Source code for satpy.tests.reader_tests.test_modis_l2

#!/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/>.
"""Unit tests for MODIS L2 HDF reader."""

from __future__ import annotations

import dask
import dask.array as da
import numpy as np
import pytest
from pytest_lazyfixture import lazy_fixture

from satpy import Scene, available_readers

from ..utils import CustomScheduler, make_dataid
from ._modis_fixtures import _shape_for_resolution


def _check_shared_metadata(data_arr, expect_area=False):
    assert data_arr.attrs["sensor"] == "modis"
    assert data_arr.attrs["platform_name"] == "EOS-Terra"
    assert "rows_per_scan" in data_arr.attrs
    assert isinstance(data_arr.attrs["rows_per_scan"], int)
    assert data_arr.attrs['reader'] == 'modis_l2'
    if expect_area:
        assert data_arr.attrs.get('area') is not None
    else:
        assert 'area' not in data_arr.attrs


[docs]class TestModisL2: """Test MODIS L2 reader."""
[docs] def test_available_reader(self): """Test that MODIS L2 reader is available.""" assert 'modis_l2' in available_readers()
[docs] def test_scene_available_datasets(self, modis_l2_nasa_mod35_file): """Test that datasets are available.""" scene = Scene(reader='modis_l2', filenames=modis_l2_nasa_mod35_file) available_datasets = scene.all_dataset_names() assert len(available_datasets) > 0 assert 'cloud_mask' in available_datasets assert 'latitude' in available_datasets assert 'longitude' in available_datasets
[docs] @pytest.mark.parametrize( ('input_files', 'has_5km', 'has_500', 'has_250', 'default_res'), [ [lazy_fixture('modis_l2_nasa_mod35_file'), True, False, False, 1000], ] ) def test_load_longitude_latitude(self, input_files, has_5km, has_500, has_250, default_res): """Test that longitude and latitude datasets are loaded correctly.""" from .test_modis_l1b import _load_and_check_geolocation scene = Scene(reader='modis_l2', filenames=input_files) shape_5km = _shape_for_resolution(5000) shape_500m = _shape_for_resolution(500) shape_250m = _shape_for_resolution(250) default_shape = _shape_for_resolution(default_res) with dask.config.set(scheduler=CustomScheduler(max_computes=1 + has_5km + has_500 + has_250)): _load_and_check_geolocation(scene, "*", default_res, default_shape, True, check_callback=_check_shared_metadata) _load_and_check_geolocation(scene, 5000, 5000, shape_5km, has_5km, check_callback=_check_shared_metadata) _load_and_check_geolocation(scene, 500, 500, shape_500m, has_500, check_callback=_check_shared_metadata) _load_and_check_geolocation(scene, 250, 250, shape_250m, has_250, check_callback=_check_shared_metadata)
[docs] def test_load_quality_assurance(self, modis_l2_nasa_mod35_file): """Test loading quality assurance.""" scene = Scene(reader='modis_l2', filenames=modis_l2_nasa_mod35_file) dataset_name = 'quality_assurance' scene.load([dataset_name]) quality_assurance_id = make_dataid(name=dataset_name, resolution=1000) assert quality_assurance_id in scene quality_assurance = scene[quality_assurance_id] assert quality_assurance.shape == _shape_for_resolution(1000) _check_shared_metadata(quality_assurance, expect_area=True)
[docs] @pytest.mark.parametrize( ('input_files', 'loadables', 'request_resolution', 'exp_resolution', 'exp_area'), [ [lazy_fixture('modis_l2_nasa_mod35_mod03_files'), ["cloud_mask"], 1000, 1000, True], [lazy_fixture('modis_l2_imapp_mask_byte1_geo_files'), ["cloud_mask", "land_sea_mask", "snow_ice_mask"], None, 1000, True], ] ) def test_load_category_dataset(self, input_files, loadables, request_resolution, exp_resolution, exp_area): """Test loading category products.""" scene = Scene(reader='modis_l2', filenames=input_files) kwargs = {"resolution": request_resolution} if request_resolution is not None else {} scene.load(loadables, **kwargs) for ds_name in loadables: cat_id = make_dataid(name=ds_name, resolution=exp_resolution) assert cat_id in scene cat_data_arr = scene[cat_id] assert isinstance(cat_data_arr.data, da.Array) cat_data_arr = cat_data_arr.compute() assert cat_data_arr.shape == _shape_for_resolution(exp_resolution) assert cat_data_arr.values[0, 0] == 0.0 assert cat_data_arr.attrs.get('resolution') == exp_resolution # mask variables should be integers assert np.issubdtype(cat_data_arr.dtype, np.integer) assert cat_data_arr.attrs.get('_FillValue') is not None _check_shared_metadata(cat_data_arr, expect_area=exp_area)
[docs] @pytest.mark.parametrize( ('input_files', 'exp_area'), [ [lazy_fixture('modis_l2_nasa_mod35_file'), False], [lazy_fixture('modis_l2_nasa_mod35_mod03_files'), True], ] ) def test_load_250m_cloud_mask_dataset(self, input_files, exp_area): """Test loading 250m cloud mask.""" scene = Scene(reader='modis_l2', filenames=input_files) dataset_name = 'cloud_mask' scene.load([dataset_name], resolution=250) cloud_mask_id = make_dataid(name=dataset_name, resolution=250) assert cloud_mask_id in scene cloud_mask = scene[cloud_mask_id] assert isinstance(cloud_mask.data, da.Array) cloud_mask = cloud_mask.compute() assert cloud_mask.shape == _shape_for_resolution(250) assert cloud_mask.values[0, 0] == 0.0 # mask variables should be integers assert np.issubdtype(cloud_mask.dtype, np.integer) assert cloud_mask.attrs.get('_FillValue') is not None _check_shared_metadata(cloud_mask, expect_area=exp_area)
[docs] @pytest.mark.parametrize( ('input_files', 'loadables', 'exp_resolution', 'exp_area', 'exp_value'), [ [lazy_fixture('modis_l2_nasa_mod06_file'), ["surface_pressure"], 5000, True, 4.0], # snow mask is considered a category product, factor/offset ignored [lazy_fixture('modis_l2_imapp_snowmask_file'), ["snow_mask"], 1000, False, 1.0], [lazy_fixture('modis_l2_imapp_snowmask_geo_files'), ["snow_mask"], 1000, True, 1.0], ] ) def test_load_l2_dataset(self, input_files, loadables, exp_resolution, exp_area, exp_value): """Load and check an L2 variable.""" scene = Scene(reader='modis_l2', filenames=input_files) scene.load(loadables) for ds_name in loadables: assert ds_name in scene data_arr = scene[ds_name] assert isinstance(data_arr.data, da.Array) data_arr = data_arr.compute() assert data_arr.values[0, 0] == exp_value assert data_arr.shape == _shape_for_resolution(exp_resolution) assert data_arr.attrs.get('resolution') == exp_resolution _check_shared_metadata(data_arr, expect_area=exp_area)