Source code for satpy.readers.abi_l1b

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2016-2019 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 <>.
"""Advance Baseline Imager reader for the Level 1b format.

The files read by this reader are described in the official PUG document:

import logging

import numpy as np

import satpy
from satpy.readers.abi_base import NC_ABI_BASE

logger = logging.getLogger(__name__)

[docs] class NC_ABI_L1B(NC_ABI_BASE): """File reader for individual ABI L1B NetCDF4 files.""" def __init__(self, filename, filename_info, filetype_info, clip_negative_radiances=None): """Open the NetCDF file with xarray and prepare the Dataset for reading.""" super().__init__(filename, filename_info, filetype_info) if clip_negative_radiances is None: clip_negative_radiances = satpy.config.get("readers.clip_negative_radiances") self.clip_negative_radiances = clip_negative_radiances
[docs] def get_dataset(self, key, info): """Load a dataset.""" logger.debug('Reading in get_dataset %s.', key['name']) # For raw cal, don't apply scale and offset, return raw file counts if key['calibration'] == 'counts': radiances =['Rad'].copy() else: radiances = self['Rad'] # mapping of calibration types to calibration functions cal_dictionary = { 'reflectance': self._vis_calibrate, 'brightness_temperature': self._ir_calibrate, 'radiance': self._rad_calibrate, 'counts': self._raw_calibrate, } try: func = cal_dictionary[key['calibration']] res = func(radiances) except KeyError: raise ValueError("Unknown calibration '{}'".format(key['calibration'])) # convert to satpy standard units if res.attrs['units'] == '1' and key['calibration'] != 'counts': res *= 100 res.attrs['units'] = '%' self._adjust_attrs(res, key) return res
[docs] def _adjust_attrs(self, data, key): data.attrs.update({'platform_name': self.platform_name, 'sensor': self.sensor}) # Add orbital parameters projection =["goes_imager_projection"] data.attrs['orbital_parameters'] = { 'projection_longitude': float(projection.attrs['longitude_of_projection_origin']), 'projection_latitude': float(projection.attrs['latitude_of_projection_origin']), 'projection_altitude': float(projection.attrs['perspective_point_height']), 'satellite_nominal_latitude': float(self['nominal_satellite_subpoint_lat']), 'satellite_nominal_longitude': float(self['nominal_satellite_subpoint_lon']), 'satellite_nominal_altitude': float(self['nominal_satellite_height']) * 1000., 'yaw_flip': bool(self['yaw_flip_flag']), } data.attrs.update(key.to_dict()) # remove attributes that could be confusing later # if calibration type is raw counts, we leave them in if key['calibration'] != 'counts': data.attrs.pop('_FillValue', None) data.attrs.pop('scale_factor', None) data.attrs.pop('add_offset', None) data.attrs.pop('_Unsigned', None) data.attrs.pop('ancillary_variables', None) # Can't currently load DQF # although we could compute these, we'd have to update in calibration data.attrs.pop('valid_range', None) # add in information from the filename that may be useful to the user for attr in ('observation_type', 'scene_abbr', 'scan_mode', 'platform_shortname', 'suffix'): if attr in self.filename_info: data.attrs[attr] = self.filename_info[attr] # copy global attributes to metadata for attr in ('scene_id', 'orbital_slot', 'instrument_ID', 'production_site', 'timeline_ID'): data.attrs[attr] = # only include these if they are present for attr in ('fusion_args',): if attr in data.attrs[attr] =[attr]
[docs] def _rad_calibrate(self, data): """Calibrate any channel to radiances. This no-op method is just to keep the flow consistent - each valid cal type results in a calibration method call """ res = data res.attrs = data.attrs return res
[docs] def _raw_calibrate(self, data): """Calibrate any channel to raw counts. Useful for cases where a copy requires no calibration. """ res = data res.attrs = data.attrs res.attrs['units'] = '1' res.attrs['long_name'] = 'Raw Counts' res.attrs['standard_name'] = 'counts' return res
[docs] def _vis_calibrate(self, data): """Calibrate visible channels to reflectance.""" solar_irradiance = self['esun'] esd = self["earth_sun_distance_anomaly_in_AU"].astype(float) factor = np.pi * esd * esd / solar_irradiance res = data * factor res.attrs = data.attrs res.attrs['units'] = '1' res.attrs['long_name'] = 'Bidirectional Reflectance' res.attrs['standard_name'] = 'toa_bidirectional_reflectance' return res
[docs] def _get_minimum_radiance(self, data): """Estimate minimum radiance from Rad DataArray.""" attrs = data.attrs scale_factor = attrs["scale_factor"] add_offset = attrs["add_offset"] count_zero_rad = - add_offset / scale_factor count_pos = np.ceil(count_zero_rad) min_rad = count_pos * scale_factor + add_offset return min_rad
[docs] def _ir_calibrate(self, data): """Calibrate IR channels to BT.""" fk1 = float(self["planck_fk1"]) fk2 = float(self["planck_fk2"]) bc1 = float(self["planck_bc1"]) bc2 = float(self["planck_bc2"]) if self.clip_negative_radiances: min_rad = self._get_minimum_radiance(data) data = data.clip(min=min_rad) res = (fk2 / np.log(fk1 / data + 1) - bc1) / bc2 res.attrs = data.attrs res.attrs['units'] = 'K' res.attrs['long_name'] = 'Brightness Temperature' res.attrs['standard_name'] = 'toa_brightness_temperature' return res