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move add_sv_states tutorial to sv_models
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‎notebooks/tutorials/parsers.ipynb‎

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" rinex_obs_3.where('gps_millis', rinex_obs_3['gps_millis', 0], 'eq'))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cf51b89e",
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"metadata": {},
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"source": [
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"# Precise Ephemerides Processing"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c5de62f",
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"metadata": {},
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"source": [
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"The data required to calculate with precise ephemerides uses .sp3 and .clk files, which can be downloaded from [CDDIS](https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/gnss_mgex.html) or [CORS](https://geodesy.noaa.gov/UFCORS/).\n",
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"\n",
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"The .sp3 files provide post-processed, accurate, and precise information regarding 3-D satellite position in the Earth-Centered Earth-Fixed (ECEF) frame at intervals of 15mins each. \n",
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"\n",
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"Similarly, the .clk files provide post-processed, accurate and precise information on satellite clock errors at intervals of 30secs each. \n",
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"\n",
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"These .sp3 and .clk files are available for any GNSS constellation, and hence, provide a common processing platform for applications that involve multi-GNSS satellite signals (without requiring to parse the broadcast ephemeris from each constellation separately one at a time). Also, unlike broadcast ephemeris that can suffer from signal-in-space anomalies, the .sp3 and .clk files are guaranteed to provide accurate satellite information. However, note that, these files are only available in a post-processed manner, and not in real-time"
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]
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},
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{
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"cell_type": "markdown",
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"id": "67fc4f2e",
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"metadata": {},
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"source": [
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"We show how to analyze this precise ephemerides functionality for the Android derived dataset in the following cells, \n",
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"\n",
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"1. Load the derived data from AndroidDerived"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "364e7fb3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"# load Android Google Challenge data\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2021/Pixel4_derived_clkdiscnt.csv --quiet -O \"Pixel4_derived_clkdiscnt.csv\"\n",
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"derived_data = glp.AndroidDerived2021(\"Pixel4_derived_clkdiscnt.csv\", remove_timing_outliers=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4e4ee7c8",
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"metadata": {},
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"source": [
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"2. Specify the paths to the .sp3 and .clk files, as well as the desired constellations from which one intends to extract satellite information"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e62ed8ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# download .sp3 data file\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/sp3/COD0MGXFIN_20211180000_01D_05M_ORB.SP3 --quiet -O \"COD0MGXFIN_20211180000_01D_05M_ORB.SP3\"\n",
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"# Specify .sp3 file path to extract precise ephemerides\n",
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"sp3_path = \"COD0MGXFIN_20211180000_01D_05M_ORB.SP3\"\n",
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"\n",
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"# download .clk data file\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/clk/COD0MGXFIN_20211180000_01D_30S_CLK.CLK --quiet -O \"COD0MGXFIN_20211180000_01D_30S_CLK.CLK\"\n",
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"# Specify .clk file path to extract precise ephemerides\n",
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"clk_path = \"COD0MGXFIN_20211180000_01D_30S_CLK.CLK\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "2567ff0f",
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"metadata": {},
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"source": [
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"3. Remove the rows in NavData class that refer to satellite information (3-D satellite position, 3-D satellite velocity, clock bias and clock drift), \n",
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"\n",
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" i.e., `SV_KEYS =['x_sv_m', 'y_sv_m', 'z_sv_m', 'vx_sv_mps','vy_sv_mps','vz_sv_mps', 'b_sv_m', 'b_dot_sv_mps']`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3761b169",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define the keys relevant for satellite information, and remove the data within these fields\n",
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"SV_KEYS = ['x_sv_m', 'y_sv_m', 'z_sv_m', \\\n",
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" 'vx_sv_mps','vy_sv_mps','vz_sv_mps', \\\n",
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" 'b_sv_m', 'b_dot_sv_mps']\n",
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"derived_data[SV_KEYS] = np.nan"
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]
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},
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{
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"cell_type": "markdown",
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"id": "458b0a46",
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"metadata": {},
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"source": [
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"4. Populate the columns of SV_KEYS with information extracted via precise ephemerides"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f4e3f349",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Update derived_data class with satellite information computed via precise ephemerides\n",
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"# derived_gps_glonass = derived_data.where(\"gnss_id\",(\"gps\",\"glonass\"))\n",
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"derived_multi_gnss = glp.add_sv_states_sp3_and_clk(derived_data, sp3_path, \\\n",
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" clk_path, verbose = False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "10563cec",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Check that all the desired fields related to satellite information have useful\n",
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"# information, and norm of computed satellite position matches the altitude of GNSS constellation\n",
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"sat_alt = np.linalg.norm(derived_multi_gnss[[\"x_sv_m\",\"y_sv_m\",\"z_sv_m\"],9:12],axis=0)\n",
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"print('Distance of two satellites from the center of the Earth (expected around 26000000 m)')\n",
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"print(\"Three GPS SVs calculated to be at:\", sat_alt,\"\\n\")\n",
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"\n",
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"print(\"Small section of calculated positions:\")\n",
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"print(derived_multi_gnss.copy(cols=[2,3,4,5],rows=[\"gnss_id\"]+SV_KEYS))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95e58b71",

‎notebooks/tutorials/sv_models.ipynb‎

Lines changed: 121 additions & 8 deletions
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@@ -4,19 +4,124 @@
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"cell_type": "markdown",
55
"metadata": {},
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"source": [
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"This tutorial explains how to estimate SV states, both as estimation for\n",
8-
"a single time instance and measurements and as a wrapper for an entire\n",
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"set of received measurements."
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"Load `gnss_lib_py` into the Python workspace"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gnss_lib_py as glp"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"For this tutorial, we will work with the `AndroidDerived2022` dataset.\n",
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"This serves the dual purpose of showing how each functionality works and\n",
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"allowing us to compare the SV states estimated in `sv_models.py` to that\n",
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"estimated by Google. The latter verifies state computation from our method."
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"# Adding SV States with Precise Ephemerides (SP3 & CLK)"
24+
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This tutorial explains how to calculate satellite states using precise SP3 and CLK files.\n",
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"\n",
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"The data required to calculate with precise ephemerides uses .sp3 and .clk files, which can be downloaded from [CDDIS](https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/gnss_mgex.html) or [CORS](https://geodesy.noaa.gov/UFCORS/).\n",
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"\n",
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"The .sp3 files provide post-processed, accurate, and precise information regarding 3-D satellite position in the Earth-Centered Earth-Fixed (ECEF) frame at intervals of 15mins each. \n",
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"\n",
36+
"Similarly, the .clk files provide post-processed, accurate and precise information on satellite clock errors at intervals of 30secs each. \n",
37+
"\n",
38+
"These .sp3 and .clk files are available for any GNSS constellation, and hence, provide a common processing platform for applications that involve multi-GNSS satellite signals (without requiring to parse the broadcast ephemeris from each constellation separately one at a time). Also, unlike broadcast ephemeris that can suffer from signal-in-space anomalies, the .sp3 and .clk files are guaranteed to provide accurate satellite information. However, note that, these files are only available in a post-processed manner, and not in real-time\n",
39+
"\n",
40+
"We show how to analyze this precise ephemerides functionality for the Android derived dataset in the following cells, \n",
41+
"\n",
42+
"1. Load the derived data from AndroidDerived and remove the rows in NavData class that refer to satellite information (3-D satellite position, 3-D satellite velocity, clock bias and clock drift),"
43+
]
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},
45+
{
46+
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"# load Android Google Challenge data\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2021/Pixel4_derived_clkdiscnt.csv --quiet -O \"Pixel4_derived_clkdiscnt.csv\"\n",
55+
"derived_data = glp.AndroidDerived2021(\"Pixel4_derived_clkdiscnt.csv\", remove_timing_outliers=False)\n",
56+
"# Define the keys relevant for satellite information, and remove the data within these fields\n",
57+
"SV_KEYS = ['x_sv_m', 'y_sv_m', 'z_sv_m', \\\n",
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" 'vx_sv_mps','vy_sv_mps','vz_sv_mps', \\\n",
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" 'b_sv_m', 'b_dot_sv_mps']\n",
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"derived_data.remove(rows=SV_KEYS,inplace=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"2. Specify the paths to the .sp3 and .clk files using the `file_path` variable. If files are not specified, they will be automatically downloaded using the ephemeris downloader."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# download .sp3 data file\n",
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"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/sp3/COD0MGXFIN_20211180000_01D_05M_ORB.SP3 --quiet -O \"COD0MGXFIN_20211180000_01D_05M_ORB.SP3\"\n",
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"# Specify .sp3 file path to extract precise ephemerides\n",
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"sp3_path = \"COD0MGXFIN_20211180000_01D_05M_ORB.SP3\"\n",
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"\n",
81+
"# download .clk data file\n",
82+
"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/clk/COD0MGXFIN_20211180000_01D_30S_CLK.CLK --quiet -O \"COD0MGXFIN_20211180000_01D_30S_CLK.CLK\"\n",
83+
"# Specify .clk file path to extract precise ephemerides\n",
84+
"clk_path = \"COD0MGXFIN_20211180000_01D_30S_CLK.CLK\""
85+
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"3. Populate the columns of SV_KEYS with information extracted via precise ephemerides"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Update derived_data class with satellite information computed via precise ephemerides\n",
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"derived_multi_gnss = glp.add_sv_states(derived_data, source=\"precise\", file_paths=[sp3_path, clk_path],\n",
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" verbose = False)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Check that all the desired fields related to satellite information have useful information and the norm of computed satellite position matches the altitude of GNSS constellations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"sat_alt = np.linalg.norm(derived_multi_gnss[[\"x_sv_m\",\"y_sv_m\",\"z_sv_m\"],9:12],axis=0)\n",
120+
"print('Distance of two satellites from the center of the Earth (expected around 26000000 m)')\n",
121+
"print(\"Three GPS SVs calculated to be at:\", sat_alt,\"\\n\")\n",
122+
"\n",
123+
"print(\"Small section of calculated positions:\")\n",
124+
"print(derived_multi_gnss.copy(cols=[2,3,4,5],rows=[\"gnss_id\"]+SV_KEYS))"
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]
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},
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{
@@ -30,6 +135,15 @@
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"cell_type": "markdown",
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"metadata": {},
32137
"source": [
138+
"This tutorial explains how to estimate SV states, both as estimation for\n",
139+
"a single time instance and measurements and as a wrapper for an entire\n",
140+
"set of received measurements.\n",
141+
"\n",
142+
"For this tutorial, we will work with the `AndroidDerived2022` dataset.\n",
143+
"This serves the dual purpose of showing how each functionality works and\n",
144+
"allowing us to compare the SV states estimated in `sv_models.py` to that\n",
145+
"estimated by Google. The latter verifies state computation from our method.\n",
146+
"\n",
33147
"Load the test dataset for the Android Derived 2022 dataset"
34148
]
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},
@@ -40,7 +154,6 @@
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"outputs": [],
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"source": [
42156
"import numpy as np\n",
43-
"import gnss_lib_py as glp\n",
44157
"# download Android data file\n",
45158
"!wget https://raw.githubusercontent.com/Stanford-NavLab/gnss_lib_py/main/data/unit_test/android_2022/device_gnss.csv --quiet -O \"device_gnss.csv\"\n",
46159
"# load Android Google Challenge data\n",

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