathymetry)) + + for field_name in ("dem_source", "density_dataset_source"): + value = getattr(self, field_name) + if _is_dataarray(value): + continue + if is_filepath_like(value): + object.__setattr__(self, field_name, str(value)) + continue + if not value: + object.__setattr__(self, field_name, "") + continue + raise TypeError( + f"{field_name} attribute must be a DataArray, str, or " + f"Path-like object, not a {type(value).__name__}" + ) + def _sanity_check(self) -> None: if ( np.isnan(self.min_dist) @@ -503,12 +545,35 @@ def _sanity_check(self) -> None: "0.0 <= min_dist < max_dist: " f"got min_dist={self.min_dist}, max_dist={self.max_dist}" ) + # check distance msk type is valid + if not is_in_literal(self.distance_mask_type, TCorrDistanceMaskType): + raise ValueError( + f"invalid 'distance_mask_type': {self.distance_mask_type}. " + f"Expected one of: {get_args(TCorrDistanceMaskType.__value__)}" + ) + + if _is_dataarray(self.dem_source): + if not self.dem_source.tcorr.is_valid_dem: + raise ValueError( + "invalid dem_source DataArray: must be a 2D array of floats" + ) + elif not self.dem_source: + raise TypeError( + "invalid dem_source: must be an xarray.DataArray or file path" + ) + if _is_dataarray(self.density_dataset_source): + if not self.density_dataset_source.tcorr.is_valid_dem(): + raise ValueError( + "density_dataset_source is an xr.DataArray object but is not a valid DEM. " + "Check that it has the correct dimensions and coordinates." + ) def to_series( self, series_name: str | None = "value", index_name: str | None = "parameter", index_prefix: str | Sequence[str] | None = None, + include_data_array: bool = True, ) -> pd.Series: """Return parameters as a pandas Series. @@ -525,12 +590,21 @@ def to_series( first level is ``index_prefix``. E.g. if ``index_prefix="zone1"``, then the Series index will be of the form: ("zone1", parameter_name,...). This is useful to avoid index collisions when combining parameter Series. + include_data_array: bool, default True + Control how fields 'dem_source` and `density_dataset_source` are treated if + they contain an ``xarray.DataArray`` object. If True, include the full + array. If False, replace the array with a string defining output. Returns ------- pd.Series A pandas Series containing the parameters. """ + pars_dict = self.to_dict() + if not include_data_array: + for f in ("density_dataset_source", "dem_source"): + if _is_dataarray(pars_dict[f]): + pars_dict[f] = "xarray.DataArray" ds = pd.Series(data=self.to_dict(), name=series_name).rename_axis(index_name) if index_prefix is not None: @@ -629,49 +703,6 @@ class TerrainCorrector: It supports multiple calculation "zones", each with its own parameters and data sources. - Attributes - ---------- - params : dict - A dictionary of TerrainCorrectionParameters objects defining the "zones" to be computed. - dems : dict[str, DataArray | None] - A dictionary storing user supplied DEMs by zone name. Values will be None if no - DEM was provided for that zone, in which case the ``dem_source`` attribute of the - associated TerrainCorrectionParameters object will be used to obtain the DEM. Note that - DEMs specified by ``dem_source`` are never stored here, but are loaded on-the-fly - during computation. - _density_models : dict[str, xr.DataArray | None] - A dictionary storing user supplied density models by zone name. Values will be - None if no density model was provided for that zone. Note that internally generated - density models are never stored here, but are created on-the-fly during computation. - - Parameters - ---------- - params: TerrainCorrectionParameters or list-like of TerrainCorrectionParameters - The TerrainCorrectionParameters object(s) defining the "zones" to be computed. - dem : xarray.DataArray, list-like or None, default is None - User supplied DEM(s) corresponding to each zone defined in ``params``. - For example if ``params=[p1, p2, p3]``, and you wish to specify a DEM for - p2 only, then the argument must be ``dem=[None, dem_for_p2, None]``. If None, - the dem will be loaded from the ``dem_source`` attribute of the corresponding - TerrainCorrectionParameters object. If a dem is specified here, then the - ``dem_source`` attribute is ignored. - density_model : xarray.DataArray, list-like or None, default is None - User supplied density model(s) corresponding to each zone defined in ``params``. - If `density_model` is specified then it must include a density model or None for - each zone in ``params``. For example if ``params=[p1, p2, p3]``, and you wish - to specify a density model for p2 only, then the argument must be - ``density_model=[None, model_for_p2, None]``. - - If ``density_model`` is None, then: - - if ``density_dataset_source`` attribute of the associated - TerrainCorrectionParameters object is not None, the density model will be - loaded from the specified source. - - otherwise generate a default density model from the loaded DEM, using the - ``terrain_density``, ``water_density``, and ``sea_level_elevation`` - attributes of the associated TerrainCorrectionParameters object. - - Notes - ----- A typical workflow using this class would be: - Define one or more TerrainCorrectionParameters objects for the desired zones. @@ -679,17 +710,22 @@ class TerrainCorrector: - Add additional zones as needed using ``add_calculation_zone``. - Call ``compute()`` on a set of points. + Parameters + ---------- + params: TerrainCorrectionParameters or list-like of TerrainCorrectionParameters + The TerrainCorrectionParameters object(s) defining the "zones" to be computed. + + Attributes + ---------- + params : dict + A dictionary of TerrainCorrectionParmeter objects defining the "zones" to be computed. """ def __init__( self, params: TerrainCorrectionParameters | Sequence[TerrainCorrectionParameters], - dem: xr.DataArray | Sequence[None | xr.DataArray] | None = None, - density_model: xr.DataArray | Sequence[None | xr.DataArray] | None = None, ) -> None: self.params: dict[str, TerrainCorrectionParameters] = {} - self.dems: dict[str, xr.DataArray | None] = {} - self._density_models: dict[str, xr.DataArray | None] = {} if isinstance(params, TerrainCorrectionParameters): params = [params] @@ -705,65 +741,16 @@ def __init__( f" of TerrainCorrectionParameters objects, not '{type(params)}'" ) - if dem is None: - dem = [None] * len(params) - elif isinstance(dem, xr.DataArray): - if len(params) > 1: - raise ValueError( - "if params is a list-like of TerrainCorrectionParameters objects, " - "then dem must be a list-like of the same length" - ) - dem = [dem] - elif isinstance(dem, Iterable): - if len(dem) != len(params): - raise ValueError( - "if dem is a list-like, it must be the same length as params" - ) - if not all((d is None or isinstance(d, xr.DataArray)) for d in dem): - raise TypeError( - "if dem is a list-like, all items must be xarray.DataArray or None" - ) - dem = list(dem) - else: - raise TypeError( - f"dem arg must be a DataArray, None or a list-like, not '{type(dem)}'" - ) - - if density_model is None: - density_model = [None] * len(params) - elif isinstance(density_model, (xr.DataArray, xr.Dataset)): - density_model = [density_model] - elif isinstance(density_model, Iterable): - density_model = list(density_model) - else: - raise TypeError( - "density_model arg must be a DataArray, None or a list-like, " - f"not '{type(density_model)}'" - ) - - # check everything is consistent - if not (len(params) == len(dem) == len(density_model)): - raise ValueError("inconsistent params and dem and density_model args") + for p in params: + self.add_zone(params=p) - for p, t, d in zip(params, dem, density_model): - self.add_zone(params=p, dem=t, density_model=d) - - def add_zone( - self, - params: TerrainCorrectionParameters, - dem: xr.DataArray | None = None, - density_model: xr.DataArray | None = None, - ) -> None: - """Add a terrain correction calculation zone and associated dem and/or density model. + def add_zone(self, params: TerrainCorrectionParameters) -> None: + """Add a terrain correction calculation zone to the TerrainCorrector. Parameters ---------- params : TerrainCorrectionParameters The parameters defining the terrain correction zone. - dem : xarray.DataArray, optional - The digital elevation model corresponding to the terrain correction zone. - density_model : xarray.DataArray, optional - The density model corresponding to the terrain correction zone. """ if not isinstance(params, TerrainCorrectionParameters): raise TypeError( @@ -771,36 +758,40 @@ def add_zone( f"not {type(params)}" ) - key = str(params.name) - self.params[key] = params - - if dem is None or isinstance(dem, xr.DataArray): - self.dems[key] = dem - else: - raise TypeError(f"dem must be a xarray.DataArray or None, not {type(dem)}") - - if density_model is None or isinstance(density_model, xr.DataArray): - self._density_models[key] = density_model - else: - raise TypeError( - "density_model must be a xarray.DataArray or None, " - f"not {type(density_model)}" - ) + self.params[params.name] = params @property def zones(self) -> list[str]: - """Return list of defined terrain correction zones sorted by min_dist.""" + """List of zone names sorted by min_dist.""" zones = [(k, v.min_dist) for k, v in self.params.items()] return [str(z[0]) for z in sorted(zones, key=lambda x: x[1])] + def _has_constant_xy_coord_source(self) -> bool: + """Return True if all params specify the same easting and northing source fields. + + Returns + ------- + bool + """ + return ( + len({p.site_easting_field for p in self.params.values()}) == 1 + and len({p.site_northing_field for p in self.params.values()}) == 1 + ) + + def _has_constant_z_coord_source(self) -> bool: + """Return True if all params specify the same height source fields. + + Returns + ------- + bool + """ + return len({p.site_height_field for p in self.params.values()}) == 1 + def compute( self, points: SitesLike | Points3D, site_id: npt.ArrayLike | None = None, show_progress: bool = True, - method: str = "harmonica", - site_height_field: str = "height_ellipsoidal", - site_xy_fields: tuple[str, str] = ("easting", "northing"), ) -> "TerrainCorrectionData": """Compute terrain corrections for a set of points. @@ -809,24 +800,17 @@ def compute( points : GravitySites or sequence of array_likes (x, y, z) The observation points where terrain corrections are to be computed. Must be in the same coordinate reference system as the dem. - If ``points`` is a ``GravitySites`` object, then data colums corresponding to - ``site_xy_fields`` (default: ("easting", "northing")) and - ``site_height_field`` (default: "height_ellipsoidal") must have been set. - If points is a sequence of array_likes, then it must be of the form - (x, y, z), where x, y, and z are arrays of equal length. + If ``points`` is a ``GravitySites`` object, then the site coordinate fields + must be defined on the ``TerrainCorrectionParameters`` objects used by + this ``TerrainCorrector``. If points is a sequence of array_likes, then it + must be of the form (x, y, z), where x, y, and z are arrays of equal + length. site_id : array_like, optional - An array of site IDs corresponding to each point. If None, then a simple - RangeIndex will be used. + An array of site IDs corresponding to each point if ``points`` is a Points3D + instance (see above). If None, then a RangeIndex will be generated. + Ignored if points is a `SitesLike` object. show_progress : bool, default is True. Report progress, including a progress bar if the tqdm package is installed. - method : str, default is "harmonica" - The terrain correction calculation method to use. Currently only "harmonica" - is supported. - site_height_field : str, default is "height_ellipsoidal" - When ``points`` is a ``GravitySites`` object, get site elevation from this field. - site_xy_fields : tuple of str, default is ("easting", "northing") - When ``points`` is a ``GravitySites`` object, get site x and y coordinates from - these fields. Returns ------- @@ -835,36 +819,65 @@ def compute( TerrainCorrectionParameters used. """ - if site_id is not None: - site_id = np.atleast_1d(site_id) - + sites_like_input = False if isinstance(points, SitesLike): - x, y, z = points.get_points( - site_xy_fields[0], site_xy_fields[1], site_height_field - ) - if site_id is None: - site_id = points.data.index.to_numpy().astype(str) - - elif is_list_like(points): - x, y, z = points - if site_id is None: - site_id = np.arange(len(x), dtype=int).astype(str) - + sites_like_input = True + elif is_points3d_like(points): + sites_like_input = False else: raise TypeError( "points must be a sequence of arrays of form (x, y, z) " f"or a GravitySites object, not {type(points)}" ) + # Establish if we need to get points for each zone. + # - If the points is a tuple, get 1 set of x,y,z now + # - If the points is a GravitySites object, check if the site coordinate fields + # are the same for all zones. + # - If True, we will get 1 set of x,y,z now + # - If False, we will get x,y,z for each zone in the loop + + if sites_like_input: + site_id = points.get_site_ids() + + if ( + self._has_constant_xy_coord_source() + and self._has_constant_z_coord_source() + ): + get_xyz_per_zone = False + p = self.params[self.zones[0]] + x, y, z = points.get_points( + xcol=p.site_easting_field, + ycol=p.site_northing_field, + zcol=p.site_height_field, + ) + + else: + get_xyz_per_zone = True + z = None + if self._has_constant_xy_coord_source(): + p = self.params[self.zones[0]] + x, y, _ = points.get_points( + xcol=p.site_easting_field, + ycol=p.site_northing_field, + zcol="", + ) + else: + x, y = None, None + warnings.warn( + "TerrainCorrector parameters specify variable easting " + "and/or northing fields. Easting and Northing will not be " + "written to TerrainCorrectionData output." + ) + + else: + get_xyz_per_zone = False + x, y, z = to_points3D(points) + if site_id is None: + site_id = np.arange(len(x), dtype=int).astype(str) + else: + site_id = to_1d_ndarray(site_id, expected_size=x.size).astype(str) - x = to_1d_ndarray(x).astype(np.float64) - y = to_1d_ndarray(y, expected_size=x.size).astype(np.float64) - z = to_1d_ndarray(z, expected_size=x.size).astype(np.float64) - site_id = to_1d_ndarray(site_id, expected_size=x.size).astype(str) - if len(site_id) != len(x): - raise ValueError( - f"site_id must be same length as points, not {len(site_id)} != {len(x)}" - ) # empty object to store results results = TerrainCorrectionData( @@ -872,7 +885,6 @@ def compute( easting=x, northing=y, params=None, - site_height_field=z, ) full_warning_displayed = False @@ -882,46 +894,56 @@ def compute( if show_progress: print(f"\nCalculating terrain corrections for zone: {zone}") + # get points if necessary + if get_xyz_per_zone: + try: + x, y, z = points.get_points( + xcol=pars.site_easting_field, + ycol=pars.site_northing_field, + zcol=pars.site_height_field, + ) + except Exception as e: + raise ValueError( + f"Error extracting site coordinates from GravitySites object: {e}" + ) + # get the dem for this zone - if zone in self.dems and isinstance(self.dems[zone], xr.DataArray): - # defined as an argument - dem = self.dems[zone].copy() # ty:ignore[unresolved-attribute] + # maybe do not copy here + if _is_dataarray(pars.dem_source): + dem = pars.dem_source.copy() elif pars.dem_source: # defined as a source file to be loaded dem = load_dem(pars.dem_source) else: raise ValueError( - f"No dem array or data source specified for zone='{zone}'" + f"DEM not specified or zone='{zone}': TerrainCorrectionParameter " + "object must provide source file or an xarray.DataArray object." ) # get the density model if defined - if zone in self._density_models and isinstance( - self._density_models[zone], xr.DataArray - ): + if _is_dataarray(pars.density_dataset_source): # defined as an argument - dens_model = self._density_models[zone] + density_model = pars.density_dataset_source.copy() elif pars.density_dataset_source: # defined as a source file to be loaded - dens_model = load_dem(pars.density_dataset_source) + density_model = load_dem(pars.density_dataset_source) else: # not specified, will auto generate from DEM - dens_model = None + density_model = None tc = calculate_terrain_correction( dem=dem, points=(x, y, z), min_dist=pars.min_dist, max_dist=pars.max_dist, - density_dataset=dens_model, + density_dataset=density_model, terrain_density=pars.terrain_density, water_density=pars.water_density, distance_mask_type=pars.distance_mask_type, show_progress=show_progress, - # method=method, compute_topography=pars.compute_topography, compute_bathymetry=pars.compute_bathymetry, ) - pars.tcorr_method = method # print a summary if something went wrong n_missing_tc = 0 @@ -944,8 +966,12 @@ def compute( ) results.set_corrections( - params=pars, topography_corrections=tc[0], bathymetry_corrections=tc[1] + params=pars, + topography_corrections=tc[0] if pars.compute_topography else None, + bathymetry_corrections=tc[1] if pars.compute_bathymetry else None, + elevations=z, ) + return results @@ -958,6 +984,19 @@ class TerrainCorrectionData(GSolveTable): file and then reloaded and re-instantiated, supporting a workflow where terrain corrections need only be computed once. + Parameters + ---------- + site_id : array_like of str + The unique site identifiers as a sequence. All elements are converted to str. Will be + used to index the ``obj.data`` DataFrame. + params : TerrainCorrectionParameters or sequence of TerrainCorrectionParameters, optional + The parameters for the various terrain correction zones. + terrain_corrections : array_like or list of array_like + The terrain correction values for each zone. + **kwargs : dict + Additional columns to be included in ``obj.data`` DataFrame. This could include + site location information such as easting, northing, latitude, longitude etc. + Attributes ---------- params : dict @@ -972,9 +1011,9 @@ class TerrainCorrectionData(GSolveTable): for each zone, and the total terrain correction. For a given zone defined by TerrainCorrectionParameters object = ``obj``, the output columns will be: - - 'tcorr:{obj.name}:topo' : the topography only component of the terrain + - 'tcorr:{obj.name}:topo' : the topography only component of the terrain correction. Ommited if ``compute_topography`` is False. - - 'tcorr:{obj.name}:bath' : the bathymetry only component of the terrain + - 'tcorr:{obj.name}:bath' : the bathymetry only component of the terrain correction. Ommited if ``compute_bathymetry`` is False. The total terrain correction column will be labeled ``'tcorr:total'``. This is @@ -983,19 +1022,6 @@ class TerrainCorrectionData(GSolveTable): Columns are ordered by minimum distance of the corresponding zone, with the total terrain correction column last. - - Parameters - ---------- - site_id : array_like of str - The unique site identifiers as a sequence. All elements are converted to str. Will be - used to index the ``obj.data`` DataFrame. - params : TerrainCorrectionParameters or sequence of TerrainCorrectionParameters, optional - The parameters for the various terrain correction zones. - terrain_corrections : array_like or list of array_like - The terrain correction values for each zone. - **kwargs : dict - Additional columns to be included in ``obj.data`` DataFrame. This could include - site location information such as easting, northing, latitude, longitude etc. """ _known_fields = { @@ -1029,7 +1055,7 @@ def __init__( | None = None, **kwargs, ) -> None: - self.params = {} + self.params: dict[str, TerrainCorrectionParameters] = {} if params is not None and terrain_corrections is None: raise ValueError( @@ -1138,6 +1164,7 @@ def set_corrections( params: TerrainCorrectionParameters, topography_corrections: npt.ArrayLike | None = None, bathymetry_corrections: npt.ArrayLike | None = None, + elevations: npt.ArrayLike | None = None, ) -> None: """Add a set of terrain correction parameters and values. @@ -1148,10 +1175,12 @@ def set_corrections( ---------- params : TerrainCorrectionParameters The parameters for the terrain correction calculations. - topography_corrections : array-like + topography_corrections : array-like, optional The terrain correction values. - bathymetry_corrections : array-like + bathymetry_corrections : array-like, optional Corrections for bathymetry + elevations : array-like, optional + The elevations used to calculate these terrain corrections. """ if not isinstance(params, TerrainCorrectionParameters): raise TypeError( @@ -1164,54 +1193,99 @@ def set_corrections( "bathymetry_corrections" ) - tcor_prefix = "tcorr:" - tcor_base_name = f"{tcor_prefix}{params.name}" - self.params[tcor_base_name] = params + tcor_prefix = "tcorr" + + self.params[f"{tcor_prefix}:{params.name}"] = params.copy() corrs_dict = { - "topography_corrections": ( - topography_corrections if params.compute_topography else None - ), - "bathymetry_corrections": ( - bathymetry_corrections if params.compute_bathymetry else None - ), + "topography_corrections": [ + topography_corrections, + f"{tcor_prefix}:{params.name}:topo", + ], + "bathymetry_corrections": [ + bathymetry_corrections, + f"{tcor_prefix}:{params.name}:bath", + ], + "elevations": [elevations, f"elev:{params.name}"], } - for corr_type, corrs in corrs_dict.items(): + for corr_type, (corrs, col_name) in corrs_dict.items(): if corrs is None: continue - corrs = np.atleast_1d(corrs).astype(float) - if len(corrs.shape) != 1: - raise ValueError( - f"{corr_type} must be a 1D array, not {len(corrs.shape)}D" + try: + c = ( + to_1d_ndarray(corrs, expected_size=len(self.data)) + .astype(float) + .round(decimals=6) ) - - if len(corrs) != len(self.data): + except ValueError as e: raise ValueError( - f"{corr_type} must have same length as site_id " - f"({self.data.shape[0]}), not {len(corrs)}" + f"{corr_type} must be a 1d array of floats of the len as site_id: {e} " ) - topo_col_name = f"{tcor_base_name}:{corr_type[:4]}" - self.set_column(topo_col_name, corrs, dtype=float) + self.set_column(label=col_name, data=c, dtype=float) # now set the total column - tcorr_total_col_name = f"{tcor_prefix}total" + tcorr_total_col_name = f"{tcor_prefix}:total" if tcorr_total_col_name in self.data.columns: self.data = self.data.drop(columns=[tcorr_total_col_name]) + existing_tcorr_cols = [ c for c in self.data.columns if (c.startswith(tcor_prefix) and c != tcorr_total_col_name) ] + if len(existing_tcorr_cols) == 0: self.set_column(tcorr_total_col_name, 0.0, dtype=float) else: - df = self.data[existing_tcorr_cols].round(decimals=6) + temp_df = self.data[existing_tcorr_cols] self.data = self.data.drop(columns=existing_tcorr_cols) - df[tcorr_total_col_name] = df.sum(axis=1, skipna=False) + temp_df[tcorr_total_col_name] = temp_df.sum(axis=1, skipna=False) + + self.data = pd.concat([self.data, temp_df], axis=1) + + def set_height_ellipsoidal( + self, + elevations: SitesLike | FloatArray, + input_height_field: str = "height_ellipsoidal", + output_height_field: str = "height_ellipsoidal", + ) -> None: + """Add ellipsoidal heights to terrain correction outputs. - self.data = pd.concat([self.data, df], axis=1) + Terrain correction calculations typically use elevations sampled + from the DEM being used. This method allows site elevations to be + included in terrain correction outputs. These can then be used to + QC results and identify errors introduced when sampling a DEM. + + Parameters + ---------- + elevations : SitesLike or array-like object + The elevation/height data to add to ``obj.data``. + input_height_field : str, default is 'height_ellipsoidal' + Column containing height data if ``elevations`` is a SitesLike object. + output_height_field : str, default is 'height_ellipsoidal' + Write heights to column ``output_height_field`` in the objects data + attribute. + + Raises + ------ + KeyError + If ``elevations`` is a SitesLike and ``elevations.data`` has no + column named ``input_height_field``. + ValueError + If size of ``elevations`` is incompatible with ``obj.data`` + """ + insertion_point = self.data.columns.get_loc["easting"] + 1 + self.data.insert(insertion_point, output_height_field, np.nan) + + if isinstance(elevations, SitesLike): + if not hasattr(elevations, "data"): + raise TypeError( + "elevations arg is a SitesLike object but does not have a " + ) + # z = elevations.data[input_height_field].astype(float).to_numpy() + # df = elevations.data[] def __repr__(self) -> str: zones = ", ".join( @@ -1324,19 +1398,38 @@ def from_dataframe( return obj def _params_to_dataframe(self, paths_to_str: bool = True) -> pd.DataFrame: - """Convert the parameters to a DataFrame.""" # noqa: DOC201 - params_df = self._params_to_series().to_frame() - return params_df.reset_index() + """Convert the parameters to a DataFrame. + + Parameters + ---------- + paths_to_str : bool, default True + + Returns + ------- + pd.DataFrame + A pandas DataFrame containing the parameters for all zones, indexed by + zone and parameter name. + """ + return self._params_to_series().to_frame().reset_index() def _params_to_series(self) -> pd.Series: - """Convert the parameters to a Series.""" # noqa: DOC201 - pars = [] - for k, v in self.params.items(): - # TerrainCorrectionParameters.to_series() - s = v.to_series(series_name="parameter_value", index_prefix=[k, "zone"]) - pars.append(s) - params_sr = pd.concat(pars) - return params_sr + """Convert the parameters to a Series. + + Returns + ------- + pd.Series + A pandas Series containing the parameters for all zones, indexed by zone and parameter name. + """ + params_list: list[pd.Series] = [] + for zone, p in self.params.items(): + params_list.append( + p.to_series( + series_name="parameter_value", + index_prefix=[zone, "zone"], + include_data_array=False, + ) + ) + return pd.concat(params_list) def to_excel( self, @@ -1378,12 +1471,8 @@ def to_excel( else: sheet_name = self._default_excel_sheet_name[0] - if params_sheet_name is None: - params_sheet_name = f"{sheet_name}_params" - - df_tcorr = prepare_writable_df(self.data, normalize_column_names=True) write_excel_worksheet( - df_tcorr, + prepare_writable_df(self.data, normalize_column_names=True), fname, sheet_name=sheet_name, if_workbook_exists=if_workbook_exists, @@ -1391,13 +1480,16 @@ def to_excel( **kwargs, ) - df_params = prepare_writable_df( - self._params_to_series().to_frame().reset_index(level=1), - normalize_column_names=True, - ) + # write params to a seprarate sheet + if params_sheet_name is None: + params_sheet_name = f"{sheet_name}_params" + write_excel_worksheet( - df_params, - fname, + prepare_writable_df( + self._params_to_series().to_frame().reset_index(level=1), + normalize_column_names=True, + ), + excel_file=fname, sheet_name=params_sheet_name, if_workbook_exists="append", if_sheet_exists=if_sheet_exists, @@ -1567,29 +1659,21 @@ def get_corrections( site_id_missing = site_id_idx.difference(tcorrs.index) if site_id_missing.empty: - rval = tcorrs.loc[site_id_found, cols] - else: - if site_id_found.empty: - msg = ( - "no terrain corrrection data found for any of the " - "specified site_id's" - ) - else: - msg = ( - "no terrain corrrection data found for site_id's " - f"{site_id_missing.to_list()}" - ) - if if_missing == "raise": - raise ValueError(msg) - if if_missing == "drop": - warnings.warn(f"{msg}, dropping from ouput") - rval = tcorrs.loc[site_id_found, cols] - else: # must be 'fill' - warnings.warn(f"{msg}, filling with {fill_value}") - rval = tcorrs.loc[site_id_found, cols].copy() - rval_fill = pd.DataFrame( - index=site_id_missing, columns=cols, data=fill_value - ) - rval = pd.concat([rval, rval_fill]).loc[site_id_idx] + return tcorrs.loc[site_id_found, cols] - return rval + err_msg = ( + f"no terrain corrrection data found for {len(site_id_missing)} of " + f"{len(site_id_idx)} site_id's " + ) + + if if_missing == "raise": + raise ValueError(err_msg) + + elif if_missing == "drop": + warnings.warn(f"{err_msg}, dropping from ouput") + return tcorrs.loc[site_id_found, cols] + + warnings.warn(f"{err_msg}, filling with {fill_value}") + rval = tcorrs.loc[site_id_found, cols].copy() + rval_fill = pd.DataFrame(index=site_id_missing, columns=cols, data=fill_value) + return pd.concat([rval, rval_fill]).loc[site_id_idx] diff --git a/src/gsolve/scintrex.py b/src/gsolve/scintrex.py index 5294708..bf24738 100644 --- a/src/gsolve/scintrex.py +++ b/src/gsolve/scintrex.py @@ -37,7 +37,7 @@ from gsolve.core.utils import ( generate_loop_intervals, generate_loop_names, - is_list_like, + is_datetime_array, loops_from_gaps, to_naive_utc_datetime, ) @@ -489,7 +489,7 @@ def set_loop( elif isinstance(datetimes, Mapping): dates = to_naive_utc_datetime(list(datetimes.keys())) loop_ids = [str(l) for l in datetimes.values()] - elif isinstance(datetimes, DatetimeArray): + elif is_datetime_array(datetimes): dates = to_naive_utc_datetime(datetimes) loop_ids = generate_loop_names( len(dates), start=loop_start, step=loop_step diff --git a/src/gsolve/sites.py b/src/gsolve/sites.py index 78330f7..b0e78d7 100644 --- a/src/gsolve/sites.py +++ b/src/gsolve/sites.py @@ -20,7 +20,6 @@ import typing as _typing from collections.abc import Iterable, Mapping, Sequence -from typing import Self import numpy as _np import numpy.typing as _npt @@ -31,14 +30,14 @@ FilePath, IfSheetExists, IfWorkbookExists, - Points2D, - Points3D, + Points3DTrue, Renamer, ) from gsolve.core.data import COMMON_FIELDS, DataFieldSpecification, GSolveTable from gsolve.core.excel_io import read_excel_worksheet, write_excel_worksheet from gsolve.core.utils import ( GSolveDataWarning, + is_filepath_like, is_list_like, normalize_field_names, prepare_writable_df, @@ -46,6 +45,7 @@ ) from gsolve.core.xr_methods import load_dem, prepare_dem + __all__ = [ "GravitySites", "combine_gravity_sites", @@ -196,7 +196,7 @@ def from_excel( ignore_unknown_fields: bool = True, mapper: Renamer | None = None, **kwargs, - ) -> Self: + ) -> _typing.Self: """ Create a GravitySites object from an excel workbook. @@ -580,7 +580,7 @@ def sample_elevation( None if ``output_col`` is defined, otherwise a Series of sampled elevations. """ - if isinstance(dem, FilePath): + if is_filepath_like(dem): _dem = load_dem(dem) elif isinstance(dem, DatasetOrArray): _dem = prepare_dem(dem) @@ -601,10 +601,8 @@ def sample_elevation( return _pd.Series(index=self.data.index, data=z) def get_points( - self, xcol: str, ycol: str, zcol: str | None = None - ) -> tuple[ - _npt.NDArray[_np.float64], _npt.NDArray[_np.float64], _npt.NDArray[_np.float64] - ]: + self, xcol: str, ycol: str, zcol: str = "" + ) -> Points3DTrue: """Get site point coordinates as numpy arrays. Parameters @@ -613,20 +611,33 @@ def get_points( The column name for x coordinates. ycol : str The column name for y coordinates. - zcol : str or None, optional - The column name for z coordinates. If None, z coordinates will be set to NaN. + zcol : str + The column name for z coordinates. If an empty str, z coordinates + will be set to NaN. Returns ------- - tuple of numpy.ndarray + (ndarray, ndarray, ndarray) The x, y, and z coordinates as numpy arrays. """ - x = self.data.loc[:, xcol].to_numpy().copy().astype(_np.float64) - y = self.data.loc[:, ycol].to_numpy().copy().astype(_np.float64) - if zcol is None: - return x, y, _np.full_like(x, _np.nan, dtype=_np.float64) + x = to_1d_ndarray(self.data.loc[:, xcol]).copy().astype(_np.float64) + y = to_1d_ndarray(self.data.loc[:, ycol]).copy().astype(_np.float64) + + if not zcol: + z = _np.full_like(x, fill_value=_np.nan) else: - return x, y, self.data.loc[:, zcol].to_numpy().copy().astype(_np.float64) + z = to_1d_ndarray(self.data.loc[:, zcol]).copy().astype(_np.float64) + + return x, y, z + + def get_site_ids(self) -> _npt.NDArray[_np.str_]: + """Return site_id's as as numpy ndarray. + + Returns + ------- + numpy.ndarray[str] + """ + return to_1d_ndarray(self.data.index).copy().astype(str) def _siteid_exists(site_id: str, other: _pd.DataFrame | GSolveTable) -> bool: @@ -756,7 +767,7 @@ def write_to_csv( self, csv_file: FilePath, normalize_column_names: bool = True, - bool_to_int: bool = True, + bool_to_int: bool = True, include_unknown_fields: bool = False, **kwargs, ) -> None: @@ -857,7 +868,7 @@ def from_dict( cls, data: Mapping, set_active: bool = True, - ) -> Self: + ) -> _typing.Self: """Create a ReferenceGravity object from a dictionary. This method provides a simple mechanism for users to add reference diff --git a/tests/test_terrain_corrections_consistency.py b/tests/test_terrain_corrections_consistency.py index 97d4b3a..e3b2a5e 100644 --- a/tests/test_terrain_corrections_consistency.py +++ b/tests/test_terrain_corrections_consistency.py @@ -60,8 +60,8 @@ def sites(ripple_dem) -> GravitySites: sites = GravitySites( site_id=[str(n) for n in range(1, n_points + 1)], - latitude=np.random.uniform(-90.0, 90.0, size=n_points), - longitude=np.random.uniform(-180.0, 180.0, size=n_points), + latitude=rng.uniform(-90.0, 90.0, size=n_points), + longitude=rng.uniform(-180.0, 180.0, size=n_points), height_ellipsoidal=z, easting=x, northing=y, @@ -77,7 +77,7 @@ def pre_calced_tcorr_data(tc_params) -> TerrainCorrectionData: # - Catch changes that alter the results # -> Investigate causes data = [ - "site_id,easting,northing,site_height_field,tcorr:topo:topo,tcorr:bath:bath,tcorr:both:topo,tcorr:both:bath,tcorr:total", + "site_id,easting,northing,elev:topo,tcorr:tt:topo,tcorr:bb:bath,tcorr:both:topo,tcorr:both:bath,tcorr:total", "1,547.9120971119266,-258.4039515348376,5.909,0.046479,0.012204,0.046479,0.012204,0.117366", "2,-122.24312049589537,853.5299776972035,-1.582,NaN,NaN,NaN,NaN,NaN", "3,717.1958398227648,287.730240161329,7.86,0.030694,0.002449,0.030694,0.002449,0.06628600000000001", @@ -99,26 +99,32 @@ def pre_calced_tcorr_data(tc_params) -> TerrainCorrectionData: @pytest.fixture -def tc_params() -> list[TerrainCorrectionParameters]: +def tc_params(ripple_dem) -> list[TerrainCorrectionParameters]: p1 = TerrainCorrectionParameters( - name="topo", + name="tt", min_dist=tc_min_dist, max_dist=tc_max_dist, distance_mask_type="radial", compute_bathymetry=False, + dem_source=ripple_dem, + site_height_field="height_dem", ) p2 = TerrainCorrectionParameters( - name="bath", + name="bb", min_dist=tc_min_dist, max_dist=tc_max_dist, distance_mask_type="radial", compute_topography=False, + dem_source=ripple_dem, + site_height_field="height_dem", ) p3 = TerrainCorrectionParameters( name="both", min_dist=tc_min_dist, max_dist=tc_max_dist, distance_mask_type="radial", + dem_source=ripple_dem, + site_height_field="height_dem", ) return [p1, p2, p3] @@ -126,9 +132,8 @@ def tc_params() -> list[TerrainCorrectionParameters]: def test_terrain_correction_consistency( ripple_dem, sites, tc_params, pre_calced_tcorr_data ): - dem = ripple_dem - tc = TerrainCorrector(params=tc_params, dem=[dem] * len(tc_params)) - results = tc.compute(sites, site_height_field="height_dem", show_progress=False) + tc = TerrainCorrector(params=tc_params) + results = tc.compute(sites, show_progress=False) to_close_dist = dem_max - tc_max_dist bad_points = results.data.easting.abs().gt( @@ -142,6 +147,7 @@ def test_terrain_correction_consistency( # test that pre-calculated results are close to calculated results for col in tcorr_cols: + assert col in pre_calced_tcorr_data.data.columns assert np.allclose( 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