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result_mixed_graphics.py
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result_mixed_graphics.py
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#!/usr/bin/env python3.8.2
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
if __name__ == "__main__":
(f, (ax1,ax2)) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [2.5, 1]})
## FIRST PLOT ##
category_names = ['RA(%70) - First sample', 'RA(%20) - Second sample', 'RA(%10) - Third sample']
results = {
'BWA': [66.52, 20.10, 13.38],
'SBG': [66.80, 20.02, 13.18],
'VG': [66.48, 19.98, 13.54]
}
actual_results = {
'BWA': [70.0, 90.0, 100.0],
'SBG': [70.0, 90.0, 100.0],
'VG': [70.0, 90.0, 100.0]
}
labels = list(results.keys())
actual_results_list = np.array(list(actual_results.values()))
data = np.array(list(results.values()))
data_cum = data.cumsum(axis=1)
category_colors = plt.get_cmap('RdYlBu')(np.linspace(0.15, 0.85, data.shape[1]))
ax1.invert_yaxis()
ax1.xaxis.set_visible(False)
ax1.set_xlim(0, np.sum(data, axis=1).max())
for i, (colname, color) in enumerate(zip(category_names, category_colors)):
widths = data[:, i]
actual_value = actual_results_list[:,i]
starts = data_cum[:, i] - widths
if i == 0:
color_ = 'navy'
elif i == 1:
color_ = 'darkgreen'
else:
color_ = 'darkorange'
ax1.barh(labels, widths, left=starts, height=0.5, label=colname, color=color_)
xcenters = starts + widths / 2
r, g, b, _ = color
text_color = 'white' if r * g * b < 0.5 else 'darkgrey'
# text_color = 'black'
for y, (x, c) in enumerate(zip(xcenters, widths)):
ax1.text(x, y, str(float(c)), ha='center', va='center', color=text_color)
# ax1.legend(ncol=len(category_names), bbox_to_anchor=(0, 1), loc='lower left', fontsize='small')
ax1.axvline(70, color="black", ymin=0.05, ymax=0.225, linestyle='dotted')
ax1.axvline(70, color="black", ymin=0.41, ymax=0.59, linestyle='dotted')
ax1.axvline(70, color="black", ymin=0.78, ymax=0.96, linestyle='dotted')
ax1.axvline(90, color="black", ymin=0.05, ymax=0.225, linestyle='dotted')
ax1.axvline(90, color="black", ymin=0.41, ymax=0.59, linestyle='dotted')
ax1.axvline(90, color="black", ymin=0.78, ymax=0.96, linestyle='dotted')
left, width = .25, .5
bottom, height = .25, .5
right = left + width
top = bottom + height
ax1.text(0.35*(left+right), 0.72*(bottom+top), 'RA:70%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.80*(left+right), 0.72*(bottom+top), 'RA:20%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.94*(left+right), 0.72*(bottom+top), 'RA:10%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.35*(left+right), 0.36*(bottom+top), 'RA:70%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.80*(left+right), 0.36*(bottom+top), 'RA:20%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.94*(left+right), 0.36*(bottom+top), 'RA:10%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.35*(left+right), 0.02*(bottom+top), 'RA:70%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.80*(left+right), 0.02*(bottom+top), 'RA:20%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.text(0.94*(left+right), 0.02*(bottom+top), 'RA:10%',
horizontalalignment='center',
verticalalignment='center',
fontsize=7, color='black',
transform=ax1.transAxes)
ax1.grid(which='major', linestyle='-', linewidth='0.05', alpha=0.3, color='black')
ax1.grid(which='minor', linestyle='--', linewidth='0.001', alpha=0.1, color='gray')
ax1.grid(True)
## SECOND PLOT ##
n_clusters = 5
# sample = np.arange(0, 6)
# bwa_sa_before_est = np.array([40, 66, 57, 31, 32])
# bwa_sa_after_est = np.array([94, 127, 121, 83, 80])
# sbg_sa_before_est = np.array([26, 32, 32, 21, 31])
# sbg_sa_after_est = np.array([216, 284, 346, 384, 333])
# vg_sa_before_est = np.array([227, 239, 234, 220, 221])
# vg_sa_after_est = np.array([280, 300, 297, 271, 266])
index = np.arange(1, 2)
bar_width = 0.2
ax2.set(xlim=(0.5, 1.5), ylim=(0, 700))
ax2.bar(index - bar_width, 45.2, bar_width, color='navy', edgecolor='black', label='BWA')
ax2.bar(index - bar_width, 101.0, bar_width, color='dodgerblue', bottom = 45.2, edgecolor='black', label='BWA + Estimation')
ax2.bar(index, 28.4, bar_width, color='darkgreen', edgecolor='black', label='SBG')
ax2.bar(index, 312.6, bar_width, color='seagreen', bottom = 28.4, edgecolor='black', label='SBG + Estimation')
ax2.bar(index + bar_width, 228.2, bar_width, color='darkorange', edgecolor='black', label='VG')
ax2.bar(index + bar_width, 282.8, bar_width, color='orange', bottom = 228.2, edgecolor='black', label='VG + Estimation')
# ax2.title.set_text("Run time")
ax2.set_ylabel('Run time (sec)')
ax2.set_xticks(np.arange(0.8, 1.4, 0.2))
ax2.set_yticks(np.arange(0, 700, 50))
ax2.set_xticklabels(['BWA', 'SBG', 'VG'], rotation=90)
# ax2.minorticks_on()
ax2.grid(which='major', linestyle='-', linewidth='0.05', alpha=0.3, color='black')
ax2.grid(which='minor', linestyle='--', linewidth='0.001', alpha=0.1, color='gray')
ax2.grid(True)
ax2.legend(loc='upper right', fontsize='x-small')
plt.tight_layout()
# plt.savefig('mixed_samples_accuracy.pdf', format='pdf')
plt.show()