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

from typing import Any, Dict, Sequence
import numpy as np
from functools import reduce
from gn3.settings import TMPDIR
import plotly.graph_objects as go
import plotly.figure_factory as ff
from gn3.random import random_string
from gn3.computations.slink import slink
from plotly.subplots import make_subplots
from gn3.computations.correlations2 import compute_correlation
from gn3.db.genotypes import (
    build_genotype_file, load_genotype_samples, parse_genotype_file)
from gn3.db.traits import (
    retrieve_trait_data,
    retrieve_trait_info,
    generate_traits_filename)
from gn3.computations.qtlreaper import (
    run_reaper,
    generate_traits_file,
    chromosome_sorter_key_fn,
    parse_reaper_main_results,
    organise_reaper_main_results,
    parse_reaper_permutation_results)

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
    dtype: (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]["value"]:
            sample_data.append(tdata[strain]["value"])
            if var_exists:
                if tdata[strain]["variance"]:
                    sample_data.append(tdata[strain]["variance"])
                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):
        # pylint: disable=[R0911]
        if strain in trait_data["data"]:
            if dtype == "val":
                return accumulator + (trait_data["data"][strain]["value"], )
            if dtype == "var":
                return accumulator + (trait_data["data"][strain]["variance"], )
            if dtype == "N":
                return accumulator + (trait_data["data"][strain]["ndata"], )
            if dtype == "all":
                return accumulator + __export_all_types(trait_data["data"], strain)
            raise KeyError("Type `%s` is incorrect" % dtype)
        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, 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())
        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_i[0] == tdata_j[0]:
            return 0.0
        corr_vals = compute_correlation(tdata_i[1], tdata_j[1])
        corr = corr_vals[0]
        if (1 - corr) < 0:
            return 0.0
        return 1 - corr

    def __cluster(tdata_i):
        return tuple(
            __compute_corr(tdata_i, tdata_j)
            for tdata_j in enumerate(traits_data_list))

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

def build_heatmap(traits_names, conn: Any):
    """
    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...
    """
    threshold = 0 # webqtlConfig.PUBLICTHRESH
    traits = [
        retrieve_trait_info(threshold, fullname, conn)
        for fullname in traits_names]
    traits_data_list = [retrieve_trait_data(t, conn) for t in traits]
    genotype_filename = build_genotype_file(traits[0]["riset"])
    # genotype = parse_genotype_file(genotype_filename)
    strains = load_genotype_samples(genotype_filename)
    exported_traits_data_list = [
        export_trait_data(td, strains) for td in traits_data_list]
    clustered = cluster_traits(exported_traits_data_list)
    slinked = slink(clustered)
    traits_order = compute_traits_order(slinked)
    ordered_traits_names = [
        traits[idx]["trait_fullname"] for idx in traits_order]
    strains_and_values = retrieve_strains_and_values(
        traits_order, strains, exported_traits_data_list)
    traits_filename = "{}/traits_test_file_{}.txt".format(
        TMPDIR, random_string(10))
    generate_traits_file(
        strains_and_values[0][1],
        [t[2] for t in strains_and_values],
        traits_filename)

    main_output, _permutations_output = run_reaper(
        genotype_filename, traits_filename, separate_nperm_output=True)

    qtlresults = parse_reaper_main_results(main_output)
    # permudata = parse_reaper_permutation_results(permutations_output)
    organised = organise_reaper_main_results(qtlresults)

    traits_ids = [# sort numerically, but retain the ids as strings
        str(i) for i in sorted({int(row["ID"]) for row in qtlresults})]
    chromosome_names = sorted(
        {row["Chr"] for row in qtlresults}, key=chromosome_sorter_key_fn)
    # loci_names = sorted({row["Locus"] for row in qtlresults})
    ordered_traits_names = dict(
        zip(traits_ids,
            [traits[idx]["trait_fullname"] for idx in traits_order]))

    return generate_clustered_heatmap(
        process_traits_data_for_heatmap(
            organised, traits_ids, chromosome_names),
        clustered,
        "single_heatmap_{}".format(random_string(10)),
        y_axis=tuple(
            ordered_traits_names[traits_ids[order]]
            for order in traits_order),
        y_label="Traits",
        x_axis=chromosome_names,
        x_label="Chromosomes")

def compute_traits_order(slink_data, neworder: tuple = tuple()):
    """
    Compute the order of the traits for clustering from `slink_data`.

    This function tries to reproduce the creation and update of the `neworder`
    variable in
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L120
    and in the `web.webqtl.heatmap.Heatmap.draw` function in GN1
    """
    def __order_maker(norder, slnk_dt):
        if isinstance(slnk_dt[0], int) and isinstance(slnk_dt[1], int):
            return norder + (slnk_dt[0], slnk_dt[1])

        if isinstance(slnk_dt[0], int):
            return __order_maker((norder + (slnk_dt[0], )), slnk_dt[1])

        if isinstance(slnk_dt[1], int):
            return __order_maker(norder, slnk_dt[0]) + (slnk_dt[1], )

        return __order_maker(__order_maker(norder, slnk_dt[0]), slnk_dt[1])

    return __order_maker(neworder, slink_data)

def retrieve_strains_and_values(orders, strainlist, traits_data_list):
    """
    Get the strains and their corresponding values from `strainlist` and
    `traits_data_list`.

    This migrates the code in
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L215-221
    """
    # This feels nasty! There's a lot of mutation of values here, that might
    # indicate something untoward in the design of this function and its
    # dependents  ==>  Review
    strains = []
    values = []
    rets = []
    for order in orders:
        temp_val = traits_data_list[order]
        for i, strain in enumerate(strainlist):
            if temp_val[i] is not None:
                strains.append(strain)
                values.append(temp_val[i])
        rets.append([order, strains[:], values[:]])
        strains = []
        values = []

    return rets

def nearest_marker_finder(genotype):
    """
    Returns a function to be used with `genotype` to compute the nearest marker
    to the trait passed to the returned function.

    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L425-434
    """
    def __compute_distances(chromo, trait):
        loci = chromo.get("loci", None)
        if not loci:
            return None
        return tuple(
            {
                "name": locus["name"],
                "distance": abs(locus["Mb"] - trait["mb"])
            } for locus in loci)

    def __finder(trait):
        _chrs = tuple(
            _chr for _chr in genotype["chromosomes"]
            if str(_chr["name"]) == str(trait["chr"]))
        if len(_chrs) == 0:
            return None
        distances = tuple(
            distance for dists in
            filter(
                lambda x: x is not None,
                (__compute_distances(_chr, trait) for _chr in _chrs))
            for distance in dists)
        nearest = min(distances, key=lambda d: d["distance"])
        return nearest["name"]
    return __finder

def get_nearest_marker(traits_list, genotype):
    """
    Retrieves the nearest marker for each of the traits in the list.

    DESCRIPTION:
    This migrates the code in
    https://github.com/genenetwork/genenetwork1/blob/master/web/webqtl/heatmap/Heatmap.py#L419-L438
    """
    if not genotype["Mbmap"]:
        return [None] * len(traits_list)

    marker_finder = nearest_marker_finder(genotype)
    return [marker_finder(trait) for trait in traits_list]

def get_lrs_from_chr(trait, chr_name):
    """
    Retrieve the LRS values for a specific chromosome in the given trait.
    """
    chromosome = trait["chromosomes"].get(chr_name)
    if chromosome:
        return [
            locus["LRS"] for locus in
            sorted(chromosome["loci"], key=lambda loc: loc["Locus"])]
    return [None]

def process_traits_data_for_heatmap(data, trait_names, chromosome_names):
    """
    Process the traits data in a format useful for generating heatmap diagrams.
    """
    hdata = [
        [get_lrs_from_chr(data[trait], chr_name) for trait in trait_names]
        for chr_name in chromosome_names]
    return hdata

def generate_clustered_heatmap(
        data, clustering_data, image_filename_prefix, x_axis=None,
        x_label: str = "", y_axis=None, y_label: str = "",
        output_dir: str = TMPDIR,
        colorscale=(
            (0.0, '#5D5D5D'), (0.4999999999999999, '#ABABAB'),
            (0.5, '#F5DE11'), (1.0, '#FF0D00'))):
    """
    Generate a dendrogram, and heatmaps for each chromosome, and put them all
    into one plot.
    """
    num_cols = 1 + len(x_axis)
    fig = make_subplots(
        rows=1,
        cols=num_cols,
        shared_yaxes="rows",
        horizontal_spacing=0.001,
        subplot_titles=["distance"] + x_axis,
        figure=ff.create_dendrogram(
            np.array(clustering_data), orientation="right", labels=y_axis))
    hms = [go.Heatmap(
        name=chromo,
        y=y_axis,
        z=data_array,
        showscale=False) for chromo, data_array in zip(x_axis, data)]
    for i, heatmap in enumerate(hms):
        fig.add_trace(heatmap, row=1, col=(i + 2))

    fig.update_layout(
        {
            "width": 1500,
            "height": 800,
            "xaxis": {
                "mirror": False,
                "showgrid": True
            }
        })

    x_axes_layouts = {
        "xaxis{}".format(i+1 if i > 0 else ""): {
            "mirror": False,
            "showticklabels": True if i == 0 else False,
            "ticks": "outside" if i == 0 else ""
        }
        for i in range(num_cols)}

    fig.update_layout(
        {
            "width": 4000,
            "height": 800,
            "yaxis": {
                "mirror": False,
                "ticks": ""
            },
            **x_axes_layouts})
    fig.update_traces(
        showlegend=False,
        colorscale=colorscale,
        selector={"type": "heatmap"})
    fig.update_traces(
        showlegend=True,
        showscale=True,
        selector={"name": x_axis[-1]})
    image_filename = "{}/{}.html".format(output_dir, image_filename_prefix)
    fig.write_html(image_filename)
    return image_filename, fig