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empirical_wcss.py
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empirical_wcss.py
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#!/usr/bin/env python
import os
import numpy as np
print np.__version__
import csv
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from munsell_wcs import build_wcs_map, build_chiplist, lookup_chip
from collections import Counter
from itertools import izip
# Counting and transfering to csv file
hue = np.genfromtxt('/home/aurimas/Amgen/perception/chips_and_speakers.csv', delimiter =',', usecols=(0),dtype=None)
hue_value = np.genfromtxt('/home/aurimas/Amgen/perception/chips_and_speakers.csv', delimiter =',', usecols=(1))
speakers = np.genfromtxt('/home/aurimas/Amgen/perception/chips_and_speakers.csv', delimiter =',', usecols=(2))
x = hue
y = hue_value
z = speakers
def create_file():
with open('foci-exp.csv', 'rU') as emp:
emp_csv = csv.reader(emp, delimiter = '\t')
emp_map = { }
cnt = Counter()
for row in emp_csv:
chip_count = [row[4]]
for word in chip_count:
cnt[word] += 1
chips_cas = cnt
with open('chips_and_speakers_sorted.csv', 'wb') as f:
w = csv.writer(f, delimiter=',')
for c in chips_cas.iteritems():
w.writerow([c[0][0], c[0][1:], c[1]])
a = izip(*csv.reader(open("chips_and_speakers_sorted.csv", "rb")))
csv.writer(open("chips_and_speakers0.csv", "wb")).writerows(a)
#print chips_cas.values()
#print chips_cas.keys()
#sort = sorted(chips_cas)
#hueaval = sorted(hues_and_values)
#speaks = sorted(speak)
#print hueaval, speak
#print hue, hue_value, speakers
def randrange(n, vmin, vmax):
return (vmax-vmin)*np.random.rand(n) + vmin
#fig = plt.figure()
#ax = fig.gca(projection='3d')
#Axes3D.plot(hue_value,hue, speakers)
hue_num = [(ord(X) - ord('A')) for X in hue]
tosortlist = zip(hue_num, hue_value, speakers)
sortedlist = sorted(tosortlist)
unzipedlist = zip(*sortedlist)
hue_num = np.array(unzipedlist[0])
hue_value = np.array(unzipedlist[1])
speakers = np.array(unzipedlist[2])
#print hue_num
X = hue_num.reshape((10, 41))
Y = hue_value.reshape((10, 41))
Z = speakers.reshape((10, 41))
#plt.imshow(Z[1:9], interpolation='nearest', cmap = cm.coolwarm, label='B&K')
#plt.gca().invert_yaxis()
#plt.yticks([0, 1, 2, 3, 4, 5, 6, 7], ['B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'])
#plt.show()
#ax.plot_surface(X, Y, Z, rstride=1, cstride=1, linewidth=0, antialiased=False, cmap=cm.RdYlGn)
#plt.show()
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
#n = 100
#for c, m, zl, zh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
# xs = randrange(n, 23, 32)
# ys = randrange(n, 0, 100)
# zs = randrange(n, zl, zh)
# ax.scatter(xs, ys, zs, c=c, marker=m)
#ax.set_xlabel('X Label')
#ax.set_ylabel('Y Label')
#ax.set_zlabel('Z Label')
#plt.show()