Locked History Actions


Strategies for tools that create more than one output file

Handling Multiple Output Files in Galaxy

Tools which create more than one output file are common. There are several different methods to accommodate this need. Each one of these has their advantages and weaknesses; careful thought should be employed to determine the best method for a particular tool.

Static Multiple Outputs

Handling cases when tools create a static number of outputs is simple. Simply include an <output> tag for each output desired within the tool XML file:

   1 <tool id="example_tool" name="Multiple output" description="example">
   2     <command>example_tool.sh $input1 $tool_option1 $output1 $output2</command>
   3     <inputs>
   4         ...
   5     </inputs>
   6     ...
   7     <outputs>
   8         <data format="interval" name="output1" metadata_source="input1" />
   9         <data format="pdf" name="output2" />
  10     </outputs>
  11 </tool>

Variable Static Outputs determined by parameter values

In cases when the number of output files varies, but can be determined based upon a user's parameter selection, the use of the filter tag can be used. The text contents of the <filter> tag are evaled and if the expression is True a dataset will be created as normal. If the expression is False, the output dataset will not be created; instead a NoneDataset object will be created and made available - when used on the command line the text None will appear instead of a file path. The local namespace of the filter has been populated with the tool parameters.

   1 <tool id="example_tool" name="Multiple output" description="example">
   2     <command>example_tool.sh $input1 $tool_option1 $output1 $output2</command>
   3     <inputs>
   4        ...
   5        <param name="tool_option1" type="select" label="Type of output">
   6            <option value="1">Single File</option>
   7            <option value="2">Two Files</option>
   8        </param>
   9        <conditional name="condition1">
  10            <param name="tool_option2" type="select" label="Conditional output">
  11                <option value="yes">Yes</option>
  12                <option value="no">No</option>
  13            </param>
  14            ...
  15        </condition>
  16        ...
  17     </inputs>
  18     ...
  19     <outputs>
  20         <data format="interval" name="output1" metadata_source="input1" />
  21         <data format="pdf" name="output2" >
  22             <filter>tool_option1 == "2"</filter>
  23         </data>
  24         <data format="txt" name="output3" >
  25             <filter>condition1['tool_option2'] == "yes"</filter>
  26         </data>
  27     </outputs>
  28 </tool>

The command line generated when tool_option1 is set to Single File is:

example_tool.sh input1_FILE_PATH 1 output1_FILE_PATH None

The command line generated when tool_option1 is set to Two Files is:

example_tool.sh input1_FILE_PATH 2 output1_FILE_PATH output2_FILE_PATH

The datatype of an output can be determined by conditional parameter settings as in tools/filter/pasteWrapper.xml

   1  <outputs>
   2    <data format="input" name="out_file1" metadata_source="input1">
   3      <change_format>
   4        <when input_dataset="input1" attribute="ext" value="bed" format="interval"/>
   5      </change_format>
   6    </data>
   7  </outputs>

There are times when a single history item is desired, but this history item is composed of multiple files which are only useful when considered together. This is done by having a single (primary) output and storing additional files in a directory (single-level) associated with the primary dataset.

A common usage of this strategy is to have the primary dataset be an HTML file and then store additional content (reports, pdfs, images, etc) in the dataset extra files directory. The content of this directory can be referenced using relative links with in the primary (HTML) file, clicking on the eye icon to view the dataset will display the HTML page.

If you want to wrap or create a tool that generates an html history item that shows the user links to a number of related output objects (files, images..), you need to know where to write the objects and how to reference them when your tool generates html which gets written to the html file. Galaxy will not write that html for you at present.

The fastqc wrapper (see tools/rgenetics/rgFastQC.py) is an existing tool example where the java application generates html and image outputs but these need to be massaged to make them Galaxy friendly. In other cases, the application or your wrapper must take care of all the fiddly detailed work of writing valid html to display to the user. In either situation, the Html datatype offers a flexible way to display very complex collections of related outputs inside a single history item or to present a complex html page generated by an application. There are some things you need to take care of for this to work:

Use an html output file

If you want to the tool to write a single output in the history showing links to lots of files and/or images, use an Html datatype as the only output for the tool in the xml wrapper. eg:

<data format="html" name="html_file" label="myToolOutput_${tool_name}.html">

Pass the application a specific output directory

The application or script must be set up to write all the output files and/or images to a new special subdirectory passed as a command line parameter from Galaxy every time the tool is run. The paths for images and other files will end up looking something like galaxy_dist/database/files/000/dataset_56/img1.jpg when you prepend the Galaxy provided path to the filenames you want to use. The command line must pass that path to your script and it is specified using the files_path property of the html file output. For example:

<command>myscript.pl "$input1" "$html_file" "$html_file.files_path" </command>

Write valid html

The application must create and write valid html to setup the page $html_file seen by the user when they view (eye icon) the file. It must create and write that new file at the path passed by Galaxy as the $html_file command line parameter. All application outputs that will be included as links in that html code should be placed in the specific output directory $html_file.files_path passed on the command line. The external application is responsible for creating that directory before writing images and files into it. When generating the html, The files written by the application to $html_file.files_path are referenced in links directly by their name, without any other path decoration - eg:

<a href="file1.xls">Some special output</a>
<img src="image1.jpg" >

The Galaxy Tool Factory includes code to gather all output files and create a page with links and clickable pdf thumbnail images which may be useful as a starting point - eg see https://bitbucket.org/fubar/rgalaxy/src/9932187787e592238c2c6fb514a39ff3a705a9af/tools/rgenetics/rgToolFactory.py?at=default

Composite Datatypes

Html is a subclass of composite datasets so new subclasses of composite can be used to implement even more specific structured outputs (as seen with Rgenetics) but this requires adding the new definition to Galaxy - whereas Html files require no extension of the core framework. For more information visit CompositeDatatypes

Number of Output datasets cannot be determined until tool run

There are times when the number of output datasets varies entirely based upon the content of an input dataset and the user needs to see all of these outputs as new individual history items rather than as a collection of related objects linked in a single new html page in the history. Tools can optionally describe how to "discover" an arbitrary number of files that will be added after the job's completion to the user's history as new datasets. Whenever possible, one of the above strategies should be used instead since these discovered datasets cannot be used with workflows and require the user to refresh their history before they are shown.

Discovering datasets (arbitrarily) require a fixed 'parent' output dataset to key on - this dataset will act as the reference for our additional datasets. Sometimes the parent dataset to use makes sense from context but in instances where one does not readily make sense tool authors can just create an arbitrary text output like a report of the dataset generation.

Each discovered dataset requires a unique 'designation' (used to describe functional tests, the default output name, etc...) and should be located in the job's working direcotry or (ideally) a sub-directory thereof. Regular expressions are used to describe how to discover the datasets and (though not required) a unique such pattern should be specified for each homogeneous group of such files.


Consider a tool that creates a bunch of text files or bam files and writes them (with extension that matches the Galaxy datatype - e.g. txt or bam to the split sub-directory of the working directory. Such outputs can be discovered by adding the following block of XML to your tool description:

   1     <outputs>
   2         <data format="txt" name="report">
   3                 <discover_datasets pattern="__designation_and_ext__" directory="split" visible="true" />
   4         </data>
   5     </outputs>

So for instance, if the tool creates 4 files (in addition to the report) such as split/samp1.bam, split/samp2.bam, split/samp3.bam, and split/samp4.bam - then 4 discovered datasets will be created of type bam with designations of samp1, samp2, samp3, and samp4.

If the tool doesn't create the files in split with extensions or does but with extensions that do not match Galaxy's datatypes - a slightly different pattern can be used and the extension/format can be statically specified:

   1     <outputs>
   2         <data format="txt" name="report">
   3                 <discover_datasets pattern="__designation__" ext="tabular" directory="tables" visible="true" />
   4         </data>
   5     </outputs>

So in this example, if the tool creates 3 tabular files such as tables/part1.tsv, tables/part2.tsv, and tables/part3.tsv - then 3 discovered datasets will be created of type tabular with designations of part1.tsv, part2.tsv, and part3.tsv.

It may not be desirable for the extension (.tsv) to appear in the designation this way. These patterns __designation__ and __designation_and_ext__ are replaced with regular expressions that capture metadata from the file name using named groups. A tool author can explicitly define these regular expressions instead of using these shortcuts - for instance __designation__ is just (?P<designation>.*) and __designation_and_ext__ is (?P<designation>.*)\.(?P<ext>[^\._]+)?. So the above example can be modified as:

   1     <outputs>
   2         <data format="txt" name="report">
   3                 <discover_datasets pattern="(?P&lt;designation&gt;.+)\.tsv" ext="tabular" directory="tables" visible="true" />
   4         </data>
   5     </outputs>

As a result - three datasets are still be captured - but this time with designations of part1, part2, and part3.

Notice here the < and > in the tool pattern had to be replaced with \&lt; and &gt; to be properly embedded in XML (this is very ugly - apologies).

The metadata elements that can be captured via regular expression named groups this way include ext, designation, name, dbkey, and visible. Each pattern must declare at least either a designation or a name - the other metadata parts ext, dbkey, and visible are all optional and may also be declared explicitly in via attributes on the discover_datasets element (as shown in the above examples).

If no discover_datasets element is nested with a tool output - Galaxy will still look for datasets using the named pattern __default__ which expands to primary_DATASET_ID_(?P<designation>[^_]+)_(?P<visible>[^_]+)_(?P<ext>[^_]+)(_(?P<dbkey>[^_]+))?. Many tools use this mechanism as it traditionally was the only way to discover datasets and has the nice advantage of not requiring an explicit declaration and encoding everything (including the output to map to) right in the name of the file itself.

For instance consider the following output declaration:

   1     <outputs>
   2         <data format="interval" name="output1" metadata_source="input1" />
   3     </outputs>

If $output1.id (accessible in the tool command block) is 546 and the tool (likely a wrapper) produces the files primary_546_output2_visible_bed and primary_546_output3_visible_pdf in the job's working directory - then after execution is complete these two additional datasets (a bed file and a pdf file) are added to the user's history.

More information:

Legacy information:

In the past, it would be necessary to set the attribute force_history_refresh to True to force the user's history to fully refresh after the tool run has completed. This functionality is now broken and force_history_refresh is ignored by Galaxy. Users now //MUST// manually refresh their history to see these files. A Trello card used to track the progress on fixing this and eliminating the need to refresh histories in this manner can be found here.

Discovered datasets are available via post job hooks (a deprecated feature) by using the designation - e.g. __collected_datasets__['primary'][designation].

In the past these datasets were typically written to $__new_file_path__ instead of the working directory. This is not very scalable and $__new_file_path__ should generally not be used. If you set the option collect_outputs_from in universe_wsgi.ini ensure job_working_directory is listed as an option (if not the only option).

Generating Dataset Collections

Discovered Datasets can also be combined with dataset collections as shown in:

Galaxy Tool Generating Dataset Collections