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9e93243 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | import numpy as np
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
mpl.use('Agg')
def hist_box(data_frame, field, name="hist_box", path="./", title=None):
title = title if title else field
fig, axs = plt.subplots(1,2,figsize=(10,4))
data_frame[field].plot.hist(bins=100, title=title, ax=axs[0])
data_frame.boxplot(field, ax=axs[1])
plt.title(title)
plt.suptitle("")
plt.savefig(os.path.join(path, '{}.png'.format(name)), bbox_inches='tight')
plt.close()
def hist(data_frame, field, name="hist", path="./", title=None):
title = title if title else field
plt.hist(data_frame[field])
plt.title(title)
plt.savefig(os.path.join(path, '{}.png'.format(name)), bbox_inches='tight')
plt.close()
def hist_box_list(data_list, name="hist_box", path="./", title=None):
fig, axs = plt.subplots(1,2,figsize=(10,4))
axs[0].hist(data_list, bins=100)
axs[0].set_title(title)
axs[1].boxplot(data_list)
axs[1].set_title(title)
plt.savefig(os.path.join(path, '{}.png'.format(name)), bbox_inches='tight')
plt.close()
def scatter_hist(x, y, name, path, field=None, lims=None):
fig, axs = plt.subplots(1, 2, figsize=(10, 4))
n = len(x)
xy = np.vstack([x+ 0.00001 * np.random.rand(n), y+ 0.00001 * np.random.rand(n)])
z = gaussian_kde(xy)(xy)
axs[0].scatter(x, y, c=z, s=3, marker='o', alpha=0.2)
lims = [np.min([axs[0].get_xlim(), axs[0].get_ylim()]), np.max([axs[0].get_xlim(), axs[0].get_ylim()])] if lims is None else lims
axs[0].plot(lims, lims, 'k-', alpha=0.75)
axs[0].set_aspect('equal')
axs[0].set_xlim(lims)
axs[0].set_ylim(lims)
xlabel = ""
ylabel = ""
if "delta" in field:
if "data" in field:
axs[0].set_xlabel(r'$\Delta LogD$ (experimental)')
axs[0].set_ylabel(r'$\Delta LogD$ (calculated)')
xlabel = 'Delta LogD (experimental)'
ylabel = 'Delta LogD (calculated)'
elif "predict" in field:
axs[0].set_xlabel(r'$\Delta LogD$ (desirable)')
axs[0].set_ylabel(r'$\Delta LogD$ (generated)')
xlabel = 'Delta LogD (desirable)'
ylabel = 'Delta LogD (generated)'
if "single" in field:
if "data" in field:
xlabel, ylabel = 'LogD (experimental)', 'LogD (calculated)'
axs[0].set_xlabel(xlabel)
axs[0].set_ylabel(ylabel)
elif "predict" in field:
xlabel, ylabel = 'LogD (desirable)', 'LogD (generated)'
axs[0].set_xlabel(xlabel)
axs[0].set_ylabel(ylabel)
bins = np.histogram(np.hstack((x, y)), bins=100)[1] # get the bin edges
kwargs = dict(histtype='stepfilled', alpha=0.3, density=False, bins=bins, stacked=False)
axs[1].hist(x, **kwargs, color='b', label=xlabel)
axs[1].hist(y, **kwargs, color='r', label=ylabel)
plt.ylabel('Frequency')
plt.legend(loc='upper left')
plt.savefig(os.path.join(path, '{}.png'.format(name)), bbox_inches='tight')
plt.close()
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