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#!/usr/bin/env python
import sys
import numpy as np
import scipy.spatial.distance as spd
import scipy.cluster.hierarchy as sph
from scipy import stats
import matplotlib
#matplotlib.use('Agg')
import pylab
import pandas as pd
import matplotlib.pyplot as plt
class ReadCmd:
def __init__( self ):
import argparse as ap
import textwrap
p = ap.ArgumentParser( description= "TBA" )
arg = p.add_argument
arg( '-i', '--inp', '--in', metavar='INPUT_FILE', type=str, nargs='?', default=sys.stdin,
help= "The input matrix" )
arg( '-o', '--out', metavar='OUTPUT_FILE', type=str, nargs='?', default=None,
help= "The output image file [image on screen of not specified]" )
arg( '-m', '--metadata_file', type=str, default='None',
help= "The input metadata file [default None]" )
DataMatrix.input_parameters( p )
BarPlot.input_parameters( p )
self.args = p.parse_args()
def check_consistency( self ):
pass
def get_args( self ):
return self.args
class DataMatrix:
datatype = 'data_matrix'
@staticmethod
def input_parameters( parser ):
dm_param = parser.add_argument_group('Input data matrix parameters')
arg = dm_param.add_argument
arg( '--sep', type=str, default='\t' )
arg( '-f', '--feat', type=str, default=None, required = True,
help = "Name of the feature to plot"
"[or the ending string if --endswith is specified]")
arg( '--endswith', action='store_true',
help = "Match the ending part of the feature name" )
arg( '--fname_row', type=int, default=0,
help = "row number containing the names of the features "
"[default 0, specify -1 if no names are present in the matrix")
arg( '--sname_row', type=int, default=0,
help = "column number containing the names of the samples "
"[default 0, specify -1 if no names are present in the matrix")
arg( '--skip_rows', type=str, default=None,
help = "Row numbers to skip (0-indexed, comma separated) from the input file"
"[default None, meaning no rows skipped")
arg( '--def_na', type=float, default=None,
help = "Set the default value for missing values [default None which means no replacement]")
def __init__( self, input_file, args ):
self.args = args
toskip = [int(l) for l in self.args.skip_rows.split(",")] if self.args.skip_rows else None
self.table = pd.read_table(
input_file, sep = self.args.sep, skipinitialspace = True, skiprows = toskip,
header = self.args.fname_row if self.args.fname_row > -1 else None,
index_col = self.args.sname_row if self.args.sname_row > -1 else None
)
rows = []
if self.args.endswith:
for n in self.table.index:
if n.endswith( self.args.feat ):
rows.append( n )
elif self.args.feat in self.table.index:
rows.append( self.args.feat )
self.table = self.table.reindex( index=rows )
if not len(rows):
sys.stderr.write("Error, feat "+self.args.feat+" not found!")
sys.exit()
if len(rows) > 1:
sys.stderr.write("Error, multiple features matching "+self.args.feat+" !")
sys.exit()
if not self.args.def_na is None:
self.table = self.table.fillna( self.args.def_na )
def get_numpy_matrix( self ):
return self.table
def get_snames( self ):
return list(self.table.index)
def get_fnames( self ):
return list(self.table.columns)
def save_matrix( self, output_file ):
self.table.to_csv( output_file, sep = '\t' )
class MetadataMatrix:
datatype = 'metadata_matrix'
@staticmethod
def input_parameters( parser ):
dm_param = parser.add_argument_group('Input metadata file')
arg = dm_param.add_argument
arg( '--sep', type=str, default='\t' )
arg( '--fname_row', type=int, default=0,
help = "row number containing the names of the features "
"[default 0, specify -1 if no names are present in the matrix")
arg( '--def_na', type=float, default=None,
help = "Set the default value for missing values [default None which means no replacement]")
def __init__( self, input_file, args ):
self.args = args
self.table = pd.read_table(
input_file, sep = self.args.sep, skipinitialspace = True,
#header = self.args.fname_row if self.args.fname_row > -1 else None,
index_col = self.args.sname_row if self.args.sname_row > -1 else None
)
if not self.args.def_na is None:
self.table = self.table.fillna( self.args.def_na )
def get_snames( self ):
return list(self.table.index)
def get_fnames( self ):
return list(self.table.columns)
def get_table( self ):
return self.table
class BarPlot:
datatype = 'barplot'
@staticmethod
def input_parameters( parser ):
hm_param = parser.add_argument_group('Heatmap options')
arg = hm_param.add_argument
arg( '--dpi', type=int, default=72,
help = "Image resolution in dpi [default 72]")
arg( '-C', '--color_condition', type=str, default=None,
help = "The name of the metadata column used for coloring")
arg( '-H', '--hatch_condition', type=str, default=None,
help = "The name of the metadata column used for hatching")
arg( '-G', '--group_condition', type=str, default=None,
help = "The name of the metadata column used for grouping")
arg( '-t', '--title', type=str, default=None,
help = "The title of the plot [default no title]")
arg( '-l', '--log_scale', action='store_true',
help = "Log scale" )
def __init__( self, numpy_matrix, metadata_matrix, args = None ):
self.numpy_matrix = numpy_matrix
self.mmatrix = metadata_matrix
self.args = args
def draw( self ):
fig = plt.figure( figsize=(20,8) )
ax = fig.add_subplot(111)
width = 0.65
names = list(self.numpy_matrix.index)
n0 = names[0]
tp = self.numpy_matrix.to_dict()
keys = sorted(tp)
if self.args.color_condition not in self.mmatrix:
self.args.color_condition = None
cond_values = [None] if self.args.color_condition is None else sorted(set(self.mmatrix[self.args.color_condition]) )
if self.args.hatch_condition not in self.mmatrix:
self.args.hatch_condition = None
hatch_values = [None] if self.args.hatch_condition is None else sorted(set(self.mmatrix[self.args.hatch_condition]) )
if self.args.group_condition:
group_values = list(sorted(set(self.mmatrix[self.args.group_condition])))
keys = sorted( keys, key=lambda x:group_values.index(self.mmatrix[self.args.group_condition][x]) )
else:
keys, group_values = sorted( keys ), []
ind = np.arange( len(tp) )
pos = ind-width/2
hatches = ['//','\\\\','++','--','xx']
cols = ['r','g','c','b']
minv,maxv = 0.0, max([v[n0] for v in tp.values()])
bar_sets = []
for i,c in (enumerate(cond_values) if len(cond_values) > 0 else None):
for j,h in enumerate(hatch_values):
values = [(tp[k][n0] if (c is None or self.mmatrix[self.args.color_condition][k] == c)
and (h is None or self.mmatrix[self.args.hatch_condition][k] == h) else 0.0) for k in keys]
b = ax.bar(pos, values, width, hatch=hatches[j%len(hatches)] if len(hatch_values) > 1 else "", color=cols[i%len(cols)])
cond = self.args.color_condition + " "+str(c).strip()+", " if c else ""
hatch = self.args.hatch_condition + " "+str(h).strip()+", " if h else ""
bar_sets.append( (b,cond+hatch) )
v0 = ind[0]-0.5
vm1 = v0
ax.plot([v0,v0],[minv,maxv],"--",linewidth=2,color='k')
for g in group_values:
vm1 = v0
v0 += list(self.mmatrix[self.args.group_condition]).count(g)
ax.plot([v0,v0],[minv,maxv],"--",linewidth=2,color='k')
ax.text( (vm1+v0)*0.5, maxv * 0.9, str(g), horizontalalignment='center', verticalalignment='center' )
#ax.text( (vm1+v0)*0.5, maxv * 0.9, str(round(g,1)), horizontalalignment='center', verticalalignment='center' )
if self.args.color_condition or self.args.hatch_condition:
leg = ax.legend( zip(*bar_sets)[0], zip(*bar_sets)[1], bbox_to_anchor=(1.02, 0,0.3,1), loc=1,
ncol=1, mode="expand", borderaxespad=0., frameon = False)
ax.set_xlim(-width,ind[-1]+width)
ax.set_ylim(0,maxv)
ax.set_xticks( ind )
ax.set_xticklabels( keys, rotation = 90 )
ax.set_title( self.args.title or "" )
if not self.args.out:
plt.show()
else:
fig.savefig( self.args.out, bbox_inches='tight', dpi = self.args.dpi,
bbox_extra_artists=((fig.get_axes()[0].get_legend(),) if self.args.color_condition or self.args.hatch_condition else None) ) #dpi = self.args.dpi )
if __name__ == '__main__':
read = ReadCmd( )
read.check_consistency()
args = read.get_args()
dm = DataMatrix( args.inp, args )
mdm = MetadataMatrix( args.metadata_file, args )
bp = BarPlot( dm.get_numpy_matrix(), mdm.get_table(),args )
bp.draw()
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