def __call__(self, projectables, nonprojectables=None, **info):
if len(projectables) != 3:
raise ValueError("Expected 3 datasets, got %d" %
(len(projectables), ))
try:
the_data = np.rollaxis(
np.ma.dstack([projectable for projectable in projectables]),
axis=2)
except ValueError:
raise IncompatibleAreas
# info = projectables[0].info.copy()
# info.update(projectables[1].info)
# info.update(projectables[2].info)
info = combine_info(*projectables)
info.update(self.info)
info['id'] = DatasetID(self.info['name'])
# FIXME: should this be done here ?
info["wavelength_range"] = None
info.pop("units", None)
sensor = set()
for projectable in projectables:
current_sensor = projectable.info.get("sensor", None)
if current_sensor:
if isinstance(current_sensor, (str, bytes, six.text_type)):
sensor.add(current_sensor)
else:
sensor |= current_sensor
if len(sensor) == 0:
sensor = None
elif len(sensor) == 1:
sensor = list(sensor)[0]
info["sensor"] = sensor
info["mode"] = "RGB"
return Projectable(data=the_data, **info)
def __call__(self, projectables, nonprojectables=None, **info):
if len(projectables) != 3:
raise ValueError("Expected 3 datasets, got %d" %
(len(projectables), ))
try:
the_data = np.rollaxis(
np.ma.dstack([projectable for projectable in projectables]),
axis=2)
except ValueError:
raise IncompatibleAreas
# info = projectables[0].info.copy()
# info.update(projectables[1].info)
# info.update(projectables[2].info)
info = combine_info(*projectables)
info.update(self.info)
info['id'] = DatasetID(self.info['name'])
# FIXME: should this be done here ?
info["wavelength_range"] = None
info.pop("units", None)
sensor = set()
for projectable in projectables:
current_sensor = projectable.info.get("sensor", None)
if current_sensor:
if isinstance(current_sensor, (str, bytes, six.text_type)):
sensor.add(current_sensor)
else:
sensor |= current_sensor
if len(sensor) == 0:
sensor = None
elif len(sensor) == 1:
sensor = list(sensor)[0]
info["sensor"] = sensor
info["mode"] = "RGB"
return Projectable(data=the_data, **info)
def __call__(self, projectables, nonprojectables=None, **info):
if len(projectables) != 3:
raise ValueError("Expected 3 datasets, got %d" %
(len(projectables), ))
# Collect information that is the same between the projectables
info = combine_info(*projectables)
# Update that information with configured information (including name)
info.update(self.info)
# Force certain pieces of metadata that we *know* to be true
info["wavelength"] = None
info["mode"] = self.info.get("mode", "RGB")
return Projectable(data=np.rollaxis(np.ma.dstack(
[projectable for projectable in projectables]),
axis=2),
**info)
def __call__(self, projectables, nonprojectables=None, **info):
if len(projectables) != 3:
raise ValueError("Expected 3 datasets, got %d" %
(len(projectables), ))
# Collect information that is the same between the projectables
info = combine_info(*projectables)
# Update that information with configured information (including name)
info.update(self.info)
# Force certain pieces of metadata that we *know* to be true
info["wavelength"] = None
info["mode"] = self.info.get("mode", "RGB")
return Projectable(data=np.rollaxis(
np.ma.dstack([projectable for projectable in projectables]),
axis=2),
**info)
def combine_info(self, all_infos):
"""Combine metadata for multiple datasets.
When loading data from multiple files it can be non-trivial to combine
things like start_time, end_time, start_orbit, end_orbit, etc.
By default this method will produce a dictionary containing all values
that were equal across **all** provided info dictionaries.
Additionally it performs the logical comparisons to produce the
following if they exist:
- start_time
- end_time
- start_orbit
- end_orbit
Also, concatenate the areas.
"""
combined_info = combine_info(*all_infos)
if 'start_time' not in combined_info and 'start_time' in all_infos[0]:
combined_info['start_time'] = min(i['start_time']
for i in all_infos)
if 'end_time' not in combined_info and 'end_time' in all_infos[0]:
combined_info['end_time'] = max(i['end_time'] for i in all_infos)
if 'start_orbit' not in combined_info and 'start_orbit' in all_infos[0]:
combined_info['start_orbit'] = min(i['start_orbit']
for i in all_infos)
if 'end_orbit' not in combined_info and 'end_orbit' in all_infos[0]:
combined_info['end_orbit'] = max(i['end_orbit'] for i in all_infos)
try:
area = SwathDefinition(
lons=np.ma.vstack([info['area'].lons for info in all_infos]),
lats=np.ma.vstack([info['area'].lats for info in all_infos]))
area.name = '_'.join([info['area'].name for info in all_infos])
combined_info['area'] = area
except KeyError:
pass
return combined_info
def __call__(self, datasets, optional_datasets=[], **info):
if len(datasets) != 3:
raise ValueError("Expected 3 datasets, got %d" % (len(datasets), ))
area = None
# raise IncompatibleAreas
p1, p2, p3 = datasets
if optional_datasets:
high_res = optional_datasets[0]
low_res = datasets[["red", "green",
"blue"].index(self.high_resolution_band)]
if high_res.info["area"] != low_res.info["area"]:
if np.mod(high_res.shape[0], low_res.shape[0]) or \
np.mod(high_res.shape[1], low_res.shape[1]):
raise IncompatibleAreas(
"High resolution band is not mapped the same area as the low resolution bands"
)
else:
f0 = high_res.shape[0] / low_res.shape[0]
f1 = high_res.shape[1] / low_res.shape[1]
if p1.shape != high_res.shape:
p1 = np.ma.repeat(np.ma.repeat(p1, f0, axis=0),
f1,
axis=1)
p1.info["area"] = high_res.info["area"]
if p2.shape != high_res.shape:
p2 = np.ma.repeat(np.ma.repeat(p2, f0, axis=0),
f1,
axis=1)
p2.info["area"] = high_res.info["area"]
if p3.shape != high_res.shape:
p3 = np.ma.repeat(np.ma.repeat(p3, f0, axis=0),
f1,
axis=1)
p3.info["area"] = high_res.info["area"]
area = high_res.info["area"]
if self.high_resolution_band == "red":
LOG.debug("Sharpening image with high resolution red band")
ratio = high_res.data / p1.data
r = high_res.data
g = p2.data * ratio
b = p3.data * ratio
elif self.high_resolution_band == "green":
LOG.debug("Sharpening image with high resolution green band")
ratio = high_res.data / p2.data
r = p1.data * ratio
g = high_res.data
b = p3.data * ratio
elif self.high_resolution_band == "blue":
LOG.debug("Sharpening image with high resolution blue band")
ratio = high_res.data / p3.data
r = p1.data * ratio
g = p2.data * ratio
b = high_res.data
else:
# no sharpening
r = p1.data
g = p2.data
b = p3.data
mask = p1.mask | p2.mask | p3.mask | high_res.mask
else:
r, g, b = p1.data, p2.data, p3.data
mask = p1.mask | p2.mask | p3.mask
# Collect information that is the same between the projectables
info = combine_info(*datasets)
# Update that information with configured information (including name)
info.update(self.info)
# Force certain pieces of metadata that we *know* to be true
info["wavelength"] = None
info.setdefault("standard_name", "true_color")
info["mode"] = self.info.get("mode", "RGB")
if area is not None:
info['area'] = area
return Projectable(data=np.concatenate(([r], [g], [b]), axis=0),
mask=np.array([[mask, mask, mask]]),
**info)
def __call__(self, datasets, optional_datasets=[], **info):
if len(datasets) != 3:
raise ValueError("Expected 3 datasets, got %d" % (len(datasets), ))
area = None
# raise IncompatibleAreas
p1, p2, p3 = datasets
if optional_datasets:
high_res = optional_datasets[0]
low_res = datasets[["red", "green", "blue"].index(self.high_resolution_band)]
if high_res.info["area"] != low_res.info["area"]:
if np.mod(high_res.shape[0], low_res.shape[0]) or \
np.mod(high_res.shape[1], low_res.shape[1]):
raise IncompatibleAreas(
"High resolution band is not mapped the same area as the low resolution bands")
else:
f0 = high_res.shape[0] / low_res.shape[0]
f1 = high_res.shape[1] / low_res.shape[1]
if p1.shape != high_res.shape:
p1 = np.ma.repeat(np.ma.repeat(p1, f0, axis=0), f1, axis=1)
p1.info["area"] = high_res.info["area"]
if p2.shape != high_res.shape:
p2 = np.ma.repeat(np.ma.repeat(p2, f0, axis=0), f1, axis=1)
p2.info["area"] = high_res.info["area"]
if p3.shape != high_res.shape:
p3 = np.ma.repeat(np.ma.repeat(p3, f0, axis=0), f1, axis=1)
p3.info["area"] = high_res.info["area"]
area = high_res.info["area"]
if self.high_resolution_band == "red":
LOG.debug("Sharpening image with high resolution red band")
ratio = high_res.data / p1.data
r = high_res.data
g = p2.data * ratio
b = p3.data * ratio
elif self.high_resolution_band == "green":
LOG.debug("Sharpening image with high resolution green band")
ratio = high_res.data / p2.data
r = p1.data * ratio
g = high_res.data
b = p3.data * ratio
elif self.high_resolution_band == "blue":
LOG.debug("Sharpening image with high resolution blue band")
ratio = high_res.data / p3.data
r = p1.data * ratio
g = p2.data * ratio
b = high_res.data
else:
# no sharpening
r = p1.data
g = p2.data
b = p3.data
mask = p1.mask | p2.mask | p3.mask | high_res.mask
else:
r, g, b = p1.data, p2.data, p3.data
mask = p1.mask | p2.mask | p3.mask
# Collect information that is the same between the projectables
info = combine_info(*datasets)
# Update that information with configured information (including name)
info.update(self.info)
# Force certain pieces of metadata that we *know* to be true
info["wavelength"] = None
info.setdefault("standard_name", "true_color")
info["mode"] = self.info.get("mode", "RGB")
if area is not None:
info['area'] = area
return Projectable(data=np.concatenate(
([r], [g], [b]), axis=0),
mask=np.array([[mask, mask, mask]]),
**info)