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sentinel1_xml_mappings.py
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"""
xpath mapping from xml file, with convertion functions
"""
import os.path
import warnings
import zipfile
from datetime import datetime
import aiohttp
import fsspec
import geopandas as gpd
import numpy as np
import pandas as pd
import pyproj
import xarray
import xarray as xr
from numpy.polynomial import Polynomial
from shapely.geometry import Point, Polygon
namespaces = {
"xfdu": "urn:ccsds:schema:xfdu:1",
"s1sarl1": "http://www.esa.int/safe/sentinel-1.0/sentinel-1/sar/level-1",
"s1sar": "http://www.esa.int/safe/sentinel-1.0/sentinel-1/sar",
"s1": "http://www.esa.int/safe/sentinel-1.0/sentinel-1",
"safe": "http://www.esa.int/safe/sentinel-1.0",
"gml": "http://www.opengis.net/gml",
}
# xpath convertion function: they take only one args (list returned by xpath)
scalar = lambda x: x[0]
scalar_int = lambda x: int(x[0])
scalar_float = lambda x: float(x[0])
date_converter = lambda x: datetime.strptime(x[0], "%Y-%m-%dT%H:%M:%S.%f")
datetime64_array = lambda x: np.array(
[np.datetime64(date_converter([sx])).astype("datetime64[ns]") for sx in x]
)
int_1Darray_from_string = lambda x: np.fromstring(x[0], dtype=int, sep=" ")
float_2Darray_from_string_list = lambda x: np.vstack(
[np.fromstring(e, dtype=float, sep=" ") for e in x]
)
list_of_float_1D_array_from_string = lambda x: [
np.fromstring(e, dtype=float, sep=" ") for e in x
]
int_1Darray_from_join_strings = lambda x: np.fromstring(" ".join(x), dtype=int, sep=" ")
float_1Darray_from_join_strings = lambda x: np.fromstring(
" ".join(x), dtype=float, sep=" "
)
int_array = lambda x: np.array(x, dtype=int)
bool_array = lambda x: np.array(x, dtype=bool)
float_array = lambda x: np.array(x, dtype=float)
uniq_sorted = lambda x: np.array(sorted(set(x)))
ordered_category = lambda x: pd.Categorical(x).reorder_categories(x, ordered=True)
normpath = lambda paths: [os.path.normpath(p) for p in paths]
def get_test_file(fname):
"""
get test file from https://cyclobs.ifremer.fr/static/sarwing_datarmor/xsardata/
file is unzipped and extracted to `config['data_dir']`
Parameters
----------
fname: str
file name to get (without '.zip' extension)
Returns
-------
str
path to file, relative to `config['data_dir']`
"""
config = {"data_dir": "/tmp"}
def url_get(url, cache_dir=os.path.join(config["data_dir"], "fsspec_cache")):
"""
Get fil from url, using caching.
Parameters
----------
url: str
cache_dir: str
Cache dir to use. default to `os.path.join(config['data_dir'], 'fsspec_cache')`
Raises
------
FileNotFoundError
Returns
-------
filename: str
The local file name
Notes
-----
Due to fsspec, the returned filename won't match the remote one.
"""
if "://" in url:
with fsspec.open(
"filecache::%s" % url,
https={"client_kwargs": {"timeout": aiohttp.ClientTimeout(total=3600)}},
filecache={
"cache_storage": os.path.join(
os.path.join(config["data_dir"], "fsspec_cache")
)
},
) as f:
fname = f.name
else:
fname = url
return fname
res_path = config["data_dir"]
base_url = "https://cyclobs.ifremer.fr/static/sarwing_datarmor/xsardata"
file_url = "%s/%s.zip" % (base_url, fname)
if not os.path.exists(os.path.join(res_path, fname)):
warnings.warn("Downloading %s" % file_url)
local_file = url_get(file_url)
warnings.warn("Unzipping %s" % os.path.join(res_path, fname))
with zipfile.ZipFile(local_file, "r") as zip_ref:
zip_ref.extractall(res_path)
return os.path.join(res_path, fname)
def or_ipf28(xpath):
"""change xpath to match ipf <2.8 or >2.9 (for noise range)"""
xpath28 = xpath.replace("noiseRange", "noise").replace("noiseAzimuth", "noise")
if xpath28 != xpath:
xpath += " | %s" % xpath28
return xpath
def list_poly_from_list_string_coords(str_coords_list):
footprints = []
for gmlpoly in str_coords_list:
footprints.append(
Polygon(
[
(float(lon), float(lat))
for lat, lon in [latlon.split(",") for latlon in gmlpoly.split(" ")]
]
)
)
return footprints
# xpath_mappings:
# first level key is xml file type
# second level key is variable name
# mappings may be 'xpath', or 'tuple(func,xpath)', or 'dict'
# - xpath is an lxml xpath
# - func is a decoder function fed by xpath
# - dict is a nested dict, to create more hierarchy levels.
xpath_mappings = {
"manifest": {
"ipf_version": (
scalar_float,
"//xmlData/safe:processing/safe:facility/safe:software/@version",
),
"swath_type": (scalar, "//s1sarl1:instrumentMode/s1sarl1:mode"),
# 'product': (scalar, '/xfdu:XFDU/informationPackageMap/xfdu:contentUnit/@textInfo'),
"polarizations": (
ordered_category,
"//s1sarl1:standAloneProductInformation/s1sarl1:transmitterReceiverPolarisation",
),
"footprints": (
list_poly_from_list_string_coords,
"//safe:frame/safe:footPrint/gml:coordinates",
),
"product_type": (
scalar,
"//s1sarl1:standAloneProductInformation/s1sarl1:productType",
),
"mission": (scalar, "//safe:platform/safe:familyName"),
"satellite": (scalar, "//safe:platform/safe:number"),
"start_date": (date_converter, "//safe:acquisitionPeriod/safe:startTime"),
"stop_date": (date_converter, "//safe:acquisitionPeriod/safe:stopTime"),
"aux_cal": (
scalar,
'//metadataSection/metadataObject/metadataWrap/xmlData/safe:processing/safe:resource/safe:processing/safe:resource[@role="AUX_CAL"]/@name',
),
"aux_pp1": (
scalar,
'//metadataSection/metadataObject/metadataWrap/xmlData/safe:processing/safe:resource/safe:processing/safe:resource[@role="AUX_PP1"]/@name',
),
"aux_ins": (
scalar,
'//metadataSection/metadataObject/metadataWrap/xmlData/safe:processing/safe:resource/safe:processing/safe:resource[@role="AUX_INS"]/@name',
),
"aux_cal_sl2": (
scalar,
'//metadataSection/metadataObject/metadataWrap/xmlData/safe:processing/safe:resource[@role="AUX_CAL"]/@name',
),
"annotation_files": (
normpath,
'/xfdu:XFDU/dataObjectSection/*[@repID="s1Level1ProductSchema"]/byteStream/fileLocation/@href',
),
"measurement_files": (
normpath,
'/xfdu:XFDU/dataObjectSection/*[@repID="s1Level1MeasurementSchema"]/byteStream/fileLocation/@href',
),
"noise_files": (
normpath,
'/xfdu:XFDU/dataObjectSection/*[@repID="s1Level1NoiseSchema"]/byteStream/fileLocation/@href',
),
"calibration_files": (
normpath,
'/xfdu:XFDU/dataObjectSection/*[@repID="s1Level1CalibrationSchema"]/byteStream/fileLocation/@href',
),
"xsd_product_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1Level1ProductSchema"]/metadataReference/@href',
),
"xsd_Noise_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1Level1NoiseSchema"]/metadataReference/@href',
),
"xsd_RFI_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1Level1RfiSchema"]/metadataReference/@href',
),
"xsd_calibration_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1Level1CalibrationSchema"]/metadataReference/@href',
),
"xsd_objecttype_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1ObjectTypesSchema"]/metadataReference/@href',
),
"xsd_measurement_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1Level1MeasurementSchema"]/metadataReference/@href',
),
"xsd_level1product_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1Level1ProductPreviewSchema"]/metadataReference/@href',
),
"xsd_overlay_file": (
normpath,
'/xfdu:XFDU/metadataSection/metadataObject[@ID="s1Level1MapOverlaySchema"]/metadataReference/@href',
),
"instrument_configuration_id": (
scalar,
"//s1sarl1:standAloneProductInformation/s1sarl1:instrumentConfigurationID/text()",
),
},
"calibration": {
"polarization": (scalar, "/calibration/adsHeader/polarisation"),
# 'number_of_vector': '//calibration/calibrationVectorList/@count',
"line": (
np.array,
"//calibration/calibrationVectorList/calibrationVector/line",
),
"sample": (
int_1Darray_from_string,
"//calibration/calibrationVectorList/calibrationVector[1]/pixel",
),
"sigma0_lut": (
float_2Darray_from_string_list,
"//calibration/calibrationVectorList/calibrationVector/sigmaNought",
),
"gamma0_lut": (
float_2Darray_from_string_list,
"//calibration/calibrationVectorList/calibrationVector/gamma",
),
"azimuthTime": (
datetime64_array,
"/calibration/calibrationVectorList/calibrationVector/azimuthTime",
),
},
"noise": {
"mode": (scalar, "/noise/adsHeader/mode"),
"polarization": (scalar, "/noise/adsHeader/polarisation"),
"range": {
"line": (
int_array,
or_ipf28("/noise/noiseRangeVectorList/noiseRangeVector/line"),
),
"sample": (
lambda x: [np.fromstring(s, dtype=int, sep=" ") for s in x],
or_ipf28("/noise/noiseRangeVectorList/noiseRangeVector/pixel"),
),
"noiseLut": (
lambda x: [np.fromstring(s, dtype=float, sep=" ") for s in x],
or_ipf28("/noise/noiseRangeVectorList/noiseRangeVector/noiseRangeLut"),
),
"azimuthTime": (
datetime64_array,
"/noise/noiseRangeVectorList/noiseRangeVector/azimuthTime",
),
},
"azi": {
"swath": "/noise/noiseAzimuthVectorList/noiseAzimuthVector/swath",
"line": (
lambda x: [np.fromstring(str(s), dtype=int, sep=" ") for s in x],
"/noise/noiseAzimuthVectorList/noiseAzimuthVector/line",
),
"line_start": (
int_array,
"/noise/noiseAzimuthVectorList/noiseAzimuthVector/firstAzimuthLine",
),
"line_stop": (
int_array,
"/noise/noiseAzimuthVectorList/noiseAzimuthVector/lastAzimuthLine",
),
"sample_start": (
int_array,
"/noise/noiseAzimuthVectorList/noiseAzimuthVector/firstRangeSample",
),
"sample_stop": (
int_array,
"/noise/noiseAzimuthVectorList/noiseAzimuthVector/lastRangeSample",
),
"noiseLut": (
lambda x: [np.fromstring(str(s), dtype=float, sep=" ") for s in x],
"/noise/noiseAzimuthVectorList/noiseAzimuthVector/noiseAzimuthLut",
),
},
},
"annotation": {
"product_type": (scalar, "/product/adsHeader/productType"),
"swath_subswath": (scalar, "/product/adsHeader/swath"),
"line": (
uniq_sorted,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/line",
),
"sample": (
uniq_sorted,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/pixel",
),
"incidenceAngle": (
float_array,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/incidenceAngle",
),
"elevationAngle": (
float_array,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/elevationAngle",
),
"height": (
float_array,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/height",
),
"azimuthTime": (
datetime64_array,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/azimuthTime",
),
"slantRangeTime": (
float_array,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/slantRangeTime",
),
"longitude": (
float_array,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/longitude",
),
"latitude": (
float_array,
"/product/geolocationGrid/geolocationGridPointList/geolocationGridPoint/latitude",
),
"polarization": (scalar, "/product/adsHeader/polarisation"),
"line_time_range": (
datetime64_array,
'/product/imageAnnotation/imageInformation/*[contains(name(),"LineUtcTime")]',
),
"line_size": (
scalar,
"/product/imageAnnotation/imageInformation/numberOfLines",
),
"sample_size": (
scalar,
"/product/imageAnnotation/imageInformation/numberOfSamples",
),
"incidence_angle_mid_swath": (
scalar_float,
"/product/imageAnnotation/imageInformation/incidenceAngleMidSwath",
),
"azimuth_time_interval": (
scalar_float,
"/product/imageAnnotation/imageInformation/azimuthTimeInterval",
),
"slant_range_time_image": (
scalar_float,
"/product/imageAnnotation/imageInformation/slantRangeTime",
),
"rangePixelSpacing": (
scalar_float,
"/product/imageAnnotation/imageInformation/rangePixelSpacing",
),
"azimuthPixelSpacing": (
scalar_float,
"/product/imageAnnotation/imageInformation/azimuthPixelSpacing",
),
"denoised": (
scalar,
"/product/imageAnnotation/processingInformation/thermalNoiseCorrectionPerformed",
),
"pol": (scalar, "/product/adsHeader/polarisation"),
"pass": (scalar, "/product/generalAnnotation/productInformation/pass"),
"platform_heading": (
scalar_float,
"/product/generalAnnotation/productInformation/platformHeading",
),
"radar_frequency": (
scalar_float,
"/product/generalAnnotation/productInformation/radarFrequency",
),
"range_sampling_rate": (
scalar_float,
"/product/generalAnnotation/productInformation/rangeSamplingRate",
),
"azimuth_steering_rate": (
scalar_float,
"/product/generalAnnotation/productInformation/azimuthSteeringRate",
),
"orbit_time": (
datetime64_array,
"//product/generalAnnotation/orbitList/orbit/time",
),
"orbit_frame": (np.array, "//product/generalAnnotation/orbitList/orbit/frame"),
"orbit_pos_x": (
float_array,
"//product/generalAnnotation/orbitList/orbit/position/x",
),
"orbit_pos_y": (
float_array,
"//product/generalAnnotation/orbitList/orbit/position/y",
),
"orbit_pos_z": (
float_array,
"//product/generalAnnotation/orbitList/orbit/position/z",
),
"orbit_vel_x": (
float_array,
"//product/generalAnnotation/orbitList/orbit/velocity/x",
),
"orbit_vel_y": (
float_array,
"//product/generalAnnotation/orbitList/orbit/velocity/y",
),
"orbit_vel_z": (
float_array,
"//product/generalAnnotation/orbitList/orbit/velocity/z",
),
"number_of_bursts": (scalar_int, "/product/swathTiming/burstList/@count"),
"linesPerBurst": (scalar, "/product/swathTiming/linesPerBurst"),
"samplesPerBurst": (scalar, "/product/swathTiming/samplesPerBurst"),
"all_bursts": (np.array, "//product/swathTiming/burstList/burst"),
"burst_azimuthTime": (
datetime64_array,
"//product/swathTiming/burstList/burst/azimuthTime",
),
"burst_azimuthAnxTime": (
float_array,
"//product/swathTiming/burstList/burst/azimuthAnxTime",
),
"burst_sensingTime": (
datetime64_array,
"//product/swathTiming/burstList/burst/sensingTime",
),
"burst_byteOffset": (
np.array,
"//product/swathTiming/burstList/burst/byteOffset",
),
"burst_firstValidSample": (
float_2Darray_from_string_list,
"//product/swathTiming/burstList/burst/firstValidSample",
),
"burst_lastValidSample": (
float_2Darray_from_string_list,
"//product/swathTiming/burstList/burst/lastValidSample",
),
"nb_dcestimate": (scalar_int, "/product/dopplerCentroid/dcEstimateList/@count"),
"nb_geoDcPoly": (
scalar_int,
"/product/dopplerCentroid/dcEstimateList/dcEstimate[1]/geometryDcPolynomial/@count",
),
"nb_dataDcPoly": (
scalar_int,
"/product/dopplerCentroid/dcEstimateList/dcEstimate[1]/dataDcPolynomial/@count",
),
"nb_fineDce": (
scalar_int,
"/product/dopplerCentroid/dcEstimateList/dcEstimate[1]/fineDceList/@count",
),
"dc_azimuth_time": (
datetime64_array,
"//product/dopplerCentroid/dcEstimateList/dcEstimate/azimuthTime",
),
"dc_t0": (np.array, "//product/dopplerCentroid/dcEstimateList/dcEstimate/t0"),
"dc_geoDcPoly": (
list_of_float_1D_array_from_string,
"//product/dopplerCentroid/dcEstimateList/dcEstimate/geometryDcPolynomial",
),
"dc_dataDcPoly": (
list_of_float_1D_array_from_string,
"//product/dopplerCentroid/dcEstimateList/dcEstimate/dataDcPolynomial",
),
"dc_rmserr": (
np.array,
"//product/dopplerCentroid/dcEstimateList/dcEstimate/dataDcRmsError",
),
"dc_rmserrAboveThres": (
bool_array,
"//product/dopplerCentroid/dcEstimateList/dcEstimate/dataDcRmsErrorAboveThreshold",
),
"dc_azstarttime": (
datetime64_array,
"//product/dopplerCentroid/dcEstimateList/dcEstimate/fineDceAzimuthStartTime",
),
"dc_azstoptime": (
datetime64_array,
"//product/dopplerCentroid/dcEstimateList/dcEstimate/fineDceAzimuthStopTime",
),
"dc_slantRangeTime": (
float_array,
"///product/dopplerCentroid/dcEstimateList/dcEstimate/fineDceList/fineDce/slantRangeTime",
),
"dc_frequency": (
float_array,
"///product/dopplerCentroid/dcEstimateList/dcEstimate/fineDceList/fineDce/frequency",
),
"nb_fmrate": (
scalar_int,
"/product/generalAnnotation/azimuthFmRateList/@count",
),
"fmrate_azimuthtime": (
datetime64_array,
"//product/generalAnnotation/azimuthFmRateList/azimuthFmRate/azimuthTime",
),
"fmrate_t0": (
float_array,
"//product/generalAnnotation/azimuthFmRateList/azimuthFmRate/t0",
),
"fmrate_c0": (
float_array,
"//product/generalAnnotation/azimuthFmRateList/azimuthFmRate/c0",
),
"fmrate_c1": (
float_array,
"//product/generalAnnotation/azimuthFmRateList/azimuthFmRate/c1",
),
"fmrate_c2": (
float_array,
"//product/generalAnnotation/azimuthFmRateList/azimuthFmRate/c2",
),
"fmrate_azimuthFmRatePolynomial": (
list_of_float_1D_array_from_string,
"//product/generalAnnotation/azimuthFmRateList/azimuthFmRate/azimuthFmRatePolynomial",
),
"ap_azimuthTime": (
datetime64_array,
"/product/antennaPattern/antennaPatternList/antennaPattern/azimuthTime",
),
"ap_roll": (
float_array,
"/product/antennaPattern/antennaPatternList/antennaPattern/roll",
),
"ap_swath": (
lambda x: np.array(x),
"/product/antennaPattern/antennaPatternList/antennaPattern/swath",
),
"ap_elevationAngle": (
list_of_float_1D_array_from_string,
"/product/antennaPattern/antennaPatternList/antennaPattern/elevationAngle",
),
"ap_incidenceAngle": (
list_of_float_1D_array_from_string,
"/product/antennaPattern/antennaPatternList/antennaPattern/incidenceAngle",
),
"ap_slantRangeTime": (
list_of_float_1D_array_from_string,
"/product/antennaPattern/antennaPatternList/antennaPattern/slantRangeTime",
),
"ap_terrainHeight": (
float_array,
"/product/antennaPattern/antennaPatternList/antennaPattern/terrainHeight",
),
"ap_elevationPattern": (
list_of_float_1D_array_from_string,
"/product/antennaPattern/antennaPatternList/antennaPattern/elevationPattern",
),
"sm_nbPerSwat": (
int_array,
"/product/swathMerging/swathMergeList/swathMerge/swathBoundsList/@count",
),
"sm_swath": (
lambda x: np.array(x),
"/product/swathMerging/swathMergeList/swathMerge/swath",
),
"sm_azimuthTime": (
datetime64_array,
"/product/swathMerging/swathMergeList/swathMerge/swathBoundsList/swathBounds/azimuthTime",
),
"sm_firstAzimuthLine": (
int_array,
"/product/swathMerging/swathMergeList/swathMerge/swathBoundsList/swathBounds/firstAzimuthLine",
),
"sm_lastAzimuthLine": (
int_array,
"/product/swathMerging/swathMergeList/swathMerge/swathBoundsList/swathBounds/lastAzimuthLine",
),
"sm_firstRangeSample": (
int_array,
"/product/swathMerging/swathMergeList/swathMerge/swathBoundsList/swathBounds/firstRangeSample",
),
"sm_lastRangeSample": (
int_array,
"/product/swathMerging/swathMergeList/swathMerge/swathBoundsList/swathBounds/lastRangeSample",
),
},
"xsd": {
"all": (
str,
"/xsd:schema/xsd:complexType/xsd:sequence/xsd:element/xsd:annotation/xsd:documentation",
),
"names": (str, "/xsd:schema/xsd:complexType/xsd:sequence/xsd:element/@name"),
"sensingtime": (
str,
"/xsd:schema/xsd:complexType/xsd:sequence/xsd:element/sensingTime",
),
},
}
def signal_lut_raw(line, sample, lut_sigma0, lut_gamma0, azimuth_times):
ds = xr.Dataset()
ds["sigma0_lut"] = xr.DataArray(
lut_sigma0,
dims=["line", "sample"],
coords={"line": line, "sample": sample},
name="sigma0",
attrs={"description": "look up table sigma0"},
)
ds["gamma0_lut"] = xr.DataArray(
lut_gamma0,
dims=["line", "sample"],
coords={"line": line, "sample": sample},
name="gamma0",
attrs={"description": "look up table gamma0"},
)
ds["azimuthTime"] = xr.DataArray(
azimuth_times,
dims=["line"],
coords={"line": line},
attrs={"description": "azimuth times associated to the signal look up table"},
)
return ds
def noise_lut_range_raw(lines, samples, noiseLuts, azimuthTimes):
"""
Parameters
----------
lines: np.ndarray
1D array of lines. lut is defined at each line
samples: list of np.ndarray
arrays of samples. list length is same as samples. each array define samples where lut is defined
noiseLuts: list of np.ndarray
arrays of luts. Same structure as samples.
azimuthTimes: np.ndarray
1D array of azimuth dates associated to each lines of the noise range grid
Returns
-------
"""
ds = xr.Dataset()
# check that all the noiseLuts vector are the same size in range, in old IPF eg <=2017, there was one +/- 1 point over 634
minimum_pts = 100000
normalized_noise_luts = []
normalized_samples = []
for uu in range(len(noiseLuts)):
if len(noiseLuts[uu]) < minimum_pts:
minimum_pts = len(noiseLuts[uu])
# reduce to the smaller number of points (knowing that it is quite often that last noise value is zero )
for uu in range(len(noiseLuts)):
normalized_noise_luts.append(noiseLuts[uu][0:minimum_pts])
normalized_samples.append(samples[uu][0:minimum_pts])
tmp_noise = np.stack(normalized_noise_luts)
ds["noise_lut"] = xr.DataArray(
tmp_noise,
coords={"line": lines, "sample": samples[0][0:minimum_pts]},
dims=["line", "sample"],
)
try:
ds["azimuthTime"] = xr.DataArray(
azimuthTimes, coords={"line": lines}, dims=["line"]
)
except (
ValueError
): # for IPF2.72 for instance there is no azimuthTimes associated to the noise range LUT
ds["azimuthTime"] = xr.DataArray(
np.ones(len(lines)) * np.nan, coords={"line": lines}, dims=["line"]
)
# ds['sample'] = xr.DataArray(np.stack(normalized_samples), coords={'lines': lines, 'sample_index': np.arange(minimum_pts)},
# dims=['lines', 'sample_index'])
return ds
def noise_lut_azi_raw_grd(
line_azi,
line_azi_start,
line_azi_stop,
sample_azi_start,
sample_azi_stop,
noise_azi_lut,
swath,
):
ds = xr.Dataset()
for ii, swathi in enumerate(
swath
): # with 2018 data the noise vector are not the same size -> stacking impossible
ds["noise_lut_%s" % swathi] = xr.DataArray(
noise_azi_lut[ii], coords={"line": line_azi[ii]}, dims=["line"]
)
ds["line_start"] = xr.DataArray(
line_azi_start, coords={"swath": swath}, dims=["swath"]
)
ds["line_stop"] = xr.DataArray(
line_azi_stop, coords={"swath": swath}, dims=["swath"]
)
ds["sample_start"] = xr.DataArray(
sample_azi_start, coords={"swath": swath}, dims=["swath"]
)
ds["sample_stop"] = xr.DataArray(
sample_azi_stop, coords={"swath": swath}, dims=["swath"]
)
return ds
def noise_lut_azi_raw_slc(
line_azi,
line_azi_start,
line_azi_stop,
sample_azi_start,
sample_azi_stop,
noise_azi_lut,
swath,
):
ds = xr.Dataset()
# if 'WV' in mode: # there is no noise in azimuth for WV acquisitions
if swath == []: # WV SLC case
ds["noise_lut"] = xr.DataArray(
1.0
) # set noise_azimuth to one to make post steps like noise_azi*noise_range always possible
ds["line_start"] = xr.DataArray(line_azi_start, attrs={"swath": swath})
ds["line_stop"] = xr.DataArray(line_azi_stop, attrs={"swath": swath})
ds["sample_start"] = xr.DataArray(sample_azi_start, attrs={"swath": swath})
ds["sample_stop"] = xr.DataArray(sample_azi_stop, attrs={"swath": swath})
else:
ds["noise_lut"] = xr.DataArray(
noise_azi_lut[0], coords={"line": line_azi[0]}, dims=["line"]
) # only on subswath opened
ds["line_start"] = xr.DataArray(line_azi_start[0], attrs={"swath": swath})
ds["line_stop"] = xr.DataArray(line_azi_stop[0], attrs={"swath": swath})
ds["sample_start"] = xr.DataArray(sample_azi_start[0], attrs={"swath": swath})
ds["sample_stop"] = xr.DataArray(sample_azi_stop[0], attrs={"swath": swath})
# ds['noise_lut'] = xr.DataArray(np.stack(noise_azi_lut).T, coords={'line_index': np.arange(len(line_azi[0])), 'swath': swath},
# dims=['line_index', 'swath'])
# ds['line'] = xr.DataArray(np.stack(line_azi).T, coords={'line_index': np.arange(len(line_azi[0])), 'swath': swath},
# dims=['line_index', 'swath'])
return ds
def datetime64_array(dates):
"""list of datetime to np.datetime64 array"""
return np.array([np.datetime64(d) for d in dates])
def df_files(annotation_files, measurement_files, noise_files, calibration_files):
# get polarizations and file number from filename
pols = [os.path.basename(f).split("-")[3].upper() for f in annotation_files]
num = [
int(os.path.splitext(os.path.basename(f))[0].split("-")[8])
for f in annotation_files
]
dsid = [os.path.basename(f).split("-")[1].upper() for f in annotation_files]
# check that dsid are spatialy uniques (i.e. there is only one dsid per geographic position)
# some SAFES like WV, dsid are not uniques ('WV1' and 'WV2')
# we want them uniques, and compatibles with gdal sentinel driver (ie 'WV_012')
pols_count = len(set(pols))
subds_count = len(annotation_files) // pols_count
dsid_count = len(set(dsid))
if dsid_count != subds_count:
dsid_rad = dsid[0][:-1] # WV
dsid = ["%s_%03d" % (dsid_rad, n) for n in num]
assert (
len(set(dsid)) == subds_count
) # probably an unknown mode we need to handle
df = pd.DataFrame(
{
"polarization": pols,
"dsid": dsid,
"annotation": annotation_files,
"measurement": measurement_files,
"noise": noise_files,
"calibration": calibration_files,
},
index=num,
)
return df
def xsd_files_func(xsd_product_file):
"""
return a xarray Dataset with path of the different xsd files
:param xsd_product: str
:return:
"""
ds = xr.Dataset()
ds["xsd_product"] = xarray.DataArray(xsd_product_file)
return ds
def orbit(
time,
frame,
pos_x,
pos_y,
pos_z,
vel_x,
vel_y,
vel_z,
orbit_pass,
platform_heading,
return_xarray=True,
):
"""
Parameters
----------
return_xarray: bool, True-> return a xarray.Dataset, False-> returns a GeoDataFrame
Returns
-------
geopandas.GeoDataFrame
with 'geometry' as position, 'time' as index, 'velocity' as velocity, and 'geocent' as crs.
"""
if (frame[0] != "Earth Fixed") or (np.unique(frame).size != 1):
raise NotImplementedError('All orbit frames must be of type "Earth Fixed"')
if return_xarray is False:
crs = pyproj.crs.CRS(proj="geocent", ellps="WGS84", datum="WGS84")
res = gpd.GeoDataFrame(
{"velocity": list(map(Point, zip(vel_x, vel_y, vel_z)))},
geometry=list(map(Point, zip(pos_x, pos_y, pos_z))),
crs=crs,
index=time,
)
else:
res = xr.Dataset()
res["velocity_x"] = xr.DataArray(vel_x, dims=["time"], coords={"time": time})
res["velocity_y"] = xr.DataArray(vel_y, dims=["time"], coords={"time": time})
res["velocity_z"] = xr.DataArray(vel_z, dims=["time"], coords={"time": time})
res["position_x"] = xr.DataArray(pos_x, dims=["time"], coords={"time": time})
res["position_y"] = xr.DataArray(pos_y, dims=["time"], coords={"time": time})
res["position_z"] = xr.DataArray(pos_z, dims=["time"], coords={"time": time})
res.attrs = {
"orbit_pass": orbit_pass,
"platform_heading": platform_heading,
"frame": frame[0],
}
return res
def azimuth_fmrate(azimuthtime, t0, c0, c1, c2, polynomial):
"""
decode FM rate information from xml annotations
Parameters
----------
azimuthtime
t0
c0
c1
c2
polynomial
Returns
-------
xarray.Dataset
containing the polynomial coefficient for each of the FM rate along azimuth time coordinates
"""
if (np.sum([c.size for c in [c0, c1, c2]]) != 0) and (len(polynomial) == 0):
# old IPF annotation
polynomial = np.stack([c0, c1, c2], axis=1)
res = xr.Dataset()
res["t0"] = xr.DataArray(
t0,
dims=["azimuthTime"],
coords={"azimuthTime": azimuthtime},
attrs={"source": xpath_mappings["annotation"]["fmrate_t0"][1]},
)
res["azimuthFmRatePolynomial"] = xr.DataArray(
[Polynomial(p) for p in polynomial],
dims=["azimuthTime"],
coords={"azimuthTime": azimuthtime},
attrs={
"source": xpath_mappings["annotation"]["fmrate_azimuthFmRatePolynomial"][1]
},
)
return res
def image(
product_type,
line_time_range,
line_size,
sample_size,
incidence_angle_mid_swath,
azimuth_time_interval,
slant_range_time_image,
azimuthPixelSpacing,
rangePixelSpacing,
swath_subswath,
radar_frequency,
range_sampling_rate,
azimuth_steering_rate,
):
"""
Decode attribute describing the SAR image
Parameters
----------
product_type: str
line_time_range: int
line_size: int
sample_size: int
incidence_angle_mid_swath: float
azimuth_time_interval: float [ in seconds]
slant_range_time_image: float [ in seconds]
azimuthPixelSpacing: int [m]
rangePixelSpacing: int [m]
swath_subswath: str
radar_frequency: float [second-1]
range_sampling_rate: float
azimuth_steering_rate: float
Returns
-------
xarray.Dataset
"""
if product_type == "SLC" or product_type == "SL2":
pixel_sample_m = rangePixelSpacing / np.sin(
np.radians(incidence_angle_mid_swath)
)
else:
pixel_sample_m = rangePixelSpacing
tmp = {
"LineUtcTime": (line_time_range, "line_time_range"),
"numberOfLines": (line_size, "line_size"),
"numberOfSamples": (sample_size, "sample_size"),
"azimuthPixelSpacing": (azimuthPixelSpacing, "azimuthPixelSpacing"),
"slantRangePixelSpacing": (rangePixelSpacing, "rangePixelSpacing"),
"groundRangePixelSpacing": (pixel_sample_m, "rangePixelSpacing"),
"incidenceAngleMidSwath": (
incidence_angle_mid_swath,
"incidence_angle_mid_swath",
),
"azimuthTimeInterval": (azimuth_time_interval, "azimuth_time_interval"),
"slantRangeTime": (slant_range_time_image, "slant_range_time_image"),
"swath_subswath": (swath_subswath, "swath_subswath"),
"radarFrequency": (radar_frequency, "radar_frequency"),
"rangeSamplingRate": (range_sampling_rate, "range_sampling_rate"),
"azimuthSteeringRate": (azimuth_steering_rate, "azimuth_steering_rate"),
}
ds = xr.Dataset()
for ke in tmp:
ds[ke] = xr.DataArray(
tmp[ke][0], attrs={"source": xpath_mappings["annotation"][tmp[ke][1]][1]}
)
return ds
def bursts(
line_per_burst,
sample_per_burst,
burst_azimuthTime,
burst_azimuthAnxTime,
burst_sensingTime,
burst_byteOffset,
burst_firstValidSample,
burst_lastValidSample,
):
"""return burst as an xarray dataset"""
da = xr.Dataset()
if (line_per_burst == 0) and (sample_per_burst == 0):
pass
else:
# convert to float, so we can use NaN as missing value, instead of -1
burst_firstValidSample = burst_firstValidSample.astype(float)
burst_lastValidSample = burst_lastValidSample.astype(float)
burst_firstValidSample[burst_firstValidSample == -1] = np.nan
burst_lastValidSample[burst_lastValidSample == -1] = np.nan
da = xr.Dataset(
{
"azimuthTime": ("burst", burst_azimuthTime),