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fig2.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: 2019-05-31 14:25:27
''' Sub-panels of the model optimization figure. '''
import os
import logging
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
from matplotlib.patches import Rectangle
from argparse import ArgumentParser
from PySONIC.utils import logger, rescale, si_format, selectDirDialog
from PySONIC.plt import getStimPulses, cm2inch
from PySONIC.constants import NPC_FULL
from PySONIC.neurons import CorticalRS
from PySONIC.core import BilayerSonophore, NeuronalBilayerSonophore
# 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 PmApprox(bls, Z, fs=12, lw=2):
fig, ax = plt.subplots(figsize=cm2inch(7, 7))
for key in ['right', 'top']:
ax.spines[key].set_visible(False)
for key in ['bottom', 'left']:
ax.spines[key].set_linewidth(2)
ax.spines['bottom'].set_position('zero')
ax.set_xlabel('Z (nm)', fontsize=fs)
ax.set_ylabel('Pressure (kPa)', fontsize=fs, labelpad=-10)
ax.set_xticks([0, bls.a * 1e9])
ax.set_xticklabels(['0', 'a'])
ax.tick_params(axis='x', which='major', length=25, pad=5)
ax.set_yticks([0])
ax.set_ylim([-10, 50])
for item in ax.get_xticklabels() + ax.get_yticklabels():
item.set_fontsize(fs)
ax.plot(Z * 1e9, bls.v_PMavg(Z, bls.v_curvrad(Z), bls.surface(Z)) * 1e-3, c='g', label='$P_m$')
ax.plot(Z * 1e9, bls.PMavgpred(Z) * 1e-3, '--', c='r', label='$\~P_m$')
ax.axhline(y=0, color='k')
ax.legend(fontsize=fs, frameon=False)
fig.tight_layout()
fig.canvas.set_window_title(figbase + 'a')
return fig
def recasting(nbls, Fdrive, Adrive, fs=12, lw=2, ps=15):
# Run effective simulation
data, _ = nbls.simulate(Fdrive, Adrive, 5 / Fdrive, 0., method='full')
t, Qm, Vm = [data[key].values for key in ['t', 'Qm', 'Vm']]
t *= 1e6 # us
Qm *= 1e5 # nC/cm2
Qrange = (Qm.min(), Qm.max())
dQ = Qrange[1] - Qrange[0]
# Create figure and axes
fig, axes = plt.subplots(1, 2, figsize=cm2inch(17, 5))
for ax in axes:
ax.set_xticks([])
ax.set_yticks([])
# Plot Q-trace and V-trace
ax = axes[0]
for key in ['top', 'right']:
ax.spines[key].set_visible(False)
for key in ['bottom', 'left']:
ax.spines[key].set_position(('axes', -0.03))
ax.spines[key].set_linewidth(2)
ax.plot(t, Vm, label='Vm', c='dimgrey', linewidth=lw)
ax.plot(t, Qm, label='Qm', c='k', linewidth=lw)
ax.add_patch(Rectangle(
(t[0], Qrange[0] - 5), t[-1], dQ + 10,
fill=False, edgecolor='k', linestyle='--', linewidth=1
))
ax.yaxis.set_tick_params(width=2)
ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))
# ax.set_xlim((t.min(), t.max()))
ax.set_xticks([])
ax.set_xlabel('{}s'.format(si_format((t.max()), space=' ')), fontsize=fs)
ax.set_ylabel('$\\rm nC/cm^2$ - mV', fontsize=fs, labelpad=-15)
ax.set_yticks(ax.get_ylim())
for item in ax.get_yticklabels():
item.set_fontsize(fs)
# Plot inset on Q-trace
ax = axes[1]
for key in ['top', 'right', 'bottom', 'left']:
ax.spines[key].set_linewidth(1)
ax.spines[key].set_linestyle('--')
ax.plot(t, Vm, label='Vm', c='dimgrey', linewidth=lw)
ax.plot(t, Qm, label='Qm', c='k', linewidth=lw)
ax.set_xlim((t.min(), t.max()))
ax.set_xticks([])
ax.set_yticks([])
delta = 0.05
ax.set_ylim(Qrange[0] - delta * dQ, Qrange[1] + delta * dQ)
fig.canvas.set_window_title(figbase + 'b')
return fig
def mechSim(bls, Fdrive, Adrive, Qm, fs=12, lw=2, ps=15):
# Run mechanical simulation
data, _ = bls.simulate(Fdrive, Adrive, Qm)
t, Z, ng = [data[key].values for key in ['t', 'Z', 'ng']]
# Create figure
fig, ax = plt.subplots(figsize=cm2inch(7, 7))
fig.suptitle('Mechanical simulation', fontsize=12)
for skey in ['bottom', 'left', 'right', 'top']:
ax.spines[skey].set_visible(False)
ax.set_xticks([])
ax.set_yticks([])
# Plot variables and labels
t_plot = np.insert(t, 0, -1e-6) * 1e6
Pac = Adrive * np.sin(2 * np.pi * Fdrive * t + np.pi) # Pa
yvars = {'P_A': Pac * 1e-3, 'Z': Z * 1e9, 'n_g': ng * 1e22}
colors = {'P_A': 'k', 'Z': 'C0', 'n_g': 'C5'}
dy = 1.2
for i, ykey in enumerate(yvars.keys()):
y = yvars[ykey]
y_plot = rescale(np.insert(y, 0, y[0])) - dy * i
ax.plot(t_plot, y_plot, color=colors[ykey], linewidth=lw)
ax.text(t_plot[0] - 0.1, y_plot[0], '$\mathregular{{{}}}$'.format(ykey), fontsize=fs,
horizontalalignment='right', verticalalignment='center', color=colors[ykey])
# Acoustic pressure annotations
ax.annotate(s='', xy=(1.5, 1.1), xytext=(3.5, 1.1),
arrowprops=dict(arrowstyle='<|-|>', color='k'))
ax.text(2.5, 1.12, '1/f', fontsize=fs, color='k',
horizontalalignment='center', verticalalignment='bottom')
ax.annotate(s='', xy=(1.5, -0.1), xytext=(1.5, 1),
arrowprops=dict(arrowstyle='<|-|>', color='k'))
ax.text(1.55, 0.4, '2A', fontsize=fs, color='k',
horizontalalignment='left', verticalalignment='center')
# Periodic stabilization patch
ax.add_patch(Rectangle((2, -2 * dy - 0.1), 2, 2 * dy, color='dimgrey', alpha=0.3))
ax.text(3, -2 * dy - 0.2, 'limit cycle', fontsize=fs, color='dimgrey',
horizontalalignment='center', verticalalignment='top')
# Z_last patch
ax.add_patch(Rectangle((2, -dy - 0.1), 2, dy, edgecolor='k', facecolor='none', linestyle='--'))
# ngeff annotations
c = plt.get_cmap('tab20').colors[11]
ax.text(t_plot[-1] + 0.1, y_plot[-1], '$\mathregular{n_{g,eff}}$', fontsize=fs, color=c,
horizontalalignment='left', verticalalignment='center')
ax.scatter([t_plot[-1]], [y_plot[-1]], color=c, s=ps)
fig.canvas.set_window_title(figbase + 'c mechsim')
return fig
def cycleAveraging(bls, neuron, Fdrive, Adrive, Qm, fs=12, lw=2, ps=15):
# Run mechanical simulation
data, _ = bls.simulate(Fdrive, Adrive, Qm)
t, Z, ng = [data[key].values for key in ['t', 'Z', 'ng']]
# Compute variables evolution over last acoustic cycle
t_last = t[-NPC_FULL:] * 1e6 # us
Z_last = Z[-NPC_FULL:] # m
Cm = bls.v_Capct(Z_last) * 1e2 # uF/m2
Vm = Qm / Cm * 1e5 # mV
yvars = {
'C_m': Cm, # uF/cm2
'V_m': Vm, # mV
'\\alpha_m': neuron.alpham(Vm) * 1e3, # ms-1
'\\beta_m': neuron.betam(Vm) * 1e3, # ms-1
'p_\\infty / \\tau_p': neuron.pinf(Vm) / neuron.taup(Vm) * 1e3, # ms-1
'(1-p_\\infty) / \\tau_p': (1 - neuron.pinf(Vm)) / neuron.taup(Vm) * 1e3 # ms-1
}
# Determine colors
violets = plt.get_cmap('Paired').colors[8:10][::-1]
oranges = plt.get_cmap('Paired').colors[6:8][::-1]
colors = {
'C_m': ['k', 'dimgrey'],
'V_m': plt.get_cmap('tab20').colors[14:16],
'\\alpha_m': violets,
'\\beta_m': oranges,
'p_\\infty / \\tau_p': violets,
'(1-p_\\infty) / \\tau_p': oranges
}
# Create figure and axes
fig, axes = plt.subplots(6, 1, figsize=cm2inch(4, 15))
fig.suptitle('Cycle-averaging', fontsize=fs)
for ax in axes:
ax.set_xticks([])
ax.set_yticks([])
for skey in ['bottom', 'left', 'right', 'top']:
ax.spines[skey].set_visible(False)
# Plot variables
for ax, ykey in zip(axes, yvars.keys()):
ax.set_xticks([])
ax.set_yticks([])
for skey in ['bottom', 'left', 'right', 'top']:
ax.spines[skey].set_visible(False)
y = yvars[ykey]
ax.plot(t_last, y, color=colors[ykey][0], linewidth=lw)
ax.plot([t_last[0], t_last[-1]], [np.mean(y)] * 2, '--', color=colors[ykey][1])
ax.scatter([t_last[-1]], [np.mean(y)], s=ps, color=colors[ykey][1])
ax.text(t_last[0] - 0.1, y[0], '$\mathregular{{{}}}$'.format(ykey), fontsize=fs,
horizontalalignment='right', verticalalignment='center', color=colors[ykey][0])
fig.canvas.set_window_title(figbase + 'c cycleavg')
return fig
def Qsolution(nbls, Fdrive, Adrive, tstim, toffset, PRF, DC, fs=12, lw=2, ps=15):
# Run effective simulation
data, _ = nbls.simulate(Fdrive, Adrive, tstim, toffset, PRF, DC, method='sonic')
t, Qm, states = [data[key].values for key in ['t', 'Qm', 'stimstate']]
t *= 1e3 # ms
Qm *= 1e5 # nC/cm2
_, tpulse_on, tpulse_off = getStimPulses(t, states)
# Add small onset
t = np.insert(t, 0, -5.0)
Qm = np.insert(Qm, 0, Qm[0])
# Create figure and axes
fig, ax = plt.subplots(figsize=cm2inch(12, 6))
ax.set_xticks([])
ax.set_yticks([])
for key in ['top', 'right']:
ax.spines[key].set_visible(False)
for key in ['bottom', 'left']:
ax.spines[key].set_position(('axes', -0.03))
ax.spines[key].set_linewidth(2)
# Plot Q-trace and stimulation pulses
ax.plot(t, Qm, label='Qm', c='k', linewidth=lw)
for ton, toff in zip(tpulse_on, tpulse_off):
ax.axvspan(ton, toff, edgecolor='none', facecolor='#8A8A8A', alpha=0.2)
ax.yaxis.set_tick_params(width=2)
ax.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))
ax.set_xlim((t.min(), t.max()))
ax.set_xticks([])
ax.set_xlabel('{}s'.format(si_format((t.max()) * 1e-3, space=' ')), fontsize=fs)
ax.set_ylabel('$\\rm nC/cm^2$', fontsize=fs, labelpad=-15)
ax.set_yticks(ax.get_ylim())
for item in ax.get_yticklabels():
item.set_fontsize(fs)
ax.legend(fontsize=fs, frameon=False)
fig.canvas.set_window_title(figbase + 'e Qtrace')
return fig
def main():
ap = ArgumentParser()
# Runtime options
ap.add_argument('-v', '--verbose', default=False, action='store_true', help='Increase verbosity')
ap.add_argument('-o', '--outdir', type=str, help='Output directory')
ap.add_argument('-f', '--figset', type=str, nargs='+', help='Figure set', default='all')
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)
figset = args.figset
if figset == 'all':
figset = ['a', 'b', 'c', 'e']
logger.info('Generating panels {} of {}'.format(figset, figbase))
# Parameters
neuron = CorticalRS()
a = 32e-9 # m
Fdrive = 500e3 # Hz
Adrive = 100e3 # Pa
PRF = 100. # Hz
DC = 0.5
tstim = 150e-3 # s
toffset = 100e-3 # s
Qm = -71.9e-5 # C/cm2
bls = BilayerSonophore(a, neuron.Cm0, neuron.Cm0 * neuron.Vm0 * 1e-3)
nbls = NeuronalBilayerSonophore(a, neuron)
# Figures
figs = []
if 'a' in figset:
figs.append(PmApprox(bls, np.linspace(-0.4 * bls.Delta_, bls.a, 1000)))
if 'b' in figset:
figs.append(recasting(nbls, Fdrive, Adrive))
if 'c' in figset:
figs += [
mechSim(bls, Fdrive, Adrive, Qm),
cycleAveraging(bls, neuron, Fdrive, Adrive, Qm)
]
if 'e' in figset:
figs.append(Qsolution(nbls, Fdrive, Adrive, tstim, toffset, PRF, DC))
if args.save:
outdir = selectDirDialog() if args.outdir is None else args.outdir
if outdir == '':
logger.error('No input directory chosen')
return
for fig in figs:
figname = '{}.pdf'.format(fig.canvas.get_window_title())
fig.savefig(os.path.join(outdir, figname), transparent=True)
else:
plt.show()
if __name__ == '__main__':
main()

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