--- a/aggregate.py	Thu Aug 11 18:30:39 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,324 +0,0 @@
-# A translation of aggregate.pl into python! For analysis of Tn-Seq.
-# This script requires BioPython just like calc_fitness.py, so you need it installed along with its dependencies if you want to run these scripts on your own.
-# How to install BioPython and a list of its dependencies can be found here: http://biopython.org/DIST/docs/install/Installation.html
-
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-
-
-
-
-
-
-##### ARGUMENTS #####
-
-# Prints basic instructions on / options for using this code; called when the user forgets to enter an output file or fitness file(s)
-
-def print_usage():
-	print "Aggregate.py's usage is as follows:" + "\n\n"
-	print "\033[1m" + "Required" + "\033[0m" + "\n"
-	print "-o" + "\t\t" + "Output file for aggregated data." + "\n"
-	print "\n"
-	print "\033[1m" + "Optional" + "\033[0m" + "\n"
-	print "-c" + "\t\t" + "Check for missing genes in the data set - provide a reference genome in genbank format. Missing genes will be sent to stdout." + "\n"
-	print "-m" + "\t\t" + "Place a mark in an extra column for this set of genes. Provide a file with a list of genes seperated by newlines." + "\n"
-	print "-x" + "\t\t" + "Cutoff: Don't include fitness scores with average counts (c1+c2)/2 < x (default: 0)" + "\n"
-	print "-b" + "\t\t" + "Blanks: Exclude -b % of blank fitness scores (scores where c2 = 0) (default: 0 = 0%)" + "\n"
-	print "-w" + "\t\t" + "Use weighted algorithm to calculate averages, variance, sd, se" + "\n"
-	print "-l" + "\t\t" + "Weight ceiling: maximum value to use as a weight (default: 999,999)" + "\n"
-	print "\n"
-	print "All remainder arguements will be treated as fitness files (those files created by calc_fitness.py)" + "\n"
-	print "\n"
-	
-# Turns the arguments entered in the command line into variables so that they can actually be called. 
-
-import argparse 
-parser = argparse.ArgumentParser()
-parser.add_argument("-o", action="store", dest="summary")
-parser.add_argument("-c", action="store", dest="find_missing")
-parser.add_argument("-m", action="store", dest="marked")
-parser.add_argument("-x", action="store", dest="cutoff")
-parser.add_argument("-b", action="store", dest="blank_pc")
-parser.add_argument("-w", action="store", dest="weighted")
-parser.add_argument("-l", action="store", dest="weight_ceiling")
-parser.add_argument("fitnessfiles", nargs=argparse.REMAINDER)
-
-#Shortens the names of those variables created from the arguments - from parser.parse_args().ref_genome to arguments.ref_genome, for example.
-
-arguments = parser.parse_args()
-
-#Checks that the required arguments have been entered; if not informs the user they need to provide a name for the output file / the fitness file(s) and print's aggregate.py's usage
-
-if not arguments.summary:
-	print "\n" + "You are missing a value for the -o flag. "
-	print_usage() 
-	quit()
-
-if not arguments.fitnessfiles:
-	print "\n" + "You are missing fitness file(s); these should be entered immediately after all the flags. "
-	print_usage() 
-	quit()
-	
-#Sets the maximum weight of a fitness value, if that wasn't specified in the command line. 
-	
-if (not arguments.weight_ceiling):
-	arguments.max_weight = 999999
-	
-#Sets the cutoff to a default value of 0 if it wasn't specified in the command line.
-#Cutoff exists to discard positions with a low number of counted transcripts, because their fitness may not be as accurate - for the same reasoning that studies with low sample sizes can be innacurate. 
-	
-if (not arguments.cutoff):
-	arguments.cutoff = 0
-
-#Sets the % of blank fitness values to exclude to a default value of 0 if it wasn't specified in the command line. This can be found in the 2nd output of calc_fitness.
-
-if (not arguments.blank_pc):
-	arguments.blank_pc = 0
-
-# gets blank_pc from the output file of calc_fit / consol
-
-if arguments.blank_pc:
-	with open(arguments.blank_pc) as file:
-		blank_pc = file.read().splitlines()
-		arguments.blank_pc = float(blank_pc[1].split()[1])
-
-
-
-
-
-
-
-##### SUBROUTINES #####
-
-#A subroutine that calculates the average, variance, standard deviation (sd), and standard error (se) of a group of scores; for use when aggregating scores by gene later on
-
-import math
-def average(scores):
-	sum = 0
-	num = 0
-	
-#Finds the average of the scores
-	
-	for i in scores:
-		sum += i
-		num += 1
-	average = sum/num
-	
-#Finds the variance of the scores
-
-	xminusxbars = 0
-	for i in scores:
-		xminusxbars += (i - average)**2
-	variance = xminusxbars/(num-1)
-	
-#Finds the standard deviation and standard error of the scores; then the average / variance / standard deviation / standard error are returned
-
-	sd = math.sqrt(variance)
-	se = sd / math.sqrt(num)
-	return (average, variance, sd, se)
-
-#A subroutine that calculates the weighted average, variance, standard deviation (sd), and standard error (se) of a group of scores; the weights come from the number of reads each insertion location has
-#For use when aggregating scores by gene later on, if the weighted argument is called
-
-def weighted_average(scores,weights):
-	sum = 0
-	weighted_average = 0
-	weighted_variance = 0
-	top = 0
-	bottom = 0
-	i = 0
-	
-#Finds weighted average of the scores
-	
-	while i < len(weights):
-		if not scores[i]:
-			scores[i] = 0.0
-		top += float(weights[i])*float(scores[i])
-		bottom += float(weights[i])
-		i += 1
-	if bottom == 0:
-		return 0
-	weighted_average = top/bottom
-	
-#Finds weighted variance of the scores
-
-	top = 0
-	bottom = 0
-	i = 0
-	while i < len(weights):
-		top += float(weights[i]) * (float(scores[i]) - weighted_average)**2
-		bottom += float(weights[i])
-		i += 1
-	weighted_variance = top/bottom
-    
-#Finds weighted standard deviation and standard error of the scores; then the weighted average / variance / standard deviation / standard error are returned
-    
-	weighted_stdev = math.sqrt(weighted_variance)
-	weighted_stder = weighted_stdev/math.sqrt(len(scores))
-	return (weighted_average, weighted_variance, weighted_stdev, weighted_stder)
-
-
-
-
-
-
-
-
-
-
-##### AGGREGATION / CALCULATIONS #####
-
-#Reads the genes which should be marked in the final aggregate file into an array
-
-import os.path
-if arguments.marked:
-	with open(arguments.marked) as file:
-		marked_set = file.read().splitlines()
-
-#Creates a dictionary of dictionaries to contain a summary of all genes and their fitness values
-#Each gene is its own dictionary with w and s as keys for the various fitnesses and weights of the insertion locations within those genes respectively
-#The fitness values and weights match up, so that the weight of gene_summary[locus]["w"][2] would be gene_summary[locus]["s"][2]
-
-import csv
-gene_summary = {}
-for eachfile in arguments.fitnessfiles:
-	with open(eachfile) as csvfile:
-		lines = csv.reader(csvfile)
-		for line in lines:
-			locus = line[9]
-			w = line[12]
-			if w == 'nW':
-				continue
-			if not w:
-				w == 0
-			c1 = float(line[2])
-			c2 = float(line[3])
-			avg = (c1+c2)/2
-			if avg < float(arguments.cutoff):
-				continue
-			if avg > float(arguments.weight_ceiling):
-				avg = arguments.weight_ceiling
-			if locus not in gene_summary:
-				gene_summary[locus] = {"w" : [], "s": []}
-			gene_summary[locus]["w"].append(w)
-			gene_summary[locus]["s"].append(avg)
-
-#If finding any missing gene loci is requested in the arguments, starts out by loading all the known features from a genbank file
-
-from Bio import SeqIO
-if (arguments.find_missing):
-	output = [["locus","mean","var","sd","se","gene","Total","Blank","Not Blank","Blank Removed","M\n"]]
-	handle = open(arguments.find_missing, "rU")
-	for record in SeqIO.parse(handle, "genbank"):
-		refname = record.id
-		features = record.features
-	handle.close()
-	
-#Goes through the features to find which are genes
-	
-	for feature in features:
-		gene = ""
-		if feature.type == "gene":
-			locus = "".join(feature.qualifiers["locus_tag"])
-			if "gene" in feature.qualifiers:
-				gene = "".join(feature.qualifiers["gene"])
-		else:
-			continue
-			
-#Goes through the fitness scores of insertions within each gene, and removes whatever % of blank fitness scores were requested along with their corresponding weights
-
-		sum = 0
-		num = 0
-		avgsum = 0
-		blank_ws = 0
-		i = 0
-		if locus in gene_summary.keys():
-			for w in gene_summary[locus]["w"]:
-				if float(w) == 0:
-					blank_ws += 1
-				else:
-					sum += float(w)
-					num += 1
-			count = num + blank_ws		
-			removed = 0
-			to_remove = int(float(arguments.blank_pc)*count)
-			if blank_ws > 0:
-				i = 0
-				while i < len(gene_summary[locus]["w"]):
-					if removed == to_remove:
-						break
-					if not w:
-						del gene_summary[locus]["w"][i]
-						del gene_summary[locus]["s"][i]
-						removed += 1
-						i -= 1
-					i += 1
-
-#If all the fitness values within a gene are empty, sets mean/var to 0.10 and Xs out sd/se; marks the gene if that's requested
-
-			if num == 0:	
-				if (arguments.marked and locus in marked_set):
-					output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "M", "\n"])
-				else:
-					output.append([locus, "0.10", "0.10", "X", "X", gene, count, blank_ws, num, removed, "\n"])
-
-#Otherwise calls average() or weighted_average() to find the aggregate w / count / standard deviation / standard error of the insertions within each gene; marks the gene if that's requested
-
-			else:
-				if not arguments.weighted:
-					(average, variance, stdev, stderr) = average(gene_summary[locus]["w"])
-				else:
-					(average, variance, stdev, stderr) = weighted_average(gene_summary[locus]["w"],gene_summary[locus]["s"])
-				if (arguments.marked and locus in marked_set):
-					output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "M", "\n"])
-				else:
-					output.append([locus, average, variance, stdev, stderr, gene, count, blank_ws, num, removed, "\n"])
-		
-#If a gene doesn't have any insertions, sets mean/var to 0.10 and Xs out sd/se, plus leaves count through removed blank because there were no reads.
-
-		else:
-			if (arguments.marked and locus in marked_set):
-				output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "M", "\n"])
-			else:
-				output.append([locus, "0.10", "0.10", "X", "X", gene, "", "", "", "", "\n"])
-
-#Writes the aggregated fitness file
-
-	with open(arguments.summary, "wb") as csvfile:
-		writer = csv.writer(csvfile)
-		writer.writerows(output)
-
-#If finding missing genes is not requested, just finds the aggregate w / count / standard deviation / standard error of the insertions within each gene, and writes them to a file, plus marks the genes requested
-#This is never called through Galaxy since finding missing genes is better than not finding them.
-
-else:
-	output = [["Locus","W","Count","SD","SE","M\n"]]
-	for gene in gene_summary.keys():
-		sum = 0
-		num = 0
-		average = 0
-		if "w" not in gene_summary[gene]:
-			continue
-		for i in gene_summary[gene]["w"]:
-			sum += i
-			num += 1
-		average = sum/num
-		xminusxbars = 0
-		for i in w:
-			xminusxbars += (i-average)**2
-		if num > 1:
-			sd = math.sqrt(xminusxbars/(num-1))
-			se = sd / math.sqrt(num)
-		if (arguments.marked and locus in marked_set):
-			output.append([gene, average, num, sd, se, "M", "\n"])
-		else:
-			output.append([gene, average, num, sd, se, "\n"])
-	with open(arguments.summary, "wb") as csvfile:
-		writer = csv.writer(csvfile)
-		writer.writerows(output)
-
-
-#Test: python ../script/aggregate.py -m tigr4_normal.txt -w 1 -x 10 -l 50 -b 0 -c NC_003028b2.gbk -o aggregates/L3_2394eVI_GlucTEST.csv results/L3_2394eVI_Gluc.csv
-
-#Perl Test: perl ../script/aggregate.pl -m tigr4_normal.txt -w 1 -x 10 -l 50 -b 0 -c NC_003028b2.gbk -o aggregates/L3_2394eVI_Gluc.csv results/L3_2394eVI_Gluc.csv
\ No newline at end of file

--- a/aggregate.xml	Thu Aug 11 18:30:39 2016 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,86 +0,0 @@
-<tool id="aggregate" name="Aggregate">
-  <description>fitness calculations by gene</description>
-  <requirements>
-    <requirement type="package" version="1.64">biopython</requirement>
-  </requirements>
-  <command interpreter="python">
-    aggregate.py 
-    #if $mark.certain  == "yes":
-      -m $mark.genes
-    #end if
-    #if $weighted.algorithms == "yes":
-      -w 1 
-    #end if
-    -x $cutoff 
-    -l $weightceiling 
-    -b $blanks 
-    -c $ref
-    -o $output 
-    $input
-    #for $a in $additionalcsv
-      ${a.input2}
-    #end for
-  </command>
-  <inputs>
-    <param name="input" type="data" label="csv fitness file"/>
-      <repeat name="additionalcsv" title="Additional csv fitness file(s)">
-        <param name="input2" type="data" label="Select" />
-      </repeat>
-    <param name="ref" type="data" label="GenBank reference genome"/>
-    <conditional name="mark">
-      <param name="certain" type="select" label="Mark certain genes?">
-        <option value="no">No</option>
-        <option value="yes">Yes</option>
-      </param>
-      <when value="no">
-        <!-- do nothing -->
-      </when>
-      <when value="yes"> 
-        <param name="genes" type="data" label="Genes to mark" />
-      </when>
-    </conditional>
-    <conditional name="weighted">
-      <param name="algorithms" type="select" label="Use weighted algorithms?">
-        <option value="no">No</option>
-        <option value="yes">Yes</option>
-      </param>
-      <when value="-w 1 "/>
-      <when value=""/>
-    </conditional>
-    <param name="weightceiling" type="float" value="50.0" label="Weight ceiling"/>
-    <param name="cutoff" type="float" value="10.0" label="Cutoff"/>
-    <param name="blanks" type="data" label="txt output from Calc_fit or Consol_fit"/>
-  </inputs>
-  <outputs>
-    <data name="output" format="csv"/>
-  </outputs>
-  <help>
-
-**What it does**
-
-This tool calculates the aggregate fitness values of  mutations by gene.
-
-**The options explained**
-
-The csv fitness file(s): These are the csv (comma separated values) files containing the fitness values you want to aggregate by gene. Since they should have been produced by the "Calculate Fitness" tool, each line besides the header should represent the following information for an insertion location: position,strand,count_1,count_2,ratio,mt_freq_t1,mt_freq_t2,pop_freq_t1,pop_freq_t2,gene,D,W,nW
-
-GenBank reference genome: the reference genome of whatever model you're working with, which needs to be in standard genbank format. For more on that format see the genbank website.
-
-Marking certain genes: If you chose to mark certain genes, those genes will have an "M" under the M column of the resulting aggregate file. 
-
-Using weighted algorithms: Recommended. If you chose to use weighted algorithms, scores will be weighted by the number of reads their insertion location has, as insertions with more reads tend to be more accurate.
-
-Weight ceiling: This value lets you set a weight ceiling for the weights of fitness values. It's only relevant if you're using weighted algorithms.
-
-Cutoff: This value lets you ignore the fitness scores of any insertion locations with an average count (the number of counts from t1 and t2 divided by 2) less than it.
-
-Blanks: This value lets you exclude a % of blank fitness scores (scores with a fitness of 0) from your calculations. It should be entered as a float (e.g. 0.10 would be 10%). This can be useful when / for !!!
-
-The name of your output file: self-explanatory. Remember to have it end in ".csv".
-
-**Additional notes**
-
-The output file should have each line (besides the header) represent the following information for a particular gene: locus,mean,var,sd,se,gene,Total,Blank,Not Blank,Blank Removed,M
-    
-</help>
-</tool>
\ No newline at end of file