Topology in neuroscience
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# -*- coding: utf-8 -*-
"""
Some decorators useful for data analysis functions
"""
import numpy as np
def multi_input(f):
"""Allow a function to also be applied to each element in a dictionary"""
def wrapper(data, *args, **kwargs):
if type(data) is dict:
output_data = {}
for name in data:
output_data[name] = f(data[name], *args, **kwargs)
if all(x is None for x in output_data.values()):
return
else:
return output_data
else:
return f(data, *args, **kwargs)
return wrapper
def av_output(f):
"""Allow running a function multiple times returning the average output"""
def wrapper(average=1, *args, **kwargs):
data_av = f(*args, **kwargs)
try:
if not isinstance(data_av, np.ndarray):
data_av = np.array(data_av)
except:
pass
for i in range(average - 1):
delta = f(*args, **kwargs)
try:
if not isinstance(delta, np.ndarray):
delta = np.array(delta)
except:
pass
data_av += delta
if not isinstance(data_av, tuple):
data_av /= average
else:
data_av = [d / average for d in data_av]
return data_av
return wrapper