[galaxy-commits] commit/galaxy-central: 11 new changesets

12 Oct 2011

11 new changesets in galaxy-central:

http://bitbucket.org/galaxy/galaxy-central/changeset/c959d32f2405/
changeset:   c959d32f2405
user:        jdu...@Bruno-Proto6.illumina.com
date:        2011-05-25 00:15:03
summary:     Added new "multi" parallelism option for tools
affected #:  8 files (-1 bytes)

--- a/lib/galaxy/datatypes/data.py	Tue May 24 05:33:07 2011 -0400
+++ b/lib/galaxy/datatypes/data.py	Tue May 24 15:15:03 2011 -0700
@@ -1,4 +1,4 @@
-import logging, os, sys, time, tempfile
+import logging, os, sys, time, tempfile,  gzip
 from galaxy import util
 from galaxy.util.odict import odict
 from galaxy.util.bunch import Bunch
@@ -351,6 +351,32 @@
     @property
     def has_resolution(self):
         return False
+    
+    
+
+    def merge( split_files, output_file): 
+        """
+        Export files are usually compressed, but it doesn't have to be so. In the case that they are, use
+        zcat to cat the files and gzip -c to recompress the result, otherwise use cat
+        TODO: Move to a faster gzjoin-based technique
+        """
+        #TODO: every time I try to import this from sniff, the parser dies
+        def is_gzip( filename ):
+            temp = open( filename, "U" )
+            magic_check = temp.read( 2 )
+            temp.close()
+            if magic_check != util.gzip_magic:
+                return False
+            return True
+        
+        if len(split_files) == 1:
+            os.system( 'mv -f %s %s' % ( split_files[0], output_file ) )
+            return
+        if is_gzip(split_files[0]):
+            os.system( 'zcat %s | gzip -c > %s' % ( ' '.join(split_files), output_file ) )
+        else:
+            os.system( 'cat %s > %s' % ( ' '.join(split_files), output_file ) )
+    merge = staticmethod(merge)
 
 class Text( Data ):
     file_ext = 'txt'
@@ -446,6 +472,80 @@
             dataset.peek = 'file does not exist'
             dataset.blurb = 'file purged from disk'
 
+    def split( input_files, subdir_generator_function, split_params): 
+        """
+        Split the input files by line.
+        """
+        if split_params is None:
+            return
+            
+        if len(input_files) > 1:
+            raise Exception("Text file splitting does not support multiple files")
+        
+        lines_per_file = None
+        chunk_size = None
+        if split_params['split_mode'] == 'number_of_parts':
+            lines_per_file = []
+            # Computing the length is expensive!
+            def _file_len(fname):
+                i = 0
+                f = open(fname)
+                for i, l in enumerate(f):
+                    pass
+                f.close()
+                return i + 1
+            length = _file_len(input_files[0])
+            parts = int(split_params['split_size'])
+            if length < parts:
+                parts = length
+            len_each, remainder = divmod(length, parts)
+            while length > 0:
+                chunk = len_each
+                if remainder > 0:
+                    chunk += 1
+                lines_per_file.append(chunk)
+                remainder=- 1
+                length -= chunk
+        elif split_params['split_mode'] == 'to_size':
+            chunk_size = int(split_params['split_size'])
+        else:
+            raise Exception('Unsupported split mode %s' % split_params['split_mode'])
+        
+        f = open(input_files[0], 'rt')
+        try:
+            chunk_idx = 0
+            file_done = False
+            part_file = None
+            while not file_done:
+                if lines_per_file is None:
+                    this_chunk_size = chunk_size
+                elif chunk_idx < len(lines_per_file):
+                    this_chunk_size = lines_per_file[chunk_idx]
+                    chunk_idx += 1
+                lines_remaining = this_chunk_size
+                part_file = None
+                while lines_remaining > 0:
+                    a_line = f.readline()
+                    if a_line == '':
+                        file_done = True
+                        break
+                    if part_file is None:
+                        part_dir = subdir_generator_function()
+                        part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
+                        part_file = open(part_path, 'w')
+                    part_file.write(a_line)
+                    lines_remaining -= 1
+                if part_file is not None:
+                    part_file.close()
+        except Exception,  e:
+            log.error('Unable to split files: %s' % str(e))
+            f.close()
+            if part_file is not None:
+                part_file.close()
+            raise
+        f.close()
+    split = staticmethod(split)
+
 class Newick( Text ):
     pass
 


--- a/lib/galaxy/datatypes/sequence.py	Tue May 24 05:33:07 2011 -0400
+++ b/lib/galaxy/datatypes/sequence.py	Tue May 24 15:15:03 2011 -0700
@@ -49,6 +49,95 @@
         else:
             dataset.peek = 'file does not exist'
             dataset.blurb = 'file purged from disk'
+    
+    def split( input_files, subdir_generator_function, split_params):
+        """
+        FASTQ files are split on cluster boundaries, in increments of 4 lines
+        """
+        if split_params is None:
+            return
+        
+        def split_one( input_file, get_dir, clusters_per_file,  default_clusters=None):
+            in_file = open(input_file, 'rt')
+            part_file = None
+            part = 0
+            if clusters_per_file is None:
+                local_clusters_per_file = [default_clusters]
+            else:
+                local_clusters_per_file = [x for x in clusters_per_file] 
+                
+            for i, line in enumerate(in_file):
+                cluster_number,  line_in_cluster = divmod(i, 4)
+                current_part, remainder = divmod(cluster_number, local_clusters_per_file[part])
+                
+                if (current_part != part or part_file is None):
+                    if (part_file):
+                        part_file.close()
+                    part = current_part
+                    part_dir = get_dir()
+                    part_path = os.path.join(part_dir, os.path.basename(input_file))
+                    part_file = open(part_path, 'w')
+                    if clusters_per_file is None:
+                        local_clusters_per_file.append(default_clusters)
+                part_file.write(line)
+            if (part_file):
+                part_file.close()
+            in_file.close()
+            local_clusters_per_file[part] = remainder + 1
+            return local_clusters_per_file
+    
+        directories = []
+        def create_subdir():
+            dir = subdir_generator_function()
+            directories.append(dir)
+            return dir
+        
+        clusters_per_file = None
+        if split_params['split_mode'] == 'number_of_parts':
+            # legacy splitting. To keep things simple, just scan the 0th file and count the clusters,
+            # then split it
+            clusters_per_file = []
+            in_file = open(input_files[0], 'rt')
+            for i, line in enumerate(in_file):
+                pass
+            in_file.close()
+            length = (i+1)/4
+            
+            if length <= 0:
+                raise Exception('Invalid sequence file %s' % input_files[0])
+            parts = int(split_params['split_size'])
+            if length < parts:
+                parts = length
+            len_each, remainder = divmod(length, parts)
+            while length > 0:
+                chunk = len_each
+                if remainder > 0:
+                    chunk += 1
+                clusters_per_file.append(chunk)
+                remainder=- 1
+                length -= chunk
+            split_one(input_files[0],  create_subdir,  clusters_per_file)
+        elif split_params['split_mode'] == 'to_size':
+            # split one file and see what the cluster sizes turn out to be
+            clusters_per_file = split_one(input_files[0],  create_subdir,  None,  int(split_params['split_size']))
+        else:
+            raise Exception('Unsupported split mode %s' % split_params['split_mode'])
+        
+        # split the rest, using the same number of clusters for each file
+        current_dir_idx = [0] # use a list to get around Python 2.x lame closure support
+        def get_subdir():
+            if len(directories) <= current_dir_idx[0]:
+                raise Exception('FASTQ files do not have the same number of clusters - splitting failed')
+            result = directories[current_dir_idx[0]]
+            current_dir_idx[0] = current_dir_idx[0] + 1
+            return result
+            
+        for i in range(1,  len(input_files)):
+            current_dir_idx[0] = 0
+            split_one(input_files[i],  get_subdir,  clusters_per_file)
+    split = staticmethod(split)
+
+
 
 class Alignment( data.Text ):
     """Class describing an alignment"""


--- a/lib/galaxy/jobs/__init__.py	Tue May 24 05:33:07 2011 -0400
+++ b/lib/galaxy/jobs/__init__.py	Tue May 24 15:15:03 2011 -0700
@@ -291,6 +291,7 @@
         self.working_directory = \
             os.path.join( self.app.config.job_working_directory, str( self.job_id ) )
         self.output_paths = None
+        self.output_dataset_paths = None
         self.tool_provided_job_metadata = None
         # Wrapper holding the info required to restore and clean up from files used for setting metadata externally
         self.external_output_metadata = metadata.JobExternalOutputMetadataWrapper( job )        
@@ -657,23 +658,35 @@
     def get_session_id( self ):
         return self.session_id
 
+    def get_input_dataset_fnames( self,  ds ):
+        filenames = []
+        filenames = [ ds.file_name ]
+        #we will need to stage in metadata file names also
+        #TODO: would be better to only stage in metadata files that are actually needed (found in command line, referenced in config files, etc.)
+        for key, value in ds.metadata.items():
+            if isinstance( value, model.MetadataFile ):
+                filenames.append( value.file_name )
+        return filenames
+    
     def get_input_fnames( self ):
         job = self.get_job()
         filenames = []
         for da in job.input_datasets: #da is JobToInputDatasetAssociation object
             if da.dataset:
-                filenames.append( da.dataset.file_name )
-                #we will need to stage in metadata file names also
-                #TODO: would be better to only stage in metadata files that are actually needed (found in command line, referenced in config files, etc.)
-                for key, value in da.dataset.metadata.items():
-                    if isinstance( value, model.MetadataFile ):
-                        filenames.append( value.file_name )
+                filenames.extend(self.get_input_dataset_fnames(da.dataset))
         return filenames
 
     def get_output_fnames( self ):
-        if self.output_paths is not None:
-            return self.output_paths
+        if self.output_paths is None:
+            self.compute_outputs()
+        return self.output_paths
 
+    def get_output_datasets_and_fnames( self ):
+        if self.output_dataset_paths is None:
+            self.compute_outputs()
+        return self.output_dataset_paths
+
+    def compute_outputs( self ) :
         class DatasetPath( object ):
             def __init__( self, dataset_id, real_path, false_path = None ):
                 self.dataset_id = dataset_id
@@ -688,19 +701,25 @@
         job = self.get_job()
         # Job output datasets are combination of output datasets, library datasets, and jeha datasets.
         jeha = self.sa_session.query( model.JobExportHistoryArchive ).filter_by( job=job ).first()
+        jeha_false_path = None
         if self.app.config.outputs_to_working_directory:
             self.output_paths = []
+            output_dataset_paths = {}
             for name, data in [ ( da.name, da.dataset.dataset ) for da in job.output_datasets + job.output_library_datasets ]:
                 false_path = os.path.abspath( os.path.join( self.working_directory, "galaxy_dataset_%d.dat" % data.id ) )
-                self.output_paths.append( DatasetPath( data.id, data.file_name, false_path ) )
+                dsp = DatasetPath( data.id, data.file_name, false_path )
+                self.output_paths.append( dsp )
+                self.output_dataset_paths[name] = data,  dsp
             if jeha:
-                false_path = os.path.abspath( os.path.join( self.working_directory, "galaxy_dataset_%d.dat" % jeha.dataset.id ) )
-                self.output_paths.append( DatasetPath( jeha.dataset.id, jeha.dataset.file_name, false_path ) )
+                jeha_false_path = os.path.abspath( os.path.join( self.working_directory, "galaxy_dataset_%d.dat" % jeha.dataset.id ) )
         else:
-            self.output_paths = [ DatasetPath( da.dataset.dataset.id, da.dataset.file_name ) for da in job.output_datasets + job.output_library_datasets ]
-            if jeha:
-                self.output_paths.append( DatasetPath( jeha.dataset.id, jeha.dataset.file_name ) )
-
+            results = [ (da.name,  da.dataset,  DatasetPath( da.dataset.dataset.id, da.dataset.file_name )) for da in job.output_datasets + job.output_library_datasets ]
+            self.output_paths = [t[2] for t in results]
+            self.output_dataset_paths = dict([(t[0],  t[1:]) for t in results])
+            
+        if jeha:
+            dsp = DatasetPath( jeha.dataset.id, jeha.dataset.file_name, jeha_false_path )
+            self.output_paths.append( dsp )
         return self.output_paths
 
     def get_output_file_id( self, file ):
@@ -807,12 +826,7 @@
     def __init__(self, task, queue):
         super(TaskWrapper, self).__init__(task.job, queue)
         self.task_id = task.id
-        self.parallelism = None
-        if task.part_file:
-            #do this better
-            self.working_directory = os.path.dirname(task.part_file)
-        else:
-            self.working_directory = None
+        self.working_directory = task.working_directory
         self.status = task.states.NEW
 
     def get_job( self ):


--- a/lib/galaxy/jobs/runners/tasks.py	Tue May 24 05:33:07 2011 -0400
+++ b/lib/galaxy/jobs/runners/tasks.py	Tue May 24 15:15:03 2011 -0700
@@ -60,71 +60,58 @@
         if command_line:
             try:
                 # DBTODO read tool info and use the right kind of parallelism.  
-                # For now, the only splitter is the 'basic' one, n-ways split on one input, one output.
-                # This is incredibly simplified.  Parallelism ultimately needs to describe which inputs, how, etc.
+                # For now, the only splitter is the 'basic' one
                 job_wrapper.change_state( model.Job.states.RUNNING )
                 self.sa_session.flush()
-                parent_job = job_wrapper.get_job()
                 # Split with the tool-defined method.
-                if job_wrapper.tool.parallelism == "basic":
-                    from galaxy.jobs.splitters import basic
-                    if len(job_wrapper.get_input_fnames()) > 1 or len(job_wrapper.get_output_fnames()) > 1:
-                        log.error("The basic splitter is not capable of handling jobs with multiple inputs or outputs.")
-                        job_wrapper.change_state( model.Job.states.ERROR )
-                        job_wrapper.fail("Job Splitting Failed, the basic splitter only handles tools with one input and one output")
-                        # Requeue as a standard job?
-                        return
-                    input_file = job_wrapper.get_input_fnames()[0]
-                    working_directory = job_wrapper.working_directory
-                    # DBTODO execute an external task to do the splitting, this should happen at refactor.
-                    # Regarding number of ways split, use "hints" in tool config?
-                    # If the number of tasks is sufficiently high, we can use it to calculate job completion % and give a running status.
-                    basic.split(input_file, working_directory, 
-                                        20, #Needs serious experimentation to find out what makes the most sense.
-                                        parent_job.input_datasets[0].dataset.ext)
-                    # Tasks in this parts list are in alphabetical listdir order (15 before 5), but that should not matter.
-                    parts = [os.path.join(os.path.abspath(job_wrapper.working_directory), p, os.path.basename(input_file)) 
-                                for p in os.listdir(job_wrapper.working_directory) 
-                                if p.startswith('task_')]
-                else:
+                try:
+                    splitter = getattr(__import__('galaxy.jobs.splitters',  globals(),  locals(),  [job_wrapper.tool.parallelism.method]),  job_wrapper.tool.parallelism.method)
+                except:   
                     job_wrapper.change_state( model.Job.states.ERROR )
                     job_wrapper.fail("Job Splitting Failed, no match for '%s'" % job_wrapper.tool.parallelism)
-                # Assemble parts into task_wrappers
+                    return
+                tasks = splitter.do_split(job_wrapper)
 
                 # Not an option for now.  Task objects don't *do* anything useful yet, but we'll want them tracked outside this thread to do anything.
                 # if track_tasks_in_database:
-                tasks = []
                 task_wrappers = []
-                for part in parts:
-                    task = model.Task(parent_job, part)
+                for task in tasks:
                     self.sa_session.add(task)
-                    tasks.append(task)
                 self.sa_session.flush()
+                
                 # Must flush prior to the creation and queueing of task wrappers.
                 for task in tasks:
                     tw = TaskWrapper(task, job_wrapper.queue)
                     task_wrappers.append(tw)
                     self.app.job_manager.dispatcher.put(tw)
                 tasks_incomplete = False
+                count_complete = 0
                 sleep_time = 1
+                # sleep/loop until no more progress can be made. That is when
+                # all tasks are one of { OK, ERROR, DELETED }
+                completed_states = [ model.Task.states.OK, \
+                                    model.Task.states.ERROR, \
+                                    model.Task.states.DELETED ]
+                # TODO: Should we report an error (and not merge outputs) if one of the subtasks errored out?
+                # Should we prevent any that are pending from being started in that case?
                 while tasks_incomplete is False:
+                    count_complete = 0
                     tasks_incomplete = True
                     for tw in task_wrappers:
-                        if not tw.get_state() == model.Task.states.OK:
+                        task_state = tw.get_state()
+                        if not task_state in completed_states:
                             tasks_incomplete = False
-                    sleep( sleep_time )
-                    if sleep_time < 8:
-                        sleep_time *= 2
-                output_filename = job_wrapper.get_output_fnames()[0].real_path
-                basic.merge(working_directory, output_filename)
-                log.debug('execution finished: %s' % command_line)
-                for tw in task_wrappers:
-                    # Prevent repetitive output, e.g. "Sequence File Aligned"x20
-                    # Eventually do a reduce for jobs that output "N reads mapped", combining all N for tasks.
-                    if stdout.strip() != tw.get_task().stdout.strip():
-                        stdout += tw.get_task().stdout
-                    if stderr.strip() != tw.get_task().stderr.strip():                        
-                        stderr += tw.get_task().stderr
+                        else:
+                            count_complete = count_complete + 1
+                    if tasks_incomplete is False:
+                        # log.debug('Tasks complete: %s. Sleeping %s' % (count_complete, sleep_time))
+                        sleep( sleep_time )
+                        if sleep_time < 8:
+                            sleep_time *= 2
+                
+                log.debug('execution finished - beginning merge: %s' % command_line)
+                stdout,  stderr = splitter.do_merge(job_wrapper,  task_wrappers)
+                
             except Exception:
                 job_wrapper.fail( "failure running job", exception=True )
                 log.exception("failure running job %d" % job_wrapper.job_id)


--- a/lib/galaxy/jobs/splitters/basic.py	Tue May 24 05:33:07 2011 -0400
+++ b/lib/galaxy/jobs/splitters/basic.py	Tue May 24 15:15:03 2011 -0700
@@ -1,91 +1,23 @@
-import os, logging
+import logging
+import multi
+
 log = logging.getLogger( __name__ )
 
-def _file_len(fname):
-    i = 0
-    f = open(fname)
-    for i, l in enumerate(f):
-        pass
-    f.close()
-    return i + 1
+def set_basic_defaults(job_wrapper):
+    parent_job = job_wrapper.get_job()
+    job_wrapper.tool.parallelism.attributes['split_inputs'] = parent_job.input_datasets[0].name
+    job_wrapper.tool.parallelism.attributes['merge_outputs'] = job_wrapper.get_output_datasets_and_fnames().keys()[0]
 
-def _fq_seq_count(fname):
-    count = 0
-    f = open(fname)
-    for i, l in enumerate(f):
-        if l.startswith('@'):
-            count += 1
-    f.close()
-    return count
-
-def split_fq(input_file, working_directory, parts):
-    # Temporary, switch this to use the fq reader in lib/galaxy_utils/sequence.
-    outputs = []
-    length = _fq_seq_count(input_file)
-    if length < 1:
-        return outputs
-    if length < parts:
-        parts = length
-    len_each, remainder = divmod(length, parts)
-    f = open(input_file, 'rt')
-    for p in range(0, parts):
-        part_dir = os.path.join( os.path.abspath(working_directory), 'task_%s' % p)
-        if not os.path.exists( part_dir ):
-            os.mkdir( part_dir )
-        part_path = os.path.join(part_dir, os.path.basename(input_file))
-        part_file = open(part_path, 'w')
-        for l in range(0, len_each):
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-        if remainder > 0:
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            remainder -= 1
-        outputs.append(part_path)
-        part_file.close()
-    f.close()
-    return outputs
-
-def split_txt(input_file, working_directory, parts):
-    outputs = []
-    length = _file_len(input_file)
-    if length < parts:
-        parts = length
-    len_each, remainder = divmod(length, parts)
-    f = open(input_file, 'rt')
-    for p in range(0, parts):
-        part_dir = os.path.join( os.path.abspath(working_directory), 'task_%s' % p)
-        if not os.path.exists( part_dir ):
-            os.mkdir( part_dir )
-        part_path = os.path.join(part_dir, os.path.basename(input_file))
-        part_file = open(part_path, 'w')
-        for l in range(0, len_each):
-            part_file.write(f.readline())
-        if remainder > 0:
-            part_file.write(f.readline())
-            remainder -= 1
-        outputs.append(part_path)
-        part_file.close()
-    f.close()
-    return outputs
+def do_split (job_wrapper):
+    if len(job_wrapper.get_input_fnames()) > 1 or len(job_wrapper.get_output_fnames()) > 1:
+        log.error("The basic splitter is not capable of handling jobs with multiple inputs or outputs.")
+        raise Exception,  "Job Splitting Failed, the basic splitter only handles tools with one input and one output"
+    # add in the missing information for splitting the one input and merging the one output
+    set_basic_defaults(job_wrapper)
+    return multi.do_split(job_wrapper)
     
-def split( input_file, working_directory, parts, file_type = None):
-    #Implement a better method for determining how to split.
-    if file_type.startswith('fastq'):
-        return split_fq(input_file, working_directory, parts)
-    else:
-        return split_txt(input_file, working_directory, parts)
+def do_merge( job_wrapper,  task_wrappers):
+    # add in the missing information for splitting the one input and merging the one output
+    set_basic_defaults(job_wrapper)
+    return multi.do_merge(job_wrapper,  task_wrappers)
     
-def merge( working_directory, output_file ):
-    output_file_name = os.path.basename(output_file)
-    task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
-    task_dirs.sort(key = lambda x: int(x.split('task_')[-1]))
-    for task_dir in task_dirs:
-        try:
-            os.system( 'cat %s >> %s' % ( os.path.join(task_dir, output_file_name), output_file ) )
-        except Exception, e:
-            log.error(str(e))


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/jobs/splitters/multi.py	Tue May 24 15:15:03 2011 -0700
@@ -0,0 +1,149 @@
+import os, logging,  shutil
+from galaxy import model
+
+log = logging.getLogger( __name__ )
+    
+def do_split (job_wrapper):
+    parent_job = job_wrapper.get_job()
+    working_directory = os.path.abspath(job_wrapper.working_directory)
+
+    parallel_settings = job_wrapper.tool.parallelism.attributes
+    # Syntax: split_inputs="input1,input2" shared_inputs="genome" 
+    # Designates inputs to be split or shared
+    split_inputs=parallel_settings.get("split_inputs")
+    if split_inputs is None:
+        split_inputs = []
+    else:
+        split_inputs = [x.strip() for x in split_inputs.split(",")]
+
+    shared_inputs=parallel_settings.get("shared_inputs")
+    if shared_inputs is None:
+        shared_inputs = []
+    else:
+        shared_inputs = [x.strip() for x in shared_inputs.split(",")]
+    illegal_inputs = [x for x in shared_inputs if x in split_inputs]
+    if len(illegal_inputs) > 0:
+        raise Exception("Inputs have conflicting parallelism attributes: %s" % str( illegal_inputs ))
+    
+    subdir_index = [0] # use a list to get around Python 2.x lame closure support
+    task_dirs = []
+    def get_new_working_directory_name():
+        dir=os.path.join(working_directory,  'task_%d' % subdir_index[0])
+        subdir_index[0] = subdir_index[0] + 1
+        if not os.path.exists(dir):
+            os.makedirs(dir)
+        task_dirs.append(dir)
+        return dir
+    
+    # For things like paired end alignment, we need two inputs to be split. Since all inputs to all
+    # derived subtasks need to be correlated, allow only one input type to be split
+    type_to_input_map = {}
+    for input in parent_job.input_datasets:
+        if input.name in split_inputs:
+            type_to_input_map.setdefault(input.dataset.datatype,  []).append(input.name)
+        elif input.name in shared_inputs:
+            pass # pass original file name
+        else:
+            log_error = "The input '%s' does not define a method for implementing parallelism" % str(input.name)
+            log.error(log_error)
+            raise Exception(log_error)
+
+    if len(type_to_input_map) > 1:
+        log_error = "The multi splitter does not support splitting inputs of more than one type"
+        log.error(log_error)
+        raise Exception(log_error)
+    
+    # split the first one to build up the task directories
+    input_files = []
+    for input in parent_job.input_datasets:
+        if input.name in split_inputs:
+            this_input_files = job_wrapper.get_input_dataset_fnames(input.dataset)
+            if len(this_input_files) > 1:
+                log_error = "The input '%s' is composed of multiple files - splitting is not allowed" % str(input.name)
+                log.error(log_error)
+                raise Exception(log_error)
+            input_files.extend(this_input_files)    
+    
+    input_type = type_to_input_map.keys()[0]
+    # DBTODO execute an external task to do the splitting, this should happen at refactor.
+    # If the number of tasks is sufficiently high, we can use it to calculate job completion % and give a running status.
+    try:
+        input_type.split(input_files, get_new_working_directory_name, parallel_settings)
+    except AttributeError:
+        log_error = "The type '%s' does not define a method for splitting files" % str(input_type)
+        log.error(log_error)
+        raise
+    log.debug('do_split created %d parts' % len(task_dirs))
+    # next, after we know how many divisions there are, add the shared inputs via soft links
+    for input in parent_job.input_datasets:
+        if input and input.name in shared_inputs:
+            names = job_wrapper.get_input_dataset_fnames(input.dataset)
+            for dir in task_dirs:
+                for file in names:
+                    os.symlink(file, os.path.join(dir,  os.path.basename(file)))
+    tasks = []
+    for dir in task_dirs:
+        task = model.Task(parent_job, dir)
+        tasks.append(task)
+    return tasks
+                               
+
+def do_merge( job_wrapper,  task_wrappers):
+    parent_job = job_wrapper.get_job()
+    parallel_settings = job_wrapper.tool.parallelism.attributes
+    # Syntax: merge_outputs="export" pickone_outputs="genomesize"
+    # Designates outputs to be merged, or selected from as a representative
+    merge_outputs = parallel_settings.get("merge_outputs")
+    if merge_outputs is None:
+        merge_outputs = []
+    else:
+        merge_outputs = [x.strip() for x in merge_outputs.split(",")]
+    pickone_outputs = parallel_settings.get("pickone_outputs")
+    if pickone_outputs is None:
+        pickone_outputs = []
+    else:
+        pickone_outputs = [x.strip() for x in pickone_outputs.split(",")]
+    
+    illegal_outputs = [x for x in merge_outputs if x in pickone_outputs]
+    if len(illegal_outputs) > 0:
+        raise Exception("Outputs have conflicting parallelism attributes: %s" % str( illegal_outputs ))
+
+    
+    working_directory = job_wrapper.working_directory
+    task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
+    # TODO: Output datasets can be very complex. This doesn't handle metadata files
+    outputs = job_wrapper.get_output_datasets_and_fnames()
+    pickone_done = []
+    task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
+    for output in outputs:
+        output_file_name = str(outputs[output][1])
+        base_output_name = os.path.basename(output_file_name)
+        if output in merge_outputs:
+            output_type = outputs[output][0].datatype
+            output_files = [os.path.join(dir,base_output_name) for dir in task_dirs]
+            log.debug('files %s ' % output_files)
+            output_type.merge(output_files, output_file_name)
+            log.debug('merge finished: %s' % output_file_name)
+            pass # TODO: merge all the files
+        elif output in pickone_outputs:
+            # just pick one of them
+            if output not in pickone_done:
+                task_file_name = os.path.join(task_dirs[0], base_output_name)
+                shutil.move( task_file_name,  output_file_name )
+                pickone_done.append(output)
+        else:
+            log_error = "The output '%s' does not define a method for implementing parallelism" % output
+            log.error(log_error)
+            raise Exception(log_error)
+
+    stdout = ''
+    stderr=''
+    for tw in task_wrappers:
+        # Prevent repetitive output, e.g. "Sequence File Aligned"x20
+        # Eventually do a reduce for jobs that output "N reads mapped", combining all N for tasks.
+        if stdout.strip() != tw.get_task().stdout.strip():
+            stdout += tw.get_task().stdout
+        if stderr.strip() != tw.get_task().stderr.strip():                        
+            stderr += tw.get_task().stderr
+    return (stdout,  stderr)
+    


--- a/lib/galaxy/model/__init__.py	Tue May 24 05:33:07 2011 -0400
+++ b/lib/galaxy/model/__init__.py	Tue May 24 15:15:03 2011 -0700
@@ -176,12 +176,23 @@
         self.parameters = []
         self.state = Task.states.NEW
         self.info = None
+        # TODO: Rename this to working_directory
+        # Does this necessitate a DB migration step?
         self.part_file = part_file
         self.task_runner_name = None
         self.task_runner_external_id = None
         self.job = job
         self.stdout = None
         self.stderr = None
+    
+    @property
+    def working_directory(self):
+        if self.part_file is not None:
+            if not os.path.isdir(self.part_file):
+                return os.path.dirname(self.part_file)
+            else:
+                return self.part_file
+        return None
 
     def set_state( self, state ):
         self.state = state


--- a/lib/galaxy/tools/__init__.py	Tue May 24 05:33:07 2011 -0400
+++ b/lib/galaxy/tools/__init__.py	Tue May 24 15:15:03 2011 -0700
@@ -321,6 +321,21 @@
         self.type = None
         self.version = None
 
+class ToolParallelismInfo(object):
+    """
+    Stores the information (if any) for running multiple instances of the tool in parallel
+    on the same set of inputs.
+    """
+    def __init__(self, tag):
+        self.method = tag.get('method')
+        self.attributes = dict([item for item in tag.attrib.items() if item[0] != 'method' ])
+        if len(self.attributes) == 0:
+            # legacy basic mode - provide compatible defaults
+            self.attributes['split_size'] = 20
+            self.attributes['split_mode'] = 'number_of_parts'
+            
+        
+    
 class Tool:
     """
     Represents a computational tool that can be executed through Galaxy. 
@@ -403,7 +418,7 @@
         # Parallelism for tasks, read from tool config.
         parallelism = root.find("parallelism")
         if parallelism is not None and parallelism.get("method"):
-            self.parallelism = parallelism.get("method")
+            self.parallelism = ToolParallelismInfo(parallelism)
         else:
             self.parallelism = None
         if self.app.config.start_job_runners is None:


http://bitbucket.org/galaxy/galaxy-central/changeset/6441f8214ebe/
changeset:   6441f8214ebe
user:        John Duddy
date:        2011-05-25 19:45:53
summary:     Remove zcat + gzip -c for merging gz files. Go with cat for now
affected #:  1 file (-1 bytes)

--- a/lib/galaxy/datatypes/data.py	Tue May 24 15:15:03 2011 -0700
+++ b/lib/galaxy/datatypes/data.py	Wed May 25 10:45:53 2011 -0700
@@ -1,4 +1,4 @@
-import logging, os, sys, time, tempfile,  gzip
+import logging, os, sys, time, tempfile
 from galaxy import util
 from galaxy.util.odict import odict
 from galaxy.util.bunch import Bunch
@@ -356,24 +356,11 @@
 
     def merge( split_files, output_file): 
         """
-        Export files are usually compressed, but it doesn't have to be so. In the case that they are, use
-        zcat to cat the files and gzip -c to recompress the result, otherwise use cat
-        TODO: Move to a faster gzjoin-based technique
+        TODO: Do we need to merge gzip files using gzjoin? cat seems to work,
+        but might be brittle. Need to revisit this.
         """
-        #TODO: every time I try to import this from sniff, the parser dies
-        def is_gzip( filename ):
-            temp = open( filename, "U" )
-            magic_check = temp.read( 2 )
-            temp.close()
-            if magic_check != util.gzip_magic:
-                return False
-            return True
-        
         if len(split_files) == 1:
             os.system( 'mv -f %s %s' % ( split_files[0], output_file ) )
-            return
-        if is_gzip(split_files[0]):
-            os.system( 'zcat %s | gzip -c > %s' % ( ' '.join(split_files), output_file ) )
         else:
             os.system( 'cat %s > %s' % ( ' '.join(split_files), output_file ) )
     merge = staticmethod(merge)


http://bitbucket.org/galaxy/galaxy-central/changeset/4a5512953201/
changeset:   4a5512953201
user:        John Duddy
date:        2011-06-16 21:19:07
summary:     Add ability to split compressed input
Fix off by one error on number_of_parts splitting
affected #:  1 file (-1 bytes)

--- a/lib/galaxy/datatypes/sequence.py	Tue May 24 15:15:03 2011 -0700
+++ b/lib/galaxy/datatypes/sequence.py	Thu Jun 16 12:19:07 2011 -0700
@@ -2,6 +2,7 @@
 Sequence classes
 """
 
+import gzip
 import data
 import logging
 import re
@@ -58,17 +59,22 @@
             return
         
         def split_one( input_file, get_dir, clusters_per_file,  default_clusters=None):
-            in_file = open(input_file, 'rt')
+            compress = is_gzip(input_file)
+            if compress:
+# TODO: Python 2.4, 2.5 don't have io.BufferedReader!!!
+# add a buffered reader because gzip is really slow before python 2.7
+                in_file = gzip.GzipFile(input_file, 'r')
+            else:
+                in_file = open(input_file, 'rt')
             part_file = None
             part = 0
             if clusters_per_file is None:
                 local_clusters_per_file = [default_clusters]
             else:
                 local_clusters_per_file = [x for x in clusters_per_file] 
-                
             for i, line in enumerate(in_file):
                 cluster_number,  line_in_cluster = divmod(i, 4)
-                current_part, remainder = divmod(cluster_number, local_clusters_per_file[part])
+                current_part, remainder = divmod(cluster_number, local_clusters_per_file[part]+1)
                 
                 if (current_part != part or part_file is None):
                     if (part_file):
@@ -76,6 +82,7 @@
                     part = current_part
                     part_dir = get_dir()
                     part_path = os.path.join(part_dir, os.path.basename(input_file))
+# TODO: If the input was compressed, compress the output?
                     part_file = open(part_path, 'w')
                     if clusters_per_file is None:
                         local_clusters_per_file.append(default_clusters)


http://bitbucket.org/galaxy/galaxy-central/changeset/ea221820ba19/
changeset:   ea221820ba19
user:        John Duddy
date:        2011-06-16 23:00:45
summary:     Merge
affected #:  1 file (-1 bytes)

--- a/lib/galaxy/datatypes/data.py	Thu Jun 16 12:19:07 2011 -0700
+++ b/lib/galaxy/datatypes/data.py	Thu Jun 16 14:00:45 2011 -0700
@@ -1,4 +1,4 @@
-import logging, os, sys, time, tempfile,  gzip
+import logging, os, sys, time, tempfile
 from galaxy import util
 from galaxy.util.odict import odict
 from galaxy.util.bunch import Bunch
@@ -356,24 +356,11 @@
 
     def merge( split_files, output_file): 
         """
-        Export files are usually compressed, but it doesn't have to be so. In the case that they are, use
-        zcat to cat the files and gzip -c to recompress the result, otherwise use cat
-        TODO: Move to a faster gzjoin-based technique
+        TODO: Do we need to merge gzip files using gzjoin? cat seems to work,
+        but might be brittle. Need to revisit this.
         """
-        #TODO: every time I try to import this from sniff, the parser dies
-        def is_gzip( filename ):
-            temp = open( filename, "U" )
-            magic_check = temp.read( 2 )
-            temp.close()
-            if magic_check != util.gzip_magic:
-                return False
-            return True
-        
         if len(split_files) == 1:
             os.system( 'mv -f %s %s' % ( split_files[0], output_file ) )
-            return
-        if is_gzip(split_files[0]):
-            os.system( 'zcat %s | gzip -c > %s' % ( ' '.join(split_files), output_file ) )
         else:
             os.system( 'cat %s > %s' % ( ' '.join(split_files), output_file ) )
     merge = staticmethod(merge)


http://bitbucket.org/galaxy/galaxy-central/changeset/1dfae31e27bc/
changeset:   1dfae31e27bc
user:        John Duddy
date:        2011-06-16 23:06:06
summary:     Fix issues related to splitting multiple inputs and handling
Files split evenly
affected #:  1 file (-1 bytes)

--- a/lib/galaxy/datatypes/sequence.py	Thu Jun 16 14:00:45 2011 -0700
+++ b/lib/galaxy/datatypes/sequence.py	Thu Jun 16 14:06:06 2011 -0700
@@ -58,7 +58,10 @@
         if split_params is None:
             return
         
-        def split_one( input_file, get_dir, clusters_per_file,  default_clusters=None):
+        def split_calculate_clusters( input_file, get_dir, default_clusters):
+            """
+            Split the 0th file into even sized chunks, and return the number of clusters in each
+            """
             compress = is_gzip(input_file)
             if compress:
 # TODO: Python 2.4, 2.5 don't have io.BufferedReader!!!
@@ -68,13 +71,10 @@
                 in_file = open(input_file, 'rt')
             part_file = None
             part = 0
-            if clusters_per_file is None:
-                local_clusters_per_file = [default_clusters]
-            else:
-                local_clusters_per_file = [x for x in clusters_per_file] 
+            local_clusters_per_file = []
             for i, line in enumerate(in_file):
                 cluster_number,  line_in_cluster = divmod(i, 4)
-                current_part, remainder = divmod(cluster_number, local_clusters_per_file[part]+1)
+                current_part, remainder = divmod(cluster_number, default_clusters)
                 
                 if (current_part != part or part_file is None):
                     if (part_file):
@@ -84,8 +84,7 @@
                     part_path = os.path.join(part_dir, os.path.basename(input_file))
 # TODO: If the input was compressed, compress the output?
                     part_file = open(part_path, 'w')
-                    if clusters_per_file is None:
-                        local_clusters_per_file.append(default_clusters)
+                    local_clusters_per_file.append(default_clusters)
                 part_file.write(line)
             if (part_file):
                 part_file.close()
@@ -93,6 +92,50 @@
             local_clusters_per_file[part] = remainder + 1
             return local_clusters_per_file
     
+        def split_to_size(input_file, get_dir, clusters_per_file):
+            """
+            Split the files beyond the 0th to the same number of clusters as the 0th.
+            This is used to split in a variety of ways, so these are both legal for
+            clusters_per_file:
+                [ 10000, 10000, 10000, 10000, 2 ] # to_size=10000, 40002 total
+                [ 10001, 10001, 10000, 10000 ] # number_of_parts = 4, 40002 total
+
+            """
+            compress = is_gzip(input_file)
+            if compress:
+# TODO: Python 2.4, 2.5 don't have io.BufferedReader!!!
+# add a buffered reader because gzip is really slow before python 2.7
+                in_file = gzip.GzipFile(input_file, 'r')
+            else:
+                in_file = open(input_file, 'rt')
+            part_file = None
+            part = 0
+            clusters_this_part = 0
+            for i, line in enumerate(in_file):
+                cluster_number,  line_in_cluster = divmod(i, 4)
+                if clusters_this_part  == clusters_per_file[part]:
+                    current_part = part + 1
+                else:
+                    current_part = part
+                
+                if (current_part != part or part_file is None):
+                    if (part_file):
+                        part_file.close()
+                    part = current_part
+                    clusters_this_part = 0
+                    part_dir = get_dir()
+                    part_path = os.path.join(part_dir, os.path.basename(input_file))
+# TODO: If the input was compressed, compress the output?
+                    part_file = open(part_path, 'w')
+                    if clusters_per_file is None and part > 0:
+                        local_clusters_per_file.append(default_clusters)
+                part_file.write(line)
+                if line_in_cluster == 3:
+                    clusters_this_part += 1
+            if (part_file):
+                part_file.close()
+            in_file.close()
+
         directories = []
         def create_subdir():
             dir = subdir_generator_function()
@@ -123,10 +166,11 @@
                 clusters_per_file.append(chunk)
                 remainder=- 1
                 length -= chunk
-            split_one(input_files[0],  create_subdir,  clusters_per_file)
+            split_to_size(input_files[0],  create_subdir,  clusters_per_file)
         elif split_params['split_mode'] == 'to_size':
             # split one file and see what the cluster sizes turn out to be
-            clusters_per_file = split_one(input_files[0],  create_subdir,  None,  int(split_params['split_size']))
+            clusters_per_file = split_calculate_clusters(input_files[0],  create_subdir,  
+                int(split_params['split_size']))
         else:
             raise Exception('Unsupported split mode %s' % split_params['split_mode'])
         
@@ -141,7 +185,7 @@
             
         for i in range(1,  len(input_files)):
             current_dir_idx[0] = 0
-            split_one(input_files[i],  get_subdir,  clusters_per_file)
+            split_to_size(input_files[i],  get_subdir,  clusters_per_file)
     split = staticmethod(split)
 
 


http://bitbucket.org/galaxy/galaxy-central/changeset/49c9f87e4717/
changeset:   49c9f87e4717
user:        John Duddy
date:        2011-10-07 22:25:19
summary:     Merge
affected #:  711 files (-1 bytes)
Diff too large to display.
http://bitbucket.org/galaxy/galaxy-central/changeset/1827729ed37e/
changeset:   1827729ed37e
user:        John Duddy
date:        2011-10-07 23:12:40
summary:     Upgrade of splitting code to do splitting in the tasks versus in the Galaxy process
affected #:  12 files (-1 bytes)

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/extract_dataset_parts.sh	Fri Oct 07 14:12:40 2011 -0700
@@ -0,0 +1,8 @@
+#!/bin/sh
+
+cd `dirname $0`
+for file in $1/split_info*.json
+do
+    # echo processing $file
+    python ./scripts/extract_dataset_part.py $file
+done


--- a/lib/galaxy/datatypes/data.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/datatypes/data.py	Fri Oct 07 14:12:40 2011 -0700
@@ -360,9 +360,12 @@
         but might be brittle. Need to revisit this.
         """
         if len(split_files) == 1:
-            os.system( 'mv -f %s %s' % ( split_files[0], output_file ) )
+            cmd = 'mv -f %s %s' % ( split_files[0], output_file ) 
         else:
-            os.system( 'cat %s > %s' % ( ' '.join(split_files), output_file ) )
+            cmd = 'cat %s > %s' % ( ' '.join(split_files), output_file ) 
+        result = os.system(cmd)
+        if result != 0:
+            raise Exception('Result %s from %s' % (result, cmd))
     merge = staticmethod(merge)
 
 class Text( Data ):
@@ -533,6 +536,13 @@
         f.close()
     split = staticmethod(split)
 
+class LineCount( Text ):
+    """
+    Dataset contains a single line with a single integer that denotes the
+    line count for a related dataset. Used for custom builds.
+    """
+    pass
+
 class Newick( Text ):
     pass
 


--- a/lib/galaxy/datatypes/sequence.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/datatypes/sequence.py	Fri Oct 07 14:12:40 2011 -0700
@@ -7,6 +7,7 @@
 import logging
 import re
 import string
+import os
 from cgi import escape
 from galaxy.datatypes.metadata import MetadataElement
 from galaxy.datatypes import metadata
@@ -14,8 +15,52 @@
 from galaxy import util
 from sniff import *
 
+import pkg_resources
+pkg_resources.require("simplejson")
+import simplejson
+
 log = logging.getLogger(__name__)
 
+class SequenceSplitLocations( data.Text ):
+    """
+    Class storing information about a sequence file composed of multiple gzip files concatenated as
+    one OR an uncompressed file. In the GZIP case, each sub-file's location is stored in start and end. 
+    The format of the file is JSON:
+    { "sections" : [
+            { "start" : "x", "end" : "y", "clusters" : "z" },
+            ...
+    ]}
+    """
+    def set_peek( self, dataset, is_multi_byte=False ):
+        if not dataset.dataset.purged:
+            try:
+                parsed_data = simplejson.load(open(dataset.file_name))
+                # dataset.peek = simplejson.dumps(data, sort_keys=True, indent=4)
+                dataset.peek = data.get_file_peek( dataset.file_name, is_multi_byte=is_multi_byte )
+                dataset.blurb = '%d sections' % len(parsed_data['sections'])
+            except Exception, e:
+                dataset.peek = 'Not FQTOC file'
+                dataset.blurb = 'Not FQTOC file'
+        else:
+            dataset.peek = 'file does not exist'
+            dataset.blurb = 'file purged from disk'
+
+    file_ext = "fqtoc"
+
+    def sniff( self, filename ):
+        if os.path.getsize(filename) < 50000:
+            try:
+                data = simplejson.load(open(filename))
+                sections = data['sections']
+                for section in sections:
+                    if 'start' not in section or 'end' not in section or 'clusters' not in section:
+                        return False
+                return True
+            except:
+                pass
+        return False
+
+
 class Sequence( data.Text ):
     """Class describing a sequence"""
 
@@ -50,143 +95,237 @@
         else:
             dataset.peek = 'file does not exist'
             dataset.blurb = 'file purged from disk'
+
+    def get_sequences_per_file(total_clusters, split_params):
+        if split_params['split_mode'] == 'number_of_parts':
+            # legacy basic mode - split into a specified number of parts
+            parts = int(split_params['split_size'])
+            sequences_per_file = [total_clusters/parts for i in range(parts)]
+            for i in range(total_clusters % parts):
+                sequences_per_file[i] += 1
+        elif split_params['split_mode'] == 'to_size':
+            # loop through the sections and calculate the number of clusters
+            chunk_size = long(split_params['split_size'])
+
+            chunks = total_clusters / chunk_size
+            rem = total_clusters % chunk_size
+            sequences_per_file = [chunk_size for i in range(total_clusters / chunk_size)]
+            # TODO: Should we invest the time in a better way to handle small remainders?
+            if rem > 0:
+                sequences_per_file.append(rem)
+        else:
+            raise Exception('Unsupported split mode %s' % split_params['split_mode'])
+        return sequences_per_file
+    get_sequences_per_file = staticmethod(get_sequences_per_file)
+
+    def do_slow_split( cls, input_datasets, subdir_generator_function, split_params):
+        # count the clusters so we can split
+        # TODO: if metadata is present, take the number of lines / 4
+        if input_datasets[0].metadata is not None and input_datasets[0].metadata.sequences is not None:
+            total_clusters = input_datasets[0].metadata.sequences
+        else:
+            input_file = input_datasets[0].file_name
+            compress = is_gzip(input_file)
+            if compress:
+                # gzip is really slow before python 2.7!
+                in_file = gzip.GzipFile(input_file, 'r')
+            else:
+                # TODO
+                # if a file is not compressed, seek locations can be calculated and stored
+                # ideally, this would be done in metadata
+                # TODO
+                # Add BufferedReader if python 2.7?
+                in_file = open(input_file, 'rt')
+            total_clusters = long(0)
+            for i, line in enumerate(in_file):
+                total_clusters += 1
+            in_file.close()
+            total_clusters /= 4
+
+        sequences_per_file = cls.get_sequences_per_file(total_clusters, split_params)
+        return cls.write_split_files(input_datasets, None, subdir_generator_function, sequences_per_file)
+    do_slow_split = classmethod(do_slow_split)
+
+    def do_fast_split( cls, input_datasets, toc_file_datasets, subdir_generator_function, split_params):
+        data = simplejson.load(open(toc_file_datasets[0].file_name))
+        sections = data['sections']
+        total_clusters = long(0)
+        for section in sections:
+            total_clusters += long(section['clusters'])
+        sequences_per_file = cls.get_sequences_per_file(total_clusters, split_params)
+        return cls.write_split_files(input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file)
+    do_fast_split = classmethod(do_fast_split)
+
+    def write_split_files(cls, input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file):
+        directories = []
+        def get_subdir(idx):
+            if idx < len(directories):
+                return directories[idx]
+            dir = subdir_generator_function()
+            directories.append(dir)
+            return dir
+
+        # we know how many splits and how many clusters in each. What remains is to write out instructions for the 
+        # splitting of all the input files. To decouple the format of those instructions from this code, the exact format of
+        # those instructions is delegated to scripts
+        start_sequence=0
+        for part_no in range(len(sequences_per_file)):
+            dir = get_subdir(part_no)
+            for ds_no in range(len(input_datasets)):
+                ds = input_datasets[ds_no]
+                base_name = os.path.basename(ds.file_name)
+                part_path = os.path.join(dir, base_name)
+                split_data = dict(class_name='%s.%s' % (cls.__module__, cls.__name__),
+                                  output_name=part_path,
+                                  input_name=ds.file_name,
+                                  args=dict(start_sequence=start_sequence, num_sequences=sequences_per_file[part_no]))
+                if toc_file_datasets is not None:
+                    toc = toc_file_datasets[ds_no]
+                    split_data['args']['toc_file'] = toc.file_name
+                f = open(os.path.join(dir, 'split_info_%s.json' % base_name), 'w')
+                simplejson.dump(split_data, f)
+                f.close()
+            start_sequence += sequences_per_file[part_no]
+        return directories
+    write_split_files = classmethod(write_split_files)
     
-    def split( input_files, subdir_generator_function, split_params):
+    def split( cls, input_datasets, subdir_generator_function, split_params):
         """
         FASTQ files are split on cluster boundaries, in increments of 4 lines
         """
         if split_params is None:
-            return
+            return None
+
+        # first, see if there are any associated FQTOC files that will give us the split locations
+        # if so, we don't need to read the files to do the splitting
+        toc_file_datasets = []
+        for ds in input_datasets:
+            tmp_ds = ds
+            fqtoc_file = None
+            while fqtoc_file is None and tmp_ds is not None:
+                fqtoc_file = tmp_ds.get_converted_files_by_type('fqtoc')
+                tmp_ds = tmp_ds.copied_from_library_dataset_dataset_association
+
+            if fqtoc_file is not None:
+                toc_file_datasets.append(fqtoc_file)
+
+        if len(toc_file_datasets) == len(input_datasets):
+            return cls.do_fast_split(input_datasets, toc_file_datasets, subdir_generator_function, split_params)
+        return cls.do_slow_split(input_datasets, subdir_generator_function, split_params)
+    split = classmethod(split)
+
+    def process_split_file(data):
+        """
+        This is called in the context of an external process launched by a Task (possibly not on the Galaxy machine)
+        to create the input files for the Task. The parameters:
+        data - a dict containing the contents of the split file
+        """
+        args = data['args']
+        input_name = data['input_name']
+        output_name = data['output_name']
+        start_sequence = long(args['start_sequence'])
+        sequence_count = long(args['num_sequences'])
         
-        def split_calculate_clusters( input_file, get_dir, default_clusters):
-            """
-            Split the 0th file into even sized chunks, and return the number of clusters in each
-            """
-            compress = is_gzip(input_file)
-            if compress:
-# TODO: Python 2.4, 2.5 don't have io.BufferedReader!!!
-# add a buffered reader because gzip is really slow before python 2.7
-                in_file = gzip.GzipFile(input_file, 'r')
-            else:
-                in_file = open(input_file, 'rt')
-            part_file = None
-            part = 0
-            local_clusters_per_file = []
-            for i, line in enumerate(in_file):
-                cluster_number,  line_in_cluster = divmod(i, 4)
-                current_part, remainder = divmod(cluster_number, default_clusters)
+        if 'toc_file' in args:
+            toc_file = simplejson.load(open(args['toc_file'], 'r'))
+            commands = Sequence.get_split_commands_with_toc(input_name, output_name, toc_file, start_sequence, sequence_count)
+        else:
+            commands = Sequence.get_split_commands_sequential(is_gzip(input_name), input_name, output_name, start_sequence, sequence_count)
+        for cmd in commands:
+            if 0 != os.system(cmd):
+                raise Exception("Executing '%s' failed" % cmd)
+        return True
+    process_split_file = staticmethod(process_split_file)
+    
+    def get_split_commands_with_toc(input_name, output_name, toc_file, start_sequence, sequence_count):
+        """
+        Uses a Table of Contents dict, parsed from an FQTOC file, to come up with a set of
+        shell commands that will extract the parts necessary
+        >>> three_sections=[dict(start=0, end=74, clusters=10), dict(start=74, end=148, clusters=10), dict(start=148, end=148+76, clusters=10)]
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=0, sequence_count=10)
+        ['dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=1, sequence_count=5)
+        ['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +5 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=0, sequence_count=20)
+        ['dd bs=1 skip=0 count=148 if=./input.gz 2> /dev/null >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=5, sequence_count=10)
+        ['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz', '(dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=10, sequence_count=10)
+        ['dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=5, sequence_count=20)
+        ['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz', 'dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz', '(dd bs=1 skip=148 count=76 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
+        """
+        sections = toc_file['sections']
+        result = []
+
+        current_sequence = long(0)
+        i=0
+        # skip to the section that contains my starting sequence
+        while i < len(sections) and start_sequence >= current_sequence + long(sections[i]['clusters']):
+            current_sequence += long(sections[i]['clusters'])
+            i += 1
+        if i == len(sections): # bad input data!
+            raise Exception('No FQTOC section contains starting sequence %s' % start_sequence)
+    
+        # These two variables act as an accumulator for consecutive entire blocks that
+        # can be copied verbatim (without decompressing)
+        start_chunk = long(-1)
+        end_chunk = long(-1)
+        copy_chunk_cmd = 'dd bs=1 skip=%s count=%s if=%s 2> /dev/null >> %s'
+        
+        while sequence_count > 0 and i < len(sections):
+            # we need to extract partial data. So, find the byte offsets of the chunks that contain the data we need
+            # use a combination of dd (to pull just the right sections out) tail (to skip lines) and head (to get the 
+            # right number of lines
+            sequences = long(sections[i]['clusters'])
+            skip_sequences = start_sequence-current_sequence
+            sequences_to_extract = min(sequence_count, sequences-skip_sequences)
+            start_copy = long(sections[i]['start'])
+            end_copy = long(sections[i]['end'])
+            if sequences_to_extract < sequences:
+                if start_chunk > -1:
+                    result.append(copy_chunk_cmd % (start_chunk, end_chunk-start_chunk, input_name, output_name))
+                    start_chunk = -1
+                # extract, unzip, trim, recompress
+                result.append('(dd bs=1 skip=%s count=%s if=%s 2> /dev/null )| zcat | ( tail -n +%s 2> /dev/null) | head -%s | gzip -c >> %s' % 
+                    (start_copy, end_copy-start_copy, input_name, skip_sequences*4+1, sequences_to_extract*4, output_name))
+            else: # whole section - add it to the start_chunk/end_chunk accumulator
+                if start_chunk == -1:
+                    start_chunk = start_copy
+                end_chunk = end_copy
+            sequence_count -= sequences_to_extract
+            start_sequence += sequences_to_extract
+            current_sequence += sequences
+            i += 1
+        if start_chunk > -1:
+            result.append(copy_chunk_cmd % (start_chunk, end_chunk-start_chunk, input_name, output_name))
                 
-                if (current_part != part or part_file is None):
-                    if (part_file):
-                        part_file.close()
-                    part = current_part
-                    part_dir = get_dir()
-                    part_path = os.path.join(part_dir, os.path.basename(input_file))
-# TODO: If the input was compressed, compress the output?
-                    part_file = open(part_path, 'w')
-                    local_clusters_per_file.append(default_clusters)
-                part_file.write(line)
-            if (part_file):
-                part_file.close()
-            in_file.close()
-            local_clusters_per_file[part] = remainder + 1
-            return local_clusters_per_file
-    
-        def split_to_size(input_file, get_dir, clusters_per_file):
-            """
-            Split the files beyond the 0th to the same number of clusters as the 0th.
-            This is used to split in a variety of ways, so these are both legal for
-            clusters_per_file:
-                [ 10000, 10000, 10000, 10000, 2 ] # to_size=10000, 40002 total
-                [ 10001, 10001, 10000, 10000 ] # number_of_parts = 4, 40002 total
+        if sequence_count > 0:
+            raise Exception('%s sequences not found in file' % sequence_count)
+             
+        return result
+    get_split_commands_with_toc = staticmethod(get_split_commands_with_toc)
 
-            """
-            compress = is_gzip(input_file)
-            if compress:
-# TODO: Python 2.4, 2.5 don't have io.BufferedReader!!!
-# add a buffered reader because gzip is really slow before python 2.7
-                in_file = gzip.GzipFile(input_file, 'r')
-            else:
-                in_file = open(input_file, 'rt')
-            part_file = None
-            part = 0
-            clusters_this_part = 0
-            for i, line in enumerate(in_file):
-                cluster_number,  line_in_cluster = divmod(i, 4)
-                if clusters_this_part  == clusters_per_file[part]:
-                    current_part = part + 1
-                else:
-                    current_part = part
-                
-                if (current_part != part or part_file is None):
-                    if (part_file):
-                        part_file.close()
-                    part = current_part
-                    clusters_this_part = 0
-                    part_dir = get_dir()
-                    part_path = os.path.join(part_dir, os.path.basename(input_file))
-# TODO: If the input was compressed, compress the output?
-                    part_file = open(part_path, 'w')
-                    if clusters_per_file is None and part > 0:
-                        local_clusters_per_file.append(default_clusters)
-                part_file.write(line)
-                if line_in_cluster == 3:
-                    clusters_this_part += 1
-            if (part_file):
-                part_file.close()
-            in_file.close()
 
-        directories = []
-        def create_subdir():
-            dir = subdir_generator_function()
-            directories.append(dir)
-            return dir
+    def get_split_commands_sequential(is_compressed, input_name, output_name, start_sequence, sequence_count):
+        """
+        Does a brain-dead sequential scan & extract of certain sequences
+        >>> Sequence.get_split_commands_sequential(True, './input.gz', './output.gz', start_sequence=0, sequence_count=10)
+        ['zcat "./input.gz" | ( tail -n +1 2> /dev/null) | head -40 | gzip -c > "./output.gz"']
+        >>> Sequence.get_split_commands_sequential(False, './input.fastq', './output.fastq', start_sequence=10, sequence_count=10)
+        ['tail -n +41 "./input.fastq" 2> /dev/null | head -40 > "./output.fastq"']
+        """
+        start_line = start_sequence * 4
+        line_count = sequence_count * 4
+        # TODO: verify that tail can handle 64-bit numbers
+        if is_compressed:
+            cmd = 'zcat "%s" | ( tail -n +%s 2> /dev/null) | head -%s | gzip -c' % (input_name, start_line+1, line_count)
+        else:
+            cmd = 'tail -n +%s "%s" 2> /dev/null | head -%s'  % (start_line+1, input_name, line_count)
+        cmd += ' > "%s"' % output_name
         
-        clusters_per_file = None
-        if split_params['split_mode'] == 'number_of_parts':
-            # legacy splitting. To keep things simple, just scan the 0th file and count the clusters,
-            # then split it
-            clusters_per_file = []
-            in_file = open(input_files[0], 'rt')
-            for i, line in enumerate(in_file):
-                pass
-            in_file.close()
-            length = (i+1)/4
-            
-            if length <= 0:
-                raise Exception('Invalid sequence file %s' % input_files[0])
-            parts = int(split_params['split_size'])
-            if length < parts:
-                parts = length
-            len_each, remainder = divmod(length, parts)
-            while length > 0:
-                chunk = len_each
-                if remainder > 0:
-                    chunk += 1
-                clusters_per_file.append(chunk)
-                remainder=- 1
-                length -= chunk
-            split_to_size(input_files[0],  create_subdir,  clusters_per_file)
-        elif split_params['split_mode'] == 'to_size':
-            # split one file and see what the cluster sizes turn out to be
-            clusters_per_file = split_calculate_clusters(input_files[0],  create_subdir,  
-                int(split_params['split_size']))
-        else:
-            raise Exception('Unsupported split mode %s' % split_params['split_mode'])
-        
-        # split the rest, using the same number of clusters for each file
-        current_dir_idx = [0] # use a list to get around Python 2.x lame closure support
-        def get_subdir():
-            if len(directories) <= current_dir_idx[0]:
-                raise Exception('FASTQ files do not have the same number of clusters - splitting failed')
-            result = directories[current_dir_idx[0]]
-            current_dir_idx[0] = current_dir_idx[0] + 1
-            return result
-            
-        for i in range(1,  len(input_files)):
-            current_dir_idx[0] = 0
-            split_to_size(input_files[i],  get_subdir,  clusters_per_file)
-    split = staticmethod(split)
+        return [cmd]
+    get_split_commands_sequential = staticmethod(get_split_commands_sequential)
 
 
 
@@ -690,3 +829,7 @@
                 return False
         except:
             return False
+
+if __name__ == '__main__':
+    import doctest, sys
+    doctest.testmod(sys.modules[__name__])
\ No newline at end of file


--- a/lib/galaxy/jobs/__init__.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/jobs/__init__.py	Fri Oct 07 14:12:40 2011 -0700
@@ -32,9 +32,9 @@
 class JobManager( object ):
     """
     Highest level interface to job management.
-
+    
     TODO: Currently the app accesses "job_queue" and "job_stop_queue" directly.
-          This should be decoupled.
+          This should be decoupled. 
     """
     def __init__( self, app ):
         self.app = app
@@ -71,7 +71,7 @@
 
 class JobQueue( object ):
     """
-    Job manager, waits for jobs to be runnable and then dispatches to
+    Job manager, waits for jobs to be runnable and then dispatches to 
     a JobRunner.
     """
     STOP_SIGNAL = object()
@@ -95,7 +95,7 @@
         self.running = True
         self.dispatcher = dispatcher
         self.monitor_thread = threading.Thread( target=self.__monitor )
-        self.monitor_thread.start()
+        self.monitor_thread.start()        
         log.info( "job manager started" )
         if app.config.get_bool( 'enable_job_recovery', True ):
             self.__check_jobs_at_startup()
@@ -132,7 +132,7 @@
 
     def __monitor( self ):
         """
-        Continually iterate the waiting jobs, checking is each is ready to
+        Continually iterate the waiting jobs, checking is each is ready to 
         run and dispatching if so.
         """
         # HACK: Delay until after forking, we need a way to do post fork notification!!!
@@ -180,12 +180,12 @@
                     jobs_to_check.append( self.sa_session.query( model.Job ).get( job_id ) )
             except Empty:
                 pass
-        # Iterate over new and waiting jobs and look for any that are
+        # Iterate over new and waiting jobs and look for any that are 
         # ready to run
         new_waiting_jobs = []
         for job in jobs_to_check:
             try:
-                # Check the job's dependencies, requeue if they're not done
+                # Check the job's dependencies, requeue if they're not done                    
                 job_state = self.__check_if_ready_to_run( job )
                 if job_state == JOB_WAIT:
                     if not self.track_jobs_in_database:
@@ -216,7 +216,7 @@
         self.waiting_jobs = new_waiting_jobs
         # Done with the session
         self.sa_session.remove()
-
+        
     def __check_if_ready_to_run( self, job ):
         """
         Check if a job is ready to run by verifying that each of its input
@@ -281,13 +281,13 @@
         if len( user_jobs ) >= self.app.config.user_job_limit:
             return JOB_WAIT
         return JOB_READY
-
+            
     def put( self, job_id, tool ):
         """Add a job to the queue (by job identifier)"""
         if not self.track_jobs_in_database:
             self.queue.put( ( job_id, tool.id ) )
             self.sleeper.wake()
-
+    
     def shutdown( self ):
         """Attempts to gracefully shut down the worker thread"""
         if self.parent_pid != os.getpid():
@@ -304,7 +304,7 @@
 
 class JobWrapper( object ):
     """
-    Wraps a 'model.Job' with convenience methods for running processes and
+    Wraps a 'model.Job' with convenience methods for running processes and 
     state management.
     """
     def __init__( self, job, queue ):
@@ -330,15 +330,15 @@
         self.output_dataset_paths = None
         self.tool_provided_job_metadata = None
         # Wrapper holding the info required to restore and clean up from files used for setting metadata externally
-        self.external_output_metadata = metadata.JobExternalOutputMetadataWrapper( job )
-
+        self.external_output_metadata = metadata.JobExternalOutputMetadataWrapper( job )        
+    
     def get_job( self ):
         return self.sa_session.query( model.Job ).get( self.job_id )
-
+    
     def get_id_tag(self):
         # For compatability with drmaa, which uses job_id right now, and TaskWrapper
         return str(self.job_id)
-
+    
     def get_param_dict( self ):
         """
         Restore the dictionary of parameters from the database.
@@ -347,10 +347,10 @@
         param_dict = dict( [ ( p.name, p.value ) for p in job.parameters ] )
         param_dict = self.tool.params_from_strings( param_dict, self.app )
         return param_dict
-
+        
     def get_version_string_path( self ):
         return os.path.abspath(os.path.join(self.app.config.new_file_path, "GALAXY_VERSION_STRING_%s" % self.job_id))
-
+        
     def prepare( self ):
         """
         Prepare the job to run by creating the working directory and the
@@ -372,9 +372,9 @@
         out_data = dict( [ ( da.name, da.dataset ) for da in job.output_datasets ] )
         inp_data.update( [ ( da.name, da.dataset ) for da in job.input_library_datasets ] )
         out_data.update( [ ( da.name, da.dataset ) for da in job.output_library_datasets ] )
-
-        # Set up output dataset association for export history jobs. Because job
-        # uses a Dataset rather than an HDA or LDA, it's necessary to set up a
+        
+        # Set up output dataset association for export history jobs. Because job 
+        # uses a Dataset rather than an HDA or LDA, it's necessary to set up a 
         # fake dataset association that provides the needed attributes for
         # preparing a job.
         class FakeDatasetAssociation ( object ):
@@ -401,7 +401,7 @@
         # ( this used to be performed in the "exec_before_job" hook, but hooks are deprecated ).
         self.tool.exec_before_job( self.queue.app, inp_data, out_data, param_dict )
         # Run the before queue ("exec_before_job") hook
-        self.tool.call_hook( 'exec_before_job', self.queue.app, inp_data=inp_data,
+        self.tool.call_hook( 'exec_before_job', self.queue.app, inp_data=inp_data, 
                              out_data=out_data, tool=self.tool, param_dict=incoming)
         self.sa_session.flush()
         # Build any required config files
@@ -434,7 +434,7 @@
 
     def fail( self, message, exception=False ):
         """
-        Indicate job failure by setting state and message on all output
+        Indicate job failure by setting state and message on all output 
         datasets.
         """
         job = self.get_job()
@@ -480,7 +480,7 @@
         if self.tool:
             self.tool.job_failed( self, message, exception )
         self.cleanup()
-
+        
     def change_state( self, state, info = False ):
         job = self.get_job()
         self.sa_session.refresh( job )
@@ -510,12 +510,12 @@
         job.job_runner_external_id = external_id
         self.sa_session.add( job )
         self.sa_session.flush()
-
+        
     def finish( self, stdout, stderr ):
         """
-        Called to indicate that the associated command has been run. Updates
+        Called to indicate that the associated command has been run. Updates 
         the output datasets based on stderr and stdout from the command, and
-        the contents of the output files.
+        the contents of the output files. 
         """
         # default post job setup
         self.sa_session.expunge_all()
@@ -537,7 +537,7 @@
             if os.path.exists(version_filename):
                 self.version_string = open(version_filename).read()
                 os.unlink(version_filename)
-
+            
         if self.app.config.outputs_to_working_directory:
             for dataset_path in self.get_output_fnames():
                 try:
@@ -585,7 +585,7 @@
                             else:
                                 # Security violation.
                                 log.exception( "from_work_dir specified a location not in the working directory: %s, %s" % ( source_file, self.working_directory ) )
-
+            
                 dataset.blurb = 'done'
                 dataset.peek  = 'no peek'
                 dataset.info  = context['stdout'] + context['stderr']
@@ -600,7 +600,7 @@
                         dataset.init_meta( copy_from=dataset )
                     #if a dataset was copied, it won't appear in our dictionary:
                     #either use the metadata from originating output dataset, or call set_meta on the copies
-                    #it would be quicker to just copy the metadata from the originating output dataset,
+                    #it would be quicker to just copy the metadata from the originating output dataset, 
                     #but somewhat trickier (need to recurse up the copied_from tree), for now we'll call set_meta()
                     if not self.app.config.set_metadata_externally or \
                      ( not self.external_output_metadata.external_metadata_set_successfully( dataset, self.sa_session ) \
@@ -612,7 +612,7 @@
                         #load metadata from file
                         #we need to no longer allow metadata to be edited while the job is still running,
                         #since if it is edited, the metadata changed on the running output will no longer match
-                        #the metadata that was stored to disk for use via the external process,
+                        #the metadata that was stored to disk for use via the external process, 
                         #and the changes made by the user will be lost, without warning or notice
                         dataset.metadata.from_JSON_dict( self.external_output_metadata.get_output_filenames_by_dataset( dataset, self.sa_session ).filename_out )
                     try:
@@ -653,13 +653,13 @@
         # Flush all the dataset and job changes above.  Dataset state changes
         # will now be seen by the user.
         self.sa_session.flush()
-        # Save stdout and stderr
+        # Save stdout and stderr    
         if len( stdout ) > 32768:
             log.error( "stdout for job %d is greater than 32K, only first part will be logged to database" % job.id )
         job.stdout = stdout[:32768]
         if len( stderr ) > 32768:
             log.error( "stderr for job %d is greater than 32K, only first part will be logged to database" % job.id )
-        job.stderr = stderr[:32768]
+        job.stderr = stderr[:32768]  
         # custom post process setup
         inp_data = dict( [ ( da.name, da.dataset ) for da in job.input_datasets ] )
         out_data = dict( [ ( da.name, da.dataset ) for da in job.output_datasets ] )
@@ -676,8 +676,8 @@
         # ( this used to be performed in the "exec_after_process" hook, but hooks are deprecated ).
         self.tool.exec_after_process( self.queue.app, inp_data, out_data, param_dict, job = job )
         # Call 'exec_after_process' hook
-        self.tool.call_hook( 'exec_after_process', self.queue.app, inp_data=inp_data,
-                             out_data=out_data, param_dict=param_dict,
+        self.tool.call_hook( 'exec_after_process', self.queue.app, inp_data=inp_data, 
+                             out_data=out_data, param_dict=param_dict, 
                              tool=self.tool, stdout=stdout, stderr=stderr )
         job.command_line = self.command_line
 
@@ -696,7 +696,7 @@
         self.sa_session.flush()
         log.debug( 'job %d ended' % self.job_id )
         self.cleanup()
-
+        
     def cleanup( self ):
         # remove temporary files
         try:
@@ -710,10 +710,10 @@
             galaxy.tools.imp_exp.JobImportHistoryArchiveWrapper( self.job_id ).cleanup_after_job( self.sa_session )
         except:
             log.exception( "Unable to cleanup job %d" % self.job_id )
-
+        
     def get_command_line( self ):
         return self.command_line
-
+    
     def get_session_id( self ):
         return self.session_id
 
@@ -884,13 +884,17 @@
     Should be refactored into a generalized executable unit wrapper parent, then jobs and tasks.
     """
     # Abstract this to be more useful for running tasks that *don't* necessarily compose a job.
-
+    
     def __init__(self, task, queue):
         super(TaskWrapper, self).__init__(task.job, queue)
         self.task_id = task.id
         self.working_directory = task.working_directory
+        if task.prepare_input_files_cmd is not None:
+            self.prepare_input_files_cmds = [ task.prepare_input_files_cmd ]
+        else:
+            self.prepare_input_files_cmds = None
         self.status = task.states.NEW
-
+       
     def get_job( self ):
         if self.job_id:
             return self.sa_session.query( model.Job ).get( self.job_id )
@@ -953,7 +957,7 @@
         # ( this used to be performed in the "exec_before_job" hook, but hooks are deprecated ).
         self.tool.exec_before_job( self.queue.app, inp_data, out_data, param_dict )
         # Run the before queue ("exec_before_job") hook
-        self.tool.call_hook( 'exec_before_job', self.queue.app, inp_data=inp_data,
+        self.tool.call_hook( 'exec_before_job', self.queue.app, inp_data=inp_data, 
                              out_data=out_data, tool=self.tool, param_dict=incoming)
         self.sa_session.flush()
         # Build any required config files
@@ -1000,12 +1004,12 @@
         task.state = state
         self.sa_session.add( task )
         self.sa_session.flush()
-
+            
     def get_state( self ):
         task = self.get_task()
         self.sa_session.refresh( task )
         return task.state
-
+        
     def set_runner( self, runner_url, external_id ):
         task = self.get_task()
         self.sa_session.refresh( task )
@@ -1014,15 +1018,15 @@
         # DBTODO Check task job_runner_stuff
         self.sa_session.add( task )
         self.sa_session.flush()
-
+        
     def finish( self, stdout, stderr ):
         # DBTODO integrate previous finish logic.
         # Simple finish for tasks.  Just set the flag OK.
         log.debug( 'task %s for job %d ended' % (self.task_id, self.job_id) )
         """
-        Called to indicate that the associated command has been run. Updates
+        Called to indicate that the associated command has been run. Updates 
         the output datasets based on stderr and stdout from the command, and
-        the contents of the output files.
+        the contents of the output files. 
         """
         # default post job setup_external_metadata
         self.sa_session.expunge_all()
@@ -1039,7 +1043,7 @@
             task.state = task.states.ERROR
         else:
             task.state = task.states.OK
-        # Save stdout and stderr
+        # Save stdout and stderr    
         if len( stdout ) > 32768:
             log.error( "stdout for task %d is greater than 32K, only first part will be logged to database" % task.id )
         task.stdout = stdout[:32768]
@@ -1053,7 +1057,7 @@
     def cleanup( self ):
         # There is no task cleanup.  The job cleans up for all tasks.
         pass
-
+        
     def get_command_line( self ):
         return self.command_line
 
@@ -1063,7 +1067,7 @@
     def get_output_file_id( self, file ):
         # There is no permanent output file for tasks.
         return None
-
+        
     def get_tool_provided_job_metadata( self ):
         # DBTODO Handle this as applicable for tasks.
         return None
@@ -1085,7 +1089,7 @@
     def setup_external_metadata( self, exec_dir = None, tmp_dir = None, dataset_files_path = None, config_root = None, datatypes_config = None, set_extension = True, **kwds ):
         # There is no metadata setting for tasks.  This is handled after the merge, at the job level.
         return ""
-
+        
 class DefaultJobDispatcher( object ):
     def __init__( self, app ):
         self.app = app
@@ -1115,7 +1119,7 @@
             runner = getattr( module, obj )
             self.job_runners[name] = runner( self.app )
             log.debug( 'Loaded job runner: %s' % display_name )
-
+            
     def put( self, job_wrapper ):
         try:
             if self.app.config.use_tasked_jobs and job_wrapper.tool.parallelism is not None:
@@ -1126,8 +1130,8 @@
                     self.job_runners[runner_name].put( job_wrapper )
                 else:
                     runner_name = "tasks"
-                    log.debug( "dispatching job %d to %s runner" %( job_wrapper.job_id, runner_name ) )
-                    self.job_runners[runner_name].put( job_wrapper )
+                log.debug( "dispatching job %d to %s runner" %( job_wrapper.job_id, runner_name ) )
+                self.job_runners[runner_name].put( job_wrapper )
             else:
                 runner_name = ( job_wrapper.tool.job_runner.split(":", 1) )[0]
                 log.debug( "dispatching job %d to %s runner" %( job_wrapper.job_id, runner_name ) )
@@ -1183,7 +1187,7 @@
         self.sleeper = Sleeper()
         self.running = True
         self.monitor_thread = threading.Thread( target=self.monitor )
-        self.monitor_thread.start()
+        self.monitor_thread.start()        
         log.info( "job stopper started" )
 
     def monitor( self ):
@@ -1263,4 +1267,3 @@
         return
     def shutdown( self ):
         return
-


--- a/lib/galaxy/jobs/runners/__init__.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/jobs/runners/__init__.py	Fri Oct 07 14:12:40 2011 -0700
@@ -6,6 +6,7 @@
         Compose the sequence of commands necessary to execute a job. This will
         currently include:
             - environment settings corresponding to any requirement tags
+            - preparing input files
             - command line taken from job wrapper
             - commands to set metadata (if include_metadata is True)
         """
@@ -17,10 +18,13 @@
         # Prepend version string
         if job_wrapper.version_string_cmd:
             commands = "%s &> %s; " % ( job_wrapper.version_string_cmd, job_wrapper.get_version_string_path() ) + commands
+        # prepend getting input files (if defined)
+        if hasattr(job_wrapper, 'prepare_input_files_cmds') and job_wrapper.prepare_input_files_cmds is not None:
+            commands = "; ".join( job_wrapper.prepare_input_files_cmds + [ commands ] ) 
         # Prepend dependency injection
         if job_wrapper.dependency_shell_commands:
             commands = "; ".join( job_wrapper.dependency_shell_commands + [ commands ] ) 
-        
+
         # Append metadata setting commands, we don't want to overwrite metadata
         # that was copied over in init_meta(), as per established behavior
         if include_metadata and self.app.config.set_metadata_externally:


--- a/lib/galaxy/jobs/runners/lwr.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/jobs/runners/lwr.py	Fri Oct 07 14:12:40 2011 -0700
@@ -249,6 +249,12 @@
 
         try:
             job_wrapper.prepare()
+            if hasattr(job_wrapper, 'prepare_input_files_cmds') and job_wrapper.prepare_input_files_cmds is not None:
+                for cmd in job_wrapper.prepare_input_file_cmds: # run the commands to stage the input files
+                    #log.debug( 'executing: %s' % cmd )
+                    if 0 != os.system(cmd):
+                        raise Exception('Error running file staging command: %s' % cmd)
+                job_wrapper.prepare_input_files_cmds = None # prevent them from being used in-line
             command_line = self.build_command_line( job_wrapper, include_metadata=False )
         except:
             job_wrapper.fail( "failure preparing job", exception=True )


--- a/lib/galaxy/jobs/splitters/multi.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/jobs/splitters/multi.py	Fri Oct 07 14:12:40 2011 -0700
@@ -1,5 +1,6 @@
 import os, logging,  shutil
-from galaxy import model
+from galaxy import model, util
+
 
 log = logging.getLogger( __name__ )
     
@@ -54,7 +55,7 @@
         raise Exception(log_error)
     
     # split the first one to build up the task directories
-    input_files = []
+    input_datasets = []
     for input in parent_job.input_datasets:
         if input.name in split_inputs:
             this_input_files = job_wrapper.get_input_dataset_fnames(input.dataset)
@@ -62,13 +63,13 @@
                 log_error = "The input '%s' is composed of multiple files - splitting is not allowed" % str(input.name)
                 log.error(log_error)
                 raise Exception(log_error)
-            input_files.extend(this_input_files)    
+            input_datasets.append(input.dataset)
     
     input_type = type_to_input_map.keys()[0]
     # DBTODO execute an external task to do the splitting, this should happen at refactor.
     # If the number of tasks is sufficiently high, we can use it to calculate job completion % and give a running status.
     try:
-        input_type.split(input_files, get_new_working_directory_name, parallel_settings)
+        input_type.split(input_datasets, get_new_working_directory_name, parallel_settings)
     except AttributeError:
         log_error = "The type '%s' does not define a method for splitting files" % str(input_type)
         log.error(log_error)
@@ -82,8 +83,9 @@
                 for file in names:
                     os.symlink(file, os.path.join(dir,  os.path.basename(file)))
     tasks = []
+    prepare_files = os.path.join(util.galaxy_directory(), 'extract_dataset_parts.sh') + ' %s'
     for dir in task_dirs:
-        task = model.Task(parent_job, dir)
+        task = model.Task(parent_job, dir, prepare_files % dir)
         tasks.append(task)
     return tasks
                                
@@ -106,44 +108,51 @@
     
     illegal_outputs = [x for x in merge_outputs if x in pickone_outputs]
     if len(illegal_outputs) > 0:
-        raise Exception("Outputs have conflicting parallelism attributes: %s" % str( illegal_outputs ))
+        return ('Tool file error', 'Outputs have conflicting parallelism attributes: %s' % str( illegal_outputs ))
+    
+    stdout = ''
+    stderr = ''
+    
+    try:
+        working_directory = job_wrapper.working_directory
+        task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
+        # TODO: Output datasets can be very complex. This doesn't handle metadata files
+        outputs = job_wrapper.get_output_datasets_and_fnames()
+        pickone_done = []
+        task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
+        for output in outputs:
+            output_file_name = str(outputs[output][1])
+            base_output_name = os.path.basename(output_file_name)
+            if output in merge_outputs:
+                output_type = outputs[output][0].datatype
+                output_files = [os.path.join(dir,base_output_name) for dir in task_dirs]
+                log.debug('files %s ' % output_files)
+                output_type.merge(output_files, output_file_name)
+                log.debug('merge finished: %s' % output_file_name)
+                pass # TODO: merge all the files
+            elif output in pickone_outputs:
+                # just pick one of them
+                if output not in pickone_done:
+                    task_file_name = os.path.join(task_dirs[0], base_output_name)
+                    shutil.move( task_file_name,  output_file_name )
+                    pickone_done.append(output)
+            else:
+                log_error = "The output '%s' does not define a method for implementing parallelism" % output
+                log.error(log_error)
+                raise Exception(log_error)
+    except Exception, e:
+        stdout = 'Error merging files';
+        stderr = str(e)
+        
 
-    
-    working_directory = job_wrapper.working_directory
-    task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
-    # TODO: Output datasets can be very complex. This doesn't handle metadata files
-    outputs = job_wrapper.get_output_datasets_and_fnames()
-    pickone_done = []
-    task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
-    for output in outputs:
-        output_file_name = str(outputs[output][1])
-        base_output_name = os.path.basename(output_file_name)
-        if output in merge_outputs:
-            output_type = outputs[output][0].datatype
-            output_files = [os.path.join(dir,base_output_name) for dir in task_dirs]
-            log.debug('files %s ' % output_files)
-            output_type.merge(output_files, output_file_name)
-            log.debug('merge finished: %s' % output_file_name)
-            pass # TODO: merge all the files
-        elif output in pickone_outputs:
-            # just pick one of them
-            if output not in pickone_done:
-                task_file_name = os.path.join(task_dirs[0], base_output_name)
-                shutil.move( task_file_name,  output_file_name )
-                pickone_done.append(output)
-        else:
-            log_error = "The output '%s' does not define a method for implementing parallelism" % output
-            log.error(log_error)
-            raise Exception(log_error)
-
-    stdout = ''
-    stderr=''
     for tw in task_wrappers:
         # Prevent repetitive output, e.g. "Sequence File Aligned"x20
         # Eventually do a reduce for jobs that output "N reads mapped", combining all N for tasks.
-        if stdout.strip() != tw.get_task().stdout.strip():
-            stdout += tw.get_task().stdout
-        if stderr.strip() != tw.get_task().stderr.strip():                        
-            stderr += tw.get_task().stderr
+        out = tw.get_task().stdout.strip()
+        err = tw.get_task().stderr.strip()
+        if len(out) > 0:
+            stdout += tw.working_directory + ':\n' + out
+        if len(err) > 0:
+            stderr += tw.working_directory + ':\n' + err
     return (stdout,  stderr)
     


--- a/lib/galaxy/model/__init__.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/model/__init__.py	Fri Oct 07 14:12:40 2011 -0700
@@ -204,29 +204,19 @@
                     ERROR = 'error',
                     DELETED = 'deleted' )
 
-    def __init__( self, job, part_file = None ):
+    def __init__( self, job, working_directory, prepare_files_cmd ):
         self.command_line = None
         self.parameters = []
         self.state = Task.states.NEW
         self.info = None
-        # TODO: Rename this to working_directory
-        # Does this necessitate a DB migration step?
-        self.part_file = part_file
+        self.working_directory = working_directory
         self.task_runner_name = None
         self.task_runner_external_id = None
         self.job = job
         self.stdout = None
         self.stderr = None
+        self.prepare_input_files_cmd = prepare_files_cmd
     
-    @property
-    def working_directory(self):
-        if self.part_file is not None:
-            if not os.path.isdir(self.part_file):
-                return os.path.dirname(self.part_file)
-            else:
-                return self.part_file
-        return None
-
     def set_state( self, state ):
         self.state = state
 
@@ -907,7 +897,9 @@
     def get_converted_files_by_type( self, file_type ):
         for assoc in self.implicitly_converted_datasets:
             if not assoc.deleted and assoc.type == file_type:
-                return assoc.dataset
+                if assoc.dataset:
+                    return assoc.dataset
+                return assoc.dataset_ldda
         return None
     def get_converted_dataset_deps(self, trans, target_ext):
         """
@@ -1599,7 +1591,12 @@
 class ImplicitlyConvertedDatasetAssociation( object ):
     def __init__( self, id = None, parent = None, dataset = None, file_type = None, deleted = False, purged = False, metadata_safe = True ):
         self.id = id
-        self.dataset = dataset
+        if isinstance(dataset, HistoryDatasetAssociation):
+            self.dataset = dataset
+        elif isinstance(dataset, LibraryDatasetDatasetAssociation):
+            self.dataset_ldda = dataset
+        else:
+            raise AttributeError, 'Unknown dataset type provided for dataset: %s' % type( dataset )
         if isinstance(parent, HistoryDatasetAssociation):
             self.parent_hda = parent
         elif isinstance(parent, LibraryDatasetDatasetAssociation):


--- a/lib/galaxy/model/mapping.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/model/mapping.py	Fri Oct 07 14:12:40 2011 -0700
@@ -148,6 +148,7 @@
     Column( "create_time", DateTime, default=now ),
     Column( "update_time", DateTime, default=now, onupdate=now ),
     Column( "hda_id", Integer, ForeignKey( "history_dataset_association.id" ), index=True, nullable=True ),
+    Column( "ldda_id", Integer, ForeignKey( "library_dataset_dataset_association.id" ), index=True, nullable=True ),
     Column( "hda_parent_id", Integer, ForeignKey( "history_dataset_association.id" ), index=True ),
     Column( "ldda_parent_id", Integer, ForeignKey( "library_dataset_dataset_association.id" ), index=True ),
     Column( "deleted", Boolean, index=True, default=False ),
@@ -469,9 +470,10 @@
     Column( "stderr", TEXT ),
     Column( "traceback", TEXT ),
     Column( "job_id", Integer, ForeignKey( "job.id" ), index=True, nullable=False ),
-    Column( "part_file", String(1024)),
+    Column( "working_directory", String(1024)),
     Column( "task_runner_name", String( 255 ) ),
-    Column( "task_runner_external_id", String( 255 ) ) )
+    Column( "task_runner_external_id", String( 255 ) ),
+    Column( "prepare_input_files_cmd", TEXT ) )
 
 PostJobAction.table = Table("post_job_action", metadata,
     Column("id", Integer, primary_key=True),
@@ -1211,6 +1213,9 @@
                         LibraryDatasetDatasetAssociation, 
                         primaryjoin=( ImplicitlyConvertedDatasetAssociation.table.c.ldda_parent_id == LibraryDatasetDatasetAssociation.table.c.id ) ),
                      
+                     dataset_ldda=relation( 
+                        LibraryDatasetDatasetAssociation, 
+                        primaryjoin=( ImplicitlyConvertedDatasetAssociation.table.c.ldda_id == LibraryDatasetDatasetAssociation.table.c.id ) ),
                      dataset=relation( 
                         HistoryDatasetAssociation, 
                         primaryjoin=( ImplicitlyConvertedDatasetAssociation.table.c.hda_id == HistoryDatasetAssociation.table.c.id ) ) ) )
@@ -1594,7 +1599,7 @@
                      annotations=relation( PageAnnotationAssociation, order_by=PageAnnotationAssociation.table.c.id, backref="pages" ),
                      ratings=relation( PageRatingAssociation, order_by=PageRatingAssociation.table.c.id, backref="pages" )  
                    ) )
-
+                   
 assign_mapper( context, ToolShedRepository, ToolShedRepository.table )
 
 # Set up proxy so that 


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/model/migrate/versions/0083_add_prepare_files_to_task.py	Fri Oct 07 14:12:40 2011 -0700
@@ -0,0 +1,63 @@
+"""
+Migration script to add 'prepare_input_files_cmd' column to the task table and to rename a column.
+"""
+
+from sqlalchemy import *
+from sqlalchemy.orm import *
+from migrate import *
+from migrate.changeset import *
+
+import logging
+log = logging.getLogger( __name__ )
+
+metadata = MetaData( migrate_engine )
+db_session = scoped_session( sessionmaker( bind=migrate_engine, autoflush=False, autocommit=True ) )
+
+def upgrade():
+    print __doc__
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "prepare_input_files_cmd", TEXT, nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.prepare_input_files_cmd
+    except Exception, e:
+        print "Adding prepare_input_files_cmd column to task table failed: %s" % str( e )
+        log.debug( "Adding prepare_input_files_cmd column to task table failed: %s" % str( e ) )
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "working_directory", String ( 1024 ), nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.working_directory
+    except Exception, e:
+        print "Adding working_directory column to task table failed: %s" % str( e )
+        log.debug( "Adding working_directory column to task table failed: %s" % str( e ) )
+
+    # remove the 'part_file' column - nobody used tasks before this, so no data needs to be migrated
+    try:
+        task_table.c.part_file.drop()
+    except Exception, e:
+        log.debug( "Deleting column 'part_file' from the 'task' table failed: %s" % ( str( e ) ) )
+
+def downgrade():
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        task_table.c.prepare_input_files_cmd.drop()
+    except Exception, e:
+        print "Dropping prepare_input_files_cmd column from task table failed: %s" % str( e )
+        log.debug( "Dropping prepare_input_files_cmd column from task table failed: %s" % str( e ) )
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        task_table.c.working_directory.drop()
+    except Exception, e:
+        print "Dropping working_directory column from task table failed: %s" % str( e )
+        log.debug( "Dropping working_directory column from task table failed: %s" % str( e ) )
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "part_file", String ( 1024 ), nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.part_file
+    except Exception, e:
+        print "Adding part_file column to task table failed: %s" % str( e )
+        log.debug( "Adding part_file column to task table failed: %s" % str( e ) )


--- a/lib/galaxy/util/__init__.py	Fri Oct 07 13:25:19 2011 -0700
+++ b/lib/galaxy/util/__init__.py	Fri Oct 07 14:12:40 2011 -0700
@@ -623,6 +623,9 @@
 gbrowse_build_sites = read_build_sites( os.path.join( galaxy_root_path, "tool-data", "shared", "gbrowse", "gbrowse_build_sites.txt" ) )
 genetrack_sites = read_build_sites( os.path.join( galaxy_root_path, "tool-data", "shared", "genetrack", "genetrack_sites.txt" ), check_builds=False )
 
+def galaxy_directory():
+    return os.path.abspath(galaxy_root_path)
+
 if __name__ == '__main__':
     import doctest, sys
     doctest.testmod(sys.modules[__name__], verbose=False)


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/extract_dataset_part.py	Fri Oct 07 14:12:40 2011 -0700
@@ -0,0 +1,48 @@
+"""
+Reads a JSON file and uses it to call into a datatype class to extract
+a subset of a dataset for processing.
+
+Used by jobs that split large files into pieces to be processed concurrently
+on a gid in a scatter-gather mode. This does part of the scatter.
+
+"""
+import os
+import sys
+import logging
+logging.basicConfig()
+log = logging.getLogger( __name__ )
+
+new_path = [ os.path.join( os.getcwd(), "lib" ) ]
+new_path.extend( sys.path[1:] ) # remove scripts/ from the path
+sys.path = new_path
+
+from galaxy import eggs
+import pkg_resources
+pkg_resources.require("simplejson")
+import simplejson
+
+# This junk is here to prevent loading errors
+import galaxy.model.mapping #need to load this before we unpickle, in order to setup properties assigned by the mappers
+galaxy.model.Job() #this looks REAL stupid, but it is REQUIRED in order for SA to insert parameters into the classes defined by the mappers --> it appears that instantiating ANY mapper'ed class would suffice here
+galaxy.datatypes.metadata.DATABASE_CONNECTION_AVAILABLE = False #Let metadata know that there is no database connection, and to just assume object ids are valid
+
+def __main__():
+    """
+    Argument: a JSON file
+    """
+    file_path = sys.argv.pop( 1 )
+    data = simplejson.load(open(file_path, 'r'))
+    try:
+        class_name_parts = data['class_name'].split('.')
+        module_name = '.'.join(class_name_parts[:-1])
+        class_name = class_name_parts[-1]
+        mod = __import__(module_name, globals(),  locals(),  [class_name])
+        cls = getattr(mod,  class_name)
+        if not cls.process_split_file(data):
+            sys.stderr.write('Writing split file failed\n')
+            sys.exit(1)
+    except Exception, e:
+        sys.stderr.write(str(e))
+        sys.exit(1)
+        
+__main__()


http://bitbucket.org/galaxy/galaxy-central/changeset/5b65ae5d04f3/
changeset:   5b65ae5d04f3
user:        John Duddy
date:        2011-10-10 21:07:19
summary:     Fix missing columns for Task, add migration to add ldda for associated types
affected #:  3 files (-1 bytes)

--- a/lib/galaxy/model/mapping.py	Fri Oct 07 14:12:40 2011 -0700
+++ b/lib/galaxy/model/mapping.py	Mon Oct 10 12:07:19 2011 -0700
@@ -468,6 +468,7 @@
     Column( "runner_name", String( 255 ) ),
     Column( "stdout", TEXT ),
     Column( "stderr", TEXT ),
+    Column( "info", TrimmedString ( 255 ) ),
     Column( "traceback", TEXT ),
     Column( "job_id", Integer, ForeignKey( "job.id" ), index=True, nullable=False ),
     Column( "working_directory", String(1024)),


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/model/migrate/versions/0084_add_ldda_id_to_implicit_conversion_table.py	Mon Oct 10 12:07:19 2011 -0700
@@ -0,0 +1,35 @@
+"""
+Migration script to add 'ldda_id' column to the implicitly_converted_dataset_association table.
+"""
+
+from sqlalchemy import *
+from sqlalchemy.orm import *
+from migrate import *
+from migrate.changeset import *
+
+import logging
+log = logging.getLogger( __name__ )
+
+metadata = MetaData( migrate_engine )
+db_session = scoped_session( sessionmaker( bind=migrate_engine, autoflush=False, autocommit=True ) )
+
+def upgrade():
+    print __doc__
+    metadata.reflect()
+    try:
+        Implicitly_converted_table = Table( "implicitly_converted_dataset_association", metadata, autoload=True )
+        c = Column( "ldda_id", Integer, ForeignKey( "library_dataset_dataset_association.id" ), index=True, nullable=True )
+        c.create( Implicitly_converted_table )
+        assert c is Implicitly_converted_table.c.ldda_id
+    except Exception, e:
+        print "Adding ldda_id column to implicitly_converted_dataset_association table failed: %s" % str( e )
+        log.debug( "Adding ldda_id column to implicitly_converted_dataset_association table failed: %s" % str( e ) )
+
+def downgrade():
+    metadata.reflect()
+    try:
+        Implicitly_converted_table = Table( "implicitly_converted_dataset_association", metadata, autoload=True )
+        Implicitly_converted_table.c.ldda_id.drop()
+    except Exception, e:
+        print "Dropping ldda_id column from implicitly_converted_dataset_association table failed: %s" % str( e )
+        log.debug( "Dropping ldda_id column from implicitly_converted_dataset_association table failed: %s" % str( e ) )


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/model/migrate/versions/0085_add_task_info.py	Mon Oct 10 12:07:19 2011 -0700
@@ -0,0 +1,35 @@
+"""
+Migration script to add 'info' column to the task table.
+"""
+
+from sqlalchemy import *
+from sqlalchemy.orm import *
+from migrate import *
+from migrate.changeset import *
+
+import logging
+log = logging.getLogger( __name__ )
+
+metadata = MetaData( migrate_engine )
+db_session = scoped_session( sessionmaker( bind=migrate_engine, autoflush=False, autocommit=True ) )
+
+def upgrade():
+    print __doc__
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "info", TrimmedString (255) , nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.info
+    except Exception, e:
+        print "Adding info column to table table failed: %s" % str( e )
+        log.debug( "Adding info column to task table failed: %s" % str( e ) )
+
+def downgrade():
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        task_table.c.info.drop()
+    except Exception, e:
+        print "Dropping info column from task table failed: %s" % str( e )
+        log.debug( "Dropping info column from task table failed: %s" % str( e ) )


http://bitbucket.org/galaxy/galaxy-central/changeset/dc352138c3de/
changeset:   dc352138c3de
user:        John Duddy
date:        2011-10-10 21:17:57
summary:     add FQTOC datatype to drive file splitting
affected #:  4 files (-1 bytes)

--- a/datatypes_conf.xml.sample	Mon Oct 10 12:07:19 2011 -0700
+++ b/datatypes_conf.xml.sample	Mon Oct 10 12:17:57 2011 -0700
@@ -56,11 +56,22 @@
       <converter file="fasta_to_2bit.xml" target_datatype="twobit"/><converter file="fasta_to_len.xml" target_datatype="len"/></datatype>
-    <datatype extension="fastq" type="galaxy.datatypes.sequence:Fastq" display_in_upload="true"/>
-    <datatype extension="fastqsanger" type="galaxy.datatypes.sequence:FastqSanger" display_in_upload="true"/>
-    <datatype extension="fastqsolexa" type="galaxy.datatypes.sequence:FastqSolexa" display_in_upload="true"/>
-    <datatype extension="fastqcssanger" type="galaxy.datatypes.sequence:FastqCSSanger" display_in_upload="true"/>
-    <datatype extension="fastqillumina" type="galaxy.datatypes.sequence:FastqIllumina" display_in_upload="true"/>
+    <datatype extension="fastq" type="galaxy.datatypes.sequence:Fastq" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqsanger" type="galaxy.datatypes.sequence:FastqSanger" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqsolexa" type="galaxy.datatypes.sequence:FastqSolexa" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqcssanger" type="galaxy.datatypes.sequence:FastqCSSanger" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqillumina" type="galaxy.datatypes.sequence:FastqIllumina" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fqtoc" type="galaxy.datatypes.sequence:SequenceSplitLocations" display_in_upload="true"/><datatype extension="eland" type="galaxy.datatypes.tabular:Eland" display_in_upload="true"/><datatype extension="elandmulti" type="galaxy.datatypes.tabular:ElandMulti" display_in_upload="true"/><datatype extension="genetrack" type="galaxy.datatypes.tracks:GeneTrack">


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/datatypes/converters/fastq_to_fqtoc.py	Mon Oct 10 12:17:57 2011 -0700
@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+
+import sys, os, gzip
+from galaxy.datatypes.checkers import is_gzip
+
+
+def main():
+    """
+    The format of the file is JSON:
+    { "sections" : [
+            { "start" : "x", "end" : "y", "sequences" : "z" },
+            ...
+    ]}
+    This works only for UNCOMPRESSED fastq files. The Python GzipFile does not provide seekable
+    offsets via tell(), so clients just have to split the slow way
+    """
+    input_fname = sys.argv[1]
+    if is_gzip(input_fname):
+        print 'Conversion is only possible for uncompressed files'
+        sys.exit(1)
+
+    out_file = open(sys.argv[2], 'w')
+
+    current_line = 0
+    sequences=1000000
+    lines_per_chunk = 4*sequences
+    chunk_begin = 0
+
+    in_file = open(input_name)
+
+    out_file.write('{"sections" : [');
+
+    for line in in_file:
+        current_line += 1
+        if 0 == current_line % lines_per_chunk:
+            chunk_end = in_file.tell()
+            out_file.write('{"start":"%s","end":"%s","sequences":"%s"},' % (chunk_begin, chunk_end, sequences))
+            chunk_begin = chunk_end
+        
+    chunk_end = in_file.tell()
+    out_file.write('{"start":"%s","end":"%s","sequences":"%s"}' % (chunk_begin, chunk_end, (current_line % lines_per_chunk) / 4))
+    out_file.write(']}\n')
+    
+
+if __name__ == "__main__": 
+    main()


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/datatypes/converters/fastq_to_fqtoc.xml	Mon Oct 10 12:17:57 2011 -0700
@@ -0,0 +1,13 @@
+<tool id="CONVERTER_fastq_to_fqtoc0" name="Convert FASTQ files to seek locations" version="1.0.0" hidden="true">
+  <command interpreter="python">fastq_to_fqtoc.py $input1 $output1</command>
+  <inputs>
+    <page>
+        <param format="fastq" name="input1" type="data" label="Choose FASTQ file"/>
+    </page>
+   </inputs>
+  <outputs>
+    <data format="fqtoc" name="output1"/>
+  </outputs>
+  <help>
+  </help>
+</tool>


--- a/lib/galaxy/datatypes/sequence.py	Mon Oct 10 12:07:19 2011 -0700
+++ b/lib/galaxy/datatypes/sequence.py	Mon Oct 10 12:17:57 2011 -0700
@@ -27,7 +27,7 @@
     one OR an uncompressed file. In the GZIP case, each sub-file's location is stored in start and end. 
     The format of the file is JSON:
     { "sections" : [
-            { "start" : "x", "end" : "y", "clusters" : "z" },
+            { "start" : "x", "end" : "y", "sequences" : "z" },
             ...
     ]}
     """
@@ -53,7 +53,7 @@
                 data = simplejson.load(open(filename))
                 sections = data['sections']
                 for section in sections:
-                    if 'start' not in section or 'end' not in section or 'clusters' not in section:
+                    if 'start' not in section or 'end' not in section or 'sequences' not in section:
                         return False
                 return True
             except:
@@ -96,20 +96,20 @@
             dataset.peek = 'file does not exist'
             dataset.blurb = 'file purged from disk'
 
-    def get_sequences_per_file(total_clusters, split_params):
+    def get_sequences_per_file(total_sequences, split_params):
         if split_params['split_mode'] == 'number_of_parts':
             # legacy basic mode - split into a specified number of parts
             parts = int(split_params['split_size'])
-            sequences_per_file = [total_clusters/parts for i in range(parts)]
-            for i in range(total_clusters % parts):
+            sequences_per_file = [total_sequences/parts for i in range(parts)]
+            for i in range(total_sequences % parts):
                 sequences_per_file[i] += 1
         elif split_params['split_mode'] == 'to_size':
-            # loop through the sections and calculate the number of clusters
+            # loop through the sections and calculate the number of sequences
             chunk_size = long(split_params['split_size'])
 
-            chunks = total_clusters / chunk_size
-            rem = total_clusters % chunk_size
-            sequences_per_file = [chunk_size for i in range(total_clusters / chunk_size)]
+            chunks = total_sequences / chunk_size
+            rem = total_sequences % chunk_size
+            sequences_per_file = [chunk_size for i in range(total_sequences / chunk_size)]
             # TODO: Should we invest the time in a better way to handle small remainders?
             if rem > 0:
                 sequences_per_file.append(rem)
@@ -119,10 +119,10 @@
     get_sequences_per_file = staticmethod(get_sequences_per_file)
 
     def do_slow_split( cls, input_datasets, subdir_generator_function, split_params):
-        # count the clusters so we can split
+        # count the sequences so we can split
         # TODO: if metadata is present, take the number of lines / 4
         if input_datasets[0].metadata is not None and input_datasets[0].metadata.sequences is not None:
-            total_clusters = input_datasets[0].metadata.sequences
+            total_sequences = input_datasets[0].metadata.sequences
         else:
             input_file = input_datasets[0].file_name
             compress = is_gzip(input_file)
@@ -136,23 +136,23 @@
                 # TODO
                 # Add BufferedReader if python 2.7?
                 in_file = open(input_file, 'rt')
-            total_clusters = long(0)
+            total_sequences = long(0)
             for i, line in enumerate(in_file):
-                total_clusters += 1
+                total_sequences += 1
             in_file.close()
-            total_clusters /= 4
+            total_sequences /= 4
 
-        sequences_per_file = cls.get_sequences_per_file(total_clusters, split_params)
+        sequences_per_file = cls.get_sequences_per_file(total_sequences, split_params)
         return cls.write_split_files(input_datasets, None, subdir_generator_function, sequences_per_file)
     do_slow_split = classmethod(do_slow_split)
 
     def do_fast_split( cls, input_datasets, toc_file_datasets, subdir_generator_function, split_params):
         data = simplejson.load(open(toc_file_datasets[0].file_name))
         sections = data['sections']
-        total_clusters = long(0)
+        total_sequences = long(0)
         for section in sections:
-            total_clusters += long(section['clusters'])
-        sequences_per_file = cls.get_sequences_per_file(total_clusters, split_params)
+            total_sequences += long(section['sequences'])
+        sequences_per_file = cls.get_sequences_per_file(total_sequences, split_params)
         return cls.write_split_files(input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file)
     do_fast_split = classmethod(do_fast_split)
 
@@ -165,7 +165,7 @@
             directories.append(dir)
             return dir
 
-        # we know how many splits and how many clusters in each. What remains is to write out instructions for the 
+        # we know how many splits and how many sequences in each. What remains is to write out instructions for the 
         # splitting of all the input files. To decouple the format of those instructions from this code, the exact format of
         # those instructions is delegated to scripts
         start_sequence=0
@@ -241,7 +241,7 @@
         """
         Uses a Table of Contents dict, parsed from an FQTOC file, to come up with a set of
         shell commands that will extract the parts necessary
-        >>> three_sections=[dict(start=0, end=74, clusters=10), dict(start=74, end=148, clusters=10), dict(start=148, end=148+76, clusters=10)]
+        >>> three_sections=[dict(start=0, end=74, sequences=10), dict(start=74, end=148, sequences=10), dict(start=148, end=148+76, sequences=10)]
         >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=0, sequence_count=10)
         ['dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null >> ./output.gz']
         >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=1, sequence_count=5)
@@ -261,8 +261,8 @@
         current_sequence = long(0)
         i=0
         # skip to the section that contains my starting sequence
-        while i < len(sections) and start_sequence >= current_sequence + long(sections[i]['clusters']):
-            current_sequence += long(sections[i]['clusters'])
+        while i < len(sections) and start_sequence >= current_sequence + long(sections[i]['sequences']):
+            current_sequence += long(sections[i]['sequences'])
             i += 1
         if i == len(sections): # bad input data!
             raise Exception('No FQTOC section contains starting sequence %s' % start_sequence)
@@ -277,7 +277,7 @@
             # we need to extract partial data. So, find the byte offsets of the chunks that contain the data we need
             # use a combination of dd (to pull just the right sections out) tail (to skip lines) and head (to get the 
             # right number of lines
-            sequences = long(sections[i]['clusters'])
+            sequences = long(sections[i]['sequences'])
             skip_sequences = start_sequence-current_sequence
             sequences_to_extract = min(sequence_count, sequences-skip_sequences)
             start_copy = long(sections[i]['start'])
@@ -832,4 +832,4 @@
 
 if __name__ == '__main__':
     import doctest, sys
-    doctest.testmod(sys.modules[__name__])
\ No newline at end of file
+    doctest.testmod(sys.modules[__name__])


http://bitbucket.org/galaxy/galaxy-central/changeset/d226b75b9dbc/
changeset:   d226b75b9dbc
user:        dannon
date:        2011-10-12 17:19:42
summary:     Pull and merge of Task overhaul.
affected #:  20 files (-1 bytes)

--- a/datatypes_conf.xml.sample	Mon Oct 10 18:42:31 2011 -0400
+++ b/datatypes_conf.xml.sample	Wed Oct 12 11:19:42 2011 -0400
@@ -56,11 +56,22 @@
       <converter file="fasta_to_2bit.xml" target_datatype="twobit"/><converter file="fasta_to_len.xml" target_datatype="len"/></datatype>
-    <datatype extension="fastq" type="galaxy.datatypes.sequence:Fastq" display_in_upload="true"/>
-    <datatype extension="fastqsanger" type="galaxy.datatypes.sequence:FastqSanger" display_in_upload="true"/>
-    <datatype extension="fastqsolexa" type="galaxy.datatypes.sequence:FastqSolexa" display_in_upload="true"/>
-    <datatype extension="fastqcssanger" type="galaxy.datatypes.sequence:FastqCSSanger" display_in_upload="true"/>
-    <datatype extension="fastqillumina" type="galaxy.datatypes.sequence:FastqIllumina" display_in_upload="true"/>
+    <datatype extension="fastq" type="galaxy.datatypes.sequence:Fastq" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqsanger" type="galaxy.datatypes.sequence:FastqSanger" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqsolexa" type="galaxy.datatypes.sequence:FastqSolexa" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqcssanger" type="galaxy.datatypes.sequence:FastqCSSanger" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fastqillumina" type="galaxy.datatypes.sequence:FastqIllumina" display_in_upload="true">
+        <converter file="fastq_to_fqtoc.xml" target_datatype="fqtoc"/>
+    </datatype>
+    <datatype extension="fqtoc" type="galaxy.datatypes.sequence:SequenceSplitLocations" display_in_upload="true"/><datatype extension="eland" type="galaxy.datatypes.tabular:Eland" display_in_upload="true"/><datatype extension="elandmulti" type="galaxy.datatypes.tabular:ElandMulti" display_in_upload="true"/><datatype extension="genetrack" type="galaxy.datatypes.tracks:GeneTrack">


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/extract_dataset_parts.sh	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,8 @@
+#!/bin/sh
+
+cd `dirname $0`
+for file in $1/split_info*.json
+do
+    # echo processing $file
+    python ./scripts/extract_dataset_part.py $file
+done


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/datatypes/converters/fastq_to_fqtoc.py	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,46 @@
+#!/usr/bin/env python
+
+import sys, os, gzip
+from galaxy.datatypes.checkers import is_gzip
+
+
+def main():
+    """
+    The format of the file is JSON:
+    { "sections" : [
+            { "start" : "x", "end" : "y", "sequences" : "z" },
+            ...
+    ]}
+    This works only for UNCOMPRESSED fastq files. The Python GzipFile does not provide seekable
+    offsets via tell(), so clients just have to split the slow way
+    """
+    input_fname = sys.argv[1]
+    if is_gzip(input_fname):
+        print 'Conversion is only possible for uncompressed files'
+        sys.exit(1)
+
+    out_file = open(sys.argv[2], 'w')
+
+    current_line = 0
+    sequences=1000000
+    lines_per_chunk = 4*sequences
+    chunk_begin = 0
+
+    in_file = open(input_name)
+
+    out_file.write('{"sections" : [');
+
+    for line in in_file:
+        current_line += 1
+        if 0 == current_line % lines_per_chunk:
+            chunk_end = in_file.tell()
+            out_file.write('{"start":"%s","end":"%s","sequences":"%s"},' % (chunk_begin, chunk_end, sequences))
+            chunk_begin = chunk_end
+        
+    chunk_end = in_file.tell()
+    out_file.write('{"start":"%s","end":"%s","sequences":"%s"}' % (chunk_begin, chunk_end, (current_line % lines_per_chunk) / 4))
+    out_file.write(']}\n')
+    
+
+if __name__ == "__main__": 
+    main()


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/datatypes/converters/fastq_to_fqtoc.xml	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,13 @@
+<tool id="CONVERTER_fastq_to_fqtoc0" name="Convert FASTQ files to seek locations" version="1.0.0" hidden="true">
+  <command interpreter="python">fastq_to_fqtoc.py $input1 $output1</command>
+  <inputs>
+    <page>
+        <param format="fastq" name="input1" type="data" label="Choose FASTQ file"/>
+    </page>
+   </inputs>
+  <outputs>
+    <data format="fqtoc" name="output1"/>
+  </outputs>
+  <help>
+  </help>
+</tool>


--- a/lib/galaxy/datatypes/data.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/datatypes/data.py	Wed Oct 12 11:19:42 2011 -0400
@@ -351,6 +351,22 @@
     @property
     def has_resolution(self):
         return False
+    
+    
+
+    def merge( split_files, output_file): 
+        """
+        TODO: Do we need to merge gzip files using gzjoin? cat seems to work,
+        but might be brittle. Need to revisit this.
+        """
+        if len(split_files) == 1:
+            cmd = 'mv -f %s %s' % ( split_files[0], output_file ) 
+        else:
+            cmd = 'cat %s > %s' % ( ' '.join(split_files), output_file ) 
+        result = os.system(cmd)
+        if result != 0:
+            raise Exception('Result %s from %s' % (result, cmd))
+    merge = staticmethod(merge)
 
 class Text( Data ):
     file_ext = 'txt'
@@ -446,9 +462,83 @@
             dataset.peek = 'file does not exist'
             dataset.blurb = 'file purged from disk'
 
+    def split( input_files, subdir_generator_function, split_params): 
+        """
+        Split the input files by line.
+        """
+        if split_params is None:
+            return
+            
+        if len(input_files) > 1:
+            raise Exception("Text file splitting does not support multiple files")
+        
+        lines_per_file = None
+        chunk_size = None
+        if split_params['split_mode'] == 'number_of_parts':
+            lines_per_file = []
+            # Computing the length is expensive!
+            def _file_len(fname):
+                i = 0
+                f = open(fname)
+                for i, l in enumerate(f):
+                    pass
+                f.close()
+                return i + 1
+            length = _file_len(input_files[0])
+            parts = int(split_params['split_size'])
+            if length < parts:
+                parts = length
+            len_each, remainder = divmod(length, parts)
+            while length > 0:
+                chunk = len_each
+                if remainder > 0:
+                    chunk += 1
+                lines_per_file.append(chunk)
+                remainder=- 1
+                length -= chunk
+        elif split_params['split_mode'] == 'to_size':
+            chunk_size = int(split_params['split_size'])
+        else:
+            raise Exception('Unsupported split mode %s' % split_params['split_mode'])
+        
+        f = open(input_files[0], 'rt')
+        try:
+            chunk_idx = 0
+            file_done = False
+            part_file = None
+            while not file_done:
+                if lines_per_file is None:
+                    this_chunk_size = chunk_size
+                elif chunk_idx < len(lines_per_file):
+                    this_chunk_size = lines_per_file[chunk_idx]
+                    chunk_idx += 1
+                lines_remaining = this_chunk_size
+                part_file = None
+                while lines_remaining > 0:
+                    a_line = f.readline()
+                    if a_line == '':
+                        file_done = True
+                        break
+                    if part_file is None:
+                        part_dir = subdir_generator_function()
+                        part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
+                        part_file = open(part_path, 'w')
+                    part_file.write(a_line)
+                    lines_remaining -= 1
+                if part_file is not None:
+                    part_file.close()
+        except Exception,  e:
+            log.error('Unable to split files: %s' % str(e))
+            f.close()
+            if part_file is not None:
+                part_file.close()
+            raise
+        f.close()
+    split = staticmethod(split)
+
 class LineCount( Text ):
-    """ 
-    Dataset contains a single line with a single integer that denotes the 
+    """
+    Dataset contains a single line with a single integer that denotes the
     line count for a related dataset. Used for custom builds.
     """
     pass


--- a/lib/galaxy/datatypes/sequence.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/datatypes/sequence.py	Wed Oct 12 11:19:42 2011 -0400
@@ -2,10 +2,12 @@
 Sequence classes
 """
 
+import gzip
 import data
 import logging
 import re
 import string
+import os
 from cgi import escape
 from galaxy.datatypes.metadata import MetadataElement
 from galaxy.datatypes import metadata
@@ -13,8 +15,52 @@
 from galaxy import util
 from sniff import *
 
+import pkg_resources
+pkg_resources.require("simplejson")
+import simplejson
+
 log = logging.getLogger(__name__)
 
+class SequenceSplitLocations( data.Text ):
+    """
+    Class storing information about a sequence file composed of multiple gzip files concatenated as
+    one OR an uncompressed file. In the GZIP case, each sub-file's location is stored in start and end. 
+    The format of the file is JSON:
+    { "sections" : [
+            { "start" : "x", "end" : "y", "sequences" : "z" },
+            ...
+    ]}
+    """
+    def set_peek( self, dataset, is_multi_byte=False ):
+        if not dataset.dataset.purged:
+            try:
+                parsed_data = simplejson.load(open(dataset.file_name))
+                # dataset.peek = simplejson.dumps(data, sort_keys=True, indent=4)
+                dataset.peek = data.get_file_peek( dataset.file_name, is_multi_byte=is_multi_byte )
+                dataset.blurb = '%d sections' % len(parsed_data['sections'])
+            except Exception, e:
+                dataset.peek = 'Not FQTOC file'
+                dataset.blurb = 'Not FQTOC file'
+        else:
+            dataset.peek = 'file does not exist'
+            dataset.blurb = 'file purged from disk'
+
+    file_ext = "fqtoc"
+
+    def sniff( self, filename ):
+        if os.path.getsize(filename) < 50000:
+            try:
+                data = simplejson.load(open(filename))
+                sections = data['sections']
+                for section in sections:
+                    if 'start' not in section or 'end' not in section or 'sequences' not in section:
+                        return False
+                return True
+            except:
+                pass
+        return False
+
+
 class Sequence( data.Text ):
     """Class describing a sequence"""
 
@@ -50,6 +96,239 @@
             dataset.peek = 'file does not exist'
             dataset.blurb = 'file purged from disk'
 
+    def get_sequences_per_file(total_sequences, split_params):
+        if split_params['split_mode'] == 'number_of_parts':
+            # legacy basic mode - split into a specified number of parts
+            parts = int(split_params['split_size'])
+            sequences_per_file = [total_sequences/parts for i in range(parts)]
+            for i in range(total_sequences % parts):
+                sequences_per_file[i] += 1
+        elif split_params['split_mode'] == 'to_size':
+            # loop through the sections and calculate the number of sequences
+            chunk_size = long(split_params['split_size'])
+
+            chunks = total_sequences / chunk_size
+            rem = total_sequences % chunk_size
+            sequences_per_file = [chunk_size for i in range(total_sequences / chunk_size)]
+            # TODO: Should we invest the time in a better way to handle small remainders?
+            if rem > 0:
+                sequences_per_file.append(rem)
+        else:
+            raise Exception('Unsupported split mode %s' % split_params['split_mode'])
+        return sequences_per_file
+    get_sequences_per_file = staticmethod(get_sequences_per_file)
+
+    def do_slow_split( cls, input_datasets, subdir_generator_function, split_params):
+        # count the sequences so we can split
+        # TODO: if metadata is present, take the number of lines / 4
+        if input_datasets[0].metadata is not None and input_datasets[0].metadata.sequences is not None:
+            total_sequences = input_datasets[0].metadata.sequences
+        else:
+            input_file = input_datasets[0].file_name
+            compress = is_gzip(input_file)
+            if compress:
+                # gzip is really slow before python 2.7!
+                in_file = gzip.GzipFile(input_file, 'r')
+            else:
+                # TODO
+                # if a file is not compressed, seek locations can be calculated and stored
+                # ideally, this would be done in metadata
+                # TODO
+                # Add BufferedReader if python 2.7?
+                in_file = open(input_file, 'rt')
+            total_sequences = long(0)
+            for i, line in enumerate(in_file):
+                total_sequences += 1
+            in_file.close()
+            total_sequences /= 4
+
+        sequences_per_file = cls.get_sequences_per_file(total_sequences, split_params)
+        return cls.write_split_files(input_datasets, None, subdir_generator_function, sequences_per_file)
+    do_slow_split = classmethod(do_slow_split)
+
+    def do_fast_split( cls, input_datasets, toc_file_datasets, subdir_generator_function, split_params):
+        data = simplejson.load(open(toc_file_datasets[0].file_name))
+        sections = data['sections']
+        total_sequences = long(0)
+        for section in sections:
+            total_sequences += long(section['sequences'])
+        sequences_per_file = cls.get_sequences_per_file(total_sequences, split_params)
+        return cls.write_split_files(input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file)
+    do_fast_split = classmethod(do_fast_split)
+
+    def write_split_files(cls, input_datasets, toc_file_datasets, subdir_generator_function, sequences_per_file):
+        directories = []
+        def get_subdir(idx):
+            if idx < len(directories):
+                return directories[idx]
+            dir = subdir_generator_function()
+            directories.append(dir)
+            return dir
+
+        # we know how many splits and how many sequences in each. What remains is to write out instructions for the 
+        # splitting of all the input files. To decouple the format of those instructions from this code, the exact format of
+        # those instructions is delegated to scripts
+        start_sequence=0
+        for part_no in range(len(sequences_per_file)):
+            dir = get_subdir(part_no)
+            for ds_no in range(len(input_datasets)):
+                ds = input_datasets[ds_no]
+                base_name = os.path.basename(ds.file_name)
+                part_path = os.path.join(dir, base_name)
+                split_data = dict(class_name='%s.%s' % (cls.__module__, cls.__name__),
+                                  output_name=part_path,
+                                  input_name=ds.file_name,
+                                  args=dict(start_sequence=start_sequence, num_sequences=sequences_per_file[part_no]))
+                if toc_file_datasets is not None:
+                    toc = toc_file_datasets[ds_no]
+                    split_data['args']['toc_file'] = toc.file_name
+                f = open(os.path.join(dir, 'split_info_%s.json' % base_name), 'w')
+                simplejson.dump(split_data, f)
+                f.close()
+            start_sequence += sequences_per_file[part_no]
+        return directories
+    write_split_files = classmethod(write_split_files)
+    
+    def split( cls, input_datasets, subdir_generator_function, split_params):
+        """
+        FASTQ files are split on cluster boundaries, in increments of 4 lines
+        """
+        if split_params is None:
+            return None
+
+        # first, see if there are any associated FQTOC files that will give us the split locations
+        # if so, we don't need to read the files to do the splitting
+        toc_file_datasets = []
+        for ds in input_datasets:
+            tmp_ds = ds
+            fqtoc_file = None
+            while fqtoc_file is None and tmp_ds is not None:
+                fqtoc_file = tmp_ds.get_converted_files_by_type('fqtoc')
+                tmp_ds = tmp_ds.copied_from_library_dataset_dataset_association
+
+            if fqtoc_file is not None:
+                toc_file_datasets.append(fqtoc_file)
+
+        if len(toc_file_datasets) == len(input_datasets):
+            return cls.do_fast_split(input_datasets, toc_file_datasets, subdir_generator_function, split_params)
+        return cls.do_slow_split(input_datasets, subdir_generator_function, split_params)
+    split = classmethod(split)
+
+    def process_split_file(data):
+        """
+        This is called in the context of an external process launched by a Task (possibly not on the Galaxy machine)
+        to create the input files for the Task. The parameters:
+        data - a dict containing the contents of the split file
+        """
+        args = data['args']
+        input_name = data['input_name']
+        output_name = data['output_name']
+        start_sequence = long(args['start_sequence'])
+        sequence_count = long(args['num_sequences'])
+        
+        if 'toc_file' in args:
+            toc_file = simplejson.load(open(args['toc_file'], 'r'))
+            commands = Sequence.get_split_commands_with_toc(input_name, output_name, toc_file, start_sequence, sequence_count)
+        else:
+            commands = Sequence.get_split_commands_sequential(is_gzip(input_name), input_name, output_name, start_sequence, sequence_count)
+        for cmd in commands:
+            if 0 != os.system(cmd):
+                raise Exception("Executing '%s' failed" % cmd)
+        return True
+    process_split_file = staticmethod(process_split_file)
+    
+    def get_split_commands_with_toc(input_name, output_name, toc_file, start_sequence, sequence_count):
+        """
+        Uses a Table of Contents dict, parsed from an FQTOC file, to come up with a set of
+        shell commands that will extract the parts necessary
+        >>> three_sections=[dict(start=0, end=74, sequences=10), dict(start=74, end=148, sequences=10), dict(start=148, end=148+76, sequences=10)]
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=0, sequence_count=10)
+        ['dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=1, sequence_count=5)
+        ['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +5 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=0, sequence_count=20)
+        ['dd bs=1 skip=0 count=148 if=./input.gz 2> /dev/null >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=5, sequence_count=10)
+        ['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz', '(dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=10, sequence_count=10)
+        ['dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz']
+        >>> Sequence.get_split_commands_with_toc('./input.gz', './output.gz', dict(sections=three_sections), start_sequence=5, sequence_count=20)
+        ['(dd bs=1 skip=0 count=74 if=./input.gz 2> /dev/null )| zcat | ( tail -n +21 2> /dev/null) | head -20 | gzip -c >> ./output.gz', 'dd bs=1 skip=74 count=74 if=./input.gz 2> /dev/null >> ./output.gz', '(dd bs=1 skip=148 count=76 if=./input.gz 2> /dev/null )| zcat | ( tail -n +1 2> /dev/null) | head -20 | gzip -c >> ./output.gz']
+        """
+        sections = toc_file['sections']
+        result = []
+
+        current_sequence = long(0)
+        i=0
+        # skip to the section that contains my starting sequence
+        while i < len(sections) and start_sequence >= current_sequence + long(sections[i]['sequences']):
+            current_sequence += long(sections[i]['sequences'])
+            i += 1
+        if i == len(sections): # bad input data!
+            raise Exception('No FQTOC section contains starting sequence %s' % start_sequence)
+    
+        # These two variables act as an accumulator for consecutive entire blocks that
+        # can be copied verbatim (without decompressing)
+        start_chunk = long(-1)
+        end_chunk = long(-1)
+        copy_chunk_cmd = 'dd bs=1 skip=%s count=%s if=%s 2> /dev/null >> %s'
+        
+        while sequence_count > 0 and i < len(sections):
+            # we need to extract partial data. So, find the byte offsets of the chunks that contain the data we need
+            # use a combination of dd (to pull just the right sections out) tail (to skip lines) and head (to get the 
+            # right number of lines
+            sequences = long(sections[i]['sequences'])
+            skip_sequences = start_sequence-current_sequence
+            sequences_to_extract = min(sequence_count, sequences-skip_sequences)
+            start_copy = long(sections[i]['start'])
+            end_copy = long(sections[i]['end'])
+            if sequences_to_extract < sequences:
+                if start_chunk > -1:
+                    result.append(copy_chunk_cmd % (start_chunk, end_chunk-start_chunk, input_name, output_name))
+                    start_chunk = -1
+                # extract, unzip, trim, recompress
+                result.append('(dd bs=1 skip=%s count=%s if=%s 2> /dev/null )| zcat | ( tail -n +%s 2> /dev/null) | head -%s | gzip -c >> %s' % 
+                    (start_copy, end_copy-start_copy, input_name, skip_sequences*4+1, sequences_to_extract*4, output_name))
+            else: # whole section - add it to the start_chunk/end_chunk accumulator
+                if start_chunk == -1:
+                    start_chunk = start_copy
+                end_chunk = end_copy
+            sequence_count -= sequences_to_extract
+            start_sequence += sequences_to_extract
+            current_sequence += sequences
+            i += 1
+        if start_chunk > -1:
+            result.append(copy_chunk_cmd % (start_chunk, end_chunk-start_chunk, input_name, output_name))
+                
+        if sequence_count > 0:
+            raise Exception('%s sequences not found in file' % sequence_count)
+             
+        return result
+    get_split_commands_with_toc = staticmethod(get_split_commands_with_toc)
+
+
+    def get_split_commands_sequential(is_compressed, input_name, output_name, start_sequence, sequence_count):
+        """
+        Does a brain-dead sequential scan & extract of certain sequences
+        >>> Sequence.get_split_commands_sequential(True, './input.gz', './output.gz', start_sequence=0, sequence_count=10)
+        ['zcat "./input.gz" | ( tail -n +1 2> /dev/null) | head -40 | gzip -c > "./output.gz"']
+        >>> Sequence.get_split_commands_sequential(False, './input.fastq', './output.fastq', start_sequence=10, sequence_count=10)
+        ['tail -n +41 "./input.fastq" 2> /dev/null | head -40 > "./output.fastq"']
+        """
+        start_line = start_sequence * 4
+        line_count = sequence_count * 4
+        # TODO: verify that tail can handle 64-bit numbers
+        if is_compressed:
+            cmd = 'zcat "%s" | ( tail -n +%s 2> /dev/null) | head -%s | gzip -c' % (input_name, start_line+1, line_count)
+        else:
+            cmd = 'tail -n +%s "%s" 2> /dev/null | head -%s'  % (start_line+1, input_name, line_count)
+        cmd += ' > "%s"' % output_name
+        
+        return [cmd]
+    get_split_commands_sequential = staticmethod(get_split_commands_sequential)
+
+
+
 class Alignment( data.Text ):
     """Class describing an alignment"""
 
@@ -550,3 +829,4 @@
                 return False
         except:
             return False
+


--- a/lib/galaxy/jobs/__init__.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/jobs/__init__.py	Wed Oct 12 11:19:42 2011 -0400
@@ -327,6 +327,7 @@
         self.working_directory = \
             os.path.join( self.app.config.job_working_directory, str( self.job_id ) )
         self.output_paths = None
+        self.output_dataset_paths = None
         self.tool_provided_job_metadata = None
         # Wrapper holding the info required to restore and clean up from files used for setting metadata externally
         self.external_output_metadata = metadata.JobExternalOutputMetadataWrapper( job )
@@ -716,23 +717,35 @@
     def get_session_id( self ):
         return self.session_id
 
+    def get_input_dataset_fnames( self,  ds ):
+        filenames = []
+        filenames = [ ds.file_name ]
+        #we will need to stage in metadata file names also
+        #TODO: would be better to only stage in metadata files that are actually needed (found in command line, referenced in config files, etc.)
+        for key, value in ds.metadata.items():
+            if isinstance( value, model.MetadataFile ):
+                filenames.append( value.file_name )
+        return filenames
+
     def get_input_fnames( self ):
         job = self.get_job()
         filenames = []
         for da in job.input_datasets + job.input_library_datasets: #da is JobToInputDatasetAssociation object
             if da.dataset:
-                filenames.append( da.dataset.file_name )
-                #we will need to stage in metadata file names also
-                #TODO: would be better to only stage in metadata files that are actually needed (found in command line, referenced in config files, etc.)
-                for key, value in da.dataset.metadata.items():
-                    if isinstance( value, model.MetadataFile ):
-                        filenames.append( value.file_name )
+                filenames.extend(self.get_input_dataset_fnames(da.dataset))
         return filenames
 
     def get_output_fnames( self ):
-        if self.output_paths is not None:
-            return self.output_paths
+        if self.output_paths is None:
+            self.compute_outputs()
+        return self.output_paths
 
+    def get_output_datasets_and_fnames( self ):
+        if self.output_dataset_paths is None:
+            self.compute_outputs()
+        return self.output_dataset_paths
+
+    def compute_outputs( self ) :
         class DatasetPath( object ):
             def __init__( self, dataset_id, real_path, false_path = None ):
                 self.dataset_id = dataset_id
@@ -743,23 +756,27 @@
                     return self.real_path
                 else:
                     return self.false_path
-
         job = self.get_job()
         # Job output datasets are combination of output datasets, library datasets, and jeha datasets.
         jeha = self.sa_session.query( model.JobExportHistoryArchive ).filter_by( job=job ).first()
+        jeha_false_path = None
         if self.app.config.outputs_to_working_directory:
             self.output_paths = []
+            output_dataset_paths = {}
             for name, data in [ ( da.name, da.dataset.dataset ) for da in job.output_datasets + job.output_library_datasets ]:
                 false_path = os.path.abspath( os.path.join( self.working_directory, "galaxy_dataset_%d.dat" % data.id ) )
-                self.output_paths.append( DatasetPath( data.id, data.file_name, false_path ) )
+                dsp = DatasetPath( data.id, data.file_name, false_path )
+                self.output_paths.append( dsp )
+                self.output_dataset_paths[name] = data,  dsp
             if jeha:
-                false_path = os.path.abspath( os.path.join( self.working_directory, "galaxy_dataset_%d.dat" % jeha.dataset.id ) )
-                self.output_paths.append( DatasetPath( jeha.dataset.id, jeha.dataset.file_name, false_path ) )
+                jeha_false_path = os.path.abspath( os.path.join( self.working_directory, "galaxy_dataset_%d.dat" % jeha.dataset.id ) )
         else:
-            self.output_paths = [ DatasetPath( da.dataset.dataset.id, da.dataset.file_name ) for da in job.output_datasets + job.output_library_datasets ]
-            if jeha:
-                self.output_paths.append( DatasetPath( jeha.dataset.id, jeha.dataset.file_name ) )
-
+            results = [ (da.name,  da.dataset,  DatasetPath( da.dataset.dataset.id, da.dataset.file_name )) for da in job.output_datasets + job.output_library_datasets ]
+            self.output_paths = [t[2] for t in results]
+            self.output_dataset_paths = dict([(t[0],  t[1:]) for t in results])
+        if jeha:
+            dsp = DatasetPath( jeha.dataset.id, jeha.dataset.file_name, jeha_false_path )
+            self.output_paths.append( dsp )
         return self.output_paths
 
     def get_output_file_id( self, file ):
@@ -869,12 +886,11 @@
     def __init__(self, task, queue):
         super(TaskWrapper, self).__init__(task.job, queue)
         self.task_id = task.id
-        self.parallelism = None
-        if task.part_file:
-            #do this better
-            self.working_directory = os.path.dirname(task.part_file)
+        self.working_directory = task.working_directory
+        if task.prepare_input_files_cmd is not None:
+            self.prepare_input_files_cmds = [ task.prepare_input_files_cmd ]
         else:
-            self.working_directory = None
+            self.prepare_input_files_cmds = None
         self.status = task.states.NEW
 
     def get_job( self ):
@@ -1112,8 +1128,8 @@
                     self.job_runners[runner_name].put( job_wrapper )
                 else:
                     runner_name = "tasks"
-                    log.debug( "dispatching job %d to %s runner" %( job_wrapper.job_id, runner_name ) )
-                    self.job_runners[runner_name].put( job_wrapper )
+                log.debug( "dispatching job %d to %s runner" %( job_wrapper.job_id, runner_name ) )
+                self.job_runners[runner_name].put( job_wrapper )
             else:
                 runner_name = ( job_wrapper.tool.job_runner.split(":", 1) )[0]
                 log.debug( "dispatching job %d to %s runner" %( job_wrapper.job_id, runner_name ) )
@@ -1249,4 +1265,3 @@
         return
     def shutdown( self ):
         return
-


--- a/lib/galaxy/jobs/runners/__init__.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/jobs/runners/__init__.py	Wed Oct 12 11:19:42 2011 -0400
@@ -6,6 +6,7 @@
         Compose the sequence of commands necessary to execute a job. This will
         currently include:
             - environment settings corresponding to any requirement tags
+            - preparing input files
             - command line taken from job wrapper
             - commands to set metadata (if include_metadata is True)
         """
@@ -17,10 +18,13 @@
         # Prepend version string
         if job_wrapper.version_string_cmd:
             commands = "%s &> %s; " % ( job_wrapper.version_string_cmd, job_wrapper.get_version_string_path() ) + commands
+        # prepend getting input files (if defined)
+        if hasattr(job_wrapper, 'prepare_input_files_cmds') and job_wrapper.prepare_input_files_cmds is not None:
+            commands = "; ".join( job_wrapper.prepare_input_files_cmds + [ commands ] ) 
         # Prepend dependency injection
         if job_wrapper.dependency_shell_commands:
             commands = "; ".join( job_wrapper.dependency_shell_commands + [ commands ] ) 
-        
+
         # Append metadata setting commands, we don't want to overwrite metadata
         # that was copied over in init_meta(), as per established behavior
         if include_metadata and self.app.config.set_metadata_externally:


--- a/lib/galaxy/jobs/runners/lwr.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/jobs/runners/lwr.py	Wed Oct 12 11:19:42 2011 -0400
@@ -249,6 +249,12 @@
 
         try:
             job_wrapper.prepare()
+            if hasattr(job_wrapper, 'prepare_input_files_cmds') and job_wrapper.prepare_input_files_cmds is not None:
+                for cmd in job_wrapper.prepare_input_file_cmds: # run the commands to stage the input files
+                    #log.debug( 'executing: %s' % cmd )
+                    if 0 != os.system(cmd):
+                        raise Exception('Error running file staging command: %s' % cmd)
+                job_wrapper.prepare_input_files_cmds = None # prevent them from being used in-line
             command_line = self.build_command_line( job_wrapper, include_metadata=False )
         except:
             job_wrapper.fail( "failure preparing job", exception=True )


--- a/lib/galaxy/jobs/runners/tasks.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/jobs/runners/tasks.py	Wed Oct 12 11:19:42 2011 -0400
@@ -60,71 +60,58 @@
         if command_line:
             try:
                 # DBTODO read tool info and use the right kind of parallelism.  
-                # For now, the only splitter is the 'basic' one, n-ways split on one input, one output.
-                # This is incredibly simplified.  Parallelism ultimately needs to describe which inputs, how, etc.
+                # For now, the only splitter is the 'basic' one
                 job_wrapper.change_state( model.Job.states.RUNNING )
                 self.sa_session.flush()
-                parent_job = job_wrapper.get_job()
                 # Split with the tool-defined method.
-                if job_wrapper.tool.parallelism == "basic":
-                    from galaxy.jobs.splitters import basic
-                    if len(job_wrapper.get_input_fnames()) > 1 or len(job_wrapper.get_output_fnames()) > 1:
-                        log.error("The basic splitter is not capable of handling jobs with multiple inputs or outputs.")
-                        job_wrapper.change_state( model.Job.states.ERROR )
-                        job_wrapper.fail("Job Splitting Failed, the basic splitter only handles tools with one input and one output")
-                        # Requeue as a standard job?
-                        return
-                    input_file = job_wrapper.get_input_fnames()[0]
-                    working_directory = job_wrapper.working_directory
-                    # DBTODO execute an external task to do the splitting, this should happen at refactor.
-                    # Regarding number of ways split, use "hints" in tool config?
-                    # If the number of tasks is sufficiently high, we can use it to calculate job completion % and give a running status.
-                    basic.split(input_file, working_directory, 
-                                        20, #Needs serious experimentation to find out what makes the most sense.
-                                        parent_job.input_datasets[0].dataset.ext)
-                    # Tasks in this parts list are in alphabetical listdir order (15 before 5), but that should not matter.
-                    parts = [os.path.join(os.path.abspath(job_wrapper.working_directory), p, os.path.basename(input_file)) 
-                                for p in os.listdir(job_wrapper.working_directory) 
-                                if p.startswith('task_')]
-                else:
+                try:
+                    splitter = getattr(__import__('galaxy.jobs.splitters',  globals(),  locals(),  [job_wrapper.tool.parallelism.method]),  job_wrapper.tool.parallelism.method)
+                except:   
                     job_wrapper.change_state( model.Job.states.ERROR )
                     job_wrapper.fail("Job Splitting Failed, no match for '%s'" % job_wrapper.tool.parallelism)
-                # Assemble parts into task_wrappers
+                    return
+                tasks = splitter.do_split(job_wrapper)
 
                 # Not an option for now.  Task objects don't *do* anything useful yet, but we'll want them tracked outside this thread to do anything.
                 # if track_tasks_in_database:
-                tasks = []
                 task_wrappers = []
-                for part in parts:
-                    task = model.Task(parent_job, part)
+                for task in tasks:
                     self.sa_session.add(task)
-                    tasks.append(task)
                 self.sa_session.flush()
+                
                 # Must flush prior to the creation and queueing of task wrappers.
                 for task in tasks:
                     tw = TaskWrapper(task, job_wrapper.queue)
                     task_wrappers.append(tw)
                     self.app.job_manager.dispatcher.put(tw)
                 tasks_incomplete = False
+                count_complete = 0
                 sleep_time = 1
+                # sleep/loop until no more progress can be made. That is when
+                # all tasks are one of { OK, ERROR, DELETED }
+                completed_states = [ model.Task.states.OK, \
+                                    model.Task.states.ERROR, \
+                                    model.Task.states.DELETED ]
+                # TODO: Should we report an error (and not merge outputs) if one of the subtasks errored out?
+                # Should we prevent any that are pending from being started in that case?
                 while tasks_incomplete is False:
+                    count_complete = 0
                     tasks_incomplete = True
                     for tw in task_wrappers:
-                        if not tw.get_state() == model.Task.states.OK:
+                        task_state = tw.get_state()
+                        if not task_state in completed_states:
                             tasks_incomplete = False
-                    sleep( sleep_time )
-                    if sleep_time < 8:
-                        sleep_time *= 2
-                output_filename = job_wrapper.get_output_fnames()[0].real_path
-                basic.merge(working_directory, output_filename)
-                log.debug('execution finished: %s' % command_line)
-                for tw in task_wrappers:
-                    # Prevent repetitive output, e.g. "Sequence File Aligned"x20
-                    # Eventually do a reduce for jobs that output "N reads mapped", combining all N for tasks.
-                    if stdout.strip() != tw.get_task().stdout.strip():
-                        stdout += tw.get_task().stdout
-                    if stderr.strip() != tw.get_task().stderr.strip():                        
-                        stderr += tw.get_task().stderr
+                        else:
+                            count_complete = count_complete + 1
+                    if tasks_incomplete is False:
+                        # log.debug('Tasks complete: %s. Sleeping %s' % (count_complete, sleep_time))
+                        sleep( sleep_time )
+                        if sleep_time < 8:
+                            sleep_time *= 2
+                
+                log.debug('execution finished - beginning merge: %s' % command_line)
+                stdout,  stderr = splitter.do_merge(job_wrapper,  task_wrappers)
+                
             except Exception:
                 job_wrapper.fail( "failure running job", exception=True )
                 log.exception("failure running job %d" % job_wrapper.job_id)


--- a/lib/galaxy/jobs/splitters/basic.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/jobs/splitters/basic.py	Wed Oct 12 11:19:42 2011 -0400
@@ -1,91 +1,23 @@
-import os, logging
+import logging
+import multi
+
 log = logging.getLogger( __name__ )
 
-def _file_len(fname):
-    i = 0
-    f = open(fname)
-    for i, l in enumerate(f):
-        pass
-    f.close()
-    return i + 1
+def set_basic_defaults(job_wrapper):
+    parent_job = job_wrapper.get_job()
+    job_wrapper.tool.parallelism.attributes['split_inputs'] = parent_job.input_datasets[0].name
+    job_wrapper.tool.parallelism.attributes['merge_outputs'] = job_wrapper.get_output_datasets_and_fnames().keys()[0]
 
-def _fq_seq_count(fname):
-    count = 0
-    f = open(fname)
-    for i, l in enumerate(f):
-        if l.startswith('@'):
-            count += 1
-    f.close()
-    return count
-
-def split_fq(input_file, working_directory, parts):
-    # Temporary, switch this to use the fq reader in lib/galaxy_utils/sequence.
-    outputs = []
-    length = _fq_seq_count(input_file)
-    if length < 1:
-        return outputs
-    if length < parts:
-        parts = length
-    len_each, remainder = divmod(length, parts)
-    f = open(input_file, 'rt')
-    for p in range(0, parts):
-        part_dir = os.path.join( os.path.abspath(working_directory), 'task_%s' % p)
-        if not os.path.exists( part_dir ):
-            os.mkdir( part_dir )
-        part_path = os.path.join(part_dir, os.path.basename(input_file))
-        part_file = open(part_path, 'w')
-        for l in range(0, len_each):
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-        if remainder > 0:
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            part_file.write(f.readline())
-            remainder -= 1
-        outputs.append(part_path)
-        part_file.close()
-    f.close()
-    return outputs
-
-def split_txt(input_file, working_directory, parts):
-    outputs = []
-    length = _file_len(input_file)
-    if length < parts:
-        parts = length
-    len_each, remainder = divmod(length, parts)
-    f = open(input_file, 'rt')
-    for p in range(0, parts):
-        part_dir = os.path.join( os.path.abspath(working_directory), 'task_%s' % p)
-        if not os.path.exists( part_dir ):
-            os.mkdir( part_dir )
-        part_path = os.path.join(part_dir, os.path.basename(input_file))
-        part_file = open(part_path, 'w')
-        for l in range(0, len_each):
-            part_file.write(f.readline())
-        if remainder > 0:
-            part_file.write(f.readline())
-            remainder -= 1
-        outputs.append(part_path)
-        part_file.close()
-    f.close()
-    return outputs
+def do_split (job_wrapper):
+    if len(job_wrapper.get_input_fnames()) > 1 or len(job_wrapper.get_output_fnames()) > 1:
+        log.error("The basic splitter is not capable of handling jobs with multiple inputs or outputs.")
+        raise Exception,  "Job Splitting Failed, the basic splitter only handles tools with one input and one output"
+    # add in the missing information for splitting the one input and merging the one output
+    set_basic_defaults(job_wrapper)
+    return multi.do_split(job_wrapper)
     
-def split( input_file, working_directory, parts, file_type = None):
-    #Implement a better method for determining how to split.
-    if file_type.startswith('fastq'):
-        return split_fq(input_file, working_directory, parts)
-    else:
-        return split_txt(input_file, working_directory, parts)
+def do_merge( job_wrapper,  task_wrappers):
+    # add in the missing information for splitting the one input and merging the one output
+    set_basic_defaults(job_wrapper)
+    return multi.do_merge(job_wrapper,  task_wrappers)
     
-def merge( working_directory, output_file ):
-    output_file_name = os.path.basename(output_file)
-    task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
-    task_dirs.sort(key = lambda x: int(x.split('task_')[-1]))
-    for task_dir in task_dirs:
-        try:
-            os.system( 'cat %s >> %s' % ( os.path.join(task_dir, output_file_name), output_file ) )
-        except Exception, e:
-            log.error(str(e))


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/jobs/splitters/multi.py	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,158 @@
+import os, logging,  shutil
+from galaxy import model, util
+
+
+log = logging.getLogger( __name__ )
+    
+def do_split (job_wrapper):
+    parent_job = job_wrapper.get_job()
+    working_directory = os.path.abspath(job_wrapper.working_directory)
+
+    parallel_settings = job_wrapper.tool.parallelism.attributes
+    # Syntax: split_inputs="input1,input2" shared_inputs="genome" 
+    # Designates inputs to be split or shared
+    split_inputs=parallel_settings.get("split_inputs")
+    if split_inputs is None:
+        split_inputs = []
+    else:
+        split_inputs = [x.strip() for x in split_inputs.split(",")]
+
+    shared_inputs=parallel_settings.get("shared_inputs")
+    if shared_inputs is None:
+        shared_inputs = []
+    else:
+        shared_inputs = [x.strip() for x in shared_inputs.split(",")]
+    illegal_inputs = [x for x in shared_inputs if x in split_inputs]
+    if len(illegal_inputs) > 0:
+        raise Exception("Inputs have conflicting parallelism attributes: %s" % str( illegal_inputs ))
+    
+    subdir_index = [0] # use a list to get around Python 2.x lame closure support
+    task_dirs = []
+    def get_new_working_directory_name():
+        dir=os.path.join(working_directory,  'task_%d' % subdir_index[0])
+        subdir_index[0] = subdir_index[0] + 1
+        if not os.path.exists(dir):
+            os.makedirs(dir)
+        task_dirs.append(dir)
+        return dir
+    
+    # For things like paired end alignment, we need two inputs to be split. Since all inputs to all
+    # derived subtasks need to be correlated, allow only one input type to be split
+    type_to_input_map = {}
+    for input in parent_job.input_datasets:
+        if input.name in split_inputs:
+            type_to_input_map.setdefault(input.dataset.datatype,  []).append(input.name)
+        elif input.name in shared_inputs:
+            pass # pass original file name
+        else:
+            log_error = "The input '%s' does not define a method for implementing parallelism" % str(input.name)
+            log.error(log_error)
+            raise Exception(log_error)
+
+    if len(type_to_input_map) > 1:
+        log_error = "The multi splitter does not support splitting inputs of more than one type"
+        log.error(log_error)
+        raise Exception(log_error)
+    
+    # split the first one to build up the task directories
+    input_datasets = []
+    for input in parent_job.input_datasets:
+        if input.name in split_inputs:
+            this_input_files = job_wrapper.get_input_dataset_fnames(input.dataset)
+            if len(this_input_files) > 1:
+                log_error = "The input '%s' is composed of multiple files - splitting is not allowed" % str(input.name)
+                log.error(log_error)
+                raise Exception(log_error)
+            input_datasets.append(input.dataset)
+    
+    input_type = type_to_input_map.keys()[0]
+    # DBTODO execute an external task to do the splitting, this should happen at refactor.
+    # If the number of tasks is sufficiently high, we can use it to calculate job completion % and give a running status.
+    try:
+        input_type.split(input_datasets, get_new_working_directory_name, parallel_settings)
+    except AttributeError:
+        log_error = "The type '%s' does not define a method for splitting files" % str(input_type)
+        log.error(log_error)
+        raise
+    log.debug('do_split created %d parts' % len(task_dirs))
+    # next, after we know how many divisions there are, add the shared inputs via soft links
+    for input in parent_job.input_datasets:
+        if input and input.name in shared_inputs:
+            names = job_wrapper.get_input_dataset_fnames(input.dataset)
+            for dir in task_dirs:
+                for file in names:
+                    os.symlink(file, os.path.join(dir,  os.path.basename(file)))
+    tasks = []
+    prepare_files = os.path.join(util.galaxy_directory(), 'extract_dataset_parts.sh') + ' %s'
+    for dir in task_dirs:
+        task = model.Task(parent_job, dir, prepare_files % dir)
+        tasks.append(task)
+    return tasks
+                               
+
+def do_merge( job_wrapper,  task_wrappers):
+    parent_job = job_wrapper.get_job()
+    parallel_settings = job_wrapper.tool.parallelism.attributes
+    # Syntax: merge_outputs="export" pickone_outputs="genomesize"
+    # Designates outputs to be merged, or selected from as a representative
+    merge_outputs = parallel_settings.get("merge_outputs")
+    if merge_outputs is None:
+        merge_outputs = []
+    else:
+        merge_outputs = [x.strip() for x in merge_outputs.split(",")]
+    pickone_outputs = parallel_settings.get("pickone_outputs")
+    if pickone_outputs is None:
+        pickone_outputs = []
+    else:
+        pickone_outputs = [x.strip() for x in pickone_outputs.split(",")]
+    
+    illegal_outputs = [x for x in merge_outputs if x in pickone_outputs]
+    if len(illegal_outputs) > 0:
+        return ('Tool file error', 'Outputs have conflicting parallelism attributes: %s' % str( illegal_outputs ))
+    
+    stdout = ''
+    stderr = ''
+    
+    try:
+        working_directory = job_wrapper.working_directory
+        task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
+        # TODO: Output datasets can be very complex. This doesn't handle metadata files
+        outputs = job_wrapper.get_output_datasets_and_fnames()
+        pickone_done = []
+        task_dirs = [os.path.join(working_directory, x) for x in os.listdir(working_directory) if x.startswith('task_')]
+        for output in outputs:
+            output_file_name = str(outputs[output][1])
+            base_output_name = os.path.basename(output_file_name)
+            if output in merge_outputs:
+                output_type = outputs[output][0].datatype
+                output_files = [os.path.join(dir,base_output_name) for dir in task_dirs]
+                log.debug('files %s ' % output_files)
+                output_type.merge(output_files, output_file_name)
+                log.debug('merge finished: %s' % output_file_name)
+                pass # TODO: merge all the files
+            elif output in pickone_outputs:
+                # just pick one of them
+                if output not in pickone_done:
+                    task_file_name = os.path.join(task_dirs[0], base_output_name)
+                    shutil.move( task_file_name,  output_file_name )
+                    pickone_done.append(output)
+            else:
+                log_error = "The output '%s' does not define a method for implementing parallelism" % output
+                log.error(log_error)
+                raise Exception(log_error)
+    except Exception, e:
+        stdout = 'Error merging files';
+        stderr = str(e)
+        
+
+    for tw in task_wrappers:
+        # Prevent repetitive output, e.g. "Sequence File Aligned"x20
+        # Eventually do a reduce for jobs that output "N reads mapped", combining all N for tasks.
+        out = tw.get_task().stdout.strip()
+        err = tw.get_task().stderr.strip()
+        if len(out) > 0:
+            stdout += tw.working_directory + ':\n' + out
+        if len(err) > 0:
+            stderr += tw.working_directory + ':\n' + err
+    return (stdout,  stderr)
+    


--- a/lib/galaxy/model/__init__.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/model/__init__.py	Wed Oct 12 11:19:42 2011 -0400
@@ -204,18 +204,19 @@
                     ERROR = 'error',
                     DELETED = 'deleted' )
 
-    def __init__( self, job, part_file = None ):
+    def __init__( self, job, working_directory, prepare_files_cmd ):
         self.command_line = None
         self.parameters = []
         self.state = Task.states.NEW
         self.info = None
-        self.part_file = part_file
+        self.working_directory = working_directory
         self.task_runner_name = None
         self.task_runner_external_id = None
         self.job = job
         self.stdout = None
         self.stderr = None
-
+        self.prepare_input_files_cmd = prepare_files_cmd
+    
     def set_state( self, state ):
         self.state = state
 
@@ -896,7 +897,9 @@
     def get_converted_files_by_type( self, file_type ):
         for assoc in self.implicitly_converted_datasets:
             if not assoc.deleted and assoc.type == file_type:
-                return assoc.dataset
+                if assoc.dataset:
+                    return assoc.dataset
+                return assoc.dataset_ldda
         return None
     def get_converted_dataset_deps(self, trans, target_ext):
         """
@@ -1588,7 +1591,12 @@
 class ImplicitlyConvertedDatasetAssociation( object ):
     def __init__( self, id = None, parent = None, dataset = None, file_type = None, deleted = False, purged = False, metadata_safe = True ):
         self.id = id
-        self.dataset = dataset
+        if isinstance(dataset, HistoryDatasetAssociation):
+            self.dataset = dataset
+        elif isinstance(dataset, LibraryDatasetDatasetAssociation):
+            self.dataset_ldda = dataset
+        else:
+            raise AttributeError, 'Unknown dataset type provided for dataset: %s' % type( dataset )
         if isinstance(parent, HistoryDatasetAssociation):
             self.parent_hda = parent
         elif isinstance(parent, LibraryDatasetDatasetAssociation):


--- a/lib/galaxy/model/mapping.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/model/mapping.py	Wed Oct 12 11:19:42 2011 -0400
@@ -148,6 +148,7 @@
     Column( "create_time", DateTime, default=now ),
     Column( "update_time", DateTime, default=now, onupdate=now ),
     Column( "hda_id", Integer, ForeignKey( "history_dataset_association.id" ), index=True, nullable=True ),
+    Column( "ldda_id", Integer, ForeignKey( "library_dataset_dataset_association.id" ), index=True, nullable=True ),
     Column( "hda_parent_id", Integer, ForeignKey( "history_dataset_association.id" ), index=True ),
     Column( "ldda_parent_id", Integer, ForeignKey( "library_dataset_dataset_association.id" ), index=True ),
     Column( "deleted", Boolean, index=True, default=False ),
@@ -467,11 +468,13 @@
     Column( "runner_name", String( 255 ) ),
     Column( "stdout", TEXT ),
     Column( "stderr", TEXT ),
+    Column( "info", TrimmedString ( 255 ) ),
     Column( "traceback", TEXT ),
     Column( "job_id", Integer, ForeignKey( "job.id" ), index=True, nullable=False ),
-    Column( "part_file", String(1024)),
+    Column( "working_directory", String(1024)),
     Column( "task_runner_name", String( 255 ) ),
-    Column( "task_runner_external_id", String( 255 ) ) )
+    Column( "task_runner_external_id", String( 255 ) ),
+    Column( "prepare_input_files_cmd", TEXT ) )
 
 PostJobAction.table = Table("post_job_action", metadata,
     Column("id", Integer, primary_key=True),
@@ -1206,11 +1209,12 @@
     properties=dict( parent_hda=relation( 
                         HistoryDatasetAssociation, 
                         primaryjoin=( ImplicitlyConvertedDatasetAssociation.table.c.hda_parent_id == HistoryDatasetAssociation.table.c.id ) ),
-                     
                      parent_ldda=relation( 
                         LibraryDatasetDatasetAssociation, 
                         primaryjoin=( ImplicitlyConvertedDatasetAssociation.table.c.ldda_parent_id == LibraryDatasetDatasetAssociation.table.c.id ) ),
-                     
+                     dataset_ldda=relation( 
+                        LibraryDatasetDatasetAssociation, 
+                        primaryjoin=( ImplicitlyConvertedDatasetAssociation.table.c.ldda_id == LibraryDatasetDatasetAssociation.table.c.id ) ),
                      dataset=relation( 
                         HistoryDatasetAssociation, 
                         primaryjoin=( ImplicitlyConvertedDatasetAssociation.table.c.hda_id == HistoryDatasetAssociation.table.c.id ) ) ) )
@@ -1594,7 +1598,7 @@
                      annotations=relation( PageAnnotationAssociation, order_by=PageAnnotationAssociation.table.c.id, backref="pages" ),
                      ratings=relation( PageRatingAssociation, order_by=PageRatingAssociation.table.c.id, backref="pages" )  
                    ) )
-
+                   
 assign_mapper( context, ToolShedRepository, ToolShedRepository.table )
 
 # Set up proxy so that 


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/model/migrate/versions/0083_add_prepare_files_to_task.py	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,63 @@
+"""
+Migration script to add 'prepare_input_files_cmd' column to the task table and to rename a column.
+"""
+
+from sqlalchemy import *
+from sqlalchemy.orm import *
+from migrate import *
+from migrate.changeset import *
+
+import logging
+log = logging.getLogger( __name__ )
+
+metadata = MetaData( migrate_engine )
+db_session = scoped_session( sessionmaker( bind=migrate_engine, autoflush=False, autocommit=True ) )
+
+def upgrade():
+    print __doc__
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "prepare_input_files_cmd", TEXT, nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.prepare_input_files_cmd
+    except Exception, e:
+        print "Adding prepare_input_files_cmd column to task table failed: %s" % str( e )
+        log.debug( "Adding prepare_input_files_cmd column to task table failed: %s" % str( e ) )
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "working_directory", String ( 1024 ), nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.working_directory
+    except Exception, e:
+        print "Adding working_directory column to task table failed: %s" % str( e )
+        log.debug( "Adding working_directory column to task table failed: %s" % str( e ) )
+
+    # remove the 'part_file' column - nobody used tasks before this, so no data needs to be migrated
+    try:
+        task_table.c.part_file.drop()
+    except Exception, e:
+        log.debug( "Deleting column 'part_file' from the 'task' table failed: %s" % ( str( e ) ) )
+
+def downgrade():
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        task_table.c.prepare_input_files_cmd.drop()
+    except Exception, e:
+        print "Dropping prepare_input_files_cmd column from task table failed: %s" % str( e )
+        log.debug( "Dropping prepare_input_files_cmd column from task table failed: %s" % str( e ) )
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        task_table.c.working_directory.drop()
+    except Exception, e:
+        print "Dropping working_directory column from task table failed: %s" % str( e )
+        log.debug( "Dropping working_directory column from task table failed: %s" % str( e ) )
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "part_file", String ( 1024 ), nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.part_file
+    except Exception, e:
+        print "Adding part_file column to task table failed: %s" % str( e )
+        log.debug( "Adding part_file column to task table failed: %s" % str( e ) )


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/model/migrate/versions/0084_add_ldda_id_to_implicit_conversion_table.py	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,35 @@
+"""
+Migration script to add 'ldda_id' column to the implicitly_converted_dataset_association table.
+"""
+
+from sqlalchemy import *
+from sqlalchemy.orm import *
+from migrate import *
+from migrate.changeset import *
+
+import logging
+log = logging.getLogger( __name__ )
+
+metadata = MetaData( migrate_engine )
+db_session = scoped_session( sessionmaker( bind=migrate_engine, autoflush=False, autocommit=True ) )
+
+def upgrade():
+    print __doc__
+    metadata.reflect()
+    try:
+        Implicitly_converted_table = Table( "implicitly_converted_dataset_association", metadata, autoload=True )
+        c = Column( "ldda_id", Integer, ForeignKey( "library_dataset_dataset_association.id" ), index=True, nullable=True )
+        c.create( Implicitly_converted_table )
+        assert c is Implicitly_converted_table.c.ldda_id
+    except Exception, e:
+        print "Adding ldda_id column to implicitly_converted_dataset_association table failed: %s" % str( e )
+        log.debug( "Adding ldda_id column to implicitly_converted_dataset_association table failed: %s" % str( e ) )
+
+def downgrade():
+    metadata.reflect()
+    try:
+        Implicitly_converted_table = Table( "implicitly_converted_dataset_association", metadata, autoload=True )
+        Implicitly_converted_table.c.ldda_id.drop()
+    except Exception, e:
+        print "Dropping ldda_id column from implicitly_converted_dataset_association table failed: %s" % str( e )
+        log.debug( "Dropping ldda_id column from implicitly_converted_dataset_association table failed: %s" % str( e ) )


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lib/galaxy/model/migrate/versions/0085_add_task_info.py	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,36 @@
+"""
+Migration script to add 'info' column to the task table.
+"""
+
+from sqlalchemy import *
+from sqlalchemy.orm import *
+from migrate import *
+from migrate.changeset import *
+
+import logging
+log = logging.getLogger( __name__ )
+from galaxy.model.custom_types import TrimmedString
+
+metadata = MetaData( migrate_engine )
+db_session = scoped_session( sessionmaker( bind=migrate_engine, autoflush=False, autocommit=True ) )
+
+def upgrade():
+    print __doc__
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        c = Column( "info", TrimmedString (255) , nullable=True )
+        c.create( task_table )
+        assert c is task_table.c.info
+    except Exception, e:
+        print "Adding info column to table table failed: %s" % str( e )
+        log.debug( "Adding info column to task table failed: %s" % str( e ) )
+
+def downgrade():
+    metadata.reflect()
+    try:
+        task_table = Table( "task", metadata, autoload=True )
+        task_table.c.info.drop()
+    except Exception, e:
+        print "Dropping info column from task table failed: %s" % str( e )
+        log.debug( "Dropping info column from task table failed: %s" % str( e ) )


--- a/lib/galaxy/tools/__init__.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/tools/__init__.py	Wed Oct 12 11:19:42 2011 -0400
@@ -352,6 +352,21 @@
         self.fabfile = fabfile
         self.method = method
 
+class ToolParallelismInfo(object):
+    """
+    Stores the information (if any) for running multiple instances of the tool in parallel
+    on the same set of inputs.
+    """
+    def __init__(self, tag):
+        self.method = tag.get('method')
+        self.attributes = dict([item for item in tag.attrib.items() if item[0] != 'method' ])
+        if len(self.attributes) == 0:
+            # legacy basic mode - provide compatible defaults
+            self.attributes['split_size'] = 20
+            self.attributes['split_mode'] = 'number_of_parts'
+            
+        
+    
 class Tool:
     """
     Represents a computational tool that can be executed through Galaxy. 
@@ -442,7 +457,7 @@
         # Parallelism for tasks, read from tool config.
         parallelism = root.find("parallelism")
         if parallelism is not None and parallelism.get("method"):
-            self.parallelism = parallelism.get("method")
+            self.parallelism = ToolParallelismInfo(parallelism)
         else:
             self.parallelism = None
         if self.app.config.start_job_runners is None:


--- a/lib/galaxy/util/__init__.py	Mon Oct 10 18:42:31 2011 -0400
+++ b/lib/galaxy/util/__init__.py	Wed Oct 12 11:19:42 2011 -0400
@@ -623,6 +623,9 @@
 gbrowse_build_sites = read_build_sites( os.path.join( galaxy_root_path, "tool-data", "shared", "gbrowse", "gbrowse_build_sites.txt" ) )
 genetrack_sites = read_build_sites( os.path.join( galaxy_root_path, "tool-data", "shared", "genetrack", "genetrack_sites.txt" ), check_builds=False )
 
+def galaxy_directory():
+    return os.path.abspath(galaxy_root_path)
+
 if __name__ == '__main__':
     import doctest, sys
     doctest.testmod(sys.modules[__name__], verbose=False)


--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/extract_dataset_part.py	Wed Oct 12 11:19:42 2011 -0400
@@ -0,0 +1,48 @@
+"""
+Reads a JSON file and uses it to call into a datatype class to extract
+a subset of a dataset for processing.
+
+Used by jobs that split large files into pieces to be processed concurrently
+on a gid in a scatter-gather mode. This does part of the scatter.
+
+"""
+import os
+import sys
+import logging
+logging.basicConfig()
+log = logging.getLogger( __name__ )
+
+new_path = [ os.path.join( os.getcwd(), "lib" ) ]
+new_path.extend( sys.path[1:] ) # remove scripts/ from the path
+sys.path = new_path
+
+from galaxy import eggs
+import pkg_resources
+pkg_resources.require("simplejson")
+import simplejson
+
+# This junk is here to prevent loading errors
+import galaxy.model.mapping #need to load this before we unpickle, in order to setup properties assigned by the mappers
+galaxy.model.Job() #this looks REAL stupid, but it is REQUIRED in order for SA to insert parameters into the classes defined by the mappers --> it appears that instantiating ANY mapper'ed class would suffice here
+galaxy.datatypes.metadata.DATABASE_CONNECTION_AVAILABLE = False #Let metadata know that there is no database connection, and to just assume object ids are valid
+
+def __main__():
+    """
+    Argument: a JSON file
+    """
+    file_path = sys.argv.pop( 1 )
+    data = simplejson.load(open(file_path, 'r'))
+    try:
+        class_name_parts = data['class_name'].split('.')
+        module_name = '.'.join(class_name_parts[:-1])
+        class_name = class_name_parts[-1]
+        mod = __import__(module_name, globals(),  locals(),  [class_name])
+        cls = getattr(mod,  class_name)
+        if not cls.process_split_file(data):
+            sys.stderr.write('Writing split file failed\n')
+            sys.exit(1)
+    except Exception, e:
+        sys.stderr.write(str(e))
+        sys.exit(1)
+        
+__main__()


http://bitbucket.org/galaxy/galaxy-central/changeset/44731af3e1c0/
changeset:   44731af3e1c0
user:        dannon
date:        2011-10-12 17:19:58
summary:     Merge
affected #:  9 files (-1 bytes)

--- a/lib/galaxy/visualization/tracks/data_providers.py	Wed Oct 12 11:19:42 2011 -0400
+++ b/lib/galaxy/visualization/tracks/data_providers.py	Wed Oct 12 11:19:58 2011 -0400
@@ -205,7 +205,10 @@
 
             # Score (filter data)    
             if length >= 5 and filter_cols and filter_cols[0] == "Score":
-                payload.append( float(feature[4]) )
+                try:
+                    payload.append( float( feature[4] ) )
+                except:
+                    payload.append( feature[4] )
 
             rval.append( payload )
 
@@ -804,7 +807,7 @@
     # Return full feature.
     payload = [ feature.start, 
                 feature.end, 
-                feature.name(), 
+                feature.name(),
                 feature.strand,
                 # No notion of thick start, end in GFF, so make everything
                 # thick.
@@ -828,9 +831,16 @@
     # Add filter data to payload.
     for col in filter_cols:
         if col == "Score":
-            payload.append( feature.score )
+            try: 
+                payload.append( float( feature.score ) )
+            except:
+                payload.append( feature.score )
         elif col in feature.attributes:
-            payload.append( feature.attributes[col] )
+            try:
+                payload.append( float( feature.attributes[col] ) )
+            except:
+                # Feature is not a float.
+                payload.append( feature.attributes[col] )
         else:
             # Dummy value.
             payload.append( "na" )


--- a/lib/galaxy/web/controllers/dataset.py	Wed Oct 12 11:19:42 2011 -0400
+++ b/lib/galaxy/web/controllers/dataset.py	Wed Oct 12 11:19:58 2011 -0400
@@ -1134,13 +1134,13 @@
                 if history in target_histories:
                     refresh_frames = ['history']
                 trans.sa_session.flush()
-                hist_names_str = ", ".join( [ hist.name for hist in target_histories ] )
+                hist_names_str = ", ".join( ['<a href="%s" target="_top">%s</a>' % 
+                                            ( url_for( controller="history", action="switch_to_history", \
+                                                        hist_id=trans.security.encode_id( hist.id ) ), hist.name ) \
+                                                        for hist in target_histories ] )
                 num_source = len( source_dataset_ids ) - invalid_datasets
                 num_target = len(target_histories)
                 done_msg = "%i %s copied to %i %s: %s." % (num_source, inflector.cond_plural(num_source, "dataset"), num_target, inflector.cond_plural(num_target, "history"), hist_names_str )
-                if new_history is not None:
-                    done_msg += " <a href=\"%s\" target=\"_top\">Switch to the new history.</a>" % url_for( 
-                        controller="history", action="switch_to_history", hist_id=trans.security.encode_id( new_history.id ) )
                 trans.sa_session.refresh( history )
         source_datasets = history.visible_datasets
         target_histories = [history]


--- a/static/june_2007_style/blue/trackster.css	Wed Oct 12 11:19:42 2011 -0400
+++ b/static/june_2007_style/blue/trackster.css	Wed Oct 12 11:19:58 2011 -0400
@@ -24,7 +24,7 @@
 .track{background:white;}
 .track-header{text-align:left;padding:4px 0px;color:#666;}
 .track-header .menubutton{margin-left:0px;}
-.track-content{overflow:hidden;text-align:center;border-top:1px solid #eee;border-bottom:2px solid #eee;background:#eee url('/static/images/tracks/diag_bg.gif');min-height:16px;}
+.track-content{text-align:center;border-top:1px solid #eee;border-bottom:2px solid #eee;background:#eee url('/static/images/tracks/diag_bg.gif');min-height:16px;}
 .label-track .track-content{background:white;}
 .track-tile{background:white;}
 .track-tile canvas{position:relative;z-index:100;}
@@ -62,3 +62,4 @@
 .icon.more-across{background:url('../images/fugue/arrow-transition-bw.png') no-repeat 0px 0px;}
 .intro{padding:1em;}
 .intro > .action-button{background-color:#CCC;padding:1em;}
+.feature-popup{background-color:#DDD;position:absolute;z-index:1000}


--- a/static/june_2007_style/trackster.css.tmpl	Wed Oct 12 11:19:42 2011 -0400
+++ b/static/june_2007_style/trackster.css.tmpl	Wed Oct 12 11:19:58 2011 -0400
@@ -156,7 +156,6 @@
 }
 
 .track-content {
-    overflow: hidden;
     text-align: center;
     border-top: 1px solid #eee;
     border-bottom: 2px solid #eee;
@@ -335,4 +334,8 @@
     background-color: #CCC;
     padding: 1em;
 }
-
+.feature-popup{ 
+    background-color: #DDD;
+    position: absolute;
+    z-index: 1000
+}


--- a/static/scripts/packed/trackster.js	Wed Oct 12 11:19:42 2011 -0400
+++ b/static/scripts/packed/trackster.js	Wed Oct 12 11:19:58 2011 -0400
@@ -1,1 +1,1 @@
-var class_module=function(b,a){var c=function(){var f=arguments[0];for(var e=1;e<arguments.length;e++){var d=arguments[e];for(key in d){f[key]=d[key]}}return f};a.extend=c};var requestAnimationFrame=(function(){return window.requestAnimationFrame||window.webkitRequestAnimationFrame||window.mozRequestAnimationFrame||window.oRequestAnimationFrame||window.msRequestAnimationFrame||function(b,a){window.setTimeout(b,1000/60)}})();var BEFORE=1001,CONTAINS=1002,OVERLAP_START=1003,OVERLAP_END=1004,CONTAINED_BY=1005,AFTER=1006;var compute_overlap=function(e,b){var g=e[0],f=e[1],d=b[0],c=b[1],a;if(g<d){if(f<d){a=BEFORE}else{if(f<=c){a=OVERLAP_START}else{a=CONTAINS}}}else{if(g>c){a=AFTER}else{if(f<=c){a=CONTAINED_BY}else{a=OVERLAP_END}}}return a};var is_overlap=function(c,b){var a=compute_overlap(c,b);return(a!==BEFORE&&a!==AFTER)};var trackster_module=function(f,aa){var q=f("class").extend,t=f("slotting"),N=f("painters");var ag=function(ah,ai){this.document=ah;this.default_font=ai!==undefined?ai:"9px Monaco, Lucida Console, monospace";this.dummy_canvas=this.new_canvas();this.dummy_context=this.dummy_canvas.getContext("2d");this.dummy_context.font=this.default_font;this.char_width_px=this.dummy_context.measureText("A").width;this.patterns={};this.load_pattern("right_strand","/visualization/strand_right.png");this.load_pattern("left_strand","/visualization/strand_left.png");this.load_pattern("right_strand_inv","/visualization/strand_right_inv.png");this.load_pattern("left_strand_inv","/visualization/strand_left_inv.png")};q(ag.prototype,{load_pattern:function(ah,al){var ai=this.patterns,aj=this.dummy_context,ak=new Image();ak.src=image_path+al;ak.onload=function(){ai[ah]=aj.createPattern(ak,"repeat")}},get_pattern:function(ah){return this.patterns[ah]},new_canvas:function(){var ah=this.document.createElement("canvas");if(window.G_vmlCanvasManager){G_vmlCanvasManager.initElement(ah)}ah.manager=this;return ah}});var o={};var m=function(ah,ai){o[ah.attr("id")]=ai};var n=function(ah,aj,al,ak){al=".group";var ai={};o[ah.attr("id")]=ak;ah.bind("drag",{handle:"."+aj,relative:true},function(au,av){var at=$(this);var ay=$(this).parent(),ap=ay.children(),ar=o[$(this).attr("id")],ao,an,aw,am,aq;an=$(this).parents(al);if(an.length!==0){aw=an.position().top;am=aw+an.outerHeight();if(av.offsetY<aw){$(this).insertBefore(an);var ax=o[an.attr("id")];ax.remove_drawable(ar);ax.container.add_drawable_before(ar,ax);return}else{if(av.offsetY>am){$(this).insertAfter(an);var ax=o[an.attr("id")];ax.remove_drawable(ar);ax.container.add_drawable(ar);return}}}an=null;for(aq=0;aq<ap.length;aq++){ao=$(ap.get(aq));aw=ao.position().top;am=aw+ao.outerHeight();if(ao.is(al)&&this!==ao.get(0)&&av.offsetY>=aw&&av.offsetY<=am){if(av.offsetY-aw<am-av.offsetY){ao.find(".content-div").prepend(this)}else{ao.find(".content-div").append(this)}if(ar.container){ar.container.remove_drawable(ar)}o[ao.attr("id")].add_drawable(ar);return}}for(aq=0;aq<ap.length;aq++){if(av.offsetY<$(ap.get(aq)).position().top){break}}if(aq===ap.length){if(this!==ap.get(aq-1)){ay.append(this);o[ay.attr("id")].move_drawable(ar,aq)}}else{if(this!==ap.get(aq)){$(this).insertBefore(ap.get(aq));o[ay.attr("id")].move_drawable(ar,(av.deltaY>0?aq-1:aq))}}}).bind("dragstart",function(){ai["border-top"]=ah.css("border-top");ai["border-bottom"]=ah.css("border-bottom");$(this).css({"border-top":"1px solid blue","border-bottom":"1px solid blue"})}).bind("dragend",function(){$(this).css(ai)})};aa.moveable=n;var af=16,I=9,F=20,V=I+2,B=100,K=12000,T=200,E=5,w=10,M=5000,x=100,p="There was an error in indexing this dataset. ",L="A converter for this dataset is not installed. Please check your datatypes_conf.xml file.",G="No data for this chrom/contig.",u="Currently indexing... please wait",z="Tool cannot be rerun: ",a="Loading data...",ab="Ready for display",d=10,v=5,D=5;function y(ah){return Math.round(ah*1000)/1000}var c=function(ah){this.num_elements=ah;this.clear()};q(c.prototype,{get:function(ai){var ah=this.key_ary.indexOf(ai);if(ah!==-1){if(this.obj_cache[ai].stale){this.key_ary.splice(ah,1);delete this.obj_cache[ai]}else{this.move_key_to_end(ai,ah)}}return this.obj_cache[ai]},set:function(ai,aj){if(!this.obj_cache[ai]){if(this.key_ary.length>=this.num_elements){var ah=this.key_ary.shift();delete this.obj_cache[ah]}this.key_ary.push(ai)}this.obj_cache[ai]=aj;return aj},move_key_to_end:function(ai,ah){this.key_ary.splice(ah,1);this.key_ary.push(ai)},clear:function(){this.obj_cache={};this.key_ary=[]},size:function(){return this.key_ary.length}});var U=function(ai,ah,aj){c.call(this,ai);this.track=ah;this.subset=(aj!==undefined?aj:true)};q(U.prototype,c.prototype,{load_data:function(aq,al,ao,ai,an){var ap=this.track.view.chrom,ak={chrom:ap,low:aq,high:al,mode:ao,resolution:ai,dataset_id:this.track.dataset_id,hda_ldda:this.track.hda_ldda};$.extend(ak,an);if(this.track.filters_manager){var ar=[];var ah=this.track.filters_manager.filters;for(var am=0;am<ah.length;am++){ar[ar.length]=ah[am].name}ak.filter_cols=JSON.stringify(ar)}var aj=this;return $.getJSON(this.track.data_url,ak,function(at){aj.set_data(aq,al,ao,at)})},get_data:function(ah,al,am,ai,ak){var aj=this.get_data_from_cache(ah,al,am);if(aj){return aj}aj=this.load_data(ah,al,am,ai,ak);this.set_data(ah,al,am,aj);return aj},DEEP_DATA_REQ:"deep",BROAD_DATA_REQ:"breadth",get_more_data:function(ap,ak,ao,aj,an,al){var aq=this.get_data_from_cache(ap,ak,ao);if(!aq){console.log("ERROR: no current data for: ",this.track,ap,ak,ao,aj,an);return}aq.stale=true;var ai=ap;if(al===this.DEEP_DATA_REQ){$.extend(an,{start_val:aq.data.length+1})}else{if(al===this.BROAD_DATA_REQ){ai=(aq.max_high?aq.max_high:aq.data[aq.data.length-1][2])+1}}var ah=this,am=this.load_data(ai,ak,ao,aj,an);new_data_available=$.Deferred();this.set_data(ap,ak,ao,new_data_available);$.when(am).then(function(ar){if(ar.data){ar.data=aq.data.concat(ar.data);if(ar.max_low){ar.max_low=aq.max_low}if(ar.message){ar.message=ar.message.replace(/[0-9]+/,ar.data.length)}}ah.set_data(ap,ak,ao,ar);new_data_available.resolve(ar)});return new_data_available},get_data_from_cache:function(ah,ai,aj){return this.get(this.gen_key(ah,ai,aj))},set_data:function(ai,aj,ak,ah){return this.set(this.gen_key(ai,aj,ak),ah)},gen_key:function(ah,aj,ak){var ai=ah+"_"+aj+"_"+ak;return ai},split_key:function(ah){return ah.split("_")}});var J=function(ai,ah,aj){U.call(this,ai,ah,aj)};q(J.prototype,U.prototype,c.prototype,{load_data:function(aj,ah,al,am,ai,ak){if(ai>1){return}return U.prototype.load_data.call(this,aj,ah,al,am,ai,ak)}});var r=function(ak,ai,ah,aj,al){this.name=ak;this.view=ai;this.container=ah;this.drag_handle_class=al;this.config=new H({track:this,params:[{key:"name",label:"Name",type:"text",default_value:ak}],saved_values:aj,onchange:function(){this.track.set_name(this.track.config.values.name)}});this.prefs=this.config.values};q(r.prototype,{init:function(){},request_draw:function(){},_draw:function(){},to_json:function(){},make_name_popup_menu:function(){},set_name:function(ah){this.old_name=this.name;this.name=ah;this.name_div.text(this.name)},revert_name:function(){this.name=this.old_name;this.name_div.text(this.name)},remove:function(){this.container.remove_drawable(this);this.container_div.fadeOut("slow",function(){$(this).remove();view.update_intro_div();view.has_changes=true})}});var A=function(al,ak,ai,ah,aj,am){r.call(this,ak,ai,ah,aj,am);this.obj_type=al;this.drawables=[]};q(A.prototype,r.prototype,{init:function(){for(var ah=0;ah<this.drawables.length;ah++){this.drawables[ah].init()}},_draw:function(){for(var ah=0;ah<this.drawables.length;ah++){this.drawables[ah]._draw()}},to_json:function(){var ai=[];for(var ah=0;ah<this.drawables.length;ah++){ai.push(this.drawables[ah].to_json())}return{name:this.name,prefs:this.prefs,obj_type:this.obj_type,drawables:ai}},add_drawable:function(ah){this.drawables.push(ah);ah.container=this},add_drawable_before:function(aj,ah){var ai=this.drawables.indexOf(ah);if(ai!=-1){this.drawables.splice(ai,0,aj);return true}return false},remove_drawable:function(ai){var ah=this.drawables.indexOf(ai);if(ah!=-1){this.drawables.splice(ah,1);ai.container=null;return true}return false},move_drawable:function(ai,aj){var ah=this.drawables.indexOf(ai);if(ah!=-1){this.drawables.splice(ah,1);this.drawables.splice(aj,0,ai);return true}return false}});var S=function(ak,ai,ah,aj){A.call(this,"DrawableGroup",ak,ai,ah,aj,"group-handle");if(!S.id_counter){S.id_counter=0}var al=S.id_counter++;this.container_div=$("<div/>").addClass("group").attr("id","group_"+al).appendTo(this.container.content_div);this.header_div=$("<div/>").addClass("track-header").appendTo(this.container_div);this.header_div.append($("<div/>").addClass(this.drag_handle_class));this.name_div=$("<div/>").addClass("group-name menubutton popup").text(this.name).appendTo(this.header_div);this.content_div=$("<div/>").addClass("content-div").attr("id","group_"+al+"_content_div").appendTo(this.container_div);m(this.container_div,this);m(this.content_div,this);n(this.container_div,this.drag_handle_class,".group",this);this.make_name_popup_menu()};q(S.prototype,r.prototype,A.prototype,{make_name_popup_menu:function(){var ai=this;var ah={};ah["Edit configuration"]=function(){var al=function(){hide_modal();$(window).unbind("keypress.check_enter_esc")},aj=function(){ai.config.update_from_form($(".dialog-box"));hide_modal();$(window).unbind("keypress.check_enter_esc")},ak=function(am){if((am.keyCode||am.which)===27){al()}else{if((am.keyCode||am.which)===13){aj()}}};$(window).bind("keypress.check_enter_esc",ak);show_modal("Configure Group",ai.config.build_form(),{Cancel:al,OK:aj})};ah.Remove=function(){ai.remove()};make_popupmenu(ai.name_div,ah)}});var ae=function(ah,ak,aj,ai){A.call(this,"View");this.container=ah;this.chrom=null;this.vis_id=aj;this.dbkey=ai;this.title=ak;this.tracks=this.drawables;this.label_tracks=[];this.tracks_to_be_redrawn=[];this.max_low=0;this.max_high=0;this.zoom_factor=3;this.min_separation=30;this.has_changes=false;this.load_chroms_deferred=null;this.init();this.canvas_manager=new ag(ah.get(0).ownerDocument);this.reset()};q(ae.prototype,A.prototype,{init:function(){var aj=this.container,ah=this;this.top_container=$("<div/>").addClass("top-container").appendTo(aj);this.browser_content_div=$("<div/>").addClass("content").css("position","relative").appendTo(aj);this.bottom_container=$("<div/>").addClass("bottom-container").appendTo(aj);this.top_labeltrack=$("<div/>").addClass("top-labeltrack").appendTo(this.top_container);this.viewport_container=$("<div/>").addClass("viewport-container").attr("id","viewport-container").appendTo(this.browser_content_div);this.content_div=this.viewport_container;m(this.viewport_container,ah);this.intro_div=$("<div/>").addClass("intro");var ak=$("<div/>").text("Add Datasets to Visualization").addClass("action-button").appendTo(this.intro_div).click(function(){add_tracks()});this.nav_labeltrack=$("<div/>").addClass("nav-labeltrack").appendTo(this.bottom_container);this.nav_container=$("<div/>").addClass("nav-container").prependTo(this.top_container);this.nav=$("<div/>").addClass("nav").appendTo(this.nav_container);this.overview=$("<div/>").addClass("overview").appendTo(this.bottom_container);this.overview_viewport=$("<div/>").addClass("overview-viewport").appendTo(this.overview);this.overview_close=$("<a href='javascript:void(0);'>Close Overview</a>").addClass("overview-close").hide().appendTo(this.overview_viewport);this.overview_highlight=$("<div/>").addClass("overview-highlight").hide().appendTo(this.overview_viewport);this.overview_box_background=$("<div/>").addClass("overview-boxback").appendTo(this.overview_viewport);this.overview_box=$("<div/>").addClass("overview-box").appendTo(this.overview_viewport);this.default_overview_height=this.overview_box.height();this.nav_controls=$("<div/>").addClass("nav-controls").appendTo(this.nav);this.chrom_select=$("<select/>").attr({name:"chrom"}).css("width","15em").addClass("no-autocomplete").append("<option value=''>Loading</option>").appendTo(this.nav_controls);var ai=function(al){if(al.type==="focusout"||(al.keyCode||al.which)===13||(al.keyCode||al.which)===27){if((al.keyCode||al.which)!==27){ah.go_to($(this).val())}$(this).hide();$(this).val("");ah.location_span.show();ah.chrom_select.show()}};this.nav_input=$("<input/>").addClass("nav-input").hide().bind("keyup focusout",ai).appendTo(this.nav_controls);this.location_span=$("<span/>").addClass("location").appendTo(this.nav_controls);this.location_span.click(function(){ah.location_span.hide();ah.chrom_select.hide();ah.nav_input.val(ah.chrom+":"+ah.low+"-"+ah.high);ah.nav_input.css("display","inline-block");ah.nav_input.select();ah.nav_input.focus()});if(this.vis_id!==undefined){this.hidden_input=$("<input/>").attr("type","hidden").val(this.vis_id).appendTo(this.nav_controls)}this.zo_link=$("<a id='zoom-out' />").click(function(){ah.zoom_out();ah.request_redraw()}).appendTo(this.nav_controls);this.zi_link=$("<a id='zoom-in' />").click(function(){ah.zoom_in();ah.request_redraw()}).appendTo(this.nav_controls);this.load_chroms_deferred=this.load_chroms({low:0});this.chrom_select.bind("change",function(){ah.change_chrom(ah.chrom_select.val())});this.browser_content_div.click(function(al){$(this).find("input").trigger("blur")});this.browser_content_div.bind("dblclick",function(al){ah.zoom_in(al.pageX,this.viewport_container)});this.overview_box.bind("dragstart",function(al,am){this.current_x=am.offsetX}).bind("drag",function(al,an){var ao=an.offsetX-this.current_x;this.current_x=an.offsetX;var am=Math.round(ao/ah.viewport_container.width()*(ah.max_high-ah.max_low));ah.move_delta(-am)});this.overview_close.click(function(){ah.reset_overview()});this.viewport_container.bind("draginit",function(al,am){if(al.clientX>ah.viewport_container.width()-16){return false}}).bind("dragstart",function(al,am){am.original_low=ah.low;am.current_height=al.clientY;am.current_x=am.offsetX}).bind("drag",function(an,ap){var al=$(this);var aq=ap.offsetX-ap.current_x;var am=al.scrollTop()-(an.clientY-ap.current_height);al.scrollTop(am);ap.current_height=an.clientY;ap.current_x=ap.offsetX;var ao=Math.round(aq/ah.viewport_container.width()*(ah.high-ah.low));ah.move_delta(ao)}).bind("mousewheel",function(an,ap,am,al){if(am){var ao=Math.round(-am/ah.viewport_container.width()*(ah.high-ah.low));ah.move_delta(ao)}});this.top_labeltrack.bind("dragstart",function(al,am){return $("<div />").css({height:ah.browser_content_div.height()+ah.top_labeltrack.height()+ah.nav_labeltrack.height()+1,top:"0px",position:"absolute","background-color":"#ccf",opacity:0.5,"z-index":1000}).appendTo($(this))}).bind("drag",function(ap,aq){$(aq.proxy).css({left:Math.min(ap.pageX,aq.startX),width:Math.abs(ap.pageX-aq.startX)});var am=Math.min(ap.pageX,aq.startX)-ah.container.offset().left,al=Math.max(ap.pageX,aq.startX)-ah.container.offset().left,ao=(ah.high-ah.low),an=ah.viewport_container.width();ah.update_location(Math.round(am/an*ao)+ah.low,Math.round(al/an*ao)+ah.low)}).bind("dragend",function(aq,ar){var am=Math.min(aq.pageX,ar.startX),al=Math.max(aq.pageX,ar.startX),ao=(ah.high-ah.low),an=ah.viewport_container.width(),ap=ah.low;ah.low=Math.round(am/an*ao)+ap;ah.high=Math.round(al/an*ao)+ap;$(ar.proxy).remove();ah.request_redraw()});this.add_label_track(new ad(this,{content_div:this.top_labeltrack}));this.add_label_track(new ad(this,{content_div:this.nav_labeltrack}));$(window).bind("resize",function(){ah.resize_window()});$(document).bind("redraw",function(){ah.redraw()});this.reset();$(window).trigger("resize");this.update_intro_div()},update_intro_div:function(){if(this.num_tracks===0){this.intro_div.appendTo(this.viewport_container)}else{this.intro_div.remove()}},update_location:function(ah,ai){this.location_span.text(commatize(ah)+" - "+commatize(ai));this.nav_input.val(this.chrom+":"+commatize(ah)+"-"+commatize(ai))},load_chroms:function(aj){aj.num=x;$.extend(aj,(this.vis_id!==undefined?{vis_id:this.vis_id}:{dbkey:this.dbkey}));var ah=this,ai=$.Deferred();$.ajax({url:chrom_url,data:aj,dataType:"json",success:function(al){if(al.chrom_info.length===0){alert("Invalid chromosome: "+aj.chrom);return}if(al.reference){ah.add_label_track(new C(ah))}ah.chrom_data=al.chrom_info;var ao='<option value="">Select Chrom/Contig</option>';for(var an=0,ak=ah.chrom_data.length;an<ak;an++){var am=ah.chrom_data[an].chrom;ao+='<option value="'+am+'">'+am+"</option>"}if(al.prev_chroms){ao+='<option value="previous">Previous '+x+"</option>"}if(al.next_chroms){ao+='<option value="next">Next '+x+"</option>"}ah.chrom_select.html(ao);ah.chrom_start_index=al.start_index;ai.resolve(al)},error:function(){alert("Could not load chroms for this dbkey:",ah.dbkey)}});return ai},change_chrom:function(al,ai,an){if(!al||al==="None"){return}var ak=this;if(al==="previous"){ak.load_chroms({low:this.chrom_start_index-x});return}if(al==="next"){ak.load_chroms({low:this.chrom_start_index+x});return}var am=$.grep(ak.chrom_data,function(ap,aq){return ap.chrom===al})[0];if(am===undefined){ak.load_chroms({chrom:al},function(){ak.change_chrom(al,ai,an)});return}else{if(al!==ak.chrom){ak.chrom=al;ak.chrom_select.val(ak.chrom);ak.max_high=am.len-1;ak.reset();ak.request_redraw(true);for(var ao=0,ah=ak.tracks.length;ao<ah;ao++){var aj=ak.tracks[ao];if(aj.init){aj.init()}}}if(ai!==undefined&&an!==undefined){ak.low=Math.max(ai,0);ak.high=Math.min(an,ak.max_high)}ak.reset_overview();ak.request_redraw()}},go_to:function(al){var ap=this,ah,ak,ai=al.split(":"),an=ai[0],ao=ai[1];if(ao!==undefined){try{var am=ao.split("-");ah=parseInt(am[0].replace(/,/g,""),10);ak=parseInt(am[1].replace(/,/g,""),10)}catch(aj){return false}}ap.change_chrom(an,ah,ak)},move_fraction:function(aj){var ah=this;var ai=ah.high-ah.low;this.move_delta(aj*ai)},move_delta:function(aj){var ah=this;var ai=ah.high-ah.low;if(ah.low-aj<ah.max_low){ah.low=ah.max_low;ah.high=ah.max_low+ai}else{if(ah.high-aj>ah.max_high){ah.high=ah.max_high;ah.low=ah.max_high-ai}else{ah.high-=aj;ah.low-=aj}}ah.request_redraw()},add_drawable:function(ah){A.prototype.add_drawable.call(this,ah);ah.init();this.has_changes=true;this.update_intro_div()},add_label_track:function(ah){ah.view=this;ah.init();this.label_tracks.push(ah)},remove_drawable:function(aj,ai){A.prototype.remove_drawable.call(this,aj);if(ai){var ah=this;aj.container_div.fadeOut("slow",function(){$(this).remove();ah.update_intro_div()});this.has_changes=true}},reset:function(){this.low=this.max_low;this.high=this.max_high;this.viewport_container.find(".yaxislabel").remove()},request_redraw:function(ap,ah,ao,ai){var an=this,al=(ai?[ai]:an.tracks),aj;var ai;for(var am=0;am<al.length;am++){ai=al[am];aj=-1;for(var ak=0;ak<an.tracks_to_be_redrawn.length;ak++){if(an.tracks_to_be_redrawn[ak][0]===ai){aj=ak;break}}if(aj<0){an.tracks_to_be_redrawn.push([ai,ah,ao])}else{an.tracks_to_be_redrawn[am][1]=ah;an.tracks_to_be_redrawn[am][2]=ao}}requestAnimationFrame(function(){an._redraw(ap)})},_redraw:function(ar){var ao=this.low,ak=this.high;if(ao<this.max_low){ao=this.max_low}if(ak>this.max_high){ak=this.max_high}var aq=this.high-this.low;if(this.high!==0&&aq<this.min_separation){ak=ao+this.min_separation}this.low=Math.floor(ao);this.high=Math.ceil(ak);this.resolution=Math.pow(E,Math.ceil(Math.log((this.high-this.low)/T)/Math.log(E)));this.zoom_res=Math.pow(w,Math.max(0,Math.ceil(Math.log(this.resolution,w)/Math.log(w))));var ah=(this.low/(this.max_high-this.max_low)*this.overview_viewport.width())||0;var an=((this.high-this.low)/(this.max_high-this.max_low)*this.overview_viewport.width())||0;var at=13;this.overview_box.css({left:ah,width:Math.max(at,an)}).show();if(an<at){this.overview_box.css("left",ah-(at-an)/2)}if(this.overview_highlight){this.overview_highlight.css({left:ah,width:an})}this.update_location(this.low,this.high);if(!ar){var aj,ai,ap;for(var al=0,am=this.tracks_to_be_redrawn.length;al<am;al++){aj=this.tracks_to_be_redrawn[al][0];ai=this.tracks_to_be_redrawn[al][1];ap=this.tracks_to_be_redrawn[al][2];if(aj){aj._draw(ai,ap)}}this.tracks_to_be_redrawn=[];for(al=0,am=this.label_tracks.length;al<am;al++){this.label_tracks[al]._draw()}}},zoom_in:function(ai,aj){if(this.max_high===0||this.high-this.low<this.min_separation){return}var ak=this.high-this.low,al=ak/2+this.low,ah=(ak/this.zoom_factor)/2;if(ai){al=ai/this.viewport_container.width()*(this.high-this.low)+this.low}this.low=Math.round(al-ah);this.high=Math.round(al+ah);this.request_redraw()},zoom_out:function(){if(this.max_high===0){return}var ai=this.high-this.low,aj=ai/2+this.low,ah=(ai*this.zoom_factor)/2;this.low=Math.round(aj-ah);this.high=Math.round(aj+ah);this.request_redraw()},resize_window:function(){this.viewport_container.height(this.container.height()-this.top_container.height()-this.bottom_container.height());this.nav_container.width(this.container.width());this.request_redraw()},set_overview:function(ah){$.when(ah.get_overview_tile()).then(function(ai){view.overview_viewport.find(".track-tile").remove();view.overview_close.show();view.overview_viewport.append(ai.canvas);view.overview_highlight.show().height(ai.canvas.height());view.overview_viewport.height(ai.canvas.height()+view.overview_box.outerHeight());view.resize_window();if(view.overview_track){view.overview_track.set_is_overview(false)}view.overview_track=ah;ah.set_is_overview(true)});view.has_changes=true},reset_overview:function(){this.overview_viewport.find(".track-tile").remove();this.overview_viewport.height(this.default_overview_height);this.overview_box.height(this.default_overview_height);this.overview_close.hide();this.overview_highlight.hide();view.resize_window();if(view.overview_track){view.overview_track.set_is_overview(false)}view.overview_track=null}});var s=function(aj,an){this.track=aj;this.name=an.name;this.params=[];var av=an.params;for(var ak=0;ak<av.length;ak++){var ap=av[ak],ai=ap.name,au=ap.label,al=unescape(ap.html),aw=ap.value,ar=ap.type;if(ar==="number"){this.params[this.params.length]=new g(ai,au,al,aw,ap.min,ap.max)}else{if(ar=="select"){this.params[this.params.length]=new P(ai,au,al,aw)}else{console.log("WARNING: unrecognized tool parameter type:",ai,ar)}}}this.parent_div=$("<div/>").addClass("dynamic-tool").hide();this.parent_div.bind("drag",function(ay){ay.stopPropagation()}).click(function(ay){ay.stopPropagation()}).bind("dblclick",function(ay){ay.stopPropagation()});var at=$("<div class='tool-name'>").appendTo(this.parent_div).text(this.name);var aq=this.params;var ao=this;$.each(this.params,function(az,aC){var aB=$("<div>").addClass("param-row").appendTo(ao.parent_div);var ay=$("<div>").addClass("param-label").text(aC.label).appendTo(aB);var aA=$("<div/>").addClass("slider").html(aC.html).appendTo(aB);aA.find(":input").val(aC.value);$("<div style='clear: both;'/>").appendTo(aB)});this.parent_div.find("input").click(function(){$(this).select()});var ax=$("<div>").addClass("param-row").appendTo(this.parent_div);var am=$("<input type='submit'>").attr("value","Run on complete dataset").appendTo(ax);var ah=$("<input type='submit'>").attr("value","Run on visible region").css("margin-left","3em").appendTo(ax);var ao=this;ah.click(function(){ao.run_on_region()});am.click(function(){ao.run_on_dataset()})};q(s.prototype,{get_param_values_dict:function(){var ah={};this.parent_div.find(":input").each(function(){var ai=$(this).attr("name"),aj=$(this).val();ah[ai]=JSON.stringify(aj)});return ah},get_param_values:function(){var ai=[];var ah={};this.parent_div.find(":input").each(function(){var aj=$(this).attr("name"),ak=$(this).val();if(aj){ai[ai.length]=ak}});return ai},run_on_dataset:function(){var ah=this;ah.run({dataset_id:this.track.original_dataset_id,tool_id:ah.name},null,function(ai){show_modal(ah.name+" is Running",ah.name+" is running on the complete dataset. Tool outputs are in dataset's history.",{Close:hide_modal})})},run_on_region:function(){var ai={dataset_id:this.track.original_dataset_id,chrom:this.track.view.chrom,low:this.track.view.low,high:this.track.view.high,tool_id:this.name},al=this.track,aj=ai.tool_id+al.tool_region_and_parameters_str(ai.chrom,ai.low,ai.high),ah,am;if(al.container===view){var ak=new S(this.name,this.track.view,this.track.container);al.container.add_drawable(ak);al.container.remove_drawable(al);ak.add_drawable(al);al.container_div.appendTo(ak.content_div);ah=ak}else{ah=al.container}if(al instanceof e){am=new X(aj,view,ah,"hda");am.change_mode(al.mode);ah.add_drawable(am)}am.content_div.text("Starting job.");this.run(ai,am,function(an){am.dataset_id=an.dataset_id;am.content_div.text("Running job.");am.init()})},run:function(ai,aj,ak){$.extend(ai,this.get_param_values_dict());var ah=function(){$.getJSON(rerun_tool_url,ai,function(al){if(al==="no converter"){aj.container_div.addClass("error");aj.content_div.text(L)}else{if(al.error){aj.container_div.addClass("error");aj.content_div.text(z+al.message)}else{if(al==="pending"){aj.container_div.addClass("pending");aj.content_div.text("Converting input data so that it can be used quickly with tool.");setTimeout(ah,2000)}else{ak(al)}}}})};ah()}});var P=function(ai,ah,aj,ak){this.name=ai;this.label=ah;this.html=aj;this.value=ak};var g=function(aj,ai,al,am,ak,ah){P.call(this,aj,ai,al,am);this.min=ak;this.max=ah};var h=function(ai,ah,aj,ak){this.name=ai;this.index=ah;this.tool_id=aj;this.tool_exp_name=ak};var Y=function(ai,ah,aj,ak){h.call(this,ai,ah,aj,ak);this.low=-Number.MAX_VALUE;this.high=Number.MAX_VALUE;this.min=Number.MAX_VALUE;this.max=-Number.MAX_VALUE;this.container=null;this.slider=null;this.slider_label=null};q(Y.prototype,{applies_to:function(ah){if(ah.length>this.index){return true}return false},keep:function(ah){if(!this.applies_to(ah)){return true}var ai=parseFloat(ah[this.index]);return(isNaN(ai)||(ai>=this.low&&ai<=this.high))},update_attrs:function(ai){var ah=false;if(!this.applies_to(ai)){return ah}if(ai[this.index]<this.min){this.min=Math.floor(ai[this.index]);ah=true}if(ai[this.index]>this.max){this.max=Math.ceil(ai[this.index]);ah=true}return ah},update_ui_elt:function(){if(this.min!=this.max){this.container.show()}else{this.container.hide()}var aj=function(am,ak){var al=ak-am;return(al<=2?0.01:1)};var ai=this.slider.slider("option","min"),ah=this.slider.slider("option","max");if(this.min<ai||this.max>ah){this.slider.slider("option","min",this.min);this.slider.slider("option","max",this.max);this.slider.slider("option","step",aj(this.min,this.max));this.slider.slider("option","values",[this.min,this.max])}}});var ac=function(ar,ay){this.track=ar;this.filters=[];for(var at=0;at<ay.length;at++){var au=ay[at],az=au.name,ah=au.type,ak=au.index,ax=au.tool_id,aw=au.tool_exp_name;if(ah==="int"||ah==="float"){this.filters[at]=new Y(az,ak,ax,aw)}else{console.log("ERROR: unsupported filter: ",az,ah)}}var al=function(aA,aB,aC){aA.click(function(){var aD=aB.text();max=parseFloat(aC.slider("option","max")),input_size=(max<=1?4:max<=1000000?max.toString().length:6),multi_value=false;if(aC.slider("option","values")){input_size=2*input_size+1;multi_value=true}aB.text("");$("<input type='text'/>").attr("size",input_size).attr("maxlength",input_size).attr("value",aD).appendTo(aB).focus().select().click(function(aE){aE.stopPropagation()}).blur(function(){$(this).remove();aB.text(aD)}).keyup(function(aI){if(aI.keyCode===27){$(this).trigger("blur")}else{if(aI.keyCode===13){var aG=aC.slider("option","min"),aE=aC.slider("option","max"),aH=function(aJ){return(isNaN(aJ)||aJ>aE||aJ<aG)},aF=$(this).val();if(!multi_value){aF=parseFloat(aF);if(aH(aF)){alert("Parameter value must be in the range ["+aG+"-"+aE+"]");return $(this)}}else{aF=aF.split("-");aF=[parseFloat(aF[0]),parseFloat(aF[1])];if(aH(aF[0])||aH(aF[1])){alert("Parameter value must be in the range ["+aG+"-"+aE+"]");return $(this)}}aC.slider((multi_value?"values":"value"),aF)}}})})};this.parent_div=$("<div/>").addClass("filters").hide();this.parent_div.bind("drag",function(aA){aA.stopPropagation()}).click(function(aA){aA.stopPropagation()}).bind("dblclick",function(aA){aA.stopPropagation()}).bind("keydown",function(aA){aA.stopPropagation()});var av=$("<div/>").addClass("sliders").appendTo(this.parent_div);var ap=this;$.each(this.filters,function(aD,aF){aF.container=$("<div/>").addClass("slider-row").appendTo(av);var aE=$("<div/>").addClass("elt-label").appendTo(aF.container);var aC=$("<span/>").addClass("slider-name").text(aF.name+"  ").appendTo(aE);var aB=$("<span/>");var aH=$("<span/>").addClass("slider-value").appendTo(aE).append("[").append(aB).append("]");var aA=$("<div/>").addClass("slider").appendTo(aF.container);aF.control_element=$("<div/>").attr("id",aF.name+"-filter-control").appendTo(aA);var aG=[0,0];aF.control_element.slider({range:true,min:Number.MAX_VALUE,max:-Number.MIN_VALUE,values:[0,0],slide:function(aJ,aK){var aI=aK.values;aB.text(aI[0]+"-"+aI[1]);aF.low=aI[0];aF.high=aI[1];ap.track.request_draw(true,true)},change:function(aI,aJ){aF.control_element.slider("option","slide").call(aF.control_element,aI,aJ)}});aF.slider=aF.control_element;aF.slider_label=aB;al(aH,aB,aF.control_element);$("<div style='clear: both;'/>").appendTo(aF.container)});if(this.filters.length!==0){var am=$("<div/>").addClass("param-row").appendTo(av);var ao=$("<input type='submit'/>").attr("value","Run on complete dataset").appendTo(am);var aj=this;ao.click(function(){aj.run_on_dataset()})}var aq=$("<div/>").addClass("display-controls").appendTo(this.parent_div),an=$("<span/>").addClass("elt-label").text("Transparency:").appendTo(aq),ai=$("<select/>").attr("name","alpha_dropdown").appendTo(aq);this.alpha_filter=null;$("<option/>").attr("value",-1).text("== None ==").appendTo(ai);for(var at=0;at<this.filters.length;at++){$("<option/>").attr("value",at).text(this.filters[at].name).appendTo(ai)}ai.change(function(){$(this).children("option:selected").each(function(){var aA=parseInt($(this).val());ap.alpha_filter=(aA>=0?ap.filters[aA]:null);ap.track.request_draw(true,true)})});$("<div style='clear: both;'/>").appendTo(this.parent_div)};q(ac.prototype,{reset_filters:function(){for(var ah=0;ah<this.filters.length;ah++){filter=this.filters[ah];filter.slider.slider("option","values",[filter.min,filter.max])}this.alpha_filter=null},run_on_dataset:function(){var ap=function(au,ar,at){if(!(ar in au)){au[ar]=at}return au[ar]};var aj={},ah,ai,ak;for(var al=0;al<this.filters.length;al++){ah=this.filters[al];if(ah.tool_id){if(ah.min!=ah.low){ai=ap(aj,ah.tool_id,[]);ai[ai.length]=ah.tool_exp_name+" >= "+ah.low}if(ah.max!=ah.high){ai=ap(aj,ah.tool_id,[]);ai[ai.length]=ah.tool_exp_name+" <= "+ah.high}}}var an=[];for(var aq in aj){an[an.length]=[aq,aj[aq]]}var ao=an.length;(function am(ay,av){var at=av[0],au=at[0],ax=at[1],aw="("+ax.join(") and (")+")",ar={cond:aw,input:ay,target_dataset_id:ay,tool_id:au},av=av.slice(1);$.getJSON(run_tool_url,ar,function(az){if(az.error){show_modal("Filter Dataset","Error running tool "+au,{Close:hide_modal})}else{if(av.length===0){show_modal("Filtering Dataset","Filter(s) are running on the complete dataset. Outputs are in dataset's history.",{Close:hide_modal})}else{am(az.dataset_id,av)}}})})(this.track.dataset_id,an)}});var Q=function(ai,ah){N.AlphaGenerator.call(this,ah);this.filter=ai};Q.prototype.gen_alpha=function(ah){if(this.filter.high===Number.MAX_VALUE||this.filter.low===-Number.MAX_VALUE||this.filter.low===this.filter.high){return this.default_alpha}return((parseFloat(ah[this.filter.index])-this.filter.low)/(this.filter.high-this.filter.low))};var H=function(ah){this.track=ah.track;this.params=ah.params;this.values={};this.restore_values((ah.saved_values?ah.saved_values:{}));this.onchange=ah.onchange};q(H.prototype,{restore_values:function(ah){var ai=this;$.each(this.params,function(aj,ak){if(ah[ak.key]!==undefined){ai.values[ak.key]=ah[ak.key]}else{ai.values[ak.key]=ak.default_value}})},build_form:function(){var ai=this;var ah=$("<div />");$.each(this.params,function(am,ak){if(!ak.hidden){var aj="param_"+am;var ao=ai.values[ak.key];var ar=$("<div class='form-row' />").appendTo(ah);ar.append($("<label />").attr("for",aj).text(ak.label+":"));if(ak.type==="bool"){ar.append($('<input type="checkbox" />').attr("id",aj).attr("name",aj).attr("checked",ao))}else{if(ak.type==="text"){ar.append($('<input type="text"/>').attr("id",aj).val(ao).click(function(){$(this).select()}))}else{if(ak.type==="color"){var an=$("<input />").attr("id",aj).attr("name",aj).val(ao);var ap=$("<div class='tipsy tipsy-north' style='position: absolute;' />").hide();var al=$("<div style='background-color: black; padding: 10px;'></div>").appendTo(ap);var aq=$("<div/>").appendTo(al).farbtastic({width:100,height:100,callback:an,color:ao});$("<div />").append(an).append(ap).appendTo(ar).bind("click",function(at){ap.css({left:$(this).position().left+($(an).width()/2)-60,top:$(this).position().top+$(this.height)}).show();$(document).bind("click.color-picker",function(){ap.hide();$(document).unbind("click.color-picker")});at.stopPropagation()})}else{ar.append($("<input />").attr("id",aj).attr("name",aj).val(ao))}}}}});return ah},update_from_form:function(ah){var aj=this;var ai=false;$.each(this.params,function(ak,am){if(!am.hidden){var an="param_"+ak;var al=ah.find("#"+an).val();if(am.type==="float"){al=parseFloat(al)}else{if(am.type==="int"){al=parseInt(al)}else{if(am.type==="bool"){al=ah.find("#"+an).is(":checked")}}}if(al!==aj.values[am.key]){aj.values[am.key]=al;ai=true}}});if(ai){this.onchange()}}});var b=function(aj,ai,ah,ak){this.index=aj;this.low=aj*T*ai;this.high=(aj+1)*T*ai;this.resolution=ai;this.canvas=$("<div class='track-tile'/>").append(ah);this.data=ak;this.stale=false};var l=function(aj,ai,ah,ak,al){b.call(this,aj,ai,ah,ak);this.max_val=al};var R=function(ak,aj,ai,an,am,ah){b.call(this,ak,aj,ai,an);this.message=am;this.feature_mapper=ah;var al=this;$(this.canvas).mousemove(function(ap){var ao=al.feature_mapper.get_feature_data(ap.offsetX,ap.offsetY)})};var j=function(ak,ai,ah,aj,al,am){r.call(this,ak,ai,ah,{},"draghandle");this.data_url=(al?al:default_data_url);this.data_url_extra_params={};this.data_query_wait=(am?am:M);this.dataset_check_url=converted_datasets_state_url;if(!j.id_counter){j.id_counter=0}this.container_div=$("<div />").addClass("track").attr("id","track_"+j.id_counter++).css("position","relative");if(!this.hidden){this.header_div=$("<div class='track-header' />").appendTo(this.container_div);if(this.view.editor){this.drag_div=$("<div/>").addClass(this.drag_handle_class).appendTo(this.header_div)}this.name_div=$("<div class='menubutton popup' />").appendTo(this.header_div);this.name_div.text(this.name);this.name_div.attr("id",this.name.replace(/\s+/g,"-").replace(/[^a-zA-Z0-9\-]/g,"").toLowerCase())}this.content_div=$("<div class='track-content'>").appendTo(this.container_div);this.container.content_div.append(this.container_div)};q(j.prototype,r.prototype,{get_type:function(){if(this instanceof ad){return"LabelTrack"}else{if(this instanceof C){return"ReferenceTrack"}else{if(this instanceof k){return"LineTrack"}else{if(this instanceof Z){return"ReadTrack"}else{if(this instanceof X){return"ToolDataFeatureTrack"}else{if(this instanceof W){return"VcfTrack"}else{if(this instanceof e){return"FeatureTrack"}}}}}}}return""},init:function(){var ah=this;ah.enabled=false;ah.tile_cache.clear();ah.data_manager.clear();ah.initial_canvas=undefined;ah.content_div.css("height","auto");ah.container_div.removeClass("nodata error pending");if(!ah.dataset_id){return}$.getJSON(converted_datasets_state_url,{hda_ldda:ah.hda_ldda,dataset_id:ah.dataset_id,chrom:ah.view.chrom},function(ai){if(!ai||ai==="error"||ai.kind==="error"){ah.container_div.addClass("error");ah.content_div.text(p);if(ai.message){var ak=ah.view.tracks.indexOf(ah);var aj=$(" <a href='javascript:void(0);'></a>").text("View error").click(function(){show_modal("Trackster Error","<pre>"+ai.message+"</pre>",{Close:hide_modal})});ah.content_div.append(aj)}}else{if(ai==="no converter"){ah.container_div.addClass("error");ah.content_div.text(L)}else{if(ai==="no data"||(ai.data!==undefined&&(ai.data===null||ai.data.length===0))){ah.container_div.addClass("nodata");ah.content_div.text(G)}else{if(ai==="pending"){ah.container_div.addClass("pending");ah.content_div.text(u);setTimeout(function(){ah.init()},ah.data_query_wait)}else{if(ai.status==="data"){if(ai.valid_chroms){ah.valid_chroms=ai.valid_chroms;ah.make_name_popup_menu()}ah.content_div.text(ab);if(ah.view.chrom){ah.content_div.text("");ah.content_div.css("height",ah.height_px+"px");ah.enabled=true;$.when(ah.predraw_init()).done(function(){ah.container_div.removeClass("nodata error pending");ah.request_draw()})}}}}}}})},predraw_init:function(){},});var O=function(ao,am){var ai=this,ap=ai.view;n(ai.container_div,ai.drag_handle_class,".group",ai);this.filters_manager=new ac(this,(ao!==undefined?ao:{}));this.filters_available=false;this.filters_visible=false;this.tool=(am!==undefined&&obj_length(am)>0?new s(this,am):undefined);this.is_overview=false;if(ai.hidden){return}if(this.filters_manager){this.filters_div=this.filters_manager.parent_div;this.header_div.after(this.filters_div)}if(this.tool){this.dynamic_tool_div=this.tool.parent_div;this.header_div.after(this.dynamic_tool_div)}if(ai.display_modes!==undefined){if(ai.mode_div===undefined){ai.mode_div=$("<div class='right-float menubutton popup' />").appendTo(ai.header_div);var aj=(ai.config&&ai.config.values.mode?ai.config.values.mode:ai.display_modes[0]);ai.mode=aj;ai.mode_div.text(aj);var ah={};for(var ak=0,an=ai.display_modes.length;ak<an;ak++){var al=ai.display_modes[ak];ah[al]=function(aq){return function(){ai.change_mode(aq)}}(al)}make_popupmenu(ai.mode_div,ah)}else{ai.mode_div.hide()}}this.make_name_popup_menu()};q(O.prototype,r.prototype,j.prototype,{to_json:function(){return{track_type:this.get_type(),name:this.name,hda_ldda:this.hda_ldda,dataset_id:this.dataset_id,prefs:this.prefs,mode:this.mode,}},change_mode:function(ai){var ah=this;ah.mode_div.text(ai);ah.mode=ai;ah.config.values.mode=ai;ah.tile_cache.clear();ah.request_draw();return ah},make_name_popup_menu:function(){var ai=this;var ah={};ah[(this.is_overview?"Hide overview":"Set as overview")]=function(){if(ai.is_overview){ai.view.reset_overview()}else{ai.view.set_overview(ai)}};ah["Edit configuration"]=function(){var am=function(){hide_modal();$(window).unbind("keypress.check_enter_esc")},ak=function(){ai.config.update_from_form($(".dialog-box"));hide_modal();$(window).unbind("keypress.check_enter_esc")},al=function(an){if((an.keyCode||an.which)===27){am()}else{if((an.keyCode||an.which)===13){ak()}}};$(window).bind("keypress.check_enter_esc",al);show_modal("Configure Track",ai.config.build_form(),{Cancel:am,OK:ak})};if(ai.filters_available>0){var aj=(ai.filters_div.is(":visible")?"Hide filters":"Show filters");ah[aj]=function(){ai.filters_visible=(ai.filters_div.is(":visible"));if(ai.filters_visible){ai.filters_manager.reset_filters()}ai.filters_div.toggle();ai.make_name_popup_menu()}}if(ai.tool){var aj=(ai.dynamic_tool_div.is(":visible")?"Hide tool":"Show tool");ah[aj]=function(){if(!ai.dynamic_tool_div.is(":visible")){ai.set_name(ai.name+ai.tool_region_and_parameters_str())}else{menu_option_text="Show dynamic tool";ai.revert_name()}ai.dynamic_tool_div.toggle();ai.make_name_popup_menu()}}ah.Remove=function(){ai.remove()};make_popupmenu(ai.name_div,ah)},set_is_overview:function(ah){this.is_overview=ah;this.make_name_popup_menu()},get_overview_tile:function(){var ah=this;view=ah.view,resolution=Math.pow(E,Math.ceil(Math.log((view.max_high-view.max_low)/T)/Math.log(E))),view_width=view.container.width(),w_scale=view_width/(view.max_high-view.max_low),overview_tile=$.Deferred();$.when(ah.data_manager.get_data(view.max_low,view.max_high,"Auto",resolution,ah.data_url_extra_params)).then(function(ai){var ak=ah._gen_tile_cache_key(view_width,w_scale,0),am=ah.tile_cache.get(ak);if(!am){am=ah.draw_tile(ai,"Auto",resolution,0,w_scale);ah.tile_cache.set(ak,am)}var ap=$(am.canvas.find("canvas")),al=ap.clone(),ao=ap.get(0).getContext("2d"),aj=al.get(0).getContext("2d"),an=ao.getImageData(0,0,ao.canvas.width,ao.canvas.height);aj.putImageData(an,-ah.left_offset,(am.data.dataset_type==="summary_tree"?V:0));new_tile=new b(-1,resolution,al);overview_tile.resolve(new_tile)});return overview_tile},_gen_tile_cache_key:function(ai,aj,ah){return ai+"_"+aj+"_"+ah},request_draw:function(ai,ah){this.view.request_redraw(false,ai,ah,this)},_draw:function(aj,ar){if(!this.enabled){return}if(!(this instanceof C)&&(!this.dataset_id)){return}var aq=this.view.low,an=this.view.high,ao=an-aq,ak=this.view.container.width(),av=ak/ao,am=this.view.resolution,au=$("<div style='position: relative;'></div>");if(!ar){this.content_div.children().remove()}this.content_div.append(au);this.max_height=0;var ai=Math.floor(aq/am/T);var ap=true;var at=[];var ah=0;while((ai*T*am)<an){tile=this.draw_helper(aj,ak,ai,am,au,av);if(tile){at.push(tile)}else{ap=false}ai+=1;ah++}var al=this;if(ap){al.postdraw_actions(at,ak,av,ar)}},postdraw_actions:function(al,am,an,ah){var aj=this;var ak=false;for(var ai=0;ai<al.length;ai++){if(al[ai].message){ak=true;break}}if(ak){for(var ai=0;ai<al.length;ai++){tile=al[ai];if(!tile.message){tile.canvas.css("padding-top",F)}}}},draw_helper:function(ai,aj,ak,an,au,ay,av,ao){var al=this,at=this._gen_tile_cache_key(aj,ay,ak),ap=ak*T*an,ax=ap+T*an;var aq=(ai?undefined:al.tile_cache.get(at));if(aq){al.show_tile(aq,au,ay);return aq}var ar=function(az){return("isResolved" in az)};var am=true;var ah=al.data_manager.get_data(ap,ax,al.mode,an,al.data_url_extra_params);if(ar(ah)){am=false}var aw;if(view.reference_track&&ay>view.canvas_manager.char_width_px){aw=view.reference_track.data_manager.get_data(ap,ax,al.mode,an,view.reference_track.data_url_extra_params);if(ar(aw)){am=false}}if(am){q(ah,ao);var aq=al.draw_tile(ah,al.mode,an,ak,ay,aw);if(aq!==undefined){al.tile_cache.set(at,aq);al.show_tile(aq,au,ay)}return aq}$.when(ah,aw).then(function(){view.request_redraw()});return null},show_tile:function(ao,aq,ar){var aj=this,ai=ao.canvas,an=ai;if(ao.message){var at=$("<div/>"),ap=$("<div/>").addClass("tile-message").text(ao.message).css({height:F-1,width:ao.canvas.width}).appendTo(at),al=$("<a href='javascript:void(0);'/>").addClass("icon more-down").appendTo(ap),ah=$("<a href='javascript:void(0);'/>").addClass("icon more-across").appendTo(ap);at.append(ai);an=at;al.click(function(){ao.stale=true;aj.data_manager.get_more_data(ao.low,ao.high,aj.mode,ao.resolution,{},aj.data_manager.DEEP_DATA_REQ);aj.request_draw()}).dblclick(function(au){au.stopPropagation()});ah.click(function(){ao.stale=true;aj.data_manager.get_more_data(ao.low,ao.high,aj.mode,ao.resolution,{},aj.data_manager.BROAD_DATA_REQ);aj.request_draw()}).dblclick(function(au){au.stopPropagation()})}var am=this.view.high-this.view.low,ak=(ao.low-this.view.low)*ar;if(this.left_offset){ak-=this.left_offset}an.css({position:"absolute",top:0,left:ak,height:""});aq.append(an);aj.max_height=Math.max(aj.max_height,an.height());aj.content_div.css("height",aj.max_height+"px");aq.children().css("height",aj.max_height+"px")},_get_tile_bounds:function(ah,ai){var ak=ah*T*ai,al=T*ai,aj=(ak+al<=this.view.max_high?ak+al:this.view.max_high);return[ak,aj]},tool_region_and_parameters_str:function(aj,ah,ak){var ai=this,al=(aj!==undefined&&ah!==undefined&&ak!==undefined?aj+":"+ah+"-"+ak:"all");return" - region=["+al+"], parameters=["+ai.tool.get_param_values().join(", ")+"]"}});var ad=function(ai,ah){this.hidden=true;j.call(this,"label",ai,ah,{});this.container_div.addClass("label-track")};q(ad.prototype,j.prototype,{init:function(){this.enabled=true},_draw:function(){var aj=this.view,ak=aj.high-aj.low,an=Math.floor(Math.pow(10,Math.floor(Math.log(ak)/Math.log(10)))),ah=Math.floor(aj.low/an)*an,al=this.view.container.width(),ai=$("<div style='position: relative; height: 1.3em;'></div>");while(ah<aj.high){var am=(ah-aj.low)/ak*al;ai.append($("<div class='label'>"+commatize(ah)+"</div>").css({position:"absolute",left:am-1}));ah+=an}this.content_div.children(":first").remove();this.content_div.append(ai)}});var C=function(ah){this.hidden=true;j.call(this,"reference",ah,{content_div:ah.top_labeltrack},{});O.call(this);ah.reference_track=this;this.left_offset=200;this.height_px=12;this.container_div.addClass("reference-track");this.content_div.css("background","none");this.content_div.css("min-height","0px");this.content_div.css("border","none");this.data_url=reference_url;this.data_url_extra_params={dbkey:ah.dbkey};this.data_manager=new J(D,this,false);this.tile_cache=new c(v)};q(C.prototype,r.prototype,O.prototype,{init:function(){this.enabled=true},draw_tile:function(ar,an,am,ai,at){var al=this,aj=T*am;if(at>this.view.canvas_manager.char_width_px){if(ar===null){al.content_div.css("height","0px");return}var ak=this.view.canvas_manager.new_canvas();var aq=ak.getContext("2d");ak.width=Math.ceil(aj*at+al.left_offset);ak.height=al.height_px;aq.font=aq.canvas.manager.default_font;aq.textAlign="center";ar=ar.data;for(var ao=0,ap=ar.length;ao<ap;ao++){var ah=Math.round(ao*at);aq.fillText(ar[ao],ah+al.left_offset,10)}return new b(ai,am,ak,ar)}this.content_div.css("height","0px")}});var k=function(am,ak,aj,an,ah,al){var ai=this;this.display_modes=["Histogram","Line","Filled","Intensity"];this.mode="Histogram";j.call(this,am,ak,aj,al);O.call(this);this.min_height_px=16;this.max_height_px=400;this.height_px=80;this.hda_ldda=an;this.dataset_id=ah;this.original_dataset_id=ah;this.data_manager=new U(D,this);this.tile_cache=new c(v);this.left_offset=0;this.config=new H({track:this,params:[{key:"name",label:"Name",type:"text",default_value:am},{key:"color",label:"Color",type:"color",default_value:"black"},{key:"min_value",label:"Min Value",type:"float",default_value:undefined},{key:"max_value",label:"Max Value",type:"float",default_value:undefined},{key:"mode",type:"string",default_value:this.mode,hidden:true},{key:"height",type:"int",default_value:this.height_px,hidden:true}],saved_values:al,onchange:function(){ai.set_name(ai.prefs.name);ai.vertical_range=ai.prefs.max_value-ai.prefs.min_value;$("#linetrack_"+ai.track_id+"_minval").text(ai.prefs.min_value);$("#linetrack_"+ai.track_id+"_maxval").text(ai.prefs.max_value);ai.tile_cache.clear();ai.request_draw()}});this.prefs=this.config.values;this.height_px=this.config.values.height;this.vertical_range=this.config.values.max_value-this.config.values.min_value;this.add_resize_handle()};q(k.prototype,r.prototype,O.prototype,{add_resize_handle:function(){var ah=this;var ak=false;var aj=false;var ai=$("<div class='track-resize'>");$(ah.container_div).hover(function(){ak=true;ai.show()},function(){ak=false;if(!aj){ai.hide()}});ai.hide().bind("dragstart",function(al,am){aj=true;am.original_height=$(ah.content_div).height()}).bind("drag",function(am,an){var al=Math.min(Math.max(an.original_height+an.deltaY,ah.min_height_px),ah.max_height_px);$(ah.content_div).css("height",al);ah.height_px=al;ah.request_draw(true)}).bind("dragend",function(al,am){ah.tile_cache.clear();aj=false;if(!ak){ai.hide()}ah.config.values.height=ah.height_px}).appendTo(ah.container_div)},predraw_init:function(){var ah=this,ai=ah.view.tracks.indexOf(ah);ah.vertical_range=undefined;return $.getJSON(ah.data_url,{stats:true,chrom:ah.view.chrom,low:null,high:null,hda_ldda:ah.hda_ldda,dataset_id:ah.dataset_id},function(aj){ah.container_div.addClass("line-track");var al=aj.data;if(isNaN(parseFloat(ah.prefs.min_value))||isNaN(parseFloat(ah.prefs.max_value))){ah.prefs.min_value=al.min;ah.prefs.max_value=al.max;$("#track_"+ai+"_minval").val(ah.prefs.min_value);$("#track_"+ai+"_maxval").val(ah.prefs.max_value)}ah.vertical_range=ah.prefs.max_value-ah.prefs.min_value;ah.total_frequency=al.total_frequency;ah.container_div.find(".yaxislabel").remove();var am=$("<div />").addClass("yaxislabel").attr("id","linetrack_"+ai+"_minval").text(y(ah.prefs.min_value));var ak=$("<div />").addClass("yaxislabel").attr("id","linetrack_"+ai+"_maxval").text(y(ah.prefs.max_value));ak.css({position:"absolute",top:"24px",left:"10px"});ak.prependTo(ah.container_div);am.css({position:"absolute",bottom:"2px",left:"10px"});am.prependTo(ah.container_div)})},draw_tile:function(au,am,al,aj,at){if(this.vertical_range===undefined){return}var ah=this._get_tile_bounds(aj,al),an=ah[0],ar=ah[1],ai=Math.ceil((ar-an)*at),ap=this.height_px;var ak=this.view.canvas_manager.new_canvas();ak.width=ai,ak.height=ap;var aq=ak.getContext("2d");var ao=new N.LinePainter(au.data,an,ar,this.prefs,am);ao.draw(aq,ai,ap);return new b(aj,al,ak,au.data)}});var e=function(ah,an,ai,am,ap,ao,ak,al){var aj=this;this.display_modes=["Auto","Histogram","Dense","Squish","Pack"];j.call(this,ah,an,ai,ao);O.call(this,ak,al);this.config=new H({track:this,params:[{key:"name",label:"Name",type:"text",default_value:ah},{key:"block_color",label:"Block color",type:"color",default_value:"#444"},{key:"label_color",label:"Label color",type:"color",default_value:"black"},{key:"show_counts",label:"Show summary counts",type:"bool",default_value:true},{key:"mode",type:"string",default_value:this.mode,hidden:true},],saved_values:ao,onchange:function(){aj.set_name(aj.prefs.name);aj.tile_cache.clear();aj.request_draw()}});this.prefs=this.config.values;this.height_px=0;this.container_div.addClass("feature-track");this.hda_ldda=am;this.dataset_id=ap;this.original_dataset_id=ap;this.show_labels_scale=0.001;this.showing_details=false;this.summary_draw_height=30;this.inc_slots={};this.start_end_dct={};this.tile_cache=new c(d);this.data_manager=new U(20,this);this.left_offset=200;this.painter=N.LinkedFeaturePainter};q(e.prototype,r.prototype,O.prototype,{postdraw_actions:function(ax,ah,ay,aw){O.prototype.postdraw_actions.call(this,ax,aw);var ak=this;if(aw){var am=ak.content_div.children();var an=false;for(var al=am.length-1,ar=0;al>=ar;al--){var aj=$(am[al]);if(an){aj.remove()}else{if(aj.children().length!==0){an=true}}}}if(ak.mode=="Histogram"){var aq=-1;for(var al=0;al<ax.length;al++){var av=ax[al].max_val;if(av>aq){aq=av}}for(var al=0;al<ax.length;al++){var au=ax[al];if(au.max_val!==aq){au.canvas.remove();ak.draw_helper(true,ah,au.index,au.resolution,au.canvas.parent(),ay,[],{max:aq})}}}if(ak.filters_manager){var ai=ak.filters_manager.filters;for(var ap=0;ap<ai.length;ap++){ai[ap].update_ui_elt()}var ao=false,at;for(var al=0;al<ax.length;al++){if(ax[al].data.length){at=ax[al].data[0];for(var ap=0;ap<ai.length;ap++){if(ai[ap].applies_to(at)){ao=true;break}}}}if(ak.filters_available!==ao){ak.filters_available=ao;if(!ak.filters_available){ak.filters_div.hide()}ak.make_name_popup_menu()}}},update_auto_mode:function(ah){if(this.mode=="Auto"){if(ah=="no_detail"){ah="feature spans"}else{if(ah=="summary_tree"){ah="coverage histogram"}}this.mode_div.text("Auto ("+ah+")")}},incremental_slots:function(al,ai,ak){var aj=this.view.canvas_manager.dummy_context,ah=this.inc_slots[al];if(!ah||(ah.mode!==ak)){ah=new (t.FeatureSlotter)(al,ak==="Pack",B,function(am){return aj.measureText(am)});ah.mode=ak;this.inc_slots[al]=ah}return ah.slot_features(ai)},get_summary_tree_data:function(al,ao,aj,ax){if(ax>aj-ao){ax=aj-ao}var at=Math.floor((aj-ao)/ax),aw=[],ak=0;var am=0,an=0,ar,av=0,ap=[],au,aq;var ai=function(aA,az,aB,ay){aA[0]=az+aB*ay;aA[1]=az+(aB+1)*ay};while(av<ax&&am!==al.length){var ah=false;for(;av<ax&&!ah;av++){ai(ap,ao,av,at);for(an=am;an<al.length;an++){ar=al[an].slice(1,3);if(is_overlap(ar,ap)){ah=true;break}}if(ah){break}}data_start_index=an;aw[aw.length]=au=[ap[0],0];for(;an<al.length;an++){ar=al[an].slice(1,3);if(is_overlap(ar,ap)){au[1]++}else{break}}if(au[1]>ak){ak=au[1]}av++}return{max:ak,delta:at,data:aw}},draw_tile:function(aw,az,aE,aI,ar,ak){var aB=this,am=aB._get_tile_bounds(aI,aE),aL=am[0],ai=am[1],ay=ai-aL,aC=Math.ceil(ay*ar),aP=25,al=this.left_offset,ax,an;if(az==="Auto"){if(aw.dataset_type==="summary_tree"){az=aw.dataset_type}else{if(aw.extra_info==="no_detail"){az="no_detail"}else{var aO=aw.data;if(this.view.high-this.view.low>K){az="Squish"}else{az="Pack"}}}this.update_auto_mode(az)}if(az==="summary_tree"||az==="Histogram"){an=this.summary_draw_height;this.container_div.find(".yaxislabel").remove();var ah=$("<div />").addClass("yaxislabel");ah.text(aw.max);ah.css({position:"absolute",top:"24px",left:"10px",color:this.prefs.label_color});ah.prependTo(this.container_div);var aj=this.view.canvas_manager.new_canvas();aj.width=aC+al;aj.height=an+V;if(aw.dataset_type!="summary_tree"){var at=this.get_summary_tree_data(aw.data,aL,ai,200);if(aw.max){at.max=aw.max}aw=at}var aM=new N.SummaryTreePainter(aw,aL,ai,this.prefs);var aD=aj.getContext("2d");aD.translate(al,V);aM.draw(aD,aC,an);return new l(aI,aE,aj,aw.data,aw.max)}var ax,ap=1;if(az==="no_detail"||az==="Squish"||az==="Pack"){ap=this.incremental_slots(ar,aw.data,az);ax=this.inc_slots[ar].slots}var aq=[];if(aw.data){var au=this.filters_manager.filters;for(var aF=0,aH=aw.data.length;aF<aH;aF++){var ao=aw.data[aF];var aG=false;var av;for(var aK=0,aN=au.length;aK<aN;aK++){av=au[aK];av.update_attrs(ao);if(!av.keep(ao)){aG=true;break}}if(!aG){aq.push(ao)}}}var aA=(this.filters_manager.alpha_filter?new Q(this.filters_manager.alpha_filter):null);var aM=new (this.painter)(aq,aL,ai,this.prefs,az,aA,ak);var an=Math.max(af,aM.get_required_height(ap));var aj=this.view.canvas_manager.new_canvas();var aJ=null;aj.width=aC+al;aj.height=an;var aD=aj.getContext("2d");aD.fillStyle=this.prefs.block_color;aD.font=aD.canvas.manager.default_font;aD.textAlign="right";this.container_div.find(".yaxislabel").remove();if(aw.data){aD.translate(al,0);aJ=aM.draw(aD,aC,an,ax);aJ.translation=-al}return new R(aI,aE,aj,aw.data,aw.message,aJ)}});var W=function(al,aj,ai,an,ah,ak,am){e.call(this,al,aj,ai,an,ah,ak,am);this.painter=N.VariantPainter};q(W.prototype,r.prototype,O.prototype,e.prototype);var Z=function(al,aj,ai,an,ah,ak,am){e.call(this,al,aj,ai,an,ah,ak,am);this.config=new H({track:this,params:[{key:"name",label:"Name",type:"text",default_value:al},{key:"block_color",label:"Block color",type:"color",default_value:"#444"},{key:"label_color",label:"Label color",type:"color",default_value:"black"},{key:"show_insertions",label:"Show insertions",type:"bool",default_value:false},{key:"show_differences",label:"Show differences only",type:"bool",default_value:true},{key:"show_counts",label:"Show summary counts",type:"bool",default_value:true},{key:"mode",type:"string",default_value:this.mode,hidden:true},],saved_values:ak,onchange:function(){this.track.set_name(this.track.prefs.name);this.track.tile_cache.clear();this.track.request_draw()}});this.prefs=this.config.values;this.painter=N.ReadPainter;this.make_name_popup_menu()};q(Z.prototype,r.prototype,O.prototype,e.prototype);var X=function(al,aj,ai,an,ah,ak,am){e.call(this,al,aj,ai,an,ah,ak,am,{});this.data_url=raw_data_url;this.data_query_wait=1000;this.dataset_check_url=dataset_state_url};q(X.prototype,r.prototype,O.prototype,e.prototype,{predraw_init:function(){var ai=this;var ah=function(){if(ai.data_manager.size()===0){setTimeout(ah,300)}else{ai.data_url=default_data_url;ai.data_query_wait=M;ai.dataset_state_url=converted_datasets_state_url;$.getJSON(ai.dataset_state_url,{dataset_id:ai.dataset_id,hda_ldda:ai.hda_ldda},function(aj){})}};ah()}});aa.View=ae;aa.DrawableGroup=S;aa.LineTrack=k;aa.FeatureTrack=e;aa.ReadTrack=Z};var slotting_module=function(c,b){var e=c("class").extend;var d=2,a=5;b.FeatureSlotter=function(j,h,f,g){this.slots={};this.start_end_dct={};this.w_scale=j;this.include_label=h;this.max_rows=f;this.measureText=g};e(b.FeatureSlotter.prototype,{slot_features:function(m){var p=this.w_scale,s=this.slots,h=this.start_end_dct,y=[],A=[],n=0,z=this.max_rows;for(var w=0,x=m.length;w<x;w++){var l=m[w],o=l[0];if(s[o]!==undefined){n=Math.max(n,s[o]);A.push(s[o])}else{y.push(w)}}var q=function(G,H){for(var F=0;F<=z;F++){var D=false,I=h[F];if(I!==undefined){for(var C=0,E=I.length;C<E;C++){var B=I[C];if(H>B[0]&&G<B[1]){D=true;break}}}if(!D){return F}}return -1};for(var w=0,x=y.length;w<x;w++){var l=m[y[w]],o=l[0],u=l[1],f=l[2],r=l[3],g=Math.floor(u*p),k=Math.ceil(f*p),v=this.measureText(r).width,j;if(r!==undefined&&this.include_label){v+=(d+a);if(g-v>=0){g-=v;j="left"}else{k+=v;j="right"}}var t=q(g,k);if(t>=0){if(h[t]===undefined){h[t]=[]}h[t].push([g,k]);s[o]=t;n=Math.max(n,t)}else{}}return n+1}})};var painters_module=function(k,y){var v=k("class").extend;var q=function(J,B,H,A,G,E){if(E===undefined){E=4}var D=A-B;var C=G-H;var F=Math.floor(Math.sqrt(D*D+C*C)/E);var K=D/F;var I=C/F;var z;for(z=0;z<F;z++,B+=K,H+=I){if(z%2!==0){continue}J.fillRect(B,H,E,1)}};var r=function(B,A,z,E){var D=A-E/2,C=A+E/2,F=z-Math.sqrt(E*3/2);B.beginPath();B.moveTo(D,F);B.lineTo(C,F);B.lineTo(A,z);B.lineTo(D,F);B.strokeStyle=this.fillStyle;B.fill();B.stroke();B.closePath()};var g=function(z){this.default_alpha=(z?z:1)};g.prototype.gen_alpha=function(z){return this.default_alpha};var n=function(B,D,z,A,C){this.data=B;this.view_start=D;this.view_end=z;this.prefs=v({},this.default_prefs,A);this.mode=C};n.prototype.default_prefs={};var w=function(B,D,z,A,C){n.call(this,B,D,z,A,C)};w.prototype.default_prefs={show_counts:false};w.prototype.draw=function(M,z,L){var E=this.view_start,O=this.view_end-this.view_start,N=z/O;var J=this.data.data,I=this.data.delta,G=this.data.max,B=L;delta_x_px=Math.ceil(I*N);M.save();for(var C=0,D=J.length;C<D;C++){var H=Math.floor((J[C][0]-E)*N);var F=J[C][1];if(!F){continue}var K=F/G*L;if(F!==0&&K<1){K=1}M.fillStyle=this.prefs.block_color;M.fillRect(H,B-K,delta_x_px,K);var A=4;if(this.prefs.show_counts&&(M.measureText(F).width+A)<delta_x_px){M.fillStyle=this.prefs.label_color;M.textAlign="center";M.fillText(F,H+(delta_x_px/2),10)}}M.restore()};var c=function(z,D,F,G,B){n.call(this,z,D,F,G,B);if(this.prefs.min_value===undefined){var H=Infinity;for(var A=0,C=this.data.length;A<C;A++){H=Math.min(H,this.data[A][1])}this.prefs.min_value=H}if(this.prefs.max_value===undefined){var E=-Infinity;for(var A=0,C=this.data.length;A<C;A++){E=Math.max(E,this.data[A][1])}this.prefs.max_value=E}};c.prototype.default_prefs={min_value:undefined,max_value:undefined,mode:"Histogram",color:"#000",overflow_color:"#F66"};c.prototype.draw=function(N,M,K){var F=false,H=this.prefs.min_value,D=this.prefs.max_value,J=D-H,z=K,A=this.view_start,L=this.view_end-this.view_start,B=M/L,I=this.mode,T=this.data;N.save();var U=Math.round(K+H/J*K);if(I!=="Intensity"){N.fillStyle="#aaa";N.fillRect(0,U,M,1)}N.beginPath();var R,E,C;if(T.length>1){C=Math.ceil((T[1][0]-T[0][0])*B)}else{C=10}for(var O=0,P=T.length;O<P;O++){N.fillStyle=this.prefs.color;R=Math.round((T[O][0]-A)*B);E=T[O][1];var Q=false,G=false;if(E===null){if(F&&I==="Filled"){N.lineTo(R,z)}F=false;continue}if(E<H){G=true;E=H}else{if(E>D){Q=true;E=D}}if(I==="Histogram"){E=Math.round(E/J*z);N.fillRect(R,U,C,-E)}else{if(I==="Intensity"){E=255-Math.floor((E-H)/J*255);N.fillStyle="rgb("+E+","+E+","+E+")";N.fillRect(R,0,C,z)}else{E=Math.round(z-(E-H)/J*z);if(F){N.lineTo(R,E)}else{F=true;if(I==="Filled"){N.moveTo(R,z);N.lineTo(R,E)}else{N.moveTo(R,E)}}}}N.fillStyle=this.prefs.overflow_color;if(Q||G){var S;if(I==="Histogram"||I==="Intensity"){S=C}else{R-=2;S=4}if(Q){N.fillRect(R,0,S,3)}if(G){N.fillRect(R,z-3,S,3)}}N.fillStyle=this.prefs.color}if(I==="Filled"){if(F){N.lineTo(R,U);N.lineTo(0,U)}N.fill()}else{N.stroke()}N.restore()};var o=function(z){this.feature_positions={};this.slot_height=z;this.translation=0};o.prototype.map_feature_data=function(A,C,z,B){if(!this.feature_positions[C]){this.feature_positions[C]=[]}this.feature_positions[C].push({data:A,x_start:z,x_end:B})};o.prototype.get_feature_data=function(z,D){var C=Math.floor(D/this.slot_height),B;if(!this.feature_positions[C]){return null}z+=this.translation;for(var A=0;A<this.feature_positions[C].length;A++){B=this.feature_positions[C][A];if(z>=B.x_start&&z<=B.x_end){return B.data}}};var p=function(C,E,z,B,D,A){n.call(this,C,E,z,B,D);this.alpha_generator=(A?A:new g())};p.prototype.default_prefs={block_color:"#FFF",connector_color:"#FFF"};v(p.prototype,{get_required_height:function(A){var z=y_scale=this.get_row_height(),B=this.mode;if(B==="no_detail"||B==="Squish"||B==="Pack"){z=A*y_scale}return z+Math.max(Math.round(y_scale/2),5)},draw:function(L,J,H,G){var R=this.data,E=this.view_start,N=this.view_end;L.save();L.fillStyle=this.prefs.block_color;L.textAlign="right";var I=this.view_end-this.view_start,F=J/I,M=this.get_row_height(),Q=new o(M),C;for(var O=0,P=R.length;O<P;O++){var B=R[O],D=B[0],K=B[1],z=B[2],A=(G&&G[D]!==undefined?G[D]:null);if((K<N&&z>E)&&(this.mode=="Dense"||A!==null)){C=this.draw_element(L,this.mode,B,A,E,N,F,M,J);Q.map_feature_data(B,A,C[0],C[1])}}L.restore();return Q},draw_element:function(F,B,H,D,C,E,G,A,z){console.log("WARNING: Unimplemented function.");return[0,0]}});var d=10,j=3,m=5,x=10,f=1,t=3,e=3,a=9,l=2,h="#ccc";var s=function(C,E,z,B,D,A){p.call(this,C,E,z,B,D,A)};v(s.prototype,p.prototype,{get_row_height:function(){var A=this.mode,z;if(A==="Dense"){z=d}else{if(A==="no_detail"){z=j}else{if(A==="Squish"){z=m}else{z=x}}}return z},draw_element:function(L,E,T,G,N,ae,ai,ak,z){var 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ah=0;ah<this.drawables.length;ah++){this.drawables[ah].init()}},_draw:function(){for(var ah=0;ah<this.drawables.length;ah++){this.drawables[ah]._draw()}},to_json:function(){var ai=[];for(var ah=0;ah<this.drawables.length;ah++){ai.push(this.drawables[ah].to_json())}return{name:this.name,prefs:this.prefs,obj_type:this.obj_type,drawables:ai}},add_drawable:function(ah){this.drawables.push(ah);ah.container=this},add_drawable_before:function(aj,ah){var ai=this.drawables.indexOf(ah);if(ai!=-1){this.drawables.splice(ai,0,aj);return true}return false},remove_drawable:function(ai){var ah=this.drawables.indexOf(ai);if(ah!=-1){this.drawables.splice(ah,1);ai.container=null;return true}return false},move_drawable:function(ai,aj){var ah=this.drawables.indexOf(ai);if(ah!=-1){this.drawables.splice(ah,1);this.drawables.splice(aj,0,ai);return true}return false}});var R=function(ak,ai,ah,aj){z.call(this,"DrawableGroup",ak,ai,ah,aj,"group-handle");if(!R.id_counter){R.id_counter=0}var al=R.id_counter++;this.container_div=$("<div/>").addClass("group").attr("id","group_"+al).appendTo(this.container.content_div);this.header_div=$("<div/>").addClass("track-header").appendTo(this.container_div);this.header_div.append($("<div/>").addClass(this.drag_handle_class));this.name_div=$("<div/>").addClass("group-name menubutton popup").text(this.name).appendTo(this.header_div);this.content_div=$("<div/>").addClass("content-div").attr("id","group_"+al+"_content_div").appendTo(this.container_div);m(this.container_div,this);m(this.content_div,this);n(this.container_div,this.drag_handle_class,".group",this);this.make_name_popup_menu()};q(R.prototype,r.prototype,z.prototype,{make_name_popup_menu:function(){var ai=this;var ah={};ah["Edit configuration"]=function(){var al=function(){hide_modal();$(window).unbind("keypress.check_enter_esc")},aj=function(){ai.config.update_from_form($(".dialog-box"));hide_modal();$(window).unbind("keypress.check_enter_esc")},ak=function(am){if((am.keyCode||am.which)===27){al()}else{if((am.keyCode||am.which)===13){aj()}}};$(window).bind("keypress.check_enter_esc",ak);show_modal("Configure Group",ai.config.build_form(),{Cancel:al,OK:aj})};ah.Remove=function(){ai.remove()};make_popupmenu(ai.name_div,ah)}});var ae=function(ah,ak,aj,ai){z.call(this,"View");this.container=ah;this.chrom=null;this.vis_id=aj;this.dbkey=ai;this.title=ak;this.label_tracks=[];this.tracks_to_be_redrawn=[];this.max_low=0;this.max_high=0;this.zoom_factor=3;this.min_separation=30;this.has_changes=false;this.load_chroms_deferred=null;this.init();this.canvas_manager=new ag(ah.get(0).ownerDocument);this.reset()};q(ae.prototype,z.prototype,{init:function(){var aj=this.container,ah=this;this.top_container=$("<div/>").addClass("top-container").appendTo(aj);this.browser_content_div=$("<div/>").addClass("content").css("position","relative").appendTo(aj);this.bottom_container=$("<div/>").addClass("bottom-container").appendTo(aj);this.top_labeltrack=$("<div/>").addClass("top-labeltrack").appendTo(this.top_container);this.viewport_container=$("<div/>").addClass("viewport-container").attr("id","viewport-container").appendTo(this.browser_content_div);this.content_div=this.viewport_container;m(this.viewport_container,ah);this.intro_div=$("<div/>").addClass("intro");var ak=$("<div/>").text("Add Datasets to Visualization").addClass("action-button").appendTo(this.intro_div).click(function(){add_tracks()});this.nav_labeltrack=$("<div/>").addClass("nav-labeltrack").appendTo(this.bottom_container);this.nav_container=$("<div/>").addClass("nav-container").prependTo(this.top_container);this.nav=$("<div/>").addClass("nav").appendTo(this.nav_container);this.overview=$("<div/>").addClass("overview").appendTo(this.bottom_container);this.overview_viewport=$("<div/>").addClass("overview-viewport").appendTo(this.overview);this.overview_close=$("<a href='javascript:void(0);'>Close Overview</a>").addClass("overview-close").hide().appendTo(this.overview_viewport);this.overview_highlight=$("<div/>").addClass("overview-highlight").hide().appendTo(this.overview_viewport);this.overview_box_background=$("<div/>").addClass("overview-boxback").appendTo(this.overview_viewport);this.overview_box=$("<div/>").addClass("overview-box").appendTo(this.overview_viewport);this.default_overview_height=this.overview_box.height();this.nav_controls=$("<div/>").addClass("nav-controls").appendTo(this.nav);this.chrom_select=$("<select/>").attr({name:"chrom"}).css("width","15em").addClass("no-autocomplete").append("<option value=''>Loading</option>").appendTo(this.nav_controls);var ai=function(al){if(al.type==="focusout"||(al.keyCode||al.which)===13||(al.keyCode||al.which)===27){if((al.keyCode||al.which)!==27){ah.go_to($(this).val())}$(this).hide();$(this).val("");ah.location_span.show();ah.chrom_select.show()}};this.nav_input=$("<input/>").addClass("nav-input").hide().bind("keyup focusout",ai).appendTo(this.nav_controls);this.location_span=$("<span/>").addClass("location").appendTo(this.nav_controls);this.location_span.click(function(){ah.location_span.hide();ah.chrom_select.hide();ah.nav_input.val(ah.chrom+":"+ah.low+"-"+ah.high);ah.nav_input.css("display","inline-block");ah.nav_input.select();ah.nav_input.focus()});if(this.vis_id!==undefined){this.hidden_input=$("<input/>").attr("type","hidden").val(this.vis_id).appendTo(this.nav_controls)}this.zo_link=$("<a id='zoom-out' />").click(function(){ah.zoom_out();ah.request_redraw()}).appendTo(this.nav_controls);this.zi_link=$("<a id='zoom-in' />").click(function(){ah.zoom_in();ah.request_redraw()}).appendTo(this.nav_controls);this.load_chroms_deferred=this.load_chroms({low:0});this.chrom_select.bind("change",function(){ah.change_chrom(ah.chrom_select.val())});this.browser_content_div.click(function(al){$(this).find("input").trigger("blur")});this.browser_content_div.bind("dblclick",function(al){ah.zoom_in(al.pageX,this.viewport_container)});this.overview_box.bind("dragstart",function(al,am){this.current_x=am.offsetX}).bind("drag",function(al,an){var ao=an.offsetX-this.current_x;this.current_x=an.offsetX;var am=Math.round(ao/ah.viewport_container.width()*(ah.max_high-ah.max_low));ah.move_delta(-am)});this.overview_close.click(function(){ah.reset_overview()});this.viewport_container.bind("draginit",function(al,am){if(al.clientX>ah.viewport_container.width()-16){return false}}).bind("dragstart",function(al,am){am.original_low=ah.low;am.current_height=al.clientY;am.current_x=am.offsetX}).bind("drag",function(an,ap){var al=$(this);var aq=ap.offsetX-ap.current_x;var am=al.scrollTop()-(an.clientY-ap.current_height);al.scrollTop(am);ap.current_height=an.clientY;ap.current_x=ap.offsetX;var ao=Math.round(aq/ah.viewport_container.width()*(ah.high-ah.low));ah.move_delta(ao)}).bind("mousewheel",function(an,ap,am,al){if(am){var ao=Math.round(-am/ah.viewport_container.width()*(ah.high-ah.low));ah.move_delta(ao)}});this.top_labeltrack.bind("dragstart",function(al,am){return $("<div />").css({height:ah.browser_content_div.height()+ah.top_labeltrack.height()+ah.nav_labeltrack.height()+1,top:"0px",position:"absolute","background-color":"#ccf",opacity:0.5,"z-index":1000}).appendTo($(this))}).bind("drag",function(ap,aq){$(aq.proxy).css({left:Math.min(ap.pageX,aq.startX),width:Math.abs(ap.pageX-aq.startX)});var am=Math.min(ap.pageX,aq.startX)-ah.container.offset().left,al=Math.max(ap.pageX,aq.startX)-ah.container.offset().left,ao=(ah.high-ah.low),an=ah.viewport_container.width();ah.update_location(Math.round(am/an*ao)+ah.low,Math.round(al/an*ao)+ah.low)}).bind("dragend",function(aq,ar){var am=Math.min(aq.pageX,ar.startX),al=Math.max(aq.pageX,ar.startX),ao=(ah.high-ah.low),an=ah.viewport_container.width(),ap=ah.low;ah.low=Math.round(am/an*ao)+ap;ah.high=Math.round(al/an*ao)+ap;$(ar.proxy).remove();ah.request_redraw()});this.add_label_track(new ad(this,{content_div:this.top_labeltrack}));this.add_label_track(new ad(this,{content_div:this.nav_labeltrack}));$(window).bind("resize",function(){ah.resize_window()});$(document).bind("redraw",function(){ah.redraw()});this.reset();$(window).trigger("resize");this.update_intro_div()},update_intro_div:function(){if(this.num_tracks===0){this.intro_div.appendTo(this.viewport_container)}else{this.intro_div.remove()}},update_location:function(ah,ai){this.location_span.text(commatize(ah)+" - "+commatize(ai));this.nav_input.val(this.chrom+":"+commatize(ah)+"-"+commatize(ai))},load_chroms:function(aj){aj.num=x;$.extend(aj,(this.vis_id!==undefined?{vis_id:this.vis_id}:{dbkey:this.dbkey}));var ah=this,ai=$.Deferred();$.ajax({url:chrom_url,data:aj,dataType:"json",success:function(al){if(al.chrom_info.length===0){alert("Invalid chromosome: "+aj.chrom);return}if(al.reference){ah.add_label_track(new B(ah))}ah.chrom_data=al.chrom_info;var ao='<option value="">Select Chrom/Contig</option>';for(var an=0,ak=ah.chrom_data.length;an<ak;an++){var am=ah.chrom_data[an].chrom;ao+='<option value="'+am+'">'+am+"</option>"}if(al.prev_chroms){ao+='<option value="previous">Previous '+x+"</option>"}if(al.next_chroms){ao+='<option value="next">Next '+x+"</option>"}ah.chrom_select.html(ao);ah.chrom_start_index=al.start_index;ai.resolve(al)},error:function(){alert("Could not load chroms for this dbkey:",ah.dbkey)}});return ai},change_chrom:function(am,ai,ao){if(!am||am==="None"){return}var aj=this;if(am==="previous"){aj.load_chroms({low:this.chrom_start_index-x});return}if(am==="next"){aj.load_chroms({low:this.chrom_start_index+x});return}var an=$.grep(aj.chrom_data,function(ap,aq){return ap.chrom===am})[0];if(an===undefined){aj.load_chroms({chrom:am},function(){aj.change_chrom(am,ai,ao)});return}else{if(am!==aj.chrom){aj.chrom=am;aj.chrom_select.val(aj.chrom);aj.max_high=an.len-1;aj.reset();aj.request_redraw(true);for(var al=0,ah=aj.drawables.length;al<ah;al++){var ak=aj.drawables[al];if(ak.init){ak.init()}}}if(ai!==undefined&&ao!==undefined){aj.low=Math.max(ai,0);aj.high=Math.min(ao,aj.max_high)}aj.reset_overview();aj.request_redraw()}},go_to:function(al){var ap=this,ah,ak,ai=al.split(":"),an=ai[0],ao=ai[1];if(ao!==undefined){try{var am=ao.split("-");ah=parseInt(am[0].replace(/,/g,""),10);ak=parseInt(am[1].replace(/,/g,""),10)}catch(aj){return false}}ap.change_chrom(an,ah,ak)},move_fraction:function(aj){var ah=this;var ai=ah.high-ah.low;this.move_delta(aj*ai)},move_delta:function(aj){var ah=this;var ai=ah.high-ah.low;if(ah.low-aj<ah.max_low){ah.low=ah.max_low;ah.high=ah.max_low+ai}else{if(ah.high-aj>ah.max_high){ah.high=ah.max_high;ah.low=ah.max_high-ai}else{ah.high-=aj;ah.low-=aj}}ah.request_redraw()},add_drawable:function(ah){z.prototype.add_drawable.call(this,ah);ah.init();this.has_changes=true;this.update_intro_div()},add_label_track:function(ah){ah.view=this;ah.init();this.label_tracks.push(ah)},remove_drawable:function(aj,ai){z.prototype.remove_drawable.call(this,aj);if(ai){var ah=this;aj.container_div.fadeOut("slow",function(){$(this).remove();ah.update_intro_div()});this.has_changes=true}},reset:function(){this.low=this.max_low;this.high=this.max_high;this.viewport_container.find(".yaxislabel").remove()},request_redraw:function(ap,ah,ao,ai){var an=this,al=(ai?[ai]:an.drawables),aj;var ai;for(var am=0;am<al.length;am++){ai=al[am];aj=-1;for(var ak=0;ak<an.tracks_to_be_redrawn.length;ak++){if(an.tracks_to_be_redrawn[ak][0]===ai){aj=ak;break}}if(aj<0){an.tracks_to_be_redrawn.push([ai,ah,ao])}else{an.tracks_to_be_redrawn[am][1]=ah;an.tracks_to_be_redrawn[am][2]=ao}}requestAnimationFrame(function(){an._redraw(ap)})},_redraw:function(ar){var ao=this.low,ak=this.high;if(ao<this.max_low){ao=this.max_low}if(ak>this.max_high){ak=this.max_high}var aq=this.high-this.low;if(this.high!==0&&aq<this.min_separation){ak=ao+this.min_separation}this.low=Math.floor(ao);this.high=Math.ceil(ak);this.resolution=Math.pow(D,Math.ceil(Math.log((this.high-this.low)/S)/Math.log(D)));this.zoom_res=Math.pow(w,Math.max(0,Math.ceil(Math.log(this.resolution,w)/Math.log(w))));var ah=(this.low/(this.max_high-this.max_low)*this.overview_viewport.width())||0;var an=((this.high-this.low)/(this.max_high-this.max_low)*this.overview_viewport.width())||0;var at=13;this.overview_box.css({left:ah,width:Math.max(at,an)}).show();if(an<at){this.overview_box.css("left",ah-(at-an)/2)}if(this.overview_highlight){this.overview_highlight.css({left:ah,width:an})}this.update_location(this.low,this.high);if(!ar){var aj,ai,ap;for(var al=0,am=this.tracks_to_be_redrawn.length;al<am;al++){aj=this.tracks_to_be_redrawn[al][0];ai=this.tracks_to_be_redrawn[al][1];ap=this.tracks_to_be_redrawn[al][2];if(aj){aj._draw(ai,ap)}}this.tracks_to_be_redrawn=[];for(al=0,am=this.label_tracks.length;al<am;al++){this.label_tracks[al]._draw()}}},zoom_in:function(ai,aj){if(this.max_high===0||this.high-this.low<this.min_separation){return}var ak=this.high-this.low,al=ak/2+this.low,ah=(ak/this.zoom_factor)/2;if(ai){al=ai/this.viewport_container.width()*(this.high-this.low)+this.low}this.low=Math.round(al-ah);this.high=Math.round(al+ah);this.request_redraw()},zoom_out:function(){if(this.max_high===0){return}var ai=this.high-this.low,aj=ai/2+this.low,ah=(ai*this.zoom_factor)/2;this.low=Math.round(aj-ah);this.high=Math.round(aj+ah);this.request_redraw()},resize_window:function(){this.viewport_container.height(this.container.height()-this.top_container.height()-this.bottom_container.height());this.nav_container.width(this.container.width());this.request_redraw()},set_overview:function(ah){$.when(ah.get_overview_tile()).then(function(ai){view.overview_viewport.find(".track-tile").remove();view.overview_close.show();view.overview_viewport.append(ai.canvas);view.overview_highlight.show().height(ai.canvas.height());view.overview_viewport.height(ai.canvas.height()+view.overview_box.outerHeight());view.resize_window();if(view.overview_track){view.overview_track.set_is_overview(false)}view.overview_track=ah;ah.set_is_overview(true)});view.has_changes=true},reset_overview:function(){this.overview_viewport.find(".track-tile").remove();this.overview_viewport.height(this.default_overview_height);this.overview_box.height(this.default_overview_height);this.overview_close.hide();this.overview_highlight.hide();view.resize_window();if(view.overview_track){view.overview_track.set_is_overview(false)}view.overview_track=null}});var s=function(aj,an){this.track=aj;this.name=an.name;this.params=[];var av=an.params;for(var ak=0;ak<av.length;ak++){var ap=av[ak],ai=ap.name,au=ap.label,al=unescape(ap.html),aw=ap.value,ar=ap.type;if(ar==="number"){this.params[this.params.length]=new g(ai,au,al,aw,ap.min,ap.max)}else{if(ar=="select"){this.params[this.params.length]=new O(ai,au,al,aw)}else{console.log("WARNING: unrecognized tool parameter type:",ai,ar)}}}this.parent_div=$("<div/>").addClass("dynamic-tool").hide();this.parent_div.bind("drag",function(ay){ay.stopPropagation()}).click(function(ay){ay.stopPropagation()}).bind("dblclick",function(ay){ay.stopPropagation()});var at=$("<div class='tool-name'>").appendTo(this.parent_div).text(this.name);var aq=this.params;var ao=this;$.each(this.params,function(az,aC){var aB=$("<div>").addClass("param-row").appendTo(ao.parent_div);var ay=$("<div>").addClass("param-label").text(aC.label).appendTo(aB);var aA=$("<div/>").addClass("slider").html(aC.html).appendTo(aB);aA.find(":input").val(aC.value);$("<div style='clear: both;'/>").appendTo(aB)});this.parent_div.find("input").click(function(){$(this).select()});var ax=$("<div>").addClass("param-row").appendTo(this.parent_div);var am=$("<input type='submit'>").attr("value","Run on complete dataset").appendTo(ax);var ah=$("<input type='submit'>").attr("value","Run on visible region").css("margin-left","3em").appendTo(ax);var ao=this;ah.click(function(){ao.run_on_region()});am.click(function(){ao.run_on_dataset()})};q(s.prototype,{get_param_values_dict:function(){var ah={};this.parent_div.find(":input").each(function(){var ai=$(this).attr("name"),aj=$(this).val();ah[ai]=JSON.stringify(aj)});return ah},get_param_values:function(){var ai=[];var ah={};this.parent_div.find(":input").each(function(){var aj=$(this).attr("name"),ak=$(this).val();if(aj){ai[ai.length]=ak}});return ai},run_on_dataset:function(){var ah=this;ah.run({dataset_id:this.track.original_dataset_id,tool_id:ah.name},null,function(ai){show_modal(ah.name+" is Running",ah.name+" is running on the complete dataset. Tool outputs are in dataset's history.",{Close:hide_modal})})},run_on_region:function(){var ai={dataset_id:this.track.original_dataset_id,chrom:this.track.view.chrom,low:this.track.view.low,high:this.track.view.high,tool_id:this.name},al=this.track,aj=ai.tool_id+al.tool_region_and_parameters_str(ai.chrom,ai.low,ai.high),ah,am;if(al.container===view){var ak=new R(this.name,this.track.view,this.track.container);al.container.add_drawable(ak);al.container.remove_drawable(al);ak.add_drawable(al);al.container_div.appendTo(ak.content_div);ah=ak}else{ah=al.container}if(al instanceof e){am=new W(aj,view,ah,"hda");am.change_mode(al.mode);ah.add_drawable(am)}am.content_div.text("Starting job.");this.run(ai,am,function(an){am.dataset_id=an.dataset_id;am.content_div.text("Running job.");am.init()})},run:function(ai,aj,ak){$.extend(ai,this.get_param_values_dict());var ah=function(){$.getJSON(rerun_tool_url,ai,function(al){if(al==="no converter"){aj.container_div.addClass("error");aj.content_div.text(K)}else{if(al.error){aj.container_div.addClass("error");aj.content_div.text(y+al.message)}else{if(al==="pending"){aj.container_div.addClass("pending");aj.content_div.text("Converting input data so that it can be used quickly with tool.");setTimeout(ah,2000)}else{ak(al)}}}})};ah()}});var O=function(ai,ah,aj,ak){this.name=ai;this.label=ah;this.html=aj;this.value=ak};var g=function(aj,ai,al,am,ak,ah){O.call(this,aj,ai,al,am);this.min=ak;this.max=ah};var h=function(ai,ah,aj,ak){this.name=ai;this.index=ah;this.tool_id=aj;this.tool_exp_name=ak};var X=function(ai,ah,aj,ak){h.call(this,ai,ah,aj,ak);this.low=-Number.MAX_VALUE;this.high=Number.MAX_VALUE;this.min=Number.MAX_VALUE;this.max=-Number.MAX_VALUE;this.container=null;this.slider=null;this.slider_label=null};q(X.prototype,{applies_to:function(ah){if(ah.length>this.index){return true}return false},keep:function(ah){if(!this.applies_to(ah)){return true}var ai=ah[this.index];return(isNaN(ai)||(ai>=this.low&&ai<=this.high))},update_attrs:function(ai){var ah=false;if(!this.applies_to(ai)){return ah}if(ai[this.index]<this.min){this.min=Math.floor(ai[this.index]);ah=true}if(ai[this.index]>this.max){this.max=Math.ceil(ai[this.index]);ah=true}return ah},update_ui_elt:function(){if(this.min!=this.max){this.container.show()}else{this.container.hide()}var aj=function(am,ak){var al=ak-am;return(al<=2?0.01:1)};var ai=this.slider.slider("option","min"),ah=this.slider.slider("option","max");if(this.min<ai||this.max>ah){this.slider.slider("option","min",this.min);this.slider.slider("option","max",this.max);this.slider.slider("option","step",aj(this.min,this.max));this.slider.slider("option","values",[this.min,this.max])}}});var ac=function(ar,ay){this.track=ar;this.filters=[];for(var at=0;at<ay.length;at++){var au=ay[at],az=au.name,ah=au.type,ak=au.index,ax=au.tool_id,aw=au.tool_exp_name;if(ah==="int"||ah==="float"){this.filters[at]=new X(az,ak,ax,aw)}else{console.log("ERROR: unsupported filter: ",az,ah)}}var al=function(aA,aB,aC){aA.click(function(){var aD=aB.text();max=parseFloat(aC.slider("option","max")),input_size=(max<=1?4:max<=1000000?max.toString().length:6),multi_value=false;if(aC.slider("option","values")){input_size=2*input_size+1;multi_value=true}aB.text("");$("<input type='text'/>").attr("size",input_size).attr("maxlength",input_size).attr("value",aD).appendTo(aB).focus().select().click(function(aE){aE.stopPropagation()}).blur(function(){$(this).remove();aB.text(aD)}).keyup(function(aI){if(aI.keyCode===27){$(this).trigger("blur")}else{if(aI.keyCode===13){var aG=aC.slider("option","min"),aE=aC.slider("option","max"),aH=function(aJ){return(isNaN(aJ)||aJ>aE||aJ<aG)},aF=$(this).val();if(!multi_value){aF=parseFloat(aF);if(aH(aF)){alert("Parameter value must be in the range ["+aG+"-"+aE+"]");return $(this)}}else{aF=aF.split("-");aF=[parseFloat(aF[0]),parseFloat(aF[1])];if(aH(aF[0])||aH(aF[1])){alert("Parameter value must be in the range ["+aG+"-"+aE+"]");return $(this)}}aC.slider((multi_value?"values":"value"),aF)}}})})};this.parent_div=$("<div/>").addClass("filters").hide();this.parent_div.bind("drag",function(aA){aA.stopPropagation()}).click(function(aA){aA.stopPropagation()}).bind("dblclick",function(aA){aA.stopPropagation()}).bind("keydown",function(aA){aA.stopPropagation()});var av=$("<div/>").addClass("sliders").appendTo(this.parent_div);var ap=this;$.each(this.filters,function(aD,aF){aF.container=$("<div/>").addClass("slider-row").appendTo(av);var aE=$("<div/>").addClass("elt-label").appendTo(aF.container);var aC=$("<span/>").addClass("slider-name").text(aF.name+"  ").appendTo(aE);var aB=$("<span/>");var aH=$("<span/>").addClass("slider-value").appendTo(aE).append("[").append(aB).append("]");var aA=$("<div/>").addClass("slider").appendTo(aF.container);aF.control_element=$("<div/>").attr("id",aF.name+"-filter-control").appendTo(aA);var aG=[0,0];aF.control_element.slider({range:true,min:Number.MAX_VALUE,max:-Number.MIN_VALUE,values:[0,0],slide:function(aJ,aK){var aI=aK.values;aB.text(aI[0]+"-"+aI[1]);aF.low=aI[0];aF.high=aI[1];ap.track.request_draw(true,true)},change:function(aI,aJ){aF.control_element.slider("option","slide").call(aF.control_element,aI,aJ)}});aF.slider=aF.control_element;aF.slider_label=aB;al(aH,aB,aF.control_element);$("<div style='clear: both;'/>").appendTo(aF.container)});if(this.filters.length!==0){var am=$("<div/>").addClass("param-row").appendTo(av);var ao=$("<input type='submit'/>").attr("value","Run on complete dataset").appendTo(am);var aj=this;ao.click(function(){aj.run_on_dataset()})}var aq=$("<div/>").addClass("display-controls").appendTo(this.parent_div),an=$("<span/>").addClass("elt-label").text("Transparency:").appendTo(aq),ai=$("<select/>").attr("name","alpha_dropdown").appendTo(aq);this.alpha_filter=null;$("<option/>").attr("value",-1).text("== None ==").appendTo(ai);for(var at=0;at<this.filters.length;at++){$("<option/>").attr("value",at).text(this.filters[at].name).appendTo(ai)}ai.change(function(){$(this).children("option:selected").each(function(){var aA=parseInt($(this).val());ap.alpha_filter=(aA>=0?ap.filters[aA]:null);ap.track.request_draw(true,true)})});$("<div style='clear: both;'/>").appendTo(this.parent_div)};q(ac.prototype,{reset_filters:function(){for(var ah=0;ah<this.filters.length;ah++){filter=this.filters[ah];filter.slider.slider("option","values",[filter.min,filter.max])}this.alpha_filter=null},run_on_dataset:function(){var ap=function(au,ar,at){if(!(ar in au)){au[ar]=at}return au[ar]};var aj={},ah,ai,ak;for(var al=0;al<this.filters.length;al++){ah=this.filters[al];if(ah.tool_id){if(ah.min!=ah.low){ai=ap(aj,ah.tool_id,[]);ai[ai.length]=ah.tool_exp_name+" >= "+ah.low}if(ah.max!=ah.high){ai=ap(aj,ah.tool_id,[]);ai[ai.length]=ah.tool_exp_name+" <= "+ah.high}}}var an=[];for(var aq in aj){an[an.length]=[aq,aj[aq]]}var ao=an.length;(function am(ay,av){var at=av[0],au=at[0],ax=at[1],aw="("+ax.join(") and (")+")",ar={cond:aw,input:ay,target_dataset_id:ay,tool_id:au},av=av.slice(1);$.getJSON(run_tool_url,ar,function(az){if(az.error){show_modal("Filter Dataset","Error running tool "+au,{Close:hide_modal})}else{if(av.length===0){show_modal("Filtering Dataset","Filter(s) are running on the complete dataset. Outputs are in dataset's history.",{Close:hide_modal})}else{am(az.dataset_id,av)}}})})(this.track.dataset_id,an)}});var P=function(ai,ah){M.AlphaGenerator.call(this,ah);this.filter=ai};P.prototype.gen_alpha=function(ah){if(this.filter.high===Number.MAX_VALUE||this.filter.low===-Number.MAX_VALUE||this.filter.low===this.filter.high){return this.default_alpha}return((parseFloat(ah[this.filter.index])-this.filter.low)/(this.filter.high-this.filter.low))};var G=function(ah){this.track=ah.track;this.params=ah.params;this.values={};this.restore_values((ah.saved_values?ah.saved_values:{}));this.onchange=ah.onchange};q(G.prototype,{restore_values:function(ah){var ai=this;$.each(this.params,function(aj,ak){if(ah[ak.key]!==undefined){ai.values[ak.key]=ah[ak.key]}else{ai.values[ak.key]=ak.default_value}})},build_form:function(){var ai=this;var ah=$("<div />");$.each(this.params,function(am,ak){if(!ak.hidden){var aj="param_"+am;var ao=ai.values[ak.key];var ar=$("<div class='form-row' />").appendTo(ah);ar.append($("<label />").attr("for",aj).text(ak.label+":"));if(ak.type==="bool"){ar.append($('<input type="checkbox" />').attr("id",aj).attr("name",aj).attr("checked",ao))}else{if(ak.type==="text"){ar.append($('<input type="text"/>').attr("id",aj).val(ao).click(function(){$(this).select()}))}else{if(ak.type==="color"){var an=$("<input />").attr("id",aj).attr("name",aj).val(ao);var ap=$("<div class='tipsy tipsy-north' style='position: absolute;' />").hide();var al=$("<div style='background-color: black; padding: 10px;'></div>").appendTo(ap);var aq=$("<div/>").appendTo(al).farbtastic({width:100,height:100,callback:an,color:ao});$("<div />").append(an).append(ap).appendTo(ar).bind("click",function(at){ap.css({left:$(this).position().left+($(an).width()/2)-60,top:$(this).position().top+$(this.height)}).show();$(document).bind("click.color-picker",function(){ap.hide();$(document).unbind("click.color-picker")});at.stopPropagation()})}else{ar.append($("<input />").attr("id",aj).attr("name",aj).val(ao))}}}}});return ah},update_from_form:function(ah){var aj=this;var ai=false;$.each(this.params,function(ak,am){if(!am.hidden){var an="param_"+ak;var al=ah.find("#"+an).val();if(am.type==="float"){al=parseFloat(al)}else{if(am.type==="int"){al=parseInt(al)}else{if(am.type==="bool"){al=ah.find("#"+an).is(":checked")}}}if(al!==aj.values[am.key]){aj.values[am.key]=al;ai=true}}});if(ai){this.onchange()}}});var b=function(ah,ak,aj,ai,al){this.track=ah;this.index=ak;this.low=ak*S*aj;this.high=(ak+1)*S*aj;this.resolution=aj;this.canvas=$("<div class='track-tile'/>").append(ai);this.data=al;this.stale=false};b.prototype.predisplay_actions=function(){};var l=function(ah,ak,aj,ai,al,am){b.call(this,ah,ak,aj,ai,al);this.max_val=am};q(l.prototype,b.prototype);var Q=function(ah,al,ak,aj,an,ao,am,ai){b.call(this,ah,al,ak,aj,an);this.mode=ao;this.message=am;this.feature_mapper=ai};q(Q.prototype,b.prototype);Q.prototype.predisplay_actions=function(){var ai=this,ah={};if(ai.mode!=="Pack"){return}$(this.canvas).mousemove(function(au){var ao=$(this).offset(),at=au.pageX-ao.left,ar=au.pageY-ao.top,ay=ai.feature_mapper.get_feature_data(at,ar),ap=(ay?ay[0]:null);$(this).siblings(".feature-popup").each(function(){if(!ap||$(this).attr("id")!==ap.toString()){$(this).remove()}});if(ay){var ak=ah[ap];if(!ak){var ap=ay[0],av={name:ay[3],start:ay[1],end:ay[2],strand:ay[4]},an=ai.track.filters_manager.filters,am;for(var aq=0;aq<an.length;aq++){am=an[aq];av[am.name]=ay[am.index]}var ak=$("<div/>").attr("id",ap).addClass("feature-popup"),ax,aw,az=$("<table/>").appendTo(ak),aA;for(ax in av){aw=av[ax];aA=$("<tr/>").appendTo(az);$("<th/>").appendTo(aA).text(ax);$("<td/>").attr("align","left").appendTo(aA).text(typeof(aw)=="number"?ab(aw,2):aw)}ah[ap]=ak}ak.appendTo($(ai.canvas).parent());var al=at+parseInt(ai.canvas.css("left"))+7,aj=ar+parseInt(ai.canvas.css("top"))+7;ak.css("left",al+"px").css("top",aj+"px")}else{if(!au.isPropagationStopped()){au.stopPropagation();$(this).siblings().each(function(){$(this).trigger(au)})}}}).mouseleave(function(){$(this).siblings(".feature-popup").remove()})};var j=function(ak,ai,ah,aj,al,am){r.call(this,ak,ai,ah,{},"draghandle");this.data_url=(al?al:default_data_url);this.data_url_extra_params={};this.data_query_wait=(am?am:L);this.dataset_check_url=converted_datasets_state_url;if(!j.id_counter){j.id_counter=0}this.container_div=$("<div />").addClass("track").attr("id","track_"+j.id_counter++).css("position","relative");if(!this.hidden){this.header_div=$("<div class='track-header' />").appendTo(this.container_div);if(this.view.editor){this.drag_div=$("<div/>").addClass(this.drag_handle_class).appendTo(this.header_div)}this.name_div=$("<div class='menubutton popup' />").appendTo(this.header_div);this.name_div.text(this.name);this.name_div.attr("id",this.name.replace(/\s+/g,"-").replace(/[^a-zA-Z0-9\-]/g,"").toLowerCase())}this.content_div=$("<div class='track-content'>").appendTo(this.container_div);this.container.content_div.append(this.container_div)};q(j.prototype,r.prototype,{get_type:function(){if(this instanceof ad){return"LabelTrack"}else{if(this instanceof B){return"ReferenceTrack"}else{if(this instanceof k){return"LineTrack"}else{if(this instanceof Y){return"ReadTrack"}else{if(this instanceof W){return"ToolDataFeatureTrack"}else{if(this instanceof V){return"VcfTrack"}else{if(this instanceof e){return"FeatureTrack"}}}}}}}return""},init:function(){var ah=this;ah.enabled=false;ah.tile_cache.clear();ah.data_manager.clear();ah.initial_canvas=undefined;ah.content_div.css("height","auto");ah.container_div.removeClass("nodata error pending");if(!ah.dataset_id){return}$.getJSON(converted_datasets_state_url,{hda_ldda:ah.hda_ldda,dataset_id:ah.dataset_id,chrom:ah.view.chrom},function(ai){if(!ai||ai==="error"||ai.kind==="error"){ah.container_div.addClass("error");ah.content_div.text(p);if(ai.message){var aj=$(" <a href='javascript:void(0);'></a>").text("View error").click(function(){show_modal("Trackster Error","<pre>"+ai.message+"</pre>",{Close:hide_modal})});ah.content_div.append(aj)}}else{if(ai==="no converter"){ah.container_div.addClass("error");ah.content_div.text(K)}else{if(ai==="no data"||(ai.data!==undefined&&(ai.data===null||ai.data.length===0))){ah.container_div.addClass("nodata");ah.content_div.text(F)}else{if(ai==="pending"){ah.container_div.addClass("pending");ah.content_div.text(u);setTimeout(function(){ah.init()},ah.data_query_wait)}else{if(ai.status==="data"){if(ai.valid_chroms){ah.valid_chroms=ai.valid_chroms;ah.make_name_popup_menu()}ah.content_div.text(aa);if(ah.view.chrom){ah.content_div.text("");ah.content_div.css("height",ah.height_px+"px");ah.enabled=true;$.when(ah.predraw_init()).done(function(){ah.container_div.removeClass("nodata error pending");ah.request_draw()})}}}}}}})},predraw_init:function(){},});var N=function(ao,am){var ai=this,ap=ai.view;n(ai.container_div,ai.drag_handle_class,".group",ai);this.filters_manager=new ac(this,(ao!==undefined?ao:{}));this.filters_available=false;this.filters_visible=false;this.tool=(am!==undefined&&obj_length(am)>0?new s(this,am):undefined);this.is_overview=false;if(ai.hidden){return}if(this.filters_manager){this.filters_div=this.filters_manager.parent_div;this.header_div.after(this.filters_div)}if(this.tool){this.dynamic_tool_div=this.tool.parent_div;this.header_div.after(this.dynamic_tool_div)}if(ai.display_modes!==undefined){if(ai.mode_div===undefined){ai.mode_div=$("<div class='right-float menubutton popup' />").appendTo(ai.header_div);var aj=(ai.config&&ai.config.values.mode?ai.config.values.mode:ai.display_modes[0]);ai.mode=aj;ai.mode_div.text(aj);var ah={};for(var ak=0,an=ai.display_modes.length;ak<an;ak++){var al=ai.display_modes[ak];ah[al]=function(aq){return function(){ai.change_mode(aq)}}(al)}make_popupmenu(ai.mode_div,ah)}else{ai.mode_div.hide()}}this.make_name_popup_menu()};q(N.prototype,r.prototype,j.prototype,{to_json:function(){return{track_type:this.get_type(),name:this.name,hda_ldda:this.hda_ldda,dataset_id:this.dataset_id,prefs:this.prefs,mode:this.mode,}},change_mode:function(ai){var ah=this;ah.mode_div.text(ai);ah.mode=ai;ah.config.values.mode=ai;ah.tile_cache.clear();ah.request_draw();return ah},make_name_popup_menu:function(){var ai=this;var ah={};ah[(this.is_overview?"Hide overview":"Set as overview")]=function(){if(ai.is_overview){ai.view.reset_overview()}else{ai.view.set_overview(ai)}};ah["Edit configuration"]=function(){var am=function(){hide_modal();$(window).unbind("keypress.check_enter_esc")},ak=function(){ai.config.update_from_form($(".dialog-box"));hide_modal();$(window).unbind("keypress.check_enter_esc")},al=function(an){if((an.keyCode||an.which)===27){am()}else{if((an.keyCode||an.which)===13){ak()}}};$(window).bind("keypress.check_enter_esc",al);show_modal("Configure Track",ai.config.build_form(),{Cancel:am,OK:ak})};if(ai.filters_available>0){var aj=(ai.filters_div.is(":visible")?"Hide filters":"Show filters");ah[aj]=function(){ai.filters_visible=(ai.filters_div.is(":visible"));if(ai.filters_visible){ai.filters_manager.reset_filters()}ai.filters_div.toggle();ai.make_name_popup_menu()}}if(ai.tool){var aj=(ai.dynamic_tool_div.is(":visible")?"Hide tool":"Show tool");ah[aj]=function(){if(!ai.dynamic_tool_div.is(":visible")){ai.set_name(ai.name+ai.tool_region_and_parameters_str())}else{menu_option_text="Show dynamic tool";ai.revert_name()}ai.dynamic_tool_div.toggle();ai.make_name_popup_menu()}}ah.Remove=function(){ai.remove()};make_popupmenu(ai.name_div,ah)},set_is_overview:function(ah){this.is_overview=ah;this.make_name_popup_menu()},get_overview_tile:function(){var ah=this;view=ah.view,resolution=Math.pow(D,Math.ceil(Math.log((view.max_high-view.max_low)/S)/Math.log(D))),view_width=view.container.width(),w_scale=view_width/(view.max_high-view.max_low),overview_tile=$.Deferred();$.when(ah.data_manager.get_data(view.max_low,view.max_high,"Auto",resolution,ah.data_url_extra_params)).then(function(ai){var ak=ah._gen_tile_cache_key(view_width,w_scale,0),am=ah.tile_cache.get(ak);if(!am){am=ah.draw_tile(ai,"Auto",resolution,0,w_scale);ah.tile_cache.set(ak,am)}var ap=$(am.canvas.find("canvas")),al=ap.clone(),ao=ap.get(0).getContext("2d"),aj=al.get(0).getContext("2d"),an=ao.getImageData(0,0,ao.canvas.width,ao.canvas.height);aj.putImageData(an,-ah.left_offset,(am.data.dataset_type==="summary_tree"?U:0));new_tile=new b(ah,-1,resolution,al);overview_tile.resolve(new_tile)});return overview_tile},_gen_tile_cache_key:function(ai,aj,ah){return ai+"_"+aj+"_"+ah},request_draw:function(ai,ah){this.view.request_redraw(false,ai,ah,this)},_draw:function(aj,ar){if(!this.enabled){return}if(!(this instanceof B)&&(!this.dataset_id)){return}var aq=this.view.low,an=this.view.high,ao=an-aq,ak=this.view.container.width(),av=ak/ao,am=this.view.resolution,au=$("<div style='position: relative;'></div>");if(!ar){this.content_div.children().remove()}this.content_div.append(au);this.max_height=0;var ai=Math.floor(aq/am/S);var ap=true;var at=[];var ah=0;while((ai*S*am)<an){tile=this.draw_helper(aj,ak,ai,am,au,av);if(tile){at.push(tile)}else{ap=false}ai+=1;ah++}var al=this;if(ap){al.postdraw_actions(at,ak,av,ar)}},postdraw_actions:function(al,am,an,ah){var aj=this;var ak=false;for(var ai=0;ai<al.length;ai++){if(al[ai].message){ak=true;break}}if(ak){for(var ai=0;ai<al.length;ai++){tile=al[ai];if(!tile.message){tile.canvas.css("padding-top",E)}}}},draw_helper:function(ai,aj,ak,an,au,ay,av,ao){var al=this,at=this._gen_tile_cache_key(aj,ay,ak),ap=ak*S*an,ax=ap+S*an;var aq=(ai?undefined:al.tile_cache.get(at));if(aq){al.show_tile(aq,au,ay);return aq}var ar=function(az){return("isResolved" in az)};var am=true;var ah=al.data_manager.get_data(ap,ax,al.mode,an,al.data_url_extra_params);if(ar(ah)){am=false}var aw;if(view.reference_track&&ay>view.canvas_manager.char_width_px){aw=view.reference_track.data_manager.get_data(ap,ax,al.mode,an,view.reference_track.data_url_extra_params);if(ar(aw)){am=false}}if(am){q(ah,ao);var aq=al.draw_tile(ah,al.mode,an,ak,ay,aw);if(aq!==undefined){al.tile_cache.set(at,aq);al.show_tile(aq,au,ay)}return aq}$.when(ah,aw).then(function(){view.request_redraw()});return null},show_tile:function(ao,aq,ar){var aj=this,ai=ao.canvas,an=ai;if(ao.message){var at=$("<div/>"),ap=$("<div/>").addClass("tile-message").text(ao.message).css({height:E-1,width:ao.canvas.width}).appendTo(at),al=$("<a href='javascript:void(0);'/>").addClass("icon more-down").appendTo(ap),ah=$("<a href='javascript:void(0);'/>").addClass("icon more-across").appendTo(ap);at.append(ai);an=at;al.click(function(){ao.stale=true;aj.data_manager.get_more_data(ao.low,ao.high,aj.mode,ao.resolution,{},aj.data_manager.DEEP_DATA_REQ);aj.request_draw()}).dblclick(function(au){au.stopPropagation()});ah.click(function(){ao.stale=true;aj.data_manager.get_more_data(ao.low,ao.high,aj.mode,ao.resolution,{},aj.data_manager.BROAD_DATA_REQ);aj.request_draw()}).dblclick(function(au){au.stopPropagation()})}ao.predisplay_actions();var am=this.view.high-this.view.low,ak=(ao.low-this.view.low)*ar;if(this.left_offset){ak-=this.left_offset}an.css({position:"absolute",top:0,left:ak,height:""});aq.append(an);aj.max_height=Math.max(aj.max_height,an.height());aj.content_div.css("height",aj.max_height+"px");aq.children().css("height",aj.max_height+"px")},_get_tile_bounds:function(ah,ai){var ak=ah*S*ai,al=S*ai,aj=(ak+al<=this.view.max_high?ak+al:this.view.max_high);return[ak,aj]},tool_region_and_parameters_str:function(aj,ah,ak){var ai=this,al=(aj!==undefined&&ah!==undefined&&ak!==undefined?aj+":"+ah+"-"+ak:"all");return" - region=["+al+"], parameters=["+ai.tool.get_param_values().join(", ")+"]"}});var ad=function(ai,ah){this.hidden=true;j.call(this,"label",ai,ah,{});this.container_div.addClass("label-track")};q(ad.prototype,j.prototype,{init:function(){this.enabled=true},_draw:function(){var aj=this.view,ak=aj.high-aj.low,an=Math.floor(Math.pow(10,Math.floor(Math.log(ak)/Math.log(10)))),ah=Math.floor(aj.low/an)*an,al=this.view.container.width(),ai=$("<div style='position: relative; height: 1.3em;'></div>");while(ah<aj.high){var am=(ah-aj.low)/ak*al;ai.append($("<div class='label'>"+commatize(ah)+"</div>").css({position:"absolute",left:am-1}));ah+=an}this.content_div.children(":first").remove();this.content_div.append(ai)}});var B=function(ah){this.hidden=true;j.call(this,"reference",ah,{content_div:ah.top_labeltrack},{});N.call(this);ah.reference_track=this;this.left_offset=200;this.height_px=12;this.container_div.addClass("reference-track");this.content_div.css("background","none");this.content_div.css("min-height","0px");this.content_div.css("border","none");this.data_url=reference_url;this.data_url_extra_params={dbkey:ah.dbkey};this.data_manager=new I(C,this,false);this.tile_cache=new c(v)};q(B.prototype,r.prototype,N.prototype,{init:function(){this.enabled=true},draw_tile:function(ar,an,am,ai,at){var al=this,aj=S*am;if(at>this.view.canvas_manager.char_width_px){if(ar===null){al.content_div.css("height","0px");return}var ak=this.view.canvas_manager.new_canvas();var aq=ak.getContext("2d");ak.width=Math.ceil(aj*at+al.left_offset);ak.height=al.height_px;aq.font=aq.canvas.manager.default_font;aq.textAlign="center";ar=ar.data;for(var ao=0,ap=ar.length;ao<ap;ao++){var ah=Math.round(ao*at);aq.fillText(ar[ao],ah+al.left_offset,10)}return new b(al,ai,am,ak,ar)}this.content_div.css("height","0px")}});var k=function(am,ak,aj,an,ah,al){var ai=this;this.display_modes=["Histogram","Line","Filled","Intensity"];this.mode="Histogram";j.call(this,am,ak,aj,al);N.call(this);this.min_height_px=16;this.max_height_px=400;this.height_px=80;this.hda_ldda=an;this.dataset_id=ah;this.original_dataset_id=ah;this.data_manager=new T(C,this);this.tile_cache=new c(v);this.left_offset=0;this.config=new G({track:this,params:[{key:"name",label:"Name",type:"text",default_value:am},{key:"color",label:"Color",type:"color",default_value:"black"},{key:"min_value",label:"Min Value",type:"float",default_value:undefined},{key:"max_value",label:"Max Value",type:"float",default_value:undefined},{key:"mode",type:"string",default_value:this.mode,hidden:true},{key:"height",type:"int",default_value:this.height_px,hidden:true}],saved_values:al,onchange:function(){ai.set_name(ai.prefs.name);ai.vertical_range=ai.prefs.max_value-ai.prefs.min_value;$("#linetrack_"+ai.dataset_id+"_minval").text(ai.prefs.min_value);$("#linetrack_"+ai.dataset_id+"_maxval").text(ai.prefs.max_value);ai.tile_cache.clear();ai.request_draw()}});this.prefs=this.config.values;this.height_px=this.config.values.height;this.vertical_range=this.config.values.max_value-this.config.values.min_value;this.add_resize_handle()};q(k.prototype,r.prototype,N.prototype,{add_resize_handle:function(){var ah=this;var ak=false;var aj=false;var ai=$("<div class='track-resize'>");$(ah.container_div).hover(function(){ak=true;ai.show()},function(){ak=false;if(!aj){ai.hide()}});ai.hide().bind("dragstart",function(al,am){aj=true;am.original_height=$(ah.content_div).height()}).bind("drag",function(am,an){var al=Math.min(Math.max(an.original_height+an.deltaY,ah.min_height_px),ah.max_height_px);$(ah.content_div).css("height",al);ah.height_px=al;ah.request_draw(true)}).bind("dragend",function(al,am){ah.tile_cache.clear();aj=false;if(!ak){ai.hide()}ah.config.values.height=ah.height_px}).appendTo(ah.container_div)},predraw_init:function(){var ah=this;ah.vertical_range=undefined;return $.getJSON(ah.data_url,{stats:true,chrom:ah.view.chrom,low:null,high:null,hda_ldda:ah.hda_ldda,dataset_id:ah.dataset_id},function(ai){ah.container_div.addClass("line-track");var ak=ai.data;if(isNaN(parseFloat(ah.prefs.min_value))||isNaN(parseFloat(ah.prefs.max_value))){ah.prefs.min_value=ak.min;ah.prefs.max_value=ak.max;$("#track_"+ah.dataset_id+"_minval").val(ah.prefs.min_value);$("#track_"+ah.dataset_id+"_maxval").val(ah.prefs.max_value)}ah.vertical_range=ah.prefs.max_value-ah.prefs.min_value;ah.total_frequency=ak.total_frequency;ah.container_div.find(".yaxislabel").remove();var al=$("<div />").addClass("yaxislabel").attr("id","linetrack_"+ah.dataset_id+"_minval").text(ab(ah.prefs.min_value,3));var aj=$("<div />").addClass("yaxislabel").attr("id","linetrack_"+ah.dataset_id+"_maxval").text(ab(ah.prefs.max_value,3));aj.css({position:"absolute",top:"24px",left:"10px"});aj.prependTo(ah.container_div);al.css({position:"absolute",bottom:"2px",left:"10px"});al.prependTo(ah.container_div)})},draw_tile:function(au,am,al,aj,at){if(this.vertical_range===undefined){return}var ah=this._get_tile_bounds(aj,al),an=ah[0],ar=ah[1],ai=Math.ceil((ar-an)*at),ap=this.height_px;var ak=this.view.canvas_manager.new_canvas();ak.width=ai,ak.height=ap;var aq=ak.getContext("2d");var ao=new M.LinePainter(au.data,an,ar,this.prefs,am);ao.draw(aq,ai,ap);return new b(this.track,aj,al,ak,au.data)}});var e=function(ah,an,ai,am,ap,ao,ak,al){var aj=this;this.display_modes=["Auto","Histogram","Dense","Squish","Pack"];j.call(this,ah,an,ai,ao);N.call(this,ak,al);this.config=new G({track:this,params:[{key:"name",label:"Name",type:"text",default_value:ah},{key:"block_color",label:"Block color",type:"color",default_value:"#444"},{key:"label_color",label:"Label color",type:"color",default_value:"black"},{key:"show_counts",label:"Show summary counts",type:"bool",default_value:true},{key:"mode",type:"string",default_value:this.mode,hidden:true},],saved_values:ao,onchange:function(){aj.set_name(aj.prefs.name);aj.tile_cache.clear();aj.request_draw()}});this.prefs=this.config.values;this.height_px=0;this.container_div.addClass("feature-track");this.hda_ldda=am;this.dataset_id=ap;this.original_dataset_id=ap;this.show_labels_scale=0.001;this.showing_details=false;this.summary_draw_height=30;this.inc_slots={};this.start_end_dct={};this.tile_cache=new c(d);this.data_manager=new T(20,this);this.left_offset=200;this.painter=M.LinkedFeaturePainter};q(e.prototype,r.prototype,N.prototype,{postdraw_actions:function(ax,ah,ay,aw){N.prototype.postdraw_actions.call(this,ax,aw);var ak=this;if(aw){var am=ak.content_div.children();var an=false;for(var al=am.length-1,ar=0;al>=ar;al--){var aj=$(am[al]);if(an){aj.remove()}else{if(aj.children().length!==0){an=true}}}}if(ak.mode=="Histogram"){var aq=-1;for(var al=0;al<ax.length;al++){var av=ax[al].max_val;if(av>aq){aq=av}}for(var al=0;al<ax.length;al++){var au=ax[al];if(au.max_val!==aq){au.canvas.remove();ak.draw_helper(true,ah,au.index,au.resolution,au.canvas.parent(),ay,[],{max:aq})}}}if(ak.filters_manager){var ai=ak.filters_manager.filters;for(var ap=0;ap<ai.length;ap++){ai[ap].update_ui_elt()}var ao=false,at;for(var al=0;al<ax.length;al++){if(ax[al].data.length){at=ax[al].data[0];for(var ap=0;ap<ai.length;ap++){if(ai[ap].applies_to(at)){ao=true;break}}}}if(ak.filters_available!==ao){ak.filters_available=ao;if(!ak.filters_available){ak.filters_div.hide()}ak.make_name_popup_menu()}}},update_auto_mode:function(ah){if(this.mode=="Auto"){if(ah=="no_detail"){ah="feature spans"}else{if(ah=="summary_tree"){ah="coverage histogram"}}this.mode_div.text("Auto ("+ah+")")}},incremental_slots:function(al,ai,ak){var aj=this.view.canvas_manager.dummy_context,ah=this.inc_slots[al];if(!ah||(ah.mode!==ak)){ah=new (t.FeatureSlotter)(al,ak==="Pack",A,function(am){return aj.measureText(am)});ah.mode=ak;this.inc_slots[al]=ah}return ah.slot_features(ai)},get_summary_tree_data:function(al,ao,aj,ax){if(ax>aj-ao){ax=aj-ao}var at=Math.floor((aj-ao)/ax),aw=[],ak=0;var am=0,an=0,ar,av=0,ap=[],au,aq;var ai=function(aA,az,aB,ay){aA[0]=az+aB*ay;aA[1]=az+(aB+1)*ay};while(av<ax&&am!==al.length){var ah=false;for(;av<ax&&!ah;av++){ai(ap,ao,av,at);for(an=am;an<al.length;an++){ar=al[an].slice(1,3);if(is_overlap(ar,ap)){ah=true;break}}if(ah){break}}data_start_index=an;aw[aw.length]=au=[ap[0],0];for(;an<al.length;an++){ar=al[an].slice(1,3);if(is_overlap(ar,ap)){au[1]++}else{break}}if(au[1]>ak){ak=au[1]}av++}return{max:ak,delta:at,data:aw}},draw_tile:function(aw,az,aE,aI,ar,ak){var aB=this,am=aB._get_tile_bounds(aI,aE),aL=am[0],ai=am[1],ay=ai-aL,aC=Math.ceil(ay*ar),aP=25,al=this.left_offset,ax,an;if(az==="Auto"){if(aw.dataset_type==="summary_tree"){az=aw.dataset_type}else{if(aw.extra_info==="no_detail"){az="no_detail"}else{var aO=aw.data;if(this.view.high-this.view.low>J){az="Squish"}else{az="Pack"}}}this.update_auto_mode(az)}if(az==="summary_tree"||az==="Histogram"){an=this.summary_draw_height;this.container_div.find(".yaxislabel").remove();var ah=$("<div />").addClass("yaxislabel");ah.text(aw.max);ah.css({position:"absolute",top:"24px",left:"10px",color:this.prefs.label_color});ah.prependTo(this.container_div);var aj=this.view.canvas_manager.new_canvas();aj.width=aC+al;aj.height=an+U;if(aw.dataset_type!="summary_tree"){var at=this.get_summary_tree_data(aw.data,aL,ai,200);if(aw.max){at.max=aw.max}aw=at}var aM=new M.SummaryTreePainter(aw,aL,ai,this.prefs);var aD=aj.getContext("2d");aD.translate(al,U);aM.draw(aD,aC,an);return new l(aB,aI,aE,aj,aw.data,aw.max)}var ax,ap=1;if(az==="no_detail"||az==="Squish"||az==="Pack"){ap=this.incremental_slots(ar,aw.data,az);ax=this.inc_slots[ar].slots}var aq=[];if(aw.data){var au=this.filters_manager.filters;for(var aF=0,aH=aw.data.length;aF<aH;aF++){var ao=aw.data[aF];var aG=false;var av;for(var aK=0,aN=au.length;aK<aN;aK++){av=au[aK];av.update_attrs(ao);if(!av.keep(ao)){aG=true;break}}if(!aG){aq.push(ao)}}}var aA=(this.filters_manager.alpha_filter?new P(this.filters_manager.alpha_filter):null);var aM=new (this.painter)(aq,aL,ai,this.prefs,az,aA,ak);var an=Math.max(af,aM.get_required_height(ap));var aj=this.view.canvas_manager.new_canvas();var aJ=null;aj.width=aC+al;aj.height=an;var aD=aj.getContext("2d");aD.fillStyle=this.prefs.block_color;aD.font=aD.canvas.manager.default_font;aD.textAlign="right";this.container_div.find(".yaxislabel").remove();if(aw.data){aD.translate(al,0);aJ=aM.draw(aD,aC,an,ax);aJ.translation=-al}return new Q(aB,aI,aE,aj,aw.data,az,aw.message,aJ)}});var V=function(al,aj,ai,an,ah,ak,am){e.call(this,al,aj,ai,an,ah,ak,am);this.painter=M.VariantPainter};q(V.prototype,r.prototype,N.prototype,e.prototype);var Y=function(al,aj,ai,an,ah,ak,am){e.call(this,al,aj,ai,an,ah,ak,am);this.config=new G({track:this,params:[{key:"name",label:"Name",type:"text",default_value:al},{key:"block_color",label:"Block color",type:"color",default_value:"#444"},{key:"label_color",label:"Label color",type:"color",default_value:"black"},{key:"show_insertions",label:"Show insertions",type:"bool",default_value:false},{key:"show_differences",label:"Show differences only",type:"bool",default_value:true},{key:"show_counts",label:"Show summary counts",type:"bool",default_value:true},{key:"mode",type:"string",default_value:this.mode,hidden:true},],saved_values:ak,onchange:function(){this.track.set_name(this.track.prefs.name);this.track.tile_cache.clear();this.track.request_draw()}});this.prefs=this.config.values;this.painter=M.ReadPainter;this.make_name_popup_menu()};q(Y.prototype,r.prototype,N.prototype,e.prototype);var W=function(al,aj,ai,an,ah,ak,am){e.call(this,al,aj,ai,an,ah,ak,am,{});this.data_url=raw_data_url;this.data_query_wait=1000;this.dataset_check_url=dataset_state_url};q(W.prototype,r.prototype,N.prototype,e.prototype,{predraw_init:function(){var ai=this;var ah=function(){if(ai.data_manager.size()===0){setTimeout(ah,300)}else{ai.data_url=default_data_url;ai.data_query_wait=L;ai.dataset_state_url=converted_datasets_state_url;$.getJSON(ai.dataset_state_url,{dataset_id:ai.dataset_id,hda_ldda:ai.hda_ldda},function(aj){})}};ah()}});Z.View=ae;Z.DrawableGroup=R;Z.LineTrack=k;Z.FeatureTrack=e;Z.ReadTrack=Y};var slotting_module=function(c,b){var e=c("class").extend;var d=2,a=5;b.FeatureSlotter=function(j,h,f,g){this.slots={};this.start_end_dct={};this.w_scale=j;this.include_label=h;this.max_rows=f;this.measureText=g};e(b.FeatureSlotter.prototype,{slot_features:function(m){var p=this.w_scale,s=this.slots,h=this.start_end_dct,y=[],A=[],n=0,z=this.max_rows;for(var w=0,x=m.length;w<x;w++){var l=m[w],o=l[0];if(s[o]!==undefined){n=Math.max(n,s[o]);A.push(s[o])}else{y.push(w)}}var q=function(G,H){for(var F=0;F<=z;F++){var D=false,I=h[F];if(I!==undefined){for(var C=0,E=I.length;C<E;C++){var B=I[C];if(H>B[0]&&G<B[1]){D=true;break}}}if(!D){return F}}return -1};for(var w=0,x=y.length;w<x;w++){var l=m[y[w]],o=l[0],u=l[1],f=l[2],r=l[3],g=Math.floor(u*p),k=Math.ceil(f*p),v=this.measureText(r).width,j;if(r!==undefined&&this.include_label){v+=(d+a);if(g-v>=0){g-=v;j="left"}else{k+=v;j="right"}}var t=q(g,k);if(t>=0){if(h[t]===undefined){h[t]=[]}h[t].push([g,k]);s[o]=t;n=Math.max(n,t)}else{}}return n+1}})};var painters_module=function(k,y){var v=k("class").extend;var q=function(J,B,H,A,G,E){if(E===undefined){E=4}var D=A-B;var C=G-H;var F=Math.floor(Math.sqrt(D*D+C*C)/E);var K=D/F;var I=C/F;var z;for(z=0;z<F;z++,B+=K,H+=I){if(z%2!==0){continue}J.fillRect(B,H,E,1)}};var r=function(B,A,z,E){var D=A-E/2,C=A+E/2,F=z-Math.sqrt(E*3/2);B.beginPath();B.moveTo(D,F);B.lineTo(C,F);B.lineTo(A,z);B.lineTo(D,F);B.strokeStyle=this.fillStyle;B.fill();B.stroke();B.closePath()};var g=function(z){this.default_alpha=(z?z:1)};g.prototype.gen_alpha=function(z){return this.default_alpha};var n=function(B,D,z,A,C){this.data=B;this.view_start=D;this.view_end=z;this.prefs=v({},this.default_prefs,A);this.mode=C};n.prototype.default_prefs={};var w=function(B,D,z,A,C){n.call(this,B,D,z,A,C)};w.prototype.default_prefs={show_counts:false};w.prototype.draw=function(M,z,L){var E=this.view_start,O=this.view_end-this.view_start,N=z/O;var J=this.data.data,I=this.data.delta,G=this.data.max,B=L;delta_x_px=Math.ceil(I*N);M.save();for(var C=0,D=J.length;C<D;C++){var H=Math.floor((J[C][0]-E)*N);var F=J[C][1];if(!F){continue}var K=F/G*L;if(F!==0&&K<1){K=1}M.fillStyle=this.prefs.block_color;M.fillRect(H,B-K,delta_x_px,K);var A=4;if(this.prefs.show_counts&&(M.measureText(F).width+A)<delta_x_px){M.fillStyle=this.prefs.label_color;M.textAlign="center";M.fillText(F,H+(delta_x_px/2),10)}}M.restore()};var c=function(z,D,F,G,B){n.call(this,z,D,F,G,B);if(this.prefs.min_value===undefined){var H=Infinity;for(var A=0,C=this.data.length;A<C;A++){H=Math.min(H,this.data[A][1])}this.prefs.min_value=H}if(this.prefs.max_value===undefined){var E=-Infinity;for(var A=0,C=this.data.length;A<C;A++){E=Math.max(E,this.data[A][1])}this.prefs.max_value=E}};c.prototype.default_prefs={min_value:undefined,max_value:undefined,mode:"Histogram",color:"#000",overflow_color:"#F66"};c.prototype.draw=function(N,M,K){var F=false,H=this.prefs.min_value,D=this.prefs.max_value,J=D-H,z=K,A=this.view_start,L=this.view_end-this.view_start,B=M/L,I=this.mode,T=this.data;N.save();var U=Math.round(K+H/J*K);if(I!=="Intensity"){N.fillStyle="#aaa";N.fillRect(0,U,M,1)}N.beginPath();var R,E,C;if(T.length>1){C=Math.ceil((T[1][0]-T[0][0])*B)}else{C=10}for(var O=0,P=T.length;O<P;O++){N.fillStyle=this.prefs.color;R=Math.round((T[O][0]-A)*B);E=T[O][1];var Q=false,G=false;if(E===null){if(F&&I==="Filled"){N.lineTo(R,z)}F=false;continue}if(E<H){G=true;E=H}else{if(E>D){Q=true;E=D}}if(I==="Histogram"){E=Math.round(E/J*z);N.fillRect(R,U,C,-E)}else{if(I==="Intensity"){E=255-Math.floor((E-H)/J*255);N.fillStyle="rgb("+E+","+E+","+E+")";N.fillRect(R,0,C,z)}else{E=Math.round(z-(E-H)/J*z);if(F){N.lineTo(R,E)}else{F=true;if(I==="Filled"){N.moveTo(R,z);N.lineTo(R,E)}else{N.moveTo(R,E)}}}}N.fillStyle=this.prefs.overflow_color;if(Q||G){var S;if(I==="Histogram"||I==="Intensity"){S=C}else{R-=2;S=4}if(Q){N.fillRect(R,0,S,3)}if(G){N.fillRect(R,z-3,S,3)}}N.fillStyle=this.prefs.color}if(I==="Filled"){if(F){N.lineTo(R,U);N.lineTo(0,U)}N.fill()}else{N.stroke()}N.restore()};var o=function(z){this.feature_positions={};this.slot_height=z;this.translation=0};o.prototype.map_feature_data=function(A,C,z,B){if(!this.feature_positions[C]){this.feature_positions[C]=[]}this.feature_positions[C].push({data:A,x_start:z,x_end:B})};o.prototype.get_feature_data=function(z,D){var C=Math.floor(D/this.slot_height),B;if(!this.feature_positions[C]){return null}z+=this.translation;for(var A=0;A<this.feature_positions[C].length;A++){B=this.feature_positions[C][A];if(z>=B.x_start&&z<=B.x_end){return B.data}}};var p=function(C,E,z,B,D,A){n.call(this,C,E,z,B,D);this.alpha_generator=(A?A:new g())};p.prototype.default_prefs={block_color:"#FFF",connector_color:"#FFF"};v(p.prototype,{get_required_height:function(A){var z=y_scale=this.get_row_height(),B=this.mode;if(B==="no_detail"||B==="Squish"||B==="Pack"){z=A*y_scale}return z+Math.max(Math.round(y_scale/2),5)},draw:function(L,J,H,G){var R=this.data,E=this.view_start,N=this.view_end;L.save();L.fillStyle=this.prefs.block_color;L.textAlign="right";var I=this.view_end-this.view_start,F=J/I,M=this.get_row_height(),Q=new o(M),C;for(var O=0,P=R.length;O<P;O++){var B=R[O],D=B[0],K=B[1],z=B[2],A=(G&&G[D]!==undefined?G[D]:null);if((K<N&&z>E)&&(this.mode=="Dense"||A!==null)){C=this.draw_element(L,this.mode,B,A,E,N,F,M,J);Q.map_feature_data(B,A,C[0],C[1])}}L.restore();return Q},draw_element:function(F,B,H,D,C,E,G,A,z){console.log("WARNING: Unimplemented function.");return[0,0]}});var d=10,j=3,m=5,x=10,f=1,t=3,e=3,a=9,l=2,h="#ccc";var s=function(C,E,z,B,D,A){p.call(this,C,E,z,B,D,A)};v(s.prototype,p.prototype,{get_row_height:function(){var A=this.mode,z;if(A==="Dense"){z=d}else{if(A==="no_detail"){z=j}else{if(A==="Squish"){z=m}else{z=x}}}return z},draw_element:function(L,E,T,G,N,ae,ai,ak,z){var Q=T[0],ag=T[1],Y=T[2],O=T[3],Z=Math.floor(Math.max(0,(ag-N)*ai)),M=Math.ceil(Math.min(z,Math.max(0,(Y-N)*ai))),X=Z,aj=M,W=(E==="Dense"?0:(0+G))*ak,K,ac,P=null,al=null,C=this.prefs.block_color,ab=this.prefs.label_color;L.globalAlpha=this.alpha_generator.gen_alpha(T);if(E=="Dense"){G=1}if(E==="no_detail"){L.fillStyle=C;L.fillRect(Z,W+5,M-Z,f)}else{var J=T[4],V=T[5],aa=T[6],D=T[7];if(V&&aa){P=Math.floor(Math.max(0,(V-N)*ai));al=Math.ceil(Math.min(z,Math.max(0,(aa-N)*ai)))}var ah,R;if(E==="Squish"||E==="Dense"){ah=1;R=e}else{ah=5;R=a}if(!D){if(T.strand){if(T.strand==="+"){L.fillStyle=L.canvas.manager.get_pattern("right_strand_inv")}else{if(T.strand==="-"){L.fillStyle=L.canvas.manager.get_pattern("left_strand_inv")}}}else{L.fillStyle=C}L.fillRect(Z,W,M-Z,R)}else{var I,S;if(E==="Squish"||E==="Dense"){L.fillStyle=h;I=W+Math.floor(e/2)+1;S=1}else{if(J){var I=W;var S=R;if(J==="+"){L.fillStyle=L.canvas.manager.get_pattern("right_strand")}else{if(J==="-"){L.fillStyle=L.canvas.manager.get_pattern("left_strand")}}}else{L.fillStyle=h;I+=(e/2)+1;S=1}}L.fillRect(Z,I,M-Z,S);for(var af=0,B=D.length;af<B;af++){var F=D[af],A=Math.floor(Math.max(0,(F[0]-N)*ai)),U=Math.ceil(Math.min(z,Math.max((F[1]-N)*ai)));if(A>U){continue}L.fillStyle=C;L.fillRect(A,W+(R-ah)/2+1,U-A,ah);if(P!==undefined&&aa>V&&!(A>al||U<P)){var ad=Math.max(A,P),H=Math.min(U,al);L.fillRect(ad,W+1,H-ad,R);if(D.length==1&&E=="Pack"){if(J==="+"){L.fillStyle=L.canvas.manager.get_pattern("right_strand_inv")}else{if(J==="-"){L.fillStyle=L.canvas.manager.get_pattern("left_strand_inv")}}if(ad+14<H){ad+=2;H-=2}L.fillRect(ad,W+1,H-ad,R)}}}}if(E==="Pack"&&ag>N){L.fillStyle=ab;if(N===0&&Z-L.measureText(O).width<0){L.textAlign="left";L.fillText(O,M+l,W+8);aj+=L.measureText(O).width+l}else{L.textAlign="right";L.fillText(O,Z-l,W+8);X-=L.measureText(O).width+l}}}L.globalAlpha=1;return[X,aj]}});var b=function(C,E,z,B,D,A){p.call(this,C,E,z,B,D,A)};v(b.prototype,p.prototype,{draw_element:function(S,N,H,D,V,B,K,T,Q){var H=data[i],J=H[0],R=H[1],C=H[2],M=H[3],F=Math.floor(Math.max(0,(R-V)*K)),I=Math.ceil(Math.min(Q,Math.max(0,(C-V)*K))),E=(N==="Dense"?0:(0+D))*T,z,W,A=null,L=null;if(no_label){S.fillStyle=block_color;S.fillRect(F+left_offset,E+5,I-F,1)}else{var U=H[4],P=H[5],G=H[6];z=9;W=1;S.fillRect(F+left_offset,E,I-F,z);if(N!=="Dense"&&M!==undefined&&R>V){S.fillStyle=label_color;if(V===0&&F-S.measureText(M).width<0){S.textAlign="left";S.fillText(M,I+2+left_offset,E+8)}else{S.textAlign="right";S.fillText(M,F-2+left_offset,E+8)}S.fillStyle=block_color}var O=U+" / "+P;if(R>V&&S.measureText(O).width<(I-F)){S.fillStyle="white";S.textAlign="center";S.fillText(O,left_offset+F+(I-F)/2,E+8);S.fillStyle=block_color}}return[F,I]}});var u=function(D,F,z,C,E,A,B){p.call(this,D,F,z,C,E,A);this.ref_seq=B};u.prototype.default_prefs=v({},p.prototype.default_prefs,{show_insertions:false});v(u.prototype,p.prototype,{get_row_height:function(){var z,A=this.mode;if(A==="Dense"){z=d}else{if(A==="Squish"){z=m}else{z=x;if(this.prefs.show_insertions){z*=2}}}return z},draw_read:function(V,Q,M,aa,B,U,J,G,F){V.textAlign="center";var T=this,A=[aa,B],P=0,W=0,S=0;ref_seq=this.ref_seq,char_width_px=V.canvas.manager.char_width_px;var af=[];if((Q==="Pack"||this.mode==="Auto")&&G!==undefined&&M>char_width_px){S=Math.round(M/2)}if(!J){J=[[0,G.length]]}for(var N=0,Y=J.length;N<Y;N++){var K=J[N],C="MIDNSHP=X"[K[0]],O=K[1];if(C==="H"||C==="S"){P-=O}var H=U+P,ae=Math.floor(Math.max(0,(H-aa)*M)),I=Math.floor(Math.max(0,(H+O-aa)*M));if(ae===I){I+=1}switch(C){case"H":break;case"S":case"M":case"=":if(is_overlap([H,H+O],A)){var R=G.slice(W,W+O);if(S>0){V.fillStyle=this.prefs.block_color;V.fillRect(ae-S,F+1,I-ae,9);V.fillStyle=h;for(var ac=0,z=R.length;ac<z;ac++){if(this.prefs.show_differences&&ref_seq){var L=ref_seq[H-aa+ac];if(!L||L.toLowerCase()===R[ac].toLowerCase()){continue}}if(H+ac>=aa&&H+ac<=B){var ad=Math.floor(Math.max(0,(H+ac-aa)*M));V.fillText(R[ac],ad,F+9)}}}else{V.fillStyle=this.prefs.block_color;V.fillRect(ae,F+4,I-ae,e)}}W+=O;P+=O;break;case"N":V.fillStyle=h;V.fillRect(ae-S,F+5,I-ae,1);P+=O;break;case"D":V.fillStyle="red";V.fillRect(ae-S,F+4,I-ae,3);P+=O;break;case"P":break;case"I":var Z=ae-S;if(is_overlap([H,H+O],A)){var R=G.slice(W,W+O);if(this.prefs.show_insertions){var E=ae-(I-ae)/2;if((Q==="Pack"||this.mode==="Auto")&&G!==undefined&&M>char_width_px){V.fillStyle="yellow";V.fillRect(E-S,F-9,I-ae,9);af[af.length]={type:"triangle",data:[Z,F+4,5]};V.fillStyle=h;switch(seq_tile_overlap){case (OVERLAP_START):R=R.slice(aa-H);break;case (OVERLAP_END):R=R.slice(0,H-B);break;case (CONTAINED_BY):break;case (CONTAINS):R=R.slice(aa-H,H-B);break}for(var ac=0,z=R.length;ac<z;ac++){var ad=Math.floor(Math.max(0,(H+ac-aa)*M));V.fillText(R[ac],ad-(I-ae)/2,F)}}else{V.fillStyle="yellow";V.fillRect(E,F+(this.mode!=="Dense"?2:5),I-ae,(Q!=="Dense"?e:t))}}else{if((Q==="Pack"||this.mode==="Auto")&&G!==undefined&&M>char_width_px){af[af.length]={type:"text",data:[R.length,Z,F+9]}}else{}}}W+=O;break;case"X":W+=O;break}}V.fillStyle="yellow";var ab,D,ag;for(var X=0;X<af.length;X++){ab=af[X];D=ab.type;ag=ab.data;if(D==="text"){V.save();V.font="bold "+V.font;V.fillText(ag[0],ag[1],ag[2]);V.restore()}else{if(D=="triangle"){r(V,ag[0],ag[1],ag[2])}}}},draw_element:function(S,N,F,C,V,A,J,T,Q){var I=F[0],R=F[1],B=F[2],K=F[3],E=Math.floor(Math.max(0,(R-V)*J)),G=Math.ceil(Math.min(Q,Math.max(0,(B-V)*J))),D=(N==="Dense"?0:(0+C))*T,W=this.prefs.block_color,H=this.prefs.label_color,P=0;if((N==="Pack"||this.mode==="Auto")&&J>S.canvas.manager.char_width_px){var P=Math.round(J/2)}S.fillStyle=W;if(F[5] instanceof Array){var O=Math.floor(Math.max(0,(F[4][0]-V)*J)),M=Math.ceil(Math.min(Q,Math.max(0,(F[4][1]-V)*J))),L=Math.floor(Math.max(0,(F[5][0]-V)*J)),z=Math.ceil(Math.min(Q,Math.max(0,(F[5][1]-V)*J)));if(F[4][1]>=V&&F[4][0]<=A&&F[4][2]){this.draw_read(S,N,J,V,A,F[4][0],F[4][2],F[4][3],D)}if(F[5][1]>=V&&F[5][0]<=A&&F[5][2]){this.draw_read(S,N,J,V,A,F[5][0],F[5][2],F[5][3],D)}if(L>M){S.fillStyle=h;q(S,M-P,D+5,L-P,D+5)}}else{S.fillStyle=W;this.draw_read(S,N,J,V,A,R,F[4],F[5],D)}if(N==="Pack"&&R>V){S.fillStyle=this.prefs.label_color;var U=1;if(U===0&&E-S.measureText(K).width<0){S.textAlign="left";S.fillText(K,G+l-P,D+8)}else{S.textAlign="right";S.fillText(K,E-l-P,D+8)}S.fillStyle=W}return[0,0]}});y.AlphaGenerator=g;y.SummaryTreePainter=w;y.LinePainter=c;y.LinkedFeaturePainter=s;y.ReadPainter=u;y.VariantPainter=b};(function(d){var c={};var b=function(e){return c[e]};var a=function(f,g){var e={};g(b,e);c[f]=e};a("class",class_module);a("slotting",slotting_module);a("painters",painters_module);a("trackster",trackster_module);for(key in c.trackster){d[key]=c.trackster[key]}})(window);
\ No newline at end of file


--- a/static/scripts/packed/trackster_ui.js	Wed Oct 12 11:19:42 2011 -0400
+++ b/static/scripts/packed/trackster_ui.js	Wed Oct 12 11:19:58 2011 -0400
@@ -1,1 +1,1 @@
-var add_bookmark=function(b,a){var g=$("#bookmarks-container"),d=$("<div/>").addClass("bookmark").appendTo(g),c=$("<div/>").addClass("delete-icon-container").appendTo(d).click(function(){d.slideUp("fast");d.remove();view.has_changes=true;return false}),e=$("<a href=''/>").addClass("icon-button delete").appendTo(c),f=$("<div/>").addClass("position").appendTo(d),h=$("<a href=''/>").text(b).appendTo(f).click(function(){view.go_to(b);return false});annotation_div=get_editable_text_elt(a,true).addClass("annotation").appendTo(d);view.has_changes=true;return d};var addable_objects={LineTrack:LineTrack,FeatureTrack:FeatureTrack,ReadTrack:ReadTrack,DrawableGroup:DrawableGroup};var track_from_dict=function(c,b){var a=new addable_objects[c.track_type](c.name,view,b,c.hda_ldda,c.dataset_id,c.prefs,c.filters,c.tool);if(c.mode){a.change_mode(c.mode)}return a};var drawable_collection_from_dict=function(f,a){var e=new addable_objects[f.obj_type](f.name,view,a,f.prefs,view.viewport_container,view);for(var d=0;d<f.drawables.length;d++){var b=f.drawables[d],c;if(b.track_type){c=track_from_dict(b,e)}else{c=drawable_collection_from_dict(b)}e.add_drawable(c);e.content_div.append(c.container_div)}return e};var drawable_from_dict=function(b,a){return(b.track_type?track_from_dict(b,a):drawable_collection_from_dict(b,a))};var create_visualization=function(b,e,g,c,a,d,f){view=new View(b,e,g,c);view.editor=true;$.when(view.load_chroms_deferred).then(function(){if(a){var k=a.chrom,p=a.start,h=a.end,m=a.overview;if(k&&(p!==undefined)&&h){view.change_chrom(k,p,h)}}if(d){var o;for(var j=0;j<d.length;j++){o=d[j];view.add_drawable(drawable_from_dict(o,view))}}var n;for(var j=0;j<view.tracks.length;j++){if(view.tracks[j].name==m){view.set_overview(view.tracks[j]);break}}if(f){var l;for(var j=0;j<f.length;j++){l=f[j];add_bookmark(l.position,l.annotation)}}view.has_changes=false});return view};var init_keyboard_nav=function(a){$(document).keydown(function(b){if($(b.srcElement).is(":input")){return}switch(b.which){case 37:a.move_fraction(0.25);break;case 38:var c=Math.round(a.viewport_container.height()/15);a.viewport_container.scrollTo("-="+c+"px");break;case 39:a.move_fraction(-0.25);break;case 40:var c=Math.round(a.viewport_container.height()/15);a.viewport_container.scrollTo("+="+c+"px");break}})};
\ No newline at end of file
+var add_bookmark=function(b,a){var g=$("#bookmarks-container"),d=$("<div/>").addClass("bookmark").appendTo(g),c=$("<div/>").addClass("delete-icon-container").appendTo(d).click(function(){d.slideUp("fast");d.remove();view.has_changes=true;return false}),e=$("<a href=''/>").addClass("icon-button delete").appendTo(c),f=$("<div/>").addClass("position").appendTo(d),h=$("<a href=''/>").text(b).appendTo(f).click(function(){view.go_to(b);return false});annotation_div=get_editable_text_elt(a,true).addClass("annotation").appendTo(d);view.has_changes=true;return d};var addable_objects={LineTrack:LineTrack,FeatureTrack:FeatureTrack,ReadTrack:ReadTrack,DrawableGroup:DrawableGroup};var track_from_dict=function(c,b){var a=new addable_objects[c.track_type](c.name,view,b,c.hda_ldda,c.dataset_id,c.prefs,c.filters,c.tool);if(c.mode){a.change_mode(c.mode)}return a};var drawable_collection_from_dict=function(f,a){var e=new addable_objects[f.obj_type](f.name,view,a,f.prefs,view.viewport_container,view);for(var d=0;d<f.drawables.length;d++){var b=f.drawables[d],c;if(b.track_type){c=track_from_dict(b,e)}else{c=drawable_collection_from_dict(b)}e.add_drawable(c);e.content_div.append(c.container_div)}return e};var drawable_from_dict=function(b,a){return(b.track_type?track_from_dict(b,a):drawable_collection_from_dict(b,a))};var create_visualization=function(b,e,g,c,a,d,f){view=new View(b,e,g,c);view.editor=true;$.when(view.load_chroms_deferred).then(function(){if(a){var k=a.chrom,p=a.start,h=a.end,m=a.overview;if(k&&(p!==undefined)&&h){view.change_chrom(k,p,h)}}if(d){var o;for(var j=0;j<d.length;j++){o=d[j];view.add_drawable(drawable_from_dict(o,view))}}var n;for(var j=0;j<view.drawables.length;j++){if(view.drawables[j].name==m){view.set_overview(view.drawables[j]);break}}if(f){var l;for(var j=0;j<f.length;j++){l=f[j];add_bookmark(l.position,l.annotation)}}view.has_changes=false});return view};var init_keyboard_nav=function(a){$(document).keydown(function(b){if($(b.srcElement).is(":input")){return}switch(b.which){case 37:a.move_fraction(0.25);break;case 38:var c=Math.round(a.viewport_container.height()/15);a.viewport_container.scrollTo("-="+c+"px");break;case 39:a.move_fraction(-0.25);break;case 40:var c=Math.round(a.viewport_container.height()/15);a.viewport_container.scrollTo("+="+c+"px");break}})};
\ No newline at end of file


--- a/static/scripts/trackster.js	Wed Oct 12 11:19:42 2011 -0400
+++ b/static/scripts/trackster.js	Wed Oct 12 11:19:58 2011 -0400
@@ -309,10 +309,19 @@
     CACHED_TILES_FEATURE = 10,
     CACHED_TILES_LINE = 5,
     CACHED_DATA = 5;
-
-function round_1000(num) {
-    return Math.round(num * 1000) / 1000;    
-};
+    
+/**
+ * Round a number to a given number of decimal places.
+ */
+function round(num, places) {
+    // Default rounding is to integer.
+    if (!places) {
+        places = 0;
+    }
+    
+    var val = Math.pow(10, places);
+    return Math.round(num * val) / val;
+}
 
 /**
  * Generic cache that handles key/value pairs.
@@ -799,8 +808,6 @@
     this.vis_id = vis_id;
     this.dbkey = dbkey;
     this.title = title;
-    // Alias tracks to point at drawables. TODO: changes tracks to 'drawables' or something similar.
-    this.tracks = this.drawables;
     this.label_tracks = [];
     this.tracks_to_be_redrawn = [];
     this.max_low = 0;
@@ -1097,10 +1104,10 @@
                 view.reset();
                 view.request_redraw(true);
 
-                for (var track_id = 0, len = view.tracks.length; track_id < len; track_id++) {
-                    var track = view.tracks[track_id];
-                    if (track.init) {
-                        track.init();
+                for (var i = 0, len = view.drawables.length; i < len; i++) {
+                    var drawable = view.drawables[i];
+                    if (drawable.init) {
+                        drawable.init();
                     }
                 }
             }
@@ -1192,7 +1199,7 @@
         var 
             view = this,
             // Either redrawing a single track or all view's tracks.
-            track_list = (track ? [track] : view.tracks),
+            track_list = (track ? [track] : view.drawables),
             track_index;
         
         // Add/update tracks in track list to redraw list.
@@ -1629,7 +1636,7 @@
             // No element to filter on.
             return true;
         }
-        var val = parseFloat(element[this.index]);
+        var val = element[this.index];
         return (isNaN(val) || (val >= this.low && val <= this.high));
     },
     /**
@@ -2085,7 +2092,8 @@
 /**
  * Tiles drawn by tracks.
  */
-var Tile = function(index, resolution, canvas, data) {
+var Tile = function(track, index, resolution, canvas, data) {
+    this.track = track;
     this.index = index;
     this.low = index * DENSITY * resolution;
     this.high = (index + 1) * DENSITY * resolution;
@@ -2096,26 +2104,114 @@
     this.stale = false;
 };
 
-var SummaryTreeTile = function(index, resolution, canvas, data, max_val) {
-    Tile.call(this, index, resolution, canvas, data);
+/**
+ * Perform pre-display actions.
+ */
+Tile.prototype.predisplay_actions = function() {};
+
+var SummaryTreeTile = function(track, index, resolution, canvas, data, max_val) {
+    Tile.call(this, track, index, resolution, canvas, data);
     this.max_val = max_val;
 };
+extend(SummaryTreeTile.prototype, Tile.prototype);
 
-var FeatureTrackTile = function(index, resolution, canvas, data, message, feature_mapper) {
-    Tile.call(this, index, resolution, canvas, data);
+var FeatureTrackTile = function(track, index, resolution, canvas, data, mode, message, feature_mapper) {
+    Tile.call(this, track, index, resolution, canvas, data);
+    this.mode = mode;
     this.message = message;
     this.feature_mapper = feature_mapper;
+};
+extend(FeatureTrackTile.prototype, Tile.prototype);
+
+/**
+ * Sets up support for popups.
+ */
+FeatureTrackTile.prototype.predisplay_actions = function() {
+    //
+    // Add support for popups.
+    //
+    var tile = this,
+        popups = {};
+        
+    // Only show popups in Pack mode.
+    if (tile.mode !== "Pack") { return; }
     
-    //
-    // Set up display of feature data on mouseover.
-    //
-    var tile = this;
     $(this.canvas).mousemove(function (e) {
-        var feature_data = tile.feature_mapper.get_feature_data(e.offsetX, e.offsetY);
-        
-        // TODO: show popup with feature's information.
+        // Get feature data for position.
+        var 
+            this_offset = $(this).offset(),
+            offsetX = e.pageX - this_offset.left,
+            offsetY = e.pageY - this_offset.top,
+            feature_data = tile.feature_mapper.get_feature_data(offsetX, offsetY),
+            feature_uid = (feature_data ? feature_data[0] : null);
+        // Hide visible popup if not over a feature or over a different feature.
+        $(this).siblings(".feature-popup").each(function() {
+            if ( !feature_uid || 
+                 $(this).attr("id") !== feature_uid.toString() ) {
+                $(this).remove();
+            }
+        });
+            
+        if (feature_data) {
+            // Get or create popup.
+            var popup = popups[feature_uid];
+            if (!popup) {
+                // Create feature's popup element.            
+                var 
+                    feature_uid = feature_data[0],
+                    feature_dict = {
+                        name: feature_data[3],
+                        start: feature_data[1],
+                        end: feature_data[2],
+                        strand: feature_data[4]
+                    },
+                    filters = tile.track.filters_manager.filters,
+                    filter;
+                
+                // Add filter values to feature dict.   
+                for (var i = 0; i < filters.length; i++) {
+                    filter = filters[i];
+                    feature_dict[filter.name] = feature_data[filter.index];
+                }
+                
+                // Build popup.
+                
+                var popup = $("<div/>").attr("id", feature_uid).addClass("feature-popup"),
+                    key, value, 
+                    table = $("<table/>").appendTo(popup), row;
+                for (key in feature_dict) {
+                    value = feature_dict[key];
+                    row = $("<tr/>").appendTo(table);
+                    $("<th/>").appendTo(row).text(key);
+                    $("<td/>").attr("align", "left").appendTo(row)
+                              .text(typeof(value) == 'number' ? round(value, 2) : value);
+                }
+                popups[feature_uid] = popup;
+            }
+            
+            // Attach popup to canvas's parent.
+            popup.appendTo($(tile.canvas).parent());
+            
+            // Offsets are within canvas, but popup must be positioned relative to parent element.
+            // parseInt strips "px" from left, top measurements. +7 so that mouse pointer does not
+            // overlap popup.
+            var 
+                popupX = offsetX + parseInt( tile.canvas.css("left") ) + 7,
+                popupY = offsetY + parseInt( tile.canvas.css("top") ) + 7;
+            popup.css("left", popupX + "px").css("top", popupY + "px")
+        }
+        else if (!e.isPropagationStopped()) {
+            // Propogate event to other tiles because overlapping tiles prevent mousemove from being 
+            // called on tiles under this tile.
+            e.stopPropagation();
+            $(this).siblings().each(function() {
+                $(this).trigger(e);
+            });
+        }
+    })
+    .mouseleave(function() {
+        $(this).siblings(".feature-popup").remove();
     });
-
 };
 
 /**
@@ -2223,7 +2319,6 @@
                 track.container_div.addClass("error");
                 track.content_div.text(DATA_ERROR);
                 if (result.message) {
-                    var track_id = track.view.tracks.indexOf(track);
                     var error_link = $(" <a href='javascript:void(0);'></a>").text("View error").click(function() {
                         show_modal( "Trackster Error", "<pre>" + result.message + "</pre>", { "Close" : hide_modal } );
                     });
@@ -2492,7 +2587,7 @@
                 data = src_ctx.getImageData(0, 0, src_ctx.canvas.width, src_ctx.canvas.height);
             // Need to undo offsets when placing image data.
             tgt_ctx.putImageData(data, -track.left_offset, (tile.data.dataset_type === "summary_tree" ? SUMMARY_TREE_TOP_PADDING : 0));
-            new_tile = new Tile(-1, resolution, new_canvas);
+            new_tile = new Tile(track, -1, resolution, new_canvas);
             overview_tile.resolve(new_tile);
         });
         
@@ -2697,6 +2792,8 @@
         //
         // Show tile element.
         //
+        
+        tile.predisplay_actions();
       
         // Position tile element, recalculate left position at display time
         var range = this.view.high - this.view.low,
@@ -2812,7 +2909,7 @@
                 var c_start = Math.round(c * w_scale);
                 ctx.fillText(seq[c], c_start + track.left_offset, 10);
             }
-            return new Tile(tile_index, resolution, canvas, seq);
+            return new Tile(track, tile_index, resolution, canvas, seq);
         }
         this.content_div.css("height", "0px");
     }
@@ -2851,8 +2948,8 @@
             track.set_name(track.prefs.name);
             track.vertical_range = track.prefs.max_value - track.prefs.min_value;
             // Update the y-axis
-            $('#linetrack_' + track.track_id + '_minval').text(track.prefs.min_value);
-            $('#linetrack_' + track.track_id + '_maxval').text(track.prefs.max_value);
+            $('#linetrack_' + track.dataset_id + '_minval').text(track.prefs.min_value);
+            $('#linetrack_' + track.dataset_id + '_maxval').text(track.prefs.max_value);
             track.tile_cache.clear();
             track.request_draw();
         }
@@ -2899,8 +2996,7 @@
         }).appendTo(track.container_div);
     },
     predraw_init: function() {
-        var track = this,
-            track_id = track.view.tracks.indexOf(track);
+        var track = this;
         
         track.vertical_range = undefined;
         return $.getJSON( track.data_url, {  stats: true, chrom: track.view.chrom, low: null, high: null,
@@ -2911,8 +3007,8 @@
                 track.prefs.min_value = data.min;
                 track.prefs.max_value = data.max;
                 // Update the config
-                $('#track_' + track_id + '_minval').val(track.prefs.min_value);
-                $('#track_' + track_id + '_maxval').val(track.prefs.max_value);
+                $('#track_' + track.dataset_id + '_minval').val(track.prefs.min_value);
+                $('#track_' + track.dataset_id + '_maxval').val(track.prefs.max_value);
             }
             track.vertical_range = track.prefs.max_value - track.prefs.min_value;
             track.total_frequency = data.total_frequency;
@@ -2920,8 +3016,8 @@
             // Draw y-axis labels if necessary
             track.container_div.find(".yaxislabel").remove();
             
-            var min_label = $("<div />").addClass('yaxislabel').attr("id", 'linetrack_' + track_id + '_minval').text(round_1000(track.prefs.min_value));
-            var max_label = $("<div />").addClass('yaxislabel').attr("id", 'linetrack_' + track_id + '_maxval').text(round_1000(track.prefs.max_value));
+            var min_label = $("<div />").addClass('yaxislabel').attr("id", 'linetrack_' + track.dataset_id + '_minval').text(round(track.prefs.min_value, 3));
+            var max_label = $("<div />").addClass('yaxislabel').attr("id", 'linetrack_' + track.dataset_id + '_maxval').text(round(track.prefs.max_value, 3));
             
             max_label.css({ position: "absolute", top: "24px", left: "10px" });
             max_label.prependTo(track.container_div);
@@ -2955,7 +3051,7 @@
         var painter = new painters.LinePainter(result.data, tile_low, tile_high, this.prefs, mode);
         painter.draw(ctx, width, height);
         
-        return new Tile(tile_index, resolution, canvas, result.data);
+        return new Tile(this.track, tile_index, resolution, canvas, result.data);
     } 
 });
 
@@ -3288,7 +3384,7 @@
             // Deal with left_offset by translating.
             ctx.translate(left_offset, SUMMARY_TREE_TOP_PADDING);
             painter.draw(ctx, width, required_height);
-            return new SummaryTreeTile(tile_index, resolution, canvas, result.data, result.max);
+            return new SummaryTreeTile(track, tile_index, resolution, canvas, result.data, result.max);
         }
 
         // Start dealing with row-by-row tracks
@@ -3347,7 +3443,7 @@
             feature_mapper.translation = -left_offset;
         }
         
-        return new FeatureTrackTile(tile_index, resolution, canvas, result.data, result.message, feature_mapper);        
+        return new FeatureTrackTile(track, tile_index, resolution, canvas, result.data, mode, result.message, feature_mapper);        
     }
 });
 
@@ -3891,7 +3987,7 @@
  */
 FeaturePositionMapper.prototype.get_feature_data = function(x, y) {
     // Find slot using Y.
-    var slot = Math.floor(y/this.slot_height),
+    var slot = Math.floor( y/this.slot_height ),
         feature_dict;
 
     // May not be over a slot due to padding, margin, etc.


--- a/static/scripts/trackster_ui.js	Wed Oct 12 11:19:42 2011 -0400
+++ b/static/scripts/trackster_ui.js	Wed Oct 12 11:19:58 2011 -0400
@@ -113,9 +113,9 @@
         
         // Set overview.
         var overview_track;
-        for (var i = 0; i < view.tracks.length; i++) {
-            if (view.tracks[i].name == overview_track_name) {
-                view.set_overview(view.tracks[i]);
+        for (var i = 0; i < view.drawables.length; i++) {
+            if (view.drawables[i].name == overview_track_name) {
+                view.set_overview(view.drawables[i]);
                 break;
             }
         }


--- a/tool_conf.xml.main	Wed Oct 12 11:19:42 2011 -0400
+++ b/tool_conf.xml.main	Wed Oct 12 11:19:58 2011 -0400
@@ -45,6 +45,7 @@
     <tool file="filters/tailWrapper.xml" /><tool file="filters/trimmer.xml" /><tool file="filters/wc_gnu.xml" />
+    <tool file="filters/secure_hash_message_digest.xml" /></section><section name="Convert Formats" id="convert"><tool file="filters/bed2gff.xml" />

Repository URL: https://bitbucket.org/galaxy/galaxy-central/

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[galaxy-commits] commit/galaxy-central: 11 new changesets

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