def plot_observable_correlations(results_table, base_pdf=None):
for title, corrmat, labels, filename in observable_correlations(results_table, base_pdf):
if base_pdf is not None:
rlim = np.max(np.abs(corrmat))
else:
rlim = 1
ranges = (-rlim, rlim)
fig = plt.figure()
plt.imshow(corrmat, cmap=plotutils.spectral_cm, vmin=ranges[0],
vmax=ranges[1], interpolation='none')
plt.grid(False)
if len(corrmat) < 20:
ticks = np.arange(len(corrmat)), labels
plt.xticks(*ticks, rotation=90)
plt.yticks(*ticks)
else:
...
#empty = matplotlib.patches.Rectangle((0,0), 1, 1, fill=False,
# edgecolor='none',
# visible=False)
#plt.legend([empty]*len(corrmat), labels)
plt.title(title)
plt.tight_layout()
plt.colorbar()
yield (filename,) , fig
def export_obscorrs(data_table, output_dir, prefix, base_pdf=None):
import pandas as pd
from smpdflib.corrutils import observable_correlations
for title, corrmat, labels, filename in observable_correlations(data_table, base_pdf):
name = prefix + "_" + normalize_name(title)
filename = osp.join(output_dir, name + ".csv")
pd.DataFrame(corrmat, index=labels, columns=labels).to_csv(filename, sep='\t')
