import inspect
from functools import partial
import SPCA
from SPCA import *
# FIX: Add a docstring for this function or remove it
[docs]def get_lambdaparams(function):
return inspect.getfullargspec(function).args[1:]
# FIX: Add a docstring for this function
[docs]def get_fitted_params(function, dparams):
if type(function) == partial:
name = function.func.__name__
else:
name = function.__name__
if name=='detec_model_bliss':
if 'sigF' in dparams:
params = []
else:
params = ['sigF']
else:
params = get_lambdaparams(function)
params = [param for param in params if param not in dparams]
return params
# FIX - this is currently empty!!!
[docs]def load_past_params(path):
"""Load the fitted parameters from a previous run.
Args:
path (string): Path to the file containing past mcmc result (must be a table saved as .npy file).
Returns:
ndarray: p0 (the previously fitted values)
"""
return
[docs]def make_lambdafunc(function, dparams=[], obj=[], debug=False):
"""Create a lambda function called dynamic_funk that will fix the parameters listed in dparams with the values in obj.
Note: The module where the original function is needs to be loaded in this file.
Args:
function (string): Name of the original function.
dparams (list, optional): List of all input parameters the user does not wish to fit (default is None.)
obj (string, optional): Object containing all initial and fixed parameter values (default is None.)
debug (bool, optional): If true, will print mystr so the user can read the command because executing it (default is False).
Returns:
function: dynamic_funk (the lambda function with fixed parameters.)
"""
full_args = inspect.getfullargspec(function).args[1:]
freeze_kwargs = dict([[dparams[i], obj[dparams[i]]] for i in range(len(dparams)) if dparams[i] in full_args])
dynamic_funk = partial(function, **freeze_kwargs)
if debug:
print(inspect.getfullargspec(dynamic_funk).args[1:])
print()
return dynamic_funk