comparison home/ubuntu/lefse_to_export/plot_res.py @ 1:db64b6287cd6 draft

Modified datatypes

1:db64b6287cd6

#!/usr/bin/env python

import os,sys
import matplotlib
matplotlib.use('Agg')
from pylab import *

from lefse import *
import argparse

colors = ['r','g','b','m','c','y','k','w']

def read_params(args):
	parser = argparse.ArgumentParser(description='Plot results')
	parser.add_argument('input_file', metavar='INPUT_FILE', type=str, help="tab delimited input file")
	parser.add_argument('output_file', metavar='OUTPUT_FILE', type=str, help="the file for the output image")
	parser.add_argument('--feature_font_size', dest="feature_font_size", type=int, default=7, help="the file for the output image")
	parser.add_argument('--format', dest="format", choices=["png","svg","pdf"], default='png', type=str, help="the format for the output file")
	parser.add_argument('--dpi',dest="dpi", type=int, default=72)
	parser.add_argument('--title',dest="title", type=str, default="")
	parser.add_argument('--title_font_size',dest="title_font_size", type=str, default="12")
	parser.add_argument('--class_legend_font_size',dest="class_legend_font_size", type=str, default="10")
	parser.add_argument('--width',dest="width", type=float, default=7.0 )
	parser.add_argument('--height',dest="height", type=float, default=4.0, help="only for vertical histograms")
	parser.add_argument('--left_space',dest="ls", type=float, default=0.2 )
	parser.add_argument('--right_space',dest="rs", type=float, default=0.1 )
	parser.add_argument('--orientation',dest="orientation", type=str, choices=["h","v"], default="h" )
	parser.add_argument('--autoscale',dest="autoscale", type=int, choices=[0,1], default=1 )
	parser.add_argument('--background_color',dest="back_color", type=str, choices=["k","w"], default="w", help="set the color of the background")
	parser.add_argument('--subclades', dest="n_scl", type=int, default=1, help="number of label levels to be dislayed (starting from the leaves, -1 means all the levels, 1 is default )")
	parser.add_argument('--max_feature_len', dest="max_feature_len", type=int, default=60, help="Maximum length of feature strings (def 60)")
	parser.add_argument('--all_feats', dest="all_feats", type=str, default="")
	args = parser.parse_args()
	return vars(args)

def read_data(input_file,output_file):
	with open(input_file, 'r') as inp:
		rows = [line.strip().split()[:-1] for line in inp.readlines() if len(line.strip().split())>3]
	classes = list(set([v[2] for v in rows if len(v)>2]))
	if len(classes) < 1: 
		print "No differentially abundant features found in "+input_file
		os.system("touch "+output_file)
		sys.exit()
	data = {}
	data['rows'] = rows
	data['cls'] = classes
	return data

def plot_histo_hor(path,params,data,bcl):
	cls2 = []
	if params['all_feats'] != "":
		cls2 = sorted(params['all_feats'].split(":"))
	cls = sorted(data['cls'])
	if bcl: data['rows'].sort(key=lambda ab: fabs(float(ab[3]))*(cls.index(ab[2])*2-1))
	else: 
		mmax = max([fabs(float(a)) for a in zip(*data['rows'])[3]])
		data['rows'].sort(key=lambda ab: fabs(float(ab[3]))/mmax+(cls.index(ab[2])+1))
	pos = arange(len(data['rows']))
	head = 0.75
	tail = 0.5
	ht = head + tail
	ints = max(len(pos)*0.2,1.5)
	fig = plt.figure(figsize=(params['width'], ints + ht), edgecolor=params['back_color'],facecolor=params['back_color'])
        ax = fig.add_subplot(111,frame_on=False,axis_bgcolor=params['back_color'])
	ls, rs = params['ls'], 1.0-params['rs']
	plt.subplots_adjust(left=ls,right=rs,top=1-head*(1.0-ints/(ints+ht)), bottom=tail*(1.0-ints/(ints+ht)))

	fig.canvas.set_window_title('LDA results')

	l_align = {'horizontalalignment':'left', 'verticalalignment':'baseline'}
        r_align = {'horizontalalignment':'right', 'verticalalignment':'baseline'}
	added = []
	m = 1 if data['rows'][0][2] == cls[0] else -1
	for i,v in enumerate(data['rows']):
		indcl = cls.index(v[2])
		lab = str(v[2]) if str(v[2]) not in added else ""
		added.append(str(v[2])) 
		col = colors[indcl%len(colors)] 
		if len(cls2) > 0: 
			col = colors[cls2.index(v[2])%len(colors)]
		vv = fabs(float(v[3])) * (m*(indcl*2-1)) if bcl else fabs(float(v[3]))
		ax.barh(pos[i],vv, align='center', color=col, label=lab, height=0.8, edgecolor=params['fore_color'])
	mv = max([abs(float(v[3])) for v in data['rows']])	
	for i,r in enumerate(data['rows']):
		indcl = cls.index(data['rows'][i][2])
		if params['n_scl'] < 0: rr = r[0]
		else: rr = r[0].split(".")[-min(r[0].count("."),params['n_scl'])]
		if len(rr) > params['max_feature_len']: rr = rr[:params['max_feature_len']/2-2]+" [..]"+rr[-params['max_feature_len']/2+2:]
                if m*(indcl*2-1) < 0 and bcl: ax.text(mv/40.0,float(i)-0.3,rr, l_align, size=params['feature_font_size'],color=params['fore_color'])
                else: ax.text(-mv/40.0,float(i)-0.3,rr, r_align, size=params['feature_font_size'],color=params['fore_color'])
	ax.set_title(params['title'],size=params['title_font_size'],y=1.0+head*(1.0-ints/(ints+ht))*0.8,color=params['fore_color'])

	ax.set_yticks([])
	ax.set_xlabel("LDA SCORE (log 10)")
	ax.xaxis.grid(True)
	xlim = ax.get_xlim()
	if params['autoscale']: 
		ran = arange(0.0001,round(round((abs(xlim[0])+abs(xlim[1]))/10,4)*100,0)/100)
		if len(ran) > 1 and len(ran) < 100:
			ax.set_xticks(arange(xlim[0],xlim[1]+0.0001,min(xlim[1]+0.0001,round(round((abs(xlim[0])+abs(xlim[1]))/10,4)*100,0)/100)))
	ax.set_ylim((pos[0]-1,pos[-1]+1))
	leg = ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=5, borderaxespad=0., frameon=False,prop={'size':params['class_legend_font_size']})

	def get_col_attr(x):
                return hasattr(x, 'set_color') and not hasattr(x, 'set_facecolor')
	for o in leg.findobj(get_col_attr):
                o.set_color(params['fore_color'])
	for o in ax.findobj(get_col_attr):
                o.set_color(params['fore_color'])

	
	plt.savefig(path,format=params['format'],facecolor=params['back_color'],edgecolor=params['fore_color'],dpi=params['dpi'])
	plt.close()

def plot_histo_ver(path,params,data):
	cls = data['cls']
	mmax = max([fabs(float(a)) for a in zip(*data['rows'])[1]])
	data['rows'].sort(key=lambda ab: fabs(float(ab[3]))/mmax+(cls.index(ab[2])+1))
	pos = arange(len(data['rows']))	
	if params['n_scl'] < 0: nam = [d[0] for d in data['rows']]
	else: nam = [d[0].split(".")[-min(d[0].count("."),params['n_scl'])] for d in data['rows']]
	fig = plt.figure(edgecolor=params['back_color'],facecolor=params['back_color'],figsize=(params['width'], params['height'])) 
	ax = fig.add_subplot(111,axis_bgcolor=params['back_color'])
	plt.subplots_adjust(top=0.9, left=params['ls'], right=params['rs'], bottom=0.3)	
	fig.canvas.set_window_title('LDA results')   
	l_align = {'horizontalalignment':'left', 'verticalalignment':'baseline'}
	r_align = {'horizontalalignment':'right', 'verticalalignment':'baseline'} 
	added = []
	for i,v in enumerate(data['rows']):
		indcl = data['cls'].index(v[2])
		lab = str(v[2]) if str(v[2]) not in added else ""
		added.append(str(v[2])) 
		col = colors[indcl%len(colors)]
		vv = fabs(float(v[3])) 
		ax.bar(pos[i],vv, align='center', color=col, label=lab)
	xticks(pos,nam,rotation=-20, ha = 'left',size=params['feature_font_size'])	
	ax.set_title(params['title'],size=params['title_font_size'])
	ax.set_ylabel("LDA SCORE (log 10)")
	ax.yaxis.grid(True) 
	a,b = ax.get_xlim()
	dx = float(len(pos))/float((b-a))
	ax.set_xlim((0-dx,max(pos)+dx))	
	plt.savefig(path,format=params['format'],facecolor=params['back_color'],edgecolor=params['fore_color'],dpi=params['dpi'])
	plt.close()	

if __name__ == '__main__':
	params = read_params(sys.argv)
	params['fore_color'] = 'w' if params['back_color'] == 'k' else 'k'
	data = read_data(params['input_file'],params['output_file'])
	if params['orientation'] == 'v': plot_histo_ver(params['output_file'],params,data)
	else: plot_histo_hor(params['output_file'],params,data,len(data['cls']) == 2)