annotate trunk/experiments/scripts/cnbh-syllables/results_plotting/spider_plot.py @ 381:e6f006238034

- Python results plotting (finally).
- Proper results reporting script.
- Test on ALL talkers. The results script then generates a summary based on all the various subsets.
- Fixed chown users (hopefully sudos to be deleted entirely soon)
- More...

#!/usr/bin/env python
"""
spider_plot.py

Created by Thomas Walters on 2010-09-12.
Copyright 2010 Google. All rights reserved.
"""

import numpy as np
import pylab as p
import mpl_toolkits.mplot3d.axes3d as p3
import matplotlib as mpl
from matplotlib import cm
import matplotlib.ticker as ticker 

total_value_count=185

central_vtl=15
central_vtl_scaling=112.32

# Read in a file with lines in the form
# Pitch Scale Percentage
xs=[]
ys=[]
zs=[]
f = open('plottable_results.txt', 'r')
for line in f:
  if line[0] != "#":
    values = line.strip().split(' ')
    xs.append(central_vtl*central_vtl_scaling/float(values[1]))
    ys.append(float(values[0]))
    zs.append(float(values[2]))


# Define a tiny sphere, centered on the origin, which
# we'll shift to the desired position. 
u=np.r_[0:2*np.pi:50j]
v=np.r_[0:np.pi:50j]
sx=0.01*np.outer(np.cos(u),np.sin(v))
sy=0.01*np.outer(np.sin(u),np.sin(v))
sz=2.5*np.outer(np.ones(np.size(u)),np.cos(v))

fig=p.figure()
ax = p3.Axes3D(fig, azim=-80, elev=60)

colormap = cm.get_cmap('jet', 100)

# Note: here I fake out the lack of proper logarihmic scales on 3D axes in
# matplotlib by just plotting log values on a linear scale and renaming
# the labels.
# (This doesn't work: ax.w_yaxis.set_scale('log') ax.w_xaxis.set_scale('log'))

# Plot the values seven at a time as dark lines.
# These are the individual spokes of the spoke pattern.
n=7
for i in xrange(0,8):
  ax.plot(np.log(xs[i*n:(i+1)*n]), np.log(ys[i*n:(i+1)*n]), zs[i*n:(i+1)*n], color=[0,0,0])

for x,y,z in zip(xs,ys,zs):
  ax.plot(np.log([x, x]), np.log([y, y]), [z, 0], color=[0.8,0.8,0.8])
  ax.plot_surface(sx+np.log(x),sy+np.log(y),sz+z, color=colormap(int(z)), linewidth=0)

ax.set_ylabel('GPR/Hz')
ax.set_xlabel('VTL/cm')
ax.set_zlabel('Percent correct')
ax.set_ylim3d(np.log([131,225]))
ax.set_xlim3d(np.log([9.9, 22.1]))
ax.set_zlim3d([-1, 101])
ax.w_zaxis.set_major_locator(ticker.FixedLocator([0, 20, 40, 60, 80, 100]))

ax.w_xaxis.set_major_locator(ticker.FixedLocator(np.log([10,15,22])))
ax.w_xaxis.set_ticklabels(['10', '15', '22'])
ax.w_yaxis.set_major_locator(ticker.FixedLocator(np.log([132, 172, 224])))
ax.w_yaxis.set_ticklabels(['132', '172', '224'])

#for a in ax.w_xaxis.get_ticklines()+ax.w_xaxis.get_ticklabels(): 
#    a.set_visible(False) 

#for a in ax.w_yaxis.get_ticklines()+ax.w_yaxis.get_ticklabels(): 
#    a.set_visible(False) 


#p.show()
p.savefig('results.png')