data = np.genfromtxt("data/ExpRawData-E-TABM-84-A-AFFY-44.tab",names=True,usecols=tuple(range(1,30)),dtype=float, delimiter="\t")

print len(data)
print len(data.dtype.names)

data_array = data.view((np.float, len(data.dtype.names)))
data_array = data_array.transpose()

print data_array

import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import linkage, dendrogram

data_dist = pdist(data_array) # computing the distance
data_link = linkage(data_dist) # computing the linkage

dendrogram(data_link,labels=data.dtype.names)
plt.xlabel('Samples')
plt.ylabel('Distance')
plt.suptitle('Samples clustering', fontweight='bold', fontsize=14);

# Compute and plot first dendrogram.
fig = plt.figure(figsize=(8,8))
# x ywidth height
ax1 = fig.add_axes([0.05,0.1,0.2,0.6])
Y = linkage(data_dist, method='single')
Z1 = dendrogram(Y, orientation='right',labels=data.dtype.names) # adding/removing the axes
ax1.set_xticks([])

# Compute and plot second dendrogram.
ax2 = fig.add_axes([0.3,0.71,0.6,0.2])
Z2 = dendrogram(Y)
ax2.set_xticks([])
ax2.set_yticks([])

#Compute and plot the heatmap
axmatrix = fig.add_axes([0.3,0.1,0.6,0.6])
idx1 = Z1['leaves']
idx2 = Z2['leaves']
D = squareform(data_dist)
D = D[idx1,:]
D = D[:,idx2]
im = axmatrix.matshow(D, aspect='auto', origin='lower', cmap=plt.cm.YlGnBu)
axmatrix.set_xticks([])
axmatrix.set_yticks([])

# Plot colorbar.
axcolor = fig.add_axes([0.91,0.1,0.02,0.6])
plt.colorbar(im, cax=axcolor)

from fastcluster import *
%timeit data_link = linkage(data_array, method='single', metric='euclidean', preserve_input=True)
dendrogram(data_link,labels=data.dtype.names)
plt.xlabel('Samples')
plt.ylabel('Distance')
plt.suptitle('Samples clustering', fontweight='bold', fontsize=14);
show()

from Bio.Cluster import *
handle = open("../data/ExpRawData-E-TABM-84-A-AFFY-44.tab")
record = read(handle)

genetree = record.treecluster(method='s')
genetree.scale()
exptree = record.treecluster(dist='u', transpose=1)
record.save("../results/biopython_clustering", genetree, exptree)