Source code for satpy.readers.amsr2_l2_gaasp

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2017 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.
# satpy is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
# A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with
# satpy.  If not, see <>.
"""GCOM-W1 AMSR2 Level 2 files from the GAASP software.

GAASP output files are in the NetCDF4 format. Software is provided by NOAA
and is also distributed by the CSPP group. More information on the products
supported by this reader can be found here: for more information.

GAASP includes both swath/granule products and gridded products. Swath
products are provided in files with "MBT", "OCEAN", "SNOW", or "SOIL" in the
filename. Gridded products are in files with "SEAICE-SH" or "SEAICE-NH" in the
filename where SH stands for South Hemisphere and NH stands for North
Hemisphere. These gridded products are on the EASE2 North pole and South pole
grids. See for more details.

Note that since SEAICE products can be on both the northern or
southern hemisphere or both depending on what files are provided to Satpy, this
reader appends a `_NH` and `_SH` suffix to all variable names that are
dynamically discovered from the provided files.


import logging
from datetime import datetime
from typing import Tuple

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

from satpy import CHUNK_SIZE
from satpy._compat import cached_property
from satpy.readers.file_handlers import BaseFileHandler

logger = logging.getLogger(__name__)

[docs]class GAASPFileHandler(BaseFileHandler): """Generic file handler for GAASP output files.""" y_dims: Tuple[str, ...] = ( 'Number_of_Scans', ) x_dims: Tuple[str, ...] = ( 'Number_of_hi_rez_FOVs', 'Number_of_low_rez_FOVs', ) time_dims = ( 'Time_Dimension', ) is_gridded = False dim_resolutions = { 'Number_of_hi_rez_FOVs': 5000, 'Number_of_low_rez_FOVs': 10000, } @cached_property def nc(self): """Get the xarray dataset for this file.""" chunks = {dim_name: CHUNK_SIZE for dim_name in self.y_dims + self.x_dims + self.time_dims} nc = xr.open_dataset(self.filename, decode_cf=True, mask_and_scale=False, chunks=chunks) if len(self.time_dims) == 1: nc = nc.rename({self.time_dims[0]: 'time'}) return nc @property def start_time(self): """Get start time of observation.""" try: return self.filename_info['start_time'] except KeyError: time_str =['time_coverage_start'] return datetime.strptime(time_str, "%Y-%m-%dT%H:%M:%S.%fZ") @property def end_time(self): """Get end time of observation.""" try: return self.filename_info['end_time'] except KeyError: time_str =['time_coverage_end'] return datetime.strptime(time_str, "%Y-%m-%dT%H:%M:%S.%fZ") @property def sensor_names(self): """Sensors who have data in this file.""" return {['instrument_name'].lower()} @property def platform_name(self): """Name of the platform whose data is stored in this file.""" return['platform_name'] def _get_var_name_without_suffix(self, var_name): var_suffix = self.filetype_info.get('var_suffix', "") if var_suffix: var_name = var_name[:-len(var_suffix)] return var_name def _scale_data(self, data_arr, attrs): # handle scaling # take special care for integer/category fields scale_factor = attrs.pop('scale_factor', 1.) add_offset = attrs.pop('add_offset', 0.) scaling_needed = not (scale_factor == 1 and add_offset == 0) if scaling_needed: data_arr = data_arr * scale_factor + add_offset return data_arr, attrs @staticmethod def _nan_for_dtype(data_arr_dtype): # don't force the conversion from 32-bit float to 64-bit float # if we don't have to if data_arr_dtype.type == np.float32: return np.float32(np.nan) if np.issubdtype(data_arr_dtype, np.timedelta64): return np.timedelta64('NaT') if np.issubdtype(data_arr_dtype, np.datetime64): return np.datetime64('NaT') return np.nan def _fill_data(self, data_arr, attrs): fill_value = attrs.pop('_FillValue', None) is_int = np.issubdtype(data_arr.dtype, np.integer) has_flag_comment = 'comment' in attrs if is_int and has_flag_comment: # category product fill_out = fill_value attrs['_FillValue'] = fill_out else: fill_out = self._nan_for_dtype(data_arr.dtype) if fill_value is not None: data_arr = data_arr.where(data_arr != fill_value, fill_out) return data_arr, attrs
[docs] def get_dataset(self, dataid, ds_info): """Load, scale, and collect metadata for the specified DataID.""" orig_var_name = self._get_var_name_without_suffix(dataid['name']) data_arr =[orig_var_name].copy() attrs = data_arr.attrs.copy() data_arr, attrs = self._scale_data(data_arr, attrs) data_arr, attrs = self._fill_data(data_arr, attrs) attrs.update({ 'platform_name': self.platform_name, 'sensor': sorted(self.sensor_names)[0], 'start_time': self.start_time, 'end_time': self.end_time, }) dim_map = dict(zip(data_arr.dims, ('y', 'x'))) # rename dims data_arr = data_arr.rename(**dim_map) # drop coords, the base reader will recreate these data_arr = data_arr.reset_coords(drop=True) data_arr.attrs = attrs return data_arr
def _available_if_this_file_type(self, configured_datasets): for is_avail, ds_info in (configured_datasets or []): if is_avail is not None: # some other file handler said it has this dataset # we don't know any more information than the previous # file handler so let's yield early yield is_avail, ds_info continue yield self.file_type_matches(ds_info['file_type']), ds_info def _add_lonlat_coords(self, data_arr, ds_info): lat_coord = None lon_coord = None for coord_name in data_arr.coords: if 'longitude' in coord_name.lower(): lon_coord = coord_name if 'latitude' in coord_name.lower(): lat_coord = coord_name ds_info['coordinates'] = [lon_coord, lat_coord] def _get_ds_info_for_data_arr(self, var_name, data_arr): var_suffix = self.filetype_info.get('var_suffix', "") ds_info = { 'file_type': self.filetype_info['file_type'], 'name': var_name + var_suffix, } x_dim_name = data_arr.dims[1] if x_dim_name in self.dim_resolutions: ds_info['resolution'] = self.dim_resolutions[x_dim_name] if not self.is_gridded and data_arr.coords: self._add_lonlat_coords(data_arr, ds_info) return ds_info def _is_2d_yx_data_array(self, data_arr): has_y_dim = data_arr.dims[0] in self.y_dims has_x_dim = data_arr.dims[1] in self.x_dims return has_y_dim and has_x_dim def _available_new_datasets(self): possible_vars = list( + list( for var_name, data_arr in possible_vars: if data_arr.ndim != 2: # we don't currently handle non-2D variables continue if not self._is_2d_yx_data_array(data_arr): # we need 'traditional' y/x dimensions currently continue ds_info = self._get_ds_info_for_data_arr(var_name, data_arr) yield True, ds_info
[docs] def available_datasets(self, configured_datasets=None): """Dynamically discover what variables can be loaded from this file. See :meth:`satpy.readers.file_handlers.BaseHandler.available_datasets` for more information. """ yield from self._available_if_this_file_type(configured_datasets) yield from self._available_new_datasets()
[docs]class GAASPGriddedFileHandler(GAASPFileHandler): """GAASP file handler for gridded products like SEAICE.""" y_dims = ( 'Number_of_Y_Dimension', ) x_dims = ( 'Number_of_X_Dimension', ) dim_resolutions = { 'Number_of_X_Dimension': 10000, } is_gridded = True @staticmethod def _get_extents(data_shape, res): # assume data is centered at projection center x_min = -(data_shape[1] / 2.0) * res x_max = (data_shape[1] / 2.0) * res y_min = -(data_shape[0] / 2.0) * res y_max = (data_shape[0] / 2.0) * res return x_min, y_min, x_max, y_max
[docs] def get_area_def(self, dataid): """Create area definition for equirectangular projected data.""" var_suffix = self.filetype_info.get('var_suffix', '') area_name = 'gaasp{}'.format(var_suffix) orig_var_name = self._get_var_name_without_suffix(dataid['name']) data_shape =[orig_var_name].shape crs = CRS(self.filetype_info['grid_epsg']) res = dataid['resolution'] extent = self._get_extents(data_shape, res) area_def = AreaDefinition( area_name, area_name, area_name, crs, data_shape[1], data_shape[0], extent ) return area_def
[docs]class GAASPLowResFileHandler(GAASPFileHandler): """GAASP file handler for files that only have low resolution products.""" x_dims = ( 'Number_of_low_rez_FOVs', ) dim_resolutions = { 'Number_of_low_rez_FOVs': 10000, }