- Author:
- WeiweiAi <wai484@aucklanduni.ac.nz>
- Date:
- 2022-01-20 12:46:30+13:00
- Desc:
- revert the scaling in the currents
- Permanent Source URI:
- https://models.physiomeproject.org/workspace/701/rawfile/1451a14f3fbe8fd7aacd0d0a73d87cec591b26de/Simulation/src/clampPlot.py
# To reproduce the data needed for clamping experiments in associated original paper,
def clampPlot(simFign, current,outKeys):
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# The prefix of the saved output file name
filename = '../simulatedData/sim%s.csv' % simFign
# Figure name
figfile = 'sim%s' % simFign
# Set figure dimension (width, height) in inches.
fw, fh = 6, 6
fig = plt.figure(figsize=(fw,fh))
# Set subplots
subpRow, subpCol = 1, 1
ax, lns = {}, {}
# This gives list with the colors from the cycle, which you can use to iterate over.
cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']
# Set subplots
lfontsize, labelfontsize = 12, 12 # legend, label fontsize
# Read data from the files
x_name = 'V'
y_name = outKeys
y_labels = ['Normalized Current %s' % current]
for i, varName in enumerate(y_name):
ax[i] = fig.add_subplot(subpRow, subpCol, i+1)
data = pd.read_csv(filename)
x_data = data[x_name]
y_data = data[varName]
ny_data=y_data/max(abs(y_data))
ax[i].plot(x_data, ny_data, color=cycle[1], label = 'CellML')
ofilename ='../originalData/%s.csv' %simFign
odata = pd.read_csv(ofilename)
ox_data = odata['x']
oy_data = odata['Curve1']
ax[i].plot(ox_data, oy_data, '.', color=cycle[0], label = 'Testrow_et_al_2018')
plt.tick_params(direction='in', axis='both')
ax[i].legend(loc = 'best', fontsize=lfontsize, frameon=False)
ax[i].set_xlabel ('Voltage (mV)', fontsize= labelfontsize)
ax[i].set_ylabel (y_labels[i], fontsize= labelfontsize)
ax[i].set_title('%s in the primary publication' % (simFign))
figfiles = '../sim%s.png' % (simFign)
plt.savefig(figfiles)
plt.show()
