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fig5.py
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# -*- coding: utf-8 -*-
# @Author: Theo
# @Date: 2018-06-06 18:38:04
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-03-11 11:33:46
''' Sub-panels of the NICE and SONIC accuracies comparative figure. '''
import os
import logging
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from argparse import ArgumentParser
from PySONIC.utils import *
from PySONIC.neurons import *
from PySONIC.batches import createAStimQueue
from PySONIC.plt import plotComp, plotSpikingMetrics
from utils import *
# 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 Qprofiles_vs_amp(neuron, a, Fdrive, CW_Athrs, tstim, toffset, inputdir):
''' Comparison of resulting charge profiles for CW stimuli at sub-threshold,
threshold and supra-threshold amplitudes. '''
Athr = CW_Athrs[neuron].loc[Fdrive * 1e-3] # kPa
amps = np.array([Athr - 5., Athr, Athr + 20.]) * 1e3 # Pa
subdir = os.path.join(inputdir, neuron)
sonic_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], amps, [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], amps, [tstim], [toffset], [None], [1.], 'full'))
regimes = ['AT - 5 kPa', 'AT', 'AT + 20 kPa']
fig = plotComp(
sum([[x, y] for x, y in zip(full_fpaths, sonic_fpaths)], []),
'Qm',
labels=sum([['', x] for x in regimes], []),
lines=['-', '--'] * len(regimes),
colors=plt.get_cmap('Paired').colors[:2 * len(regimes)],
fs=8, patches='one', xticks=[0, 250], yticks=[getNeuronsDict()[neuron].Vm0, 25],
straightlegend=True, figsize=cm2inch(12.5, 5.8)
)
fig.axes[0].get_xaxis().set_label_coords(0.5, -0.05)
fig.subplots_adjust(bottom=0.2, right=0.95, top=0.95)
fig.canvas.set_window_title(figbase + 'a Qprofiles')
return fig
def spikemetrics_vs_amp(neuron, a, Fdrive, amps, tstim, toffset, inputdir):
''' Comparison of spiking metrics for CW stimuli at various supra-threshold amplitudes. '''
subdir = os.path.join(inputdir, neuron)
sonic_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], amps, [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], amps, [tstim], [toffset], [None], [1.], 'full'))
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
metrics_files = {x: '{}_spikemetrics_vs_amplitude_{}.csv'.format(neuron, x)
for x in ['full', 'sonic']}
metrics_fpaths = {key: os.path.join(inputdir, value) for key, value in metrics_files.items()}
xlabel = 'Amplitude (kPa)'
metrics = getSpikingMetrics(
subdir, neuron, amps * 1e-3, xlabel, data_fpaths, metrics_fpaths)
fig = plotSpikingMetrics(amps * 1e-3, xlabel, {neuron: metrics}, logscale=True)
fig.canvas.set_window_title(figbase + 'a spikemetrics')
return fig
def Qprofiles_vs_freq(neuron, a, freqs, CW_Athrs, tstim, toffset, inputdir):
''' Comparison of resulting charge profiles for supra-threshold CW stimuli
at low and high US frequencies. '''
subdir = os.path.join(inputdir, neuron)
sonic_fpaths, full_fpaths = [], []
for Fdrive in freqs:
Athr = CW_Athrs[neuron].loc[Fdrive * 1e-3] # kPa
Adrive = (Athr + 20.) * 1e3 # Pa
sonic_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'full'))
fig = plotComp(
sum([[x, y] for x, y in zip(full_fpaths, sonic_fpaths)], []),
'Qm',
labels=sum([['', '{}Hz'.format(si_format(f))] for f in freqs], []),
lines=['-', '--'] * len(freqs), colors=plt.get_cmap('Paired').colors[6:10], fs=8,
patches='one', xticks=[0, 250], yticks=[getNeuronsDict()[neuron].Vm0, 25],
straightlegend=True, figsize=cm2inch(12.5, 5.8),
inset={'xcoords': [5, 40], 'ycoords': [-35, 45], 'xlims': [57.5, 58.5], 'ylims': [10, 35]}
)
fig.axes[0].get_xaxis().set_label_coords(0.5, -0.05)
fig.subplots_adjust(bottom=0.2, right=0.95, top=0.95)
fig.canvas.set_window_title(figbase + 'b Qprofiles')
return fig
def spikemetrics_vs_freq(neuron, a, freqs, CW_Athrs, tstim, toffset, inputdir):
''' Comparison of spiking metrics for supra-threshold CW stimuli at various US frequencies. '''
subdir = os.path.join(inputdir, neuron)
sonic_fpaths, full_fpaths = [], []
for Fdrive in freqs:
Athr = CW_Athrs[neuron].loc[Fdrive * 1e-3] # kPa
Adrive = (Athr + 20.) * 1e3 # Pa
sonic_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'full'))
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
metrics_files = {x: '{}_spikemetrics_vs_frequency_{}.csv'.format(neuron, x)
for x in ['full', 'sonic']}
metrics_fpaths = {key: os.path.join(inputdir, value) for key, value in metrics_files.items()}
xlabel = 'Frequency (kHz)'
metrics = getSpikingMetrics(
subdir, neuron, freqs * 1e-3, xlabel, data_fpaths, metrics_fpaths)
fig = plotSpikingMetrics(freqs * 1e-3, xlabel, {neuron: metrics}, logscale=True)
fig.canvas.set_window_title(figbase + 'b spikemetrics')
return fig
def Qprofiles_vs_radius(neuron, radii, Fdrive, CW_Athrs, tstim, toffset, inputdir):
''' Comparison of resulting charge profiles for supra-threshold CW stimuli
for small and large sonophore radii. '''
subdir = os.path.join(inputdir, neuron)
sonic_fpaths, full_fpaths = [], []
for a in radii:
Athr = CW_Athrs[neuron].loc[a * 1e9] # kPa
Adrive = (Athr + 20.) * 1e3 # Pa
sonic_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'full'))
tmp = plt.get_cmap('Paired').colors
colors = tmp[2:4] + tmp[10:12]
fig = plotComp(
sum([[x, y] for x, y in zip(full_fpaths, sonic_fpaths)], []),
'Qm',
labels=sum([['', '{:.0f} nm'.format(a * 1e9)] for a in radii], []),
lines=['-', '--'] * len(radii), colors=colors, fs=8,
patches='one', xticks=[0, 250], yticks=[getNeuronsDict()[neuron].Vm0, 25],
straightlegend=True, figsize=cm2inch(12.5, 5.8)
)
fig.axes[0].get_xaxis().set_label_coords(0.5, -0.05)
fig.subplots_adjust(bottom=0.2, right=0.95, top=0.95)
fig.canvas.set_window_title(figbase + 'c Qprofiles')
return fig
def spikemetrics_vs_radius(neuron, radii, Fdrive, CW_Athrs, tstim, toffset, inputdir):
''' Comparison of spiking metrics for supra-threshold CW stimuli
with various sonophore diameters. '''
subdir = os.path.join(inputdir, neuron)
sonic_fpaths, full_fpaths = [], []
for a in radii:
Athr = CW_Athrs[neuron].loc[np.round(a * 1e9, 1)] # kPa
Adrive = (Athr + 20.) * 1e3 # Pa
sonic_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'full'))
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
metrics_files = {x: '{}_spikemetrics_vs_radius_{}.csv'.format(neuron, x)
for x in ['full', 'sonic']}
metrics_fpaths = {key: os.path.join(inputdir, value) for key, value in metrics_files.items()}
xlabel = 'Sonophore radius (nm)'
metrics = getSpikingMetrics(
subdir, neuron, radii * 1e9, xlabel, data_fpaths, metrics_fpaths)
fig = plotSpikingMetrics(radii * 1e9, xlabel, {neuron: metrics}, logscale=True)
fig.canvas.set_window_title(figbase + 'c spikemetrics')
return fig
def Qprofiles_vs_DC(neurons, a, Fdrive, Adrive, tstim, toffset, PRF, DC, inputdir):
''' Comparison of resulting charge profiles for PW stimuli at 5% duty cycle
for different neuron types. '''
sonic_fpaths, full_fpaths = [], []
for neuron in neurons:
subdir = os.path.join(inputdir, neuron)
sonic_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [PRF], [DC], 'sonic'))
full_fpaths += getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [PRF], [DC], 'full'))
colors = list(plt.get_cmap('Paired').colors[:6])
del colors[2:4]
fig = plotComp(
sum([[x, y] for x, y in zip(full_fpaths, sonic_fpaths)], []),
'Qm',
labels=sum([['', '{}, {:.0f}% DC'.format(x, DC * 1e2)] for x in neurons], []),
lines=['-', '--'] * len(neurons), colors=colors, fs=8, patches='one',
xticks=[0, 250], yticks=[min(getNeuronsDict()[n].Vm0 for n in neurons), 50],
straightlegend=True, figsize=cm2inch(12.5, 5.8)
)
fig.axes[0].get_xaxis().set_label_coords(0.5, -0.05)
fig.subplots_adjust(bottom=0.2, right=0.95, top=0.95)
fig.canvas.set_window_title(figbase + 'd Qprofiles')
return fig
def spikemetrics_vs_DC(neurons, a, Fdrive, Adrive, tstim, toffset, PRF, DCs, inputdir):
''' Comparison of spiking metrics for PW stimuli at various duty cycle for
different neuron types. '''
metrics_dict = {}
xlabel = 'Duty cycle (%)'
colors = list(plt.get_cmap('Paired').colors[:6])
del colors[2:4]
colors_dict = {}
for i, neuron in enumerate(neurons):
subdir = os.path.join(inputdir, neuron)
sonic_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [PRF], DCs, 'sonic'))
full_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], [PRF], DCs, 'full'))
metrics_files = {x: '{}_spikemetrics_vs_DC_{}.csv'.format(neuron, x)
for x in ['full', 'sonic']}
metrics_fpaths = {key: os.path.join(inputdir, value) for key, value in metrics_files.items()}
sonic_fpaths = sonic_fpaths[1:] + [sonic_fpaths[0]]
full_fpaths = full_fpaths[1:] + [full_fpaths[0]]
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
metrics_dict[neuron] = getSpikingMetrics(
subdir, neuron, DCs * 1e2, xlabel, data_fpaths, metrics_fpaths)
colors_dict[neuron] = {'full': colors[2 * i], 'sonic': colors[2 * i + 1]}
fig = plotSpikingMetrics(DCs * 1e2, xlabel, metrics_dict, spikeamp=False, colors=colors_dict)
fig.canvas.set_window_title(figbase + 'd spikemetrics')
return fig
def Qprofiles_vs_PRF(neuron, a, Fdrive, Adrive, tstim, toffset, PRFs, DC, inputdir):
''' Comparison of resulting charge profiles for PW stimuli at 5% duty cycle
with different pulse repetition frequencies. '''
subdir = os.path.join(inputdir, neuron)
sonic_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], PRFs, [DC], 'sonic'))
full_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], PRFs, [DC], 'full'))
patches = [False, True] * len(PRFs)
patches[-1] = False
fig = plotComp(
sum([[x, y] for x, y in zip(full_fpaths, sonic_fpaths)], []),
'Qm',
labels=sum([['', '{}Hz PRF'.format(si_format(PRF, space=' '))] for PRF in PRFs], []),
lines=['-', '--'] * len(PRFs), colors=plt.get_cmap('Paired').colors[4:12], fs=8,
patches=patches,
xticks=[0, 250], yticks=[getNeuronsDict()[neuron].Vm0, 50],
straightlegend=True, figsize=cm2inch(12.5, 5.8)
)
fig.axes[0].get_xaxis().set_label_coords(0.5, -0.05)
fig.subplots_adjust(bottom=0.2, right=0.95, top=0.95)
fig.canvas.set_window_title(figbase + 'e Qprofiles')
return fig
def spikemetrics_vs_PRF(neuron, a, Fdrive, Adrive, tstim, toffset, PRFs, DC, inputdir):
''' Comparison of spiking metrics for PW stimuli at 5% duty cycle
with different pulse repetition frequencies. '''
xlabel = 'PRF (Hz)'
subdir = os.path.join(inputdir, neuron)
sonic_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], PRFs, [DC], 'sonic'))
full_fpaths = getSims(subdir, neuron, a, createAStimQueue(
[Fdrive], [Adrive], [tstim], [toffset], PRFs, [DC], 'full'))
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
metrics_files = {x: '{}_spikemetrics_vs_PRF_{}.csv'.format(neuron, x)
for x in ['full', 'sonic']}
metrics_fpaths = {key: os.path.join(inputdir, value) for key, value in metrics_files.items()}
metrics = getSpikingMetrics(
subdir, neuron, PRFs, xlabel, data_fpaths, metrics_fpaths)
fig = plotSpikingMetrics(PRFs, xlabel, {neuron: metrics}, spikeamp=False, logscale=True)
fig.canvas.set_window_title(figbase + 'e spikemetrics')
return fig
def main():
ap = ArgumentParser()
# Runtime options
ap.add_argument('-v', '--verbose', default=False, action='store_true', help='Increase verbosity')
ap.add_argument('-i', '--inputdir', type=str, help='Input directory')
ap.add_argument('-f', '--figset', type=str, help='Figure set', default='a')
ap.add_argument('-s', '--save', default=False, action='store_true',
help='Save output figures as pdf')
args = ap.parse_args()
loglevel = logging.DEBUG if args.verbose is True else logging.INFO
logger.setLevel(loglevel)
inputdir = selectDirDialog() if args.inputdir is None else args.inputdir
if inputdir == '':
logger.error('No input directory chosen')
return
figset = args.figset
logger.info('Generating panel {} of {}'.format(figset, figbase))
# Parameters
radii = np.array([16, 22.6, 32, 45.3, 64]) * 1e-9 # m
a = 32e-9 # m
tstim = 150e-3 # s
toffset = 100e-3 # s
freqs = np.array([20e3, 100e3, 500e3, 1e6, 2e6, 3e6, 4e6]) # Hz
Fdrive = 500e3 # Hz
amps = np.array([50, 100, 300, 600]) * 1e3 # Pa
Adrive = 100e3 # Pa
PRFs_sparse = np.array([1e1, 1e2, 1e3, 1e4]) # Hz
PRFs_dense = sum([[x, 2 * x, 5 * x] for x in PRFs_sparse[:-1]], []) + [PRFs_sparse[-1]] # Hz
PRF = 100 # Hz
DCs = np.array([5, 10, 25, 50, 75, 100]) * 1e-2
DC = 0.05
# Get threshold amplitudes if needed
if 'a' in figset or 'b' in figset:
CW_Athr_vs_Fdrive = getCWtitrations_vs_Fdrive(
['RS'], a, freqs, tstim, toffset, os.path.join(inputdir, 'CW_Athrs_vs_freqs.csv'))
if 'c' in figset:
CW_Athr_vs_radius = getCWtitrations_vs_radius(
['RS'], radii, Fdrive, tstim, toffset, os.path.join(inputdir, 'CW_Athrs_vs_radius.csv'))
# Generate figures
figs = []
if figset == 'a':
figs.append(Qprofiles_vs_amp('RS', a, Fdrive, CW_Athr_vs_Fdrive, tstim, toffset, inputdir))
figs.append(spikemetrics_vs_amp('RS', a, Fdrive, amps, tstim, toffset, inputdir))
if figset == 'b':
figs.append(Qprofiles_vs_freq(
'RS', a, [freqs.min(), freqs.max()], CW_Athr_vs_Fdrive, tstim, toffset, inputdir))
figs.append(spikemetrics_vs_freq('RS', a, freqs, CW_Athr_vs_Fdrive, tstim, toffset, inputdir))
if figset == 'c':
figs.append(Qprofiles_vs_radius(
'RS', [radii.min(), radii.max()], Fdrive, CW_Athr_vs_radius, tstim, toffset, inputdir))
figs.append(spikemetrics_vs_radius(
'RS', radii, Fdrive, CW_Athr_vs_radius, tstim, toffset, inputdir))
if figset == 'd':
figs.append(Qprofiles_vs_DC(
['RS', 'LTS'], a, Fdrive, Adrive, tstim, toffset, PRF, DC, inputdir))
figs.append(spikemetrics_vs_DC(
['RS', 'LTS'], a, Fdrive, Adrive, tstim, toffset, PRF, DCs, inputdir))
if figset == 'e':
figs.append(Qprofiles_vs_PRF(
'LTS', a, Fdrive, Adrive, tstim, toffset, PRFs_sparse, DC, inputdir))
figs.append(spikemetrics_vs_PRF(
'LTS', a, Fdrive, Adrive, tstim, toffset, PRFs_dense, DC, inputdir))
if args.save:
for fig in figs:
figname = '{}.pdf'.format(fig.canvas.get_window_title())
fig.savefig(os.path.join(inputdir, figname), transparent=True)
else:
plt.show()
if __name__ == '__main__':
main()

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