def generate_classification(categorize_file, output_file):
"""Generates Cloudnet classification product.
This function reads the initial classification masks from a
categorize file and creates a more comprehensive classification
for different atmospheric targets. The results are written in a
netCDF file.
Args:
categorize_file (str): Categorize file name.
output_file (str): Output file name.
Examples:
>>> from cloudnetpy.products import generate_classification
>>> generate_classification('categorize.nc', 'classification.nc')
"""
data_handler = DataSource(categorize_file)
categorize_bits = CategorizeBits(categorize_file)
classification = _get_target_classification(categorize_bits)
data_handler.append_data(classification, 'target_classification')
status = _get_detection_status(categorize_bits)
data_handler.append_data(status, 'detection_status')
output.update_attributes(data_handler.data, CLASSIFICATION_ATTRIBUTES)
output.save_product_file('classification', data_handler, output_file)
data_handler.close()
def generate_classification(categorize_file, output_file, keep_uuid=False):
"""Generates Cloudnet classification product.
This function reads the initial classification masks from a
categorize file and creates a more comprehensive classification
for different atmospheric targets. The results are written in a
netCDF file.
Args:
categorize_file (str): Categorize file name.
output_file (str): Output file name.
keep_uuid (bool, optional): If True, keeps the UUID of the old file,
if that exists. Default is False when new UUID is generated.
Returns:
str: UUID of the generated file.
Examples:
>>> from cloudnetpy.products import generate_classification
>>> generate_classification('categorize.nc', 'classification.nc')
"""
data_handler = DataSource(categorize_file)
categorize_bits = CategorizeBits(categorize_file)
classification = _get_target_classification(categorize_bits)
data_handler.append_data(classification, 'target_classification')
status = _get_detection_status(categorize_bits)
data_handler.append_data(status, 'detection_status')
output.update_attributes(data_handler.data, CLASSIFICATION_ATTRIBUTES)
uuid = output.save_product_file('classification', data_handler,
output_file, keep_uuid)
data_handler.close()
return uuid
def __init__(self, categorize_file):
super().__init__(categorize_file)
self.lwp = self.getvar('lwp')
self.lwp_error = self.getvar('lwp_error')
self.is_rain = self.getvar('is_rain')
self.dheight = utils.mdiff(self.getvar('height'))
self.atmosphere = self._get_atmosphere(categorize_file)
self.categorize_bits = CategorizeBits(categorize_file)
def __init__(self, categorize_file: str):
super().__init__(categorize_file)
self.lwp = self.getvar("lwp")
self.lwp[self.lwp < 0] = 0
self.lwp_error = self.getvar("lwp_error")
self.is_rain = get_is_rain(categorize_file)
self.dheight = utils.mdiff(self.getvar("height"))
self.atmosphere = self._get_atmosphere(categorize_file)
self.categorize_bits = CategorizeBits(categorize_file)
def generate_classification(categorize_file: str,
output_file: str,
keep_uuid: bool = False,
uuid: Optional[str] = None) -> str:
"""Generates Cloudnet classification product.
This function reads the initial classification masks from a
categorize file and creates a more comprehensive classification
for different atmospheric targets. The results are written in a
netCDF file.
Args:
categorize_file: Categorize file name.
output_file: Output file name.
keep_uuid: If True, keeps the UUID of the old file, if that exists. Default is False when new UUID is generated.
uuid: Set specific UUID for the file.
Returns:
str: UUID of the generated file.
Examples:
>>> from cloudnetpy.products import generate_classification
>>> generate_classification('categorize.nc', 'classification.nc')
"""
product_container = DataSource(categorize_file)
categorize_bits = CategorizeBits(categorize_file)
classification = _get_target_classification(categorize_bits)
product_container.append_data(classification, 'target_classification')
status = _get_detection_status(categorize_bits)
product_container.append_data(status, 'detection_status')
bases, tops = _get_cloud_base_and_top_heights(classification,
product_container)
product_container.append_data(bases, 'cloud_base_height_amsl')
product_container.append_data(tops, 'cloud_top_height_amsl')
product_container.append_data(bases - product_container.altitude,
'cloud_base_height_agl')
product_container.append_data(tops - product_container.altitude,
'cloud_top_height_agl')
date = product_container.get_date()
attributes = output.add_time_attribute(CLASSIFICATION_ATTRIBUTES, date)
output.update_attributes(product_container.data, attributes)
uuid = output.save_product_file('classification', product_container,
output_file, keep_uuid, uuid)
product_container.close()
return uuid
def _find_valid_fields(nc_file, names):
"""Returns valid field names and corresponding data."""
valid_names, valid_data = names[:], []
nc = netCDF4.Dataset(nc_file)
try:
bits = CategorizeBits(nc_file)
except KeyError:
bits = None
for name in names:
if name in nc.variables:
valid_data.append(nc.variables[name][:])
elif bits and name in CategorizeBits.category_keys:
valid_data.append(bits.category_bits[name])
elif bits and name in CategorizeBits.quality_keys:
valid_data.append(bits.quality_bits[name])
else:
valid_names.remove(name)
nc.close()
return valid_data, valid_names
def _find_valid_fields(nc_file: str, names: list) -> Tuple[list, list]:
"""Returns valid field names and corresponding data."""
valid_names, valid_data = names[:], []
try:
bits = CategorizeBits(nc_file)
except KeyError:
bits = None
nc = netCDF4.Dataset(nc_file)
for name in names:
if name in nc.variables:
valid_data.append(nc.variables[name][:])
elif bits and name in CategorizeBits.category_keys:
valid_data.append(bits.category_bits[name])
elif bits and name in CategorizeBits.quality_keys:
valid_data.append(bits.quality_bits[name])
else:
valid_names.remove(name)
nc.close()
if not valid_names:
raise ValueError('No fields to be plotted')
return valid_data, valid_names