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164 | #!/usr/bin/env python3
import argparse
import re
import matplotlib.pyplot as plt
import seaborn as sns
import statistics
#define command line arguments
parser = argparse.ArgumentParser()
#required input
parser.add_argument('-i', '--input', metavar = '<Input Data>', required = True,
help = "_boostraps.tsv file generated in previous step")
parser.add_argument('-o', '--outdir', metavar = '<Direcotry Name>', required = True,
help = "Directory to write output plots")
#plotting options
parser.add_argument('--hist', action = 'store_true',
help = "Plot histogram of bootstraps")
parser.add_argument('--kde', action = 'store_true',
help = "Plot kernel density estimate plot of bootstraps")
parser.add_argument('--ecdf', action = 'store_true',
help = "Plot emperical cumulative distribution function of bootstraps")
#plot additions
parser.add_argument('--cdf', action = 'store_true', required = False,
help= "add cumulative density function to plot")
parser.add_argument('--color_tail', action = 'store_true', required = False,
help = "Shade in area under curve that corresponds to the part of the distribution that is greater than or less than the median. Default color: tab:orange.")
#styling options
parser.add_argument('--comp_line_color', default = 'tab:orange',
help = "Color comparison line. Default = tab:orange")
parser.add_argument('--plot_color', default = 'tab:blue',
help = "Color for histogram, kde, or ecdf plot")
parser.add_argument('--blank_bars', action = 'store_true',
help = "Make bars solid color as opposed to empty")
parser.add_argument('--background_theme', default = 'white',
help = "Change plot background color. Options: white, dark")
args = parser.parse_args()
#class to plot comparisons
class PlotComparisons:
def __init__(self, input):
input = self.input
#function to make histogram
def histogram_plot(mutation, observed, bootstraps):
sns.set_style(args.background_theme)
plt.figure(figsize=(5,5)).tight_layout()
plt.margins(x=0)
data = [int(x) for x in bootstraps]
ax = sns.histplot(data=data, discrete = True, color = args.plot_color)
plt.axvline(x = observed, color=args.comp_line_color, linewidth = 3)
#add kde: Always need kde for coloring tail, but don't want to make it visible if kde argument isn't specified
if args.kde == True and args.cdf == True:
alpha = 0
if args.kde == False:
alpha = 0
if args.kde == True and args.cdf == False:
alpha = 1
#add kde
ax2 = ax.twinx()
sns.kdeplot(data=data, color = args.plot_color, alpha = alpha, ax = ax2, linewidth = 3, warn_singular=False)
ax2.set(ylabel=None)
ax2.tick_params(right=False)
ax2.set(yticklabels=[])
#add cdf if specified
if args.cdf == True:
ax3 = ax.twinx()
sns.kdeplot(data=data, cumulative = True, ax = ax3, linewidth = 3, color = 'black', alpha = 1, warn_singular=False)
#fill tails if specified
if args.color_tail == True:
median_boot = statistics.median(bootstraps)
kde_x, kde_y = ax2.lines[0].get_data()
if int(observed) < median_boot:
ax2.fill_between(kde_x, kde_y, where=(kde_x < int(observed)), interpolate=True, color=args.comp_line_color, alpha = 0.75)
else:
ax2.fill_between(kde_x, kde_y, where=(kde_x > int(observed)), interpolate=True, color=args.comp_line_color, alpha = 0.75)
#make plot
plt.title(f'{mutation}')
plt.tight_layout()
pdf = f"{args.outdir}/{mutation}.pdf"
plt.savefig(pdf)
#function to make kde plot
def kde_plot(mutation, observed, bootstraps):
plt.figure(figsize=(5,5)).tight_layout()
plt.margins(x=0)
data = [int(x) for x in bootstraps]
#default kde plot
ax = sns.kdeplot(data=data, color = args.plot_color, fill = True)
if args.color_tail == True:
median_boot = statistics.median(bootstraps)
kde_x, kde_y = ax.lines[0].get_data()
if int(observed) < median_boot:
ax.fill_between(kde_x, kde_y, where=(kde_x < int(observed)), interpolate=True, color=args.comp_line_color, alpha = 0.75)
else:
ax.fill_between(kde_x, kde_y, where=(kde_x > int(observed)), interpolate=True, color=args.comp_line_color, alpha = 0.75)
#make plot
plt.title(f'{mutation}')
plt.tight_layout()
pdf = f"{args.outdir}/{mutation}.pdf"
plt.savefig(pdf)
#function to make ecdf plot
def ecdf_plot(mutation, observed, bootstraps):
plt.figure(figsize=(5,5)).tight_layout()
plt.margins(x=0)
data = [int(x) for x in bootstraps]
#default ecdf plot
ax = sns.ecdfplot(data=data)
plt.axvline(x = observed, color=args.comp_line_color, linewidth = 3)
#make plot
plt.title(f'{mutation}')
plt.tight_layout()
pdf = f"{args.outdir}/{mutation}.pdf"
plt.savefig(pdf)
def make_plot(input):
#read data file and make plots
with open(input, 'r') as input:
lines = input.readlines()
for line in lines:
header = bool(re.match('^#', line))
if header == False:
try:
bootstraps = line.split('\t')[2].rstrip('\n,').split(',')
bootstrap_data = [int(x) for x in bootstraps]
mutation = line.split('\t')[0]
observed = int(line.split('\t')[1])
if args.hist == True:
PlotComparisons.histogram_plot(mutation, observed, bootstrap_data)
if args.kde == True and args.hist == False:
PlotComparisons.kde_plot(mutation, observed, bootstrap_data)
if args.ecdf == True:
PlotComparisons.ecdf_plot(mutation, observed, bootstrap_data)
except:
mutation = line.split('\t')[0]
print(f'WARNING: No mutations of class {mutation.upper()} found in comparison data.')
PlotComparisons.make_plot(args.input)
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