def _check_file(self, f):
"""Run tests on a morphology file."""
L.info('File: %s', f)
full_result = True
full_summary = OrderedDict()
try:
data = load_data(f)
except Exception as e: # pylint: disable=W0703
L.error('Failed to load data... skipping tests for this file')
L.error(e.args)
return False, {f: OrderedDict([('ALL', False)])}
try:
result, summary = self._check_loop(data, 'structural_checks')
full_result &= result
full_summary.update(summary)
nrn = fst_core.FstNeuron(data)
result, summary = self._check_loop(nrn, 'neuron_checks')
full_result &= result
full_summary.update(summary)
except Exception as e: # pylint: disable=W0703
L.error('Check failed: %s', str(type(e)) + str(e.args))
full_result = False
full_summary['ALL'] = full_result
for m, s in full_summary.items():
self._log_msg(m, s)
return full_result, {f: full_summary}
def _check_file(self, f):
'''Run tests on a morphology file'''
L.info('File: %s', f)
full_result = True
full_summary = OrderedDict()
try:
data = load_data(f)
except Exception as e: # pylint: disable=W0703
L.error('Failed to load data... skipping tests for this file')
L.error(e.args)
return False, {f: OrderedDict([('ALL', False)])}
try:
result, summary = self._check_loop(data, 'structural_checks')
full_result &= result
full_summary.update(summary)
nrn = fst_core.FstNeuron(data)
result, summary = self._check_loop(nrn, 'neuron_checks')
full_result &= result
full_summary.update(summary)
except Exception as e: # pylint: disable=W0703
L.error('Check failed: %s', str(type(e)) + str(e.args))
full_result = False
full_summary['ALL'] = full_result
for m, s in full_summary.items():
self._log_msg(m, s)
return full_result, {f: full_summary}
def fit_results_to_dict(fit_results, min_bound=None, max_bound=None):
"""Create a JSON-comparable dict from a FitResults object.
Arguments:
fit_results (FitResults): object containing fit parameters,\
errors and type
min_bound: optional min value to add to dictionary if min isn't\
a fit parameter.
max_bound: optional max value to add to dictionary if max isn't\
a fit parameter.
Returns:
JSON-compatible dictionary with fit results
Note:
Supported fit types: 'norm', 'expon', 'uniform'
"""
type_map = {'norm': 'normal', 'expon': 'exponential', 'uniform': 'uniform'}
param_map = {'uniform': lambda p: [('min', p[0]), ('max', p[0] + p[1])],
'norm': lambda p: [('mu', p[0]), ('sigma', p[1])],
'expon': lambda p: [('lambda', 1.0 / p[1])]}
d = OrderedDict({'type': type_map[fit_results.type]})
d.update(param_map[fit_results.type](fit_results.params))
if min_bound is not None and 'min' not in d:
d['min'] = min_bound
if max_bound is not None and 'max' not in d:
d['max'] = max_bound
return d
def fit_results_to_dict(fit_results, min_bound=None, max_bound=None):
'''Create a JSON-comparable dict from a FitResults object
Parameters:
fit_results (FitResults): object containing fit parameters,\
errors and type
min_bound: optional min value to add to dictionary if min isn't\
a fit parameter.
max_bound: optional max value to add to dictionary if max isn't\
a fit parameter.
Returns:
JSON-compatible dictionary with fit results
Note:
Supported fit types: 'norm', 'expon', 'uniform'
'''
type_map = {'norm': 'normal', 'expon': 'exponential', 'uniform': 'uniform'}
param_map = {'uniform': lambda p: [('min', p[0]), ('max', p[0] + p[1])],
'norm': lambda p: [('mu', p[0]), ('sigma', p[1])],
'expon': lambda p: [('lambda', 1.0 / p[1])]}
d = OrderedDict({'type': type_map[fit_results.type]})
d.update(param_map[fit_results.type](fit_results.params))
if min_bound is not None and 'min' not in d:
d['min'] = min_bound
if max_bound is not None and 'max' not in d:
d['max'] = max_bound
return d