changeset 1:2056139d55d6 draft

Uploaded

--- a/rgToolFactory.py	Sun Oct 19 06:04:51 2014 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,662 +0,0 @@
-# rgToolFactory.py
-# see https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
-# 
-# copyright ross lazarus (ross stop lazarus at gmail stop com) May 2012
-# 
-# all rights reserved
-# Licensed under the LGPL
-# suggestions for improvement and bug fixes welcome at https://bitbucket.org/fubar/galaxytoolfactory/wiki/Home
-#
-# july 2014
-# added buffered read of sterror after run
-#
-# august 2013
-# found a problem with GS if $TMP or $TEMP missing - now inject /tmp and warn
-#
-# july 2013
-# added ability to combine images and individual log files into html output
-# just make sure there's a log file foo.log and it will be output
-# together with all images named like "foo_*.pdf
-# otherwise old format for html
-#
-# January 2013
-# problem pointed out by Carlos Borroto
-# added escaping for <>$ - thought I did that ages ago...
-#
-# August 11 2012 
-# changed to use shell=False and cl as a sequence
-
-# This is a Galaxy tool factory for simple scripts in python, R or whatever ails ye.
-# It also serves as the wrapper for the new tool.
-# 
-# you paste and run your script
-# Only works for simple scripts that read one input from the history.
-# Optionally can write one new history dataset,
-# and optionally collect any number of outputs into links on an autogenerated HTML page.
-
-# DO NOT install on a public or important site - please.
-
-# installed generated tools are fine if the script is safe.
-# They just run normally and their user cannot do anything unusually insecure
-# but please, practice safe toolshed.
-# Read the fucking code before you install any tool 
-# especially this one
-
-# After you get the script working on some test data, you can
-# optionally generate a toolshed compatible gzip file
-# containing your script safely wrapped as an ordinary Galaxy script in your local toolshed for
-# safe and largely automated installation in a production Galaxy.
-
-# If you opt for an HTML output, you get all the script outputs arranged
-# as a single Html history item - all output files are linked, thumbnails for all the pdfs.
-# Ugly but really inexpensive.
-# 
-# Patches appreciated please. 
-#
-#
-# long route to June 2012 product
-# Behold the awesome power of Galaxy and the toolshed with the tool factory to bind them
-# derived from an integrated script model  
-# called rgBaseScriptWrapper.py
-# Note to the unwary:
-#   This tool allows arbitrary scripting on your Galaxy as the Galaxy user
-#   There is nothing stopping a malicious user doing whatever they choose
-#   Extremely dangerous!!
-#   Totally insecure. So, trusted users only
-#
-# preferred model is a developer using their throw away workstation instance - ie a private site.
-# no real risk. The universe_wsgi.ini admin_users string is checked - only admin users are permitted to run this tool.
-#
-
-import sys 
-import shutil 
-import subprocess 
-import os 
-import time 
-import tempfile 
-import optparse
-import tarfile
-import re
-import shutil
-import math
-
-progname = os.path.split(sys.argv[0])[1] 
-myversion = 'V000.2 June 2012' 
-verbose = False 
-debug = False
-toolFactoryURL = 'https://bitbucket.org/fubar/galaxytoolfactory'
-buffsize = 1048576
-
-
-def timenow():
-    """return current time as a string
-    """
-    return time.strftime('%d/%m/%Y %H:%M:%S', time.localtime(time.time()))
-
-html_escape_table = {
-     "&": "&amp;",
-     ">": "&gt;",
-     "<": "&lt;",
-     "$": "\$"
-     }
-
-def html_escape(text):
-     """Produce entities within text."""
-     return "".join(html_escape_table.get(c,c) for c in text)
-
-def cmd_exists(cmd):
-     return subprocess.call("type " + cmd, shell=True, 
-           stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0
-
-
-class ScriptRunner:
-    """class is a wrapper for an arbitrary script
-    """
-
-    def __init__(self,opts=None,treatbashSpecial=True):
-        """
-        cleanup inputs, setup some outputs
-        
-        """
-        self.useGM = cmd_exists('gm')
-        self.useIM = cmd_exists('convert')
-        self.useGS = cmd_exists('gs')
-        self.temp_warned = False # we want only one warning if $TMP not set
-        self.treatbashSpecial = treatbashSpecial
-        if opts.output_dir: # simplify for the tool tarball
-            os.chdir(opts.output_dir)
-        self.thumbformat = 'png'
-        self.opts = opts
-        self.toolname = re.sub('[^a-zA-Z0-9_]+', '', opts.tool_name) # a sanitizer now does this but..
-        self.toolid = self.toolname
-        self.myname = sys.argv[0] # get our name because we write ourselves out as a tool later
-        self.pyfile = self.myname # crude but efficient - the cruft won't hurt much
-        self.xmlfile = '%s.xml' % self.toolname
-        s = open(self.opts.script_path,'r').readlines()
-        s = [x.rstrip() for x in s] # remove pesky dos line endings if needed
-        self.script = '\n'.join(s)
-        fhandle,self.sfile = tempfile.mkstemp(prefix=self.toolname,suffix=".%s" % (opts.interpreter))
-        tscript = open(self.sfile,'w') # use self.sfile as script source for Popen
-        tscript.write(self.script)
-        tscript.close()
-        self.indentedScript = '\n'.join([' %s' % x for x in s]) # for restructured text in help
-        self.escapedScript = '\n'.join([html_escape(x) for x in s])
-        self.elog = os.path.join(self.opts.output_dir,"%s_error.log" % self.toolname)
-        self.tlog = os.path.join(self.opts.output_dir,"%s_runner.log" % self.toolname)
-        if opts.output_dir: # may not want these complexities 
-            art = '%s.%s' % (self.toolname,opts.interpreter)
-            artpath = os.path.join(self.opts.output_dir,art) # need full path
-            artifact = open(artpath,'w') # use self.sfile as script source for Popen
-            artifact.write(self.script)
-            artifact.close()
-        self.cl = []
-        self.html = []
-        a = self.cl.append
-        a(opts.interpreter)
-        if self.treatbashSpecial and opts.interpreter in ['bash','sh']:
-            a(self.sfile)
-        else:
-            a('-') # stdin
-        a(opts.input_tab)
-        a(opts.output_tab)
-        self.outFormats = 'tabular' # TODO make this an option at tool generation time
-        self.inputFormats = 'tabular' # TODO make this an option at tool generation time
-        self.test1Input = '%s_test1_input.xls' % self.toolname
-        self.test1Output = '%s_test1_output.xls' % self.toolname
-        self.test1HTML = '%s_test1_output.html' % self.toolname
-
-    def makeXML(self):
-        """
-        Create a Galaxy xml tool wrapper for the new script as a string to write out
-        fixme - use templating or something less fugly than this example of what we produce
-
-        <tool id="reverse" name="reverse" version="0.01">
-            <description>a tabular file</description>
-            <command interpreter="python">
-            reverse.py --script_path "$runMe" --interpreter "python" 
-            --tool_name "reverse" --input_tab "$input1" --output_tab "$tab_file" 
-            </command>
-            <inputs>
-            <param name="input1"  type="data" format="tabular" label="Select a suitable input file from your history"/><param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="reverse"/>
-
-            </inputs>
-            <outputs>
-            <data format="tabular" name="tab_file" label="${job_name}"/>
-
-            </outputs>
-            <help>
-            
-**What it Does**
-
-Reverse the columns in a tabular file
-
-            </help>
-            <configfiles>
-            <configfile name="runMe">
-            
-# reverse order of columns in a tabular file
-import sys
-inp = sys.argv[1]
-outp = sys.argv[2]
-i = open(inp,'r')
-o = open(outp,'w')
-for row in i:
-     rs = row.rstrip().split('\t')
-     rs.reverse()
-     o.write('\t'.join(rs))
-     o.write('\n')
-i.close()
-o.close()
- 
-
-            </configfile>
-            </configfiles>
-            </tool>
-        
-        """ 
-        newXML="""<tool id="%(toolid)s" name="%(toolname)s" version="%(tool_version)s">
-            %(tooldesc)s
-            %(command)s
-            <inputs>
-            %(inputs)s
-            </inputs>
-            <outputs>
-            %(outputs)s
-            </outputs>
-            <configfiles>
-            <configfile name="runMe">
-            %(script)s
-            </configfile>
-            </configfiles>
-            %(tooltests)s
-            <help>
-            %(help)s
-            </help>
-            </tool>""" # needs a dict with toolname, toolid, interpreter, scriptname, command, inputs as a multi line string ready to write, outputs ditto, help ditto
-               
-        newCommand="""<command interpreter="python">
-            %(toolname)s.py --script_path "$runMe" --interpreter "%(interpreter)s" 
-            --tool_name "%(toolname)s" %(command_inputs)s %(command_outputs)s 
-            </command>""" # may NOT be an input or htmlout
-        tooltestsTabOnly = """<tests><test>
-        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
-        <param name="job_name" value="test1"/>
-        <param name="runMe" value="$runMe"/>
-        <output name="tab_file" file="%(test1Output)s" ftype="tabular"/>
-        </test></tests>"""
-        tooltestsHTMLOnly = """<tests><test>
-        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
-        <param name="job_name" value="test1"/>
-        <param name="runMe" value="$runMe"/>
-        <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="5"/>
-        </test></tests>"""
-        tooltestsBoth = """<tests><test>
-        <param name="input1" value="%(test1Input)s" ftype="tabular"/>
-        <param name="job_name" value="test1"/>
-        <param name="runMe" value="$runMe"/>
-        <output name="tab_file" file="%(test1Output)s" ftype="tabular" />
-        <output name="html_file" file="%(test1HTML)s" ftype="html" lines_diff="10"/>
-        </test></tests>"""
-        xdict = {}
-        xdict['tool_version'] = self.opts.tool_version
-        xdict['test1Input'] = self.test1Input
-        xdict['test1HTML'] = self.test1HTML
-        xdict['test1Output'] = self.test1Output   
-        if self.opts.make_HTML and self.opts.output_tab <> 'None':
-            xdict['tooltests'] = tooltestsBoth % xdict
-        elif self.opts.make_HTML:
-            xdict['tooltests'] = tooltestsHTMLOnly % xdict
-        else:
-            xdict['tooltests'] = tooltestsTabOnly % xdict
-        xdict['script'] = self.escapedScript 
-        # configfile is least painful way to embed script to avoid external dependencies
-        # but requires escaping of <, > and $ to avoid Mako parsing
-        if self.opts.help_text:
-            xdict['help'] = open(self.opts.help_text,'r').read()
-        else:
-            xdict['help'] = 'Please ask the tool author for help as none was supplied at tool generation'
-        coda = ['**Script**','Pressing execute will run the following code over your input file and generate some outputs in your history::']
-        coda.append(self.indentedScript)
-        coda.append('**Attribution** This Galaxy tool was created by %s at %s\nusing the Galaxy Tool Factory.' % (self.opts.user_email,timenow()))
-        coda.append('See %s for details of that project' % (toolFactoryURL))
-        coda.append('Please cite: Creating re-usable tools from scripts: The Galaxy Tool Factory. Ross Lazarus; Antony Kaspi; Mark Ziemann; The Galaxy Team. ')
-        coda.append('Bioinformatics 2012; doi: 10.1093/bioinformatics/bts573')
-        xdict['help'] = '%s\n%s' % (xdict['help'],'\n'.join(coda))
-        if self.opts.tool_desc:
-            xdict['tooldesc'] = '<description>%s</description>' % self.opts.tool_desc
-        else:
-            xdict['tooldesc'] = ''
-        xdict['command_outputs'] = '' 
-        xdict['outputs'] = '' 
-        if self.opts.input_tab <> 'None':
-            xdict['command_inputs'] = '--input_tab "$input1" ' # the space may matter a lot if we append something
-            xdict['inputs'] = '<param name="input1"  type="data" format="%s" label="Select a suitable input file from your history"/> \n' % self.inputFormats
-        else:
-            xdict['command_inputs'] = '' # assume no input - eg a random data generator       
-            xdict['inputs'] = ''
-        xdict['inputs'] += '<param name="job_name" type="text" label="Supply a name for the outputs to remind you what they contain" value="%s"/> \n' % self.toolname
-        xdict['toolname'] = self.toolname
-        xdict['toolid'] = self.toolid
-        xdict['interpreter'] = self.opts.interpreter
-        xdict['scriptname'] = self.sfile
-        if self.opts.make_HTML:
-            xdict['command_outputs'] += ' --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" '
-            xdict['outputs'] +=  ' <data format="html" name="html_file" label="${job_name}.html"/>\n'
-        if self.opts.output_tab <> 'None':
-            xdict['command_outputs'] += ' --output_tab "$tab_file"'
-            xdict['outputs'] += ' <data format="%s" name="tab_file" label="${job_name}"/>\n' % self.outFormats
-        xdict['command'] = newCommand % xdict
-        xmls = newXML % xdict
-        xf = open(self.xmlfile,'w')
-        xf.write(xmls)
-        xf.write('\n')
-        xf.close()
-        # ready for the tarball
-
-
-    def makeTooltar(self):
-        """
-        a tool is a gz tarball with eg
-        /toolname/tool.xml /toolname/tool.py /toolname/test-data/test1_in.foo ...
-        """
-        retval = self.run()
-        if retval:
-            print >> sys.stderr,'## Run failed. Cannot build yet. Please fix and retry'
-            sys.exit(1)
-        self.makeXML()
-        tdir = self.toolname
-        os.mkdir(tdir)
-        if self.opts.input_tab <> 'None': # no reproducible test otherwise? TODO: maybe..
-            testdir = os.path.join(tdir,'test-data')
-            os.mkdir(testdir) # make tests directory
-            shutil.copyfile(self.opts.input_tab,os.path.join(testdir,self.test1Input))
-            if self.opts.output_tab <> 'None':
-                shutil.copyfile(self.opts.output_tab,os.path.join(testdir,self.test1Output))
-            if self.opts.make_HTML:
-                shutil.copyfile(self.opts.output_html,os.path.join(testdir,self.test1HTML))
-            if self.opts.output_dir:
-                shutil.copyfile(self.tlog,os.path.join(testdir,'test1_out.log'))
-        op = '%s.py' % self.toolname # new name
-        outpiname = os.path.join(tdir,op) # path for the tool tarball
-        pyin = os.path.basename(self.pyfile) # our name - we rewrite ourselves (TM)
-        notes = ['# %s - a self annotated version of %s generated by running %s\n' % (op,pyin,pyin),]
-        notes.append('# to make a new Galaxy tool called %s\n' % self.toolname)
-        notes.append('# User %s at %s\n' % (self.opts.user_email,timenow()))
-        pi = open(self.pyfile,'r').readlines() # our code becomes new tool wrapper (!) - first Galaxy worm
-        notes += pi
-        outpi = open(outpiname,'w')
-        outpi.write(''.join(notes))
-        outpi.write('\n')
-        outpi.close()
-        stname = os.path.join(tdir,self.sfile)
-        if not os.path.exists(stname):
-            shutil.copyfile(self.sfile, stname)
-        xtname = os.path.join(tdir,self.xmlfile)
-        if not os.path.exists(xtname):
-            shutil.copyfile(self.xmlfile,xtname)
-        tarpath = "%s.gz" % self.toolname
-        tar = tarfile.open(tarpath, "w:gz")
-        tar.add(tdir,arcname=self.toolname)
-        tar.close()
-        shutil.copyfile(tarpath,self.opts.new_tool)
-        shutil.rmtree(tdir)
-        ## TODO: replace with optional direct upload to local toolshed?
-        return retval
-
-
-    def compressPDF(self,inpdf=None,thumbformat='png'):
-        """need absolute path to pdf
-           note that GS gets confoozled if no $TMP or $TEMP
-           so we set it
-        """
-        assert os.path.isfile(inpdf), "## Input %s supplied to %s compressPDF not found" % (inpdf,self.myName)
-        our_env = os.environ.copy()
-        if not (our_env.get('TMP',None) or our_env.get('TEMP',None)):
-            our_env['TMP'] = '/tmp'
-            if not self.temp_warned:
-               print >> sys.stdout,'## WARNING - no $TMP or $TEMP!!! Please fix - using /tmp temporarily'
-               self.temp_warned = True          
-        hlog = os.path.join(self.opts.output_dir,"compress_%s.txt" % os.path.basename(inpdf))
-        sto = open(hlog,'w')
-        outpdf = '%s_compressed' % inpdf
-        cl = ["gs", "-sDEVICE=pdfwrite", "-dNOPAUSE", "-dUseCIEColor", "-dBATCH","-dPDFSETTINGS=/printer", "-sOutputFile=%s" % outpdf,inpdf]
-        x = subprocess.Popen(cl,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
-        retval1 = x.wait()
-        sto.close()
-        if retval1 == 0:
-            os.unlink(inpdf)
-            shutil.move(outpdf,inpdf)
-            os.unlink(hlog)
-        else:
-            x = open(hlog,'r').readlines()
-            print >> sys.stdout,x
-        hlog = os.path.join(self.opts.output_dir,"thumbnail_%s.txt" % os.path.basename(inpdf))
-        sto = open(hlog,'w')
-        outpng = '%s.%s' % (os.path.splitext(inpdf)[0],thumbformat)
-        if self.useGM:        
-            cl2 = ['gm', 'convert', inpdf, outpng]
-        else: # assume imagemagick
-            cl2 = ['convert', inpdf, outpng]
-        x = subprocess.Popen(cl2,stdout=sto,stderr=sto,cwd=self.opts.output_dir,env=our_env)
-        retval2 = x.wait()
-        sto.close()
-        if retval2 <> 0:
-             x = open(hlog,'r').readlines()
-             print >> sys.stdout,x
-        else:
-             os.unlink(hlog)
-        retval = retval1 or retval2
-        return retval
-
-
-    def getfSize(self,fpath,outpath):
-        """
-        format a nice file size string
-        """
-        size = ''
-        fp = os.path.join(outpath,fpath)
-        if os.path.isfile(fp):
-            size = '0 B'
-            n = float(os.path.getsize(fp))
-            if n > 2**20:
-                size = '%1.1f MB' % (n/2**20)
-            elif n > 2**10:
-                size = '%1.1f KB' % (n/2**10)
-            elif n > 0:
-                size = '%d B' % (int(n))
-        return size
-
-    def makeHtml(self):
-        """ Create an HTML file content to list all the artifacts found in the output_dir
-        """
-
-        galhtmlprefix = """<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
-        <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> 
-        <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> 
-        <meta name="generator" content="Galaxy %s tool output - see http://g2.trac.bx.psu.edu/" /> 
-        <title></title> 
-        <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> 
-        </head> 
-        <body> 
-        <div class="toolFormBody"> 
-        """ 
-        galhtmlattr = """<hr/><div class="infomessage">This tool (%s) was generated by the <a href="https://bitbucket.org/fubar/galaxytoolfactory/overview">Galaxy Tool Factory</a></div><br/>""" 
-        galhtmlpostfix = """</div></body></html>\n"""
-
-        flist = os.listdir(self.opts.output_dir)
-        flist = [x for x in flist if x <> 'Rplots.pdf']
-        flist.sort()
-        html = []
-        html.append(galhtmlprefix % progname)
-        html.append('<div class="infomessage">Galaxy Tool "%s" run at %s</div><br/>' % (self.toolname,timenow()))
-        fhtml = []
-        if len(flist) > 0:
-            logfiles = [x for x in flist if x.lower().endswith('.log')] # log file names determine sections
-            logfiles.sort()
-            logfiles = [x for x in logfiles if os.path.abspath(x) <> os.path.abspath(self.tlog)]
-            logfiles.append(os.path.abspath(self.tlog)) # make it the last one
-            pdflist = []
-            npdf = len([x for x in flist if os.path.splitext(x)[-1].lower() == '.pdf'])
-            for rownum,fname in enumerate(flist):
-                dname,e = os.path.splitext(fname)
-                sfsize = self.getfSize(fname,self.opts.output_dir)
-                if e.lower() == '.pdf' : # compress and make a thumbnail
-                    thumb = '%s.%s' % (dname,self.thumbformat)
-                    pdff = os.path.join(self.opts.output_dir,fname)
-                    retval = self.compressPDF(inpdf=pdff,thumbformat=self.thumbformat)
-                    if retval == 0:
-                        pdflist.append((fname,thumb))
-                    else:
-                        pdflist.append((fname,fname))
-                if (rownum+1) % 2 == 0:
-                    fhtml.append('<tr class="odd_row"><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize))
-                else:
-                    fhtml.append('<tr><td><a href="%s">%s</a></td><td>%s</td></tr>' % (fname,fname,sfsize))
-            for logfname in logfiles: # expect at least tlog - if more
-                if os.path.abspath(logfname) == os.path.abspath(self.tlog): # handled later
-                    sectionname = 'All tool run'
-                    if (len(logfiles) > 1):
-                        sectionname = 'Other'
-                    ourpdfs = pdflist
-                else:
-                    realname = os.path.basename(logfname)
-                    sectionname = os.path.splitext(realname)[0].split('_')[0] # break in case _ added to log
-                    ourpdfs = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] == sectionname]
-                    pdflist = [x for x in pdflist if os.path.basename(x[0]).split('_')[0] <> sectionname] # remove
-                nacross = 1
-                npdf = len(ourpdfs)
-
-                if npdf > 0:
-                    nacross = math.sqrt(npdf) ## int(round(math.log(npdf,2)))
-                    if int(nacross)**2 != npdf:
-                        nacross += 1
-                    nacross = int(nacross)
-                    width = min(400,int(1200/nacross))
-                    html.append('<div class="toolFormTitle">%s images and outputs</div>' % sectionname)
-                    html.append('(Click on a thumbnail image to download the corresponding original PDF image)<br/>')
-                    ntogo = nacross # counter for table row padding with empty cells
-                    html.append('<div><table class="simple" cellpadding="2" cellspacing="2">\n<tr>')
-                    for i,paths in enumerate(ourpdfs): 
-                        fname,thumb = paths
-                        s= """<td><a href="%s"><img src="%s" title="Click to download a PDF of %s" hspace="5" width="%d" 
-                           alt="Image called %s"/></a></td>\n""" % (fname,thumb,fname,width,fname)
-                        if ((i+1) % nacross == 0):
-                            s += '</tr>\n'
-                            ntogo = 0
-                            if i < (npdf - 1): # more to come
-                               s += '<tr>'
-                               ntogo = nacross
-                        else:
-                            ntogo -= 1
-                        html.append(s)
-                    if html[-1].strip().endswith('</tr>'):
-                        html.append('</table></div>\n')
-                    else:
-                        if ntogo > 0: # pad
-                           html.append('<td>&nbsp;</td>'*ntogo)
-                        html.append('</tr></table></div>\n')
-                logt = open(logfname,'r').readlines()
-                logtext = [x for x in logt if x.strip() > '']
-                html.append('<div class="toolFormTitle">%s log output</div>' % sectionname)
-                if len(logtext) > 1:
-                    html.append('\n<pre>\n')
-                    html += logtext
-                    html.append('\n</pre>\n')
-                else:
-                    html.append('%s is empty<br/>' % logfname)
-        if len(fhtml) > 0:
-           fhtml.insert(0,'<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>\n')
-           fhtml.append('</table></div><br/>')
-           html.append('<div class="toolFormTitle">All output files available for downloading</div>\n')
-           html += fhtml # add all non-pdf files to the end of the display
-        else:
-            html.append('<div class="warningmessagelarge">### Error - %s returned no files - please confirm that parameters are sane</div>' % self.opts.interpreter)
-        html.append(galhtmlpostfix)
-        htmlf = file(self.opts.output_html,'w')
-        htmlf.write('\n'.join(html))
-        htmlf.write('\n')
-        htmlf.close()
-        self.html = html
-
-
-    def run(self):
-        """
-        scripts must be small enough not to fill the pipe!
-        """
-        my_env = os.environ.copy()
-        if self.treatbashSpecial and self.opts.interpreter in ['bash','sh']:
-          retval = self.runBash(pth)
-        else:
-            if self.opts.output_dir:
-                ste = open(self.elog,'w')
-                sto = open(self.tlog,'w')
-                sto.write('## Toolfactory generated command line = %s\n' % ' '.join(self.cl))
-                sto.flush()
-                p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,stdin=subprocess.PIPE,cwd=self.opts.output_dir,env=my_env)
-            else:
-                p = subprocess.Popen(self.cl,shell=False,stdin=subprocess.PIPE,env=my_env)
-            p.stdin.write(self.script)
-            p.stdin.close()
-            retval = p.wait()
-            if self.opts.output_dir:
-                sto.close()
-                ste.close()
-                # get stderr, allowing for case where it's very large
-                tmp_stderr = open( self.elog, 'rb' )
-                stderr = ''
-                try:
-                    while True:
-                        stderr += tmp_stderr.read( buffsize )
-                        if not stderr or len( stderr ) % buffsize != 0:
-                            break
-                except OverflowError:
-                    pass
-                tmp_stderr.close()
-                if retval != 0:
-                    raise Exception, stderr
-            if self.opts.make_HTML:
-                self.makeHtml()
-        return retval
-
-    def runBash(self):
-        """
-        cannot use - for bash so use self.sfile
-        """
-        if self.opts.output_dir:
-            s = '## Toolfactory generated command line = %s\n' % ' '.join(self.cl)
-            ste = open(self.elog,'w')
-            sto = open(self.tlog,'w')
-            sto.write(s)
-            sto.flush()
-            p = subprocess.Popen(self.cl,shell=False,stdout=sto,stderr=ste,cwd=self.opts.output_dir)
-        else:
-            p = subprocess.Popen(self.cl,shell=False)            
-        retval = p.wait()
-        if self.opts.output_dir:
-            sto.close()
-            ste.close()
-            # get stderr, allowing for case where it's very large
-            tmp_stderr = open(self.elog, 'rb' )
-            stderr = ''
-            try:
-                while True:
-                    stderr += tmp_stderr.read( buffsize )
-                    if not stderr or len( stderr ) % buffsize != 0:
-                        break
-            except OverflowError:
-                pass
-            tmp_stderr.close()
-            if retval != 0:
-                raise Exception, stderr
-        if self.opts.make_HTML:
-            self.makeHtml()
-        return retval
-  
-
-def main():
-    u = """
-    This is a Galaxy wrapper. It expects to be called by a special purpose tool.xml as:
-    <command interpreter="python">rgBaseScriptWrapper.py --script_path "$scriptPath" --tool_name "foo" --interpreter "Rscript"
-    </command>
-    """
-    op = optparse.OptionParser()
-    a = op.add_option
-    a('--script_path',default=None)
-    a('--tool_name',default=None)
-    a('--interpreter',default=None)
-    a('--output_dir',default=None)
-    a('--output_html',default=None)
-    a('--input_tab',default="None")
-    a('--output_tab',default="None")
-    a('--user_email',default='Unknown')
-    a('--bad_user',default=None)
-    a('--make_Tool',default=None)
-    a('--make_HTML',default=None)
-    a('--help_text',default=None)
-    a('--tool_desc',default=None)
-    a('--new_tool',default=None)
-    a('--tool_version',default=None)
-    opts, args = op.parse_args()
-    assert not opts.bad_user,'UNAUTHORISED: %s is NOT authorized to use this tool until Galaxy admin adds %s to admin_users in universe_wsgi.ini' % (opts.bad_user,opts.bad_user)
-    assert opts.tool_name,'## Tool Factory expects a tool name - eg --tool_name=DESeq'
-    assert opts.interpreter,'## Tool Factory wrapper expects an interpreter - eg --interpreter=Rscript'
-    assert os.path.isfile(opts.script_path),'## Tool Factory wrapper expects a script path - eg --script_path=foo.R'
-    if opts.output_dir:
-        try:
-            os.makedirs(opts.output_dir)
-        except:
-            pass
-    r = ScriptRunner(opts)
-    if opts.make_Tool:
-        retcode = r.makeTooltar()
-    else:
-        retcode = r.run()
-    os.unlink(r.sfile)
-    if retcode:
-        sys.exit(retcode) # indicate failure to job runner
-
-
-if __name__ == "__main__":
-    main()
-
-

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/rglasso.xml	Tue Oct 21 22:53:49 2014 -0400
@@ -0,0 +1,348 @@
+<tool id="rglasso" name="Lasso" version="0.01">
+  <description>and ridge regression using glmnet</description>
+  <requirements>
+      <requirement type="package" version="3.1.1">R_3_1_1</requirement>
+      <requirement type="package" version="1.3.18">graphicsmagick</requirement>
+      <requirement type="package" version="9.10">ghostscript</requirement>
+      <requirement type="package" version="2.14">glmnet_lars_2_14</requirement>
+  </requirements>
+   <stdio>
+     <exit_code range="4"   level="fatal"   description="Number of subject ids must match total number of samples in the input matrix" />
+   </stdio>
+  <command interpreter="python">
+     rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "rglasso" 
+    --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
+  </command>
+  <inputs>
+     <param name="title" type="text" value="lasso test" size="80" label="Title for job outputs" help="Typing a short, meaningful text here will help remind you (and explain to others) what the outputs represent">
+      <sanitizer invalid_char="">
+        <valid initial="string.letters,string.digits"><add value="_" /> </valid>
+      </sanitizer>
+    </param>
+    <param name="input1"  type="data" format="tabular" label="Select an input tabular file. Each row should contain a different measurement (phenotype) to be analysed."
+    multiple='False' help="Tabular data with samples as rows, phenotypes as columns with a header row of names and unique sample ID in first column" />
+    <param name="yvar_cols" label="Select columns containing variables to use as dependent (y) in lasso regressions" type="data_column" data_ref="input1" numerical="True" 
+         multiple="True" use_header_names="True"  help = "Each selected variable will be run and reported separately."/>
+    <param name="xvar_cols" label="Select columns containing variables to use as predictor (x) vars in each y lasso" type="data_column" data_ref="input1" numerical="True" 
+         multiple="True" use_header_names="True" />
+    <param name="logtrans" type="select" label="Perform a log transformation on all predictors" >
+            <option value="False" selected="true">No log transformation of predictors</option>
+            <option value="True">Log 2 transform predictors before model</option>
+    </param>
+    <param name="do_standard" type="select" label="Standardise x vars" 
+         help="If all measurements on same scale, may not be needed. Coefficients are always returned on the original scale.">
+            <option value="False" selected="true">No standardisation of predictors</option>
+            <option value="True">Standardise predictors before model</option>
+    </param>
+    <param name="alpha" type="float" value="0.95" size="5" min="0.01" max="1.0" label="Alpha - see glmnet docs. 1-> lasso. 0.025-> ridge regression"
+     help="Default 0.95 allows lasso to cope better with expected predictor collinearity. Use (eg) 0.5 for hybrid regularised regression or (eg) 0.025 for ridge regression"/>
+    <param name="nfold" type="integer" value="10" size="5" label="Number of folds for internal cross validation"
+     help="Default of 10 is usually ok"/>
+    <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"
+     help="Conventional default value of 0.05 recommended"/>
+    <param name="fdrtype" type="select" label="FDR (Type II error) control method" 
+         help="Use fdr or bh typically to control for the number of tests in a reliable way">
+            <option value="fdr" selected="true">fdr</option>
+            <option value="BH">Benjamini Hochberg</option>
+            <option value="BY">Benjamini Yukateli</option>
+            <option value="bonferroni">Bonferroni</option>
+            <option value="hochberg">Hochberg</option>
+            <option value="holm">Holm</option>
+            <option value="hommel">Hommel</option>
+            <option value="none">no control for multiple tests</option>
+    </param>
+  </inputs>
+  <outputs>
+    <data format="html" name="html_file" label="${title}.html"/>
+    <data format="tabular" name="model_file" label="${title}_modelres.xls"/>
+  </outputs>
+ <tests>
+<test>
+<param name='finput' value='test_bams2mx.xls' ftype='tabular' />
+ <param name='treatment_name' value='case' />
+ <param name='title' value='edgeRtest' />
+ <param name='useNDF' value='' />
+ <param name='fdrtype' value='fdr' />
+ <param name='priordf' value="0" />
+ <param name='fdrthresh' value="0.05" />
+ <param name='control_name' value='control' />
+ <param name='subjectids' value='' />
+  <param name='Treat_cols' value='3,4,5,9' />
+ <param name='Control_cols' value='2,6,7,8' />
+ <output name='outtab' file='edgeRtest1out.xls' compare='diff' />
+ <output name='html_file' file='edgeRtest1out.html'  compare='diff' lines_diff='20' />
+</test>
+</tests>
+
+<configfiles>
+<configfile name="runme">
+<![CDATA[
+library('glmnet')
+library('lars')
+
+do_lasso = function(x=NA,y=NA,do_standard=T,p.cutoff=0.05,debugOn=T,defaultFam="gaussian",logtransform=T,descr='description',
+                    indx=1,target='target',sane=F,alpha=1.0,nfold=10)
+{
+  logf = file(paste(target,"rglasso.log",sep='_'), open = "wt")
+  sink(logf,type = c("output", "message"))
+  res = NULL
+  phe_is_bin = (length(unique(y)) == 2)
+  cat('i=',indx,'target=',target,'logtrans=',logtransform,'is binary=',phe_is_bin,'dim(x)=',dim(x),'length(y)=',length(y),'\n')
+  fam = "gaussian"
+  if (defaultFam %in% c("poisson","binomial","gaussian","multinomial")) fam=defaultFam
+  if (phe_is_bin == T) {
+    fam = "binomial"
+  } 
+  do_standard = do_standard
+  standardize = do_standard 
+  normalize = do_standard
+  if (fam == "binomial") 
+  {
+    larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.logistic = "modified.Newton" ),T)
+  } else {
+    larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.gaussian="covariance"),T)
+  }
+  if (class(larsres) == "try-error")
+  {
+    print.noquote(paste('Unable to run glmnet on your data for',target))
+    return(NA)
+  }
+  mt = paste(descr,'glmnet on',target)
+  outpdf = paste(target,descr,'glmnetPath.pdf',sep='_')
+  pdf(outpdf)
+  try(
+      {
+      plot(larsres,main=mtitle,label=T) 
+      grid()
+      dev.off()
+      },T)
+  outpdf = paste(target,descr,'glmnetDeviance.pdf',sep='_')
+  pdf(outpdf)
+  mt2 = paste(descr,'Deviance for',target)
+  try( {
+        plot(larsres,xvar="dev",main=mt2,label=T)
+        grid()
+        dev.off()
+        },T)
+  larscv = NA
+  if (fam=="binomial") {
+    tmain = paste(target,'AUC')
+    outpdf = paste(target,descr,'glmnetCV_AUC.pdf',sep='_')
+    larscv = try(cv.glmnet(x=x,y=y,family=fam,type.measure='auc'),T)
+  } else {
+    tmain = paste(target,'CV MSE')
+    outpdf = paste(target,descr,'glmnetCV_MSE.pdf',sep='_')
+    larscv = try(cv.glmnet(x,y,family=fam,type.measure='mse'),T)
+  }
+  if (class(larscv) == "try-error") { return(NA) }
+  pdf(outpdf)
+  try(
+     {
+     plot(larscv,main=tmain)
+     grid()
+     dev.off()
+     },T)
+  best_lambda = larscv\$lambda.min
+  bestpred = as.matrix(coef(larscv, s = "lambda.min"))
+  inmodel = which(bestpred != 0)
+  coefs = bestpred[inmodel,1]
+  preds = rownames(bestpred)[inmodel]
+  names(coefs) = preds
+  if (debugOn) {cat(indx,'best_lambda=',best_lambda,'saving',fam,'respreds=',names(coefs),'as predictors of',target,'coefs=',coefs,'\n')}
+  res = try(data.frame(i=indx,pred=target,regulator=names(coefs),coef=coefs,glmnet_model=fam),T)
+  if (class(res) == "try-error") { return(NA) }
+  print.noquote(res)
+  return(res)
+  sink()
+}
+
+dolasso_generic = function(predvars=NA,depvars=NA,debugOn=T,p.cutoff = 0.05,maxsteps=100, alpha=alpha,nfold=10,xcolnames=c(),ycolnames=c(),
+                           descr="describe me",logtransform=F,do_standard=F,defaultFam="gaussian")
+{
+  xdat = predvars
+  if (logtransform) {
+        small = 1e-10
+        predvars = try(log(predvars+small),T)
+        descr = paste('logxformed',descr,sep='_')
+  }
+  if (class(predvars) == "try-error")
+          {
+          print.noquote('Unable to log transform your predictors')
+          return(NA)
+          }
+  xm = as.matrix(xdat)
+  res = NULL
+  depnames = ycolnames
+  ndep = length(depnames)
+  for (i in c(1:max(1,ndep)))   {
+    target = depnames[i]
+    if (length(target) < 1) { target='y' }
+    if (i %% 100 == 0) { cat(i,target,'\n') }
+    if (ndep <= 1) {
+       y=depvars
+    } else {
+       y = depvars[,i]
+    }
+    x = xm
+    ok = complete.cases(x,y)
+    if (sum(! ok) > 0) {
+      y = y[(ok)]
+      x = xm[(ok),]
+    }
+    if (sum(ok) == 0 ) {
+      print(paste("No complete cases found for",target,"in input x dim =",dim(xm),"length y=",length(y)))
+    } else {
+      regres = do_lasso(x=x,y=y,do_standard=do_standard,p.cutoff=p.cutoff,debugOn=debugOn,defaultFam=defaultFam,
+                        logtransform=logtransform,descr=descr,indx=i,target=target,alpha=alpha,nfold=nfold)
+      if (! is.na(regres)) { res = rbind(res,regres) }
+    }
+    
+  }
+  if (! is.na(res)) {
+    rownames(res) = c(1:length(res\$i))
+  }
+  return(res)
+}
+
+
+
+
+corPlot=function(xdat=c(),main='main title',is_raw=T)
+{
+  library(pheatmap)   
+  library(gplots)
+  if (is_raw) {
+    cxdat = cor(xdat,method="spearman",use="pairwise.complete.obs")
+  } else {
+    cxdat=xdat
+  }
+  xro = nrow(cxdat)
+  if (xro > 1000) stop("Too many rows for heatmap, who can read?!")
+  fontsize_col = 5.0
+  pheatmap(cxdat, main=main, show_colnames = F, width=30, height=30,
+           fontsize_row=fontsize_col, border_color=NA)
+}
+
+
+
+
+mdsPlot = function(dm,myTitle,groups)
+{
+  
+  samples = colnames(dm)
+  mt = paste(unlist(strsplit(myTitle,' ')),collapse=" ")
+  outpdfname=paste(mt,"MDS.pdf",sep='_')
+  gu = unique(groups)
+  colours = rainbow(length(gu),start=0.1,end=0.9)
+  pcols = colours[match(groups,gu)]
+  
+  mydata = t(dm) 
+  d = dist(mydata) 
+  fit = cmdscale(d,eig=TRUE, k=2) 
+  
+  x = fit\$points[,1]
+  y = fit\$points[,2]
+  pdf(outpdfname)
+  plot(x, y, xlab="Dimension 1", ylab="Dimension 2",
+       main=paste(mt,"MDS Plot"),type="n", col=pcols)
+  text(x, y, labels = row.names(mydata), cex=0.3, col=pcols)
+  grid(col="lightgray",lty="dotted")
+  dev.off()
+}
+
+runTest = function(n=10)
+{  
+  set.seed (NULL)
+  Y = data.frame(y1=runif (n),y2=runif(n))
+  Xv <- runif(n*n)
+  X <- matrix(Xv, nrow = n, ncol = n)
+  
+  mydf <- data.frame(Y, X)
+  
+  regres_out = dolasso_generic(predvars=X,depvars=Y,debugOn=T,p.cutoff = 0.05,maxsteps=10000,nfold=10,
+                               descr='randomdata',logtransform=F,do_standard=do_standard,defaultFam="gaussian",alpha=0.05)
+  return(regres_out)
+}
+]]>
+alpha = $alpha
+nfold = $nfold
+Out_Dir = "$html_file.files_path"
+Input =  "$input1"
+yvar_cols = "$yvar_cols"
+xvar_cols= "$xvar_cols"
+fdrtype = "$fdrtype"
+fdrthresh = $fdrthresh
+myTitle = "$title"
+outtab = "$model_file"
+logtrans = as.logical("$logtrans")
+do_standard = as.logical("$do_standard")
+indat = read.table(Input,head=T,sep='\t')
+datcols = colnames(indat)
+if (length(yvar_cols) == 1) {yvar_cols = c(yvar_cols)}
+if (length(xvar_cols) == 1) {xvar_cols = c(xvar_cols)}
+yvar_cols = as.numeric(strsplit(yvar_cols,",")[[1]])
+xvar_cols = as.numeric(strsplit(xvar_cols,",")[[1]])
+y = indat[,yvar_cols]
+x = indat[,xvar_cols]
+xcolnames = colnames(indat)[xvar_cols]
+ycolnames = colnames(indat)[yvar_cols]
+cat('Got yvar=',paste(ycolnames,collapse=','),'cols',yvar_cols,'n preds=',length(xcolnames),'\n')
+if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
+regres_out = dolasso_generic(predvars=x,depvars=y,debugOn=T,p.cutoff = fdrthresh,maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames,
+                             descr=myTitle,logtransform=logtrans,do_standard=do_standard,defaultFam="gaussian",alpha=alpha)
+print.noquote(regres_out)
+write.table(regres_out,outtab,quote=FALSE, sep="\t",row.names=F)
+sessionInfo()
+
+</configfile>
+</configfiles>
+<help>
+    
+**What it does**
+
+Runs Bioconductor glmnet code on your data
+glmnet allows you to use extremely efficient code providing blazing fast coordinate descent solution to lasso (or by altering alpha from 1.0, hybrid ridge/elastic-net) 
+regularised regression models. These are highly optimised forward stepwise regressions which (in very broad terms) use penalties on
+the number of coefficients in a clever way and usually find a pretty good, sane, sparse set of predictors for the dependent variable. 
+
+These provide all optimal solutions starting with all zero regression parameters, allowing one to become non-zero at a time, stepwise.
+
+Internal cross validation is used to optimise the choice of lambda based on CV AUC for logistic (binomial outcome) models, or CV mse for gaussian.
+
+Sufficiently big jobs will take a while (eg each lasso regression with 20k features on 1k samples takes about 2 minutes on our aged cluster)
+
+**Input**
+
+Assuming you have more measurements than samples, you supply data as a tabular text file where each row is a sample and columns
+are variables. You specify which columns are dependent (predictors) and which are observations for each sample. Each of multiple
+dependent variable columns will be run and reported independently.
+**Output**
+
+For each selected dependent regression variable, a brief report of the model coefficients predicted at the
+'optimal' nfold CV value of lambda.
+
+**Attributions**
+
+glmnet_ is a tutorial introduction worth reading - based on the vignette but easier to follow.
+
+
+
+See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package.
+
+Galaxy_ (that's what you are using right now!) for gluing everything together 
+
+Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is 
+licensed to you under the LGPL_ like other rgenetics artefacts
+
+.. _LGPL: http://www.gnu.org/copyleft/lesser.html
+.. _glmnet: http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html
+.. _Galaxy: http://getgalaxy.org
+
+http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html
+
+</help>
+
+</tool>
+
+
+

--- a/rglasso_cox.xml	Sun Oct 19 06:04:51 2014 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,490 +0,0 @@
-<tool id="rglasso_cox" name="Lasso" version="0.02">
-  <description>and cox regression using elastic net</description>
-  <requirements>
-      <requirement type="package" version="3.1.1">R_3_1_1</requirement>
-      <requirement type="package" version="1.3.18">graphicsmagick</requirement>
-      <requirement type="package" version="9.10">ghostscript</requirement>
-      <requirement type="package" version="2.14">glmnet_lars_2_14</requirement>
-  </requirements>
-  <command interpreter="python">
-     rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "rglasso" 
-    --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
-  </command>
-  <inputs>
-     <param name="title" type="text" value="lasso test" size="80" label="Title for job outputs" help="Typing a short, meaningful text here will help remind you (and explain to others) what the outputs represent">
-      <sanitizer invalid_char="">
-        <valid initial="string.letters,string.digits"><add value="_" /> </valid>
-      </sanitizer>
-    </param>
-    <param name="input1"  type="data" format="tabular" label="Select an input tabular text file from your history. Rows represent samples; Columns are measured phenotypes"
-    multiple='False' help="Tabular text data with samples as rows, phenotypes as columns with a header row of column identifiers" />
-    <param name="xvar_cols" label="Select columns containing numeric variables to use as predictor (x) variables" type="data_column" data_ref="input1" numerical="True" 
-         multiple="True" use_header_names="True" />
-    <param name="force_xvar_cols" label="Select numeric columns containing variables ALWAYS included as predictors in cross validation" type="data_column" data_ref="input1" numerical="True" 
-         multiple="True" use_header_names="True" optional="True"/>
-    <conditional name="model">
-        <param name="fam" type="select" label="GLM Link function for models" 
-             help="Binary dependant variables will automatically be set to Binomial no matter what this is set to">
-             "gaussian","binomial","poisson","multinomial","cox","mgaussian"
-                <option value="gaussian" selected="true">Gaussian - continuous dependent (y)</option>
-                <option value="binomial">Binomial dependent variables</option>
-                <option value="poisson">Poisson (eg counts)</option>
-                <option value="cox">Cox models - require special setup for y variables - see below</option>
-        </param>
-        <when value="gaussian">
-            <param name="yvar_cols" label="Select numeric columns containing variables to use as the dependent (y) in elasticnet" type="data_column" data_ref="input1" numerical="True" 
-             multiple="True" use_header_names="True"  help = "If multiple, each will be modelled against all the x variables and reported separately."/>
-        </when>
-        <when value="binomial">
-            <param name="yvar_cols" label="Select numeric columns containing variables to use as the dependent (y) in elasticnet" type="data_column" data_ref="input1" numerical="True" 
-             multiple="True" use_header_names="True"  help = "If multiple, each will be modelled against all the x variables and reported separately."/>
-        </when>
-        <when value="poisson">
-            <param name="yvar_cols" label="Select columns containing variables to use as the dependent (y) in elasticnet" type="data_column" data_ref="input1" numerical="True" 
-             multiple="True" use_header_names="True"  help = "If multiple, each will be modelled against all the x variables and reported separately."/>
-        </when>
-        <when value="cox">
-             <param name="cox_time" label="Select column containing time under observation for Cox regression"
-                 type="data_column" data_ref="input1" numerical="True" optional="False"
-                 multiple="False" use_header_names="True"  help = "This MUST contain a time period - eg continuous years or days to failure or right censoring"/>
-             <param name="cox_status" label="Select column containing status = 1 for outcome of interest at the end of the time under observation or 0 for right censoring"
-                 type="data_column" data_ref="input1" numerical="True" optional="False"
-                 multiple="False" use_header_names="True"  help = "This MUST contain 1 for subjects who had an event at that time or 0 for a right censored observation"/>
-        </when>
-    </conditional>
-    <param name="logtrans" type="select" label="Perform a log transformation on all predictors" help="A tiny number (1e-10) will be added to prevent taking logs of zero">
-            <option value="False" selected="true">No log transformation of predictors</option>
-            <option value="True">Log 2 transform predictors before modelling - will fail if negative values!</option>
-    </param>
-    <param name="do_standard" type="select" label="Standardise x vars" 
-         help="If all measurements on same scale, may not be needed. Coefficients are always returned on the original scale.">
-            <option value="False" selected="true">No standardisation of predictors</option>l
-            
-            <option value="True">Standardise predictors before model</option>
-    </param>
-    <param name="alpha" type="float" value="0.95" size="5" min="0.01" max="1.0" label="Alpha - see glmnet docs. 1 for pure lasso. 0.0 for pure ridge regression"
-     help="Default 0.95 allows lasso to cope better with expected predictor collinearity. Use (eg) 0.5 for hybrid regularised regression or (eg) 0.025 for ridge regression"/>
-    <param name="nfold" type="integer" value="10" size="5" label="Number of folds for internal cross validation"
-     help="Default of 10 is usually ok"/>
-  </inputs>
-  <outputs>
-    <data format="html" name="html_file" label="${title}.html"/>
-    <data format="tabular" name="model_file" label="${title}_modelres.xls"/>
-  </outputs>
- <tests>
-    <test>
-     <param name='input1' value='cox_test.xls' ftype='tabular' />
-     <param name='treatment_name' value='case' />
-     <param name='title' value='Cox glmnet test' />
-     <param name='nfold' value='10' />
-     <param name='logtrans' value='False' />
-     <param name='alpha' value='0.95' />
-     <param name='do_standard' value="True" />
-     <param name='cox_time' value='1' />
-     <param name='cox_status' value='2' />
-     <param name='fam' value='cox' />
-     <param name='model.yvar_cols' value='' />
-     <param name='xvar_cols' value='3,4,5' />
-     <param name='force_xvar_cols' value='3' />
-     <output name='model_file'> 
-          <assert_contents>
-                <has_text text="rhubarb" />
-                <has_text text="TRUE" />
-                <!-- &#009; is XML escape code for tab -->
-                <has_line line="regulator&#009;partial_likelihood&#009;forced_in&#009;glmnet_model&#009;best_lambda" />
-                <has_n_columns n="5" />
-           </assert_contents>
-     </output>
-     <output name='html_file' file='coxlassotest.html'  compare='diff' lines_diff='10' />
-    </test>
-</tests>
-<help>
-
-**Before you start**
-
-Please read the glmnet documentation @ glmnet_
-
-This Galaxy wrapper merely exposes that code and the glmnet_ documentation is essential reading
-before getting useful results here.
-
-**What it does**
-
-From documentation at glmnet_ ::
-
- Glmnet is a package that fits a generalized linear model via penalized maximum likelihood.
- The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda.
- The algorithm is extremely fast, and can exploit sparsity in the input matrix x.
- It fits linear, logistic and multinomial, poisson, and Cox regression models.
- A variety of predictions can be made from the fitted models.
-
-All solutions starting with all zero regression parameters, allowing one to become non-zero at a time 
-in a forward stepwise manner, are estimated by coordinate descent, each point corresponding to a value for lambda.
-
-Internal cross validation is used to optimise the choice of lambda based on CV AUC for logistic (binomial outcome) models, or CV mse for gaussian.
-
-Sufficiently big jobs will take a while (eg each lasso regression with 20k features on 1k samples takes about 2-3 minutes on our aged cluster)
-
-**Input**
-
-Assuming you have more measurements than samples, you supply data as a tabular text file where each row is a sample and columns
-are variables. You specify which columns are dependent (predictors) and which are observations for each sample. Each of multiple
-dependent variable columns will be run and reported independently. Prectors can be forced in to the model.
-
-**Output**
-
-For each selected dependent regression variable, a brief report of the model coefficients predicted at the
-'optimal' nfold CV value of lambda.
-
-**Attributions**
-
-glmnet_ is the R package exposed by this Galaxy tool.
-
-Galaxy_ (that's what you are using right now!) for gluing everything together 
-
-Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is 
-licensed to you under the LGPL_ like other rgenetics artefacts
-
-.. _LGPL: http://www.gnu.org/copyleft/lesser.html
-.. _glmnet: http://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html
-.. _Galaxy: http://getgalaxy.org
-
-
-</help>
-
-<configfiles>
-<configfile name="runme">
-<![CDATA[
-library('glmnet')
-library('lars')
-
-dolasso_cox = function(x,y,debugOn=F,maxsteps=10000,nfold=10,xcolnames,ycolnames,
-                             descr='Cox test',logtransform=F,do_standard=F,alpha=0.9,penalty=c())
-{
-  logf = file("cox_rglasso.log", open = "wt")
-  sink(logf,type = c("output", "message"))
-  res = NULL
-  
-  do_standard = do_standard
-  standardize = do_standard 
-  normalize = do_standard
-  larsres = try(glmnet(x,y,family='cox',standardize=standardize,alpha=alpha ),T)
-  if (class(larsres) == "try-error")
-  {
-    print.noquote('Unable to run cox glmnet on your data')
-    print(larsres)
-    sink()
-    return(NA)
-  }
-  minlambda = min(larsres\$lambda)
-  print(paste('All partial likelihood coefficients from Cox model at minimum lambda of ',minlambda,':',sep=''))
-  print(coef(larsres,s=minlambda))
-  outpdf = paste('cox',descr,'glmnetdev.pdf',sep='_')
-  try(
-      {
-      pdf(outpdf)
-      plot(larsres,main='cox glmnet',label=T) 
-      grid()
-      dev.off()
-      },T)
-  
-  larscv = NA
- 
-  larscv = try(cv.glmnet(x,y,family=fam,type.measure='deviance',penalty=penalty),T)
-  if (class(larscv) == "try-error") { return(NA) }
-  outpdf = paste('cox',descr,'glmnet_cvdeviance.pdf',sep='_')
-
-  try(
-     {
-     pdf(outpdf)
-     plot(larscv,main='Deviance',label=T)
-     grid()
-     dev.off()
-     },T)
-  best_lambda = larscv\$lambda.min
-  bestcoef = coef(larscv, s = "lambda.min")
-  inmodel = which(bestcoef != 0)
-  coefs = bestcoef[inmodel,1]
-  preds = rownames(bestcoef)[inmodel]
-  names(coefs) = preds
-  pen = as.logical( ! penalty[inmodel])
-  if (debugOn) {
-      print.noquote(paste('best_lambda=',best_lambda,'saving cox respreds=',paste(names(coefs),collapse=','),'as predictors of survival. Coefs=',paste(coefs,collapse=',')))
-      }
-  res = try(data.frame(regulator=names(coefs),partial_likelihood=coefs,forced_in=pen,glmnet_model='cox',best_lambda=best_lambda),T)
-  if (class(res) == "try-error") { return(NA) }
-  if (debugOn) {
-      print.noquote('CV results:')
-      print.noquote(res)
-      }
-  sink()
-  return(res)
-
-}
-
-do_lasso = function(x=NA,y=NA,do_standard=T,debugOn=T,defaultFam="gaussian",logtransform=T,descr='description',
-                    indx=1,target='target',sane=F,alpha=1.0,nfold=10,penalty=c())
-{ 
-  logf = file(paste(target,"rglasso.log",sep='_'), open = "wt")
-  sink(logf,type = c("output", "message"))
-  res = NA
-  phe_is_bin = (length(unique(y)) == 2)
-  forcedin = paste(colnames(x)[which(penalty == 0)],collapse=',')
-  print.noquote(paste('i=',indx,'target=',target,'logtrans=',logtransform,'is binary=',phe_is_bin,'dim(x)=',paste(dim(x),collapse=','),'length(y)=',length(y),'force=',forcedin))
-  fam = "gaussian"
-  if (defaultFam %in% c("poisson","binomial","gaussian","multinomial")) fam=defaultFam
-  if (phe_is_bin == T) {
-    fam = "binomial"
-  } 
-  do_standard = do_standard
-  standardize = do_standard 
-  normalize = do_standard
-  if (fam == "binomial") 
-  {
-    larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.logistic = "modified.Newton" ),T)
-  } else {
-    larsres = try(glmnet(x,y,family=fam,standardize=standardize,maxit=10000,alpha=alpha,type.gaussian="covariance"),T)
-  }
-  if (class(larsres) == "try-error")
-  {
-    print.noquote(paste('Unable to run glmnet on your data for',target))
-    sink()
-    return(NA)
-  }
-  mt = paste(descr,'glmnet on',target)
-  outpdf = paste(target,descr,'glmnetPath.pdf',sep='_')
-  pdf(outpdf)
-  try(
-      {
-      plot(larsres,main=mt,label=T) 
-      grid()
-      dev.off()
-      },T)
-  outpdf = paste(target,descr,'glmnetDeviance.pdf',sep='_')
-  pdf(outpdf)
-  mt2 = paste(descr,'Deviance for',target)
-  try( {
-        plot(larsres,xvar="dev",main=mt2,label=T)
-        grid()
-        dev.off()
-        },T)
-  larscv = NA
-  if (fam=="binomial") {
-    tmain = paste(target,'AUC')
-    outpdf = paste(target,descr,'glmnetCV_AUC.pdf',sep='_')
-    larscv = try(cv.glmnet(x=x,y=y,family=fam,type.measure='auc',penalty=penalty),T)
-  } else {
-    tmain = paste(target,'CV MSE')
-    outpdf = paste(target,descr,'glmnetCV_MSE.pdf',sep='_')
-    larscv = try(cv.glmnet(x,y,family=fam,type.measure='mse',penalty=penalty),T)
-  }
-  if (class(larscv) == "try-error") { return(NA) }
-  pdf(outpdf)
-  try(
-     {
-     plot(larscv,main=tmain)
-     grid()
-     dev.off()
-     },T)
-  ipenalty = c(0,penalty)
-  ### must include intercept which is always forced
-  best_lambda = larscv\$lambda.min
-  bestpred = as.matrix(coef(larscv, s = "lambda.min"))
-  inmodel = which(bestpred != 0)
-  coefs = bestpred[inmodel,1]
-  iforced = ipenalty[inmodel]
-  forced = ! as.logical(iforced)
-  preds = rownames(bestpred)[inmodel]
-  names(coefs) = preds
-  if (debugOn) {cat(indx,'best_lambda=',best_lambda,'saving',fam,'respreds=',names(coefs),'as predictors of',target,'coefs=',coefs,'\n')}
-  res = try(data.frame(i=indx,pred=target,regulator=names(coefs),coef=coefs,forced_in=forced,glmnet_model=fam),T)
-  if (class(res) == "try-error") {
-    sink()
-    return(NA) }
-  print.noquote(res)
-  sink()
-  return(res)
-}
-
-dolasso_generic = function(predvars=NA,depvars=NA,debugOn=T,maxsteps=100, alpha=alpha,nfold=10,xcolnames=c(),ycolnames=c(),
-                           descr="describe me",logtransform=F,do_standard=F,defaultFam="gaussian",penalty=penalty)
-{
-  xdat = predvars
-  if (logtransform) {
-        small = 1e-10
-        try( { lpred = log(predvars) },T)
-        if (class(lpred) == "try-error")
-            try( { lpred = log(predvars+small) }, T)
-            if (class(lpred) == "try-error")
-            {
-            print.noquote('Unable to logtransform your data')
-            return(NA)
-            }
-        descr = paste('logx',descr,sep='_')
-  }
-
-  xm = as.matrix(xdat)
-  res = NA
-  depnames = ycolnames
-  ndep = length(depnames)
-  for (i in c(1:max(1,ndep)))   {
-    target = depnames[i]
-    if (length(target) < 1) { target='y' }
-    if (i %% 100 == 0) { cat(i,target,'\n') }
-    if (ndep <= 1) {
-       y=depvars
-    } else {
-       y = depvars[,i]
-    }
-    x = xm
-    ok = complete.cases(x,y)
-    if (sum(! ok) > 0) {
-      y = y[(ok)]
-      x = xm[(ok),]
-    }
-    if (sum(ok) == 0 ) {
-      print(paste("No complete cases found for",target,"in input x dim =",paste(dim(xm),collapse=','),"length y=",length(y)))
-    } else {
-      regres = do_lasso(x=x,y=y,do_standard=do_standard,debugOn=debugOn,defaultFam=defaultFam,
-                        logtransform=logtransform,descr=descr,indx=i,target=target,alpha=alpha,nfold=nfold,penalty=penalty)
-      if (! is.na(regres)) { res = rbind(res,regres) }
-    }
-    
-  }
-  if (! is.na(res)) {
-    rownames(res) = c(1:length(res\$i))
-  }
-  return(res)
-}
-
-
-
-
-corPlot=function(xdat=c(),main='main title',is_raw=T)
-{
-  library(pheatmap)   
-  library(gplots)
-  if (is_raw) {
-    cxdat = cor(xdat,method="spearman",use="pairwise.complete.obs")
-  } else {
-    cxdat=xdat
-  }
-  xro = nrow(cxdat)
-  if (xro > 1000) stop("Too many rows for heatmap, who can read?!")
-  fontsize_col = 5.0
-  pheatmap(cxdat, main=main, show_colnames = F, width=30, height=30,
-           fontsize_row=fontsize_col, border_color=NA)
-}
-
-
-
-
-mdsPlot = function(dm,myTitle,groups)
-{
-  
-  samples = colnames(dm)
-  mt = paste(unlist(strsplit(myTitle,' ')),collapse=" ")
-  outpdfname=paste(mt,"MDS.pdf",sep='_')
-  gu = unique(groups)
-  colours = rainbow(length(gu),start=0.1,end=0.9)
-  pcols = colours[match(groups,gu)]
-  
-  mydata = t(dm) 
-  d = dist(mydata) 
-  fit = cmdscale(d,eig=TRUE, k=2) 
-  
-  x = fit\$points[,1]
-  y = fit\$points[,2]
-  pdf(outpdfname)
-  plot(x, y, xlab="Dimension 1", ylab="Dimension 2",
-       main=paste(mt,"MDS Plot"),type="n", col=pcols)
-  text(x, y, labels = row.names(mydata), cex=0.3, col=pcols)
-  grid(col="lightgray",lty="dotted")
-  dev.off()
-}
-
-runTest = function(n=10)
-{  
-  set.seed (NULL)
-  Y = data.frame(y1=runif (n),y2=runif(n))
-  Xv <- runif(n*n)
-  X <- matrix(Xv, nrow = n, ncol = n)
-  
-  mydf <- data.frame(Y, X)
-  
-  regres_out = dolasso_generic(predvars=X,depvars=Y,debugOn=T,p.cutoff = 0.05,maxsteps=10000,nfold=10,
-                               descr='randomdata',logtransform=F,do_standard=do_standard,defaultFam="gaussian",alpha=0.05)
-  return(regres_out)
-}
-]]>
-options(width=120)
-alpha = $alpha
-nfold = $nfold
-Out_Dir = "$html_file.files_path"
-Input =  "$input1"
-myTitle = "$title"
-outtab = "$model_file"
-logtrans = as.logical("$logtrans")
-do_standard = as.logical("$do_standard")
-fam = "$model.fam"
-xvar_cols_in = "$xvar_cols"
-force_xvar_cols_in = "$force_xvar_cols"
-xvar_cols = as.numeric(strsplit(xvar_cols_in,",")[[1]])
-force_xvar_cols = c()
-if (force_xvar_cols_in > "")
-{
-  force_xvar_cols = as.numeric(strsplit(force_xvar_cols_in,",")[[1]])
-  allx = c(xvar_cols,force_xvar_cols)
-  xvar_cols = unique(allx)
-  xvar_cols = xvar_cols[order(xvar_cols)]
-}
-
-indat = read.table(Input,head=T,sep='\t')
-datcols = colnames(indat)
-
-x = indat[,xvar_cols]
-xcolnames = datcols[xvar_cols]
-penalties = rep(1,length(datcols))
-penalty = penalties[xvar_cols]
-if (force_xvar_cols_in > "")
-{
-  penalties[force_xvar_cols] = 0
-  penalty = penalties[xvar_cols]
-  forcedin = paste(datcols[which(penalties == 0)],collapse=',')
-}
-
-if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
-
-#if $model.fam == "cox":
-  cox_time = $model.cox_time
-  cox_status = $model.cox_status
-  yvar_cols = c(cox_time,cox_status)
-  ycolnames = c('time','status')
-  y = data.frame(time = as.double(indat[,cox_time]),status = as.double(indat[,cox_status]))
-  y = as.matrix(y)
-  x = as.matrix(x)
-  print.noquote(paste('Cox model: Got yvar=',paste(ycolnames,collapse=','),'cols',paste(yvar_cols,sep=','),'n preds=',length(xcolnames),
-    'forced in=',paste(force_xvar_cols,collapse=',')))
-  regres_out = dolasso_cox(x=x,y=y,debugOn=F,maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames,
-                             descr=myTitle,logtransform=logtrans,do_standard=do_standard,alpha=alpha,penalty=penalty)
-#else:
-    yvar_cols = "$model.yvar_cols"
-    yvar_cols = as.numeric(strsplit(yvar_cols,",")[[1]])
-    y = indat[,yvar_cols]
-    ycolnames = colnames(indat)[yvar_cols]
-    print.noquote(paste('Got yvar=',paste(ycolnames,collapse=','),'cols',paste(yvar_cols,collapse=','),'n preds=',length(xcolnames),'forced in=',paste(force_xvar_cols,collapse=',')))
-    regres_out = dolasso_generic(predvars=x,depvars=y,debugOn=F, maxsteps=10000,nfold=nfold,xcolnames=xcolnames,ycolnames=ycolnames,
-                             descr=myTitle,logtransform=logtrans,do_standard=do_standard,defaultFam=fam,alpha=alpha,penalty=penalty)
-#end if
-print.noquote('Results preview:')
-print.noquote(regres_out)
-write.table(regres_out,outtab,quote=FALSE, sep="\t",row.names=F)
-print.noquote('SessionInfo for this R session:')
-sessionInfo()
-
-</configfile>
-</configfiles>
-
-</tool>
-
-
-

Binary file test-data/cox_coxlassotest_glmnet_cvdeviance.pdf has changed

Binary file test-data/cox_coxlassotest_glmnetdev.pdf has changed

--- a/test-data/cox_test.xls	Sun Oct 19 06:04:51 2014 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,502 +0,0 @@
-time	status	rhubarb	vegemite	apple
-575.966708	0	452.405468	30.339584	32286.089057
-1539.245319	1	329.689929	30.603839	15735.863202
-2072.798422	0	534.379263	32.474983	22639.136685
-1638.450154	0	362.522161	30.925996	31108.364370
-1771.625630	0	417.167751	31.444652	31317.491931
-4413.484706	1	459.947526	30.069033	11232.037882
-1519.376431	1	576.868414	30.102709	16539.948172
-4805.620290	0	554.756164	30.685550	9979.125726
-657.497124	0	439.481951	32.305768	22301.057122
-181.498205	1	581.498091	32.549519	10499.737992
-1630.552909	0	580.670671	30.825057	22321.821037
-2285.043061	1	588.034529	32.799742	8067.710675
-3697.405336	0	400.195630	31.826099	20975.730587
-3821.226480	0	561.466202	32.333230	17887.547850
-4810.837196	0	447.004938	32.547717	25383.925946
-3195.877681	1	482.169788	31.420740	27659.409774
-4556.750833	1	415.144072	31.960725	18401.138992
-3797.192061	1	484.492359	30.789882	7431.214452
-3734.484446	1	587.339066	32.052766	29443.923798
-567.665985	1	377.599777	31.203617	30148.076752
-2551.367122	0	447.623689	31.795708	15922.972458
-3.724234	1	534.080187	31.175131	8259.786167
-3528.906130	0	471.293653	31.225006	18307.543198
-3649.459162	1	443.804109	32.390564	9720.974163
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-

--- a/test-data/coxlassotest.html	Sun Oct 19 06:04:51 2014 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,140 +0,0 @@
-<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> 
-        <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> 
-        <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> 
-        <meta name="generator" content="Galaxy rgToolFactory.py tool output - see http://g2.trac.bx.psu.edu/" /> 
-        <title></title> 
-        <link rel="stylesheet" href="/static/style/base.css" type="text/css" /> 
-        </head> 
-        <body> 
-        <div class="toolFormBody"> 
-        
-<div class="infomessage">Galaxy Tool "rglasso" run at 19/10/2014 13:11:14</div><br/>
-<div class="toolFormTitle">cox images and outputs</div>
-(Click on a thumbnail image to download the corresponding original PDF image)<br/>
-<div><table class="simple" cellpadding="2" cellspacing="2">
-<tr>
-<td><a href="cox_Coxglmnettest_glmnet_cvdeviance.pdf"><img src="cox_Coxglmnettest_glmnet_cvdeviance.png" title="Click to download a PDF of cox_Coxglmnettest_glmnet_cvdeviance.pdf" hspace="5" width="400" 
-                           alt="Image called cox_Coxglmnettest_glmnet_cvdeviance.pdf"/></a></td>
-
-<td><a href="cox_Coxglmnettest_glmnetdev.pdf"><img src="cox_Coxglmnettest_glmnetdev.png" title="Click to download a PDF of cox_Coxglmnettest_glmnetdev.pdf" hspace="5" width="400" 
-                           alt="Image called cox_Coxglmnettest_glmnetdev.pdf"/></a></td>
-</tr>
-
-</table></div>
-
-<div class="toolFormTitle">cox log output</div>
-
-<pre>
-
-[1] "All partial likelihood coefficients from Cox model at minimum lambda of 0.00138091491730048:"
-
-3 x 1 sparse Matrix of class "dgCMatrix"
-
-                    1
-
-rhubarb  1.124517e-03
-
-vegemite 1.444990e-01
-
-apple    3.344565e-06
-
-
-</pre>
-
-<div class="toolFormTitle">rglasso log output</div>
-
-<pre>
-
-Loading required package: Matrix
-
-Loading required package: methods
-
-Loaded glmnet 1.9-8
-
-Loaded lars 1.2
-
-Warning messages:
-
-1: In if (class(larsres) == "try-error") { :
-
-  the condition has length > 1 and only the first element will be used
-
-2: In plot.window(...) : "label" is not a graphical parameter
-
-3: In plot.xy(xy, type, ...) : "label" is not a graphical parameter
-
-4: In axis(side = side, at = at, labels = labels, ...) :
-
-  "label" is not a graphical parameter
-
-5: In axis(side = side, at = at, labels = labels, ...) :
-
-  "label" is not a graphical parameter
-
-6: In box(...) : "label" is not a graphical parameter
-
-7: In title(...) : "label" is not a graphical parameter
-
-
-</pre>
-
-<div class="toolFormTitle">Other log output</div>
-
-<pre>
-
-## Toolfactory generated command line = Rscript - None None
-
-[1] Cox model: Got yvar= time,status cols 1 n preds= 3 forced in= 3
-
-[2] Cox model: Got yvar= time,status cols 2 n preds= 3 forced in= 3
-
-[1] Results preview:
-
-         regulator partial_likelihood forced_in glmnet_model best_lambda
-
-rhubarb    rhubarb        0.001180324      TRUE          cox  0.01508333
-
-vegemite  vegemite        0.093827440     FALSE          cox  0.01508333
-
-[1] SessionInfo for this R session:
-
-R version 3.1.0 (2014-04-10)
-
-Platform: x86_64-unknown-linux-gnu (64-bit)
-
-locale:
-
- [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C               LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
-
- [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8    LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
-
- [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-
-[1] methods   stats     graphics  grDevices utils     datasets  base     
-
-other attached packages:
-
-[1] lars_1.2     glmnet_1.9-8 Matrix_1.1-4
-
-loaded via a namespace (and not attached):
-
-[1] grid_3.1.0      lattice_0.20-29
-
-
-</pre>
-
-<div class="toolFormTitle">All output files available for downloading</div>
-
-<div><table class="colored" cellpadding="3" cellspacing="3"><tr><th>Output File Name (click to view)</th><th>Size</th></tr>
-
-<tr><td><a href="cox_Coxglmnettest_glmnet_cvdeviance.pdf">cox_Coxglmnettest_glmnet_cvdeviance.pdf</a></td><td>6.4 KB</td></tr>
-<tr class="odd_row"><td><a href="cox_Coxglmnettest_glmnetdev.pdf">cox_Coxglmnettest_glmnetdev.pdf</a></td><td>5.2 KB</td></tr>
-<tr><td><a href="cox_rglasso.log">cox_rglasso.log</a></td><td>228 B</td></tr>
-<tr class="odd_row"><td><a href="rglasso.Rscript">rglasso.Rscript</a></td><td>9.8 KB</td></tr>
-<tr><td><a href="rglasso_error.log">rglasso_error.log</a></td><td>671 B</td></tr>
-<tr class="odd_row"><td><a href="rglasso_runner.log">rglasso_runner.log</a></td><td>1.1 KB</td></tr>
-</table></div><br/>
-</div></body></html>
-

--- a/test-data/coxlassotest_modelres.xls	Sun Oct 19 06:04:51 2014 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,3 +0,0 @@
-regulator	partial_likelihood	forced_in	glmnet_model	best_lambda
-rhubarb	0.0011803238918088	TRUE	cox	0.0150833254995026
-vegemite	0.0938274399971897	FALSE	cox	0.0150833254995026

--- a/tool_dependencies.xml	Sun Oct 19 06:04:51 2014 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,30 +0,0 @@
-<?xml version="1.0"?>
-<tool_dependency>
-  <package name="R_3_1_1" version="3.1.1">
-      <repository changeset_revision="6ca21e466ef6" name="package_r_3_1_1" owner="fubar" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
-    </package>
-    <package name="graphicsmagick" version="1.3.18">
-      <repository changeset_revision="2fd4eb971ba5" name="package_graphicsmagick_1_3" owner="iuc" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
-    </package>
-    <package name="ghostscript" version="9.10">
-      <repository changeset_revision="9345d2740f0c" name="package_ghostscript_9_10" owner="devteam" prior_installation_required="True" toolshed="https://testtoolshed.g2.bx.psu.edu" />
-    </package>
-    <package name="glmnet_lars_2_14" version="2.14">
-      <install version="1.0">
-          <actions>
-              <action type="setup_r_environment">
-                  <repository changeset_revision="6ca21e466ef6" name="package_r_3_1_1" owner="fubar" toolshed="https://testtoolshed.g2.bx.psu.edu">
-                      <package name="R_3_1_1" version="3.1.1" />
-                    </repository>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/glmnet_1.9-8.tar.gz</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/lars_1.2.tar.gz</package>
-                    <package>https://github.com/fubar2/galaxy_tool_source/blob/master/RELEASE_2_14/lattice_0.20-29.tar.gz</package>
-                </action>
-            </actions>
-        </install>
-        <readme>
-        Yeee Haaa!
-        Lasso for Galaxy
-        </readme>
-    </package>
-</tool_dependency>