def global_avg(data, weights=None, dims=['lon', 'lat']):
""" Compute (area-weighted) global average over a DataArray
or Dataset. If `weights` are not passed, they will be computed
by using the areas of each grid cell in the dataset.
.. note::
Handles missing values (nans and infs).
"""
if isinstance(data, DataArray):
if weights is None: # Compute gaussian weights in latitude
weights = area_grid(data.lon, data.lat)
# Saving for later - compute latitudinal weighting
# gw = weights.sum('lon')
# weights = 2.*gw/gw.sum('lat')
weights = weights.where(xu.isfinite(data))
total_weights = weights.sum(dims)
return (data * weights).sum(dims) / total_weights
elif isinstance(data, Dataset):
# Create a new temporary Dataset
new_data = Dataset()
# Iterate over the contents of the original Dataset,
# which are all DataArrays, and compute the global avg
# on those elements.
for v in data.data_vars:
coords = data[v].coords
if not ('lon' in coords):
new_data[v] = data[v]
else:
new_data[v] = global_avg(data[v], weights)
# Collapse remaining lat, lon dimensions if they're here
leftover_dims = [d for d in dims if d in new_data.coords]
if leftover_dims:
new_data = new_data.sum(leftover_dims)
return new_data
def global_avg(data, weights=None, dims=['lon', 'lat']):
""" Compute (area-weighted) global average over a DataArray
or Dataset. If `weights` are not passed, they will be computed
by using the areas of each grid cell in the dataset.
.. note::
Handles missing values (nans and infs).
"""
if isinstance(data, DataArray):
if weights is None: # Compute gaussian weights in latitude
weights = area_grid(data.lon, data.lat)
# Saving for later - compute latitudinal weighting
# gw = weights.sum('lon')
# weights = 2.*gw/gw.sum('lat')
weights = weights.where(xu.isfinite(data))
total_weights = weights.sum(dims)
return (data*weights).sum(dims)/total_weights
elif isinstance(data, Dataset):
# Create a new temporary Dataset
new_data = Dataset()
# Iterate over the contents of the original Dataset,
# which are all DataArrays, and compute the global avg
# on those elements.
for v in data.data_vars:
coords = data[v].coords
if not ('lon' in coords):
new_data[v] = data[v]
else:
new_data[v] = global_avg(data[v], weights)
# Collapse remaining lat, lon dimensions if they're here
leftover_dims = [d for d in dims if d in new_data.coords]
if leftover_dims:
new_data = new_data.sum(leftover_dims)
return new_data
def __call__(self, datasets, optional_datasets=None, **info):
if len(datasets) != 3:
raise ValueError("Expected 3 datasets, got %d" % (len(datasets), ))
if not all(x.shape == datasets[0].shape for x in datasets[1:]) or \
(optional_datasets and
optional_datasets[0].shape != datasets[0].shape):
raise IncompatibleAreas('RatioSharpening requires datasets of '
'the same size. Must resample first.')
new_attrs = {}
if optional_datasets:
datasets = self.check_areas(datasets + optional_datasets)
high_res = datasets[-1]
p1, p2, p3 = datasets[:3]
if 'rows_per_scan' in high_res.attrs:
new_attrs.setdefault('rows_per_scan',
high_res.attrs['rows_per_scan'])
new_attrs.setdefault('resolution', high_res.attrs['resolution'])
if self.high_resolution_band == "red":
LOG.debug("Sharpening image with high resolution red band")
ratio = high_res / p1
# make ratio a no-op (multiply by 1) where the ratio is NaN or
# infinity or it is negative.
ratio = ratio.where(xu.isfinite(ratio) | (ratio >= 0), 1.)
r = high_res
g = p2 * ratio
b = p3 * ratio
g.attrs = p2.attrs.copy()
b.attrs = p3.attrs.copy()
elif self.high_resolution_band == "green":
LOG.debug("Sharpening image with high resolution green band")
ratio = high_res / p2
ratio = ratio.where(xu.isfinite(ratio) | (ratio >= 0), 1.)
r = p1 * ratio
g = high_res
b = p3 * ratio
r.attrs = p1.attrs.copy()
b.attrs = p3.attrs.copy()
elif self.high_resolution_band == "blue":
LOG.debug("Sharpening image with high resolution blue band")
ratio = high_res / p3
ratio = ratio.where(xu.isfinite(ratio) | (ratio >= 0), 1.)
r = p1 * ratio
g = p2 * ratio
b = high_res
r.attrs = p1.attrs.copy()
g.attrs = p2.attrs.copy()
else:
# no sharpening
r = p1
g = p2
b = p3
else:
datasets = self.check_areas(datasets)
r, g, b = datasets[:3]
# combine the masks
mask = ~(da.isnull(r.data) | da.isnull(g.data) | da.isnull(b.data))
r = r.where(mask)
g = g.where(mask)
b = b.where(mask)
# Collect information that is the same between the projectables
# we want to use the metadata from the original datasets since the
# new r, g, b arrays may have lost their metadata during calculations
info = combine_metadata(*datasets)
info.update(new_attrs)
# Update that information with configured information (including name)
info.update(self.attrs)
# Force certain pieces of metadata that we *know* to be true
info.setdefault("standard_name", "true_color")
return super(RatioSharpenedRGB, self).__call__((r, g, b), **info)
def __call__(self, datasets, optional_datasets=None, **info):
if len(datasets) != 3:
raise ValueError("Expected 3 datasets, got %d" % (len(datasets), ))
if not all(x.shape == datasets[0].shape for x in datasets[1:]) or \
(optional_datasets and
optional_datasets[0].shape != datasets[0].shape):
raise IncompatibleAreas('RatioSharpening requires datasets of '
'the same size. Must resample first.')
new_attrs = {}
if optional_datasets:
datasets = self.check_areas(datasets + optional_datasets)
high_res = datasets[-1]
p1, p2, p3 = datasets[:3]
if 'rows_per_scan' in high_res.attrs:
new_attrs.setdefault('rows_per_scan',
high_res.attrs['rows_per_scan'])
new_attrs.setdefault('resolution', high_res.attrs['resolution'])
if self.high_resolution_band == "red":
LOG.debug("Sharpening image with high resolution red band")
ratio = high_res / p1
# make ratio a no-op (multiply by 1) where the ratio is NaN or
# infinity or it is negative.
ratio = ratio.where(xu.isfinite(ratio) | (ratio >= 0), 1.)
r = high_res
g = p2 * ratio
b = p3 * ratio
g.attrs = p2.attrs.copy()
b.attrs = p3.attrs.copy()
elif self.high_resolution_band == "green":
LOG.debug("Sharpening image with high resolution green band")
ratio = high_res / p2
ratio = ratio.where(xu.isfinite(ratio) | (ratio >= 0), 1.)
r = p1 * ratio
g = high_res
b = p3 * ratio
r.attrs = p1.attrs.copy()
b.attrs = p3.attrs.copy()
elif self.high_resolution_band == "blue":
LOG.debug("Sharpening image with high resolution blue band")
ratio = high_res / p3
ratio = ratio.where(xu.isfinite(ratio) | (ratio >= 0), 1.)
r = p1 * ratio
g = p2 * ratio
b = high_res
r.attrs = p1.attrs.copy()
g.attrs = p2.attrs.copy()
else:
# no sharpening
r = p1
g = p2
b = p3
else:
datasets = self.check_areas(datasets)
r, g, b = datasets[:3]
# combine the masks
mask = ~(da.isnull(r.data) | da.isnull(g.data) | da.isnull(b.data))
r = r.where(mask)
g = g.where(mask)
b = b.where(mask)
# Collect information that is the same between the projectables
# we want to use the metadata from the original datasets since the
# new r, g, b arrays may have lost their metadata during calculations
info = combine_metadata(*datasets)
info.update(new_attrs)
# Update that information with configured information (including name)
info.update(self.attrs)
# Force certain pieces of metadata that we *know* to be true
info.setdefault("standard_name", "true_color")
return super(RatioSharpenedRGB, self).__call__((r, g, b), **info)
