#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
"""
import numpy as np
import matplotlib.pyplot as plt
import genlinmod as glm
import OMB_texturegenerator as ombtg
exp = '20180710'
stimnr = 8
steps = glm.loadstim(exp, stimnr)
texture = ombtg.texture_generator()
noiselim = ombtg.noiselim
#texture = np.flipud(texture)
#%%
def clip_trajectory(stimulus, squaredim):
pos_filter = np.where(stimulus > 0)
neg_filter = not pos_filter
out = np.zeros(stimulus.shape)
out = np.fmod(stimulus, squaredim[:, np.newaxis] * 1.5, where=pos_filter)
out = np.fmod(-stimulus, squaredim[:, np.newaxis] * 1.5, where=neg_filter)
return out
#clipped = clip_trajectory(stimulus, np.array([200, 200]))
#def conv(k, x):
# return np.convolve(k, x, 'full')[:-k.shape[0]+1]
def normalizestas(stas):
stas = np.array(stas)
b = np.abs(stas).max(axis=1)
stas_normalized = stas / b.repeat(stas.shape[1]).reshape(stas.shape)
return stas_normalized
#%%
exp_name = '20180802'
stim_nr = 1
data = iof.load(exp_name, stim_nr)
stimulus = glm.loadstim(exp_name, stim_nr)
clusters = data['clusters']
#%%
#stas = np.array(data['stas'])
#stas_normalized = np.abs(stas).max(axis=1)
#stas_normalized = a / stas_normalized.repeat(stas.shape[1]).reshape(stas.shape)
frametimes = asc.ft_nblinks(exp_name, stim_nr)[1]
#stas = normalizestas(data['stas'])
stas = np.array(data['stas'])
predstas = np.zeros(stas.shape)
predmus = np.zeros(stas.shape[0])
start = dt.datetime.now()
allspikes = np.zeros((stas.shape[0], frametimes.shape[0]), dtype=np.int8)
import analysis_scripts as asc
expstim = ('20180802', 1)
data = iof.load(*expstim)
_, metadata = asc.read_spikesheet(expstim[0])
sta = data['stas'][3]
filter_length = sta.shape[0]
frame_rate = metadata['refresh_rate']
time_res = 1 / frame_rate
tstop = data['total_frames'] * time_res
t = np.arange(0, tstop, time_res)
k_in = sta
stim = glm.loadstim(*expstim)
else:
filter_length = 40
frame_rate = 60
time_res = (1 / frame_rate)
tstop = 100 # simulation length in seconds
t = np.arange(0, tstop, time_res)
# Set the seed for PRNG for reproducibility
np.random.seed(12221)
# np.random.seed(45212) # sum is 0.01 for tstop=500
stim = np.random.normal(size=t.shape)
tmini = t[:filter_length]
cross_corrs = np.zeros((fff.nclusters, xval_splits))
t = np.linspace(0, fff.filter_length * fff.frame_duration * 1000,
fff.filter_length)
plotlabels = ['Contrast']
# Temporary hack until FFF gets its own class.
fff.readpars()
if fff.param_file['Nblinks'] == 1:
t_fff = np.linspace(0, fff.filter_length * fff.frame_duration * 1000,
fff.filter_length * 2)
else:
t_fff = t
stimulus = glm.loadstim(exp, fff_stimnr)
for i, cluster in enumerate(fff.clusters):
for xvi in range(xval_splits):
sp_tr, sp_te, stim_tr, stim_te = train_test_split(
all_spikes[i],
stimulus,
test_size=xval_fraction,
split_pos=xval_fraction * xvi)
res = glm.minimize_loglhd(np.zeros(fff.filter_length),
0,
stim_tr,
fff.frame_duration,
sp_tr,
