def coefficients(category = None, rebin_type = 'log', n = 80, legend = True, save = True, show = False):
data_path = get.data('pca', category)
mkdir.plots(category = 'all', data_type = 'pca/coefficients')
for i in range(n):
x = np.zeros([100])
k = 0
plt.figure()
plt.grid()
for data_file in data_path:
data_category = data_file.split('/')[1]
dataset = h5py.File(data_file, 'r')
coefficients_normal = dataset['coefficients_normal']
[m,n] = coefficients_normal.shape
plt.scatter(x[:n], coefficients_normal[i,:], color = COLORS[k%len(COLORS)], label = data_category)
x += 1
k += 1
dataset.close()
plt.scatter(x[0] + 2, np.array([0]), color = 'white')
plt.title('coefficient ' + str(i))
if legend:
plt.legend()
if save:
name = 'supernova_data/all/plots/pca/coefficients/coefficient_' + str(i) + '.eps'
plt.savefig(name, format='eps', dpi = 3500)
if show:
plt.show()
plt.close()
def pcomponents(category = None, components = [[0,1]], legend = True, save = True, show = False):
data_path = get.data('pca', category)
mkdir.plots(category = 'all', data_type = 'pca/pcomponents')
for component in components:
k = 0
plots = []
plot_names = []
plt.figure()
plt.grid()
i = component[0]
j = component[1]
for data_file in data_path:
data_category = data_file.split('/')[1]
dataset = h5py.File(data_file, 'r')
coefficients_reduced = dataset['coefficients_reduced'][:]
cx = coefficients_reduced[i,:]
cy = coefficients_reduced[j,:]
p = plt.scatter(cx, cy, color = COLORS[k%len(COLORS)], label = category)
plots.append(p)
plot_names.append(data_category)
k += 1
if legend:
plt.legend(plot_names, loc='right', bbox_to_anchor = (1.1, 0.2), fancybox = True)
plt.grid()
plt.xlabel('c' + str(i))
plt.ylabel('c' + str(j))
plt.title('c' + str(i) + ' vs ' + 'c' + str(j))
if save:
name = 'supernova_data/all/plots/pca/pcomponents/' + 'c' + str(i) + '_vs_' + 'c' + str(j) + '.eps'
plt.savefig(name, format='eps', dpi = 3500)
if show:
plt.show()
plt.close()
