def read_region(config, *args, **kwargs):
"""Snip-out target regions from nc4 file
Quick and dirty hax to reduce the size of data read in from netCDF files.
Keeps a memory leak in the module from blowing up the script. Not
the best way to handle this.
Parameters
----------
config : dict
Run configuration dictionary. Used to parse out target regions.
*args :
Passed on to read().
**kwargs :
Passed on to read().
Returns
-------
years : array-like
regions : array-like
data : array-like
"""
years, regions, data = read(*args, **kwargs)
if configs.is_allregions(config):
regions_msk = np.ones(regions.shape, dtype='bool')
else:
target_regions = configs.get_regions(config, regions)
regions_msk = np.isin(regions, target_regions)
return years, regions[regions_msk], data[..., regions_msk]
def iterate_regions(filepath, config={}):
"""
Config options: column
"""
years, regions, data = read(filepath, config.get('column', 'rebased'))
config['regionorder'] = list(regions)
if configs.is_allregions(config):
yield 'all', years, data
return
regions = list(regions)
for region in configs.get_regions(config, regions):
if region == 'global':
region = ''
ii = regions.index(region)
yield regions[ii], years, data[:, ii]
def iterate_regions(filepath, column, config={}):
if column is not None or 'costs' not in filepath:
years, regions, data = read(
filepath, column if column is not None else 'rebased')
else:
years, regions, data1 = read(filepath, 'costs_lb')
years, regions, data2 = read(filepath, 'costs_ub')
data = ((data1 + data2) / 2) / 1e5
config['regionorder'] = list(regions)
if configs.is_allregions(config):
yield 'all', years, data
return
regions = list(regions)
for region in configs.get_regions(config, regions):
if region == 'global':
region = ''
ii = regions.index(region)
yield regions[ii], years, data[:, ii]
def iterate_regions(filepath, column, config={}):
global deltamethod_vcv
do_deltamethod = False if configs.is_parallel_deltamethod(
config) else config.get('deltamethod', None)
if column is not None or 'costs' not in filepath:
years, regions, data = read_region(
config, filepath, column if column is not None else 'rebased',
do_deltamethod)
else:
years, regions, data1 = read_region(config, filepath, 'costs_lb',
do_deltamethod)
years, regions, data2 = read_region(config, filepath, 'costs_ub',
do_deltamethod)
data = data2 / 1e5
if deltamethod_vcv is not None and not config.get('deltamethod', False):
## Inferred that these were deltamethod files
config['deltamethod'] = True
if config.get('multiimpact_vcv',
None) is not None and deltamethod_vcv is not None:
assert isinstance(config['multiimpact_vcv'], np.ndarray)
# Extend data to conform to multiimpact_vcv
foundindex = None
for ii in range(config['multiimpact_vcv'].shape[0] -
deltamethod_vcv.shape[0] + 1):
if np.allclose(
deltamethod_vcv,
config['multiimpact_vcv'][ii:(ii +
deltamethod_vcv.shape[0]),
ii:(ii +
deltamethod_vcv.shape[1])]):
foundindex = ii
break
if foundindex is None:
print np.sum(
np.abs(deltamethod_vcv - config['multiimpact_vcv']
[:deltamethod_vcv.shape[0], :deltamethod_vcv.shape[1]]))
print np.sum(
np.abs(deltamethod_vcv -
config['multiimpact_vcv'][deltamethod_vcv.shape[0]:,
deltamethod_vcv.shape[1]:]))
assert foundindex is not None, "Cannot find the VCV for " + filepath + " within the master VCV."
newdata = np.zeros(
tuple([config['multiimpact_vcv'].shape[0]] + list(data.shape[1:])))
if len(data.shape) == 2:
newdata[foundindex:(foundindex +
deltamethod_vcv.shape[0]), :] = data
else:
newdata[foundindex:(foundindex +
deltamethod_vcv.shape[0]), :, :] = data
data = newdata
deltamethod_vcv = None # reset for next file
config['regionorder'] = list(regions)
if configs.is_allregions(config):
yield 'all', years, data
return
regions = list(regions)
for region in configs.get_regions(config, regions):
ii = regions.index(region)
if config.get('deltamethod',
False) and not configs.is_parallel_deltamethod(config):
yield regions[ii], years, data[:, :, ii]
else:
yield regions[ii], years, data[:, ii]