#modules----------------------------------------

import pickle

import scipy.stats.mstats as ssm

import numpy as np

import matplotlib as plt

from pylab import *

#first find actual data -------------------------

print "OBSERVED DATA (fig4_observed.py)"

execfile("fig4_observed.py")

#second generate bootstrap data -----------------

print "BOOTSTRAP CONFIDENCE LIMITS"

execfile("fig4_boot.py") #this can take a long time (e.g. 24 hours) if you use many (e.g. 2000) resamples

#load

#observed data

plot_timespread = pickle.load(open('save_plot_timespread.p', 'rb'))

#bootstrap data

bootdata = pickle.load(open('save_a4_2boot_bootdata.p','rb'))

#find CIs, using ssm

ci_upper=np.zeros( (1,100))

ci_lower=np.zeros( (1,100))

m_boot=np.zeros( (1,100))

for i in range(100):

ci_upper[0,i]=ssm.scoreatpercentile(bootdata[i,:],97.5)

ci_lower[0,i]=ssm.scoreatpercentile(bootdata[i,:],02.5)

m_boot[0,i]=np.mean(bootdata[i,:])

print "PLOTTING"

# plot -------------------------------------------

# thank you tomas http://www.staff.ncl.ac.uk/tom.holderness/software/pythonlinearfit

plt.clf()

# plot sample data

plot(plot_timespread,'ro',label='Sample observations')

# plot line of best fit

plot(m_boot[(0,)],'b-',label='bootstrap_mean')

# plot confidence limits

plot(ci_lower[(0,)],'b--',label='confidence limits (95%)')

plot(ci_upper[(0,)],'b--')

# configure legend

legend(loc=4) #lower left http://matplotlib.org/users/legend_guide.html

leg = gca().get_legend()

ltext = leg.get_texts()

setp(ltext, fontsize=10)

xlabel('maximum score percentile')

ylabel('average gap between 1st and 10th plays/hours')

savefig('figure4.png', dpi=None, facecolor='w', edgecolor='w',

orientation='portrait', papertype=None, format=None,

transparent=False, bbox_inches=None, pad_inches=0.1)

generatepaperfigs=0

if generatepaperfigs:

savefig('../cogsci13/figures/prcentile_vs_timespread.png', dpi=300, facecolor='w', edgecolor='w',

orientation='portrait', papertype=None, format=None,

transparent=False, bbox_inches=None, pad_inches=0.1)