diff --git a/.circleci/config.yml b/.circleci/config.yml index b9935b6a6..99ac4c7ab 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -50,6 +50,7 @@ jobs: command: | export PATH=${CONDA_PREFIX}/bin:${PATH} ${MINTPY_HOME}/tests/test_asc_desc2horz_vert.py + ${MINTPY_HOME}/tests/objects/ionex.py - run: name: Testing MintPy on Example Dataset 1/4 - FernandinaSenDT128 (ISCE/topsStack) diff --git a/.gitignore b/.gitignore index f16951406..0b35299ff 100644 --- a/.gitignore +++ b/.gitignore @@ -1,15 +1,134 @@ *.DS_Store *.pyc *.idea/* -*ipynb_checkpoints* -*.egg* +isce.log + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python build/ +develop-eggs/ dist/ -docs/api_docs/ -docs/resources/*logo* -docs/resources/*.ai -docs/resources/*.pdf -docs/deps.svg -isce.log -mintpy/modis* -site/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ \ No newline at end of file diff --git a/docs/api/module_hierarchy.md b/docs/api/module_hierarchy.md index 197e54e0a..6fa3c127a 100644 --- a/docs/api/module_hierarchy.md +++ b/docs/api/module_hierarchy.md @@ -9,6 +9,7 @@ Hierarchy of sub-modules within MintPy. Level _N_ modules depends on level _N-1_ /objects cluster colors + constants giant ramp sensor @@ -18,50 +19,50 @@ Hierarchy of sub-modules within MintPy. Level _N_ modules depends on level _N-1_ l1regls lstl1 arg_group - constants - ptime + map + ptime (including progress) utils0 /simulation fractal ------------------ level 1 -------------------- /objects conncomp (objects/ramp) + ionex (objects/ptime, utils/map) stack (utils/ptime) /utils time_func (utils/ptime) - map (utils/utils0) /simulation decorrelation (utils/ptime) defo_model (utils/utils0) variance (utils/ptime) ------------------ level 2 -------------------- /utils - readfile (utils/{ptime, utils0}, objects/{stack, giant, sensor}) - s1_utils (utils/{ptime, time_func}, objects/{stack}) + readfile (objects/{stack, giant, sensor}, utils/{ptime, utils0}) + s1_utils (objects/{stack}, utils/{ptime, time_func}) ------------------ level 3 -------------------- /objects - resample (utils/{utils0, ptime, readfile}) + resample (utils/{utils0, ptime, readfile}) coord (utils/{utils0, utils1, readfile}) /simulation - iono (utils/{utils0, ptime, readfile}) + iono (utils/{utils0, ptime, readfile}) /utils writefile (utils/{readfile}) network (objects/{stack, sensor}, utils/{readfile}) ------------------ level 4 -------------------- /objects - gps (objects/{stack, coord}, utils/{ptime, utils1, readfile}) - stackDict (objects/{stack}, utils/{ptime, utils0, readfile}) + gps (objects/{stack, coord}, utils/{ptime, utils1, readfile}) + stackDict (objects/{stack}, utils/{ptime, utils0, readfile}) /simulation - simulation (objects/{stack}, utils/{ptime, network}, simulation/{fractal, decorrelation, defo_model}) + simulation (objects/{stack}, utils/{ptime, network}, simulation/{fractal, decorrelation, defo_model}) /utils - attribute (objects/{coord}, utils/{readfile}) - utils1 (objects/{stack, ramp}, utils/{ptime, utils0, readfile, writefile}) + attribute (objects/{coord}, utils/{readfile}) + utils1 (objects/{stack, ramp}, utils/{ptime, utils0, readfile, writefile}) ------------------ level 5 -------------------- /utils - plot (objects/{stack, coord, colors}, utils/{ptime, utils0, readfile, network, map}) - utils (objects/{stack, coord}, utils/{ptime, utils1, readfile}) + plot (objects/{stack, coord, colors}, utils/{ptime, utils0, readfile, network, map}) + utils (objects/{stack, coord}, utils/{ptime, utils1, readfile}) isce_utils (utils/{ptime, readfile, writefile, utils1}) ------------------ level 6 -------------------- /objects - insar_vs_gps (objects/{stack, giant}, utils/{readfile, gps, plot, utils}) + insar_vs_gps (objects/{stack, giant}, utils/{readfile, gps, plot, utils}) ``` diff --git a/mintpy/iono_tec.py b/mintpy/iono_tec.py index cb6bf0807..673475643 100755 --- a/mintpy/iono_tec.py +++ b/mintpy/iono_tec.py @@ -16,22 +16,20 @@ import numpy as np import mintpy -from mintpy.objects import timeseries +from mintpy.objects import timeseries, ionex +from mintpy.objects.constants import SPEED_OF_LIGHT from mintpy.utils import ptime, readfile, writefile, utils as ut from mintpy.simulation import iono -SPEED_OF_LIGHT = 299792458 # m/s - - ##################################################################################### REFERENCE = """references: - Yunjun, Z., Fattahi, H., Pi, X., Rosen, P., Simons, M., Agram, P., & Aoki, Y. (2022). Range - Geolocation Accuracy of C-/L-band SAR and its Implications for Operational Stack Coregistration. - IEEE Trans. Geosci. Remote Sens., 60, doi:10.1109/TGRS.2022.3168509. - Schaer, S., Gurtner, W., & Feltens, J. (1998). IONEX: The ionosphere map exchange format version 1.1. - Paper presented at the Proceedings of the IGS AC workshop, Darmstadt, Germany, Darmstadt, Germany. + Yunjun, Z., Fattahi, H., Pi, X., Rosen, P., Simons, M., Agram, P., & Aoki, Y. (2022). + Range Geolocation Accuracy of C-/L-band SAR and its Implications for Operational + Stack Coregistration. IEEE Trans. Geosci. Remote Sens., 60, doi:10.1109/TGRS.2022.3168509. + Schaer, S., Gurtner, W., & Feltens, J. (1998). IONEX: The ionosphere map exchange format + version 1.1. Paper presented at the Proceedings of the IGS AC workshop, Darmstadt, Germany. """ EXAMPLE = """example: @@ -40,20 +38,18 @@ """ def create_parser(): - parser = argparse.ArgumentParser(description='Calculate ionospheric ramps using Global Iono Maps (GIM) from GNSS-based TEC products.', + parser = argparse.ArgumentParser(description='Calculate ionospheric ramps using Global Iono Maps' + ' from GNSS-based TEC products.', formatter_class=argparse.RawTextHelpFormatter, epilog=REFERENCE+'\n'+EXAMPLE) parser.add_argument('dis_file', help='displacement time-series HDF5 file, i.e. timeseries.h5') parser.add_argument('-g','--geomtry', dest='geom_file', type=str, required=True, help='geometry file including incidence/azimuthAngle.') - parser.add_argument('-s','--sol','--tec-sol', dest='tec_sol', default='jpl', - help='TEC solution center (default: %(default)s). \n' - ' jpl - JPL (Final)\n' - ' igs - IGS (Final)\n' - ' cod - CODE (Final)\n' - 'Check more at:\n' - ' https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html') - parser.add_argument('--tec-dir', dest='tec_dir', default='${WEATHER_DIR}/GIM_IGS', + parser.add_argument('-s','--sol','--sol-code', dest='sol_code', default='jpl', + choices={'cod','esa','igs','jpl','upc','uqr'}, + help='GIM solution center code (default: %(default)s).\n' + 'https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html') + parser.add_argument('--tec-dir', dest='tec_dir', default='${WEATHER_DIR}/IONEX', help='directory of downloaded GNSS TEC data (default: %(default)s).') # output @@ -62,13 +58,15 @@ def create_parser(): #parser.add_argument('-o', dest='cor_dis_file', help='Output file name for the corrected time-series.') # GIM extraction - tec_cfg = parser.add_argument_group('GIM extraction', - 'Parameters to extract TEC at point of interest from GIM (mainly for impact demonstration).') - tec_cfg.add_argument('-i','--interp', dest='interp_method', default='linear3d', choices={'nearest', 'linear2d', 'linear3d'}, - help='Interpolation method to grab the GIM value at the point of interest (default: %(default)s).') + tec_cfg = parser.add_argument_group('GIM extraction', 'Parameters to extract TEC at point of interest from ' + 'GIM (mainly for impact demonstration).') + tec_cfg.add_argument('-i','--interp', dest='interp_method', default='linear3d', + choices={'nearest', 'linear2d', 'linear3d'}, + help='Interpolation method to grab the GIM value at the point of interest' + ' (default: %(default)s).') tec_cfg.add_argument('--norotate', dest='rotate_tec_map', action='store_false', - help="Rotate TEC maps along the longitude direction to compensate the correlation between\n" - "the ionosphere and the Sun's position, as suggested by Schaer et al. (1998).\n" + help="Rotate TEC maps along the longitude direction to compensate the correlation\n" + "between the ionosphere and the Sun's position (Schaer et al. (1998).\n" "For 'interp_method == linear3d' ONLY. (default: %(default)s).") tec_cfg.add_argument('--ratio', dest='sub_tec_ratio', type=str, help='Ratio to calculate the sub-orbital TEC from the total TEC.\n' @@ -110,7 +108,7 @@ def cmd_line_parse(iargs=None): if not inps.iono_file: geom_dir = os.path.dirname(inps.geom_file) - inps.iono_file = os.path.join(geom_dir, 'TEC{}lr{}.h5'.format(inps.tec_sol[0], suffix)) + inps.iono_file = os.path.join(geom_dir, 'TEC{}lr{}.h5'.format(inps.sol_code[0], suffix)) #if not inps.cor_dis_file: # dis_dir = os.path.dirname(inps.dis_file) @@ -148,7 +146,8 @@ def run_or_skip(iono_file, grib_files, dis_file, geom_file): date_list_ion = timeseries(iono_file).get_date_list() if ds_size_ion != ds_size_dis or any (x not in date_list_ion for x in date_list_dis): flag = 'run' - print(f'2) output file does NOT have the same len/wid as the geometry file {geom_file} or does NOT contain all dates') + print(f'2) output file does NOT have the same len/wid as the geometry file {geom_file}' + ' or does NOT contain all dates') else: print('2) output file has the same len/wid as the geometry file and contains all dates') @@ -166,17 +165,17 @@ def run_or_skip(iono_file, grib_files, dis_file, geom_file): ##################################################################################### -def download_igs_tec(date_list, tec_dir, tec_sol='jpl'): - """Download IGS TEC products for the input list of dates. +def download_ionex_files(date_list, tec_dir, sol_code='jpl'): + """Download IGS TEC products in IONEX format for the input list of dates. Parameters: date_list - list of str, in YYYYMMDD tec_dir - str, path to IGS_TEC directory, e.g. ~/data/aux/IGS_TEC - tec_sol - str, TEC solution center, e.g. jpl, cod, igs + sol_code - str, TEC solution center, e.g. jpl, cod, igs Returns: fnames - list of str, path of the downloaded TEC files """ print("\n------------------------------------------------------------------------------") - print("downloading GNSS-based TEC products from NASA's Archive of Space Geodesy Data (CDDIS) ...") - print('Link: https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html') + print("downloading GNSS-based TEC products in IONEX format from NASA/CDDIS ...") + print('https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html') num_date = len(date_list) n = len(str(num_date)) print(f'number of TEC files to download: {num_date}') @@ -185,7 +184,7 @@ def download_igs_tec(date_list, tec_dir, tec_sol='jpl'): # output file names/sizes fnames = [] for date_str in date_list: - fnames.append(iono.get_igs_tec_filename(tec_dir, date_str, sol=tec_sol)) + fnames.append(ionex.get_ionex_filename(date_str, tec_dir=tec_dir, sol_code=sol_code)) # remove all existing files debug_mode = False @@ -209,23 +208,24 @@ def download_igs_tec(date_list, tec_dir, tec_sol='jpl'): num_date2dload = len(date_list2dload) if num_date2dload == 0: - print(f'ALL files exists with consistent file size (~{fsizec:.0f} KB) --> skip re-downloading.\n') + print(f'ALL files exists with consistent file size (~{fsizec:.0f} KB)' + ' --> skip re-downloading.\n') else: for i, date_str in enumerate(date_list2dload): print('-'*20) print('DATE {}/{}: {}'.format(i+1, num_date2dload, date_str)) - iono.dload_igs_tec(date_str, tec_dir, sol=tec_sol, print_msg=True) + ionex.dload_ionex(date_str, tec_dir=tec_dir, sol_code=sol_code, print_msg=True) # print file size info, after downloading fsizes = [os.path.getsize(i) / 1024 if os.path.isfile(i) else 0 for i in fnames] for i in range(num_date): - print('[{i:0{n}d}/{N}] {f}: {s:.2f} KB'.format(n=n, i=i+1, N=num_date, f=fnames[i], s=fsizes[i])) + print(f'[{i+1:0{n}d}/{num_date}] {fnames[i]}: {fsizes[i]:.2f} KB') return fnames -def calc_iono_ramp_timeseries_igs(tec_dir, tec_sol, interp_method, ts_file, geom_file, iono_file, +def calc_iono_ramp_timeseries_igs(tec_dir, sol_code, interp_method, ts_file, geom_file, iono_file, rotate_tec_map=True, sub_tec_ratio=None, update_mode=True): """Calculate the time-series of 2D ionospheric delay from IGS TEC data. Considering the variation of the incidence angle along range direction. @@ -255,21 +255,20 @@ def calc_iono_ramp_timeseries_igs(tec_dir, tec_sol, interp_method, ts_file, geom print('Local solar time:', local_dt.strftime("%I:%M %p")) # read IGS TEC - vtec_list = [] print('read IGS TEC file ...') - print('interpolation method: {}'.format(interp_method)) + print(f'interpolation method: {interp_method}') + if interp_method == 'linear3d': + print(f'rotate TEC maps: {rotate_tec_map}') + + vtec_list = [] prog_bar = ptime.progressBar(maxValue=len(date_list)) for i, date_str in enumerate(date_list): # read zenith TEC - tec_file = iono.get_igs_tec_filename(tec_dir, date_str, sol=tec_sol) - vtec = iono.get_igs_tec_value( - tec_file, - utc_sec, - lat=iono_lat, - lon=iono_lon, - interp_method=interp_method, - rotate_tec_map=rotate_tec_map, - ) + tec_file = ionex.get_ionex_filename(date_str, tec_dir=tec_dir, sol_code=sol_code) + vtec = ionex.get_ionex_value(tec_file, utc_sec, + lat=iono_lat, lon=iono_lon, + interp_method=interp_method, + rotate_tec_map=rotate_tec_map) vtec_list.append(vtec) prog_bar.update(i+1, suffix=date_str) prog_bar.close() @@ -331,7 +330,7 @@ def vtec2iono_ramp_timeseries(date_list, vtec_list, geom_file, iono_file, sub_te dates = ptime.date_list2vector(date_list)[0] ydays = np.array([x.timetuple().tm_yday for x in dates]) fc = np.loadtxt(top_perc_file, dtype=bytes).astype(np.float32) - print('multiply VTEC adaptively based on the day of the year from: {}'.format(top_perc_file)) + print(f'multiply VTEC adaptively based on the day of the year from: {top_perc_file}') sub_perc = fc[:,2][np.array(ydays)] vtec_list = (np.array(vtec_list).flatten() * sub_perc).tolist() @@ -401,13 +400,13 @@ def main(iargs=None): # download date_list = timeseries(inps.dis_file).get_date_list() - tec_files = download_igs_tec(date_list, tec_dir=inps.tec_dir, tec_sol=inps.tec_sol) + tec_files = download_ionex_files(date_list, tec_dir=inps.tec_dir, sol_code=inps.sol_code) # calculate if run_or_skip(inps.iono_file, tec_files, inps.dis_file, inps.geom_file) == 'run': calc_iono_ramp_timeseries_igs( tec_dir=inps.tec_dir, - tec_sol=inps.tec_sol, + sol_code=inps.sol_code, interp_method=inps.interp_method, ts_file=inps.dis_file, geom_file=inps.geom_file, diff --git a/mintpy/objects/ionex.py b/mintpy/objects/ionex.py new file mode 100644 index 000000000..cc8a125da --- /dev/null +++ b/mintpy/objects/ionex.py @@ -0,0 +1,389 @@ +#!/usr/bin/env python3 +############################################################ +# Program is part of MintPy # +# Copyright (c) 2013, Zhang Yunjun, Heresh Fattahi # +# Author: Zhang Yunjun, Jun 2022 # +############################################################ +# Links: +# IGS (NASA): https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html +# IMPC (DLR): https://impc.dlr.de/products/total-electron-content/near-real-time-tec/nrt-tec-global/ +# Recommend import: +# from mintpy.objects import ionex + + +import os +import re +import datetime as dt + +import numpy as np +from scipy import interpolate +from matplotlib import pyplot as plt +from matplotlib.animation import FuncAnimation +from cartopy import crs as ccrs + +from mintpy.utils import ptime +from mintpy.utils.map import draw_lalo_label, round_to_1 + + +IGS_SOLUTION_NAMES = { + 'cod' : 'CODE (1-hour)', + 'esa' : 'ESA (2-hour)', + 'igs' : 'IGS (2-hour)', + 'jpl' : 'JPL (2-hour)', + 'upc' : 'UPC (2-hour)', + 'uqr' : 'UPC (15-min; rapid)' +} + + +################################## Download #################################### + +def dload_ionex(date_str, tec_dir, sol_code='jpl', date_fmt='%Y%m%d', print_msg=False): + """Download IGS vertical TEC files in IONEX format. + + Parameters: date_str - str, date in date_fmt format. + tec_dir - str, local directory to save the downloaded files. + sol_code - str, IGS TEC analysis center code. + date_fmt - str, date format code + Returns: fname_dst_uncomp - str, path to the local uncompressed IONEX file. + """ + + # get the source (remote) and destination (local) file path/url + kwargs = dict(sol_code=sol_code, date_fmt=date_fmt) + fname_src = get_ionex_filename(date_str, tec_dir=None, **kwargs) + fname_dst = get_ionex_filename(date_str, tec_dir=tec_dir, **kwargs) + '.Z' + fname_dst_uncomp = fname_dst[:-2] + + # download - compose cmd + cmd = 'wget --continue --auth-no-challenge "{}"'.format(fname_src) + if os.path.isfile(fname_dst) and os.path.getsize(fname_dst) > 1000: + cmd += ' --timestamping' + cmd = cmd + ' --quiet' if not print_msg else print(cmd) + + # downlload - run cmd in output dir + pwd = os.getcwd() + os.chdir(tec_dir) + os.system(cmd) + os.chdir(pwd) + + # uncompress + # if output file 1) does not exist or 2) smaller than 400k in size or 3) older + if (not os.path.isfile(fname_dst_uncomp) + or os.path.getsize(fname_dst_uncomp) < 600e3 + or os.path.getmtime(fname_dst_uncomp) < os.path.getmtime(fname_dst)): + cmd = "gzip --force --keep --decompress {}".format(fname_dst) + if print_msg: + print(cmd) + os.system(cmd) + + return fname_dst_uncomp + + +#################################### Read ###################################### + +def get_ionex_value(tec_file, utc_sec, lat, lon, interp_method='linear3d', rotate_tec_map=False, + print_msg=True): + """Get the TEC value from input IONEX file for the input lat/lon/datetime. + + Reference: + Schaer, S., Gurtner, W., & Feltens, J. (1998). IONEX: The ionosphere map exchange format + version 1.1. Paper presented at the Proceedings of the IGS AC workshop, Darmstadt, Germany. + + Parameters: tec_file - str, path of local TEC file + utc_sec - float or 1D np.ndarray, UTC time of the day in seconds + lat/lon - float or 1D np.ndarray, latitude / longitude in degrees + interp_method - str, interpolation method + rotate_tec_map - bool, rotate the TEC map along the SUN direction. + for "interp_method = linear3d" only. + print_msg - bool, print out progress bar or not. + Returns: tec_val - float or 1D np.ndarray, vertical TEC value in TECU + """ + + def interp_3d_rotate(interpfs, mins, lats, lons, utc_min, lat, lon): + ind0 = np.where((mins - utc_min) <= 0)[0][-1] + ind1 = ind0 + 1 + lon0 = lon + (utc_min - mins[ind0]) * 360. / (24. * 60.) + lon1 = lon + (utc_min - mins[ind1]) * 360. / (24. * 60.) + tec_val0 = interpfs[ind0](lon0, lat) + tec_val1 = interpfs[ind1](lon1, lat) + tec_val = ( (mins[ind1] - utc_min) / (mins[ind1] - mins[ind0]) * tec_val0 + + (utc_min - mins[ind0]) / (mins[ind1] - mins[ind0]) * tec_val1 ) + return tec_val + + # time info + utc_min = utc_sec / 60. + + # read TEC file + mins, lats, lons, tec_maps = read_ionex(tec_file)[:4] + + # resample + if interp_method == 'nearest': + lon_ind = np.abs(lons - lon).argmin() + lat_ind = np.abs(lats - lat).argmin() + time_ind = np.abs(mins - utc_min).argmin() + tec_val = tec_maps[time_ind, lat_ind, lon_ind] + + elif interp_method in ['linear', 'linear2d', 'bilinear']: + time_ind = np.abs(mins.reshape(-1,1) - utc_min).argmin(axis=0) + + if isinstance(utc_min, np.ndarray): + num_pts = len(utc_min) + tec_val = np.zeros(num_pts, dtype=np.float32) + prog_bar = ptime.progressBar(maxValue=num_pts, print_msg=print_msg) + for i in range(num_pts): + tec_val[i] = interpolate.interp2d( + x=lons, + y=lats, + z=tec_maps[time_ind[i], :, :], + kind='linear', + )(lon[i], lat[i]) + + prog_bar.update(i+1, every=200) + prog_bar.close() + + else: + tec_val = interpolate.interp2d( + x=lons, + y=lats, + z=tec_maps[time_ind[0], :, :], + kind='linear', + )(lon, lat) + + elif interp_method in ['linear3d', 'trilinear']: + if not rotate_tec_map: + # option 1: interpolate between consecutive TEC maps + # testings shows better agreement with SAR obs than option 2. + tec_val = interpolate.interpn( + points=(mins, np.flip(lats), lons), + values=np.flip(tec_maps, axis=1), + xi=(utc_min, lat, lon), + method='linear', + ) + + else: + # option 2: interpolate between consecutive rotated TEC maps + # reference: equation (3) in Schaer and Gurtner (1998) + + # prepare interpolation functions in advance to speed up + interpfs = [] + for i in range(len(mins)): + interpfs.append( + interpolate.interp2d( + x=lons, + y=lats, + z=tec_maps[i, :, :], + kind='linear', + ), + ) + + if isinstance(utc_min, np.ndarray): + num_pts = len(utc_min) + tec_val = np.zeros(num_pts, dtype=np.float32) + prog_bar = ptime.progressBar(maxValue=num_pts, print_msg=print_msg) + for i in range(num_pts): + tec_val[i] = interp_3d_rotate( + interpfs, + mins, lats, lons, + utc_min[i], lat[i], lon[i], + ) + prog_bar.update(i+1, every=200) + prog_bar.close() + + else: + tec_val = interp_3d_rotate( + interpfs, + mins, lats, lons, + utc_min, lat, lon, + ) + + else: + msg = f'Un-recognized interp_method input: {interp_method}!' + msg += '\nSupported inputs: nearest, linear2d, linear3d.' + raise ValueError(msg) + + return tec_val + + +def get_ionex_filename(date_str, tec_dir=None, sol_code='jpl', date_fmt='%Y%m%d'): + """Get the file name of IONEX files. + + Parameters: date_str - str, date in date_fmt format + tec_dir - str, path to the local TEC file directory + Set to None for the http path. + sol_code - str, GIM analysis center code in 3 digit + https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html + date_fmt - str, date format code + https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes + Returns: tec_file - str, path to the local uncompressed TEC file OR remote compressed TECfile + """ + dd = dt.datetime.strptime(date_str, date_fmt) + doy = '{:03d}'.format(dd.timetuple().tm_yday) + yy = str(dd.year)[2:4] + + # file name base + fname = "{a}g{d}0.{y}i.Z".format(a=sol_code.lower(), d=doy, y=yy) + + # full path + if tec_dir: + # local uncompressed file path + tec_file = os.path.join(tec_dir, fname[:-2]) + else: + # remote compressed file path + url_dir = "https://cddis.nasa.gov/archive/gnss/products/ionex" + tec_file = os.path.join(url_dir, str(dd.year), doy, fname) + + return tec_file + + +def get_ionex_date(tec_file, date_fmt='%Y-%m-%d'): + """Get the date in the specified format from the input IONEX filename. + + Parameters: tec_file - str, path to the TEC file in IONEX format + date_fmt - str, date format code + Returns: date_str - str, date in date_fmt format + date_obj - datetime.datetime object + """ + fbase = os.path.basename(tec_file) + year = fbase.split('.')[1][:2] + doy = fbase.split('.')[0].split('g')[1][:3] + date_obj = dt.datetime.strptime(year, '%y') + dt.timedelta(days=int(doy)-1) + date_str = dt.datetime.strftime(date_obj, date_fmt) + return date_str, date_obj + + +def get_ionex_height(tec_file): + """Get the height of the thin-shell ionosphere from IONEX file. + + Parameters: tec_file - str, path to the TEC file in IONEX format + Returns: ion_hgt - float, height above the surface in meters + """ + + with open(tec_file, 'r') as f: + lines = f.readlines() + for line in lines: + if line.strip().endswith('DHGT'): + ion_hgt = float(line.split()[0]) + break + + return ion_hgt + + +def read_ionex(tec_file): + """Read TEC file in IONEX format. + + Parameters: tec_file - str, path to the TEC file in IONEX format + Returns: mins - 1D np.ndarray in size of (num_map), time of the day in minutes + lats - 1D np.ndarray in size of (num_lat), latitude in degrees + lons - 1D np.ndarray in size of (num_lon), longitude in degrees + tec_maps - 3D np.ndarray in size of (num_map, num_lat, num_lon), vertical TEC in TECU + rms_maps - 3D np.ndarray in size of (num_map, num_lat, num_lon), vertical TEC RMS in TECU + Examples: tec_dir = os.path.expanduser('~/data/aux/IONEX') + tec_file = get_ionex_filename('20190519', tec_dir=tec_dir, sol_code='jpl') + mins, lats, lons, tec_maps = read_ionex(tec_file)[:4] + """ + + # functions for parsing ionex file + # link: https://github.com/daniestevez/jupyter_notebooks/blob/master/IONEX.ipynb + def parse_map(tec_map, key='TEC', exponent=-1): + tec_map = re.split(f'.*END OF {key} MAP', tec_map)[0] + tec_map = [np.fromstring(l, sep=' ') for l in re.split('.*LAT/LON1/LON2/DLON/H\\n', tec_map)[1:]] + return np.stack(tec_map) * 10**exponent + + # read IONEX file + with open(tec_file, 'r') as f: + fc = f.read() + + # read header + header = fc.split('END OF HEADER')[0].split('\n') + for line in header: + if line.strip().endswith('# OF MAPS IN FILE'): + num_map = int(line.split()[0]) + elif line.strip().endswith('DLAT'): + lat0, lat1, lat_step = [float(x) for x in line.split()[:3]] + elif line.strip().endswith('DLON'): + lon0, lon1, lon_step = [float(x) for x in line.split()[:3]] + elif line.strip().endswith('EXPONENT'): + exponent = float(line.split()[0]) + + # spatial coordinates + num_lat = int((lat1 - lat0) / lat_step + 1) + num_lon = int((lon1 - lon0) / lon_step + 1) + lats = np.arange(lat0, lat0 + num_lat * lat_step, lat_step) + lons = np.arange(lon0, lon0 + num_lon * lon_step, lon_step) + + # time stamps + min_step = 24 * 60 / (num_map - 1) + mins = np.arange(0, num_map * min_step, min_step) + + # read TEC and its RMS maps + tec_maps = np.array([parse_map(t, key='TEC', exponent=exponent) + for t in fc.split('START OF TEC MAP')[1:]], dtype=np.float32) + rms_maps = np.array([parse_map(t, key='RMS', exponent=exponent) + for t in fc.split('START OF RMS MAP')[1:]], dtype=np.float32) + + return mins, lats, lons, tec_maps, rms_maps + + +#################################### Plot ###################################### + +def plot_ionex(tec_file, save_fig=False): + """Plot the IONEX file as animation. + + Parameters: tec_file - str, path to the local uncompressed IONEX file + save_fig - bool, save the animation to file + Returns: out_fig - str, path to the output animation file + """ + + # read TEC file + sol_code = os.path.basename(tec_file)[:3] + sol_name = IGS_SOLUTION_NAMES.get(sol_code, 'Unknown') + mins, lats, lons, tec_maps = read_ionex(tec_file)[:4] + + # basic info + num_map = len(mins) + lat_step = np.median(np.diff(lats)) + lon_step = np.median(np.diff(lons)) + N = np.max(lats) - lat_step / 2; S = np.min(lats) + lat_step / 2 + W = np.min(lons) - lon_step / 2; E = np.max(lons) + lon_step / 2 + vmax = round_to_1(np.nanmax(tec_maps) * 0.9) + date_obj = get_ionex_date(tec_file)[1] + + # init figure + proj_obj = ccrs.PlateCarree() + fig, ax = plt.subplots(figsize=[9, 4], subplot_kw=dict(projection=proj_obj)) + im = ax.imshow(tec_maps[0,:,:], vmin=0, vmax=vmax, extent=(W, E, S, N), + origin='upper', animated=True, interpolation='nearest') + ax.coastlines() + draw_lalo_label(ax, geo_box=(W, N, E, S), projection=proj_obj, print_msg=False) + # colorbar + cbar = fig.colorbar(im, shrink=0.5) + cbar.set_label('TECU') + fig.tight_layout() + + # update image + global ind + ind = 0 + def animate(*args): + global ind + ind += 1 + if ind >= num_map: + ind -= num_map + + # update image & title + im.set_array(tec_maps[ind,:,:]) + dt_obj = date_obj + dt.timedelta(minutes=mins[ind]) + ax.set_title(f'{dt_obj.isoformat()} - {sol_name}') + return im, + + # play animation + ani = FuncAnimation(fig, animate, interval=200, blit=True, save_count=num_map) + + # output + out_fig = '{}.gif'.format(os.path.abspath(tec_file)) + if save_fig: + print('saving animation to {}'.format(out_fig)) + ani.save(out_fig, writer='imagemagick', dpi=300) + + print('showing animation ...') + plt.show() + + return out_fig diff --git a/mintpy/simulation/iono.py b/mintpy/simulation/iono.py index ba090d1e9..33ac32ea5 100644 --- a/mintpy/simulation/iono.py +++ b/mintpy/simulation/iono.py @@ -2,16 +2,11 @@ ############################################################ # Program is part of MintPy # # Copyright (c) 2013, Zhang Yunjun, Heresh Fattahi # -# Author: Zhang Yunjun, Heresh Fattahi, Feb 2020 # +# Author: Zhang Yunjun, Feb 2020 # ############################################################ # Useful links: -# IGS (at NASA's archive): https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html +# IGS (NASA): https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html # IMPC (DLR): https://impc.dlr.de/products/total-electron-content/near-real-time-tec/nrt-tec-global/ -# Contents -# Ionospheric Mapping Functions -# JPL high resolution GIM - I/O -# IGS low resolution GIM - download, I/O, plot -# Test # Recommend usage: # from mintpy.simulation import iono @@ -24,11 +19,7 @@ import matplotlib.pyplot as plt from scipy import interpolate -from mintpy.utils import ( - ptime, - readfile, - utils0 as ut, -) +from mintpy.utils import ptime, readfile, utils0 as ut # constants @@ -50,8 +41,6 @@ # Yunjun, Z., Fattahi, H., Pi, X., Rosen, P., Simons, M., Agram, P., & Aoki, Y. (2022). Range # Geolocation Accuracy of C-/L-band SAR and its Implications for Operational Stack Coregistration. # IEEE Trans. Geosci. Remote Sens., 60, doi:10.1109/TGRS.2022.3168509. -# Schaer, S., Gurtner, W., & Feltens, J. (1998). IONEX: The ionosphere map exchange format version 1.1. -# Paper presented at the Proceedings of the IGS AC workshop, Darmstadt, Germany, Darmstadt, Germany. def vtec2range_delay(vtec, inc_angle, freq, obs_type='phase'): """Calculate/predict the range delay in SAR from TEC in zenith direction. @@ -112,6 +101,7 @@ def prep_geometry_iono(geom_file, box=None, iono_height=450e3, print_msg=True): iono_lat/lon - float, latitude/longitude of LOS vector at the thin-shell in deg iono_height - float, height of the assume effective thin-shell ionosphere in m """ + def get_center_lat_lon(geom_file, box=None): """Get the lat/lon of the scene center""" meta = readfile.read_attribute(geom_file) @@ -544,435 +534,3 @@ def check_date_list_against_reference(date_list, dset_list, date_list_ref, fill_ dset_list[flag] = dset_list_bk return date_list, dset_list - - - -################################## IGS TEC ##################################### -# Low resolution ionospheric TEC products from IGS -# Including solutions from various centers, e.g., CODE, JPL, etc. -# Webiste: https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html -# Spatial resolution in latitude / longitude [deg]: 2.5 / 5.0 -# Temporal resolution [hour]: 2.0 / 1.0 - -def calc_igs_iono_ramp(tec_dir, date_str, geom_file, box=None, print_msg=True): - """Get 2D ionospheric delay from IGS TEC data for one acquisition. - due to the variation of the incidence angle along LOS. - - Parameters: tec_dir - str, path of the local TEC directory, i.e. ~/data/aux/IGS_TEC - date_str - str, date of interest in YYYYMMDD format - geom_file - str, path of the geometry file including incidenceAngle data - box - tuple of 4 int, subset in (x0, y0, x1, y1) - Returns: range_delay - 2D np.ndarray for the range delay in meters - vtec - float, TEC value in zenith direction in TECU - iono_lat/lon - float, latitude/longitude in degrees of LOS vector in ionosphere shell - iono_height - float, height in meter of LOS vector in ionosphere shell - iono_inc_angle - float, incidence angle in degrees of LOS vector in ionosphere shell - """ - # geometry - tec_file = dload_igs_tec(date_str, tec_dir, print_msg=print_msg) - iono_height = grab_ionex_height(tec_file) - (iono_inc_angle, - iono_lat, - iono_lon) = prep_geometry_iono(geom_file, box=box, iono_height=iono_height)[:3] - - # time - meta = readfile.read_attribute(geom_file) - utc_sec = float(meta['CENTER_LINE_UTC']) - if print_msg: - h, s = divmod(utc_sec, 3600) - m, s = divmod(s, 60) - print('UTC time: {:02.0f}:{:02.0f}:{:02.1f}'.format(h, m, s)) - - # extract zenith TEC - freq = SPEED_OF_LIGHT / float(meta['WAVELENGTH']) - vtec = get_igs_tec_value(tec_file, utc_sec, iono_lon, iono_lat) - rang_delay = vtec2range_delay(vtec, iono_inc_angle, freq) - - return rang_delay, vtec, iono_lat, iono_lon, iono_height, iono_inc_angle - - -def get_igs_tec_value(tec_file, utc_sec, lat, lon, interp_method='linear3d', rotate_tec_map=False, print_msg=True): - """Get the TEC value based on input lat/lon/datetime - Parameters: tec_file - str, path of local TEC file - utc_sec - float or 1D np.ndarray, UTC time of the day in seconds - lat/lon - float or 1D np.ndarray, latitude / longitude in degrees - interp_method - str, interpolation method - rotate_tec_map - bool, rotate the TEC map along the SUN direction, for linear3d only. - print_msg - bool, print out progress bar or not. - Returns: tec_val - float or 1D np.ndarray, TEC value in TECU - """ - - def interp_3d_rotate(interpfs, lons, lats, mins, lon, lat, utc_min): - ind0 = np.where((mins - utc_min) <=0)[0][-1] - ind1 = ind0 + 1 - lon0 = lon + (utc_min - mins[ind0]) * 360. / (24. * 60.) - lon1 = lon + (utc_min - mins[ind1]) * 360. / (24. * 60.) - tec_val0 = interpfs[ind0](lon0, lat) - tec_val1 = interpfs[ind1](lon1, lat) - tec_val = ( (mins[ind1] - utc_min) / (mins[ind1] - mins[ind0]) * tec_val0 - + (utc_min - mins[ind0]) / (mins[ind1] - mins[ind0]) * tec_val1 ) - return tec_val - - # read TEC file - lons, lats, mins, tecs = read_ionex_tec(tec_file)[:4] - tec_maps = tecs[0] - - # time info - utc_min = utc_sec / 60. - - # resample - if interp_method == 'nearest': - lon_ind = np.abs(lons - lon).argmin() - lat_ind = np.abs(lats - lat).argmin() - time_ind = np.abs(mins - utc_min).argmin() - tec_val = tec_maps[lon_ind, lat_ind, time_ind] - - elif interp_method in ['linear', 'linear2d', 'bilinear']: - time_ind = np.abs(mins.reshape(-1,1) - utc_min).argmin(axis=0) - if isinstance(time_ind, np.ndarray): - num = len(time_ind) - tec_val = np.zeros(num, dtype=np.float32) - prog_bar = ptime.progressBar(maxValue=num, print_msg=print_msg) - for i in range(num): - tec_val[i] = interpolate.interp2d(lons, lats, tec_maps[:, :, time_ind[i]].T, kind='linear')(lon[i], lat[i]) - prog_bar.update(i+1, every=200) - prog_bar.close() - else: - tec_val = interpolate.interp2d(lons, lats, tec_maps[:, :, time_ind].T, kind='linear')(lon, lat) - - elif interp_method in ['linear3d', 'trilinear']: - if not rotate_tec_map: - # option 1: interpolate between consecutive TEC maps - # testings shows better agreement with SAR obs than option 2. - tec_val = interpolate.interpn((lons, np.flip(lats), mins), - np.flip(tec_maps, axis=1), - (lon, lat, utc_min), - method='linear') - - else: - # option 2: interpolate between consecutive rotated TEC maps - # reference: equation (3) in Schaer and Gurtner (1998) - - # prepare interpolation functions in advance to speed up - interpfs = [] - for i in range(len(mins)): - interpfs.append(interpolate.interp2d(lons, lats, tec_maps[:, :, i].T, kind='linear')) - - if isinstance(utc_min, np.ndarray): - num = len(utc_min) - tec_val = np.zeros(num, dtype=np.float32) - prog_bar = ptime.progressBar(maxValue=num, print_msg=print_msg) - for i in range(num): - tec_val[i] = interp_3d_rotate(interpfs, lons, lats, mins, lon[i], lat[i], utc_min[i]) - prog_bar.update(i+1, every=200) - prog_bar.close() - else: - tec_val = interp_3d_rotate(interpfs, lons, lats, mins, lon, lat, utc_min) - - return tec_val - - -def get_igs_tec_filename(tec_dir, date_str, sol='jpl', datefmt='%Y%m%d'): - """Get the local file name of downloaded IGS TEC product.""" - dd = dt.datetime.strptime(date_str, datefmt) - doy = '{:03d}'.format(dd.timetuple().tm_yday) - yy = str(dd.year)[2:4] - - fname = "{a}g{d}0.{y}i.Z".format(a=sol.lower(), d=doy, y=yy) - fbase = fname[:-2] - tec_file = os.path.join(tec_dir, fbase) - return tec_file - - -def dload_igs_tec(d, out_dir, sol='jpl', datefmt='%Y%m%d', print_msg=False): - """Download IGS vertical TEC files computed by JPL - Link: https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html - """ - # date info - dd = dt.datetime.strptime(d, datefmt) - doy = '{:03d}'.format(dd.timetuple().tm_yday) - yy = str(dd.year)[2:4] - - # file name and http path - fbase = "{a}g{d}0.{y}i.Z".format(a=sol.lower(), d=doy, y=yy) - src_dir = "https://cddis.nasa.gov/archive/gnss/products/ionex/{0}/{1}".format(dd.year, doy) - - # input/output filename - fname_src = os.path.join(src_dir, fbase) - fname_dst = os.path.join(out_dir, fbase) - fname_dst_uncomp = fname_dst[:-2] - - # download - cmd = 'wget --continue --auth-no-challenge "{}"'.format(fname_src) - if os.path.isfile(fname_dst) and os.path.getsize(fname_dst) > 1000: - cmd += ' --timestamping ' - if not print_msg: - cmd += ' --quiet ' - else: - print(cmd) - - # run cmd in output dir - pwd = os.getcwd() - os.chdir(out_dir) - os.system(cmd) - os.chdir(pwd) - - # uncompress - # if output file 1) does not exist or 2) smaller than 400k in size or 3) older - if (not os.path.isfile(fname_dst_uncomp) - or os.path.getsize(fname_dst_uncomp) < 600e3 - or os.path.getmtime(fname_dst_uncomp) < os.path.getmtime(fname_dst)): - cmd = "gzip --force --keep --decompress {}".format(fname_dst) - if print_msg: - print(cmd) - os.system(cmd) - - return fname_dst_uncomp - - -def grab_ionex_height(tec_file): - """Grab the height of the thin-shell ionosphere from IONEX file""" - # read ionex file into list of lines - with open(tec_file, 'r') as f: - lines = f.readlines() - - # search for height - DHGT - iono_height = None - for line in lines: - c = [i.strip() for i in line.strip().replace('\n', '').split()] - if c[-1] == 'DHGT': - iono_height = float(c[0]) * 1e3 - break - return iono_height - - -def read_ionex_tec(igs_file): - """Read IGS TEC file in IONEX format. - Download from https://cddis.nasa.gov/Data_and_Derived_Products/GNSS/atmospheric_products.html. - Parameters: igs_file - str, path of the TEC file - Returns: lon - 1D np.ndarray for the longitude in size of (num_lon,) in degrees - lat - 1D np.ndarray for the latitude in size of (num_lat,) in degrees - tec_times - 1D np.ndarray for the time of the day in size of (num_map,) in minutes - tec_array - 4D np.ndarray for the vertical TEC value in size of (2, num_lon, num_lat, num_map) in TECU - 1 TECU = 10^16 electrons / m^2 - tec_type - str, TEC solution - """ - - igs_code_dict = { - 'igr' : 'IGS (Rapid)', - 'igs' : 'IGS (Final)', - 'jpr' : 'JPL (Rapid)', - 'jpl' : 'JPL (Final)', - 'cor' : 'CODE (Rapid)', - 'cod' : 'CODE (Final)', - } - tec_code = os.path.basename(igs_file)[:3] - tec_type = igs_code_dict.get(tec_code, None) - - ## ========================================================================= - ## - ## The following section reads the lines of the ionex file for 1 day - ## (13 maps total) into an array a[]. It also retrieves the thin-shell - ## ionosphere height used by IGS, the lat./long. spacing, etc. for use - ## later in this script. - ## - ## ========================================================================= - #print 'Transfering IONEX data format to a TEC/DTEC array for ',ymd_date - - ## Opening and reading the IONEX file into memory as a list - linestring = open(igs_file, 'r').read() - LongList = linestring.split('\n') - - ## Create two lists without the header and only with the TEC and DTEC maps (based on code from ionFR.py) - AddToList = 0 - TECLongList = [] - DTECLongList = [] - for i in range(len(LongList)-1): - ## Once LongList[i] gets to the DTEC maps, append DTECLongList - if LongList[i].split()[-1] == 'MAP': - if LongList[i].split()[-2] == 'RMS': - AddToList = 2 - if AddToList == 1: - TECLongList.append(LongList[i]) - if AddToList == 2: - DTECLongList.append(LongList[i]) - ## Determine the number of TEC/DTEC maps - if LongList[i].split()[-1] == 'FILE': - if LongList[i].split()[-3:-1] == ['MAPS','IN']: - num_maps = float(LongList[i].split()[0]) - ## Determine the shell ionosphere height (usually 450 km for IGS IONEX files) - if LongList[i].split()[-1] == 'DHGT': - ion_H = float(LongList[i].split()[0]) - ## Determine the range in lat. and long. in the ionex file - if LongList[i].split()[-1] == 'DLAT': - start_lat = float(LongList[i].split()[0]) - end_lat = float(LongList[i].split()[1]) - incr_lat = float(LongList[i].split()[2]) - if LongList[i].split()[-1] == 'DLON': - start_lon = float(LongList[i].split()[0]) - end_lon = float(LongList[i].split()[1]) - incr_lon = float(LongList[i].split()[2]) - ## Find the end of the header so TECLongList can be appended - if LongList[i].split()[0] == 'END': - if LongList[i].split()[2] == 'HEADER': - AddToList = 1 - - ## Variables that indicate the number of points in Lat. and Lon. - points_lon = ((end_lon - start_lon)/incr_lon) + 1 - points_lat = ((end_lat - start_lat)/incr_lat) + 1 ## Note that incr_lat is defined as '-' here - num_row = int(np.ceil(points_lon/16)) ## Note there are 16 columns of data in IONEX format - - ## 4-D array that will contain TEC & DTEC (a[0] and a[1], respectively) values - a = np.zeros((2,int(points_lon),int(points_lat),int(num_maps))) - - ## Selecting only the TEC/DTEC values to store in the 4-D array. - for Titer in range(2): - counterMaps = 1 - UseList = [] - if Titer == 0: - UseList = TECLongList - elif Titer == 1: - UseList = DTECLongList - for i in range(len(UseList)): - ## Pointing to first map (out of 13 maps) then by changing 'counterMaps' the other maps are selected - if UseList[i].split()[0] == ''+str(counterMaps)+'': - if UseList[i].split()[-4] == 'START': - ## Pointing to the starting Latitude then by changing 'counterLat' we select TEC data - ## at other latitudes within the selected map - counterLat = 0 - newstartLat = float(str(start_lat)) - for iLat in range(int(points_lat)): - if UseList[i+2+counterLat].split()[0].split('-')[0] == ''+str(newstartLat)+'': - ## Adding to array a[] a line of Latitude TEC data - counterLon = 0 - for row_iter in range(num_row): - for item in range(len(UseList[i+3+row_iter+counterLat].split())): - a[Titer,counterLon,iLat,counterMaps-1] = UseList[i+3+row_iter+counterLat].split()[item] - counterLon = counterLon + 1 - if '-'+UseList[i+2+counterLat].split()[0].split('-')[1] == ''+str(newstartLat)+'': - ## Adding to array a[] a line of Latitude TEC data. Same chunk as above but - ## in this case we account for the TEC values at negative latitudes - counterLon = 0 - for row_iter in range(num_row): - for item in range(len(UseList[i+3+row_iter+counterLat].split())): - a[Titer,counterLon,iLat,counterMaps-1] = UseList[i+3+row_iter+counterLat].split()[item] - counterLon = counterLon + 1 - counterLat = counterLat + row_iter + 2 - newstartLat = newstartLat + incr_lat - counterMaps = counterMaps + 1 - - ## ========================================================================= - ## - ## The section creates a new array that is a copy of a[], but with the lower - ## left-hand corner defined as the initial element (whereas a[] has the - ## upper left-hand corner defined as the initial element). This also - ## accounts for the fact that IONEX data is in 0.1*TECU. - ## - ## ========================================================================= - - ## The native sampling of the IGS maps minutes - incr_time = 24*60/int(num_maps-1) - tec_array = np.zeros((2,int(points_lon),int(points_lat),int(num_maps))) - - for Titer in range(2): - #incr = 0 - for ilat in range(int(points_lat)): - tec_array[Titer,:,ilat,:] = 0.1*a[Titer,:,int(points_lat-1-ilat),:] - - lat = np.arange(start_lat, start_lat + points_lat*incr_lat, incr_lat) - lon = np.arange(start_lon, start_lon + points_lon*incr_lon, incr_lon) - tec_times = np.arange(0, incr_time*num_maps, incr_time) - - return lon, lat, tec_times, tec_array, tec_type - - -def plot_tec_animation(tec_file, save=False): - """Plot the input tec file as animation""" - from cartopy import crs as ccrs - from matplotlib.animation import FuncAnimation - from mintpy.utils.map import draw_lalo_label - - def grab_date(tec_file, datefmt='%Y-%m-%d'): - """Grab the date in YYYYMMDD format from the TEC filename""" - tec_file = os.path.basename(tec_file) - # year - year = tec_file.split('.')[1][:2] - if year[0] == '9': - year = '19' + year - else: - year = '20' + year - year = int(year) - - # month and day - doy = int(tec_file.split('.')[0].split('g')[1][:3]) - dt_obj = dt.datetime(year, 1, 1) + dt.timedelta(doy - 1) - date_str = dt_obj.strftime(datefmt) - return date_str - - # read TEC file - lon, lat, tec_times, tec_array, tec_type = read_ionex_tec(tec_file) - vmax = ut.round_to_1(np.nanmax(tec_array[0]) * 0.9) - # SNWE info - geo_box = (np.min(lon), np.max(lat), np.max(lon), np.min(lat)) # (W, N, E, S) - extent = (geo_box[0], geo_box[2], geo_box[3], geo_box[1]) # (W, E, S, N) - # date/time info - date_str = grab_date(tec_file) - num_map = len(tec_times) - - # init figure - fig, ax = plt.subplots(figsize=[9, 4], subplot_kw=dict(projection=ccrs.PlateCarree())) - ax.coastlines() - draw_lalo_label(ax, geo_box, projection=ccrs.PlateCarree(), print_msg=False) - # init image - im = ax.imshow(tec_array[0,:,:,0].T, vmin=0, vmax=vmax, extent=extent, - origin='upper', animated=True, interpolation='nearest') - # colorbar - cbar = fig.colorbar(im, shrink=0.5) - cbar.set_label('TECU') - fig.tight_layout() - - # update image - global ind - ind = 0 - def animate(*args): - global ind - ind += 1 - if ind >= num_map: - ind -= num_map - - # update image - data = tec_array[0,:,:,ind].T - im.set_array(data) - - # update title - dt_obj = dt.datetime.strptime(date_str, '%Y-%m-%d') + dt.timedelta(minutes=tec_times[ind]) - title = dt_obj.isoformat() - if tec_type: - title += ' - {}'.format(tec_type) - ax.set_title(title) - - return im, - - # play animation - ani = FuncAnimation(fig, animate, interval=300, blit=True, save_count=num_map) - - # output - if save: - outfig = '{}.gif'.format(os.path.abspath(tec_file)) - print('saving animation to {}'.format(outfig)) - ani.save(outfig, writer='imagemagick', dpi=300) - print('showing animation ...') - plt.show() - return - - - -################################## test ######################################## - -def main(iargs=None): - tec_dir = os.path.expanduser('~/data/aux/GIM_IGS') - tec_file = dload_igs_tec('20190409', tec_dir) - plot_tec_animation(tec_file, save=True) - return -if __name__ == '__main__': - main(sys.argv[1:]) diff --git a/tests/data/jplg3190.15i b/tests/data/jplg3190.15i new file mode 100644 index 000000000..7950755af --- /dev/null +++ b/tests/data/jplg3190.15i @@ -0,0 +1,11414 @@ + 1.0 IONOSPHERE MAPS GPS IONEX VERSION / TYPE +GIM V3.0 JPL - GNISD 18-nov-2015 02:09 PGM / RUN BY / DATE +JPL'S GLOBAL IONOSPHERE MAPS YEAR 2015 DAY 319 COMMENT +Global Ionospheric Maps (GIM) are generated on an hourly DESCRIPTION +and daily basis at JPL using data from up to 100 GPS sites DESCRIPTION +of the IGS and others institutions. DESCRIPTION +The vertical TEC is modeled in a solar-geomagnetic DESCRIPTION +reference frame using bi-cubic splines on a spherical grid. DESCRIPTION +A Kalman filter is used to solve simultaneously for DESCRIPTION +instrumental biases and VTEC on the grid (as stochastic DESCRIPTION +parameters). DESCRIPTION +Contact Address: gpsiono@cobra.jpl.nasa.gov . DESCRIPTION + 2015 11 15 0 0 0 EPOCH OF FIRST MAP + 2015 11 16 0 0 0 EPOCH OF LAST MAP + 7200 INTERVAL + 13 # OF MAPS IN FILE + NONE MAPPING FUNCTION + 10.0 ELEVATION CUTOFF +One-way carrier phase leveled to code OBSERVABLES USED + 170 # OF STATIONS + 31 # OF SATELLITES + 6371.0 BASE RADIUS + 2 MAP DIMENSION + 450.0 450.0 0.0 HGT1 / HGT2 / DHGT + 87.5 -87.5 -2.5 LAT1 / LAT2 / DLAT + -180.0 180.0 5.0 LON1 / LON2 / DLON + -1 EXPONENT +TEC/RMS values in 0.1 TECU; 9999, if no value available COMMENT +DIFFERENTIAL CODE BIASES START OF AUX DATA + 01 -7.571 0.007 PRN / BIAS / RMS + 02 8.990 0.004 PRN / BIAS / RMS + 03 -5.325 0.007 PRN / BIAS / RMS + 05 2.745 0.004 PRN / BIAS / RMS + 06 -7.010 0.007 PRN / BIAS / RMS + 07 3.025 0.004 PRN / BIAS / RMS + 08 -7.290 0.007 PRN / BIAS / RMS + 09 -4.799 0.004 PRN / BIAS / RMS + 10 -5.817 0.007 PRN / BIAS / RMS + 11 3.657 0.007 PRN / BIAS / RMS + 12 3.692 0.004 PRN / BIAS / RMS + 13 3.166 0.004 PRN / BIAS / RMS + 14 1.938 0.004 PRN / BIAS / RMS + 15 2.745 0.004 PRN / BIAS / RMS + 16 2.534 0.004 PRN / BIAS / RMS + 17 2.885 0.004 PRN / BIAS / RMS + 18 3.131 0.004 PRN / BIAS / RMS + 19 5.692 0.007 PRN / BIAS / RMS + 20 1.201 0.000 PRN / BIAS / RMS + 21 3.271 0.004 PRN / BIAS / RMS + 22 7.306 0.004 PRN / BIAS / RMS + 23 8.885 0.004 PRN / BIAS / RMS + 24 -5.992 0.004 PRN / BIAS / RMS + 25 -7.852 0.004 PRN / BIAS / RMS + 26 -8.904 0.004 PRN / BIAS / RMS + 27 -5.220 0.007 PRN / BIAS / RMS + 28 2.780 0.004 PRN / BIAS / RMS + 29 2.324 0.004 PRN / BIAS / RMS + 30 -6.589 0.004 PRN / BIAS / RMS + 31 4.394 0.004 PRN / BIAS / RMS + 32 -1.992 0.007 PRN / BIAS / RMS + AJAC 29.466 0.011 STATION / BIAS / RMS + ALBH -7.517 0.011 STATION / BIAS / RMS + ALGO 10.799 0.011 STATION / BIAS / RMS + ALIC 12.343 0.014 STATION / BIAS / RMS + ALRT 1.676 0.011 STATION / BIAS / RMS + AMC2 5.255 0.011 STATION / BIAS / RMS + AQUI 14.939 0.011 STATION / BIAS / RMS + AREQ -0.289 0.018 STATION / BIAS / RMS + ARTU -10.078 0.011 STATION / BIAS / RMS + ASPA -14.289 0.025 STATION / BIAS / RMS + AUCK -0.815 0.018 STATION / BIAS / RMS + AZU1 41.150 0.011 STATION / BIAS / RMS + BARH 4.834 0.014 STATION / BIAS / RMS + BILL 44.378 0.011 STATION / BIAS / RMS + BJFS -15.832 0.014 STATION / BIAS / RMS + BLYT 43.325 0.011 STATION / BIAS / RMS + BOGI 2.904 0.011 STATION / BIAS / RMS + BOGT 7.957 0.018 STATION / BIAS / RMS + BOR1 19.255 0.011 STATION / BIAS / RMS + BRAN 5.676 0.011 STATION / BIAS / RMS + BRAZ 25.185 0.021 STATION / BIAS / RMS + BREW -2.359 0.011 STATION / BIAS / RMS + BRMU 18.027 0.014 STATION / BIAS / RMS + BUCU 9.045 0.011 STATION / BIAS / RMS + BZRG 25.571 0.011 STATION / BIAS / RMS + CAGS -11.727 0.014 STATION / BIAS / RMS + CAS1 -8.499 0.021 STATION / BIAS / RMS + CEDU -18.254 0.014 STATION / BIAS / RMS + CHIL 10.834 0.011 STATION / BIAS / RMS + CHUM 42.939 0.014 STATION / BIAS / RMS + CHUR 11.325 0.011 STATION / BIAS / RMS + CHWK 38.308 0.011 STATION / BIAS / RMS + CIT1 -8.078 0.011 STATION / BIAS / RMS + CLAR 9.185 0.011 STATION / BIAS / RMS + CMBB 18.238 0.011 STATION / BIAS / RMS + CMP9 8.659 0.011 STATION / BIAS / RMS + COCO 1.536 0.021 STATION / BIAS / RMS + CORD -3.727 0.025 STATION / BIAS / RMS + CRFP -10.464 0.011 STATION / BIAS / RMS + CRO1 4.132 0.018 STATION / BIAS / RMS + DAEJ -16.254 0.014 STATION / BIAS / RMS + DARW 13.010 0.014 STATION / BIAS / RMS + DAV1 14.764 0.021 STATION / BIAS / RMS + DGAR -8.394 0.021 STATION / BIAS / RMS + DHLG 12.062 0.011 STATION / BIAS / RMS + DRAO -0.640 0.011 STATION / BIAS / RMS + DUBO 15.852 0.011 STATION / BIAS / RMS + EBRE 1.676 0.011 STATION / BIAS / RMS + FAIR -9.903 0.014 STATION / BIAS / RMS + FALE 13.255 0.025 STATION / BIAS / RMS + FFMJ 7.536 0.011 STATION / BIAS / RMS + FLIN 15.957 0.011 STATION / BIAS / RMS + GENO 19.887 0.011 STATION / BIAS / RMS + GLSV 8.062 0.011 STATION / BIAS / RMS + GODE -4.218 0.014 STATION / BIAS / RMS + GOLD -6.569 0.011 STATION / BIAS / RMS + GOPE 4.659 0.011 STATION / BIAS / RMS + GRAS -18.604 0.011 STATION / BIAS / RMS + GRAZ 18.027 0.011 STATION / BIAS / RMS + GUAM -0.745 0.025 STATION / BIAS / RMS + GUAO -1.306 0.021 STATION / BIAS / RMS + GUAT 13.115 0.018 STATION / BIAS / RMS + HERS 5.887 0.011 STATION / BIAS / RMS + HNLC -21.482 0.025 STATION / BIAS / RMS + HNPT 10.203 0.014 STATION / BIAS / RMS + HOB2 14.378 0.018 STATION / BIAS / RMS + HOFN 23.817 0.011 STATION / BIAS / RMS + HOLB -4.254 0.011 STATION / BIAS / RMS + HOLM 2.062 0.011 STATION / BIAS / RMS + HOPB 10.027 0.011 STATION / BIAS / RMS + HRAO 3.396 0.021 STATION / BIAS / RMS + HUEG 5.220 0.011 STATION / BIAS / RMS + HYDE 6.413 0.021 STATION / BIAS / RMS + IISC -2.640 0.021 STATION / BIAS / RMS + INEG 20.168 0.014 STATION / BIAS / RMS + INVK -10.640 0.014 STATION / BIAS / RMS + IRKJ -10.078 0.014 STATION / BIAS / RMS + ISTA -10.218 0.011 STATION / BIAS / RMS + JOZ2 10.097 0.011 STATION / BIAS / RMS + JOZE -11.271 0.011 STATION / BIAS / RMS + JPLM -4.394 0.011 STATION / BIAS / RMS + KARR -3.622 0.014 STATION / BIAS / RMS + KELY -6.078 0.011 STATION / BIAS / RMS + KERG -13.552 0.028 STATION / BIAS / RMS + KGNI 17.501 0.014 STATION / BIAS / RMS + KIRU 3.887 0.011 STATION / BIAS / RMS + KOKB -7.973 0.025 STATION / BIAS / RMS + KOUC -8.218 0.018 STATION / BIAS / RMS + KOUR 4.518 0.021 STATION / BIAS / RMS + KSMV 18.097 0.014 STATION / BIAS / RMS + LAMA 13.396 0.011 STATION / BIAS / RMS + LEIJ 4.659 0.011 STATION / BIAS / RMS + LHAZ -5.832 0.014 STATION / BIAS / RMS + LONG 7.360 0.063 STATION / BIAS / RMS + LROC 13.817 0.011 STATION / BIAS / RMS + MAC1 12.869 0.021 STATION / BIAS / RMS + MADR 5.010 0.011 STATION / BIAS / RMS + MAG0 -10.815 0.018 STATION / BIAS / RMS + MARS 26.869 0.011 STATION / BIAS / RMS + MAS1 4.694 0.014 STATION / BIAS / RMS + MAT1 -3.411 0.011 STATION / BIAS / RMS + MATE 5.641 0.011 STATION / BIAS / RMS + MAUI 32.413 0.025 STATION / BIAS / RMS + MAW1 7.957 0.021 STATION / BIAS / RMS + MCM4 -8.043 0.028 STATION / BIAS / RMS + MDVJ 11.641 0.011 STATION / BIAS / RMS + MEDI 0.378 0.011 STATION / BIAS / RMS + METS -8.534 0.011 STATION / BIAS / RMS + MIKL 12.834 0.011 STATION / BIAS / RMS + MKEA 9.115 0.028 STATION / BIAS / RMS + MOBN -7.692 0.011 STATION / BIAS / RMS + MONP 20.799 0.011 STATION / BIAS / RMS + MORP -20.534 0.011 STATION / BIAS / RMS + NANO 8.939 0.011 STATION / BIAS / RMS + NICO 26.518 0.011 STATION / BIAS / RMS + NIST 4.238 0.011 STATION / BIAS / RMS + NKLG -13.657 0.025 STATION / BIAS / RMS + NNOR 3.501 0.018 STATION / BIAS / RMS + NOT1 -3.903 0.011 STATION / BIAS / RMS + NRC1 3.782 0.014 STATION / BIAS / RMS + NRIL -4.394 0.014 STATION / BIAS / RMS + NVSK 20.343 0.014 STATION / BIAS / RMS + NYAL 19.080 0.011 STATION / BIAS / RMS + OHI2 4.518 0.025 STATION / BIAS / RMS + ONSA 3.536 0.011 STATION / BIAS / RMS + OPMT -7.692 0.011 STATION / BIAS / RMS + ORID 11.466 0.011 STATION / BIAS / RMS + PADO 25.746 0.011 STATION / BIAS / RMS + PARC -8.008 0.025 STATION / BIAS / RMS + PDEL 9.255 0.014 STATION / BIAS / RMS + PENC 9.150 0.011 STATION / BIAS / RMS + PERT -5.973 0.018 STATION / BIAS / RMS + PIMO 3.887 0.025 STATION / BIAS / RMS + PIN1 19.992 0.011 STATION / BIAS / RMS + POL2 -12.078 0.014 STATION / BIAS / RMS + POLV 26.097 0.011 STATION / BIAS / RMS + POTS 4.483 0.021 STATION / BIAS / RMS + PRDS 15.992 0.011 STATION / BIAS / RMS + PTBB -11.341 0.011 STATION / BIAS / RMS + QAQ1 0.238 0.011 STATION / BIAS / RMS + QUIN -7.236 0.011 STATION / BIAS / RMS + RABT -1.236 0.011 STATION / BIAS / RMS + REYK 24.589 0.011 STATION / BIAS / RMS + RIOP 41.115 0.018 STATION / BIAS / RMS + ROCK 7.185 0.011 STATION / BIAS / RMS + SCH2 10.869 0.011 STATION / BIAS / RMS + SCIP 11.360 0.011 STATION / BIAS / RMS + SEY1 0.939 0.021 STATION / BIAS / RMS + SFDM 40.518 0.011 STATION / BIAS / RMS + SFER 25.536 0.011 STATION / BIAS / RMS + SNI1 15.711 0.014 STATION / BIAS / RMS + SOFI 14.483 0.011 STATION / BIAS / RMS + SPK1 7.746 0.014 STATION / BIAS / RMS + SPT0 4.203 0.011 STATION / BIAS / RMS + STJO 1.992 0.011 STATION / BIAS / RMS + SULP 18.904 0.011 STATION / BIAS / RMS + SUTH -7.482 0.025 STATION / BIAS / RMS + SUWN -12.675 0.014 STATION / BIAS / RMS + SYOG -13.131 0.021 STATION / BIAS / RMS + TABL -6.955 0.011 STATION / BIAS / RMS + THTI -18.464 0.028 STATION / BIAS / RMS + THU3 -7.482 0.011 STATION / BIAS / RMS + TIDB -11.622 0.014 STATION / BIAS / RMS + TIXI -5.868 0.014 STATION / BIAS / RMS + TLSE -13.868 0.011 STATION / BIAS / RMS + TORP 6.062 0.011 STATION / BIAS / RMS + TOW2 4.238 0.014 STATION / BIAS / RMS + TRAK 13.817 0.011 STATION / BIAS / RMS + TRO1 -14.815 0.011 STATION / BIAS / RMS + TSKB -20.499 0.014 STATION / BIAS / RMS + TUBI 19.290 0.011 STATION / BIAS / RMS + UCLP 8.238 0.011 STATION / BIAS / RMS + UCLU 40.799 0.011 STATION / BIAS / RMS + UNBJ 6.518 0.011 STATION / BIAS / RMS + URUM 5.360 0.014 STATION / BIAS / RMS + USUD -1.376 0.014 STATION / BIAS / RMS + UZHL 7.852 0.011 STATION / BIAS / RMS + VILL 4.448 0.011 STATION / BIAS / RMS + VNDP -6.359 0.011 STATION / BIAS / RMS + WHC1 9.887 0.011 STATION / BIAS / RMS + WHIT 14.553 0.011 STATION / BIAS / RMS + WIDC -8.710 0.011 STATION / BIAS / RMS + WILL -9.657 0.011 STATION / BIAS / RMS + WROC 9.010 0.011 STATION / BIAS / RMS + WSLR 35.080 0.011 STATION / BIAS / RMS + WSRT 1.255 0.011 STATION / BIAS / RMS + WTZR 13.571 0.011 STATION / BIAS / RMS + YAKT -9.903 0.014 STATION / BIAS / RMS + YAR2 -3.131 0.018 STATION / BIAS / RMS + YEBE 46.097 0.011 STATION / BIAS / RMS + YELL 1.501 0.011 STATION / BIAS / RMS + YKRO -2.675 0.025 STATION / BIAS / RMS + YSSK -12.990 0.014 STATION / BIAS / RMS + ZAMB -7.271 0.021 STATION / BIAS / RMS + ZECK 9.185 0.011 STATION / BIAS / RMS + ZIMJ 8.308 0.011 STATION / BIAS / RMS + ZIMM -11.131 0.011 STATION / BIAS / RMS +DIFFERENTIAL CODE BIASES END OF AUX DATA + END OF HEADER + 1 START OF TEC MAP + 2015 11 15 0 0 0 EPOCH OF CURRENT MAP + 87.5-180.0 180.0 5.0 450.0 LAT/LON1/LON2/DLON/H + 96 97 97 97 97 98 98 98 97 97 97 97 96 96 96 95 + 95 94 94 93 93 92 92 92 91 91 90 90 90 89 89 89 + 89 88 88 88 88 88 88 88 88 88 88 88 89 89 89 89 + 89 90 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27 27 27 27 27 27 27 27 27 27 22 22 22 22 22 + 22 21 21 21 21 21 21 21 21 21 21 21 21 25 25 25 + 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 + 25 25 25 25 25 25 25 25 25 + 13 END OF RMS MAP + END OF FILE diff --git a/tests/objects/ionex.py b/tests/objects/ionex.py new file mode 100755 index 000000000..97e951e02 --- /dev/null +++ b/tests/objects/ionex.py @@ -0,0 +1,101 @@ +#!/usr/bin/env python3 +############################################################ +# Program is part of MintPy # +# Copyright (c) 2013, Zhang Yunjun, Heresh Fattahi # +# Author: Zhang Yunjun, Jun 2022 # +############################################################ + + +import os +import numpy as np +from mintpy.objects import ionex + + +# test dataset at $MINTPY_HOME/tests/data directory +tec_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data") +date_str = '20151115' +sol_code = 'jpl' + + +def prep_test_data(prep_mode=True): + """Prepare the test data.""" + + os.makedirs(tec_dir, exist_ok=True) + tec_file = ionex.get_ionex_filename(date_str, + tec_dir=tec_dir, + sol_code=sol_code) + + if prep_mode: + + # download + if not os.path.isfile(tec_file): + print(f'download IONEX at {date_str} from {sol_code} to {tec_dir}') + tec_file = ionex.dload_ionex(date_str=date_str, + tec_dir=tec_dir, + sol_code=sol_code) + + # plot + print('plotting IONEX file as animation') + ionex.plot_ionex(tec_file, save_fig=False) + + return tec_file + + + +def test_read_ionex(): + """Test mintpy.objects.ionex.read_ionex()""" + + time_ind = 1 + x0, x1, y0, y1 = 3, 9, 28, 33 + tec_aoi = np.array( + [[71.8, 64.2, 55.9, 47.1, 38.6, 31.2], + [80.2, 73.9, 66.6, 58.2, 49.5, 41.1], + [83.2, 79.6, 74.6, 68. , 60.1, 51.6], + [79.6, 79.5, 78.1, 74.5, 68.5, 60.9], + [71.9, 74.5, 76.5, 76.2, 73.1, 67.3]], + ) + + # get IONEX file path + tec_file = ionex.get_ionex_filename(date_str, + tec_dir=tec_dir, + sol_code=sol_code) + + # read IONEX + mins, lats, lons, tec_maps = ionex.read_ionex(tec_file)[:4] + + # compare + assert np.allclose(tec_maps[time_ind, y0:y1, x0:x1], tec_aoi) + + +def test_get_ionex_value(): + """Test mintpy.objects.ionex.get_ionex_value()""" + + lat, lon = -21.3, -67.4 # northern Chile + utc_sec = 23 * 3600 + 7 * 60 # 23:07 UTC + + methods = ['nearest', 'linear2d', 'linear3d', 'linear3d'] + rotates = [False, False, False, True] + values = [60.8, 58.90687978, 64.96605174, 65.15525905] + + # get IONEX file path + tec_file = ionex.get_ionex_filename(date_str, + tec_dir=tec_dir, + sol_code=sol_code) + + # compare + for method, rotate, value in zip(methods, rotates, values): + tec_val = ionex.get_ionex_value(tec_file, utc_sec, lat, lon, + interp_method=method, + rotate_tec_map=rotate) + assert np.allclose(tec_val, value, atol=1e-05, rtol=1e-05) + + +if __name__ == '__main__': + + print(f'Testing {__file__}') + + prep_test_data(prep_mode=False) + + test_read_ionex() + + test_get_ionex_value()