Create a Taverna workflow file (t2flow format) using a script.

1. Ensure Python 2.7 and Git are installed, preferably using your system's package manager.

2. Go to https://github.com/jongiddy/balcazapy and copy the HTTPS clone URL on the right to the clipboard.

   Click on the clipboard-arrow icon to copy the URL to the clipboard

3. Clone the Git repository, using the copied URL

   $ git clone https://github.com/jongiddy/balcazapy.git
   

   Note, this creates a folder called balcazapy

   If you have already cloned the repository, you can update to the latest version using the command:

   $ cd balcazpy
   $ git pull
   

4. Run:

   $ cd balcazapy
   $ ./setup.sh
   

This installs a command balc into the bin directory. Add the bin directory to your PATH, copy the balc executable to somewhere in your PATH, or reference balc with an absolute path name.

1. Install Python 2.7 from https://www.python.org/

   Python 3 is also available. Balcazapy does not yet work with Python 3.

   On Windows, use the appropriate 32-bit or 64-bit MSI Installer. Use Control Panel -> System and Security -> System to check whether your Windows version is 32-bit or 64-bit. You do not need the MSI program database.

   Use the default values for installation.

2. Install Git from https://git-scm.com/

   Use the default values for installation, EXCEPT for the page titled Adjusting your PATH environment, where you should select Run Git from the Windows Command Prompt

3. Go to https://github.com/jongiddy/balcazapy and copy the HTTPS clone URL on the right to the clipboard.

   Click on the clipboard-arrow icon to copy the URL to the clipboard

4. Open a command window (Start menu -> Accessories -> Command Prompt).

5. Clone the Git repository, using the copied URL (right click to paste into the command window)

   > git clone https://github.com/jongiddy/balcazapy.git
   

   Note, this creates a folder called balcazapy

   If you have already cloned the repository, you can update to the latest version using the command:

   > cd balcazapy
   > git pull
   

6. Check the file locations in setup.bat, then run:

   > cd balcazapy
   > setup.bat
   

This installs a batch script balc.bat into the bin folder. Add the bin folder to your PATH, copy the balc.bat script to somewhere in your PATH, or reference balc.bat with an absolute path name.

The balc command converts a Zapy description file to a Taverna t2flow file.

To create a t2flow file from an existing Zapy description file, run the command:

balc myfile.py myflow.t2flow

Run balc -h to see the available options:

usage: balc [-h] [--indent] [--validate] [--zip] [--signature]
            [--flow FLOWNAME]
            source [target]

Create a Taverna 2 workflow (t2flow) file from a Zapy description file

positional arguments:
  source           Zapy (.py) description file
  target           Taverna 2 Workflow (.t2flow) filename (default: stdout)

optional arguments:
  -h, --help       show this help message and exit
  --indent         create a larger but more readable indented file
  --validate       modify workflow to validate input ports
  --zip            create a zip file containing outputs
  --signature      print workflow signature
  --flow FLOWNAME  name of the workflow in the source file (default: flow)

Zapy files are Python files. Hence, they have a .py suffix. Using the Python format allows Zapy files to be edited in highlighting editors, including Idle, the editor that comes with Python.

Python requires that (almost) all names used, but not defined, in a file are imported from libraries. To make use of Balcazapy, start with these lines:

from balcaza.t2types import *
from balcaza.t2activity import *
from balcaza.t2flow import Workflow

Create a workflow using:

flow = Workflow(title = 'Create Projection Matrix', author = "Maria and Jon",
    description = "Create a projection matrix from a stage matrix and a list of stages")

This workflow contains 3 main collections:

• flow.input - the input ports for the workflow

• flow.output - the output ports for the workflow

• flow.task - the connected tasks within the workflow

Tasks are created by passing an Activity to a workflow task name. The available activities are described below.

flow.task.MyTask << rserve.code(
    'total <- sum(vals)',
    inputs = dict(
        vals = Vector[Integer]
        ),
    outputs = dict(
        total = Integer
        )
    )

Each task contains 2 collections:

• flow.task.MyTask.input - the input ports for the task

• flow.task.MyTask.output - the output ports for the task

Manage task parallelisation and retries using:

flow.task.MyTask.parallel(maxJobs = 5)
flow.task.MyTask.retry(maxRetries = 3, initialDelay = 1000, maxDelay = 5000,
    backoffFactor = 1.0)

Link ports using the | (pipe) symbol. Output ports can be part of multiple links. Input ports must only be linked once.

flow.input.InputValues | flow.task.MyTask.input.vals
flow.task.MyTask.output.total | flow.task.AnotherTask.input.x
flow.task.MyTask.output.total | flow.output.SumOfValues

It is possible to create a chain when a task has default input and output ports.

flow.task.MyTask << rserve.code(
    'total <- sum(vals)',
    inputs = dict(vals = Vector[Integer]),
    outputs = dict(total = Integer),
    defaultInput = 'vals',
    defaultOutput = 'total'
    )
flow.input.InputValues | flow.task.MyTask | flow.output.SumOfValues

To iterate a task for all values in a List, add + to the pipe before the port to be iterated and - for the port that collects the multiple results.

flow.input.ListOfStrings |+ flow.task.ProcessSingleString |- flow.output.ProcessedStrings
flow.input.ListOfListsOfStrings |++ flow.task.ProcessSingleString |-- flow.output.MoreProcessedStrings

Force services to run in sequence using the >> operator between tasks:

flow.task.MyTask >> flow.task.AnotherTask

Activities are the boxes you see in a workflow. Activities describe a particular task to be performed. There are several types of activities.

Activities can be created and assigned to named workflow tasks.

Activities can be reused, by assigning them to multiple tasks.

In pipelines, it is possible to use activities in place of tasks, and a task will be created. This is very useful for reuse of simple activities in pipelines.

SumValues = rserve.code(
    'total <- sum(vals)',
    inputs = dict(vals = Vector[Integer]),
    outputs = dict(total = Integer),
    defaultInput = 'vals',
    defaultOutput = 'total'
    )
flow.input.ListOfListsOfValues |+ SumValues |- SumValues | flow.output.GrandTotal

In this example, the first SumValues activity processes each outer list, to create a list of totals, and the second SumValues activity sums these totals to create a grand total.

For some activities, you will need to specify a type for a port.

Available types are:

• Integer
• Number
• String
• TextFile
• PDF_File
• PNG_Image

For interaction with R code, the following additional types are available:

• Logical
• RExpression
• Vector[Logical]
• Vector[Integer]
• Vector[Number]
• Vector[String]

You can also specify lists using List[type], where type is any of the above, or another list. For example:

• List[Integer] - a list of integers
• List[RExpression] - a list of RExpressions
• List[List[String]] - a list containing lists of strings

String types can be restricted to a set of values, and Integer types to a range, using:

String['YES', 'NO']
Integer[0,...,100]

The --validate option to balc will add additional checks that input values have the correct type.

Create using:

BeanshellCode(
    """String seperatorString = "\n";
if (seperator != void) {
    seperatorString = seperator;
}
StringBuffer sb = new StringBuffer();
for (Iterator i = stringlist.iterator(); i.hasNext();) {
    String item = (String) i.next();
    sb.append(item);
    if (i.hasNext()) {
        sb.append(seperatorString);
    }
}
concatenated = sb.toString();
""",
    inputs = dict(
        stringlist = List[String],
        seperator = String
        ),
    output = dict(
        concatenated = String
        )
    )

or

BeanshellFile(
    'file.bsh',
    inputs = dict(
        stringlist = List[String],
        seperator = String
        ),
    output = dict(
        concatenated = String
        )
    )

All inputs and outputs for BeanShell are strings or lists of strings. However, it is possible to pass other types, for documentation purposes. Just remember that the Beanshell will see a String or a List type internally.

An external tool can run a shell script locally to the workflow. Create using:

ExternalTool(
    '''mv myfile file-%%myvar%%.txt
    zip out.zip * 
''',
    inputs = dict(
        myfile = TextFile,
        myvar = String
        ),
    outputs = dict(
        output = BinaryFile
        ),
    outputMap = dict(
        output = 'out.zip'
        )
    )

Any input files are available to the script as files. Any non-file inputs are available as variables which can be accessed using %% delimiters, e.g. %%myvar%%. Use inputMap and outputMap to rename files, as shown.

Note, use of ExternalTool will prevent the workflow from working on a Microsoft Windows or other non-Unix-based operating systems.

Create using:

InteractionPage(url,
    inputs = dict(
        start = Integer,
        end = Integer
        ),
    outputs = dict(
        sequences = List[List[Integer]]
        )
    )

Create using:

HTTP.GET('https://www.biovel.eu/')
HTTP.PUT(
    'https://balca.biovel.eu/openacces/{file_name}',
    inputs = dict(
        file_name = String
        ),
    escapeParameters = False
    )

For HTTP calls, the default input is the body of the HTTP request, and the default output is the body of the HTTP response.

Create using:

TextConstant('Some text')

For text constants, the default output is the text value.

For R scripts, first create an RServer using

rserve = RServer(host, port)

If the port is omitted, the default Rserve port (6311) will be used.

If the host is omitted, localhost will be used.

Create an R activity using

rserve.code(
    'total <- sum(vals)',
    inputs = dict(
        vals = Vector[Integer]
        ),
    outputs = dict(
        total = Integer
        )
    )

or

rserve.file(
    'file.r',
    inputs = dict(
        vals = Vector[Integer]
        ),
    outputs = dict(
        total = Integer
        )
    )

For R scripts that contain variables with dots in the name, you can map them from a valid Taverna name (no dots) to the R script name, using:

rserve.file(
    'file.r',
    inputs = dict(IsBeta = Logical),
    inputMap = dict(IsBeta = 'Is.Beta'),
    outputs = dict(ResultTable = RExpression),
    outputMap = dict(ResultTable = 'result.table')
    )

This can also be used to output results as multiple types:

rserve.code(
    'total <- sum(vals)',
    outputs = dict(
        total = RExpression,
        totalAsInt = Integer,
        totalAsVector = Vector[Integer]
        ),
    outputMap = dict(
        totalAsInt = 'total',
        totalAsVector = 'total'
        )
    )

Note that the List type is not available for RServer activity ports. Use the Vector type instead.

For R scripts, the default input and output is the R workspace

Create using:

XPath('/Job/JobId')
XPath('/xhtml:html/xhtml:head/xhtml:title', {'xhtml': 'https://www.w3.org/1999/xhtml'})

For XPath, the default input is the XML expression to which the XPath expression is applied, and the default output is a list of matched text elements.

It is possible to create nested workflows using the NestedWorkflow activity.

inner = Workflow(...)
...
outer = Workflow(...)
outer.task.CoreAlgorithm << NestedWorkflow(inner)

It is often more convenient to develop the nested workflow in a separate file, and then use:

outer.task.CoreAlgorithm << NestedZapyFile('inner.py', inputs=..., outputs=...)

When using an external file, provide the input and output ports as parameters. The correct call can be obtained by running balc --signature inner.py.

To connect all unconnected ports of a task as ports of the workflow, use:

flow.task.MyTask.extendUnusedInputs()
flow.task.MyTask.extendUnusedOutputs()

or, even shorter, for the above case:

flow.task.MyTask.extendUnusedPorts()

Text constants can be created and linked in one step using:

flow.task.MyTask.input.plot_title = "Initial Results"

This is equivalent to:

TextConstant("Initial Results") | flow.task.MyTask.input.plot_title

To make access to task ports less verbose, assign the task to a variable:

MyTask = flow.task.MyTask << rserve.code(...)
flow.input.values | MyTask.input.vals
MyTask.output.total | AnotherTask.input.in1

You do not need to specify input or output ports for RExpression types in RServe activities. This is most useful when connecting two RServe activities, as shown in the following complete example:

from balcaza.t2types import *
from balcaza.t2activity import *
from balcaza.t2flow import Workflow

flow = Workflow(title = 'TwiceTheSum')

rserve = RServer()

SumValues = flow.task.SumValues << rserve.code(
    'total <- sum(vals)',
    inputs = dict(vals = Vector[Integer[0,...,100]])
    )
Double = flow.task.Double << rserve.code(
    'out1 <- 2 * in1',
    outputs = dict(out1 = Integer)
    )

# Link internal script variables (transferred as RExpression types)
SumValues.output.total | Double.input.in1

SumValues.extendUnusedInputs()
Double.extendUnusedOutputs()

Tasks and activities can be chained using their default input and output ports. See examples/rest/web.py for an example.

Workflow annotations are defined during creation, but can be overridden:

flow = Workflow(title = 'Create Projection Matrix', author = "Maria and Jon",
    description = "Create a projection matrix from a stage matrix and a list of stages")
flow.title = 'Create Projection Matrix v1'

A task annotation can come from an activity, but can be overridden:

flow.task.MyTask = HTTP.GET(url, description="Fetch the page")
flow.task.MyTask.description = "Fetch a page" # override above

Port annotations can come from the type, but can be overridden

flow.input.Location = String(description="The site name", example="Terschelling")
flow.input.Location.example = "Dwingeloo"

The --zip flag to the balc command will create an output zip file containing non-list outputs. Any outputs stored in the zip file will not be output as separate workflow output ports. Lists and any non-list outputs marked as below will not be added to the zip file, and will be output as separate output ports.

A filename annotation can be added to the output port, to rename the Taverna port name inside the zip file. This option does nothing if the file is not included in the zip file. If the filename contains %% markers, the value of the named input port is replaced between the %% markers.

The annotation zip=False causes a non-list output to continue to be provided as an output port, and not to be added to the zip file.

The annotation deleteIfEmpty=True causes an output file to be completely removed if the file is empty. The file is output neither in the zip file nor as an output. This flag has no effect if --zip is not used, since Taverna does not allow output ports to be removed dynamically.

Examples:

InteractionsMethodMatrix = flow.task.InteractionsMethodMatrix << rserve.file(
    "KW_11.r",
    encoding='cp1252',
    inputs=dict(
        BetaQ_SR = String['YES', 'NO'](example= "YES"),
        percIncr = Number(description="Percentage increment of chinook abundance (0.1 = 10%)", example='0.1'),
        ),
    outputs=dict(
        F1_Fecundity_File = PDF_File(filename='CI %%Population%% F1_Fecundity.pdf', deleteIfEmpty=True),
        F2_Fecundity_File = PDF_File(filename='CI %%Population%% F2_Fecundity.pdf', deleteIfEmpty=True)
        )
    )