Add header to patch

diff --git a/format_input.py b/format_input.py

index dd3d2e0..8873ecb 100755

--- a/format_input.py

+++ b/format_input.py

@@ -55,7 +55,7 @@ def read_input_file(inp_file, CommonArea):

Description: use 2to3 to port from python2 to python3

Author: Shayan Doust <hello@shayandoust.me>

Bug-Debian: https://bugs.debian.org/936836

Last-Update: 2019-09-06

Index: lefse/format_input.py

===================================================================

--- lefse.orig/format_input.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/format_input.py	2019-09-06 12:01:19.010689029 +0100

@@ -55,7 +55,7 @@

 		return CommonArea

 def transpose(data):

 def read_params(args):

 	parser = argparse.ArgumentParser(description='LEfSe formatting modules')

@@ -170,24 +170,24 @@ def get_class_slices(data):

@@ -170,24 +170,24 @@

 def numerical_values(feats,norm):

 	mm = []

 		feats[k] = [val*mul[i] for i,val in enumerate(v)]

 		if numpy.mean(feats[k]) and (numpy.std(feats[k])/numpy.mean(feats[k])) < 1e-10:

 			feats[k] = [ float(round(kv*1e6)/1e6) for kv in feats[k]]

@@ -237,7 +237,7 @@ def add_missing_levels(ff):

@@ -237,7 +237,7 @@

 				clades2leaves[n].append( f )

 			else:

 				clades2leaves[n] = [f]

 		if k and k not in ff:

 			ff[k] = [sum(a) for a in zip(*[[float(fn) for fn in ff[vv]] for vv in v])]

 	return ff

@@ -306,7 +306,7 @@ def biom_processing(inp_file):

@@ -306,7 +306,7 @@

 	for IDMetadataEntry in CommonArea['abndData'].funcGetMetadataCopy()[IDMetadataName]:  #* Loop on all the metadata values

 		IDMetadata.append(IDMetadataEntry)

 	ResolvedData.append(IDMetadata)					#Add the IDMetadata with all its values to the resolved area

 		if  key  != IDMetadataName:

 			MetadataEntry = list()		#*  Set it up

 			MetadataEntry.append(key)	#*	And post it to the area

@@ -358,7 +358,7 @@ def  check_params_for_biom_case(params, CommonArea):

@@ -358,7 +358,7 @@

 			else:

 				FlagError = True

 		if FlagError == True:		#* If the User passed an invalid class

 			params['class'] =  2

 			params['subclass'] =  3

 	return params

@@ -375,8 +375,8 @@ if  __name__ == '__main__':

@@ -375,8 +375,8 @@

 	#*************************************************************

 	if  params['input_file'].endswith('.biom'):

 		try:

 		except ImportError:

 			sys.stderr.write("************************************************************************************************************ \n")

 			sys.stderr.write("* Error:   Breadcrumbs libraries not detected - required to process biom files - run abnormally terminated * \n")

@@ -403,16 +403,16 @@ if  __name__ == '__main__':

@@ -403,16 +403,16 @@

 	if not params['subclass'] is None: ncl += 1	

 	if not params['subject'] is None: ncl += 1	

 #	data.insert(0,first_line)

 #	data = remove_missing(data,params['missing_p'])

 	cls = {}

@@ -426,7 +426,7 @@ if  __name__ == '__main__':

@@ -426,7 +426,7 @@

 	cls['subclass'] = rename_same_subcl(cls['class'],cls['subclass'])

 #	if 'subclass' in cls.keys(): cls = group_small_subclasses(cls,params['subcl_min_card'])

 	feats = dict([(d[0],d[1:]) for d in data])

@@ -446,7 +446,7 @@ if  __name__ == '__main__':

@@ -446,7 +446,7 @@

 			if 'class' in cls: outf.write( "\t".join(list(["class"])+list(cls['class'])) + "\n" )

 			if 'subclass' in cls: outf.write( "\t".join(list(["subclass"])+list(cls['subclass'])) + "\n" )

 			if 'subject' in cls: outf.write( "\t".join(list(["subject"])+list(cls['subject']))  + "\n" )

 	with open(params['output_file'], 'wb') as back_file:

 		pickle.dump(out,back_file)    	

diff --git a/lefse.py b/lefse.py

index 0a29be0..75fc246 100755

--- a/lefse.py

+++ b/lefse.py

@@ -17,14 +17,14 @@ def init():

Index: lefse/lefse.py

===================================================================

--- lefse.orig/lefse.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/lefse.py	2019-09-06 12:01:19.010689029 +0100

@@ -17,14 +17,14 @@

 def get_class_means(class_sl,feats):

 	means = {}

 			out.write(k+"\t"+str(math.log(max(max(v),1.0),10.0))+"\t")

 			if k in res['lda_res_th']:

 				for i,vv in enumerate(v):

@@ -64,7 +64,7 @@ def test_rep_wilcoxon_r(sl,cl_hie,feats,th,multiclass_strat,mul_cor,fn,min_c,com

@@ -64,7 +64,7 @@

 	tot_ok =  0

 	alpha_mtc = th

 	all_diff = []

 		dir_cmp = "not_set" #

 		l_subcl1, l_subcl2 = (len(cl_hie[pair[0]]), len(cl_hie[pair[1]]))

 		if mul_cor != 0: alpha_mtc = th*l_subcl1*l_subcl2 if mul_cor == 2 else 1.0-math.pow(1.0-th,l_subcl1*l_subcl2)

@@ -120,8 +120,8 @@ def test_rep_wilcoxon_r(sl,cl_hie,feats,th,multiclass_strat,mul_cor,fn,min_c,com

@@ -120,8 +120,8 @@

 		if not diff and multiclass_strat: return False

 		if diff and not multiclass_strat: all_diff.append(pair)

 	if not multiclass_strat:

 			nk = 0

 			for a in all_diff:

 				if k in a: nk += 1

@@ -132,7 +132,7 @@ def test_rep_wilcoxon_r(sl,cl_hie,feats,th,multiclass_strat,mul_cor,fn,min_c,com

@@ -132,7 +132,7 @@

 def contast_within_classes_or_few_per_class(feats,inds,min_cl,ncl):

 	cols = [ff[i] for i in inds]

 	cls = [feats['class'][i] for i in inds]

 	if len(set(cls)) < ncl:

@@ -147,8 +147,8 @@ def contast_within_classes_or_few_per_class(feats,inds,min_cl,ncl):

@@ -147,8 +147,8 @@

 	return False 

 def test_lda_r(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nlogs):

         feats['class'] = list(cls['class'])

         clss = list(set(feats['class']))

         for uu,k in enumerate(fk):

@@ -160,7 +160,7 @@ def test_lda_r(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nlogs):

@@ -160,7 +160,7 @@

                                 if feats['class'][i] == c:

                                         feats[k][i] = math.fabs(feats[k][i] + lrand.normalvariate(0.0,max(feats[k][i]*0.05,0.01)))

         rdict = {}

                 if a == 'class' or a == 'subclass' or a == 'subject':

                         rdict[a] = robjects.StrVector(b)

                 else: rdict[a] = robjects.FloatVector(b)

@@ -210,7 +210,7 @@ def test_lda_r(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nlogs):

@@ -210,7 +210,7 @@

         for k in fk:

 		m = max([numpy.mean([means[k][kk][p] for kk in range(boots)]) for p in range(len(pairs))])

                 res[k] = math.copysign(1.0,m)*math.log(1.0+math.fabs(m),10)

 def test_svm(cls,feats,cl_sl,boots,fract_sample,lda_th,tol_min,nsvm):

diff --git a/lefse2circlader.py b/lefse2circlader.py

index 79cc2a0..db01218 100755

--- a/lefse2circlader.py

+++ b/lefse2circlader.py

Index: lefse/lefse2circlader.py

===================================================================

--- lefse.orig/lefse2circlader.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/lefse2circlader.py	2019-09-06 12:01:19.010689029 +0100

@@ -1,6 +1,6 @@

 #!/usr/bin/env python

 import sys

 import os

diff --git a/lefsebiom/AbundanceTable.py b/lefsebiom/AbundanceTable.py

index 77c11aa..349af50 100755

--- a/lefsebiom/AbundanceTable.py

+++ b/lefsebiom/AbundanceTable.py

@@ -4,24 +4,24 @@ Description: Class to abstract an abundance table and methods to run on such a t

Index: lefse/lefsebiom/AbundanceTable.py

===================================================================

--- lefse.orig/lefsebiom/AbundanceTable.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/lefsebiom/AbundanceTable.py	2019-09-06 12:01:19.018688976 +0100

@@ -4,24 +4,24 @@

 """

 #####################################################################################

 #####################################################################################

 __author__ = "Timothy Tickle"

@@ -35,8 +35,8 @@ __status__ = "Development"

@@ -35,8 +35,8 @@

 import csv

 import sys

 import blist

 import copy

 from datetime import date

 import numpy as np

@@ -44,690 +44,731 @@ import os

@@ -44,690 +44,731 @@

 import re

 import scipy.stats

 import string

-

-		#The original number of features in the table

-		self._iOriginalFeatureCount = -1

+    """

+    Represents an abundance table and contains common function to perform on the object.

-

-		#The name of the object relating to the file it was read from or would have been read from if it exists

-		#Keeps tract of changes to the file through the name

-		#Will be used to write out the object to a file as needed

-		self._strOriginalName = strName

-

-		#The original number of samples in the table

-		self._iOriginalSampleCount = -1

-

-		#Data sparsity type

-		self.fSparseMatrix = dictFileMetadata.get(ConstantsBreadCrumbs.c_strSparsityKey,False) if dictFileMetadata else False

-

-		### Data metadata

-		#The column (sample) metdata

-		self._dictTableMetadata = dictMetadata

-

-		#The row (feature) metadata (Row Metadata object)

-		self.rwmtRowMetadata = rwmtRowMetadata

+    """

+    Represents an abundance table and contains common function to perform on the object.

-		### Data

-

-		#The abundance data

-		self._npaFeatureAbundance = npaAbundance

-

-

-		### Logistical

-

-		#Clade prefixes for biological samples

-		self._lsCladePrefixes = ["k__","p__","c__","o__","f__","g__","s__"]

-

-		#This is not a hashable object

-		self.__hash__ = None

-

-

-		### Prep the object

-

-		self._fIsNormalized = self._fIsSummed = None

-		#If contents is not a false then set contents to appropriate objects

-		# Checking to see if the data is normalized, summed and if we need to run a filter on it.

-		if ( self._npaFeatureAbundance != None ) and self._dictTableMetadata:

-			self._iOriginalFeatureCount = self._npaFeatureAbundance.shape[0]

-			self._iOriginalSampleCount = len(self.funcGetSampleNames())

-		

-			self._fIsNormalized = ( max( [max( list(a)[1:] or [0] ) for a in self._npaFeatureAbundance] or [0] ) <= 1 )

-

-			lsLeaves = AbundanceTable.funcGetTerminalNodesFromList( [a[0] for a in self._npaFeatureAbundance], self._cFeatureDelimiter )

-			self._fIsSummed = ( len( lsLeaves ) != len( self._npaFeatureAbundance ) )

-

-			#Occurence filtering

-			#Removes features that do not have a given level iLowestAbundance in a given amount of samples iLowestSampleOccurence

-			if ( not self._fIsNormalized ) and lOccurenceFilter:

-				iLowestAbundance, iLowestSampleOccurrence = lOccurenceFilter

-				self.funcFilterAbundanceBySequenceOccurence( iLowestAbundance, iLowestSampleOccurrence )

+    This class is made from an abundance data file. What is expected is a text file delimited by

+    a character (which is given to the object). The first column is expected to be the id column

+    for each of the rows. Metadata is expected before measurement data. Columns are samples and

+

+    This object is currently not hashable.

+    """

-		#The original number of samples in the table

-		self._iOriginalSampleCount = -1

+

+    def __init__(self, npaAbundance, dictMetadata, strName, strLastMetadata, rwmtRowMetadata=None, dictFileMetadata=None, lOccurenceFilter=None, cFileDelimiter=ConstantsBreadCrumbs.c_cTab, cFeatureNameDelimiter="|"):

+        """

+        Constructor for an abundance table.

-		#Data sparsity type

-		self.fSparseMatrix = dictFileMetadata.get(ConstantsBreadCrumbs.c_strSparsityKey,False) if dictFileMetadata else False

+

+        :param	npaAbundance:	Structured Array of abundance data (Row=Features, Columns=Samples)

+        :type:	Numpy Structured Array abundance data (Row=Features, Columns=Samples)

+        :param	dictMetadata:	Dictionary of metadata {"String ID":["strValue","strValue","strValue","strValue","strValue"]}

+

+            self._fIsNormalized = (

+                max([max(list(a)[1:] or [0]) for a in self._npaFeatureAbundance] or [0]) <= 1)

-		### Data metadata

-		#The column (sample) metdata

-		self._dictTableMetadata = dictMetadata

+

+            lsLeaves = AbundanceTable.funcGetTerminalNodesFromList(

+                [a[0] for a in self._npaFeatureAbundance], self._cFeatureDelimiter)

+            self._fIsSummed = (len(lsLeaves) != len(self._npaFeatureAbundance))

-		#The row (feature) metadata (Row Metadata object)

-		self.rwmtRowMetadata = rwmtRowMetadata

-

-		### Data

-

-		#The abundance data

-		self._npaFeatureAbundance = npaAbundance

-

-

-		### Logistical

-

-		#Clade prefixes for biological samples

-		self._lsCladePrefixes = ["k__","p__","c__","o__","f__","g__","s__"]

-

-		#This is not a hashable object

-		self.__hash__ = None

-

-

-		### Prep the object

-

-		self._fIsNormalized = self._fIsSummed = None

-		#If contents is not a false then set contents to appropriate objects

-		# Checking to see if the data is normalized, summed and if we need to run a filter on it.

-		if ( self._npaFeatureAbundance != None ) and self._dictTableMetadata:

-			self._iOriginalFeatureCount = self._npaFeatureAbundance.shape[0]

-			self._iOriginalSampleCount = len(self.funcGetSampleNames())

-		

-			self._fIsNormalized = ( max( [max( list(a)[1:] or [0] ) for a in self._npaFeatureAbundance] or [0] ) <= 1 )

-

-			lsLeaves = AbundanceTable.funcGetTerminalNodesFromList( [a[0] for a in self._npaFeatureAbundance], self._cFeatureDelimiter )

-			self._fIsSummed = ( len( lsLeaves ) != len( self._npaFeatureAbundance ) )

-

-			#Occurence filtering

-			#Removes features that do not have a given level iLowestAbundance in a given amount of samples iLowestSampleOccurence

-			if ( not self._fIsNormalized ) and lOccurenceFilter:

-				iLowestAbundance, iLowestSampleOccurrence = lOccurenceFilter

-				self.funcFilterAbundanceBySequenceOccurence( iLowestAbundance, iLowestSampleOccurrence )

+

+            # Occurence filtering

+            # Removes features that do not have a given level iLowestAbundance in a given amount of samples iLowestSampleOccurence

+            if (not self._fIsNormalized) and lOccurenceFilter:

 +										The last dict is a collection of BIOM fielparameters when converting from a BIOM file

 +										[Numpy structured Array, Dictionary, Numpy structured array, dict]

-		"""

-

+        """

-		# Open file from a stream or file path

-		istmInput = open( xInputFile, 'rU' ) if isinstance(xInputFile, str) else xInputFile

-		# Flag that when incremented will switch from metadata parsing to data parsing

-                    ConstantsBreadCrumbs.c_strTypekey:ConstantsBreadCrumbs.c_strDefaultPCLFileTpe,

-                    ConstantsBreadCrumbs.c_strURLKey:ConstantsBreadCrumbs.c_strDefaultPCLURL,

-                    ConstantsBreadCrumbs.c_strSparsityKey:ConstantsBreadCrumbs. c_fDefaultPCLSparsity}]

+        """

+

+        # Open file from a stream or file path

+        istmInput = open(xInputFile, 'rU') if isinstance(

+            xInputFile, str) else xInputFile

 #	def funcAdd(self,abndTwo,strFileName=None):

 #		"""

@@ -769,7 +810,7 @@ class AbundanceTable:

@@ -769,7 +810,7 @@

 #		lsFeaturesCombined = list(set(lsFeatures1+lsFeature2))

 #

 #		#Add samples by features (Use 0.0 for empty data features, use NA for empty metadata features)

 #

 #		#Combine metadata

 #		dictMetadata1 = self.funcGetMetadataCopy()

@@ -797,1660 +838,1753 @@ class AbundanceTable:

@@ -797,1660 +838,1753 @@

 #				strLastMetadata = self.funcGetLastMetadataName(),

 #				cFileDelimiter = self.funcGetFileDelimiter(), cFeatureNameDelimiter=self.funcGetFeatureDelimiter())

+

+        BiomCommonArea[ConstantsBreadCrumbs.c_BiomFileInfo][strInsertKey] = PostBiomValue

+        return BiomCommonArea

diff --git a/lefsebiom/CClade.py b/lefsebiom/CClade.py

index 5bd1c44..48e7031 100644

--- a/lefsebiom/CClade.py

+++ b/lefsebiom/CClade.py

@@ -85,7 +85,7 @@ class CClade:

Index: lefse/lefsebiom/CClade.py

===================================================================

--- lefse.orig/lefsebiom/CClade.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/lefsebiom/CClade.py	2019-09-06 12:01:19.018688976 +0100

@@ -85,7 +85,7 @@

             #Then take a copy of the child's

             #If they now have a copy of a child's but have other children

             #Sum their children with thier current values

 				adChild = pChild.impute( )

 				if self.m_adValues:

 					for i in range( len( adChild or [] ) ):

@@ -118,7 +118,7 @@ class CClade:

@@ -118,7 +118,7 @@

                         #Union all the results from freeze of all children

                         #Call freeze but append the child clade to the clade in the call.

                         #And give an incremented depth

 				setiRet |= pChild._freeze( hashValues, iTarget, astrClade + [strChild], iDepth, fLeaves )

 			setiRet = set( ( i + 1 ) for i in setiRet )

 		else:

@@ -155,7 +155,7 @@ class CClade:

@@ -155,7 +155,7 @@

 			if self.m_hashChildren:

 				strRet += " "

 		if self.m_hashChildren:

 		return ( strRet + ">" )

diff --git a/lefsebiom/ValidateData.py b/lefsebiom/ValidateData.py

index 5943240..d95222f 100755

--- a/lefsebiom/ValidateData.py

+++ b/lefsebiom/ValidateData.py

@@ -449,7 +449,7 @@ class ValidateData:

Index: lefse/lefsebiom/ValidateData.py

===================================================================

--- lefse.orig/lefsebiom/ValidateData.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/lefsebiom/ValidateData.py	2019-09-06 12:01:19.018688976 +0100

@@ -449,7 +449,7 @@

         #Check elements

         listSize = len(parameterValue)

             if(not ValidateData.funcIsValidNumeric(parameterValue[i])):

                 return False

         return True

@@ -472,7 +472,7 @@ class ValidateData:

@@ -472,7 +472,7 @@

         #Check elements

         listSize = len(parameterValue)

             if(not ValidateData.funcIsValidString(parameterValue[i])):

                 return False

         return True

@@ -520,7 +520,7 @@ class ValidateData:

@@ -520,7 +520,7 @@

             return False

         #Check key elements

         keyListSize = len(keyList)

         for i in range(0,keyListSize):

             if keyList[i] == None:

@@ -530,7 +530,7 @@ class ValidateData:

@@ -530,7 +530,7 @@

                     return False

         #Check key elements

         itemListSize = len(itemList)

         for i in range(0,itemListSize):

diff --git a/plot_cladogram.py b/plot_cladogram.py

index 1b95358..0faba08 100755

--- a/plot_cladogram.py

+++ b/plot_cladogram.py

Index: lefse/plot_cladogram.py

===================================================================

--- lefse.orig/plot_cladogram.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/plot_cladogram.py	2019-09-06 12:01:19.018688976 +0100

@@ -3,7 +3,7 @@

 import os,sys,matplotlib,argparse,string

 matplotlib.use('Agg')

 import numpy as np

 colors = ['r','g','b','m','c',[1.0,0.5,0.0],[0.0,1.0,0.0],[0.33,0.125,0.0],[0.75,0.75,0.75],'k']

@@ -261,7 +261,7 @@ def plot_names(father,params,depth,ax,u_i,seps):

@@ -261,7 +261,7 @@

 		lab = ""

 		txt = father.last_name

 		if params['abrv_start_lev']  < l <= params['abrv_stop_lev'] + 1:

 			lab = str(ide)+": "+father.last_name 

 			txt = str(ide)

 #		ax.bar(fr_0, clto, width = fr_1-fr_0, bottom = float(l-1)/float(depth-1), alpha = params['alpha'], color=col, edgecolor=col)

@@ -288,7 +288,7 @@ def draw_tree(out_file,tree,params):

@@ -288,7 +288,7 @@

 		params['clade_sep'] *= 0.75 	 

 		seps = [params['clade_sep']*sep/(float(depth-i+1)*0.25) for i in range(1,len(tree['nlev'])+1)]

 		totseps = sum([s*nlev[i] for i,s in enumerate(seps[:-1])])

 	fig = plt.figure(edgecolor=params['back_color'],facecolor=params['back_color'])

 	ax = fig.add_subplot(111, polar=True, frame_on=False, axis_bgcolor=params['back_color'] )

diff --git a/plot_features.py b/plot_features.py

index 5371eb3..115cf61 100755

--- a/plot_features.py

+++ b/plot_features.py

Index: lefse/plot_features.py

===================================================================

--- lefse.orig/plot_features.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/plot_features.py	2019-09-06 12:01:19.018688976 +0100

@@ -3,7 +3,7 @@

 import os,sys,matplotlib,zipfile,argparse,string

 matplotlib.use('Agg')

 import random as rand

 colors = ['r','g','b','m','c']

@@ -40,7 +40,7 @@ def read_data(file_data,file_feats,params):

@@ -40,7 +40,7 @@

 	with open(file_feats, 'r') as features:

 		feats_to_plot = [(f.split()[:-1],len(f.split()) == 5) for f in features.readlines()]

 	if not feats_to_plot:

 		sys.exit(0)

 	feats,cls,class_sl,subclass_sl,class_hierarchy,params['norm_v'] = load_data(file_data, True)	 	

 	if params['feature_num'] > 0: 

@@ -51,7 +51,7 @@ def read_data(file_data,file_feats,params):

@@ -51,7 +51,7 @@

 		if params['f'] == "one" and f[0][0] != params['feature_name']: continue

 		features[f[0][0]] = {'dim':float(f[0][1]), 'abundances':feats[f[0][0]], 'sig':f[1], 'cls':cls, 'class_sl':class_sl, 'subclass_sl':subclass_sl, 'class_hierarchy':class_hierarchy} 

 	if not features:

                 sys.exit(0)

 	return features

@@ -62,7 +62,7 @@ def plot(name,k_n,feat,params):

@@ -62,7 +62,7 @@

 	max_m = 0.0

 	norm = 1.0 if float(params['norm_v']) < 0.0 else float(params['norm_v'])

 		fr,to  = v[0], v[1]

 		median = numpy.mean(feat['abundances'][fr:to])

 		if median > max_m: max_m = median

@@ -76,7 +76,7 @@ def plot(name,k_n,feat,params):

@@ -76,7 +76,7 @@

 	if max_v == 0.0: max_v = 0.0001

 	if max_v == min_v: max_v = min_v*1.1 

 	seps = []

 	xtics = []

 	x2tics = []

@@ -142,8 +142,8 @@ if __name__ == '__main__':

@@ -142,8 +142,8 @@

 	params['fore_color'] = 'w' if params['back_color'] == 'k' else 'k'

 	features = read_data(params['input_file_1'],params['input_file_2'],params)

 	if params['archive'] == "zip": file = zipfile.ZipFile(params['output_file'], "w")

 		if params['archive'] == "zip":

 			of = plot("/tmp/"+str(int(f['sig']))+"_"+"-".join(k.split("."))+"."+params['format'],k,f,params)

 			file.write(of, os.path.basename(of), zipfile.ZIP_DEFLATED)

diff --git a/plot_res.py b/plot_res.py

index f87e79e..e67153c 100755

--- a/plot_res.py

+++ b/plot_res.py

@@ -6,7 +6,7 @@ matplotlib.use('Agg')

Index: lefse/plot_res.py

===================================================================

--- lefse.orig/plot_res.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/plot_res.py	2019-09-06 12:01:19.022688949 +0100

@@ -6,7 +6,7 @@

 from pylab import *

 from collections import defaultdict

 import argparse

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

@@ -44,7 +44,7 @@ def read_data(input_file,output_file,otu_only):

@@ -44,7 +44,7 @@

             rows = [line.strip().split()[:-1] for line in inp.readlines() if len(line.strip().split())>3 and len(line.strip().split()[0].split('.'))==8] # a feature with length 8 will have an OTU id associated with it

     classes = list(set([v[2] for v in rows if len(v)>2]))

     if len(classes) < 1: 

         os.system("touch "+output_file)

         sys.exit()

     data = {}

@@ -94,9 +94,9 @@ def plot_histo_hor(path,params,data,bcl,report_features):

@@ -94,9 +94,9 @@

         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']])  

     if report_features:

     for i,r in enumerate(data['rows']):

         indcl = cls.index(data['rows'][i][2])

         if params['n_scl'] < 0: rr = r[0]

@@ -156,9 +156,9 @@ def plot_histo_ver(path,params,data,report_features):

@@ -156,9 +156,9 @@

         vv = fabs(float(v[3])) 

         ax.bar(pos[i],vv, align='center', color=col, label=lab)

     if report_features:

     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)")

diff --git a/qiime2lefse.py b/qiime2lefse.py

index dffecc3..cdd09ec 100755

--- a/qiime2lefse.py

+++ b/qiime2lefse.py

@@ -43,8 +43,8 @@ def read_params(args):

Index: lefse/qiime2lefse.py

===================================================================

--- lefse.orig/qiime2lefse.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/qiime2lefse.py	2019-09-06 12:01:19.022688949 +0100

@@ -43,8 +43,8 @@

 def qiime2lefse(  fin, fmd, fout, all_md, sel_md ):

     with (fin if fin==sys.stdin else open(fin)) as inpf :

         lines = [list(ll) for ll in 

     for i,(l1,l2) in enumerate(zip( lines[0], lines[-1] )):

         if not l2 == 'Consensus Lineage':

             lines[-1][i] = l2+"|"+l1

@@ -59,17 +59,17 @@ def qiime2lefse(  fin, fmd, fout, all_md, sel_md ):

@@ -59,17 +59,17 @@

         mdf = mdlines[0][1:]

         for l in mdlines:

         if k == 'Consensus Lineage':

             continue

         out_m.append( [md[k][kmd] for kmd in selected_md] + list(v) )

diff --git a/run_lefse.py b/run_lefse.py

index df248ef..98c42a3 100755

--- a/run_lefse.py

+++ b/run_lefse.py

Index: lefse/run_lefse.py

===================================================================

--- lefse.orig/run_lefse.py	2019-09-06 12:01:19.030688896 +0100

+++ lefse/run_lefse.py	2019-09-06 12:01:19.022688949 +0100

@@ -1,7 +1,7 @@

 #!/usr/bin/env python

 def read_params(args):

         parser = argparse.ArgumentParser(description='LEfSe 1.0')

@@ -60,38 +60,38 @@ if __name__ == '__main__':

@@ -60,38 +60,38 @@

 	wilcoxon_res = {}

 	kw_n_ok = 0

 	nf = 0

 		lda_res,lda_res_th = {},{}

 	outres = {}

 	outres['lda_res_th'] = lda_res_th

@@ -99,5 +99,5 @@ if __name__ == '__main__':

@@ -99,5 +99,5 @@

 	outres['cls_means'] = cls_means

 	outres['cls_means_kord'] = kord

 	outres['wilcox_res'] = wilcoxon_res