anesthesia_bats/Model/plots.py

import numpy as np 
import matplotlib.pyplot as plt 
import functions as r

##### Figure 2 #####
# load data for Fig2A
sims=np.load('coefC_tau_th.npy',allow_pickle=True)
# plot Fig2A
# uncomment ax.invert_xaxis() in the function "first_approx" 
# in the function "first_approx", the variables A and B must end in "_fixedy"
# in the function "first_approx", the variable X must be paratemeters1
fig1,fig2,fig5,fig6,fig7,fig8,fig9,fig10=r.first_approx(sims)

# load data for Fig2B
sims=np.load('d_tau_th.npy',allow_pickle=True)
# plot Fig2B
# uncomment ax.invert_xaxis() in the function "first_approx" 
# in the function "first_approx", the variables A and B must end in "_fixedy"
# in the function "first_approx", the variable X must be paratemeters1
fig1,fig2,fig5,fig6,fig7,fig8,fig9,fig10=r.first_approx(sims)

# load data for Fig2C
sims=np.load('coefC_tau_th.npy',allow_pickle=True)
# plot Fig2C
# comment ax.invert_xaxis() in the function "first_approx" 
# in the function "first_approx", the variables A and B must end in "_fixedx"
# in the function "first_approx", the variable X must be paratemeters2
fig1,fig2,fig5,fig6,fig7,fig8,fig9,fig10=r.first_approx(sims)


##### Figure 3 #####
# load data for Fig3A
sims=np.load('coefC_d.npy',allow_pickle=True)
# plot Fig3A
fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6=r.dif_2d(sims)
r.ns_region(sims,fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6)


##### Figure 4 #####
# load data for Fig4B
sims=np.load('w1-w2_recurrentv2.npy',allow_pickle=True)
# plot Fig4B
p1=1.0
p2=1.0
fig8, fig9, fig10, fig11, k = r.one_combi(sims,p1,p2)

# load data for Fig4C
sims=np.load('w1-w2_recurrentv2.npy',allow_pickle=True)
# plot Fig4C top
# comment ax.invert_xaxis() in the function "first_approx" 
# in the function "first_approx", the variables A and B must end in "_fixedx"
# in the function "first_approx", the variable X must be paratemeters2
fig1,fig2,fig5,fig6,fig7,fig8,fig9,fig10=r.first_approx(sims)
    
# plot Fig4C bottom
# comment ax.invert_xaxis() in the function "first_approx" 
# in the function "first_approx", the variables A and B must end in "_fixedy"
# in the function "first_approx", the variable X must be paratemeters1
fig1,fig2,fig5,fig6,fig7,fig8,fig9,fig10=r.first_approx(sims)

# load data for Fig4D-E
sims=np.load('w1-w2_recurrentv2.npy',allow_pickle=True)
# plot Fig4D-E
fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6=r.dif_2d(sims)
r.ns_region(sims,fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6)


##### Figure 10 #####
# load data for Fig10A-B
sims=np.load('coefC-HF_d-HF.npy',allow_pickle=True)
# plot Fig10A-B
fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6=r.dif_2d(sims)
r.ns_region(sims,fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6)
# plot Fig10C
p1=0.5
p2=1.7
fig8, fig9, fig10, fig11, k = r.one_combi(sims,p1,p2)

# load data for Fig10D-E-F-G
sims=np.load('coefC-HF_d-HF.npy',allow_pickle=True)
sims_tauth=np.load('coefC-HF_d-HF_15-tau_th.npy',allow_pickle=True) 
# plot Fig10D-E
fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6=r.effect_tauth(sims, sims_tauth)
r.ns_region(sims_tauth,fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6,sims) 
# plot Fig10F-G
p1=0.5
p2=1.7
fig8, fig9, fig10, fig11, k = r.one_combi(sims_tauth,p1,p2,sims)

##### Figure 11 #####
# load data for Fig11D model A
sims=np.load('modelA_coefC-HF-d_HF.npy',allow_pickle=True)
sims_tauth=np.load('modelA_coefC-HF-d_HF_15-tauth.npy',allow_pickle=True)  # _15-tau_th
# plot Fig11D model A
fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6=r.effect_tauth(sims, sims_tauth) 
r.ns_region(sims_tauth,fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6,sims) 

# load data for Fig11D model B
sims=np.load('modelB_coefC-HF-d_HF.npy',allow_pickle=True)
sims_tauth=np.load('modelB_coefC-HF-d_HF_15-tauthv2.npy',allow_pickle=True) 
# plot Fig11D model B
fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6=r.effect_tauth(sims, sims_tauth) 
r.ns_region(sims_tauth,fig1,ax1,fig2,ax2,fig3,ax3,fig4,ax4,fig5,ax5,fig6,ax6,sims)