A Python wrapper around Nextflow.
Project description
nextflow.py is a Python wrapper around the Nextflow pipeline framework. It lets you run Nextflow pipelines from Python code.
Example
>>> import nextflow >>> execution = nextflow.run("main.nf", params={"param1": "123"}) >>> print(execution.status)
Installing
pip
nextflow.py can be installed using pip:
$ pip install nextflowpy
If you get permission errors, try using sudo:
$ sudo pip install nextflowpy
Development
The repository for nextflow.py, containing the most recent iteration, can be found here. To clone the nextflow.py repository directly from there, use:
$ git clone git://github.com/goodwright/nextflow.py.git
Nextflow
nextflow.py requires the Nextflow executable to be installed and in your PATH. Instructions for installing Nextflow can be found at their website.
Testing
To test a local version of nextflow.py, cd to the nextflow.py directory and run:
$ python -m unittest discover tests
You can opt to only run unit tests or integration tests:
$ python -m unittest discover tests.unit $ python -m unittest discover tests.integration
Overview
Running
To run a pipeline, the run function is used. The only required parameter is the path to the pipeline file:
>>> pipeline = nextflow.run("pipelines/my-pipeline.nf")
This will return an Execution object, which represents the pipeline execution that just took place (see below for details on this object). You can customise the execution with various options:
>>> execution = pipeline.run("my-pipeline.nf", run_path="./rundir", output_path="./outputs", params={"param1": "123"}, profiles=["docker", "test"], version="22.0.1", configs=["env.config"], timezone="UTC", report="report.html", timeline="timeline.html", dag="dag.html")
run_path - The location to run the pipeline from, which by default is just the current working directory.
output_path - The location to store the execution outputs (work etc.), which by default is the run_path.
params - A dictionary of parameters to pass to the pipeline as command. In the above example, this would run the pipeline with --param1=123.
profiles - A list of Nextflow profiles to use when running the pipeline. These are defined in the nextflow.config file, and can be used to configure things like the executor to use, or the container engine to use. In the above example, this would run the pipeline with -profile docker,test.
version - The version of Nextflow to use when running the pipeline. By default, the version of Nextflow installed on the system is used, but this can be overridden with this parameter.
configs - A list of config files to use when running the pipeline. These are merged with the config files specified in the pipeline itself, and can be used to override any of the settings in the pipeline config.
timezone - A timezone to pass to Nextflow - this determines the timestamps used in the log file.
report - A filename for a report file to generate. This will be an HTML file containing information about the pipeline execution.
timeline - A filename for a timeline file to generate. This will be an HTML file containing a timeline of the pipeline execution.
dag - A filename for a DAG file to generate. This will be an HTML file containing a DAG diagram of the pipeline execution.
Custom Runners
When you run a pipeline with nextflow.py, it will generate the command string that you would use at the command line if you were running the pipeline manually. This will be some variant of nextflow run some-pipeline.nf, and will include any parameters, profiles, versions, and config files that you passed in.
By default, nextflow.py will then run this command using the standard Python subprocess module. However, you can customise this behaviour by passing in a custom ‘runner’ function. This is a function which takes the command string and submits the job in some other way. For example, you could use a custom runner to submit the job to a cluster, or to a cloud platform.
This runner function is passed to the run method as the runner parameter:
>>> execution = pipeline.run("my-pipeline.nf", runner=my_custom_runner)
Once the run command string has been passed to the runner, nextflow.py will wait for the pipeline to complete by watching the execution directory, and then return the Execution object as normal.
Polling
The function described above will run the pipeline and wait while it does, with the completed Execution being returned only at the end.
An alternate method is to use run_and_poll, which returns an Execution object every few seconds representing the state of the pipeline execution at that moment in time, as a generator:
for execution in pipeline.run_and_poll(sleep=2, run_path="./rundir", params={"param1": "123"}): print("Processing intermediate execution")
By default, an Execution will be returned every second, but you can adjust this as required with the sleep paramater. This is useful if you want to get information about the progress of the pipeline execution as it proceeds.
Executions
An Execution represents a single execution of a pipeline. It has properties for:
identifier - The unique ID of that run, generated by Nextflow.
started - When the pipeline ran (as a Python datetime).
finished - When the pipeline completed (as a Python datetime).
duration - how long the pipeline ran for (if finished).
status - the status Nextflow reports on completion.
command - the command used to run the pipeline.
stdout - the stdout of the execution process.
stderr - the stderr of the execution process.
log - the full text of the log file produced.
return_code - the exit code of the run - usually 0 or 1.
path - the path to the execution directory.
It also has a process_executions property, which is a list of ProcessExecution objects. Nextflow processes data by chaining together isolated ‘processes’, and each of these has a ProcessExecution object representing its execution. These have the following properties:
identifier - The unique ID generated by Nextflow, of the form xx/xxxxxx.
process - The name of the process that spawned the process execution.
name - The name of this specific process execution.
status - the status Nextflow reports on completion.
stdout - the stdout of the process execution.
stderr - the stderr of the process execution.
started - When the process execution ran (as a Python datetime).
started - When the process execution completed (as a Python datetime).
duration - how long the process execution took in seconds.
return_code - the exit code of the process execution - usually 0 or 1.
path - the local path to the process execution directory.
full_path - the absolute path to the process execution directory.
bash - the bash file contents generated for the process execution.
Process executions can have various files passed to them, and will create files during their execution too. These can be obtained as follows:
>>> process_execution.input_data() # Full absolute paths >>> process_execution.input_data(include_path=False) # Just file names >>> process_execution.all_output_data() # Full absolute paths >>> process_execution.all_output_data(include_path=False) # Just file names
Changelog
Release 0.8.1
14th November, 2023
Handle pure nextflow process statuses better.
Release 0.8.0
5th September, 2023
You can use output_path to specify where the execution contents go.
Release 0.7.1
22nd August, 2023
Fixed bug in handling empty param values.
Release 0.7.0
22nd July, 2023
An execution report can now be published with the report parameter.
A timeline report can now be published with the timeline parameter.
A DAG report can now be published with the dag parameter.
Release 0.6.2
21st July, 2023
Fixed issue in handling no path for process execution input data.
Release 0.6.1
7th July, 2023
Added option to specify timezone to Nextflow.
Release 0.6.0
24th May, 2023
Added ability to use custom runners for starting jobs.
Removed pipeline class to.
Overhauled architecture.
Release 0.5.0
28th October, 2022
Little c (-c) is now used instead of big C (-C) for passing config.
You can now pass multiple config files during pipeline execution.
Release 0.4.2
26th September, 2022
Added bash attribute to process executions.
Release 0.4.1
11th September, 2022
Fixed issue in execution polling where previous execution interferes initially.
Execution parsing now checks directory is fully ready for parsing.
Fixed issue where logs are unparseable in certain locales.
Release 0.4.0
13th July, 2022
Process executions now report their input files as paths.
Process executions now report all their output files as paths.
Executions now have properties for their originating pipeline.
Removed schema functionality.
Release 0.3.1
15th June, 2022
Process polling now accesses stdout and stderr while process is ongoing.
Release 0.3
4th June, 2022
Allow module-level run methods for directly running pipelines.
Allow for running pipelines with different Nextflow versions.
Improved datetime parsing.
Simplified process execution parsing.
Fixed concatenation of process executions with no parentheses.
Tests now check compatability with different Nextflow versions.
Release 0.2.2
21st March, 2022
Log outputs now have ANSI codes removed.
Release 0.2.1
19th February, 2022
Execution polling now handles unready execution directory.
Better detection of failed process executions mid execution.
Release 0.2
14th February, 2022
Added method for running while continuously polling pipeline execution.
Optimised process execution object creation from file state.
Release 0.1.4
12th January, 2022
Pipeline command generation no longer applies quotes if there are already quotes.
Release 0.1.3
24th November, 2021
Fixed Windows file separator issues.
Renamed NextflowProcess -> ProcessExecution.
Release 0.1.2
3rd November, 2021
Better handling of missing Nextflow executable.
Release 0.1.1
29th October, 2021
Renamed nextflow_processes to process_executions.
Added quotes around paths to handle spaces in paths.
Release 0.1
18th October, 2021
Basic Pipeline object.
Basic Execution object.
Basic ProcessExecution object.
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