Pipeline Features
==================


The Consistency module
----------------------

The CosmoSIS standard library has a particularly helpful module called ``consistency``, which is designed to translate automatically between different ways of expressing cosmological parameters.  

For example, if your values file specifies both :math:`\Omega_m` and :math:`h^2` then it will calculate :math:`\omega_m \equiv \Omega_m h^2` for you automatically.  

Any sufficient choice of the parameters can be specified and the remainder will be determined.

To use this module, just include the module ``utility/consistency/consistency_interface.py`` at the start of a pipeline.



Pre- and post-scripts
---------------------

CosmoSIS can automatically run a script before executing your pipeline; if the script returns an error code then the pipeline will not be run.  

It can also run a script after the 

You could use this feature to:
- run a check before the pipeline to make sure some test result is as expected.
- download some data that is needed for a pipeline.
- postprocess results after the pipeline is complete and make some plots.

To use this feature, add lines in the ``[runtime]`` section of the parameter file:

.. code-block:: ini

    [runtime]
    pre_script=./my_pre_script.sh
    post_script=./my_post_script.sh

Your scripts can be any executable. If the pre-script has a non-zero return value then the pipeline will not run.

Making pipeline graphs
------------------------

You will need the pygraphviz tool to use this feature.  On Conda, you can install it with:

.. code-block:: ini

    conda install -c conda-forge pygraphviz


Then you can create a graph from the command line using the ``--graph`` flag and then the graphviz ``dot`` command:

.. code-block:: bash

    cosmosis --graph des-y1.dot  examples/des-y1.ini
    dot -Tpng -o des-y1.png des-y1.dot

And example is shown below:

.. image:: /images/des-y1.png


Making a pipeline from a single likelihood function
----------------------------------------------------

*New in version 3.3*

If you have a single likelihood function that you want to use as a pipeline (so that you are really just using CosmoSIS for its sampling features), then you can do it like this:

.. code-block:: python

    import cosmosis

    def log_like(x):
        # your likelihood function here
        return log_like

    param_ranges = [
        (-1.0, 0.0, 1.0),
        # ...
        # min, start, and max of each parameter of the likelihood
    ]

    pipeline = cosmosis.LikelihoodPipeline.from_likelihood_function(log_like, param_ranges)


You can now test your pipeline, calling ``pipeline.posterior`` or ``pipeline.likelihood``, or run a sampler like this:

.. code-block:: python

    sampler_config = {
        "runtime": {
            "sampler": "emcee",
            "verbosity": "quiet",
        },

        "emcee": {
            "walkers": 100,
            # ... other emcee parameters
        },
    }

    status, output = cosmosis.run_cosmosis(sampler_config, pipeline=pipeline, output='astropy')


The output will be an astropy table with the samples and likelihoods.

You can also use derived parameters or supply non-uniform priors. See the ``LikelihoodPipeline.from_likelihood_function`` docstring for details.