# -*- coding: utf-8 -*-
#
#
# PyRates software framework for flexible implementation of neural
# network model_templates and simulations. See also:
# https://github.com/pyrates-neuroscience/PyRates
#
# Copyright (C) 2017-2018 the original authors (Richard Gast and
# Daniel Rose), the Max-Planck-Institute for Human Cognitive Brain
# Sciences ("MPI CBS") and contributors
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>
#
# CITATION:
#
# Richard Gast and Daniel Rose et. al. in preparation
"""Contains function definitions that may be used for PyRates model equations.
"""
# external _imports
import numpy as np
# meta infos
__author__ = "Richard Gast"
__status__ = "development"
# function definitions
######################
[docs]def identity(x):
return x
[docs]def index_1d(x, idx):
return x[idx]
[docs]def index_2d(x, idx1, idx2):
return x[idx1, idx2]
[docs]def index_range(x, idx1, idx2):
return x[idx1:idx2]
[docs]def index_axis(x, idx=None, axis=0):
if idx is not None:
return x[idx] if axis == 0 else x[:, idx]
else:
return x[:]
[docs]def past(x, tau):
return x
sigmoid = """
def sigmoid(x):
return 1./(1. + exp(-x))
"""
# dictionary for backend import
###############################
base_funcs = {
'maxi': {'call': 'maximum', 'func': np.maximum, 'imports': ['numpy.maximum']},
'mini': {'call': 'minimum', 'func': np.minimum, 'imports': ['numpy.minimum']},
'round': {'call': 'round', 'func': np.round, 'imports': ['numpy.round']},
'sum': {'call': 'sum', 'func': np.sum, 'imports': ['numpy.sum']},
'mean': {'call': 'mean', 'func': np.mean, 'imports': ['numpy.mean']},
'matmul': {'call': 'dot', 'func': np.dot, 'imports': ['numpy.dot']},
'matvec': {'call': 'dot', 'func': np.dot, 'imports': ['numpy.dot']},
'roll': {'call': 'roll', 'func': np.roll, 'imports': ['numpy.roll']},
'randn': {'call': 'randn', 'func': np.random.randn, 'imports': ['numpy.random.randn']},
'tanh': {'call': 'tanh', 'func': np.tanh, 'imports': ['numpy.tanh']},
'sinh': {'call': 'sinh', 'func': np.sinh, 'imports': ['numpy.sinh']},
'cosh': {'call': 'cosh', 'func': np.cosh, 'imports': ['numpy.cosh']},
'arctan': {'call': 'arctan', 'func': np.arctan, 'imports': ['numpy.arctan']},
'arcsin': {'call': 'arcsin', 'func': np.arcsin, 'imports': ['numpy.arcsin']},
'arccos': {'call': 'arccos', 'func': np.arccos, 'imports': ['numpy.arccos']},
'sin': {'call': 'sin', 'func': np.sin, 'imports': ['numpy.sin']},
'cos': {'call': 'cos', 'func': np.cos, 'imports': ['numpy.cos']},
'tan': {'call': 'tan', 'func': np.tan, 'imports': ['numpy.tan']},
'exp': {'call': 'exp', 'func': np.exp, 'imports': ['numpy.exp']},
'interp': {'call': 'interp', 'func': np.interp, 'imports': ['numpy.interp']},
'sigmoid': {'call': 'sigmoid', 'def': sigmoid, 'imports': ['numpy.exp']},
'no_op': {'call': 'identity', 'func': identity},
'index': {'call': 'index_1d', 'func': index_1d},
'index_range': {'call': 'index_range', 'func': index_range},
'index_2d': {'call': 'index_2d', 'func': index_2d},
'index_axis': {'call': 'index_axis', 'func': index_axis},
'real': {'call': 'real', 'func': np.real, 'imports': ['numpy.real']},
'imag': {'call': 'imag', 'func': np.imag, 'imports': ['numpy.imag']},
'conj': {'call': 'conjugate', 'func': np.conjugate, 'imports': ['numpy.conjugate']},
'absv': {'call': 'abs', 'func': np.abs, 'imports': ['numpy.abs']},
'sign': {'call': 'sign', 'func': np.sign, 'imports': ['numpy.sign']},
'log': {'call': 'log', 'func': np.log, 'imports': ['numpy.log']},
'past': {'call': 'past', 'func': past},
'concatenate': {'call': 'concatenate', 'func': np.concatenate, 'imports': ['numpy.concatenate']}
}