Source code for satpy.readers.modis_l2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2019 Satpy developers
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# satpy is free software: you can redistribute it and/or modify it under the
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"""Modis level 2 hdf-eos format reader.

Introduction
------------

The ``modis_l2`` reader reads and calibrates Modis L2 image data in hdf-eos format.
Since there are a multitude of different level 2 datasets not all of theses are implemented (yet).


Currently the reader supports:
    - m[o/y]d35_l2: cloud_mask dataset
    - some datasets in m[o/y]d06 files

To get a list of the available datasets for a given file refer to the "Load data" section in :doc:`../readers`.


Geolocation files
-----------------

Similar to the ``modis_l1b`` reader the geolocation files (mod03) for the 1km data are optional and if not
given 1km geolocations will be interpolated from the 5km geolocation contained within the file.

For the 500m and 250m data geolocation files are needed.


References:
    - Documentation about the format: https://modis-atmos.gsfc.nasa.gov/products

"""
import logging

import numpy as np
import xarray as xr


from satpy import CHUNK_SIZE
from satpy.readers.hdfeos_base import HDFEOSGeoReader
from satpy.readers.hdf4_utils import from_sds

logger = logging.getLogger(__name__)


[docs]class ModisL2HDFFileHandler(HDFEOSGeoReader): """File handler for MODIS HDF-EOS Level 2 files.""" def _select_hdf_dataset(self, hdf_dataset_name, byte_dimension): """Load a dataset from HDF-EOS level 2 file.""" hdf_dataset = self.sd.select(hdf_dataset_name) if byte_dimension == 0: dataset = xr.DataArray(from_sds(hdf_dataset, chunks=CHUNK_SIZE), dims=['i', 'y', 'x']).astype(np.uint8) elif byte_dimension == 2: dataset = xr.DataArray(from_sds(hdf_dataset, chunks=CHUNK_SIZE), dims=['y', 'x', 'i']).astype(np.uint8) # Reorder dimensions for consistency dataset = dataset.transpose('i', 'y', 'x') return dataset def _parse_resolution_info(self, info, resolution): if isinstance(info, list): if len(info) == 1 and isinstance(info[0], int): return info[0] # Check if the values are stored in a with resolution as a key if isinstance(info[0], dict): for elem in info: try: return elem[resolution] except KeyError: pass # The information doesn't concern the current resolution return None return info
[docs] def get_dataset(self, dataset_id, dataset_info): """Get DataArray for specified dataset.""" dataset_name = dataset_id.name if dataset_name in HDFEOSGeoReader.DATASET_NAMES: return HDFEOSGeoReader.get_dataset(self, dataset_id, dataset_info) dataset_name_in_file = dataset_info['file_key'] # The dataset asked correspond to a given set of bits of the HDF EOS dataset if 'byte' in dataset_info and 'byte_dimension' in dataset_info: byte_dimension = dataset_info['byte_dimension'] # Where the information is stored dataset = self._select_hdf_dataset(dataset_name_in_file, byte_dimension) byte_information = self._parse_resolution_info(dataset_info['byte'], dataset_id.resolution) # At which bit starts the information bit_start = self._parse_resolution_info(dataset_info['bit_start'], dataset_id.resolution) # How many bits store the information bit_count = self._parse_resolution_info(dataset_info['bit_count'], dataset_id.resolution) # Only one byte: select the byte information if isinstance(byte_information, int): byte_dataset = dataset[byte_information, :, :] # Two bytes: recombine the two bytes elif isinstance(byte_information, list) and len(byte_information) == 2: # We recombine the two bytes dataset_a = dataset[byte_information[0], :, :] dataset_b = dataset[byte_information[1], :, :] dataset_a = np.uint16(dataset_a) dataset_a = np.left_shift(dataset_a, 8) # dataset_a << 8 byte_dataset = np.bitwise_or(dataset_a, dataset_b).astype(np.uint16) shape = byte_dataset.shape # We replicate the concatenated byte with the right shape byte_dataset = np.repeat(np.repeat(byte_dataset, 4, axis=0), 4, axis=1) # All bits carry information, we update bit_start consequently bit_start = np.arange(16, dtype=np.uint16).reshape((4, 4)) bit_start = np.tile(bit_start, (shape[0], shape[1])) # Compute the final bit mask dataset = bits_strip(bit_start, bit_count, byte_dataset) # Apply quality assurance filter if 'quality_assurance' in dataset_info: quality_assurance_required = self._parse_resolution_info( dataset_info['quality_assurance'], dataset_id.resolution ) if quality_assurance_required is True: # Get quality assurance dataset recursively from satpy import DatasetID quality_assurance_dataset_id = DatasetID( name='quality_assurance', resolution=1000 ) quality_assurance_dataset_info = { 'name': 'quality_assurance', 'resolution': [1000], 'byte_dimension': 2, 'byte': [0], 'bit_start': 0, 'bit_count': 1, 'file_key': 'Quality_Assurance' } quality_assurance = self.get_dataset( quality_assurance_dataset_id, quality_assurance_dataset_info ) # Duplicate quality assurance dataset to create relevant filter duplication_factor = [int(dataset_dim / quality_assurance_dim) for dataset_dim, quality_assurance_dim in zip(dataset.shape, quality_assurance.shape)] quality_assurance = np.tile(quality_assurance, duplication_factor) # Replace unassured data by NaN value dataset[np.where(quality_assurance == 0)] = np.NaN # No byte manipulation required else: dataset = self.load_dataset(dataset_name) return dataset
[docs]def bits_strip(bit_start, bit_count, value): """Extract specified bit from bit representation of integer value. Parameters ---------- bit_start : int Starting index of the bits to extract (first bit has index 0) bit_count : int Number of bits starting from bit_start to extract value : int Number from which to extract the bits Returns ------- int Value of the extracted bits """ bit_mask = pow(2, bit_start + bit_count) - 1 return np.right_shift(np.bitwise_and(value, bit_mask), bit_start)