genotypes-tsne

#!/usr/bin/env python

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import offsetbox
import pickle
from sklearn import manifold


loadfile = 'learn_evolve'
iter_1 = 975
iter_2 = 1000
# ---- t-SNE settings ------
perplexity = 10
n_iter = 1000

def upper_tri_masking(A):
    m = A.shape[0]
    r = np.arange(m)
    mask = r[:,None] < r
    return A[mask]

plt.rc('text', usetex=True)
font = {'family': 'serif', 'size': 16, 'serif': ['computer modern roman']}
plt.rc('font', **font)
plt.rc('legend', **{'fontsize': 16})

folder = 'save/' + loadfile

J = np.zeros((100, 351)) # 100 organisms (50 iter_1, 50 iter_2) x 351 edges
#  -------------------------------- iter_1 ------------------------------
filename = 'save/' + loadfile + '/isings/gen[' + str(iter_1) + ']-isings.pickle'

startstr = 'Loading simulation:' + filename
print(startstr)

isings = pickle.load(open(filename, 'rb'))

orgNum = 0
for I in isings:
    J[orgNum, :] = upper_tri_masking(I.J)
    orgNum += 1

iter_1_label = 'Gen: ' + str(iter_1)
#  ------------------------------- iter_2 --------------------------------
filename = 'save/' + loadfile + '/isings/gen[' + str(iter_2) + ']-isings.pickle'

startstr = 'Loading simulation:' + filename
print(startstr)

isings = pickle.load(open(filename, 'rb'))

for I in isings:
    J[orgNum, :] = upper_tri_masking(I.J)
    orgNum += 1

iter_2_label = 'Gen: ' + str(iter_2)
#  ----------------------------------------------------------------------
print("Computing t-SNE embedding")
tsne = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=perplexity, n_iter=n_iter)
J_tsne = tsne.fit_transform(J)

x1 = J_tsne[:50, 0]
y1 = J_tsne[:50, 1]
x2 = J_tsne[50:100, 0]
y2 = J_tsne[50:100, 1]

plt.figure(1)
plt.subplot(111)

plt.scatter(x1, y1, label=iter_1_label)
plt.scatter(x2, y2, marker='+', label=iter_2_label)
plt.legend(loc=2)

titlestr = 'Connectivity t-SNE Projection\n Generation: ' + str(iter_1) + ', ' + str(iter_2)
plt.title(titlestr)


ax = plt.gca()
ax.set_xticklabels([])

plt.show()