Previous changeset 1:f3012521ea79 (2015-03-13) Next changeset 3:3a0ade59306a (2015-04-01) |
Commit message:
Uploaded corrected dynamic options |
modified:
cummeRbund.xml cummeRbund_options.py |
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diff -r f3012521ea79 -r ac2ebc60ef5d cummeRbund.xml --- a/cummeRbund.xml Fri Mar 13 15:30:17 2015 -0400 +++ b/cummeRbund.xml Mon Mar 16 15:43:02 2015 -0400 |
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@@ -1,5 +1,5 @@ <?xml version="1.0"?> -<tool id="cummeRbund" name="Plot CuffDiff" version="1.0.0"> +<tool id="cummeRbund" name="Plot CuffDiff" version="1.0.1"> <description>data with cummeRbund</description> <requirements> <requirement type="set_environment">CUMMERBUND_SCRIPT_PATH</requirement> |
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diff -r f3012521ea79 -r ac2ebc60ef5d cummeRbund_options.py --- a/cummeRbund_options.py Fri Mar 13 15:30:17 2015 -0400 +++ b/cummeRbund_options.py Mon Mar 16 15:43:02 2015 -0400 |
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@@ -1,23 +1,11 @@ -from galaxy import eggs -eggs.require( 'SQLAlchemy' ) -eggs.require( 'pysqlite>=2' ) -from sqlalchemy import * -from sqlalchemy.sql import and_ -from sqlalchemy.orm import sessionmaker -from sqlalchemy.orm import scoped_session +import sqlite3 def get_genes( database_path ): - dburi = 'sqlite:///%s' % database_path - engine = create_engine( dburi ) - meta = MetaData( bind=engine ) - db_session = Session = scoped_session( sessionmaker( bind=engine, autoflush=False, autocommit=True ) ) - gene_ids = db_session.execute( 'select gene_short_name, gene_id from genes order by gene_short_name' ) + conn = sqlite3.connect( database_path ) + gene_ids = conn.execute( 'SELECT gene_short_name, gene_id FROM genes ORDER BY gene_short_name' ) return [ ( gene_id[ 0 ], gene_id[ 1 ], False ) for gene_id in gene_ids ] def get_samples( database_path ): - dburi = 'sqlite:///%s' % database_path - engine = create_engine( dburi ) - meta = MetaData( bind=engine ) - db_session = Session = scoped_session( sessionmaker( bind=engine, autoflush=False, autocommit=True ) ) - samples = db_session.execute( 'select sample_name from samples order by sample_name' ) + conn = sqlite3.connect( database_path ) + samples = conn.execute( 'SELECT sample_name FROM samples ORDER BY sample_name' ) return [ ( sample[ 0 ], sample[ 0 ], False ) for sample in samples ] |