"""
This module will contain functions to be used in computation of the data used to
generate various kinds of heatmaps.
"""

from functools import reduce
from typing import Any, Dict, Sequence
from gn3.computations.slink import slink
from gn3.db.traits import retrieve_trait_data, retrieve_trait_info
from gn3.computations.correlations2 import compute_correlation

def export_trait_data(
        trait_data: dict, strainlist: Sequence[str], dtype: str = "val",
        var_exists: bool = False, n_exists: bool = False):
    """
    Export data according to `strainlist`. Mostly used in calculating
    correlations.

    DESCRIPTION:
    Migrated from
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L166-L211

    PARAMETERS
    trait: (dict)
      The dictionary of key-value pairs representing a trait
    strainlist: (list)
      A list of strain names
    type: (str)
      ... verify what this is ...
    var_exists: (bool)
      A flag indicating existence of variance
    n_exists: (bool)
      A flag indicating existence of ndata
    """
    def __export_all_types(tdata, strain):
        sample_data = []
        if tdata[strain]["val"]:
            sample_data.append(tdata[strain]["val"])
            if var_exists:
                if tdata[strain].var:
                    sample_data.append(tdata[strain]["var"])
                else:
                    sample_data.append(None)
            if n_exists:
                if tdata[strain]["ndata"]:
                    sample_data.append(tdata[strain]["ndata"])
                else:
                    sample_data.append(None)
        else:
            if var_exists and n_exists:
                sample_data += [None, None, None]
            elif var_exists or n_exists:
                sample_data += [None, None]
            else:
                sample_data.append(None)

        return tuple(sample_data)

    def __exporter(accumulator, strain):
        if tdata.has_key(strain):
            if dtype == "val":
                return accumulator + (tdata[strain]["val"], )
            if dtype == "var":
                return accumulator + (tdata[strain]["var"], )
            if dtype == "N":
                return tdata[strain]["ndata"]
            if dtype == "all":
                return accumulator + __export_all_types(
                    accumulator, tdata, strain)
            else:
                raise KeyError("Type `%s` is incorrect" % dtype)
        else:
            if var_exists and n_exists:
                return accumulator + (None, None, None)
            if var_exists or n_exists:
                return accumulator + (None, None)
            return accumulator + (None,)

    return reduce(__exporter(strain), strainlist, tuple())

def trait_display_name(trait: Dict):
    """
    Given a trait, return a name to use to display the trait on a heatmap.

    DESCRIPTION
    Migrated from
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/base/webqtlTrait.py#L141-L157
    """
    if trait.get("db", None) and trait.get("trait_name", None):
        if trait["db"]["dataset_type"] == "Temp":
            desc = trait["description"]
            if desc.find("PCA") >= 0:
                return "%s::%s" % (
                    trait["db"]["displayname"],
                    desc[desc.rindex(':')+1:].strip())
            return "%s::%s" % (
                trait["db"]["displayname"],
                desc[:desc.index('entered')].strip())
        else:
            prefix = "%s::%s" % (
                trait["db"]["dataset_name"], trait["trait_name"])
            if trait["cellid"]:
                return "%s::%s" % (prefix, trait["cellid"])
            return prefix
    return trait["description"]

def cluster_traits(traits_data_list: Sequence[Dict]):
    """
    Clusters the trait values.

    DESCRIPTION
    Attempts to replicate the clustering of the traits, as done at
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L138-L162
    """
    def __compute_corr(tdata_i, tdata_j):
        if tdata_j[0] < tdata_i[0]:
            corr, nOverlap = compute_correlation(tdata_i, tdata_j)
            if (1 - corr) < 0:
                return 0.0
            return 1 - corr
        return 0.0

    def __cluster(tdata_i):
        res2 = tuple(
            __compute_corr(tdata_i, tdata_j) for tdata_j in enumerate(traits))

    return tuple(__cluster(tdata_i) for tdata_i in enumerate(traits_data_list))

def heatmap_data(
        fd, search_result, conn: Any, colorScheme=None, userPrivilege=None,
        userName=None):
    """
    heatmap function

    DESCRIPTION
    This function is an attempt to reproduce the initialisation at
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L46-L64
    and also the clustering and slink computations at
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L138-L165
    with the help of the `gn3.computations.heatmap.cluster_traits` function.

    It does not try to actually draw the heatmap image.

    PARAMETERS:
    TODO: Elaborate on the parameters here...
    """
    cluster_checked = fd.formdata.getvalue("clusterCheck", "")
    strainlist = [strain for strain in fd.strainlist if strain not in fd.parlist]
    genotype = fd.genotype

    def __retrieve_traitlist_and_datalist(threshold, fullname):
        trait = retrieve_trait_info(threshold, fullname, conn)
        return (trait, export_trait_data(retrieve_trait_data(trait), strainlist))

    traits_details = [
        __retrieve_traitlist_and_datalist(threshold, fullname)
        for fullname in search_result]
    traits_list = map(lambda x: x[0], traits_details)
    traits_data_list = map(lambda x: x[1], traits_details)

    return {
        "target_description_checked": fd.formdata.getvalue(
            "targetDescriptionCheck", ""),
        "cluster_checked": cluster_checked,
        "slink_data": (
            slink(cluster_traits(traits_list, strainlist))
            if cluster_checked else False)
        "sessionfile": fd.formdata.getvalue("session"),
        "genotype": genotype,
        "nLoci": sum(map(lambda x: len(x), genotype))
        "strainlist": strainlist,
        "ppolar": fd.ppolar,
        "mpolar":fd.mpolar,
        "traits_list": traits_list
        "traits_data_list": traits_data_list
    }