"""
This module deals with partial correlations.

It is an attempt to migrate over the partial correlations feature from
GeneNetwork1.
"""

from typing import Sequence
from functools import reduce

def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]):
    """
    Fetches data for the control traits.

    This migrates `web/webqtl/correlation/correlationFunction.controlStrain` in
    GN1, with a few modifications to the arguments passed in.

    PARAMETERS:
    controls: A map of sample names to trait data. Equivalent to the `cvals`
        value in the corresponding source function in GN1.
    sampleslist: A list of samples. Equivalent to `strainlst` in the
        corresponding source function in GN1
    """
    def __process_control__(trait_data):
        def __process_sample__(acc, sample):
            if sample in trait_data["data"].keys():
                sample_item = trait_data["data"][sample]
                val = sample_item["value"]
                if val is not None:
                    return (
                        acc[0] + (sample,),
                        acc[1] + (val,),
                        acc[2] + (sample_item["variance"],))
            return acc
        return reduce(
            __process_sample__, sampleslist, (tuple(), tuple(), tuple()))

    return reduce(
        lambda acc, item: (
            acc[0] + (item[0],),
            acc[1] + (item[1],),
            acc[2] + (item[2],),
            acc[3] + (len(item[0]),),
        ),
        [__process_control__(trait_data) for trait_data in controls],
        (tuple(), tuple(), tuple(), tuple()))

def dictify_by_samples(samples_vals_vars: Sequence[Sequence]) -> dict:
    """
    Build a sequence of dictionaries from a sequence of separate sequences of
    samples, values and variances.

    This is a partial migration of
    `web.webqtl.correlation.correlationFunction.fixStrains` function in GN1.
    This implementation extracts code that will find common use, and that will
    find use in more than one place.
    """
    return tuple(
        {
            sample: {"sample_name": sample, "value": val, "variance": var}
            for sample, val, var in zip(*trait_line)
        } for trait_line in zip(*(samples_vals_vars[0:3])))