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plot_figure_5_results.py
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plot_figure_5_results.py
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import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.patches as patches
from matplotlib.ticker import FormatStrFormatter
import matplotlib as mpl
mpl.style.use('classic') # Use Matplotlib v1 defaults (plot was designed on this!)
mpl.rc('text', usetex=True)
from cycler import cycler
import numpy
from os.path import join, exists
import re
import sys
plt.switch_backend('pdf')
plt.tick_params(axis='both', which='minor', labelsize=16)
model_prediction_colour = [0,0.45,0.74]
fig = plt.figure(0, figsize=(8,12), dpi=900)
#fig.text(0.51, 0.9, r'{0}'.format('Title'), ha='center', va='center', fontsize=16)
gs = gridspec.GridSpec(5, 3, height_ratios=[3,3,3,3,3], width_ratios=[1,1,1] )
left_alignment_for_panel_label = -1.00
protocol_names = ['steady_activation','inactivation','deactivation']
for column in range(0,3):
protocol = protocol_names[column]
# Plot voltage protocols
if column==0:
ax1 = plt.subplot(gs[0,column])
ax1_col0 = ax1
ax1.set_title('Steady Activation (Pr3)')
else:
ax1 = plt.subplot(gs[0,column], sharey=ax1_col0)
if column==1:
ax1.set_title('Inactivation (Pr4)')
elif column==2:
ax1.set_title('Deactivation (Pr5)')
# Load voltage data
voltage_file = 'figure_5_' + protocol + '/' + 'figure_5_' + protocol + '_protocol.txt'
data = numpy.loadtxt(voltage_file, skiprows=0)
all_time = data[:, 0]
voltages = data[:,1:]
ax1.plot(all_time,voltages,'k-',lw=0.75)
ax1.set_xlabel('Time (s)', fontsize=12)
ax1.set_ylim([-130, 65])
ax1.set_xlim([0, numpy.amax(all_time)])
# Plot voltage protocols
ax2 = plt.subplot(gs[1,column])
# Experimental data
data_file = 'figure_5_' + protocol + '/' + 'figure_5_' + protocol + '_experiment.txt'
data = numpy.loadtxt(data_file, skiprows=0)
all_time = data[:, 0]
experimental_currents = data[:,1:]
ax2.plot(all_time,experimental_currents,'r-',lw=0.5)
ax2.set_xlabel('Time (s)', fontsize=12)
# Simulation
ax3 = plt.subplot(gs[2,column],sharex=ax2,sharey=ax2)
data_file = 'figure_5_' + protocol + '/' + 'figure_5_' + protocol + '_prediction.txt'
data = numpy.loadtxt(data_file, skiprows=0)
all_time = data[:, 0]
simulated_currents = data[:,1:]
ax3.plot(all_time,simulated_currents,'-',color=model_prediction_colour,lw=0.8)
ax3.set_xlabel('Time (s)', fontsize=12)
if column==0:
ax1.set_ylabel('Voltage\n(mV)', fontsize=14,rotation=0)
ax2.set_ylabel('Experimental\nCurrent (nA)', fontsize=14,rotation=0)
ax3.set_ylabel('Predicted\nCurrent (nA)', fontsize=14,rotation=0)
start_of_zoom_time = 0.6
length_of_zoom_time = 5.9
ax2.set_ylim([-1,2])
elif column==2:
start_of_zoom_time = 2.4
length_of_zoom_time = 5.6
ax2.set_ylim([-3.5,2])
elif column==1:
start_of_zoom_time = 1.2
length_of_zoom_time = 0.3
ax2.set_ylim([-5,10])
ax2.locator_params(axis='x', nbins=4)
ax2.set_xlim([start_of_zoom_time,start_of_zoom_time+length_of_zoom_time])
# Put a zoom section on
lower_voltage, tmp = ax1.get_ylim()
tmp, upper_v_time = ax1.get_xlim()
voltage_at_next_axes = -208
patch_vertices = numpy.array([[start_of_zoom_time,lower_voltage],
[0,voltage_at_next_axes],
[upper_v_time,voltage_at_next_axes],
[start_of_zoom_time+length_of_zoom_time,lower_voltage]])
ax1.add_artist(plt.Polygon(patch_vertices,
closed=True,
edgecolor="none",
facecolor="grey",
alpha=0.15,
clip_on=False
)
)
# Shift axis labels
axes_list = [ax1, ax2, ax3]
for ax in axes_list:
if column==0:
ax.get_yaxis().set_label_coords(-0.6,0.30)
ax.get_xaxis().set_label_coords(+0.5,-0.19)
if column == 0:
# Add subfigure text labels, relative to axes top left
ax1.text(left_alignment_for_panel_label, 1.05, 'A', verticalalignment='top', horizontalalignment='left', transform=ax1.transAxes, fontsize=20, fontweight='bold')
ax2.text(left_alignment_for_panel_label, 1.05, 'B', verticalalignment='top', horizontalalignment='left', transform=ax2.transAxes, fontsize=20, fontweight='bold')
ax3.text(left_alignment_for_panel_label, 1.05, 'C', verticalalignment='top', horizontalalignment='left', transform=ax3.transAxes, fontsize=20, fontweight='bold')
# Probably easier just to plot all the summary graphs without looping!
def get_model_name(argument):
switcher = {
0: "tentusscher",
1: "mazhari",
2: "diveroli",
3: "wang",
4: "zeng",
5: "experiment",
6: "prediction",
}
return switcher.get(argument, "nothing")
color_cycle = [[1,0,1], [0.47,0.67,0.19], 'c', [0.49,0.18,0.56],'DarkOrange','r',model_prediction_colour]
line_width_cycle = [0.5,0.5,0.5,0.5,0.5,2,2]
line_style_cycle = ['-','-','-','-','-','--','-']
###########################
# S.S. activation IV curve
###########################
ax4 = plt.subplot(gs[3,0])
###########################
# S.S. activation tau curve - not plotting this as it isn't something usually seen!
###########################
#ax5 = plt.subplot(gs[4,0])
###########################
# deactivation - deactivation tau curve
###########################
ax6 = plt.subplot(gs[3,2])
###########################
# deactivation - recovery from inactivtion tau curve
###########################
ax7 = plt.subplot(gs[4,2])
###########################
# inactivtion- instantaneous inactivation tau curve
###########################
ax8 = plt.subplot(gs[3,1])
legend_entry = []
for model_idx in range(0,7):
file_name = 'figure_5_steady_activation/figure_5_steady_activation_iv_curve/figure_5_steady_activation_iv_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
ax4.plot(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
# file_name = 'figure_5_steady_activation/figure_5_steady_activation_tau_v_curve/figure_5_steady_activation_tau_v_' + get_model_name(model_idx) +'.txt'
# data = numpy.loadtxt(file_name,skiprows=0)
# ax5.semilogy(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
file_name = 'figure_5_deactivation/figure_5_deactivation_tau_v/figure_5_deactivation_tau_v_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
[a] = ax6.semilogy(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
legend_entry.append(a)
# Don't plot inactivation or instantaneous inactivation tau curves for TT or Zeng, simulated curves not comparable.
if (get_model_name(model_idx) != "tentusscher") and (get_model_name(model_idx) != "zeng"):
file_name = 'figure_5_inactivation/figure_5_instantaneous_inactivation_tau/figure_5_instantaneous_inactivation_tau_v_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
ax8.plot(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
if (get_model_name(model_idx) != "tentusscher") and (get_model_name(model_idx) != "zeng"):
file_name = 'figure_5_deactivation/figure_5_inactivation_tau_v/figure_5_inactivation_tau_v_' + get_model_name(model_idx) +'.txt'
data = numpy.loadtxt(file_name,skiprows=0)
ax7.plot(data[:,0],data[:,1],'.'+line_style_cycle[model_idx],color=color_cycle[model_idx],lw=line_width_cycle[model_idx])
ax4.set_xlabel('Voltage (mV)', fontsize=12)
ax4.set_ylabel('Current\n(normalized)', fontsize=12)
ax4.get_yaxis().set_label_coords(-0.26,0.5)
ax4.get_xaxis().set_label_coords(0.5,-0.19)
#ax5.set_xlabel('Voltage (mV)', fontsize=12)
#ax5.set_ylabel(r'Time constant $\tau$ (ms)', fontsize=12)
#ax5.set_ylim([8,10000])
#ax5.get_yaxis().set_label_coords(-0.3,0.4)
#ax5.get_xaxis().set_label_coords(0.5,-0.19)
ax6.set_xlabel('Voltage (mV)', fontsize=12)
ax6.set_ylabel(r'Deactivation $\tau$ (ms)', fontsize=12)
ax6.get_yaxis().set_label_coords(-0.16,0.5)
ax6.set_ylim([1,4000])
ax6.get_xaxis().set_label_coords(0.5,-0.19)
ax7.set_xlabel('Voltage (mV)', fontsize=12)
ax7.set_ylabel(r'Recovery inact. $\tau$ (ms)', fontsize=12)
ax7.get_yaxis().set_label_coords(-0.16,0.4)
ax7.get_xaxis().set_label_coords(0.5,-0.19)
ax8.set_xlabel('Voltage (mV)', fontsize=12)
ax8.set_ylabel(r'Inactivation $\tau$ (ms)', fontsize=12)
ax8.get_yaxis().set_label_coords(-0.16,0.5)
ax8.get_xaxis().set_label_coords(0.5,-0.19)
ax8.set_xlim([-50, 50])
ax6.locator_params(axis='x', nbins=4)
ax7.locator_params(axis='x', nbins=4)
ax7.locator_params(axis='y', nbins=6)
#ax5.locator_params(axis='y', nbins=6)
ax8.locator_params(axis='y', nbins=6)
ax4.text(-130, -0.4, 'Summary\nPlots', ha='center', fontsize=14)
ax7.legend(legend_entry, ["ten Tusscher `04","Mazhari `01","Di Veroli `13","Wang `97","Zeng `95","Experiment", "New model"], title="Legend", bbox_to_anchor=(-2.7, 0, 2.35, 1.5), loc='lower left', handletextpad=0.5,borderpad=0.5,labelspacing=0.35,columnspacing=4.5, ncol=2, borderaxespad=0.,fontsize=12)
ax4.text(left_alignment_for_panel_label, 1.05, 'D', verticalalignment='top', horizontalalignment='left',
transform=ax4.transAxes,fontsize=20, fontweight='bold')
#legend = ax4.legend(loc='center', shadow=False)
gs.update(wspace=0.35, hspace=0.4)
#fig.set_tight_layout(True)
#gs.tight_layout(fig, renderer=None, pad=0, h_pad=None, w_pad=None, rect=None)
#plt.tight_layout()
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
plt.subplots_adjust(top=0.75, wspace=0.25)
plt.savefig('figure_5.pdf', bbox_inches='tight', dpi=900, pad_inches=0.05)