nansat.mappers package
Submodules
nansat.mappers.envisat module
- class nansat.mappers.envisat.Envisat
Bases:
objectMethods/data shared between Envisat mappers
This class is needed to read awkward N1 format of ENVISAT Mostly it support reading of variables from ADS (additional data sets) which are TIE_POINTS_ADS (MERIS) or GEOLOCATION_GRID_ADS (ASAR)
- allADSParams = {'ASA_': {'list': {'first_line_incidence_angle': {'dataType': gdal.GDT_Float32, 'offset': 113, 'units': 'deg'}, 'first_line_lats': {'dataType': gdal.GDT_Int32, 'offset': 157, 'units': '(10)^-6 deg'}, 'first_line_longs': {'dataType': gdal.GDT_Int32, 'offset': 201, 'units': '(10)^-6 deg'}, 'first_line_samp_numbers': {'dataType': gdal.GDT_Float32, 'offset': 25, 'units': ''}, 'first_line_slant_range_times': {'dataType': gdal.GDT_Float32, 'offset': 69, 'units': 'ns'}, 'last_line_incidence_angle': {'dataType': gdal.GDT_Float32, 'offset': 367, 'units': 'deg'}, 'last_line_lats': {'dataType': gdal.GDT_Int32, 'offset': 411, 'units': '(10)^-6 deg'}, 'last_line_longs': {'dataType': gdal.GDT_Int32, 'offset': 455, 'units': '(10)^-6 deg'}, 'last_line_samp_numbers': {'dataType': gdal.GDT_Int32, 'offset': 279, 'units': ''}, 'last_line_slant_range_times': {'dataType': gdal.GDT_Float32, 'offset': 323, 'units': 'ns'}, 'num_lines': {'dataType': gdal.GDT_Int16, 'offset': 13, 'units': ''}}, 'name': 'DS_NAME="GEOLOCATION GRID ADS "\n', 'width': 11}, 'MER_': {'list': {'DME altitude': {'dataType': gdal.GDT_Int32, 'offset': 581, 'units': 'm'}, 'DME latitude corrections': {'dataType': gdal.GDT_Int32, 'offset': 1149, 'units': '(10)^-6 deg'}, 'DME longitude corrections': {'dataType': gdal.GDT_Int32, 'offset': 1433, 'units': '(10)^-6 deg'}, 'DME roughness': {'dataType': gdal.GDT_UInt32, 'offset': 865, 'units': 'm'}, 'latitude': {'dataType': gdal.GDT_Int32, 'offset': 13, 'units': '(10)^-6 deg'}, 'longitude': {'dataType': gdal.GDT_Int32, 'offset': 297, 'units': '(10)^-6 deg'}, 'mean sea level pressure': {'dataType': gdal.GDT_UInt16, 'offset': 3137, 'units': 'hPa'}, 'meridional winds': {'dataType': gdal.GDT_Int16, 'offset': 2995, 'units': 'm*s-1'}, 'relative humidity': {'dataType': gdal.GDT_UInt16, 'offset': 3421, 'units': '%'}, 'sun azimuth angles': {'dataType': gdal.GDT_Int32, 'offset': 2001, 'units': '(10)^-6 deg'}, 'sun zenith angles': {'dataType': gdal.GDT_UInt32, 'offset': 1717, 'units': '(10)^-6 deg'}, 'total ozone': {'dataType': gdal.GDT_UInt16, 'offset': 3279, 'units': 'DU'}, 'viewing azimuth angles': {'dataType': gdal.GDT_Int32, 'offset': 2569, 'units': '(10)^-6 deg'}, 'viewing zenith angles': {'dataType': gdal.GDT_UInt32, 'offset': 2285, 'units': '(10)^-6 deg'}, 'zonal winds': {'dataType': gdal.GDT_Int16, 'offset': 2853, 'units': 'm*s-1'}}, 'name': 'DS_NAME="Tie points ADS "\n', 'width': 71}}
- structFmt = {gdal.GDT_Float32: '>f', gdal.GDT_Int16: '>h', gdal.GDT_Int32: '>i', gdal.GDT_UInt16: '>H', gdal.GDT_UInt32: '>I'}
- lonlatNames = {'ASA_': ['first_line_longs', 'first_line_lats'], 'MER_': ['longitude', 'latitude']}
- setup_ads_parameters(filename, gdalMetadata)
Select set of params and read offset of ADS
- read_offset_from_header(gadsDSName)
Read offset of ADS from text header.
Find a location of gadsDSName. Adjust the location with textOffset and read the text at the location. Convert the text to integer and set it into offsetDict.
- Returns:
offsetDict – offset of DS, size of DS, number of records, size of record
- Return type:
dictionary
- read_binary_line(offset, fmtString, length)
Read line with binary data at given offset
Open file Read values of given size, at given offset, given times Convert to list of values of given format and return
- Parameters:
offset (int, start of reading)
fmtString (str, data type format)
lentgh (number of values to read)
- Returns:
binaryValues – values which are read from the file. the number of elements is length
- Return type:
list
- read_scaling_gads(indeces)
Read Scaling Factor GADS to get scalings of MERIS L1/L2
- Parameters:
filename (string)
indeces (list)
- Return type:
list
- get_array_from_ADS(adsName)
Create VRT with a band from Envisat ADS metadata
Read offsets of the <adsName> ADS. Read 2D matrix of binary values from ADS from file. Read last line ADS (in case of ASAR). Zoom array with ADS data to <zoomSize>. Zooming is needed to create smooth matrices. Array is zoomed to small size because it is stred in memory. Later the VRT with zoomed array is VRT.get_resized_vrt() in order to match the size of the Nansat onject. Create VRT from the ADS array.
- Parameters:
adsName (str) – name of variable from ADS to read. should match allADSParams
- Returns:
adsVrt – vrt with a band created from ADS array
- Return type:
- create_VRT_from_ADS(adsName, zoomSize=500)
Create VRT with a band from Envisat ADS metadata
Read offsets of the <adsName> ADS. Read 2D matrix of binary values from ADS from file. Zoom array with ADS data to <zoomSize>. Zooming is needed to create smooth matrices. Array is zoomed to small size because it is stred in memory. Later the VRT with zoomed array is VRT.get_resized_vrt() in order to match the size of the Nansat object.
Create VRT from the ADS array.
- Parameters:
adsName (str) – name of variable from ADS to read. should match allADSParams
zoomSize (int, optional, 500) – size, to which original matrix from ADSR is zoomed using scipy.zoom
- Returns:
adsVrt
- Return type:
VRT, vrt with a band created from ADS array
- get_ads_vrts(gdalDataset, adsNames, zoomSize=500, step=1, **kwargs)
Create list with VRTs with zoomed and resized ADS arrays
- For given names of variables (which should match self.allADSParams):
Get VRT with zoomed ADS array Get resized VRT
- Parameters:
gdalDataset (GDAL Dataset) – input dataset
adsNames (list with strings) – names of varaiables from self.allADSParams[‘list’]
zoomSize (int, 500) – size to which the ADS array will be zoomed by scipy.zoom
step (int, 1) – step, at which data will be given
- Returns:
adsVRTs – list with resized VRT with zoomed arrays
- Return type:
list with VRT
- add_geolocation_from_ads(gdalDataset, zoomSize=500, step=1)
Add geolocation domain metadata to the dataset
Get VRTs with zoomed arrays of lon and lat Create geolocation object and add to the metadata
- Parameters:
gdalDataset (GDAL Dataset) – input dataset
zoomSize (int, optional, 500) – size, to which the ADS array will be zoomed using scipy array of this size will be stored in memory
step (int) – step of pixel and line in GeolocationArrays. lat/lon grids are generated at that step
Modifies
--------
metadata (Adds Geolocation Array)
nansat.mappers.globcolour module
- class nansat.mappers.globcolour.Globcolour
Bases:
objectMapper for GLOBCOLOR L3M products
- varname2wkv = {'BBP_mean': 'volume_backscattering_coefficient_of_radiative_flux_in_sea_water_due_to_suspended_particles', 'CDM_mean': 'volume_absorption_coefficient_of_radiative_flux_in_sea_water_due_to_dissolved_organic_matter', 'CHL1_mean': 'mass_concentration_of_chlorophyll_a_in_sea_water', 'CHL2_mean': 'mass_concentration_of_chlorophyll_a_in_sea_water', 'KD490_mean': 'volume_attenuation_coefficient_of_downwelling_radiative_flux_in_sea_water', 'L412_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L443_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L490_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L510_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L531_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L555_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L620_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L670_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L681_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'L709_mean': 'surface_upwelling_spectral_radiance_in_air_emerging_from_sea_water', 'PAR_mean': 'surface_downwelling_photosynthetic_radiative_flux_in_air'}
- make_rrsw_meta_entry(nlwMetaEntry)
Make metaEntry for calculation of Rrsw
nansat.mappers.hdf4_mapper module
- class nansat.mappers.hdf4_mapper.HDF4Mapper(x_size=1, y_size=1, metadata=None, nomem=False, **kwargs)
Bases:
VRT- find_metadata(iMetadata, iKey, default='')
Find metadata which has similar key
- Parameters:
iMetadata (dict) – input metadata, usually gdalMetadata
iKey (str) – key to search for
default (str) – default value
nansat.mappers.mapper_aapp_l1b module
nansat.mappers.mapper_aapp_l1c module
nansat.mappers.mapper_amsr2_l1r module
nansat.mappers.mapper_amsr2_l3 module
- class nansat.mappers.mapper_amsr2_l3.Mapper(filename, gdalDataset, gdalMetadata, **kwargs)
Bases:
VRTMapper for Level-3 AMSR2 data from https://gcom-w1.jaxa.jp
- freqs = [6, 7, 10, 18, 23, 36, 89]
nansat.mappers.mapper_amsre_uham_leadfraction module
nansat.mappers.mapper_arome module
nansat.mappers.mapper_asar module
nansat.mappers.mapper_ascat module
nansat.mappers.mapper_aster_l1a module
nansat.mappers.mapper_aster_l1b module
nansat.mappers.mapper_case2reg module
nansat.mappers.mapper_cmems module
- nansat.mappers.mapper_cmems.get_gcmd_keywords_mapping()
nansat.mappers.mapper_csks module
nansat.mappers.mapper_ecmwf_metno module
nansat.mappers.mapper_emodnet module
nansat.mappers.mapper_generic module
- class nansat.mappers.mapper_generic.Mapper(inputFileName, gdalDataset, gdalMetadata, logLevel=30, rmMetadatas=['NETCDF_VARNAME', '_Unsigned', 'ScaleRatio', 'ScaleOffset', 'dods_variable'], **kwargs)
Bases:
VRT- repare_projection(projection)
Replace odd symbols in projection string ‘|’ => ‘,’; ‘&’ => ‘”’
- add_gcps_from_metadata(geoMetadata)
Get GCPs from strings in metadata and insert in dataset
- add_gcps_from_variables(filename)
Get GCPs from GCPPixel, GCPLine, GCPX, GCPY, GCPZ variables
nansat.mappers.mapper_geostationary module
- nansat.mappers.mapper_geostationary.arrays2LUTString(a, b)
- class nansat.mappers.mapper_geostationary.Mapper(filename, gdalDataset, gdalMetadata, **kwargs)
Bases:
VRTVRT with mapping of WKV for Geostationary satellite data
- calibration()
get calibration data for Geostationary satellites
- Returns:
satDict – calibration data for satellite ‘name’, ‘wavelengths’, ‘scale’, ‘offset’
- Return type:
dicts
nansat.mappers.mapper_globcolour_l3b module
- class nansat.mappers.mapper_globcolour_l3b.Mapper(filename, gdalDataset, gdalMetadata, latlonGrid=None, mask='', **kwargs)
Bases:
VRT,GlobcolourCreate VRT with mapping of WKV for MERIS Level 2 (FR or RR)
nansat.mappers.mapper_globcolour_l3m module
- class nansat.mappers.mapper_globcolour_l3m.Mapper(filename, gdalDataset, gdalMetadata, **kwargs)
Bases:
VRT,GlobcolourMapper for GLOBCOLOR L3M products
nansat.mappers.mapper_goci_l1 module
nansat.mappers.mapper_hirlam module
nansat.mappers.mapper_hirlam_wind_netcdf module
nansat.mappers.mapper_kmss module
nansat.mappers.mapper_landsat module
nansat.mappers.mapper_meris_l1 module
nansat.mappers.mapper_meris_l2 module
nansat.mappers.mapper_metno_hires_seaice module
nansat.mappers.mapper_metno_local_hires_seaice module
nansat.mappers.mapper_mod44w module
nansat.mappers.mapper_modis_l1 module
- class nansat.mappers.mapper_modis_l1.Mapper(filename, gdalDataset, gdalMetadata, GCP_COUNT=30, **kwargs)
Bases:
HDF4MapperVRT with mapping of WKV for MODIS Level 1 (QKM, HKM, 1KM)
nansat.mappers.mapper_ncep module
nansat.mappers.mapper_ncep_wind module
nansat.mappers.mapper_ncep_wind_online module
nansat.mappers.mapper_netcdf_cf module
Nansat NetCDF-CF mapper
Check CF-compliance of your files here: http://cfconventions.org/compliance-checker.html
- exception nansat.mappers.mapper_netcdf_cf.ContinueI
Bases:
Exception
nansat.mappers.mapper_netcdf_cf_sentinel1 module
nansat.mappers.mapper_nora10_local_vpv module
nansat.mappers.mapper_obpg_l2 module
- class nansat.mappers.mapper_obpg_l2.Mapper(filename, gdalDataset, gdalMetadata, GCP_COUNT=10, **kwargs)
Bases:
OBPGL2BaseClassMapper for SeaWIFS/MODIS/MERIS/VIIRS L2 data from OBPG
TODO: * Test on SeaWIFS * Test on MODIS Terra
nansat.mappers.mapper_obpg_l2_nc module
- class nansat.mappers.mapper_obpg_l2_nc.Mapper(filename, gdalDataset, gdalMetadata, GCP_COUNT=10, **kwargs)
Bases:
OBPGL2BaseClassMapper for SeaWIFS/MODIS/MERIS/VIIRS L2 data from OBPG in NC4 format
nansat.mappers.mapper_obpg_l3 module
- class nansat.mappers.mapper_obpg_l3.Mapper(filename, gdalDataset, gdalMetadata, **kwargs)
Bases:
VRTMapper for Level-3 Standard Mapped Image from http://oceancolor.gsfc.nasa.gov
- param2wkv = {'CDOM Index': 'volume_absorption_coefficient_of_radiative_flux_in_sea_water_due_to_dissolved_organic_matter', 'Chlorophyll a concentration': 'mass_concentration_of_chlorophyll_a_in_sea_water', 'Chlorophyll a concentration, Garver-Siegel-Maritorena Model': 'mass_concentration_of_chlorophyll_a_in_sea_water', 'Diffuse attenuation coefficient': 'volume_attenuation_coefficient_of_downwelling_radiative_flux_in_sea_water', 'Instantaneous Photosynthetically Available Radiation': 'instantaneous_downwelling_photosynthetic_photon_radiance_in_sea_water', 'Particle backscatter at 443 nm': 'volume_backscattering_coefficient_of_radiative_flux_in_sea_water_due_to_suspended_particles', 'Photosynthetically Available Radiation': 'downwelling_photosynthetic_photon_radiance_in_sea_water', 'Remote sensing reflectance': 'surface_ratio_of_upwelling_radiance_emerging_from_sea_water_to_downwelling_radiative_flux_in_air', 'Sea Surface Salinity': 'sea_surface_salinity', 'Sea Surface Temperature': 'sea_surface_temperature'}
nansat.mappers.mapper_ocean_productivity module
- class nansat.mappers.mapper_ocean_productivity.Mapper(filename, gdalDataset, gdalMetadata, **kwargs)
Bases:
VRTMapper for Ocean Productivity website http://www.science.oregonstate.edu/ocean.productivity/
- param2wkv = {'bbp': 'particle_backscatter_at_443_nm', 'chl': 'mass_concentration_of_chlorophyll_a_in_sea_water', 'ipar': 'instantaneous_downwelling_photosynthetic_photon_radiance_in_sea_water', 'par': 'downwelling_photosynthetic_photon_radiance_in_sea_water', 'sst': 'sea_surface_temperature'}
- bandNames = {'instantaneous_downwelling_photosynthetic_photon_radiance_in_sea_water': 'ipar', 'mass_concentration_of_chlorophyll_a_in_sea_water': 'algal_1', 'particle_backscatter_at_443_nm': 'bbp_443', 'sea_surface_temperature': 'SST'}
nansat.mappers.mapper_opendap_arome module
- class nansat.mappers.mapper_opendap_arome.Mapper(filename, gdal_dataset, gdal_metadata, date=None, ds=None, bands=None, cachedir=None, *args, **kwargs)
-
- baseURLs = ['http://thredds.met.no/thredds/catalog/arome25/catalog.html', 'https://thredds.met.no/thredds/dodsC/aromearcticarchive', 'http://thredds.met.no/thredds/dodsC/aromearcticarchive', 'https://thredds.met.no/thredds/dodsC/meps25epsarchive', 'http://thredds.met.no/thredds/dodsC/meps25epsarchive']
- timeVarName = 'time'
- xName = 'x'
- yName = 'y'
- timeCalendarStart = '1970-01-01'
- static get_date(filename)
Extract date and time parameters from filename and return it as a formatted string
- Parameters:
filename (str) – nn
- Returns:
str, YYYY-mm-ddThh
- Return type:
MMZ
Examples
>>> Mapper.get_date('/path/to/arome_arctic_full_2_5km_20171030T21Z.nc') '2017-10-30T21:00Z'
- convert_dstime_datetimes(ds_time)
Convert time variable to np.datetime64
nansat.mappers.mapper_opendap_globcurrent module
nansat.mappers.mapper_opendap_globcurrent_thredds module
nansat.mappers.mapper_opendap_mywave module
- class nansat.mappers.mapper_opendap_mywave.Mapper(filename, gdal_dataset, gdal_metadata, date=None, ds=None, bands=None, cachedir=None, *args, **kwargs)
Bases:
Opendap- baseURLs = ['http://thredds.met.no/thredds/dodsC/fou-hi/mywavewam4archive', 'https://thredds.met.no/thredds/dodsC/sea/mywavewam4/mywavewam4_be']
- timeVarName = 'time'
- xName = 'rlon'
- yName = 'rlat'
- timeCalendarStart = '1970-01-01'
- static get_date(filename)
Extract date and time parameters from filename and return it as a formatted (isoformat) string
- Parameters:
filename (str)
- Returns:
str, YYYY-mm-ddThh
- Return type:
MM:00Z
Examples
>>> Mapper.get_date('/path/to/MyWave_wam4_WAVE_20171029T18Z.nc') '2017-10-29T18:00:00Z'
- convert_dstime_datetimes(ds_time)
Convert time variable to np.datetime64
nansat.mappers.mapper_opendap_occci module
nansat.mappers.mapper_opendap_osisaf module
nansat.mappers.mapper_opendap_ostia module
nansat.mappers.mapper_opendap_sentinel1 module
nansat.mappers.mapper_opendap_sentinel2 module
- class nansat.mappers.mapper_opendap_sentinel2.Mapper(filename, gdal_dataset, gdal_metadata, date=None, ds=None, bands=None, cachedir=None, *args, **kwargs)
Bases:
Opendap- baseURLs = ['http://nbstds.met.no/thredds/dodsC/NBS/S2A', 'http://nbstds.met.no/thredds/dodsC/NBS/S2B']
- timeVarName = 'time'
- xName = 'x'
- yName = 'y'
- timeCalendarStart = '1981-01-01'
- GCP_STEP = 100
- static get_date(filename)
Extract date and time parameters from filename and return it as a formatted (isoformat) string
- Parameters:
filename (str) – nn
- Returns:
str, YYYY-mm-ddThh
- Return type:
MMZ
- convert_dstime_datetimes(ds_time)
Convert time variable to np.datetime64
nansat.mappers.mapper_opendap_siwtacsst module
nansat.mappers.mapper_opendap_sstcci module
nansat.mappers.mapper_pathfinder52 module
nansat.mappers.mapper_quikscat module
nansat.mappers.mapper_radarsat2 module
nansat.mappers.mapper_sentinel1_l1 module
- class nansat.mappers.mapper_sentinel1_l1.Mapper(filename, gdalDataset, gdalMetadata, fast=False, fixgcp=True, **kwargs)
Bases:
VRTCreate VRT with mapping of Sentinel-1 (A and B) stripmap mode (S1A_SM)
- Parameters:
filename (str) – name of input Sentinel-1 L1 file
gdalDataset (None)
gdalMetadata (None)
fast (bool) – Flag that triggers faster reading of metadata from Sentinel-1 file. If True, no bands are added to the dataset and georeference is not corrected. If False, all bands are added and GCPs are corrected if necessary (see Mapper.correct_geolocation_data for details).
Note
Creates self.dataset and populates it with S1 bands (when fast=False).
- read_calibration(xml, vectorListName, variable_names, pol)
Read calibration data from calibration or noise XML files :param xml: String with XML from calibration or noise files :type xml: str :param vectorListName: tag of the element that contains lists with LUT values :type vectorListName: str :param variable_names: names of LUT variable to read :type variable_names: list of str :param pol: HH, HV, etc :type pol: str
- Returns:
data – Calibration or noise data. Keys: The same as variable_names + ‘pixel’, ‘line’
- Return type:
dict
- read_annotation(annotation_files)
Read lon, lat, etc from annotation XML
- Parameters:
annotation_files (list) – strings with names of annotation files
- Returns:
data –
- geolocation data from the XML as 2D np.arrays. Keys:
pixel, line, longitude, latitude, height, incidenceAngle, elevationAngle: 2D arrays shape : tuple (shape of geolocation data arrays) x_size, y_size : int pol : list
- Return type:
dict
- correct_geolocation_data()
Correct lon/lat values in geolocation data for points high above ground (incorrect)
Each GCP in Sentinel-1 L1 image (both in the GeoTIF files and Annotation LUT) have five coordinates: X, Y, Z (height), Pixel and Line. On some scenes that cover Greenland (and probably other lands) some GCPs have height above zero even over ocean. This is incorrect, because the radar signal comes actually from the surface and not from a point above the ground as stipulated in such GCPs. Correction of GCPs in this function is equivalelnt to reverse DEM correction of SAR data.
Notes
Updates ‘pixel’ and ‘height’ in self.annotation_data
- static create_gcps(x, y, z, p, l)
Create GCPs from geolocation data
- Parameters:
x
y
z
p
l
X (N-D arrays with value of)
Y
Z
coordinates. (Pixel and Line)
lon (X and Y are typically)
lat
height. (Z -)
- Returns:
gcps
- Return type:
list with GDAL GCPs
- read_manifest_data(input_file)
Read information (time_coverage_start, etc) manifest XML
- Parameters:
input_file (str) – name of manifest file
- Returns:
data –
- manifest data. Keys:
time_coverage_start time_coverage_end platform_familyName platform_number
- Return type:
dict
- vrts_from_arrays(data, variable_names, pol='', resize=True, resample_alg=2)
Convert input dict with arrays into dict with VRTs
- Parameters:
data (dict) – 2D arrays with data from LUT
variable_names (list of str) – variable names that should be converted to VRTs
pol (str) – HH, HV, etc
resize (bool) – Shall VRT be zoomed to full size?
resample_alg (int) – Index of resampling algorithm. See VRT.get_resized_vrt()
- Returns:
vrts
- Return type:
dict with (resized) VRTs
nansat.mappers.mapper_sentinel1_l2 module
nansat.mappers.mapper_topography module
- class nansat.mappers.mapper_topography.Mapper(filename, gdal_dataset, *args, **kwargs)
Bases:
VRTMapping for the GTOPO30 (https://lta.cr.usgs.gov/GTOPO30) and the GMTED2010 (https://lta.cr.usgs.gov/GMTED2010) global elevation models.
- Parameters:
filename (string) – The vrt filename, e.g.,
gtopo30.vrtgdal_dataset (osgeo.gdal.Dataset) – The GDAL dataset returned by
gdal.Open(filename)
Example
You can create your own
gtopo30.vrtfile with gdal, e.g.:gdalbuildvrt <dem>.vrt [E,W]*.DEM
Note
Either the name of a GTOPO30 DEM file or GMTED2010 tif file, or
<path>/<dem>.vrt. The latter is an aggregation of the DEM-files available from the given DEM. The GTOPO30 vrt does not contain the Antarctic, because this is in polarstereographic projection.Remember to update this mapper by adding allowed filenames to the list of accepted filenames (accepted_names) if you create or apply new DEM datasets.
nansat.mappers.mapper_viirs_l1 module
nansat.mappers.obpg module
- class nansat.mappers.obpg.OBPGL2BaseClass(x_size=1, y_size=1, metadata=None, nomem=False, **kwargs)
Bases:
VRTBase Class for Mappers for SeaWIFS/MODIS/MERIS/VIIRS L2 data from OBPG
- titles = ['HMODISA Level-2 Data', 'MODISA Level-2 Data', 'HMODIST Level-2 Data', 'MERIS Level-2 Data', 'GOCI Level-2 Data', 'VIIRSN Level-2 Data', 'SeaWiFS Level-2 Data']
nansat.mappers.opendap module
- class nansat.mappers.opendap.Opendap(x_size=1, y_size=1, metadata=None, nomem=False, **kwargs)
Bases:
VRTMethods for all OpenDAP mappers
- P2S = {'D': 86400, 'H': 3600, 'M': 2592000, 'Y': 31536000}
- test_mapper(filename)
Tests if filename fits mapper. May raise WrongMapperError
- Parameters:
filename (str) – absolute url of input file
- Raises:
WrongMapperError – if input url does not match with list of: urls for a mapper
- get_dataset(ds)
Open Dataset
- Parameters:
ds (str or netCDF4.Dataset)
- get_geospatial_variable_names()
Get names of variables with both spatial dimensions
- get_dataset_time()
Load data from time variable
- static get_layer_datetime(date, datetimes)
Get datetime of the matching layer and layer number
- get_metaitem(url, var_name, var_dimensions)
Set metadata for creating band VRT
- Parameters:
url (str,) – absolute url of an input file
var_name (str,) – name of a variable/band from netCDF file
var_dimensions – iterable array with dimensions of the variable
- create_vrt(filename, gdalDataset, gdalMetadata, date, ds, bands, cachedir)
Create VRT
- Parameters:
filename (str,) – absolute url of an input file
date (str,) – date in format YYYY-MM-DD
ds (netDCF.Dataset)
bands (list) – list of src bands
cachedir (str)
- create_metadict(filename, var_names, time_id)
Create list which contains a dictionary with metadata for each single band
- Parameters:
filename (str,) – full path to the file
var_names (iterable,) – iterable object (list) with required band names (str)
time_id (int,) – index of required slice in time dimension
- Returns:
meta_dict – list which contains a dictionary with metadata for each <var_name>
- Return type:
list
- get_time_coverage_resolution()
Try to fecth time_coverage_resolution and convert to seconds
- get_shape()
Get srcRasterXSize and srcRasterYSize from OpenDAP
- get_geotransform()
Get first two values of X,Y variables and create geoTranform
nansat.mappers.scatterometers module
- class nansat.mappers.scatterometers.Mapper(filename, gdal_dataset, metadata, quartile=0, *args, **kwargs)
Bases:
MapperNansat mapper for scatterometers
- set_gcps(lon, lat, gdal_dataset)
Set gcps
- static shift_longitudes(lon)
Apply correction of longitudes (they are defined on 0:360 degrees but also contain egative values)
TODO: consider making this core to nansat - different ways of defining longitudes (-180:180 og 0:360 degrees) often cause problems…
nansat.mappers.sentinel1 module
- class nansat.mappers.sentinel1.Sentinel1(filename, flip_gcp_line=False)
Bases:
VRTMapper class to access Sentinel-1 data with netCDF4 to work for both opendap streams and local files.
- timeVarName = 'time'
- input_filename = ''
- set_gcmd_dif_keywords()
- get_gcps(flip_gcp_line=False)
Get Ground Control Points for the dataset.
Note that OPeNDAP streams and netCDF files are read differently by gdal. The OPeNDAP streams are read by specifying the get parameters to the OPeNDAP url. The get parameters specify the reference dimensions, e.g., x and y. Since these are specified, the raster data is correctly referenced to the GCPs. However, when gdal reads a raster band from netCDF, it reads it “blindly”. This is risky, since the definition of origo may be different in gdal vs the original data (e.g., first line starts in upper left corner or in lower left corner). For Sentinel-1, the raster data is flipped in relation to the GCPs, so we need to flip the GCP line vector as well.
- add_incidence_angle_band()
- get_full_size_GCPs()
- add_look_direction_band()