- Author:
- nima <nafs080@aucklanduni.ac.nz>
- Date:
- 2022-02-02 11:43:48+13:00
- Desc:
- Update the exposure content
- Permanent Source URI:
- https://models.cellml.org/workspace/702/rawfile/93b34dd5a6265140c2485d601bef659144efcf6a/Experiments/ca_analysis.py
import numpy as np
# import scipy as sp
from scipy.integrate import cumtrapz
import matplotlib.pyplot as plt
from scipy.interpolate import make_interp_spline
def ca_analysis(time, Iup, INaCa, IpCa, Ca):
# % Constants(copied from ipsc_function)
V_tot = 3960 #% um ^ 3 from hwang et al.
Vc_tenT = 16404
VSR_tenT = 1094
V_tot_tenT = Vc_tenT + VSR_tenT
Vc = V_tot * (Vc_tenT / V_tot_tenT)
Cm = 60 #% pF
F = 96.4853415 #% coulomb_per_mmole( in model_parameters)
# % % Find first beat to analyze
inds_time_800 = np.where(time>800)[0][0]
inds_time_1600 = np.where(time>1600)[0][0]
inds1 = np.argmin(Ca[0:inds_time_800])
inds2 = np.argmin(Ca[inds_time_800:inds_time_1600]) + inds_time_800
# % % Calculate Normalized Ca2 + flux
# % take integral
intJserca = cumtrapz(Iup, time)
intIncx_ca = cumtrapz(-INaCa * 2 * Cm / (2.0 * Vc * F), time)
intIpca = cumtrapz(IpCa * Cm / (2.0 * Vc * F), time)
# % integral for first beat
fluxJserca = intJserca[inds1:inds2]-intJserca[inds1]
fluxIncx_ca = intIncx_ca[inds1:inds2]-intIncx_ca[inds1]
fluxIpca = intIpca[inds1:inds2]-intIpca[inds1]
# % Normalize flux
flux_total = fluxJserca + fluxIncx_ca + fluxIpca
ref = np.amax(flux_total)
fluxJserca_norm = fluxJserca / ref
fluxIncx_ca_norm = fluxIncx_ca / ref
fluxIpca_norm = fluxIpca / ref
Time_flux = time[inds1:inds2]
# % % plot figure 10A
plt.figure(figsize= (14,7))
plt.subplot(1,2,1)
plt.plot((time[inds1:inds2] - time[inds1])/ 1000, Ca[inds1: inds2]*1e6, color= 'red', label= 'Baseline Model', linewidth = 4)
plt.xticks(np.arange(0, 1.1, 0.5))
plt.yticks(np.arange(0, 801, 200))
plt.xlim(0, 1)
plt.ylim(0, 800)
plt.tick_params(axis='both', labelsize='18')
plt.ylabel('[Ca$^{2+}$] (nM)', fontsize=18)
plt.xlabel('Time (s)', fontsize=18)
plt.legend(fontsize= '14', loc='best')
plt.title('A', fontsize=18)
# % % plot figure 10 C
plt.subplot(1,2,2)
plt.plot((Time_flux - time[inds1])/1000, fluxJserca_norm, color= 'blue', label= 'SERCA', linewidth = 4)
plt.plot((Time_flux-time[inds1])/1000, fluxIncx_ca_norm, color= 'orangered', label= 'NCX', linewidth = 4)
plt.plot((Time_flux-time[inds1])/1000, fluxIpca_norm, color= 'gold', label= 'non-NCX (SL-pump)', linewidth = 4)
plt.xticks(np.arange(0, 1.1, 0.5))
plt.yticks(np.arange(0, 8.1, 0.2))
plt.xlim(0, 1)
plt.ylim(0, 0.8)
plt.tick_params(axis='both', labelsize='18')
plt.title('B', fontsize=18)
plt.ylabel('Ca$^{2+}$ flux normalized', fontsize = 18)
plt.xlabel('Time (s)', fontsize = 18)
plt.legend(fontsize='14', loc='best')
plt.subplots_adjust(left=0.2,
bottom=0.2,
right=0.9,
top=0.9,
wspace=0.3,
hspace=0.3)
# plt.show()
plt.savefig('Figure10.png')
return None
