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anova_start.py
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import numpy as np
import json
import matplotlib
#matplotlib.use('Agg')
import pylab as pl
import sys
import pprint as pp
import difflib
#from difflib_data import *
# Plot the different figures for the merged spikes and voltages recordings.
# This file, as the MergeSpikefiles.py should be one level up than Test/..., the output of a simulation.
import glob
import scipy.stats as stats
def get_weights(folder):
fparam = folder+'Test/Parameters/simulation_parameters.json'
f = open(fparam, 'r')
params = json.load(f)
params['multi_n']+=1
source_rew= folder+params['rewards_multi_fn']+'_'
rewards = np.zeros((params['multi_n'], params['n_iterations']))
for i in xrange(params['multi_n']):
rewards[i] = np.loadtxt(source_rew+str(i))
#for i in xrange(lend1):
# for j in xrange(params['n_actions']):
# wd1_m[i,j] = np.mean(wd1[:,i,j])
# wd1_std[i,j] = np.std(wd1[:,i,j])
# wd2_m[i,j] = np.mean(wd2[:,i,j])
# wd2_std[i,j] = np.std(wd2[:,i,j])
#for i in xrange(lenrp):
# for j in xrange(params['n_actions']*params['n_states']):
# wrp_m[i,j] = np.mean(wrp[:,i,j])
# wrp_std[i,j] = np.std(wrp[:,i,j])
return rewards
def stars(p):
if p < 0.0001:
return "****"
elif (p < 0.001):
return "***"
elif (p < 0.01):
return "**"
elif (p < 0.05):
return "*"
else:
return "-"
######################################
######################################
if len(sys.argv)<2:
print "Need 2 folders for comparison"
pass
fname ={}
for i in xrange(1,len(sys.argv)):
fname[i-1] = sys.argv[i]+'/'
params={}
for i in xrange(len(fname)):
params[i]=json.load(open(fname[i]+'Test/Parameters/simulation_parameters.json'))
#print 'Do the simulations match? ', params[:]['n_recordings']==params[:]['n_recordings']
#diff = difflib.ndiff(open(fparam1,'r').readlines(), open(fparam2,'r').readlines())
#print ''.join(diff)
rew = {}
for i in xrange(len(fname)):
rew[i] = get_weights(fname[i])
start = 4
startpd = 9
back = 40
to = 20
#shift = start*params[1]['block_len']*params[1]['t_iteration']/params[1]['resolution']
#shift_rew = start*params[1]['block_len']
#shiftpd = startpd*params[1]['block_len']*params[1]['t_iteration']/params[1]['resolution']
#shiftpd_rew = startpd*params[1]['block_len']
#p = len(fname)-1
p = 5
p2 = 6
perf = {}
for i in xrange(len(fname)):
perf[i] = np.zeros(params[1]['multi_n'], dtype=float)
j=0
#for i in xrange(shift_rew, params1['n_iterations']):
# r1[j]=sum(rewa[:,i])
# r2[j]=sum(rewb[:,i])
# j+=1
#for i in xrange(start, params1['n_blocks']):
for f in xrange(len(fname)):
j=0
if not(p==f) and not(p2==f):
for i in xrange(params[f]['multi_n']):
for q in xrange(start, params[f]['n_blocks']):
perf[f][j]+=sum(rew[f][i,q*params[f]['block_len']-back:q*params[f]['block_len']-to])
j+=1
elif not(p2==f):
for i in xrange(params[p]['multi_n']):
for q in xrange(startpd, params[p]['n_blocks']):
perf[p][j]+=sum(rew[p][i,q*params[p]['block_len']-back:q*params[p]['block_len']-to])
j+=1
else:
for i in xrange(params[p2]['multi_n']):
for q in xrange(startpd, params[p2]['n_blocks']):
perf[p2][j]+=sum(rew[p2][i,q*params[p2]['block_len']-back:q*params[p2]['block_len']-to])
j+=1
j=0
for f in xrange(len(fname)):
if not(p==f) and not(p2==f):
perf[f] = perf[f]/((params[f]['n_blocks']-start)*(back-to-1.))
elif not(p2==f):
perf[p] = perf[p]/((params[p]['n_blocks']-startpd)*(back-to-1.))
else:
perf[p2] = perf[p2]/((params[p2]['n_blocks']-startpd)*(back-to-1.))
fig = pl.figure()
ax = fig.add_subplot(111)
ax.hlines(1./3., 0, 8, colors='gray', linestyles='dotted', label='chance')
print 'PERF'
for i in xrange(len(fname)):
print fname[i], 'mean= ', np.mean(perf[i]), 'SD=', np.std(perf[i])
for j in xrange(len(fname)):
print fname[j], 'mean ', np.mean(perf[j]), 'SD=', np.std(perf[j])
print 'T-TEST: ', stats.ttest_ind(perf[i],perf[j])
print 'F-TEST: ', stats.f_oneway(perf[i], perf[j])
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print '\n'
norm = np.ones(len(perf[i]))*.3333
print 'F-TEST: ', stats.f_oneway(perf[i], norm)
print '+++++++++++++++++++++++++++++++'
print '\n'
print '\n'
bp = ax.boxplot([v for v in perf.itervalues()])
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.tick_params(axis='x', direction='out')
ax.tick_params(axis='y', length=0)
ax.grid(axis='y', color="0.9", linestyle='-', linewidth=1)
ax.set_axisbelow(True)
# colors, as before
import brewer2mpl
bmap = brewer2mpl.get_map('Set1', 'qualitative', len(fname))
colors = bmap.mpl_colors
for i in range(0, len(bp['boxes'])):
bp['boxes'][i].set_color(colors[i])
# we have two whiskers!
bp['whiskers'][i*2].set_color(colors[i])
bp['whiskers'][i*2 + 1].set_color(colors[i])
bp['whiskers'][i*2].set_linewidth(2)
bp['whiskers'][i*2 + 1].set_linewidth(2)
# top and bottom fliers
# (set allows us to set many parameters at once)
bp['fliers'][i * 2].set(markerfacecolor=colors[i],
marker='o', alpha=0.75, markersize=6,
markeredgecolor='none')
bp['fliers'][i * 2 + 1].set(markerfacecolor=colors[i],
marker='o', alpha=0.75, markersize=6,
markeredgecolor='none')
bp['medians'][i].set_color('black')
bp['medians'][i].set_linewidth(3)
# and 4 caps to remove
for c in bp['caps']:
c.set_linewidth(0)
for i in range(len(bp['boxes'])):
box = bp['boxes'][i]
box.set_linewidth(0)
boxX = []
boxY = []
for j in range(5):
boxX.append(box.get_xdata()[j])
boxY.append(box.get_ydata()[j])
boxCoords = zip(boxX,boxY)
boxPolygon = pl.Polygon(boxCoords, facecolor = colors[i], linewidth=0)
ax.add_patch(boxPolygon)
#y_max = np.max(np.concatenate((low_mut_100, high_mut_100)))
#y_min = np.min(np.concatenate((low_mut_100, high_mut_100)))
#print y_max
y = 0.30
yA = 1.1
yB = 0.
ax.annotate("", xy=(5.4, .47), xycoords='data',
xytext=(1.6, .47), textcoords='data',
arrowprops=dict(arrowstyle="-", ec='#aaaaaa',
connectionstyle="bar,fraction=0.03"))
ax.text(3.5, .43, '$ns$',
horizontalalignment='center',
verticalalignment='center', fontsize='large')
ax.annotate("", xy=(8, y), xycoords='data',
xytext=(7, y), textcoords='data',
arrowprops=dict(arrowstyle="-", ec='#aaaaaa',
connectionstyle="bar,fraction=0.1"))
ax.text(7.5, .26, '$ns$',
horizontalalignment='center',
verticalalignment='center', fontsize='large')
#ax.annotate("", xy=(8, .59), xycoords='data',
# xytext=(7, .59), textcoords='data',
# arrowprops=dict(arrowstyle="-", ec='#aaaaaa',
# connectionstyle="bar,fraction=0.1"))
#ax.text(7.5, .55, '$ns$',
# horizontalalignment='center',
# verticalalignment='center', fontsize='large')
si = 12
parms = {
'axes.labelsize': si,
'text.fontsize': si,
'legend.fontsize': si,
'xtick.labelsize': si,
'ytick.labelsize': si,
'text.usetex': False,
'figure.figsize': [6., 7.]
}
pl.rcParams.update(parms)
ax.set_ylim([0.,1.05])
#ax.set_xticklabels(['Full','no\nD1', 'no\nD2', 'no\nRP', 'no\nEfference', 'no\nLI', 'no\nSF', 'PD'])
#ax.set_xticklabels(['Full', 'no\nRP', 'no\nSF', 'PD', 'no\nD1', 'no\nD2', 'no\nEfference'])
#ax.set_ylabel('Average success ratio of the last 5 blocks')
ax.get_xaxis().set_ticklabels([])
ax.set_ylabel('Average success ratio for\nthe first '+str(int(back-to))+' trials of each block', multialignment='center')
pl.savefig('anovatest_start.pdf', bbox_inches='tight', dpi=600)
pl.savefig('anovatest_start.eps', bbox_inches='tight', dpi=600)
pl.savefig('anovatest_start.tiff', bbox_inches='tight', dpi=600)
pl.savefig('anovatest_start.png', bbox_inches='tight', dpi=600)
#pl.show()