import matplotlib.pyplot as plt
import matplotlib.colors
import matplotlib
import sys
import numpy as np
import collections
import functions as f
import figures_formatting as ff
import config
import math
out_name = ['Signature_components_spine_dendrite_no_PKAc','Signature_components_spine_dendrite_avramas','Signature_components_predictions']
paradigms = [config.blocked_PKA,config.avramas,config.predictions]
window = config.window
endi = ['spine','dendrite']
if __name__ == '__main__':
matplotlib.rcParams['axes.linewidth'] = .5
matplotlib.rcParams['lines.linewidth'] = .5
matplotlib.rcParams['patch.linewidth'] = .5
st = []
st.append([])
st.append([])
st[0].extend(f.make_st_spine(config.steady_state+config.ending[0]))
st[1].extend(f.make_st_dendrite(config.steady_state+config.ending[1]))
for i,par in enumerate(paradigms):
print out_name[i]
fig = plt.figure(figsize=(5.4,8.))
plt.rc('legend',**{'fontsize':6})
ax = []
ax.append(fig.add_subplot(3,2,1))
ax.append(fig.add_subplot(3,2,2))
ax.append(fig.add_subplot(3,2,3))
ax.append(fig.add_subplot(3,2,4))
ax.append(fig.add_subplot(3,2,5))
ax.append(fig.add_subplot(3,2,6))
if par == config.blocked_PKA:
mini = [2000000,200000,2000000]
maxi = [0,0,0]
else:
maxi = [1,1,2]
mini = [0,0,0]
for j,key in enumerate(par):
for l,ending in enumerate(config.ending):
fname = config.sp[key][0]+ending
if 'no_PKAc' in fname:
ax[4].set_ylabel('Signature (a.u.)',fontsize=10)
else:
ax[4].set_ylabel('Concentration (normalized)',fontsize=10)
time_st,camkii,pkac,epac,gibg = f.extract_data(fname,l)
dt = time_st[1]-time_st[0]
data = []
data.extend([camkii,pkac,epac,gibg])
new_data = []
len_ = len(camkii)
for k,d in enumerate(data):
if l and k<2:
new_data.append( d/st[l][k][:len_]/config.max_val[l][config.keys[k]])
else:
new_data.append( d/st[l][k]/config.max_val[l][config.keys[k]])
titles = ['CaMKII','Epac','PKA targets']
to_smoothe = []
to_smoothe.append(new_data[0])
to_smoothe.append(new_data[2])
if 'no_PKAc' in fname:
if l:
out = f.calculate_signature_dendrite(new_data)
else:
out = f.calculate_signature_spine(new_data)
titles = ['CaMKII','Epac','Signature']
to_smoothe.append(out)
else:
if l:
to_smoothe.append(new_data[1]+new_data[-1])
else:
to_smoothe.append(new_data[1])
for gugu,smooth in enumerate(to_smoothe):
if smooth.max()>maxi[gugu]:
maxi[gugu] = smooth.max()
if smooth.min()<mini[gugu]:
mini[gugu] = smooth.min()
for k, smooth in enumerate(to_smoothe):
ax[k*2+l].hold(True)
#ax2.plot(time_st/1000,smooth,config.sp[key][2],label=config.sp[key][1],lw=1)
ax[k*2+l].plot(time_st/1000,smooth,config.sp[key][2],label=config.sp[key][1],lw=1)
ff.simpleaxis_many_panels(ax[k*2+l])
if 'no_PKAc' in fname:
ax[3].legend()
ax[4].plot(time_st/1000,config.spine_thresh[0]*np.ones(time_st.shape),':',color=config.thresh,lw=1)
ax[4].plot(time_st/1000,config.spine_thresh[1]*np.ones(time_st.shape),':',color=config.thresh,lw=1)
ax[5].plot(time_st/1000,np.ones(time_st.shape)*config.dend_thresh[0],':',color=config.thresh,lw=1)
ax[5].plot(time_st/1000,np.ones(time_st.shape)*config.dend_thresh[1],':',color=config.thresh,lw=1)
elif 'trains' in fname:
ax[1].legend(loc=4)
else:
ax[0].legend()#bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
fig_labels = ['A1','A2','B1','B2','C1','C2']
if par == config.blocked_PKA:
fig_labels = ['A1','A2','B1','B2','C','D']
for m,x in enumerate(ax):
[c.set_linewidth(1) for c in x.spines.itervalues()]
for item in (x.get_xticklabels() + x.get_yticklabels()):
item.set_fontsize(8)
if m in [0,1,2,3]:
x.axes.get_xaxis().set_ticklabels([])
start, end = x.get_xlim()
x.xaxis.set_ticks([0,300,600,900])#(np.arange(start, end,math.ceil(end-start)/4.))
x.set_ylim([mini[m/2],maxi[m/2]])
#if m in [3,5]:
# x.axes.get_yaxis().set_ticklabels([])
# if 'no_PKAc' not in fname:
# if m == 1:
#x.axes.get_yaxis().set_ticklabels([])
#ax[4].set_ylabel('Relative activity ',fontsize=10)
ax[4].set_xlabel('time [s]',fontsize=10)
ax[5].set_xlabel('time [s]',fontsize=10)
ax[0].set_ylabel('Concentration (normalized)',fontsize=10)
ax[2].set_ylabel('Concentration (normalized)',fontsize=10)
for nr,wh in enumerate([0,1,2,3,4,5]):
y_lim = ax[wh].get_ylim()
print y_lim
ax[wh].text(-50,y_lim[-1]+(y_lim[-1]-y_lim[0])/20,fig_labels[nr]+' '+titles[nr/2]+' '+endi[nr%2])
if 'no_PKA' in out_name[i]:
x_lim = ax[wh].get_xlim()
if nr == 2 or nr == 3:
ax[wh].text(x_lim[0]+(x_lim[1]-x_lim[0])/3.,y_lim[1]+(y_lim[-1]-y_lim[0])/5.,'+',fontsize=30)
elif nr == 4 or nr == 5:
ax[wh].text(x_lim[0]+(x_lim[1]-x_lim[0])/3.,y_lim[1]+(y_lim[-1]-y_lim[0])/5.,'=',fontsize=30)
imlist = ['spine.png','dendrite_horizontal.png']
loc_list = [[0.25,.93,0.1,0.1],[0.53,.93,0.4,0.1]]
new_ax = []
for m, fn in enumerate(imlist):
new_ax.append(ff.add_image(fig,fn,loc_list[m]))
if 'no_PKA' in out_name[i]:
fig.subplots_adjust(hspace=.5)
fig.savefig(out_name[i]+'.pdf',format='pdf', bbox_inches='tight',pad_inches=0.1)
fig.savefig(out_name[i]+'.png',format='png', bbox_inches='tight',pad_inches=0.1)
fig.savefig(out_name[i]+'.svg',format='svg', bbox_inches='tight',pad_inches=0.1)
fig.savefig(out_name[i]+'.eps',format='eps', bbox_inches='tight',pad_inches=0.1)