import glob
import functions as f
import config_20 as config
import numpy as np
threshold = config.spine_thresh
import matplotlib.pyplot as plt
ending = 'PSD.sa1[0]_head.sa1[0]_neck.sa1[0]'
add = '_runtime_900000-all_species-conc.txt_concentrations_'
res_file = 'spine_results.csv'
basal_list = [
'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_3_min_5_Hz_lower_Ca',
'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_ISO_bath_1000_nM',
'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_1_train_100_Hz',
'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_4_trains_massed',
'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_4_trains_spaced',
'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_ISO_bath_1_train_100_Hz_1000_nM',
'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_ISO_bath_3_min_5_Hz_higher_Ca_1000_nM',
]
#{'LFS':0,'ISO':0,'4x-HFS-3s':0,'4xHFS-80s':0,'ISO+HFS':0,'ISO+LFS':0,'HFSnoPKA':0,'4xHFS-3snoPKA':0,'4xHFS-80snoPKA':0,'ISO+HFSnoPKA':0,'ISO+LFSnoPKA':0,'Carvedilol+HFS':0,'Carvedilol+LFS':,'propranolol+4xHFS':0,'ICI-118551+4xHFS':0}
pars = ['LFS','ISO','HFS','4xHFS-3s','4xHFS-80s','ISO+HFS','ISO+LFS','HFS no PKA','4xHFS-3s no PKA','4xHFS-80s no PKA','ISO+HFS no PKA','ISO+LFS no PKA','Carvedilol+HFS','Carvedilol+LFS','Propranolol+4xHFS','Carvedilol+4xHFS','Carvedilol+2xHFS','Carvedilol+3xHFS','ICI-118551+4xHFS']
p_value_low ={'LFS':'1','ISO':'0.9998','HFS':'0.0043','4xHFS-3s':'0.01','4xHFS-80s':'0.0007','ISO+HFS':'0.0028','ISO+LFS':'0.0146','HFS no PKA':'0.9018','4xHFS-3s no PKA':'0.0013','4xHFS-80s no PKA':'0.0007','ISO+HFS no PKA':'0.0051','ISO+LFS no PKA':'1','Carvedilol+HFS':'0.0704','Carvedilol+LFS':'1','Propranolol+4xHFS':'0.0110','ICI-118551+4xHFS':'0.0028'}
p_value_high = {'LFS':'1','ISO':'1.0000','HFS':'0.0114','4xHFS-3s':'0.01','4xHFS-80s':'0.0010','ISO+HFS':'0.0032','ISO+LFS':'0.0501','HFS no PKA':'0.9556','4xHFS-3s no PKA':'0.0036','4xHFS-80s no PKA':'0.0010','ISO+HFS no PKA':'0.0119','ISO+LFS no PKA':'1','Carvedilol+HFS':'0.1451','Carvedilol+LFS':'1','Propranolol+4xHFS':'0.0129','ICI-118551+4xHFS':'0.0072'}
output_name ='spine_results.tex'
strings = '''
\\begin{table}
\caption{Robustness of the spine signature threshold. STD is an abbrevation for standard deviation. p value is significance of one-sided t-test comparing time signature is above the amplitude threshold to the 10s duration threshold. Degrees of freedom = 3 for all t-tests.}
\label{tab:spine_robustness}
\\begin{tabular}{|p{3.2cm}|p{1.9cm}|p{1.9cm}|p{1.9cm}|p{1.9cm}|p{1.9cm}|p{1.9cm}|}
\hline
\multirow{2}{*}{stimulation paradigm}& \multicolumn{3}{|c|}{above the upper threshold}&\multicolumn{3}{|c|}{above the lower threshold}\\\\
\hhline{~------}
&stim. no.&mean time $\pm$ STD&p-value& stim. no.&mean time $\pm$ STD&p-value\\\\
\hline
'''
def vari(lista):
mean = 1.*sum(lista)/len(lista)
return (sum([(x-mean)**2 for x in lista] )/(len(lista)-1))/len(lista)
if __name__ == '__main__':
#f_2 = open(res_file,'w')
#f_2.write('Paradigm, seed, time above lower, time above upper\n')
st = f.make_st_spine('Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_x_AC_PDE4_ave_'+config.ending[0])
for j, fbasal in enumerate(basal_list):
lista = glob.glob(fbasal+'*')
new_list = [new_name for new_name in lista if new_name.endswith(config.ending[0]) and 'Epac' not in new_name and 'ave' not in new_name]
if fbasal == 'Model_one_short_dendrite_PKAc_times_3_switching_L_pump_neurogranin_2.0_4_trains_spaced':
new_list = [new_name for new_name in new_list if 'propranolol' not in new_name and 'ICI' not in new_name]
outs_low = []
outs_high = []
l = 0
h = 0
print fbasal
# plt.figure()
# plt.plot(time_st/1000., threshold[0]*np.ones(time_st.shape))
# plt.plot(time_st/1000., threshold[1]*np.ones(time_st.shape))
# plt.title(fname)
for i,fname in enumerate(new_list):
#f_2.write(pars[j]+', '+sseeds[i]+', ')
time_st,camkii,pkac,epac,gibg = f.extract_data(fname,0)
dt = time_st[1]-time_st[0]
data = []
data.extend([camkii/st[0],pkac/st[1],epac/st[2],gibg])
new_data = []
for k,d in enumerate(data):
new_data.append( d/config.max_val[0][config.keys[k]])
#if 'ave' in fname:
print max(d),
out = f.calculate_signature_spine(new_data)
low = sum((np.sign(out-threshold[0])+1)*dt)*0.5/1000#sum((np.sign(out-spine_threshold[0])+1)*dt*0.5)/1000
high = sum((np.sign(out-threshold[1])+1)*dt)*0.5/1000#sum((np.sign(out-spine_threshold[1])+1)*dt*0.5)/1000
#print sorted([l for l in f.over_the_threshold(out,threshold[0],dt/1000.) if l>5])
#print sorted([l for l in f.over_the_threshold(out,threshold[1],dt/1000.) if l > 10])
#f_2.write(str(low)+', '+str(high)+'\n')
outs_low.append(low)
outs_high.append(high)
print low, high
plt.plot(time_st/1000.,out)
if len(outs_low):
mean_low = round(sum(outs_low)/len(outs_low))
print len(outs_low), mean_low,
if len(outs_low)>1:
print round(vari(outs_low)**0.5)
if len(outs_high):
mean_high = round(sum(outs_high)/len(outs_high))
print len(outs_high), mean_high,
if len(outs_high)> 1:
print vari(outs_high)**0.5
# strings += pars[j]
# strings +='&'
# if len(outs_high):
# strings += str(h)+'&'+str(mean_high)
# if len(outs_high)>1:
# strings += '$\pm$'+ str(round(vari(outs_high)**0.5))
# strings +='&'
# strings += str(p_value_low[pars[j]])+'&'
# if len(outs_low):
# strings += str(l)+'&'+str(mean_low)
# if len(outs_low)>1:
# strings += '$\pm$'+ str(round(vari(outs_low)**0.5))
# strings += '&'+str(p_value_high[pars[j]])
# strings +='\\\\'
# strings += '\n\hline\n'
# strings += '''\end{tabular}\n\end{table}'''
# fout = open(output_name,'w')
# fout.write(strings)
plt.show()