def test_save_dataset_with_calibration(self):
"""Test basic writer operation."""
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_dataset_calibration()
w = MITIFFWriter(
filename=dataset.attrs['metadata_requirements']['file_pattern'],
base_dir=self.base_dir)
w.save_dataset(dataset)
def test_save_dataset_with_calibration_one_dataset(self):
"""Test saving if mitiff as dataset with only one channel."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected = np.full((100, 200), 255)
expected_key_channel = [
u'Table_calibration: BT, BT, °[C], 8, [ 50.00 49.22 48.43 47.65 46.86 46.08 45.29 '
'44.51 43.73 42.94 42.16 41.37 40.59 39.80 39.02 38.24 37.45 36.67 35.88 35.10 34.31 '
'33.53 32.75 31.96 31.18 30.39 29.61 28.82 28.04 27.25 26.47 25.69 24.90 24.12 23.33 '
'22.55 21.76 20.98 20.20 19.41 18.63 17.84 17.06 16.27 15.49 14.71 13.92 13.14 12.35 '
'11.57 10.78 10.00 9.22 8.43 7.65 6.86 6.08 5.29 4.51 3.73 2.94 2.16 1.37 0.59 -0.20 '
'-0.98 -1.76 -2.55 -3.33 -4.12 -4.90 -5.69 -6.47 -7.25 -8.04 -8.82 -9.61 -10.39 -11.18 '
'-11.96 -12.75 -13.53 -14.31 -15.10 -15.88 -16.67 -17.45 -18.24 -19.02 -19.80 -20.59 '
'-21.37 -22.16 -22.94 -23.73 -24.51 -25.29 -26.08 -26.86 -27.65 -28.43 -29.22 -30.00 '
'-30.78 -31.57 -32.35 -33.14 -33.92 -34.71 -35.49 -36.27 -37.06 -37.84 -38.63 -39.41 '
'-40.20 -40.98 -41.76 -42.55 -43.33 -44.12 -44.90 -45.69 -46.47 -47.25 -48.04 -48.82 '
'-49.61 -50.39 -51.18 -51.96 -52.75 -53.53 -54.31 -55.10 -55.88 -56.67 -57.45 -58.24 '
'-59.02 -59.80 -60.59 -61.37 -62.16 -62.94 -63.73 -64.51 -65.29 -66.08 -66.86 -67.65 '
'-68.43 -69.22 -70.00 -70.78 -71.57 -72.35 -73.14 -73.92 -74.71 -75.49 -76.27 -77.06 '
'-77.84 -78.63 -79.41 -80.20 -80.98 -81.76 -82.55 -83.33 -84.12 -84.90 -85.69 -86.47 '
'-87.25 -88.04 -88.82 -89.61 -90.39 -91.18 -91.96 -92.75 -93.53 -94.31 -95.10 -95.88 '
'-96.67 -97.45 -98.24 -99.02 -99.80 -100.59 -101.37 -102.16 -102.94 -103.73 -104.51 '
'-105.29 -106.08 -106.86 -107.65 -108.43 -109.22 -110.00 -110.78 -111.57 -112.35 '
'-113.14 -113.92 -114.71 -115.49 -116.27 -117.06 -117.84 -118.63 -119.41 -120.20 '
'-120.98 -121.76 -122.55 -123.33 -124.12 -124.90 -125.69 -126.47 -127.25 -128.04 '
'-128.82 -129.61 -130.39 -131.18 -131.96 -132.75 -133.53 -134.31 -135.10 -135.88 '
'-136.67 -137.45 -138.24 -139.02 -139.80 -140.59 -141.37 -142.16 -142.94 -143.73 '
'-144.51 -145.29 -146.08 -146.86 -147.65 -148.43 -149.22 -150.00 ]',
]
dataset = self._get_test_dataset_calibration_one_dataset()
w = MITIFFWriter(
filename=dataset.attrs['metadata_requirements']['file_pattern'],
base_dir=self.base_dir)
w.save_dataset(dataset)
filename = (
dataset.attrs['metadata_requirements']['file_pattern']).format(
start_time=dataset.attrs['start_time'])
tif = TIFF.open(os.path.join(self.base_dir, filename))
IMAGEDESCRIPTION = 270
imgdesc = (tif.GetField(IMAGEDESCRIPTION)).decode('utf-8').split('\n')
found_table_calibration = False
number_of_calibrations = 0
for key in imgdesc:
if 'Table_calibration' in key:
found_table_calibration = True
if 'BT' in key:
self.assertEqual(key, expected_key_channel[0])
number_of_calibrations += 1
self.assertTrue(
found_table_calibration,
"Expected table_calibration is not found in the imagedescription.")
self.assertEqual(number_of_calibrations, 1)
for image in tif.iter_images():
np.testing.assert_allclose(image, expected, atol=1.e-6, rtol=0)
def test_convert_proj4_string(self):
import xarray as xr
import dask.array as da
from satpy.writers.mitiff import MITIFFWriter
from pyresample.geometry import AreaDefinition
checks = [{
'epsg':
'+init=EPSG:32631',
'proj4': (' Proj string: +proj=etmerc +lat_0=0 +lon_0=3 +k=0.9996 '
'+ellps=WGS84 +datum=WGS84 +units=km +x_0=501020.000000 '
'+y_0=1515.000000\n')
}, {
'epsg':
'+init=EPSG:32632',
'proj4': (' Proj string: +proj=etmerc +lat_0=0 +lon_0=9 +k=0.9996 '
'+ellps=WGS84 +datum=WGS84 +units=km +x_0=501020.000000 '
'+y_0=1515.000000\n')
}, {
'epsg':
'+init=EPSG:32633',
'proj4':
(' Proj string: +proj=etmerc +lat_0=0 +lon_0=15 +k=0.9996 '
'+ellps=WGS84 +datum=WGS84 +units=km +x_0=501020.000000 '
'+y_0=1515.000000\n')
}, {
'epsg':
'+init=EPSG:32634',
'proj4':
(' Proj string: +proj=etmerc +lat_0=0 +lon_0=21 +k=0.9996 '
'+ellps=WGS84 +datum=WGS84 +units=km +x_0=501020.000000 '
'+y_0=1515.000000\n')
}, {
'epsg':
'+init=EPSG:32635',
'proj4':
(' Proj string: +proj=etmerc +lat_0=0 +lon_0=27 +k=0.9996 '
'+ellps=WGS84 +datum=WGS84 +units=km +x_0=501020.000000 '
'+y_0=1515.000000\n')
}]
for check in checks:
area_def = AreaDefinition(
'test',
'test',
'test',
check['epsg'],
100,
200,
(-1000., -1500., 1000., 1500.),
)
ds1 = xr.DataArray(da.zeros((10, 20), chunks=20),
dims=('y', 'x'),
attrs={'area': area_def})
w = MITIFFWriter(filename='dummy.tif', base_dir=self.base_dir)
proj4_string = w._add_proj4_string(ds1, ds1)
self.assertEqual(proj4_string, check['proj4'])
def test_save_one_dataset(self):
"""Test basic writer operation with one dataset ie. no bands."""
import os
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_one_dataset()
w = MITIFFWriter(base_dir=self.base_dir)
w.save_dataset(dataset)
tif = TIFF.open(os.path.join(self.base_dir, os.listdir(self.base_dir)[0]))
IMAGEDESCRIPTION = 270
imgdesc = (tif.GetField(IMAGEDESCRIPTION)).decode('utf-8').split('\n')
for key in imgdesc:
if 'In this file' in key:
self.assertEqual(key, ' Channels: 1 In this file: 1')
def test_save_datasets(self):
"""Test basic writer operation save_datasets."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected = np.full((100, 200), 0)
dataset = self._get_test_datasets()
w = MITIFFWriter(base_dir=self.base_dir)
w.save_datasets(dataset)
filename = (dataset[0].attrs['metadata_requirements']['file_pattern']).format(
start_time=dataset[0].attrs['start_time'])
tif = TIFF.open(os.path.join(self.base_dir, filename))
for image in tif.iter_images():
np.testing.assert_allclose(image, expected, atol=1.e-6, rtol=0)
def test_save_dataset_with_bad_value(self):
"""Test writer operation with bad values."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected = np.array([[0, 4, 1, 37, 73], [110, 146, 183, 219, 255]])
dataset = self._get_test_dataset_with_bad_values()
w = MITIFFWriter(base_dir=self.base_dir)
w.save_dataset(dataset)
filename = "{:s}_{:%Y%m%d_%H%M%S}.mitiff".format(
dataset.attrs['name'], dataset.attrs['start_time'])
tif = TIFF.open(os.path.join(self.base_dir, filename))
for image in tif.iter_images():
np.testing.assert_allclose(image, expected, atol=1.e-6, rtol=0)
def test_save_dataset_with_calibration_one_dataset(self):
"""Test saving if mitiff as dataset with only one channel."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected = np.full((100, 200), 255)
dataset = self._get_test_dataset_calibration_one_dataset()
w = MITIFFWriter(
filename=dataset.attrs['metadata_requirements']['file_pattern'],
base_dir=self.base_dir)
w.save_dataset(dataset)
filename = (
dataset.attrs['metadata_requirements']['file_pattern']).format(
start_time=dataset.attrs['start_time'])
tif = TIFF.open(os.path.join(self.base_dir, filename))
for image in tif.iter_images():
np.testing.assert_allclose(image, expected, atol=1.e-6, rtol=0)
def test_save_dataset_with_calibration(self):
"""Test writer operation with calibration."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected_ir = np.full((100, 200), 255)
expected_vis = np.full((100, 200), 0)
expected = np.stack([
expected_vis, expected_vis, expected_ir, expected_ir, expected_ir,
expected_vis
])
dataset = self._get_test_dataset_calibration()
w = MITIFFWriter(
filename=dataset.attrs['metadata_requirements']['file_pattern'],
base_dir=self.base_dir)
w.save_dataset(dataset)
filename = (
dataset.attrs['metadata_requirements']['file_pattern']).format(
start_time=dataset.attrs['start_time'])
tif = TIFF.open(os.path.join(self.base_dir, filename))
for i, image in enumerate(tif.iter_images()):
np.testing.assert_allclose(image, expected[i], atol=1.e-6, rtol=0)
def test_save_one_dataset(self):
"""Test basic writer operation."""
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_one_dataset()
w = MITIFFWriter(base_dir=self.base_dir)
w.save_dataset(dataset)
def test_init(self):
"""Test creating the writer with no arguments."""
from satpy.writers.mitiff import MITIFFWriter
MITIFFWriter()
def test_save_dataset_with_calibration(self):
"""Test basic writer operation."""
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_dataset_calibration()
w = MITIFFWriter(mitiff_dir=self.base_dir)
w.save_dataset(dataset, writer='mitiff', mitiff_dir=self.base_dir)
def test_save_dataset_with_calibration(self):
"""Test writer operation with calibration."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected_ir = np.full((100, 200), 255)
expected_vis = np.full((100, 200), 0)
expected = np.stack([
expected_vis, expected_vis, expected_ir, expected_ir, expected_ir,
expected_vis
])
expected_key_channel = [
'Table_calibration: 1-VIS0.63, Reflectance(Albedo), [%], 8, [ 0.00 0.39 0.78 1.18 1.57 '
'1.96 2.35 2.75 3.14 3.53 3.92 4.31 4.71 5.10 5.49 5.88 6.27 6.67 7.06 7.45 7.84 8.24 '
'8.63 9.02 9.41 9.80 10.20 10.59 10.98 11.37 11.76 12.16 12.55 12.94 13.33 13.73 14.12 '
'14.51 14.90 15.29 15.69 16.08 16.47 16.86 17.25 17.65 18.04 18.43 18.82 19.22 19.61 '
'20.00 20.39 20.78 21.18 21.57 21.96 22.35 22.75 23.14 23.53 23.92 24.31 24.71 25.10 '
'25.49 25.88 26.27 26.67 27.06 27.45 27.84 28.24 28.63 29.02 29.41 29.80 30.20 30.59 '
'30.98 31.37 31.76 32.16 32.55 32.94 33.33 33.73 34.12 34.51 34.90 35.29 35.69 36.08 '
'36.47 36.86 37.25 37.65 38.04 38.43 38.82 39.22 39.61 40.00 40.39 40.78 41.18 41.57 '
'41.96 42.35 42.75 43.14 43.53 43.92 44.31 44.71 45.10 45.49 45.88 46.27 46.67 47.06 '
'47.45 47.84 48.24 48.63 49.02 49.41 49.80 50.20 50.59 50.98 51.37 51.76 52.16 52.55 '
'52.94 53.33 53.73 54.12 54.51 54.90 55.29 55.69 56.08 56.47 56.86 57.25 57.65 58.04 '
'58.43 58.82 59.22 59.61 60.00 60.39 60.78 61.18 61.57 61.96 62.35 62.75 63.14 63.53 '
'63.92 64.31 64.71 65.10 65.49 65.88 66.27 66.67 67.06 67.45 67.84 68.24 68.63 69.02 '
'69.41 69.80 70.20 70.59 70.98 71.37 71.76 72.16 72.55 72.94 73.33 73.73 74.12 74.51 '
'74.90 75.29 75.69 76.08 76.47 76.86 77.25 77.65 78.04 78.43 78.82 79.22 79.61 80.00 '
'80.39 80.78 81.18 81.57 81.96 82.35 82.75 83.14 83.53 83.92 84.31 84.71 85.10 85.49 '
'85.88 86.27 86.67 87.06 87.45 87.84 88.24 88.63 89.02 89.41 89.80 90.20 90.59 90.98 '
'91.37 91.76 92.16 92.55 92.94 93.33 93.73 94.12 94.51 94.90 95.29 95.69 96.08 96.47 '
'96.86 97.25 97.65 98.04 98.43 98.82 99.22 99.61 100.00 ]',
'Table_calibration: 2-VIS0.86, Reflectance(Albedo), [%], 8, [ 0.00 0.39 0.78 1.18 1.57 '
'1.96 2.35 2.75 3.14 3.53 3.92 4.31 4.71 5.10 5.49 5.88 6.27 6.67 7.06 7.45 7.84 8.24 '
'8.63 9.02 9.41 9.80 10.20 10.59 10.98 11.37 11.76 12.16 12.55 12.94 13.33 13.73 14.12 '
'14.51 14.90 15.29 15.69 16.08 16.47 16.86 17.25 17.65 18.04 18.43 18.82 19.22 19.61 '
'20.00 20.39 20.78 21.18 21.57 21.96 22.35 22.75 23.14 23.53 23.92 24.31 24.71 25.10 '
'25.49 25.88 26.27 26.67 27.06 27.45 27.84 28.24 28.63 29.02 29.41 29.80 30.20 30.59 '
'30.98 31.37 31.76 32.16 32.55 32.94 33.33 33.73 34.12 34.51 34.90 35.29 35.69 36.08 '
'36.47 36.86 37.25 37.65 38.04 38.43 38.82 39.22 39.61 40.00 40.39 40.78 41.18 41.57 '
'41.96 42.35 42.75 43.14 43.53 43.92 44.31 44.71 45.10 45.49 45.88 46.27 46.67 47.06 '
'47.45 47.84 48.24 48.63 49.02 49.41 49.80 50.20 50.59 50.98 51.37 51.76 52.16 52.55 '
'52.94 53.33 53.73 54.12 54.51 54.90 55.29 55.69 56.08 56.47 56.86 57.25 57.65 58.04 '
'58.43 58.82 59.22 59.61 60.00 60.39 60.78 61.18 61.57 61.96 62.35 62.75 63.14 63.53 '
'63.92 64.31 64.71 65.10 65.49 65.88 66.27 66.67 67.06 67.45 67.84 68.24 68.63 69.02 '
'69.41 69.80 70.20 70.59 70.98 71.37 71.76 72.16 72.55 72.94 73.33 73.73 74.12 74.51 '
'74.90 75.29 75.69 76.08 76.47 76.86 77.25 77.65 78.04 78.43 78.82 79.22 79.61 80.00 '
'80.39 80.78 81.18 81.57 81.96 82.35 82.75 83.14 83.53 83.92 84.31 84.71 85.10 85.49 '
'85.88 86.27 86.67 87.06 87.45 87.84 88.24 88.63 89.02 89.41 89.80 90.20 90.59 90.98 '
'91.37 91.76 92.16 92.55 92.94 93.33 93.73 94.12 94.51 94.90 95.29 95.69 96.08 96.47 '
'96.86 97.25 97.65 98.04 98.43 98.82 99.22 99.61 100.00 ]',
u'Table_calibration: 3(3B)-IR3.7, BT, °[C], 8, [ 50.00 49.22 48.43 47.65 46.86 46.08 '
'45.29 44.51 43.73 42.94 42.16 41.37 40.59 39.80 39.02 38.24 37.45 36.67 35.88 35.10 '
'34.31 33.53 32.75 31.96 31.18 30.39 29.61 28.82 28.04 27.25 26.47 25.69 24.90 24.12 '
'23.33 22.55 21.76 20.98 20.20 19.41 18.63 17.84 17.06 16.27 15.49 14.71 13.92 13.14 '
'12.35 11.57 10.78 10.00 9.22 8.43 7.65 6.86 6.08 5.29 4.51 3.73 2.94 2.16 1.37 0.59 '
'-0.20 -0.98 -1.76 -2.55 -3.33 -4.12 -4.90 -5.69 -6.47 -7.25 -8.04 -8.82 -9.61 -10.39 '
'-11.18 -11.96 -12.75 -13.53 -14.31 -15.10 -15.88 -16.67 -17.45 -18.24 -19.02 -19.80 '
'-20.59 -21.37 -22.16 -22.94 -23.73 -24.51 -25.29 -26.08 -26.86 -27.65 -28.43 -29.22 '
'-30.00 -30.78 -31.57 -32.35 -33.14 -33.92 -34.71 -35.49 -36.27 -37.06 -37.84 -38.63 '
'-39.41 -40.20 -40.98 -41.76 -42.55 -43.33 -44.12 -44.90 -45.69 -46.47 -47.25 -48.04 '
'-48.82 -49.61 -50.39 -51.18 -51.96 -52.75 -53.53 -54.31 -55.10 -55.88 -56.67 -57.45 '
'-58.24 -59.02 -59.80 -60.59 -61.37 -62.16 -62.94 -63.73 -64.51 -65.29 -66.08 -66.86 '
'-67.65 -68.43 -69.22 -70.00 -70.78 -71.57 -72.35 -73.14 -73.92 -74.71 -75.49 -76.27 '
'-77.06 -77.84 -78.63 -79.41 -80.20 -80.98 -81.76 -82.55 -83.33 -84.12 -84.90 -85.69 '
'-86.47 -87.25 -88.04 -88.82 -89.61 -90.39 -91.18 -91.96 -92.75 -93.53 -94.31 -95.10 '
'-95.88 -96.67 -97.45 -98.24 -99.02 -99.80 -100.59 -101.37 -102.16 -102.94 -103.73 '
'-104.51 -105.29 -106.08 -106.86 -107.65 -108.43 -109.22 -110.00 -110.78 -111.57 '
'-112.35 -113.14 -113.92 -114.71 -115.49 -116.27 -117.06 -117.84 -118.63 -119.41 '
'-120.20 -120.98 -121.76 -122.55 -123.33 -124.12 -124.90 -125.69 -126.47 -127.25 '
'-128.04 -128.82 -129.61 -130.39 -131.18 -131.96 -132.75 -133.53 -134.31 -135.10 '
'-135.88 -136.67 -137.45 -138.24 -139.02 -139.80 -140.59 -141.37 -142.16 -142.94 '
'-143.73 -144.51 -145.29 -146.08 -146.86 -147.65 -148.43 -149.22 -150.00 ]',
u'Table_calibration: 4-IR10.8, BT, °[C], 8, [ 50.00 49.22 48.43 47.65 46.86 46.08 '
'45.29 '
'44.51 43.73 42.94 42.16 41.37 40.59 39.80 39.02 38.24 37.45 36.67 35.88 35.10 34.31 '
'33.53 32.75 31.96 31.18 30.39 29.61 28.82 28.04 27.25 26.47 25.69 24.90 24.12 23.33 '
'22.55 21.76 20.98 20.20 19.41 18.63 17.84 17.06 16.27 15.49 14.71 13.92 13.14 12.35 '
'11.57 10.78 10.00 9.22 8.43 7.65 6.86 6.08 5.29 4.51 3.73 2.94 2.16 1.37 0.59 -0.20 '
'-0.98 -1.76 -2.55 -3.33 -4.12 -4.90 -5.69 -6.47 -7.25 -8.04 -8.82 -9.61 -10.39 -11.18 '
'-11.96 -12.75 -13.53 -14.31 -15.10 -15.88 -16.67 -17.45 -18.24 -19.02 -19.80 -20.59 '
'-21.37 -22.16 -22.94 -23.73 -24.51 -25.29 -26.08 -26.86 -27.65 -28.43 -29.22 -30.00 '
'-30.78 -31.57 -32.35 -33.14 -33.92 -34.71 -35.49 -36.27 -37.06 -37.84 -38.63 -39.41 '
'-40.20 -40.98 -41.76 -42.55 -43.33 -44.12 -44.90 -45.69 -46.47 -47.25 -48.04 -48.82 '
'-49.61 -50.39 -51.18 -51.96 -52.75 -53.53 -54.31 -55.10 -55.88 -56.67 -57.45 -58.24 '
'-59.02 -59.80 -60.59 -61.37 -62.16 -62.94 -63.73 -64.51 -65.29 -66.08 -66.86 -67.65 '
'-68.43 -69.22 -70.00 -70.78 -71.57 -72.35 -73.14 -73.92 -74.71 -75.49 -76.27 -77.06 '
'-77.84 -78.63 -79.41 -80.20 -80.98 -81.76 -82.55 -83.33 -84.12 -84.90 -85.69 -86.47 '
'-87.25 -88.04 -88.82 -89.61 -90.39 -91.18 -91.96 -92.75 -93.53 -94.31 -95.10 -95.88 '
'-96.67 -97.45 -98.24 -99.02 -99.80 -100.59 -101.37 -102.16 -102.94 -103.73 -104.51 '
'-105.29 -106.08 -106.86 -107.65 -108.43 -109.22 -110.00 -110.78 -111.57 -112.35 '
'-113.14 -113.92 -114.71 -115.49 -116.27 -117.06 -117.84 -118.63 -119.41 -120.20 '
'-120.98 -121.76 -122.55 -123.33 -124.12 -124.90 -125.69 -126.47 -127.25 -128.04 '
'-128.82 -129.61 -130.39 -131.18 -131.96 -132.75 -133.53 -134.31 -135.10 -135.88 '
'-136.67 -137.45 -138.24 -139.02 -139.80 -140.59 -141.37 -142.16 -142.94 -143.73 '
'-144.51 -145.29 -146.08 -146.86 -147.65 -148.43 -149.22 -150.00 ]',
u'Table_calibration: 5-IR11.5, BT, °[C], 8, [ 50.00 49.22 48.43 47.65 46.86 46.08 '
'45.29 '
'44.51 43.73 42.94 42.16 41.37 40.59 39.80 39.02 38.24 37.45 36.67 35.88 35.10 34.31 '
'33.53 32.75 31.96 31.18 30.39 29.61 28.82 28.04 27.25 26.47 25.69 24.90 24.12 23.33 '
'22.55 21.76 20.98 20.20 19.41 18.63 17.84 17.06 16.27 15.49 14.71 13.92 13.14 12.35 '
'11.57 10.78 10.00 9.22 8.43 7.65 6.86 6.08 5.29 4.51 3.73 2.94 2.16 1.37 0.59 -0.20 '
'-0.98 -1.76 -2.55 -3.33 -4.12 -4.90 -5.69 -6.47 -7.25 -8.04 -8.82 -9.61 -10.39 -11.18 '
'-11.96 -12.75 -13.53 -14.31 -15.10 -15.88 -16.67 -17.45 -18.24 -19.02 -19.80 -20.59 '
'-21.37 -22.16 -22.94 -23.73 -24.51 -25.29 -26.08 -26.86 -27.65 -28.43 -29.22 -30.00 '
'-30.78 -31.57 -32.35 -33.14 -33.92 -34.71 -35.49 -36.27 -37.06 -37.84 -38.63 -39.41 '
'-40.20 -40.98 -41.76 -42.55 -43.33 -44.12 -44.90 -45.69 -46.47 -47.25 -48.04 -48.82 '
'-49.61 -50.39 -51.18 -51.96 -52.75 -53.53 -54.31 -55.10 -55.88 -56.67 -57.45 -58.24 '
'-59.02 -59.80 -60.59 -61.37 -62.16 -62.94 -63.73 -64.51 -65.29 -66.08 -66.86 -67.65 '
'-68.43 -69.22 -70.00 -70.78 -71.57 -72.35 -73.14 -73.92 -74.71 -75.49 -76.27 -77.06 '
'-77.84 -78.63 -79.41 -80.20 -80.98 -81.76 -82.55 -83.33 -84.12 -84.90 -85.69 -86.47 '
'-87.25 -88.04 -88.82 -89.61 -90.39 -91.18 -91.96 -92.75 -93.53 -94.31 -95.10 -95.88 '
'-96.67 -97.45 -98.24 -99.02 -99.80 -100.59 -101.37 -102.16 -102.94 -103.73 -104.51 '
'-105.29 -106.08 -106.86 -107.65 -108.43 -109.22 -110.00 -110.78 -111.57 -112.35 '
'-113.14 -113.92 -114.71 -115.49 -116.27 -117.06 -117.84 -118.63 -119.41 -120.20 '
'-120.98 -121.76 -122.55 -123.33 -124.12 -124.90 -125.69 -126.47 -127.25 -128.04 '
'-128.82 -129.61 -130.39 -131.18 -131.96 -132.75 -133.53 -134.31 -135.10 -135.88 '
'-136.67 -137.45 -138.24 -139.02 -139.80 -140.59 -141.37 -142.16 -142.94 -143.73 '
'-144.51 -145.29 -146.08 -146.86 -147.65 -148.43 -149.22 -150.00 ]',
'Table_calibration: 6(3A)-VIS1.6, Reflectance(Albedo), [%], 8, [ 0.00 0.39 0.78 1.18 '
'1.57 1.96 2.35 2.75 3.14 3.53 3.92 4.31 4.71 5.10 5.49 5.88 6.27 6.67 7.06 7.45 7.84 '
'8.24 8.63 9.02 9.41 9.80 10.20 10.59 10.98 11.37 11.76 12.16 12.55 12.94 13.33 13.73 '
'14.12 14.51 14.90 15.29 15.69 16.08 16.47 16.86 17.25 17.65 18.04 18.43 18.82 19.22 '
'19.61 20.00 20.39 20.78 21.18 21.57 21.96 22.35 22.75 23.14 23.53 23.92 24.31 24.71 '
'25.10 25.49 25.88 26.27 26.67 27.06 27.45 27.84 28.24 28.63 29.02 29.41 29.80 30.20 '
'30.59 30.98 31.37 31.76 32.16 32.55 32.94 33.33 33.73 34.12 34.51 34.90 35.29 35.69 '
'36.08 36.47 36.86 37.25 37.65 38.04 38.43 38.82 39.22 39.61 40.00 40.39 40.78 41.18 '
'41.57 41.96 42.35 42.75 43.14 43.53 43.92 44.31 44.71 45.10 45.49 45.88 46.27 46.67 '
'47.06 47.45 47.84 48.24 48.63 49.02 49.41 49.80 50.20 50.59 50.98 51.37 51.76 52.16 '
'52.55 52.94 53.33 53.73 54.12 54.51 54.90 55.29 55.69 56.08 56.47 56.86 57.25 57.65 '
'58.04 58.43 58.82 59.22 59.61 60.00 60.39 60.78 61.18 61.57 61.96 62.35 62.75 63.14 '
'63.53 63.92 64.31 64.71 65.10 65.49 65.88 66.27 66.67 67.06 67.45 67.84 68.24 68.63 '
'69.02 69.41 69.80 70.20 70.59 70.98 71.37 71.76 72.16 72.55 72.94 73.33 73.73 74.12 '
'74.51 74.90 75.29 75.69 76.08 76.47 76.86 77.25 77.65 78.04 78.43 78.82 79.22 79.61 '
'80.00 80.39 80.78 81.18 81.57 81.96 82.35 82.75 83.14 83.53 83.92 84.31 84.71 85.10 '
'85.49 85.88 86.27 86.67 87.06 87.45 87.84 88.24 88.63 89.02 89.41 89.80 90.20 90.59 '
'90.98 91.37 91.76 92.16 92.55 92.94 93.33 93.73 94.12 94.51 94.90 95.29 95.69 96.08 '
'96.47 96.86 97.25 97.65 98.04 98.43 98.82 99.22 99.61 100.00 ]'
]
dataset = self._get_test_dataset_calibration()
w = MITIFFWriter(
filename=dataset.attrs['metadata_requirements']['file_pattern'],
base_dir=self.base_dir)
w.save_dataset(dataset)
filename = (
dataset.attrs['metadata_requirements']['file_pattern']).format(
start_time=dataset.attrs['start_time'])
tif = TIFF.open(os.path.join(self.base_dir, filename))
IMAGEDESCRIPTION = 270
imgdesc = (tif.GetField(IMAGEDESCRIPTION)).decode('utf-8').split('\n')
found_table_calibration = False
number_of_calibrations = 0
for key in imgdesc:
if 'Table_calibration' in key:
found_table_calibration = True
if '1-VIS0.63' in key:
self.assertEqual(key, expected_key_channel[0])
number_of_calibrations += 1
elif '2-VIS0.86' in key:
self.assertEqual(key, expected_key_channel[1])
number_of_calibrations += 1
elif '3(3B)-IR3.7' in key:
self.assertEqual(key, expected_key_channel[2])
number_of_calibrations += 1
elif '4-IR10.8' in key:
self.assertEqual(key, expected_key_channel[3])
number_of_calibrations += 1
elif '5-IR11.5' in key:
self.assertEqual(key, expected_key_channel[4])
number_of_calibrations += 1
elif '6(3A)-VIS1.6' in key:
self.assertEqual(key, expected_key_channel[5])
number_of_calibrations += 1
else:
self.fail(
"Not a valid channel description i the given key.")
self.assertTrue(
found_table_calibration,
"Table_calibration is not found in the imagedescription.")
self.assertEqual(number_of_calibrations, 6)
for i, image in enumerate(tif.iter_images()):
np.testing.assert_allclose(image, expected[i], atol=1.e-6, rtol=0)
def test_save_dataset_palette(self):
"""Test writer operation as palette."""
import os
import numpy as np
from libtiff import TIFF
from satpy.writers.mitiff import MITIFFWriter
expected = np.full((100, 200), 0)
exp_c = ([
0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
], [
1, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
], [
2, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
])
color_map = [[0, 3], [1, 4], [2, 5]]
pal_desc = ['test', 'test2']
unit = "Test"
dataset = self._get_test_one_dataset()
palette = {
'palette': True,
'palette_color_map': color_map,
'palette_description': pal_desc,
'palette_unit': unit,
'palette_channel_name': dataset.attrs['name']
}
w = MITIFFWriter(base_dir=self.base_dir)
w.save_dataset(dataset, **palette)
filename = "{:s}_{:%Y%m%d_%H%M%S}.mitiff".format(
dataset.attrs['name'], dataset.attrs['start_time'])
tif = TIFF.open(os.path.join(self.base_dir, filename))
# Need to check PHOTOMETRIC is 3, ie palette
self.assertEqual(tif.GetField('PHOTOMETRIC'), 3)
colormap = tif.GetField('COLORMAP')
# Check the colormap of the palette image
self.assertEqual(colormap, exp_c)
IMAGEDESCRIPTION = 270
imgdesc = (tif.GetField(IMAGEDESCRIPTION)).decode('utf-8').split('\n')
found_color_info = False
unit_name_found = False
name_length_found = False
name_length = 0
names = []
unit_name = None
for key in imgdesc:
if name_length_found and name_length > len(names):
names.append(key)
continue
elif unit_name_found:
name_length = int(key)
name_length_found = True
unit_name_found = False
elif found_color_info:
unit_name = key
unit_name_found = True
found_color_info = False
elif 'COLOR INFO:' in key:
found_color_info = True
# Check the name of the palette description
self.assertEqual(name_length, 2)
# Check the name and unit name of the palette
self.assertEqual(unit_name, ' Test')
# Check the palette description of the palette
self.assertEqual(names, [' test', ' test2'])
for image in tif.iter_images():
np.testing.assert_allclose(image, expected, atol=1.e-6, rtol=0)
def test_simple_write_two_bands(self):
"""Test basic writer operation with 3 bands from 2 prerequisites"""
from satpy.writers.mitiff import MITIFFWriter
dataset = self._get_test_dataset_three_bands_two_prereq()
w = MITIFFWriter(base_dir=self.base_dir)
w.save_dataset(dataset)