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fig9.py
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# -*- coding: utf-8 -*-
# @Author: Theo Lemaire
# @Email: theo.lemaire@epfl.ch
# @Date: 2018-12-09 12:06:01
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-06-17 22:06:53
''' Sub-panels of SONIC model validation on an STN neuron (response to CW sonication). '''
import os
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from PySONIC.core import NeuronalBilayerSonophore
from PySONIC.neurons import getPointNeuron
from PySONIC.utils import logger, Intensity2Pressure
from PySONIC.plt import CompTimeSeries, GroupedTimeSeries
from PySONIC.parsers import FigureParser
# Plot parameters
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.rcParams['font.family'] = 'arial'
# Figure basename
figbase = os.path.splitext(__file__)[0]
def main():
parser = FigureParser(['a', 'b'])
parser.addInputDir()
args = parser.parse()
logger.setLevel(args['loglevel'])
figset = args['subset']
inputdir = args['inputdir']
logger.info('Generating panels {} of {}'.format(figset, figbase))
# Parameters
pneuron = getPointNeuron('STN')
a = 32e-9 # m
Fdrive = 500e3 # Hz
tstim = 1 # s
toffset = 0. # s
PRF = 1e2
DC = 1.
nbls = NeuronalBilayerSonophore(a, pneuron)
# Range of intensities
intensities = pneuron.getLowIntensities() # W/m2
# Levels depicted with individual traces
subset_intensities = [112, 114, 123] # W/m2
# convert to amplitudes and get filepaths
amplitudes = Intensity2Pressure(intensities) # Pa
fnames = ['{}.pkl'.format(nbls.filecode(Fdrive, A, tstim, toffset, PRF, DC, 'sonic'))
for A in amplitudes]
fpaths = [os.path.join(inputdir, 'STN', fn) for fn in fnames]
# Generate figures
figs = []
if 'a' in figset:
comp_plot = CompTimeSeries(fpaths, 'FR')
fig = comp_plot.render(
patches='none',
cmap='Oranges',
tbounds=(0, tstim)
)
fig.canvas.set_window_title(figbase + 'a')
figs.append(fig)
if 'b' in figset:
isubset = [np.argwhere(intensities == x)[0][0] for x in subset_intensities]
subset_amplitudes = amplitudes[isubset]
titles = ['{:.2f} kPa ({:.0f} W/m2)'.format(A * 1e-3, I)
for A, I in zip(subset_amplitudes, subset_intensities)]
figtraces = GroupedTimeSeries([fpaths[i] for i in isubset], pltscheme={'Q_m': ['Qm']})()
for fig, title in zip(figtraces, titles):
fig.axes[0].set_title(title)
fig.canvas.set_window_title(figbase + 'b {}'.format(title))
figs.append(fig)
if args['save']:
for fig in figs:
s = fig.canvas.get_window_title()
s = s.replace('(', '- ').replace('/', '_').replace(')', '')
figname = '{}.pdf'.format(s)
fig.savefig(os.path.join(args['outputdir'], figname), transparent=True)
else:
plt.show()
if __name__ == '__main__':
main()

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