# fig_4_d_psth_plots_with_GABA_scale.py ---

#

# Filename: fig_4_d_psth_plots_with_GABA_scale.py

# Description:

# Author: subha

# Maintainer:

# Created: Sun Jan 10 21:58:14 2016 (-0500)

# Version:

# Last-Updated: Sun Jan 17 16:44:39 2016 (-0500)

# By: subha

# Update #: 160

# URL:

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# 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, or

# (at your option) any later version.

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# 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.

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# You should have received a copy of the GNU General Public License

# along with this program; see the file COPYING. If not, write to

# the Free Software Foundation, Inc., 51 Franklin Street, Fifth

# Floor, Boston, MA 02110-1301, USA.

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# Code:

"""Plots the PSTH peaks with changing DeepBasket count."""

import os

import numpy as np

from collections import defaultdict

import matplotlib as mpl

import matplotlib.pyplot as plt

# import seaborn as sns

import pandas as pd

import util

# from peakdetect import peakdetect

from util import get_filenames, makepath, get_dbcnt_dict, psth, get_stim_times

from traubdata import TraubData

from scipy.stats import mode

from bisect import bisect_left

import csv

from config import CELLTYPES

plt.rc('font', size=12)

plt.rc('figure', figsize=(3, 3))

def get_psth_with_GABA_scale(window, binwidth):

return psthdict, bins

if __name__ == '__main__':

fname = 'gabascale_psth_200.0ms_window_5.0ms_bins.csv'

if os.path.exists(fname):

with open(fname, 'r') as fd:

reader = csv.DictReader(fd)

bins = [float(v)*1e-3 for v in reader.fieldnames[2:]]

bins.append(bins[-1] + bins[-1] - bins[-2])

psthdict = defaultdict(dict)

for row in reader:

gabascale = float(row['gaba_scale'])

dfile = row['filename']

psthdict[gabascale][dfile] = [float(row[v]) for v in reader.fieldnames[2:]]

else:

psthdict, bins = get_psth_with_GABA_scale(200e-3, 5e-3)

with open(fname, 'w') as fd:

writer = csv.writer(fd)

header = [ 'gaba_scale', 'filename'] + [b * 1e3 for b in bins[:-1]]

writer.writerow(header)

for ii, gabascale in enumerate(sorted(psthdict.keys())):

for fname, psth in psthdict[gabascale].items():

row = [gabascale, fname] + [v for v in psth]

writer.writerow(row)

fig = plt.figure()

ax = None

bins = np.array(bins)

for ii, gabascale in enumerate(sorted(psthdict.keys())):

ax = fig.add_subplot(len(psthdict), 1, ii+1, sharex=ax, sharey=ax)

for fname, psth in psthdict[gabascale].items():

row = [gabascale, fname] + [v for v in psth]

ax.plot(1e3*(bins[1:]+bins[:-1])/2.0, psthdict[gabascale][fname], 'k-', alpha=0.3)

ax.set_yticks((0, 200))

ax.spines['top'].set_visible(False)

ax.spines['right'].set_visible(False)

ax.xaxis.set_visible(False)

ax.xaxis.tick_bottom()

ax.yaxis.tick_left()

ax.xaxis.set_visible(True)

ax.set_xlabel('Time since stimulus (ms)')

ax.set_ylabel('Population firing rate')

fig.subplots_adjust(left=0.2, right=0.95, top=0.95, bottom=0.2, hspace=0.5)

fig.savefig('figures/Figure_4D_psth_plots_with_GABA_scale.svg', transparent=True)

plt.show()

#

# fig_4_d_psth_plots_with_GABA_scale.py ends here