def _preflight_check(self):
"""
Check time_index and coordinates accross files
"""
time_index = None
lat_lon = None
bad_files = []
for file in self.h5_files:
with Resource(file) as f:
if 'time_index' in f:
ti = f.time_index
if time_index is None:
time_index = ti.copy()
else:
check = time_index.equals(ti)
if not check:
bad_files.append(file)
ll = f.lat_lon
if lat_lon is None:
lat_lon = ll.copy()
else:
check = np.allclose(lat_lon, ll)
if not check:
bad_files.append(file)
bad_files = list(set(bad_files))
if bad_files:
msg = ("The following files' coordinates and time-index do not "
"match:\n{}".format(bad_files))
raise ResourceRuntimeError(msg)
def _init_multi_h5(h5_source, check_files=False):
"""
Initialize MultiH5 handler class based on input type
Parameters
----------
h5_source : str | list
Unix shell style pattern path with * wildcards to multi-file
resource file sets. Files must have the same time index and
coordinates but can have different datasets. Can also be an
explicit list of complete filepaths.
check_files : bool
Check to ensure files have the same coordinates and time_index
Returns
-------
multi_h5 : MultiH5 | MultiH5Path
Initialized multi h5 handler
"""
if isinstance(h5_source, str):
multi_h5 = MultiH5Path(h5_source, check_files=check_files)
elif isinstance(h5_source, (list, tuple)):
multi_h5 = MultiH5(h5_source, check_files=check_files)
else:
msg = ('Cannot initialize MultiH5 from {}, expecting a path or a '
'list of .h5 file paths'.format(type(h5_source)))
raise ResourceRuntimeError(msg)
return multi_h5
def curtail_windspeed(self, gids, curtailment):
"""
Apply temporal curtailment mask to windspeed resource at given sites
Parameters
----------
gids : int | list
gids for site or list of sites to curtail
curtailment : ndarray
Temporal multiplier for curtailment
"""
shape = (self.shape[0], )
if isinstance(gids, int):
site_pos = self.sites.index(gids)
else:
shape += (len(gids), )
site_pos = [self.sites.index(id) for id in gids]
if curtailment.shape != shape:
msg = "curtailment must be of shape: {}".format(shape)
logger.error(msg)
raise ResourceValueError(msg)
if 'windspeed' in self._res_arrays:
self._res_arrays['windspeed'][:, site_pos] *= curtailment
else:
msg = 'windspeed has not be loaded!'
logger.error(msg)
raise ResourceRuntimeError(msg)
def runnable(self):
"""
Check to see if SAMResource iterator is runnable:
- Meta must be loaded
- Variables in var_list must be loaded
Returns
------
bool
Returns True if runnable check passes
"""
if self._meta is None:
msg = 'meta has not been set!'
logger.error(msg)
raise ResourceRuntimeError(msg)
else:
for var in self.var_list:
if var not in self._res_arrays.keys():
msg = '{} has not been set!'.format(var)
logger.error(msg)
raise ResourceRuntimeError(msg)
return True
def __getitem__(self, keys):
ds, ds_slice = parse_keys(keys)
_, ds_name = os.path.split(ds)
if 'SAM' in ds_name:
site = ds_slice[0]
if isinstance(site, (int, np.integer)):
_, height = self._parse_name(ds_name)
out = self.get_SAM_df(site, height)
else:
msg = "Can only extract SAM DataFrame for a single site"
raise ResourceRuntimeError(msg)
else:
out = super().__getitem__(keys)
return out
def __getitem__(self, keys):
ds, ds_slice = parse_keys(keys)
if ds.endswith('time_index'):
out = self._get_time_index(ds, ds_slice)
elif ds.endswith('meta'):
out = self._get_meta(ds, ds_slice)
elif 'SAM' in ds:
site = ds_slice[0]
if isinstance(site, int):
out = self._get_SAM_df(ds, site)
else:
msg = "Can only extract SAM DataFrame for a single site"
raise ResourceRuntimeError(msg)
else:
out = self._get_ds(ds, ds_slice)
return out
def __getitem__(self, keys):
ds, ds_slice = parse_keys(keys)
_, ds_name = os.path.split(ds)
if ds_name.startswith('time_index'):
out = self._get_time_index(ds, ds_slice)
elif ds_name.startswith('meta'):
out = self._get_meta(ds, ds_slice)
elif ds_name.startswith('coordinates'):
out = self._get_coords(ds, ds_slice)
elif 'SAM' in ds_name:
site = ds_slice[0]
if isinstance(site, (int, np.integer)):
out = self.get_SAM_df(site) # pylint: disable=E1111
else:
msg = "Can only extract SAM DataFrame for a single site"
raise ResourceRuntimeError(msg)
else:
out = self._get_ds(ds, ds_slice)
return out
def _get_var_mean(self, var, *var_slice):
"""
Get variable means
Parameters
----------
var : str
Resource variable name
var_slice : int | list | slice
Slice of variable array to extract
Returns
-------
means : ndarray
Vector of variable means
"""
if self._mean_arrays is None:
msg = "Variable means were not computed, you must set 'means=True'"
logger.error(msg)
raise ResourceRuntimeError(msg)
if var in self.var_list:
try:
var_array = self._mean_arrays[var]
except KeyError:
msg = '{} has yet to be set!'.format(var)
logger.error(msg)
raise ResourceKeyError(msg)
means = var_array[var_slice]
else:
msg = '{} not in {}'.format(var, self.var_list)
logger.error(msg)
raise ResourceKeyError(msg)
return means