def _get_layer(self, layer_name, *ds_slice):
"""
Extract data from given dataset
Parameters
----------
layer_name : str
Exclusion layer to extract
ds_slice : tuple of int | list | slice
tuple describing slice of layer array to extract
Returns
-------
layer_data : ndarray
Array of exclusion data
"""
if layer_name not in self.layers:
msg = ('{} not in available layers: {}'.format(
layer_name, self.layers))
logger.error(msg)
raise HandlerKeyError(msg)
if len(self.h5[layer_name].shape) == 3:
slices = (0, ) + ds_slice
else:
slices = ds_slice
layer_data = ResourceDataset.extract(self.h5[layer_name], slices)
return layer_data
def _get_layer(self, layer_name, *ds_slice):
"""
Extract data from given dataset
Parameters
----------
layer_name : str
Exclusion layer to extract
ds_slice : tuple of int | list | slice
tuple describing slice of layer array to extract
Returns
-------
layer_data : ndarray
Array of exclusion data
"""
if layer_name not in self.layers:
msg = ('{} not in available layers: {}'
.format(layer_name, self.layers))
logger.error(msg)
raise HandlerKeyError(msg)
shape = self.h5.get_dset_properties(layer_name)[0]
if len(shape) == 3:
ds_slice = (layer_name, 0) + ds_slice
else:
ds_slice = (layer_name, ) + ds_slice
layer_data = self.h5[ds_slice]
return layer_data
def _get_longitude(self, *ds_slice):
"""
Extract longitude coordinates
Parameters
----------
ds_slice : tuple of int | list | slice
Pandas slicing describing which sites and columns to extract
Returns
-------
lon : ndarray
Longitude coordinates
"""
if 'longitude' not in self.h5:
msg = ('"longitude" is missing from {}'
.format(self.h5_file))
logger.error(msg)
raise HandlerKeyError(msg)
ds_slice = ('longitude', ) + ds_slice
lon = self.h5[ds_slice]
return lon
def __getitem__(self, gid):
if gid not in self._features:
msg = "Invalid feature gid {}".format(gid)
logger.error(msg)
raise HandlerKeyError(msg)
return self._features[gid]
def __getitem__(self, keys):
ds, ds_slice = parse_keys(keys)
if ds in self.datasets:
if ds.endswith('time_index'):
out = self._get_time_index(ds, ds_slice)
elif ds.endswith('meta'):
out = self._get_meta(ds, ds_slice)
else:
out = self._get_ds(ds, ds_slice)
else:
msg = '{} is not a valid Dataset'.format(ds)
raise HandlerKeyError(msg)
return out
def _features_from_table(self, trans_table):
"""
Extract features and their capacity from supply curve transmission
mapping table
Parameters
----------
trans_table : pandas.DataFrame
DataFrame of transmission features
Returns
-------
features : dict
Nested dictionary of features (lines, substations, loadcenters)
lines : {capacity}
substations : {lines}
loadcenters : {capacity}
"""
features = {}
cap_frac = self._available_capacity_frac
trans_features = trans_table.groupby('trans_line_gid').first()
for gid, feature in trans_features.iterrows():
name = feature['category'].lower()
feature_dict = {'type': name}
if name == 'transline':
feature_dict['avail_cap'] = feature['ac_cap'] * cap_frac
elif name == 'substation':
feature_dict['lines'] = json.loads(feature['trans_gids'])
elif name == 'loadcen':
feature_dict['avail_cap'] = feature['ac_cap'] * cap_frac
elif name == 'pcaloadcen':
feature_dict['avail_cap'] = None
else:
msg = ('Cannot not recognize feature type "{}" '
'for trans gid {}!'.format(name, gid))
logger.error(msg)
raise HandlerKeyError(msg)
features[gid] = feature_dict
return features
def _get_latitude(self, *ds_slice):
"""
Extract latitude coordinates
Parameters
----------
ds_slice : tuple of int | list | slice
Pandas slicing describing which sites and columns to extract
Returns
-------
lat : ndarray
Latitude coordinates
"""
if 'latitude' not in self.h5:
msg = ('"latitude" is missing from {}'.format(self.h5_file))
logger.error(msg)
raise HandlerKeyError(msg)
lat = ResourceDataset.extract(self.h5['latitude'], ds_slice)
return lat