The ZIPPY API

ZIPPY is largely designed to be run from the command line using json files as the source of record for an execution. However, that model doesn't always work -- what about when you want to run a very similar workflow across hundreds of different samples / runs? Well, there's an API for that:

The ZIPPY API lets you load, modify, save and execute ZIPPY parameters files. The API has a whole 4 functions:

def api_load_params(fname, defaults_fname=None) """ Loads a valid zippy parameters file or template file from disk. Represents the file as a native python object (c.f., the python json module) """

def api_save_params(params, fname) """ Writes a python object (structured as json) to a file. Used to write files which can then by run using 'python zippy.py your_file.json' """

def api_build_zippy(params_dictionary) """ Call ZIPPY from python, specifying a params dictionary instead of going through makeparams. Returns a zippy object, which can then be run using the call x.api_run_zippy() """

def api_run_zippy(self, mode='sge')

Example: running the same zippy settings over multiple runs. Given a input map of runs, we load a base parameters file, and set the input run directory and other parameters on a per-run basis.

import zippy
for (identifier, dir_name) in run_map.iteritems():
    #load template params file
    params = zippy.api_load_params('base_params.json')
    for i in range(len(stages)):
        params.stages[i].output_dir = os.path.join(dir_name,params.stages[i].identifier)
    #prepare the input to the data stage
    params.stages[0].samples = get_samples(identifier)
    params.scratch_path = '/path/to/scratch/{}'.format(identifier)
    wflow = zippy.api_build_zippy(params)
    zippy.api_save_params(params, 'api_params_{}.json'.format(identifier))
    wflow.api_run_zippy()

And here is the example base_params.json:

{
    "stages": [
        {
            "identifier": "data", 
            "output_dir": "", 
            "samples": "",
            "stage": "data"
        }, 
        {
            "identifier": "strelka", 
            "is_somatic": true,
            "output_dir": "",
            "previous_stage": ["data"], 
            "stage": "strelka",
            "args": "--exome --callMemMb=1024"
        }
    ], 
    "scratch_path": "", 
    "python": "path/to/python", 
    "strelka_path": "path/to/strelka", 
    "genome": "path/to/genome"
}