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plot.py
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Sun, May 5, 14:12

plot.py
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__author__ = 'anastasia'
import pandas as pd
import matplotlib.pyplot as plt
import os
import numpy as np
sensitivity_analysis_file_exact = 'data/withinTop100.tsv'
sensitivity_analysis_file_any = 'data/alternativeReconstruction.tsv'
sensitivity_analysis_file_edges = 'data/beyondTop100.tsv'
pink = '#ffffff'
green = '#1c74ac'
blue = '#5a9957'
violet = '#efa6b4'
# creating the directory for plots
if not os.path.exists('plots'):
os.mkdir('plots')
def hypotheticalVSmaAll():
fig, ax = plt.subplots()
values_MA = ['chemATLAS_dist_known']
values_HYP = ['chemATLAS_hp_dist_known']
bar1, _ = plotNumberOfReconstructedPerNTDT(sensitivity_analysis_file_any, values_MA, values_HYP, ax, [blue, blue])
bar2, _ = plotNumberOfReconstructedPerNTDT(sensitivity_analysis_file_edges, ['chemATLAS_edges'], ['chemATLAS_hp_edges'], ax, [violet, violet])
bar3, _ = plotNumberOfReconstructedPerNTDT(sensitivity_analysis_file_exact, values_MA, values_HYP, ax, [green, green])
labels = ['Only BNICE.ch-\ncurated reactions', 'All ATLASx']
ax.set_ylabel('Number of pathways')
ax.axes.xaxis.set_visible(False)
ax.text(0.2, -160, labels[0], ha='center', va='bottom')
ax.text(0.02, -160, labels[1], ha='center', va='bottom')
#ax.legend(loc="lower right")
fig.legend((bar1, bar2, bar3), ('Alternative reconstruction', 'Exact reconstruction beyond top-100', 'Exact reconstruction within top-100'))
fig.tight_layout()
plt.savefig('plots/onlyBNICEvsAllATLASx.png', bbox = [6, 9])
def plotNumberOfReconstructedPerNTDT(sensitivity_analysis_file, values_MA, values_HYP, ax, colors):
df = pd.read_csv(
sensitivity_analysis_file,
sep='\t')
df = df.drop_duplicates()
counts_MA = [df[i].to_list().count(1) for i in values_MA]
counts_HYP = [df[i].to_list().count(1) for i in values_HYP]
width = 0.1 # the width of the bars
rects1 = ax.bar(0.2, counts_MA, width, color = colors[0])
rects2 = ax.bar(0.02, counts_HYP, width, color = colors[1])
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Number of reconstruted pathways')
autolabel(rects1, ax)
autolabel(rects2, ax)
#fig.tight_layout()
return rects1, rects2
def autolabel(rects, ax):
#Attach a text label above each bar in *rects*, displaying its height.
for rect in rects:
height = rect.get_height()
ax.annotate('{}'.format(height),
xy=(rect.get_x() + rect.get_width() / 2, height-60),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom', fontsize=7)
hypotheticalVSmaAll()
######## Supplementary material plots #########
def plotLengthDistributionAll():
fig, ax = plt.subplots(figsize=(16,9))
search_setups = ['chemATLAS_dist_known', 'chemATLAS_hp_dist_known']
bar2 = plotLengthOfReconstructedPerNTDT(sensitivity_analysis_file_any, search_setups, ax, blue)
bar4 = plotLengthOfReconstructedPerNTDT(sensitivity_analysis_file_edges, ['chemATLAS_edges','chemATLAS_hp_edges'], ax, violet) #['chemATLAS_edges','chemATLAS_hp_edges']
bar3 = plotLengthOfReconstructedPerNTDT(sensitivity_analysis_file_exact, search_setups, ax, green)
ax.set_ylabel('Number of pathways', fontsize=20)
ax.set_xlabel('Pathway length', fontsize=20)
plt.xticks(np.arange(0, 30, 5), fontsize = 18)
plt.yticks(np.arange(0, 450, 25), fontsize = 18)
ax.legend((bar2, bar4, bar3), ('Alternative reconstruction', 'Exact reconstruction beyond top 100', 'Exact reconstruction within top 100'), fontsize=20)
plt.xlim(0, 27)
plt.ylim(0, 450)
plt.savefig('plots/plotGoldenDatasetPathwayLengthDistributionAll_all.png', bbox = [9, 9])
def plotLengthDistributionAllCrop():
fig, ax = plt.subplots(figsize=(9,3))
#bar1 = plotGoldenDatasetPathwayLengthDistribution(ax, pink)
search_setups = ['chemATLAS_dist_known', 'chemATLAS_hp_dist_known']
bar2 = plotLengthOfReconstructedPerNTDT(sensitivity_analysis_file_any, search_setups, ax, blue)
bar4 = plotLengthOfReconstructedPerNTDT(sensitivity_analysis_file_edges, ['chemATLAS_edges','chemATLAS_hp_edges'], ax, violet) #['chemATLAS_edges','chemATLAS_hp_edges']
bar3 = plotLengthOfReconstructedPerNTDT(sensitivity_analysis_file_exact, search_setups, ax, green)
fig.tight_layout()
plt.xticks(np.arange(10, 30, 5), fontsize = 21)
plt.yticks(np.arange(5, 11, 5), fontsize = 21)
plt.xlim(9.5, 27)
plt.ylim(0, 12)
plt.savefig('plots/plotGoldenDatasetPathwayLengthDistributionAll_crop.png', bbox = [3, 9])
def plotLengthOfReconstructedPerNTDT(sensitivity_analysis_file, search_setups, ax, color):
df = pd.read_csv(
sensitivity_analysis_file,
sep='\t')
df = df.drop_duplicates()
setup1 = search_setups[0]
setup2 = search_setups[1]
values1 = list(set(df['Length'].to_list()))
x = np.arange(2, len(values1)) # the label locations
width = 0.4 # the width of the bars
counts1 = [df[df[setup1]==1]['Length'].to_list().count(i) for i in values1]
values1 = [i-1+ width / 2 for i in values1]
rects1 = ax.bar(values1, counts1, width, label=setup1, color=color)
values2 = list(set(df['Length'].to_list()))
counts2 = [df[df[setup2]==1]['Length'].to_list().count(i) for i in values2]
values2 = [i-1 - width / 2 for i in values2]
rects2 = ax.bar(values2, counts2, width, label=setup2, color=color)
return rects1, rects2
plotLengthDistributionAll()
plotLengthDistributionAllCrop()

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