def save_image(self, img, filename=None, **kwargs): # floating_point=False,
"""Save the image to the given *filename* in ninjotiff_ format.
.. _ninjotiff: http://www.ssec.wisc.edu/~davidh/polar2grid/misc/NinJo_Satellite_Import_Formats.html
"""
filename = filename or self.get_filename(**img.data.attrs)
nt.save(img, filename, **kwargs)
def save_image(self, img, filename=None, **kwargs): # floating_point=False,
"""Save the image to the given *filename* in ninjotiff_ format.
.. _ninjotiff: http://www.ssec.wisc.edu/~davidh/polar2grid/misc/NinJo_Satellite_Import_Formats.html
"""
filename = filename or self.get_filename(**img.info)
nt.save(img, filename, **kwargs)
def test_write_bw():
"""Test saving a BW image."""
from pyninjotiff.ninjotiff import save
from pyninjotiff.tifffile import TiffFile
area = FakeArea(
{
'ellps': 'WGS84',
'lat_0': '90.0',
'lat_ts': '60.0',
'lon_0': '0.0',
'proj': 'stere'
}, (-1000000.0, -4500000.0, 2072000.0, -1428000.0), 1024, 1024)
scale = 1.0 / 120
offset = 0.0
attrs = dict([('resolution', 1050), ('polarization', None),
('platform_name', 'NOAA-18'), ('sensor', 'avhrr-3'),
('units', '%'), ('name', '1'), ('level', None),
('modifiers', ()), ('wavelength', (10.3, 10.8, 11.3)),
('calibration', 'brightness_temperature'),
('start_time', TIME - datetime.timedelta(minutes=5)),
('end_time', TIME), ('area', area),
('ancillary_variables', []),
('enhancement_history', [{
'offset': offset,
'scale': scale
}])])
kwargs = {
'ch_min_measurement_unit': np.array([0]),
'ch_max_measurement_unit': np.array([120]),
'compute': True,
'fill_value': None,
'sat_id': 6300014,
'chan_id': 100015,
'data_cat': 'P**N',
'data_source': 'SMHI',
'physic_unit': '%',
'nbits': 8
}
data = da.tile(da.repeat(da.arange(4, chunks=1024) / 3.0, 256),
1024).reshape((1, 1024, 1024))
data = xr.DataArray(data,
coords={'bands': ['L']},
dims=['bands', 'y', 'x'],
attrs=attrs)
img = FakeImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
res = tif[0].asarray()
assert (np.allclose(res[0, 0, ::256],
np.array([256, 22016, 43520, 65280])))
def test_write_rgba():
"""Test saving an RGBA image."""
area = STEREOGRAPHIC_AREA
fill_value = np.nan
arr = create_hsv_color_disk(fill_value)
attrs = dict([('platform_name', 'NOAA-18'),
('resolution', 1050),
('polarization', None),
('start_time', TIME - datetime.timedelta(minutes=55)),
('end_time', TIME - datetime.timedelta(minutes=50)),
('level', None),
('sensor', 'avhrr-3'),
('ancillary_variables', []),
('area', area),
('wavelength', None),
('optional_datasets', []),
('standard_name', 'overview'),
('name', 'overview'),
('prerequisites', [0.6, 0.8, 10.8]),
('optional_prerequisites', []),
('calibration', None),
('modifiers', None),
('mode', 'RGBA'),
('enhancement_history', [{'scale': np.array([1, 1, -1]), 'offset': np.array([0, 0, 1])},
{'scale': np.array([0.0266347, 0.03559078, 0.01329783]),
'offset': np.array([-0.02524969, -0.01996642, 3.8918446])},
{'gamma': 1.6}])])
kwargs = {'compute': True, 'fill_value': None, 'sat_id': 6300014,
'chan_id': 6500015, 'data_cat': 'PPRN', 'data_source': 'SMHI', 'nbits': 8}
alpha = np.where(np.isnan(arr[0, :, :]), 0, 1)
arr = np.nan_to_num(arr)
arr = np.vstack((arr, alpha[np.newaxis, :, :]))
data = da.from_array(arr.clip(0, 1), chunks=1024)
data = xr.DataArray(data, coords={'bands': ['R', 'G', 'B', 'A']}, dims=[
'bands', 'y', 'x'], attrs=attrs)
img = XRImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
res = tif[0].asarray()
for idx in range(4):
np.testing.assert_allclose(res[:, :, idx], np.round(
np.nan_to_num(arr[idx, :, :]) * 255).astype(np.uint8))
np.testing.assert_allclose(res[:, :, 3] == 0, alpha == 0)
def test_write_bw_inverted_ir_fill():
"""Test saving a BW image with transparency."""
area = STEREOGRAPHIC_AREA
scale = 1.0 / 120
offset = 70.0 / 120
attrs = dict([('resolution', 1050),
('polarization', None),
('platform_name', 'NOAA-18'),
('sensor', 'avhrr-3'),
('units', 'K'),
('name', '4'),
('level', None),
('modifiers', ()),
('wavelength', (10.3, 10.8, 11.3)),
('calibration', 'brightness_temperature'),
('start_time', TIME - datetime.timedelta(minutes=35)),
('end_time', TIME - datetime.timedelta(minutes=30)),
('area', area),
('ancillary_variables', []),
('enhancement_history', [{'offset': offset, 'scale': scale}])])
kwargs = {'ch_min_measurement_unit': np.array([-70]),
'ch_max_measurement_unit': np.array([50]),
'compute': True, 'fill_value': None, 'sat_id': 6300014,
'chan_id': 900015, 'data_cat': 'P**N', 'data_source': 'SMHI',
'physic_unit': 'C', 'nbits': 8}
data1 = da.tile(da.repeat(da.arange(4, chunks=1024) /
3.0, 256), 256).reshape((1, 256, 1024))
datanan = da.ones((1, 256, 1024), chunks=1024) * np.nan
data2 = da.tile(da.repeat(da.arange(4, chunks=1024) /
3.0, 256), 512).reshape((1, 512, 1024))
data = da.concatenate((data1, datanan, data2), axis=1)
data = xr.DataArray(data, coords={'bands': ['L']}, dims=[
'bands', 'y', 'x'], attrs=attrs)
img = FakeImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
page = tif[0]
res = page.asarray(colormapped=False).squeeze()
colormap = page.tags['color_map'].value
for i in range(3):
assert(np.all(np.array(colormap[i * 256:(i + 1) * 256]) == np.arange(255, -1, -1) * 256))
assert(np.all(res[0, ::256] == np.array([1, 86, 170, 255])))
assert(np.all(res[256, :] == 0))
def save_image(self, img, filename=None, compute=True, **kwargs): # floating_point=False,
"""Save the image to the given *filename* in ninjotiff_ format.
.. _ninjotiff: http://www.ssec.wisc.edu/~davidh/polar2grid/misc/NinJo_Satellite_Import_Formats.html
"""
filename = filename or self.get_filename(**img.data.attrs)
if img.mode.startswith("L") and (
"ch_min_measurement_unit" not in kwargs
or "ch_max_measurement_unit" not in kwargs
):
try:
scale, offset = img.get_scaling_from_history()
scale, offset = invert_scale_offset(scale, offset)
except ValueError as err:
logger.warning(str(err))
else:
try:
# Here we know that the data if the image is scaled between 0 and 1
dmin = offset
dmax = scale + offset
ch_min_measurement_unit, ch_max_measurement_unit = np.minimum(dmin, dmax), np.maximum(dmin, dmax)
kwargs["ch_min_measurement_unit"] = ch_min_measurement_unit
kwargs["ch_max_measurement_unit"] = ch_max_measurement_unit
except KeyError:
raise NotImplementedError(
"Don't know how to handle non-scale/offset-based enhancements yet."
)
return nt.save(img, filename, data_is_scaled_01=True, compute=compute, **kwargs)
def test_write_bw():
"""Test saving a BW image.
Reflectances.
"""
area = STEREOGRAPHIC_AREA
scale = 1.0 / 120
offset = 0.0
attrs = dict([('resolution', 1050),
('polarization', None),
('platform_name', 'NOAA-18'),
('sensor', 'avhrr-3'),
('units', '%'),
('name', '1'),
('level', None),
('modifiers', ()),
('wavelength', (0.5, 0.6, 0.7)),
('calibration', 'reflectance'),
('start_time', TIME - datetime.timedelta(minutes=5)),
('end_time', TIME),
('area', area),
('ancillary_variables', []),
('enhancement_history', [{'offset': offset, 'scale': scale}])])
kwargs = {'ch_min_measurement_unit': xr.DataArray(0),
'ch_max_measurement_unit': xr.DataArray(120),
'compute': True, 'fill_value': None, 'sat_id': 6300014,
'chan_id': 100015, 'data_cat': 'P**N', 'data_source': 'SMHI',
'physic_unit': '%', 'nbits': 8}
data = da.tile(da.repeat(da.arange(4, chunks=1024) /
3.0, 256), 1024).reshape((1, 1024, 1024))
data = xr.DataArray(data, coords={'bands': ['L']}, dims=[
'bands', 'y', 'x'], attrs=attrs)
img = FakeImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
page = tif[0]
res = page.asarray(colormapped=False).squeeze()
colormap = page.tags['color_map'].value
for i in range(3):
assert(np.all(np.array(colormap[i * 256:(i + 1) * 256]) == np.arange(256) * 256))
assert(np.all(res[0, ::256] == np.array([1, 86, 170, 255])))
def test_write_p():
"""Test saving an image in P mode.
Values are 0, 1, 2, 3, 4, Palette is black, red, green, blue, gray.
"""
area = STEREOGRAPHIC_AREA
palette = [np.array((0, 0, 0, 1)),
np.array((1, 0, 0, 1)),
np.array((0, 1, 0, 1)),
np.array((0, 0, 1, 1)),
np.array((.5, .5, .5, 1)),
]
attrs = dict([('resolution', 1050),
('polarization', None),
('platform_name', 'MSG'),
('sensor', 'seviri'),
("palette", palette),
('name', 'msg_cloudtop_height'),
('level', None),
('modifiers', ()),
('start_time', TIME - datetime.timedelta(minutes=85)),
('end_time', TIME - datetime.timedelta(minutes=80)),
('area', area),
('ancillary_variables', [])])
data = da.tile(da.repeat(da.arange(5, chunks=1024, dtype=np.uint8), 205)[:-1],
1024).reshape((1, 1024, 1024))[:, :1024]
data = xr.DataArray(data, coords={'bands': ['P']}, dims=[
'bands', 'y', 'x'], attrs=attrs)
kwargs = {'compute': True, 'fill_value': None, 'sat_id': 9000014,
'chan_id': 1900015, 'data_cat': 'GPRN', 'data_source': 'SMHI',
'physic_unit': 'NONE', "physic_value": "NONE",
"description": "NWCSAF Cloud Top Height"}
img = FakeImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
colormap, res = _load_file_values_with_colormap(filename)
np.testing.assert_array_equal(res[0, ::205], [0, 1, 2, 3, 4])
assert(len(colormap) == 768)
for i, line in enumerate(palette):
np.testing.assert_array_equal(colormap[i::256], (line[:3] * 255).astype(int))
def test_write_rgb_classified():
"""Test saving a transparent RGB."""
area = STEREOGRAPHIC_AREA
x_size, y_size = 1024, 1024
arr = np.zeros((3, y_size, x_size))
attrs = dict([('platform_name', 'NOAA-18'),
('resolution', 1050),
('polarization', None),
('start_time', TIME - datetime.timedelta(minutes=65)),
('end_time', TIME - datetime.timedelta(minutes=60)),
('level', None),
('sensor', 'avhrr-3'),
('ancillary_variables', []),
('area', area),
('wavelength', None),
('optional_datasets', []),
('standard_name', 'overview'),
('name', 'overview'),
('prerequisites', [0.6, 0.8, 10.8]),
('optional_prerequisites', []),
('calibration', None),
('modifiers', None),
('mode', 'P')])
kwargs = {'compute': True, 'fill_value': None, 'sat_id': 6300014,
'chan_id': 1700015, 'data_cat': 'PPRN', 'data_source': 'SMHI', 'nbits': 8}
data1 = da.tile(da.repeat(da.arange(4, chunks=1024), 256), 256).reshape((1, 256, 1024))
datanan = da.ones((1, 256, 1024), chunks=1024) * 4
data2 = da.tile(da.repeat(da.arange(4, chunks=1024), 256), 512).reshape((1, 512, 1024))
data = da.concatenate((data1, datanan, data2), axis=1)
data = xr.DataArray(data, coords={'bands': ['P']}, dims=['bands', 'y', 'x'], attrs=attrs)
img = XRImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
res = tif[0].asarray()
for idx in range(3):
np.testing.assert_allclose(res[:, :, idx], np.round(
np.nan_to_num(arr[idx, :, :]) * 255).astype(np.uint8))
np.testing.assert_allclose(res[:, :, 3] == 0, np.isnan(arr[0, :, :]))
def save_image(self,
img,
filename=None,
compute=True,
**kwargs): # floating_point=False,
"""Save the image to the given *filename* in ninjotiff_ format.
.. _ninjotiff: http://www.ssec.wisc.edu/~davidh/polar2grid/misc/NinJo_Satellite_Import_Formats.html
"""
filename = filename or self.get_filename(**img.data.attrs)
if img.mode.startswith("L") and (
"ch_min_measurement_unit" not in kwargs
or "ch_max_measurement_unit" not in kwargs):
try:
history = img.data.attrs["enhancement_history"]
except KeyError:
logger.warning(
"Cannot find information on previous scaling for ninjo.")
else:
if len(history) > 1:
raise NotImplementedError(
"Don't know how to process large enhancement_history yet"
)
try:
scale = history[0]["scale"]
offset = history[0]["offset"]
dmin = -offset / scale
dmax = (1 - offset) / scale
if dmin > dmax:
dmin, dmax = dmax, dmin
ch_min_measurement_unit, ch_max_measurement_unit = (
dmin.values,
dmax.values,
)
kwargs["ch_min_measurement_unit"] = ch_min_measurement_unit
kwargs["ch_max_measurement_unit"] = ch_max_measurement_unit
except KeyError:
raise NotImplementedError(
"Don't know how to handle non-scale/offset-based enhancements yet."
)
if compute:
return nt.save(img, filename, data_is_scaled_01=True, **kwargs)
else:
return delayed(nt.save)(img,
filename,
data_is_scaled_01=True,
**kwargs)
def test_write_rgb_with_a():
"""Test saving a transparent RGB."""
from pyninjotiff.ninjotiff import save
from pyninjotiff.tifffile import TiffFile
area = FakeArea(
{
'ellps': 'WGS84',
'lat_0': '90.0',
'lat_ts': '60.0',
'lon_0': '0.0',
'proj': 'stere'
}, (-1000000.0, -4500000.0, 2072000.0, -1428000.0), 1024, 1024)
x_size, y_size = 1024, 1024
arr = np.zeros((3, y_size, x_size))
radius = min(x_size, y_size) / 2.0
centre = x_size / 2, y_size / 2
for x in range(x_size):
for y in range(y_size):
rx = x - centre[0]
ry = y - centre[1]
s = ((x - centre[0])**2.0 + (y - centre[1])**2.0)**0.5 / radius
if s <= 1.0:
h = ((np.arctan2(ry, rx) / np.pi) + 1.0) / 2.0
rgb = colorsys.hsv_to_rgb(h, s, 1.0)
arr[:, y, x] = np.array(rgb)
else:
arr[:, y, x] = np.nan
attrs = dict([('platform_name', 'NOAA-18'), ('resolution', 1050),
('polarization', None),
('start_time', TIME - datetime.timedelta(minutes=55)),
('end_time', TIME - datetime.timedelta(minutes=50)),
('level', None), ('sensor', 'avhrr-3'),
('ancillary_variables', []), ('area', area),
('wavelength', None), ('optional_datasets', []),
('standard_name', 'overview'), ('name', 'overview'),
('prerequisites', [0.6, 0.8, 10.8]),
('optional_prerequisites', []), ('calibration', None),
('modifiers', None), ('mode', 'RGB'),
('enhancement_history', [{
'scale': np.array([1, 1, -1]),
'offset': np.array([0, 0, 1])
}, {
'scale':
np.array([0.0266347, 0.03559078, 0.01329783]),
'offset':
np.array([-0.02524969, -0.01996642, 3.8918446])
}, {
'gamma': 1.6
}])])
kwargs = {
'compute': True,
'fill_value': None,
'sat_id': 6300014,
'chan_id': 6500015,
'data_cat': 'PPRN',
'data_source': 'SMHI',
'nbits': 8
}
data = da.from_array(arr.clip(0, 1), chunks=1024)
data = xr.DataArray(data,
coords={'bands': ['R', 'G', 'B']},
dims=['bands', 'y', 'x'],
attrs=attrs)
from trollimage.xrimage import XRImage
img = XRImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
res = tif[0].asarray()
for idx in range(3):
np.testing.assert_allclose(
res[:, :, idx],
np.round(np.nan_to_num(arr[idx, :, :]) * 255).astype(np.uint8))
np.testing.assert_allclose(res[:, :, 3] == 0, np.isnan(arr[0, :, :]))
def test_write_ir_colormap():
"""Test saving a IR image with a colormap.
IR with a colormap.
Temperatures are -70, -40.24, -10, 20.24, 50.
"""
from pyninjotiff.ninjotiff import save
from pyninjotiff.tifffile import TiffFile
area = FakeArea(
{
'ellps': 'WGS84',
'lat_0': 90.0,
'lat_ts': 60.0,
'lon_0': 0.0,
'proj': 'stere'
}, (-1000000.0, -4500000.0, 2072000.0, -1428000.0), 1024, 1024)
scale = 1.0 / 120
offset = 70.0 / 120
attrs = dict([('resolution', 1050), ('polarization', None),
('platform_name', 'NOAA-18'), ('sensor', 'avhrr-3'),
('units', 'K'), ('name', '4'), ('level', None),
('modifiers', ()), ('wavelength', (10.3, 10.8, 11.3)),
('calibration', 'brightness_temperature'),
('start_time', TIME - datetime.timedelta(minutes=85)),
('end_time', TIME - datetime.timedelta(minutes=80)),
('area', area), ('ancillary_variables', []),
('enhancement_history', [{
'offset': offset,
'scale': scale
}])])
ir_map = [
255, 1535, 2559, 3327, 4095, 4863, 5375, 5887, 6399, 6911, 7423, 7935,
8447, 8959, 9471, 9983, 10239, 10751, 11263, 11519, 12031, 12287,
12799, 13055, 13567, 13823, 14335, 14591, 14847, 15359, 15615, 16127,
16383, 16639, 17151, 17407, 17663, 17919, 18431, 18687, 18943, 19199,
19711, 19967, 20223, 20479, 20735, 21247, 21503, 21759, 22015, 22271,
22527, 22783, 23295, 23551, 23807, 24063, 24319, 24575, 24831, 25087,
25343, 25599, 25855, 26367, 26623, 26879, 27135, 27391, 27647, 27903,
28159, 28415, 28671, 28927, 29183, 29439, 29695, 29951, 30207, 30463,
30719, 30975, 31231, 31487, 31743, 31999, 31999, 32255, 32511, 32767,
33023, 33279, 33535, 33791, 34047, 34303, 34559, 34815, 35071, 35327,
35327, 35583, 35839, 36095, 36351, 36607, 36863, 37119, 37375, 37375,
37631, 37887, 38143, 38399, 38655, 38911, 39167, 39167, 39423, 39679,
39935, 40191, 40447, 40703, 40703, 40959, 41215, 41471, 41727, 41983,
41983, 42239, 42495, 42751, 43007, 43263, 43263, 43519, 43775, 44031,
44287, 44287, 44543, 44799, 45055, 45311, 45311, 45567, 45823, 46079,
46335, 46335, 46591, 46847, 47103, 47359, 47359, 47615, 47871, 48127,
48127, 48383, 48639, 48895, 49151, 49151, 49407, 49663, 49919, 49919,
50175, 50431, 50687, 50687, 50943, 51199, 51455, 51455, 51711, 51967,
52223, 52223, 52479, 52735, 52991, 52991, 53247, 53503, 53759, 53759,
54015, 54271, 54527, 54527, 54783, 55039, 55039, 55295, 55551, 55807,
55807, 56063, 56319, 56319, 56575, 56831, 57087, 57087, 57343, 57599,
57599, 57855, 58111, 58367, 58367, 58623, 58879, 58879, 59135, 59391,
59391, 59647, 59903, 60159, 60159, 60415, 60671, 60671, 60927, 61183,
61183, 61439, 61695, 61695, 61951, 62207, 62463, 62463, 62719, 62975,
62975, 63231, 63487, 63487, 63743, 63999, 63999, 64255, 64511, 64511,
64767, 65023, 65023, 65279
]
kwargs = {
'ch_min_measurement_unit': np.array([-70]),
'ch_max_measurement_unit': np.array([50]),
'compute': True,
'fill_value': None,
'sat_id': 6300014,
'chan_id': 900015,
'data_cat': 'P**N',
'data_source': 'SMHI',
'physic_unit': 'C',
'nbits': 8,
'cmap': [ir_map] * 3
}
data = da.tile(da.repeat(da.arange(5, chunks=1024) / 4.0, 205)[:-1],
1024).reshape((1, 1024, 1024))[:, :1024]
data = xr.DataArray(data,
coords={'bands': ['L']},
dims=['bands', 'y', 'x'],
attrs=attrs)
img = FakeImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
page = tif[0]
res = page.asarray(colormapped=False).squeeze()
colormap = page.tags['color_map'].value
assert (len(colormap) == 768)
assert (np.allclose(colormap[:256], ir_map))
assert (np.allclose(colormap[256:512], ir_map))
assert (np.allclose(colormap[512:], ir_map))
assert (np.allclose(res[0, ::205], np.array([1, 64, 128, 192, 255])))
def test_write_rgb_tb():
"""Test saving a non-trasparent RGB with thumbnails."""
from pyninjotiff.ninjotiff import save
from pyninjotiff.tifffile import TiffFile
area = FakeArea(
{
'ellps': 'WGS84',
'lat_0': 90.0,
'lat_ts': 60.0,
'lon_0': 0.0,
'proj': 'stere'
}, (-1000000.0, -4500000.0, 2072000.0, -1428000.0), 1024, 1024)
x_size, y_size = 1024, 1024
arr = np.zeros((3, y_size, x_size))
radius = min(x_size, y_size) / 2.0
centre = x_size / 2, y_size / 2
for x in range(x_size):
for y in range(y_size):
rx = x - centre[0]
ry = y - centre[1]
s = ((x - centre[0])**2.0 + (y - centre[1])**2.0)**0.5 / radius
if s <= 1.0:
h = ((np.arctan2(ry, rx) / np.pi) + 1.0) / 2.0
rgb = colorsys.hsv_to_rgb(h, s, 1.0)
arr[:, y, x] = np.array(rgb)
attrs = dict([('platform_name', 'NOAA-18'), ('resolution', 1050),
('polarization', None), ('level', None),
('sensor', 'avhrr-3'), ('ancillary_variables', []),
('area', area),
('start_time', TIME - datetime.timedelta(minutes=45)),
('end_time', TIME - datetime.timedelta(minutes=40)),
('wavelength', None), ('optional_datasets', []),
('standard_name', 'overview'), ('name', 'overview'),
('prerequisites', [0.6, 0.8, 10.8]),
('optional_prerequisites', []), ('calibration', None),
('modifiers', None), ('mode', 'RGB'),
('enhancement_history', [{
'scale': np.array([1, 1, -1]),
'offset': np.array([0, 0, 1])
}, {
'scale':
np.array([0.0266347, 0.03559078, 0.01329783]),
'offset':
np.array([-0.02524969, -0.01996642, 3.8918446])
}, {
'gamma': 1.6
}])])
kwargs = {
'compute': True,
'fill_value': None,
'sat_id': 6300014,
'chan_id': 6500015,
'data_cat': 'PPRN',
'data_source': 'SMHI',
'nbits': 8,
'tile_length': 256,
'tile_width': 256
}
data = da.from_array(arr.clip(0, 1), chunks=1024)
data = xr.DataArray(data,
coords={'bands': ['R', 'G', 'B']},
dims=['bands', 'y', 'x'],
attrs=attrs)
from trollimage.xrimage import XRImage
img = XRImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=False, **kwargs)
tif = TiffFile(filename)
res = tif[0].asarray()
assert (tif.pages[0].tags['tile_length'].value == 256)
assert (tif.pages[1].tags['tile_length'].value == 128)
assert (tif.pages[0].tags['tile_width'].value == 256)
assert (tif.pages[1].tags['tile_width'].value == 128)
assert (len(tif.pages) == 2)
assert (tif.pages[0].shape == (1024, 1024, 4))
assert (tif.pages[1].shape == (512, 512, 4))
for idx in range(3):
np.testing.assert_allclose(
res[:, :, idx],
np.round(arr[idx, :, :] * 255).astype(np.uint8))
tags = {
'new_subfile_type':
0,
'image_width':
1024,
'image_length':
1024,
'bits_per_sample': (8, 8, 8, 8),
'compression':
32946,
'photometric':
2,
'orientation':
1,
'samples_per_pixel':
4,
'planar_configuration':
1,
'software':
b'tifffile/pytroll',
'datetime':
b'2020:01:17 14:17:23',
'tile_width':
256,
'tile_length':
256,
'tile_offsets':
(951, 24414, 77352, 126135, 141546, 206260, 272951, 318709, 349650,
413166, 475735, 519168, 547960, 570326, 615924, 666705),
'tile_byte_counts':
(23463, 52938, 48783, 15411, 64714, 66691, 45758, 30941, 63516,
62569, 43433, 28792, 22366, 45598, 50781, 13371),
'extra_samples':
2,
'sample_format': (1, 1, 1, 1),
'model_pixel_scale':
(0.026949458523585643, 0.027040118922685666, 0.0),
'model_tie_point':
(0.0, 0.0, 0.0, -35.00279008179894, 73.3850622630575, 0.0),
'40000':
b'NINJO',
'40001':
6300014,
'40002':
1579264321,
'40003':
1579267043,
'40004':
6500015,
'40005':
2,
'40006':
b'/tmp/tmpb4kn93qt',
'40007':
b'PPRN',
'40008':
b'',
'40009':
24,
'40010':
b'SMHI',
'40011':
1,
'40012':
1024,
'40013':
1,
'40014':
1024,
'40015':
b'NPOL',
'40016':
-35.00278854370117,
'40017':
24.72344398498535,
'40018':
6378137.0,
'40019':
6356752.5,
'40021':
60.0,
'40022':
0.0,
'40023':
0.0,
'40024':
b'None',
'40025':
b'None',
'40026':
0,
'40027':
255,
'40028':
1.0,
'40029':
0.0,
'40040':
0,
'40041':
0,
'40042':
1,
'40043':
0,
'50000':
0,
'fill_order':
1,
'rows_per_strip':
4294967295,
'resolution_unit':
2,
'predictor':
1,
'ycbcr_subsampling':
1,
'ycbcr_positioning':
1
}
read_tags = tif.pages[0].tags
assert (read_tags.keys() == tags.keys())
for key, val in tags.items():
if key in ['datetime', '40002', '40003', '40006']:
continue
assert (val == read_tags[key].value)
def test_write_bw_fill():
"""Test saving a BW image with transparency."""
from pyninjotiff.ninjotiff import save
from pyninjotiff.tifffile import TiffFile
area = FakeArea(
{
'ellps': 'WGS84',
'lat_0': 90.0,
'lat_ts': 60.0,
'lon_0': 0.0,
'proj': 'stere'
}, (-1000000.0, -4500000.0, 2072000.0, -1428000.0), 1024, 1024)
scale = 1.0 / 120
offset = 0.0
attrs = dict([('resolution', 1050), ('polarization', None),
('platform_name', 'NOAA-18'), ('sensor', 'avhrr-3'),
('units', '%'), ('name', '1'), ('level', None),
('modifiers', ()), ('wavelength', (0.5, 0.6, 0.7)),
('calibration', 'reflectance'),
('start_time', TIME - datetime.timedelta(minutes=25)),
('end_time', TIME - datetime.timedelta(minutes=20)),
('area', area), ('ancillary_variables', []),
('enhancement_history', [{
'offset': offset,
'scale': scale
}])])
kwargs = {
'ch_min_measurement_unit': np.array([0]),
'ch_max_measurement_unit': np.array([120]),
'compute': True,
'fill_value': None,
'sat_id': 6300014,
'chan_id': 100015,
'data_cat': 'P**N',
'data_source': 'SMHI',
'physic_unit': '%',
'nbits': 8
}
data1 = da.tile(da.repeat(da.arange(4, chunks=1024) / 3.0, 256),
256).reshape((1, 256, 1024))
datanan = da.ones((1, 256, 1024), chunks=1024) * np.nan
data2 = da.tile(da.repeat(da.arange(4, chunks=1024) / 3.0, 256),
512).reshape((1, 512, 1024))
data = da.concatenate((data1, datanan, data2), axis=1)
data = xr.DataArray(data,
coords={'bands': ['L']},
dims=['bands', 'y', 'x'],
attrs=attrs)
img = FakeImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
tif = TiffFile(filename)
page = tif[0]
res = page.asarray(colormapped=False).squeeze()
colormap = page.tags['color_map'].value
for i in range(3):
assert (np.all(
np.array(colormap[i * 256:(i + 1) * 256]) == np.arange(256) *
256))
assert (np.all(res[0, ::256] == np.array([1, 86, 170, 255])))
assert (np.all(res[256, :] == 0))
def test_write_bw_colormap():
"""Test saving a BW image with a colormap.
Albedo with a colormap.
Reflectances are 0, 29.76, 60, 90.24, 120.
"""
area = STEREOGRAPHIC_AREA
scale = 1.0 / 120
offset = 0.0
attrs = dict([('resolution', 1050),
('polarization', None),
('platform_name', 'NOAA-18'),
('sensor', 'avhrr-3'),
('units', '%'),
('name', '1'),
('level', None),
('modifiers', ()),
('wavelength', (0.5, 0.6, 0.7)),
('calibration', 'reflectance'),
('start_time', TIME - datetime.timedelta(minutes=75)),
('end_time', TIME - datetime.timedelta(minutes=70)),
('area', area),
('ancillary_variables', []),
('enhancement_history', [{'offset': offset, 'scale': scale}])])
cm_vis = [0, 4095, 5887, 7167, 8191, 9215, 9983, 10751, 11519, 12287, 12799,
13567, 14079, 14847, 15359, 15871, 16383, 16895, 17407, 17919, 18175,
18687, 19199, 19711, 19967, 20479, 20735, 21247, 21503, 22015, 22271,
22783, 23039, 23551, 23807, 24063, 24575, 24831, 25087, 25599, 25855,
26111, 26367, 26879, 27135, 27391, 27647, 27903, 28415, 28671, 28927,
29183, 29439, 29695, 29951, 30207, 30463, 30975, 31231, 31487, 31743,
31999, 32255, 32511, 32767, 33023, 33279, 33535, 33791, 34047, 34303,
34559, 34559, 34815, 35071, 35327, 35583, 35839, 36095, 36351, 36607,
36863, 37119, 37119, 37375, 37631, 37887, 38143, 38399, 38655, 38655,
38911, 39167, 39423, 39679, 39935, 39935, 40191, 40447, 40703, 40959,
40959, 41215, 41471, 41727, 41983, 41983, 42239, 42495, 42751, 42751,
43007, 43263, 43519, 43519, 43775, 44031, 44287, 44287, 44543, 44799,
45055, 45055, 45311, 45567, 45823, 45823, 46079, 46335, 46335, 46591,
46847, 46847, 47103, 47359, 47615, 47615, 47871, 48127, 48127, 48383,
48639, 48639, 48895, 49151, 49151, 49407, 49663, 49663, 49919, 50175,
50175, 50431, 50687, 50687, 50943, 50943, 51199, 51455, 51455, 51711,
51967, 51967, 52223, 52223, 52479, 52735, 52735, 52991, 53247, 53247,
53503, 53503, 53759, 54015, 54015, 54271, 54271, 54527, 54783, 54783,
55039, 55039, 55295, 55551, 55551, 55807, 55807, 56063, 56319, 56319,
56575, 56575, 56831, 56831, 57087, 57343, 57343, 57599, 57599, 57855,
57855, 58111, 58367, 58367, 58623, 58623, 58879, 58879, 59135, 59135,
59391, 59647, 59647, 59903, 59903, 60159, 60159, 60415, 60415, 60671,
60671, 60927, 60927, 61183, 61439, 61439, 61695, 61695, 61951, 61951,
62207, 62207, 62463, 62463, 62719, 62719, 62975, 62975, 63231, 63231,
63487, 63487, 63743, 63743, 63999, 63999, 64255, 64255, 64511, 64511,
64767, 64767, 65023, 65023, 65279]
kwargs = {'ch_min_measurement_unit': np.array([0]),
'ch_max_measurement_unit': np.array([120]),
'compute': True, 'fill_value': None, 'sat_id': 6300014,
'chan_id': 100015, 'data_cat': 'P**N', 'data_source': 'SMHI',
'physic_unit': '%', 'nbits': 8, 'cmap': [cm_vis] * 3}
data = da.tile(da.repeat(da.arange(5, chunks=1024) / 4.0, 205)[:-1],
1024).reshape((1, 1024, 1024))[:, :1024]
data = xr.DataArray(data, coords={'bands': ['L']}, dims=[
'bands', 'y', 'x'], attrs=attrs)
img = FakeImage(data)
with tempfile.NamedTemporaryFile(delete=DELETE_FILES) as tmpfile:
filename = tmpfile.name
if not DELETE_FILES:
print(filename)
save(img, filename, data_is_scaled_01=True, **kwargs)
colormap, res = _load_file_values_with_colormap(filename)
assert(len(colormap) == 768)
assert(np.allclose(colormap[:256], cm_vis))
assert(np.allclose(colormap[256:512], cm_vis))
assert(np.allclose(colormap[512:], cm_vis))
assert(np.allclose(res[0, ::205], np.array([1, 64, 128, 192, 255])))
