#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 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 <http://www.gnu.org/licenses/>.
"""Base HDF-EOS reader."""
from __future__ import annotations
import logging
import re
from ast import literal_eval
from contextlib import suppress
from datetime import datetime
import numpy as np
import xarray as xr
from pyhdf.error import HDF4Error
from pyhdf.SD import SD
from satpy import CHUNK_SIZE, DataID
from satpy.readers.file_handlers import BaseFileHandler
logger = logging.getLogger(__name__)
[docs]def interpolate(clons, clats, csatz, src_resolution, dst_resolution):
"""Interpolate two parallel datasets jointly."""
if csatz is None:
return _interpolate_no_angles(clons, clats, src_resolution, dst_resolution)
return _interpolate_with_angles(clons, clats, csatz, src_resolution, dst_resolution)
def _interpolate_with_angles(clons, clats, csatz, src_resolution, dst_resolution):
from geotiepoints.modisinterpolator import modis_1km_to_250m, modis_1km_to_500m, modis_5km_to_1km
# (src_res, dst_res, is satz not None) -> interp function
interpolation_functions = {
(5000, 1000): modis_5km_to_1km,
(1000, 500): modis_1km_to_500m,
(1000, 250): modis_1km_to_250m
}
return _find_and_run_interpolation(interpolation_functions, src_resolution, dst_resolution,
(clons, clats, csatz))
def _interpolate_no_angles(clons, clats, src_resolution, dst_resolution):
interpolation_functions = {}
try:
from geotiepoints.simple_modis_interpolator import modis_1km_to_250m as simple_1km_to_250m
from geotiepoints.simple_modis_interpolator import modis_1km_to_500m as simple_1km_to_500m
except ImportError:
raise NotImplementedError(
f"Interpolation from {src_resolution}m to {dst_resolution}m "
"without satellite zenith angle information is not "
"implemented. Try updating your version of "
"python-geotiepoints.")
else:
interpolation_functions[(1000, 500)] = simple_1km_to_500m
interpolation_functions[(1000, 250)] = simple_1km_to_250m
return _find_and_run_interpolation(interpolation_functions, src_resolution, dst_resolution,
(clons, clats))
def _find_and_run_interpolation(interpolation_functions, src_resolution, dst_resolution, args):
try:
interpolation_function = interpolation_functions[(src_resolution, dst_resolution)]
except KeyError:
error_message = "Interpolation from {}m to {}m not implemented".format(
src_resolution, dst_resolution)
raise NotImplementedError(error_message)
logger.debug("Interpolating from {} to {}".format(src_resolution, dst_resolution))
return interpolation_function(*args)
[docs]class HDFEOSBaseFileReader(BaseFileHandler):
"""Base file handler for HDF EOS data for both L1b and L2 products."""
def __init__(self, filename, filename_info, filetype_info, **kwargs):
"""Initialize the base reader."""
BaseFileHandler.__init__(self, filename, filename_info, filetype_info)
try:
self.sd = SD(self.filename)
except HDF4Error as err:
error_message = "Could not load data from file {}: {}".format(self.filename, err)
raise ValueError(error_message)
self.metadata = self._load_all_metadata_attributes()
def _load_all_metadata_attributes(self):
metadata = {}
attrs = self.sd.attributes()
for md_key in ("CoreMetadata.0", "StructMetadata.0", "ArchiveMetadata.0"):
try:
str_val = attrs[md_key]
except KeyError:
continue
else:
metadata.update(self.read_mda(str_val))
return metadata
[docs] @classmethod
def read_mda(cls, attribute):
"""Read the EOS metadata."""
line_iterator = iter(attribute.split('\n'))
return cls._read_mda(line_iterator)
@classmethod
def _read_mda(cls, lines, element=None):
current_dict = {}
for line in lines:
if not line:
continue
if line == 'END':
return current_dict
key, val = cls._split_line(line, lines)
if key in ['GROUP', 'OBJECT']:
current_dict[val] = cls._read_mda(lines, val)
elif key in ['END_GROUP', 'END_OBJECT']:
if val != element:
raise SyntaxError("Non-matching end-tag")
return current_dict
elif key in ['CLASS', 'NUM_VAL']:
pass
else:
current_dict[key] = val
logger.warning("Malformed EOS metadata, missing an END.")
return current_dict
@classmethod
def _split_line(cls, line, lines):
key, val = line.split('=')
key = key.strip()
val = val.strip()
try:
with suppress(ValueError):
val = literal_eval(val)
except SyntaxError:
key, val = cls._split_line(line + next(lines), lines)
return key, val
@property
def metadata_platform_name(self):
"""Platform name from the internal file metadata."""
try:
# Example: 'Terra' or 'Aqua'
return self.metadata['INVENTORYMETADATA']['ASSOCIATEDPLATFORMINSTRUMENTSENSOR'][
'ASSOCIATEDPLATFORMINSTRUMENTSENSORCONTAINER']['ASSOCIATEDPLATFORMSHORTNAME']['VALUE']
except KeyError:
return self._platform_name_from_filename()
def _platform_name_from_filename(self):
platform_indicator = self.filename_info["platform_indicator"]
if platform_indicator in ("t", "O"):
# t1.* or MOD*
return "Terra"
# a1.* or MYD*
return "Aqua"
@property
def start_time(self):
"""Get the start time of the dataset."""
try:
date = (self.metadata['INVENTORYMETADATA']['RANGEDATETIME']['RANGEBEGINNINGDATE']['VALUE'] + ' ' +
self.metadata['INVENTORYMETADATA']['RANGEDATETIME']['RANGEBEGINNINGTIME']['VALUE'])
return datetime.strptime(date, '%Y-%m-%d %H:%M:%S.%f')
except KeyError:
return self._start_time_from_filename()
def _start_time_from_filename(self):
return self.filename_info["start_time"]
@property
def end_time(self):
"""Get the end time of the dataset."""
try:
date = (self.metadata['INVENTORYMETADATA']['RANGEDATETIME']['RANGEENDINGDATE']['VALUE'] + ' ' +
self.metadata['INVENTORYMETADATA']['RANGEDATETIME']['RANGEENDINGTIME']['VALUE'])
return datetime.strptime(date, '%Y-%m-%d %H:%M:%S.%f')
except KeyError:
return self.start_time
def _read_dataset_in_file(self, dataset_name):
if dataset_name not in self.sd.datasets():
error_message = "Dataset name {} not included in available datasets {}".format(
dataset_name, self.sd.datasets()
)
raise KeyError(error_message)
dataset = self.sd.select(dataset_name)
return dataset
[docs] def load_dataset(self, dataset_name, is_category=False):
"""Load the dataset from HDF EOS file."""
from satpy.readers.hdf4_utils import from_sds
dataset = self._read_dataset_in_file(dataset_name)
dask_arr = from_sds(dataset, chunks=CHUNK_SIZE)
dims = ('y', 'x') if dask_arr.ndim == 2 else None
data = xr.DataArray(dask_arr, dims=dims,
attrs=dataset.attributes())
data = self._scale_and_mask_data_array(data, is_category=is_category)
return data
def _scale_and_mask_data_array(self, data, is_category=False):
"""Unscale byte data and mask invalid/fill values.
MODIS requires unscaling the in-file bytes in an unexpected way::
data = (byte_value - add_offset) * scale_factor
See the below L1B User's Guide Appendix C for more information:
https://mcst.gsfc.nasa.gov/sites/default/files/file_attachments/M1054E_PUG_2017_0901_V6.2.2_Terra_V6.2.1_Aqua.pdf
"""
good_mask, new_fill = self._get_good_data_mask(data, is_category=is_category)
scale_factor = data.attrs.pop('scale_factor', None)
add_offset = data.attrs.pop('add_offset', None)
# don't scale category products, even though scale_factor may equal 1
# we still need to convert integers to floats
if scale_factor is not None and not is_category:
if add_offset is not None and add_offset != 0:
data = data - np.float32(add_offset)
data = data * np.float32(scale_factor)
if good_mask is not None:
data = data.where(good_mask, new_fill)
return data
def _get_good_data_mask(self, data_arr, is_category=False):
try:
fill_value = data_arr.attrs["_FillValue"]
except KeyError:
return None, None
# preserve integer data types if possible
if is_category and np.issubdtype(data_arr.dtype, np.integer):
# no need to mask, the fill value is already what it needs to be
return None, None
new_fill = np.nan
data_arr.attrs.pop('_FillValue', None)
good_mask = data_arr != fill_value
return good_mask, new_fill
def _add_satpy_metadata(self, data_id: DataID, data_arr: xr.DataArray):
"""Add metadata that is specific to Satpy."""
new_attrs = {
'platform_name': 'EOS-' + self.metadata_platform_name,
'sensor': 'modis',
}
res = data_id["resolution"]
rps = self._resolution_to_rows_per_scan(res)
new_attrs["rows_per_scan"] = rps
data_arr.attrs.update(new_attrs)
def _resolution_to_rows_per_scan(self, resolution: int) -> int:
known_rps = {
5000: 2,
1000: 10,
500: 20,
250: 40,
}
return known_rps.get(resolution, 10)
[docs]class HDFEOSGeoReader(HDFEOSBaseFileReader):
"""Handler for the geographical datasets."""
# list of geographical datasets handled by the georeader
# mapping to the default variable name if not specified in YAML
DATASET_NAMES = {
'longitude': 'Longitude',
'latitude': 'Latitude',
'satellite_azimuth_angle': ('SensorAzimuth', 'Sensor_Azimuth'),
'satellite_zenith_angle': ('SensorZenith', 'Sensor_Zenith'),
'solar_azimuth_angle': ('SolarAzimuth', 'SolarAzimuth'),
'solar_zenith_angle': ('SolarZenith', 'Solar_Zenith'),
}
def __init__(self, filename, filename_info, filetype_info, **kwargs):
"""Initialize the geographical reader."""
HDFEOSBaseFileReader.__init__(self, filename, filename_info, filetype_info, **kwargs)
self.cache = {}
[docs] @staticmethod
def is_geo_loadable_dataset(dataset_name: str) -> bool:
"""Determine if this dataset should be loaded as a Geo dataset."""
return dataset_name in HDFEOSGeoReader.DATASET_NAMES
[docs] @staticmethod
def read_geo_resolution(metadata):
"""Parse metadata to find the geolocation resolution."""
# level 1 files
try:
return HDFEOSGeoReader._geo_resolution_for_l1b(metadata)
except KeyError:
try:
return HDFEOSGeoReader._geo_resolution_for_l2_l1b(metadata)
except (AttributeError, KeyError):
raise RuntimeError("Could not determine resolution from file metadata")
@staticmethod
def _geo_resolution_for_l1b(metadata):
ds = metadata['INVENTORYMETADATA']['COLLECTIONDESCRIPTIONCLASS']['SHORTNAME']['VALUE']
if ds.endswith('D03') or ds.endswith('HKM') or ds.endswith('QKM'):
return 1000
# 1km files have 5km geolocation usually
return 5000
@staticmethod
def _geo_resolution_for_l2_l1b(metadata):
# data files probably have this level 2 files
# this does not work for L1B 1KM data files because they are listed
# as 1KM data but the geo data inside is at 5km
latitude_dim = metadata['SwathStructure']['SWATH_1']['DimensionMap']['DimensionMap_2']['GeoDimension']
resolution_regex = re.compile(r'(?P<resolution>\d+)(km|KM)')
resolution_match = resolution_regex.search(latitude_dim)
return int(resolution_match.group('resolution')) * 1000
@property
def geo_resolution(self):
"""Resolution of the geographical data retrieved in the metadata."""
return self.read_geo_resolution(self.metadata)
def _load_ds_by_name(self, ds_name):
"""Attempt loading using multiple common names."""
var_names = self.DATASET_NAMES[ds_name]
if isinstance(var_names, (list, tuple)):
try:
return self.load_dataset(var_names[0])
except KeyError:
return self.load_dataset(var_names[1])
return self.load_dataset(var_names)
[docs] def get_interpolated_dataset(self, name1, name2, resolution, offset=0):
"""Load and interpolate datasets."""
try:
result1 = self.cache[(name1, resolution)]
result2 = self.cache[(name2, resolution)]
except KeyError:
result1 = self._load_ds_by_name(name1)
result2 = self._load_ds_by_name(name2) - offset
try:
sensor_zenith = self._load_ds_by_name('satellite_zenith_angle')
except KeyError:
# no sensor zenith angle, do "simple" interpolation
sensor_zenith = None
result1, result2 = interpolate(
result1, result2, sensor_zenith,
self.geo_resolution, resolution
)
self.cache[(name1, resolution)] = result1
self.cache[(name2, resolution)] = result2 + offset
[docs] def get_dataset(self, dataset_id: DataID, dataset_info: dict) -> xr.DataArray:
"""Get the geolocation dataset."""
# Name of the dataset as it appears in the HDF EOS file
in_file_dataset_name = dataset_info.get('file_key')
# Name of the dataset in the YAML file
dataset_name = dataset_id['name']
# Resolution asked
resolution = dataset_id['resolution']
if in_file_dataset_name is not None:
# if the YAML was configured with a specific name use that
data = self.load_dataset(in_file_dataset_name)
else:
# otherwise use the default name for this variable
data = self._load_ds_by_name(dataset_name)
if resolution != self.geo_resolution:
if in_file_dataset_name is not None:
# they specified a custom variable name but
# we don't know how to interpolate this yet
raise NotImplementedError(
"Interpolation for variable '{}' is not "
"configured".format(dataset_name))
# The data must be interpolated
logger.debug("Loading %s", dataset_name)
if dataset_name in ['longitude', 'latitude']:
self.get_interpolated_dataset('longitude', 'latitude',
resolution)
elif dataset_name in ['satellite_azimuth_angle', 'satellite_zenith_angle']:
# Sensor dataset names differs between L1b and L2 products
self.get_interpolated_dataset('satellite_azimuth_angle', 'satellite_zenith_angle',
resolution, offset=90)
elif dataset_name in ['solar_azimuth_angle', 'solar_zenith_angle']:
# Sensor dataset names differs between L1b and L2 products
self.get_interpolated_dataset('solar_azimuth_angle', 'solar_zenith_angle',
resolution, offset=90)
data = self.cache[dataset_name, resolution]
for key in ('standard_name', 'units'):
if key in dataset_info:
data.attrs[key] = dataset_info[key]
self._add_satpy_metadata(dataset_id, data)
return data