# HG changeset patch
# User gianmarco_piccinno
# Date 1544546566 18000
# Node ID 9f79249901b7ba9a649e7c34abd33207516787d2
# Parent  dbbd9f0349b34c2a76d8bc7453308bf0d5b02747
Deleted selected files

diff -r dbbd9f0349b3 -r 9f79249901b7 codon_usage.py
--- a/codon_usage.py	Tue Dec 11 11:07:51 2018 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,75 +0,0 @@
-#!/home/gianmarco/galaxy-python/python
-
-import Bio
-from Bio import SeqIO
-from Bio.Data import CodonTable
-import re
-import sys
-import os
-import pandas as pd
-
-def read_input(data = "example.fna"):
-
-    seqs = ""
-    with open(data, "rU") as handle:
-        for record in SeqIO.parse(handle, "fasta"):
-            seqs = seqs + str(record.seq)
-
-    return seqs
-
-def codon_usage(seqs, codonTable):
-
-    codon_usage = {}
-    tmp = [x for x in re.split(r'(\w{3})', seqs) if x != ""]
-
-    b_cod_table = CodonTable.unambiguous_dna_by_name[codonTable].forward_table
-
-
-    for cod in CodonTable.unambiguous_dna_by_name[codonTable].stop_codons:
-        b_cod_table[cod] = "_Stop"
-
-    for cod in CodonTable.unambiguous_dna_by_name[codonTable].start_codons:
-            b_cod_table[cod + " Start"] = b_cod_table[cod]
-            b_cod_table.pop(cod)
-
-    aas = set(b_cod_table.values())
-
-
-    for aa in aas:
-        codon_usage[aa] = {}
-        for codon in b_cod_table.keys():
-            if b_cod_table[codon] == aa:
-                codon_usage[aa][codon] = tmp.count(codon.split(" ")[0])
-
-
-    tups = {(outerKey, innerKey): values for outerKey, innerDict in codon_usage.iteritems() for innerKey, values in innerDict.iteritems()}
-
-    #aas_ = set(tups.keys())
-
-    #stops_ = {el for el in aas_ if el[0] == "Stop"}
-    #aas_ = list(aas_.difference(stops_))
-    #stops_ = list(stops_)
-    #aas_.sort()
-    #stops_.sort()
-
-    codon_usage_ = pd.DataFrame(pd.Series(tups), columns = ["Count"])
-    codon_usage_.index = codon_usage_.index.set_names(["AA", "Codon"])
-    #codon_usage_.index.reindex(pd.MultiIndex.from_tuples([aas_, stops_], names=('AA', 'Codon')), level=[0,1])
-
-
-    codon_usage_['Proportion'] = codon_usage_.groupby(level=0).transform(lambda x: (x / x.sum()).round(2))
-
-    return {"Dictionary": codon_usage, "Tuples": tups, "Table": codon_usage_}
-
-
-
-if __name__ == '__main__':
-
-    
-    seqs = read_input(data=sys.argv[1])
-    out = codon_usage(seqs,"Bacterial")
-
-
-    with open(sys.argv[2], "w") as outf:
-        out["Table"].to_csv(outf, sep="\t")
-    #sys.stdout.write(out['Table'])
\ No newline at end of file
diff -r dbbd9f0349b3 -r 9f79249901b7 codon_usage.xml
--- a/codon_usage.xml	Tue Dec 11 11:07:51 2018 -0500
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,45 +0,0 @@
-<tool id="codon_usage" name="Codon Usage" version="0.1.0">
-  <description>for each sequence in a file</description>
-  <requirements>
-    <requirement type=“package” version=“3.6.0”>python</requirement>
-    <requirement type=“package” version=“1.72”>biopython</requirement>
-    <requirement type=“package” version=“0.23.4”>pandas</requirement>
-  </requirements> 
-  
-  <command>codon_usage.py -i $input -t $input_type -o $output -c $codon_table</command>
-  <inputs>
-    <param name="input" format="fasta" type="data" label="Source file"/>
-
-    <param name="input_type" type="select" format="text">
-			<label>Indicate the input file format</label>
-				<option value="fasta">Fasta</option>
-				<option value="gbk">gbk</option>
-		</param>
-
-    <param name="codon_table" type="select" format="text">
-			<label>Choose the proper codon table for your organism)</label>
-        <option value="Archaeal"> Archaeal</option>
-        <option value="Bacterial">Bacterial</option>
-        <option value="Standard">Standard</option>
-        
-		</param>
-
-  </inputs>
-
-
-
-  <outputs>
-    <data format="tabular" name="output" />
-  </outputs>
-
-  <tests>
-    <test>
-      <param name="input" value="fa_gc_content_input.fa"/>
-      <output name="out_file1" file="fa_gc_content_output.txt"/>
-    </test>
-  </tests>
-
-  <help>
-This tool compute codon usage of an annotated genome [preferably Prokaryotes].
-  </help>
-</tool>