add covariance matrix

cov.py

@@ -0,0 +1,35 @@
+import numpy as np
+from matplotlib import pyplot as plt
+
+from simulation_list import SimulationList
+
+simulations = SimulationList.jsonlines_load()
+np.set_printoptions(linewidth=1000, edgeitems=4)
+
+x = simulations.as_matrix
+labels = ["$M$", "$wp$", "$wt$", "$\\gamma$", "$\\alpha$", "$v$", "water"]
+x[::, 0] = 10 ** x[::, 0]
+
+print(x)
+print(x.shape)
+corr = np.corrcoef(x.T)
+print(np.cov(x.T))
+X = np.copy(x)
+X -= X.mean(axis=0)
+manual = np.dot(X.T, X.conj()) / (x.shape[0] - 1)
+assert np.allclose(manual, np.cov(x.T))
+print(corr)
+simple_cov = corr[-1, :-1]
+# plot correclation matrix
+plt.matshow(corr)
+plt.xticks(range(len(labels)), labels, rotation=90)
+plt.yticks(range(len(labels)), labels)
+plt.colorbar()
+plt.savefig("correlation.pdf",transparent=True)
+plt.show()
+# plt.close()
+plt.bar(range(len(simple_cov)), simple_cov)
+# ax.set_xticks(index + bar_width / 2)
+ax = plt.gca()
+ax.set_xticklabels(labels)
+plt.show()