from types import FunctionType
from brian2 import have_same_dimensions
from brian2.units.fundamentalunits import Quantity
from tqdm.auto import tqdm
def _format_quantity(v, precision=3):
if isinstance(v, Quantity):
return f'{v.in_best_unit(precision=precision)}'
else:
return f'{v:.{precision}g}'
[docs]def callback_text(params, errors, best_params, best_error, index,
additional_info):
"""Default callback print-out for Fitters"""
params = []
for p, v in sorted(best_params.items()):
params.append(f'{p}={_format_quantity(v)}')
param_str = ', '.join(params)
round = f'Round {index}: '
if len(additional_info.get('objective_errors', [])) > 1:
best_error_str = _format_quantity(best_error, precision=4)
errors = []
for error, normed_error, varname in zip(additional_info['objective_errors'],
additional_info['objective_errors_normalized'],
additional_info['output_var']):
if not have_same_dimensions(error, normed_error) or error != normed_error:
raw_error_str = f', unnormalized error: {_format_quantity(error)}'
else:
raw_error_str = ''
errors.append(f'{_format_quantity(normed_error)} ({varname}{raw_error_str})')
error_sum = ' + '.join(errors)
print(f"{round}Best parameters {param_str}\n"
f"{' '*len(round)}Best error: {best_error_str} = {error_sum}")
else:
print(f"{round}Best parameters {param_str}")
if 'objective_errors_normalized' in additional_info:
best_error_normed = _format_quantity(additional_info['objective_errors_normalized'][0])
best_error_raw = _format_quantity(additional_info['objective_errors'][0])
if (not have_same_dimensions(additional_info['objective_errors_normalized'][0],
additional_info['objective_errors'][0]) or
best_error_normed != best_error_raw):
print(f"{' ' * len(round)}Best error: {best_error_normed} ({additional_info['output_var'][0]}, "
f"unnormalized error: {best_error_raw})")
else:
print(f"{' ' * len(round)}Best error: {best_error_normed} ({additional_info['output_var'][0]})")
else:
best_error_str = _format_quantity(best_error, precision=4)
print(f"{' ' * len(round)}Best error: {best_error_str} ({additional_info['output_var'][0]})")
[docs]def callback_none(params, errors, best_params, best_error, index,
additional_info):
"""Non-verbose callback"""
pass
[docs]class ProgressBar(object):
"""Setup for tqdm progress bar in Fitter"""
def __init__(self, total=None, **kwds):
self.t = tqdm(total=total, **kwds)
def __call__(self, params, errors, best_params, best_error, index,
additional_info):
self.t.update(1)
[docs]def callback_setup(set_type, n_rounds):
"""
Helper function for callback setup in Fitter, loads option:
'text', 'progressbar' or custion FunctionType
"""
if set_type == 'text':
callback = callback_text
elif set_type == 'progressbar':
callback = ProgressBar(n_rounds)
elif set_type is None:
callback = callback_none
elif type(set_type) is FunctionType:
callback = set_type
else:
raise TypeError('callback has to be a str (`text` or `progressbar`), '
'allable or None')
return callback
[docs]def make_dic(names, values):
"""Create dictionary based on list of strings and 2D array"""
result_dict = {name: value for name, value in zip(names, values)}
return result_dict