Parameters

Parameters can be added to handle a single element or a list of elements. Thus, the add_parameter() method can be used to force the final user to provide one and only one value, where the add_parameter_list() method allows the final user to give as many values he wants.

add_parameter()

Example

In the following example, a parameter named sequencer is added to the workflow. It has a list of choices and the default value is "HiSeq2000".

self.add_parameter("sequencer",
    		   "The sequencer type.", 
    		   choices = ["HiSeq2000", "ILLUMINA","SLX","SOLEXA","454","UNKNOWN"], 
    		   default="HiSeq2000")

Options

There are two positional arguments: name and help. All other options are keyword options

add_parameter_list()

The add_parameter_list() method takes the same arguments as add_parameter(). However, adding this parameter, the final user will be allowed to enter multiple values for this parameter and the object attribut self.parameter_name will be settled as a Python list.

Inputs

Just like parameters, inputs can be added to handle a single file or a list of files. Thus, the add__input_file() method can be used to force the final user to provide one and only one file, where the add__input_file_list() method allows the final user to give as many files as he wants.

add_input_file()

Example

In the following example, an input named reads is added to the workflow. The provided file is required and should be in fastq format. No file size limitation is specified.

self.add_input_file_list("reads", 
                         "Which read files should be used", 
                         file_format="fastq", 
                         required=True)

Options

There are two positional argument : name and help. All other options are keyword options.

add_input_file_list()

This method takes the same arguments as add_input_file(). However, adding this parameter, the final user will be allowed to provide multiple files and the object attribut self.parameter_name will be settled as a Python list.

add_input_directory()

The add_input_directory() method allows the user to select files from a specific directory. This kind of input can be useful for tools outputing not only files but an organized directory. The parameter get_files_fn specify the function that will be used to retrieve the files. This method can take as many arguments as required, but the first argument has to be a string representing the folder path. By default all files will be selected. From the workflow process() function, the files can be retrieved by using the get_files() method.

Example

In the following example, the add_input_directory() method is used to parse a directory and retrieve only fasta files inside this directory. get_files() will browse the directory and get all fasta files.

import os
from jflow.Workflow import Workflow

def fasta_files(folder):
    res = []
    for file in os.listdir(folder):
        if file.endswith(".fasta"):
            res.append(file)
    return res

class MyWorkflow(Workflow):
    def define_parameters(self, function="process"):
        self.add_input_directory("fastadir", "Path to folder with fasta files", 
            get_files_fn=fasta_files)

    def process(self):
        # to retrieve the files
        for fastafile in self.fastadir.get_files():
            # do something

Options

There are two positional argument : name and help. All other options are keyword options.

Multiple parameters

Jflow offers, to the developper, the possibility to structure the input data by using the notion of multiple parameters. A multi parameter is a collection of parameters linked together. Just like for parameters and inputs, it can be added to handle a single collection or a list of collections. Thus, the add_multiple_parameter() method can be used to force the final user to provide one and only one collection, where the add_multiple_parameter_list() method allows the final user to give as many collection he wants. To add a parameter within the multiple parameter, it only requires to set the option add_to of any methods previously described. The accessible object attribut self.multi_parameter_name is then a Python dictionary gathering all the values of the different parameters under the format {"sub_parameter1":value}

add_multiple_parameter()

Example

self.add_multiple_parameter("library", "Library.", required=True)
self.add_input_file("R1", "Path to R1 file.", required=True, add_to="library")
self.add_input_file("R2", "Path to R2 file.", add_to="library")
self.add_parameter("sequencer", "The sequencer type.", choices=["HiSeq2000", 
    "ILLUMINA", "UNKNOWN"], default="HiSeq2000", add_to="library")

Options

There are two positional arguments : name and help. All other options are keyword options.

add_multiple_parameter_list()

This method takes the same arguments as add_multiple_parameter(). However, adding this parameter, the final user will be allowed to provide multiple collection and the object attribut self.multi_parameter_name will be settled as a Python list of Python dictionary.

Pre process

pre_process() is executed before running the process method. Unlike process, this method does not allow to add components, but can be useful when implementing an application requiring to prepare some data before running the workflow (insert / recover information from a database, add metadata to the workflow, ...).

Post process

post_process() is executed right after the process method and cannot be used to add components. This method can be useful to perform some database transactions and to synchronize data.

Set to address

set_to_address() overwrite the value to_address defined in the jflow configuration file.

Options

There is one required argument : to_address.

Set email subject

set_subject() overwrite the value subject defined in the jflow configuration file.

Options

There is one required argument : subject.

Set email message

set_message() overwrite the value message defined in the jflow configuration file.

Options

There is one required argument : message.

Get shared resources

The method get_resource(), giving a specific resource, returns the defined value within the resources section of the jflow configuration file.

Options

There is one required argument : resource.