def create_mosaic(dataset_in,
clean_mask=None,
no_data=-9999,
dtype=None,
intermediate_product=None,
**kwargs):
"""
Creates a most-recent-to-oldest mosaic of the input dataset.
Parameters
----------
dataset_in: xarray.Dataset
A dataset retrieved from the Data Cube; should contain:
coordinates: time, latitude, longitude
variables: variables to be mosaicked (e.g. red, green, and blue bands)
clean_mask: np.ndarray
An ndarray of the same shape as `dataset_in` - specifying which values to mask out.
If no clean mask is specified, then all values are kept during compositing.
no_data: int or float
The no data value.
dtype: str or numpy.dtype
A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g.
np.int16, np.float32) to convert the data to.
Returns
-------
dataset_out: xarray.Dataset
Compositited data with the format:
coordinates: latitude, longitude
variables: same as dataset_in
"""
dataset_in = dataset_in.copy(deep=True)
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
# Mask data with clean_mask. All values where clean_mask==False are set to no_data.
for key in list(dataset_in.data_vars):
dataset_in[key].values[np.invert(clean_mask)] = no_data
dataset_in_dtypes = None
if dtype is None:
# Save dtypes because masking with Dataset.where() converts to float64.
band_list = list(dataset_in.data_vars)
dataset_in_dtypes = {}
for band in band_list:
dataset_in_dtypes[band] = dataset_in[band].dtype
if intermediate_product is not None:
dataset_out = intermediate_product.copy(deep=True)
else:
dataset_out = None
time_slices = reversed(range(len(
dataset_in.time))) if 'reverse_time' in kwargs else range(
len(dataset_in.time))
for index in time_slices:
dataset_slice = dataset_in.isel(time=index).drop('time')
if dataset_out is None:
dataset_out = dataset_slice.copy(deep=True)
utilities.clear_attrs(dataset_out)
else:
for key in list(dataset_in.data_vars):
dataset_out[key].values[dataset_out[key].values ==
-9999] = dataset_slice[key].values[
dataset_out[key].values == -9999]
dataset_out[key].attrs = OrderedDict()
# Handle datatype conversions.
dataset_out = restore_or_convert_dtypes(dtype, band_list,
dataset_in_dtypes, dataset_out,
no_data)
return dataset_out
def create_min_ndvi_mosaic(dataset_in,
clean_mask=None,
no_data=-9999,
dtype=None,
intermediate_product=None,
**kwargs):
"""
Method for calculating the pixel value for the min ndvi value.
Parameters
----------
dataset_in: xarray.Dataset
A dataset retrieved from the Data Cube; should contain:
coordinates: time, latitude, longitude
variables: variables to be mosaicked (e.g. red, green, and blue bands)
clean_mask: np.ndarray
An ndarray of the same shape as `dataset_in` - specifying which values to mask out.
If no clean mask is specified, then all values are kept during compositing.
no_data: int or float
The no data value.
dtype: str or numpy.dtype
A string denoting a Python datatype name (e.g. int, float) or a NumPy dtype (e.g.
np.int16, np.float32) to convert the data to.
Returns
-------
dataset_out: xarray.Dataset
Compositited data with the format:
coordinates: latitude, longitude
variables: same as dataset_in
"""
dataset_in = dataset_in.copy(deep=True)
# Default to masking nothing.
if clean_mask is None:
clean_mask = create_default_clean_mask(dataset_in)
dataset_in_dtypes = None
if dtype is None:
# Save dtypes because masking with Dataset.where() converts to float64.
band_list = list(dataset_in.data_vars)
dataset_in_dtypes = {}
for band in band_list:
dataset_in_dtypes[band] = dataset_in[band].dtype
# Mask out clouds and scan lines.
dataset_in = dataset_in.where((dataset_in != -9999) & clean_mask)
if intermediate_product is not None:
dataset_out = intermediate_product.copy(deep=True)
else:
dataset_out = None
time_slices = range(len(dataset_in.time))
for timeslice in time_slices:
dataset_slice = dataset_in.isel(time=timeslice).drop('time')
ndvi = (dataset_slice.nir - dataset_slice.red) / (dataset_slice.nir +
dataset_slice.red)
ndvi.values[np.invert(clean_mask)[timeslice, ::]] = 1000000000
dataset_slice['ndvi'] = ndvi
if dataset_out is None:
dataset_out = dataset_slice.copy(deep=True)
utilities.clear_attrs(dataset_out)
else:
for key in list(dataset_slice.data_vars):
dataset_out[key].values[
dataset_slice.ndvi.values <
dataset_out.ndvi.values] = dataset_slice[key].values[
dataset_slice.ndvi.values < dataset_out.ndvi.values]
# Handle datatype conversions.
dataset_out = restore_or_convert_dtypes(dtype, None, dataset_in_dtypes,
dataset_out, no_data)
return dataset_out