#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2021 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/>.
"""Tests for the 'seadas_l2' reader."""
import numpy as np
import pytest
from pyresample.geometry import SwathDefinition
from pytest_lazyfixture import lazy_fixture
from satpy import Scene, available_readers
# NOTE:
# The following fixtures are not defined in this file, but are used and injected by Pytest:
# - tmp_path_factory
[docs]@pytest.fixture(scope="module")
def seadas_l2_modis_chlor_a(tmp_path_factory):
"""Create MODIS SEADAS file."""
filename = "a1.21322.1758.seadas.hdf"
full_path = str(tmp_path_factory.mktemp("seadas_l2") / filename)
return _create_seadas_chlor_a_hdf4_file(full_path, "Aqua", "MODISA")
[docs]@pytest.fixture(scope="module")
def seadas_l2_viirs_npp_chlor_a(tmp_path_factory):
"""Create VIIRS NPP SEADAS file."""
filename = "SEADAS_npp_d20211118_t1728125_e1739327.hdf"
full_path = str(tmp_path_factory.mktemp("seadas") / filename)
return _create_seadas_chlor_a_hdf4_file(full_path, "NPP", "VIIRSN")
[docs]@pytest.fixture(scope="module")
def seadas_l2_viirs_j01_chlor_a(tmp_path_factory):
"""Create VIIRS JPSS-01 SEADAS file."""
filename = "SEADAS_j01_d20211118_t1728125_e1739327.hdf"
full_path = str(tmp_path_factory.mktemp("seadas") / filename)
return _create_seadas_chlor_a_hdf4_file(full_path, "JPSS-1", "VIIRSJ1")
def _create_seadas_chlor_a_hdf4_file(full_path, mission, sensor):
from pyhdf.SD import SD, SDC
h = SD(full_path, SDC.WRITE | SDC.CREATE)
setattr(h, "Sensor Name", sensor)
h.Mission = mission
setattr(h, "Start Time", "2021322175853191")
setattr(h, "End Time", "2021322180551214")
lon_info = {
"type": SDC.FLOAT32,
"data": np.zeros((5, 5), dtype=np.float32),
"dim_labels": ["Number of Scan Lines", "Number of Pixel Control Points"],
"attrs": {
"long_name": "Longitude\x00",
"standard_name": "longitude\x00",
"units": "degrees_east\x00",
"valid_range": (-180.0, 180.0),
}
}
lat_info = {
"type": SDC.FLOAT32,
"data": np.zeros((5, 5), np.float32),
"dim_labels": ["Number of Scan Lines", "Number of Pixel Control Points"],
"attrs": {
"long_name": "Latitude\x00",
"standard_name": "latitude\x00",
"units": "degrees_north\x00",
"valid_range": (-90.0, 90.0),
}
}
_add_variable_to_hdf4_file(h, "longitude", lon_info)
_add_variable_to_hdf4_file(h, "latitude", lat_info)
chlor_a_info = {
"type": SDC.FLOAT32,
"data": np.ones((5, 5), np.float32),
"dim_labels": ["Number of Scan Lines", "Number of Pixel Control Points"],
"attrs": {
"long_name": "Chlorophyll Concentration, OCI Algorithm\x00",
"units": "mg m^-3\x00",
"standard_name": "mass_concentration_of_chlorophyll_in_sea_water\x00",
"valid_range": (0.001, 100.0),
}
}
_add_variable_to_hdf4_file(h, "chlor_a", chlor_a_info)
l2_flags = np.zeros((5, 5), dtype=np.int32)
l2_flags[2, 2] = -1
l2_flags_info = {
"type": SDC.INT32,
"data": l2_flags,
"dim_labels": ["Number of Scan Lines", "Number of Pixel Control Points"],
"attrs": {},
}
_add_variable_to_hdf4_file(h, "l2_flags", l2_flags_info)
return [full_path]
def _add_variable_to_hdf4_file(h, var_name, var_info):
v = h.create(var_name, var_info['type'], var_info['data'].shape)
v[:] = var_info['data']
for dim_count, dimension_name in enumerate(var_info['dim_labels']):
v.dim(dim_count).setname(dimension_name)
if var_info.get('fill_value'):
v.setfillvalue(var_info['fill_value'])
for attr_key, attr_val in var_info['attrs'].items():
setattr(v, attr_key, attr_val)
[docs]@pytest.fixture(scope="module")
def seadas_l2_modis_chlor_a_netcdf(tmp_path_factory):
"""Create MODIS SEADAS NetCDF file."""
filename = "t1.21332.1758.seadas.nc"
full_path = str(tmp_path_factory.mktemp("seadas_l2") / filename)
return _create_seadas_chlor_a_netcdf_file(full_path, "Terra", "MODIS")
def _create_seadas_chlor_a_netcdf_file(full_path, mission, sensor):
from netCDF4 import Dataset
nc = Dataset(full_path, "w")
nc.createDimension("number_of_lines", 5)
nc.createDimension("pixels_per_line", 5)
nc.instrument = sensor
nc.platform = mission
nc.time_coverage_start = "2021-11-18T17:58:53.191Z"
nc.time_coverage_end = "2021-11-18T18:05:51.214Z"
lon_info = {
"data": np.zeros((5, 5), dtype=np.float32),
"dim_labels": ("number_of_lines", "pixels_per_line"),
"attrs": {
"long_name": "Longitude",
"standard_name": "longitude",
"units": "degrees_east",
"valid_min": -180.0,
"valid_max": 180.0,
}
}
lat_info = {
"data": np.zeros((5, 5), np.float32),
"dim_labels": ("number_of_lines", "pixels_per_line"),
"attrs": {
"long_name": "Latitude",
"standard_name": "latitude",
"units": "degrees_north",
"valid_min": -90.0,
"valid_max": 90.0,
}
}
nav_group = nc.createGroup("navigation_data")
_add_variable_to_netcdf_file(nav_group, "longitude", lon_info)
_add_variable_to_netcdf_file(nav_group, "latitude", lat_info)
chlor_a_info = {
"data": np.ones((5, 5), np.float32),
"dim_labels": ("number_of_lines", "pixels_per_line"),
"attrs": {
"long_name": "Chlorophyll Concentration, OCI Algorithm",
"units": "mg m^-3",
"standard_name": "mass_concentration_of_chlorophyll_in_sea_water",
"valid_min": 0.001,
"valid_max": 100.0,
}
}
l2_flags = np.zeros((5, 5), dtype=np.int32)
l2_flags[2, 2] = -1
l2_flags_info = {
"data": l2_flags,
"dim_labels": ("number_of_lines", "pixels_per_line"),
"attrs": {
"valid_min": -2147483648,
"valid_max": 2147483647,
},
}
geophys_group = nc.createGroup("geophysical_data")
_add_variable_to_netcdf_file(geophys_group, "chlor_a", chlor_a_info)
_add_variable_to_netcdf_file(geophys_group, "l2_flags", l2_flags_info)
return [full_path]
def _add_variable_to_netcdf_file(nc, var_name, var_info):
v = nc.createVariable(var_name, var_info["data"].dtype.str[1:], dimensions=var_info["dim_labels"],
fill_value=var_info.get("fill_value"))
v[:] = var_info['data']
for attr_key, attr_val in var_info['attrs'].items():
setattr(v, attr_key, attr_val)
[docs]class TestSEADAS:
"""Test the SEADAS L2 file reader."""
[docs] def test_available_reader(self):
"""Test that SEADAS L2 reader is available."""
assert 'seadas_l2' in available_readers()
[docs] @pytest.mark.parametrize(
"input_files",
[
lazy_fixture("seadas_l2_modis_chlor_a"),
lazy_fixture("seadas_l2_viirs_npp_chlor_a"),
lazy_fixture("seadas_l2_viirs_j01_chlor_a"),
])
def test_scene_available_datasets(self, input_files):
"""Test that datasets are available."""
scene = Scene(reader='seadas_l2', filenames=input_files)
available_datasets = scene.all_dataset_names()
assert len(available_datasets) > 0
assert 'chlor_a' in available_datasets
[docs] @pytest.mark.parametrize(
("input_files", "exp_plat", "exp_sensor", "exp_rps"),
[
(lazy_fixture("seadas_l2_modis_chlor_a"), "Aqua", {"modis"}, 10),
(lazy_fixture("seadas_l2_viirs_npp_chlor_a"), "Suomi-NPP", {"viirs"}, 16),
(lazy_fixture("seadas_l2_viirs_j01_chlor_a"), "NOAA-20", {"viirs"}, 16),
(lazy_fixture("seadas_l2_modis_chlor_a_netcdf"), "Terra", {"modis"}, 10),
])
@pytest.mark.parametrize("apply_quality_flags", [False, True])
def test_load_chlor_a(self, input_files, exp_plat, exp_sensor, exp_rps, apply_quality_flags):
"""Test that we can load 'chlor_a'."""
reader_kwargs = {"apply_quality_flags": apply_quality_flags}
scene = Scene(reader='seadas_l2', filenames=input_files, reader_kwargs=reader_kwargs)
scene.load(['chlor_a'])
data_arr = scene['chlor_a']
assert data_arr.dims == ("y", "x")
assert data_arr.attrs['platform_name'] == exp_plat
assert data_arr.attrs['sensor'] == exp_sensor
assert data_arr.attrs['units'] == 'mg m^-3'
assert data_arr.dtype.type == np.float32
assert isinstance(data_arr.attrs["area"], SwathDefinition)
assert data_arr.attrs["rows_per_scan"] == exp_rps
data = data_arr.data.compute()
if apply_quality_flags:
assert np.isnan(data[2, 2])
assert np.count_nonzero(np.isnan(data)) == 1
else:
assert np.count_nonzero(np.isnan(data)) == 0