Output to tsv instead of csv (#35)

* Update CHEBI.py

* Update bigg.metabolite.py

* Update bigg.reaction.py

* Update brenda.py

* Update metanetx.reaction.py

* Update rhea.py

* Update uniprot.py

CHEBI/CHEBI.py

@@ -51,4 +51,4 @@
 df['identifiersorgprefix1'] = 'CHEBI'
 df.rename(columns={'COMPOUND_ID': 'id1', 'TYPE': 'identifiersorgprefix2', 'ACCESSION_NUMBER': 'id2'}, inplace=True)
 df = df[['identifiersorgprefix1', 'id1', 'identifiersorgprefix2', 'id2']]
-df.to_csv('CHEBI.csv', index=False)
+df.to_csv('CHEBI.tsv', index=False, sep="\t")

bigg.metabolite/bigg.metabolite.py

@@ -6,4 +6,4 @@
 df3 = df2['database_links'].str.split('/', expand=True)
 result = pd.DataFrame({'id1': df2['universal_bigg_id'], 'identifiersorgprefix2': df3[3], 'id2': df3[4]})
 result['identifiersorgprefix1'] = 'bigg.metabolite'
-result[['identifiersorgprefix1', 'id1', 'identifiersorgprefix2', 'id2']].to_csv('bigg.metabolite.csv', index=False)
+result[['identifiersorgprefix1', 'id1', 'identifiersorgprefix2', 'id2']].to_csv('bigg.metabolite.tsv', index=False, sep="\t")

bigg.reaction/bigg.reaction.py

@@ -6,4 +6,4 @@
 df3 = df2['database_links'].str.split('/', expand=True)
 result = pd.DataFrame({'id1': df2['bigg_id'], 'identifiersorgprefix2': df3[3], 'id2': df3[4]})
 result['identifiersorgprefix1'] = 'bigg.reaction'
-result[['identifiersorgprefix1', 'id1', 'identifiersorgprefix2', 'id2']].to_csv('bigg.reaction.csv', index=False)
+result[['identifiersorgprefix1', 'id1', 'identifiersorgprefix2', 'id2']].to_csv('bigg.reaction.tsv', index=False, sep="\t")

brenda/brenda.py

@@ -5,8 +5,8 @@
 data = brenda['data']
 ecnumbers = data.keys()
 
-f = open('brenda.csv', 'w')
-f.write('identifiersorgprefix1,id1,identifiersorgprefix2,id2\n')
+f = open('brenda.tsv', 'w')
+f.write('identifiersorgprefix1\tid1\tidentifiersorgprefix2\tid2\n')
 for ecnumber in ecnumbers:
     if ecnumber != 'spontaneous':
         if 'proteins' in data[ecnumber]:
@@ -20,5 +20,5 @@
                     #     print(accession)
                     #     print(protein[0]['source'])
                     # print('brenda,' + ecnumber + ',' + protein[0]['source'] + ',' + accession)
-                    f.write('brenda,' + ecnumber + ',' + protein[0]['source'] + ',' + accession + '\n')
+                    f.write('brenda\t' + ecnumber + '\t' + protein[0]['source'] + '\t' + accession + '\n')
 f.close()

metanetx.reaction/metanetx.reaction.py

@@ -15,4 +15,4 @@
 result = pd.DataFrame({'idenfiersorg2': df4[0], 'id2': df4[1], 'id1': df5['ID']})
 result['idenfiersorg1'] = 'metanetx.reaction'
 result = result[['idenfiersorg1', 'id1', 'idenfiersorg2', 'id2']]
-result.to_csv('metanetx.reaction.csv', index=False)
+result.to_csv('metanetx.reaction.tsv', index=False, sep="\t")

rhea/rhea.py

@@ -10,4 +10,4 @@
 df = df[['identifiersorgprefix1', 'id1', 'identifiersorgprefix2', 'id2']]
 tmp = 'ECOCYC:' + df[df['identifiersorgprefix2']=='biocyc']['id2']
 df['id2'].loc[tmp.index] = tmp
-df.to_csv('CHEBI.csv', index=False)
+df.to_csv('rhea.tsv', index=False, sep="\t")

uniprot/uniprot.py

@@ -18,4 +18,4 @@
 df['identifiersorgprefix1'] = 'uniprot'
 df.rename(columns={0:'id1', 1:'identifiersorgprefix2', 2:'id2'}, inplace=True)
 df = df[['identifiersorgprefix1', 'id1', 'identifiersorgprefix2', 'id2']]
-df.to_csv('uniprot.csv', index=False)
+df.to_csv('uniprot.tsv', index=False, sep="\t")