Source code for satpy.readers.nwcsaf_nc

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2017-2020 Satpy developers
#
# This file is part of satpy.
#
# satpy is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.
#
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# A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
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"""Nowcasting SAF common PPS&MSG NetCDF/CF format reader.

References:
   - The NWCSAF GEO 2018 products documentation: http://www.nwcsaf.org/web/guest/archive

"""

import logging
import os
from datetime import datetime

import dask.array as da
import numpy as np
import xarray as xr
from pyproj import CRS
from pyresample.geometry import AreaDefinition

from satpy import CHUNK_SIZE
from satpy.readers.file_handlers import BaseFileHandler
from satpy.readers.utils import unzip_file

logger = logging.getLogger(__name__)

SENSOR = {'NOAA-19': 'avhrr-3',
          'NOAA-18': 'avhrr-3',
          'NOAA-15': 'avhrr-3',
          'Metop-A': 'avhrr-3',
          'Metop-B': 'avhrr-3',
          'Metop-C': 'avhrr-3',
          'EOS-Aqua': 'modis',
          'EOS-Terra': 'modis',
          'Suomi-NPP': 'viirs',
          'NOAA-20': 'viirs',
          'JPSS-1': 'viirs',
          'GOES-16': 'abi',
          'GOES-17': 'abi',
          'Himawari-8': 'ahi',
          'Himawari-9': 'ahi',
          }


PLATFORM_NAMES = {'MSG1': 'Meteosat-8',
                  'MSG2': 'Meteosat-9',
                  'MSG3': 'Meteosat-10',
                  'MSG4': 'Meteosat-11',
                  'GOES16': 'GOES-16',
                  'GOES17': 'GOES-17',
                  }


[docs]class NcNWCSAF(BaseFileHandler): """NWCSAF PPS&MSG NetCDF reader.""" def __init__(self, filename, filename_info, filetype_info): """Init method.""" super(NcNWCSAF, self).__init__(filename, filename_info, filetype_info) self._unzipped = unzip_file(self.filename) if self._unzipped: self.filename = self._unzipped self.cache = {} self.nc = xr.open_dataset(self.filename, decode_cf=True, mask_and_scale=False, chunks=CHUNK_SIZE) self.nc = self.nc.rename({'nx': 'x', 'ny': 'y'}) self.sw_version = self.nc.attrs['source'] self.pps = False self.platform_name = None self.sensor = None try: # NWCSAF/Geo: try: kwrgs = {'sat_id': self.nc.attrs['satellite_identifier']} except KeyError: kwrgs = {'sat_id': self.nc.attrs['satellite_identifier'].astype(str)} except KeyError: # NWCSAF/PPS: kwrgs = {'platform_name': self.nc.attrs['platform']} self.set_platform_and_sensor(**kwrgs)
[docs] def set_platform_and_sensor(self, **kwargs): """Set some metadata: platform_name, sensors, and pps (identifying PPS or Geo).""" try: # NWCSAF/Geo self.platform_name = PLATFORM_NAMES.get(kwargs['sat_id'], kwargs['sat_id']) except KeyError: # NWCSAF/PPS self.platform_name = kwargs['platform_name'] self.pps = True self.sensor = set([SENSOR.get(self.platform_name, 'seviri')])
[docs] def remove_timedim(self, var): """Remove time dimension from dataset.""" if self.pps and var.dims[0] == 'time': data = var[0, :, :] data.attrs = var.attrs var = data return var
[docs] def get_dataset(self, dsid, info): """Load a dataset.""" dsid_name = dsid['name'] if dsid_name in self.cache: logger.debug('Get the data set from cache: %s.', dsid_name) return self.cache[dsid_name] if dsid_name in ['lon', 'lat'] and dsid_name not in self.nc: dsid_name = dsid_name + '_reduced' logger.debug('Reading %s.', dsid_name) variable = self.nc[dsid_name] variable = self.remove_timedim(variable) variable = self.scale_dataset(dsid, variable, info) if dsid_name.endswith('_reduced'): # Get full resolution lon,lat from the reduced (tie points) grid self.upsample_geolocation(dsid, info) return self.cache[dsid['name']] return variable
[docs] def scale_dataset(self, dsid, variable, info): """Scale the data set, applying the attributes from the netCDF file. The scale and offset attributes will then be removed from the resulting variable. """ variable = remove_empties(variable) scale = variable.attrs.get('scale_factor', np.array(1)) offset = variable.attrs.get('add_offset', np.array(0)) if np.issubdtype((scale + offset).dtype, np.floating) or np.issubdtype(variable.dtype, np.floating): variable = self._mask_variable(variable) attrs = variable.attrs.copy() variable = variable * scale + offset variable.attrs = attrs if 'valid_range' in variable.attrs: variable.attrs['valid_range'] = variable.attrs['valid_range'] * scale + offset variable.attrs.pop('add_offset', None) variable.attrs.pop('scale_factor', None) variable.attrs.update({'platform_name': self.platform_name, 'sensor': self.sensor}) if not variable.attrs.get('standard_name', '').endswith('status_flag'): # TODO: do we really need to add units to everything ? variable.attrs.setdefault('units', '1') ancillary_names = variable.attrs.get('ancillary_variables', '') try: variable.attrs['ancillary_variables'] = ancillary_names.split() except AttributeError: pass if 'palette_meanings' in variable.attrs: variable = self._prepare_variable_for_palette(variable, info) if 'standard_name' in info: variable.attrs.setdefault('standard_name', info['standard_name']) variable = self._adjust_variable_for_legacy_software(variable, dsid) return variable
@staticmethod def _mask_variable(variable): if '_FillValue' in variable.attrs: variable = variable.where( variable != variable.attrs['_FillValue']) variable.attrs['_FillValue'] = np.nan if 'valid_range' in variable.attrs: variable = variable.where( variable <= variable.attrs['valid_range'][1]) variable = variable.where( variable >= variable.attrs['valid_range'][0]) if 'valid_max' in variable.attrs: variable = variable.where( variable <= variable.attrs['valid_max']) if 'valid_min' in variable.attrs: variable = variable.where( variable >= variable.attrs['valid_min']) return variable def _prepare_variable_for_palette(self, variable, info): if 'scale_offset_dataset' in info: so_dataset = self.nc[info['scale_offset_dataset']] scale = so_dataset.attrs['scale_factor'] offset = so_dataset.attrs['add_offset'] else: scale = 1 offset = 0 variable.attrs['palette_meanings'] = [int(val) for val in variable.attrs['palette_meanings'].split()] if variable.attrs['palette_meanings'][0] == 1: variable.attrs['palette_meanings'] = [0] + variable.attrs['palette_meanings'] variable = xr.DataArray(da.vstack((np.array(variable.attrs['fill_value_color']), variable.data)), coords=variable.coords, dims=variable.dims, attrs=variable.attrs) val, idx = np.unique(variable.attrs['palette_meanings'], return_index=True) variable.attrs['palette_meanings'] = val * scale + offset variable = variable[idx] return variable def _adjust_variable_for_legacy_software(self, variable, data_id): if self.sw_version == 'NWC/PPS version v2014' and data_id['name'] == 'ctth_alti': # pps 2014 valid range and palette don't match variable.attrs['valid_range'] = (0., 9000.) if self.sw_version == 'NWC/PPS version v2014' and data_id['name'] == 'ctth_alti_pal': # pps 2014 palette has the nodata color (black) first variable = variable[1:, :] if self.sw_version == 'NWC/GEO version v2016' and data_id['name'] == 'ctth_alti': # Geo 2016/18 valid range and palette don't match # Valid range is 0 to 27000 in the file. But after scaling the valid range becomes -2000 to 25000 # This now fixed by the scaling of the valid range above. pass return variable
[docs] def upsample_geolocation(self, dsid, info): """Upsample the geolocation (lon,lat) from the tiepoint grid.""" from geotiepoints import SatelliteInterpolator # Read the fields needed: col_indices = self.nc['nx_reduced'].values row_indices = self.nc['ny_reduced'].values lat_reduced = self.scale_dataset(dsid, self.nc['lat_reduced'], info) lon_reduced = self.scale_dataset(dsid, self.nc['lon_reduced'], info) shape = (self.nc['y'].shape[0], self.nc['x'].shape[0]) cols_full = np.arange(shape[1]) rows_full = np.arange(shape[0]) satint = SatelliteInterpolator((lon_reduced.values, lat_reduced.values), (row_indices, col_indices), (rows_full, cols_full)) lons, lats = satint.interpolate() self.cache['lon'] = xr.DataArray(lons, attrs=lon_reduced.attrs, dims=['y', 'x']) self.cache['lat'] = xr.DataArray(lats, attrs=lat_reduced.attrs, dims=['y', 'x']) return
[docs] def get_area_def(self, dsid): """Get the area definition of the datasets in the file. Only applicable for MSG products! """ if self.pps: # PPS: raise NotImplementedError if dsid['name'].endswith('_pal'): raise NotImplementedError crs, area_extent = self._get_projection() crs, area_extent = self._ensure_crs_extents_in_meters(crs, area_extent) nlines, ncols = self.nc[dsid['name']].shape area = AreaDefinition('some_area_name', "On-the-fly area", 'geosmsg', crs, ncols, nlines, area_extent) return area
@staticmethod def _ensure_crs_extents_in_meters(crs, area_extent): """Fix units in Earth shape, satellite altitude and 'units' attribute.""" if 'kilo' in crs.axis_info[0].unit_name: proj_dict = crs.to_dict() proj_dict["units"] = "m" if "a" in proj_dict: proj_dict["a"] *= 1000. if "b" in proj_dict: proj_dict["b"] *= 1000. if "R" in proj_dict: proj_dict["R"] *= 1000. proj_dict["h"] *= 1000. area_extent = tuple([val * 1000. for val in area_extent]) crs = CRS.from_dict(proj_dict) return crs, area_extent def __del__(self): """Delete the instance.""" if self._unzipped: try: os.remove(self._unzipped) except OSError: pass @property def start_time(self): """Return the start time of the object.""" try: # MSG: try: return datetime.strptime(self.nc.attrs['time_coverage_start'], '%Y-%m-%dT%H:%M:%SZ') except TypeError: return datetime.strptime(self.nc.attrs['time_coverage_start'].astype(str), '%Y-%m-%dT%H:%M:%SZ') except ValueError: # PPS: return datetime.strptime(self.nc.attrs['time_coverage_start'], '%Y%m%dT%H%M%S%fZ') @property def end_time(self): """Return the end time of the object.""" try: # MSG: try: return datetime.strptime(self.nc.attrs['time_coverage_end'], '%Y-%m-%dT%H:%M:%SZ') except TypeError: return datetime.strptime(self.nc.attrs['time_coverage_end'].astype(str), '%Y-%m-%dT%H:%M:%SZ') except ValueError: # PPS: return datetime.strptime(self.nc.attrs['time_coverage_end'], '%Y%m%dT%H%M%S%fZ') @property def sensor_names(self): """List of sensors represented in this file.""" return self.sensor def _get_projection(self): """Get projection from the NetCDF4 attributes.""" try: proj_str = self.nc.attrs['gdal_projection'] except TypeError: proj_str = self.nc.attrs['gdal_projection'].decode() # Check the a/b/h units radius_a = proj_str.split('+a=')[-1].split()[0] if float(radius_a) > 10e3: units = 'm' scale = 1.0 else: units = 'km' scale = 1e3 if 'units' not in proj_str: proj_str = proj_str + ' +units=' + units area_extent = (float(self.nc.attrs['gdal_xgeo_up_left']) / scale, float(self.nc.attrs['gdal_ygeo_low_right']) / scale, float(self.nc.attrs['gdal_xgeo_low_right']) / scale, float(self.nc.attrs['gdal_ygeo_up_left']) / scale) crs = CRS.from_string(proj_str) return crs, area_extent
[docs]def remove_empties(variable): """Remove empty objects from the *variable*'s attrs.""" import h5py for key, val in variable.attrs.items(): if isinstance(val, h5py._hl.base.Empty): variable.attrs.pop(key) return variable