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plot_length_distrib.py
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plot_length_distrib.py
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#!/usr/bin/env python
import sys
import os.path
import collections
import numpy as np
from scipy import stats
import matplotlib
matplotlib.use('Agg') # don't try to use $DISPLAY
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
def parse_lengths(filename):
hist = []
fits = collections.defaultdict(dict)
with open(filename) as f:
for line in f:
line = line.rstrip("\r\n")
if len(line) == 0:
continue
if line[0] == "#":
fields = line[1:].split(";")
for ff in fields[1:]:
k, v = ff.split("=")
fits[fields[0]][k] = float(v)
continue
hist.append(int(line))
return np.array(hist, dtype=float), fits
def parse_args():
import argparse
parser = argparse.ArgumentParser(description="Plot fragment length histogram")
parser.add_argument("-m", "--maxlen", type=int, default=0, help="maximum length to plot")
parser.add_argument("--lognorm", action="store_true", default=False, help="plot lognorm")
parser.add_argument("--exp2", action="store_true", default=False, help="plot lognorm")
parser.add_argument("--exp2mix", action="store_true", default=False, help="plot lognorm")
parser.add_argument("--lognormmix", action="store_true", default=False, help="plot lognormmix")
parser.add_argument("--weibull", action="store_true", default=False, help="plot weibull")
parser.add_argument("--rayleigh", action="store_true", default=False, help="plot rayleigh")
parser.add_argument("-t", "--title", help="title")
parser.add_argument("in_hist", help="in.hist")
parser.add_argument("out_pdf", default="plot_length_distrib.pdf", help="out.pdf")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
hist, fits = parse_lengths(args.in_hist)
if args.maxlen:
hist = hist[:args.maxlen]
pdf = PdfPages(args.out_pdf)
#fig_w, fig_h = plt.figaspect(9.0/16.0)
fig_w, fig_h = plt.figaspect(3.0/4.0)
fig1 = plt.figure(figsize=(fig_w, fig_h))
ax1 = fig1.add_subplot(111)
area = np.sum(hist)
#hist /= area
# black, orange, sky blue, blueish green, yellow, dark blue, vermillion, reddish purple
# pallete = ("#000000", "#906000", "#357090", "#006050", "#959025", "#004570", "#804000", "#806070")
# blue/green, orange, purple
pallete = ("#000000", "#1b9e77", "#d95f02", "#7570b3")
bins = np.arange(1, len(hist)+1)
width=0.8
ax1.bar(bins-width/2.0, hist, width=width, color=pallete[0], edgecolor=pallete[0])
show_legend = False
if args.lognorm:
meanlog = fits["lnorm"]["meanlog"]
sdlog = fits["lnorm"]["sdlog"]
fit_ln = stats.lognorm.pdf(bins, sdlog, scale=np.exp(meanlog))
ax1.plot(bins, fit_ln*area, color=pallete[3], ls="--", lw=3, label="lognormal")
show_legend = True
if args.exp2:
a = fits["exp2"]["a"]
b = fits["exp2"]["b"]
fit_exp2 = np.exp(-a*bins-b/bins)
ax1.plot(bins, fit_exp2*area, color=pallete[2], ls="-", lw=3, label="exp2")
show_legend = True
if args.exp2mix:
a = fits["exp2mix"]["a"]
b = fits["exp2mix"]["b"]
c = fits["exp2mix"]["c"]
y = fits["exp2mix"]["y"]
ssqe = fits["exp2mix"]["SSqE"]
fit_e1 = y*exp(-a*bins -b/bins)
fit_e2 = (1-y)*exp(-c*bins -b/bins)
fit_emix = fit_e1 + fit_e2
ax1.plot(bins, fit_emix*area, color=pallete[2], ls="-", lw=3, label="lognormal1+lognormal2")
ax1.plot(bins, fit_e1*area, color=pallete[1], ls=":", lw=3, label="lognormal1")
ax1.plot(bins, fit_e2*area, color=pallete[1], ls=":", lw=3, label="lognormal2")
show_legend = True
if args.lognormmix:
meanlog1 = fits["lnormmix"]["meanlog1"]
sdlog1 = fits["lnormmix"]["sdlog1"]
meanlog2 = fits["lnormmix"]["meanlog2"]
sdlog2 = fits["lnormmix"]["sdlog2"]
y = fits["lnormmix"]["gamma"]
ssqe = fits["lnormmix"]["SSqE"]
fit_ln1 = y*stats.lognorm.pdf(bins, sdlog1, scale=np.exp(meanlog1))
fit_ln2 = (1-y)*stats.lognorm.pdf(bins, sdlog2, scale=np.exp(meanlog2))
fit_ln = fit_ln1 + fit_ln2
ax1.plot(bins, fit_ln*area, color=pallete[2], ls="-", lw=3, label="lognormal1+lognormal2")
ax1.plot(bins, fit_ln1*area, color=pallete[1], ls=":", lw=3, label="lognormal1")
ax1.plot(bins, fit_ln2*area, color=pallete[1], ls=":", lw=3, label="lognormal2")
show_legend = True
# meanlog = fits["lnormgeom"]["meanlog"]
# sdlog = fits["lnormgeom"]["sdlog"]
# p = fits["lnormgeom"]["p"]
# #print(meanlog, sdlog, p)
# fit_lng = stats.lognorm.pdf(bins, sdlog, scale=np.exp(meanlog)) * stats.geom.pmf(bins, p)
# fit_lng = np.zeros(len(bins))
# for i in range(len(fit_lng)):
# ff = 0
# for j in range(i,i+100):
# if j == len(fit_ln):
# break
# #ff += fit_ln[j] * (1-p)**(j-i) * p
# ff += fit_ln[j] * stats.poisson.pmf(j-i, p)
# fit_lng[i] = ff
# ax1.plot(bins, fit_lng*area, color=pallete[2], ls="--", lw=3, label="lnormgeom")
if args.weibull:
delta = fits["weibull"]["delta"]
eta = fits["weibull"]["eta"]
d0 = fits["weibull"]["d0"]
ssqe = fits["weibull"]["SSqE"]
fit_wb = delta/eta * ((bins-d0)/eta)**(delta-1) * np.exp(-((bins-d0)/eta)**delta)
ax1.plot(bins, fit_wb*area, color=pallete[2], ls=":", lw=3, label="weibull")
show_legend = True
if args.rayleigh:
loc = fits["rayleigh"]["loc"]
scale = fits["rayleigh"]["scale"]
ssqe = fits["rayleigh"]["SSqE"]
fit_rl = stats.rayleigh.pdf(bins, loc=loc, scale=scale)
ax1.plot(bins, fit_rl*area, color=pallete[1], ls="-.", lw=3, label="rayleigh")
show_legend = True
ax1.xaxis.set_tick_params(direction="inout")
ax1.set_xlabel("Length (bp)")
ax1.set_ylabel("Frequency")
if show_legend:
ax1.legend(numpoints=1)
if args.title:
ax1.set_title(args.title)
plt.tight_layout()
pdf.savefig()
pdf.close()