# Copyright (C) 2023 Richard Stiskalek
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.

from os.path import join
from argparse import ArgumentParser

import matplotlib.pyplot as plt
import numpy
import healpy

import scienceplots  # noqa
import utils
from cache_to_disk import cache_to_disk, delete_disk_caches_for_function  # noqa
from tqdm import tqdm

try:
    import csiborgtools
except ModuleNotFoundError:
    import sys
    sys.path.append("../")
    import csiborgtools


def open_csiborg(nsim):
    """
    Open a CSiBORG halo catalogue.
    """
    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
    bounds = {"totpartmass": (None, None), "dist": (0, 155/0.705)}
    return csiborgtools.read.HaloCatalogue(nsim, paths, bounds=bounds)


def open_quijote(nsim, nobs=None):
    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
    cat = csiborgtools.read.QuijoteHaloCatalogue(nsim, paths, nsnap=4)
    if nobs is not None:
        cat = cat.pick_fiducial_observer(nobs, rmax=155.5 / 0.705)
    return cat


def plot_mass_vs_ncells(nsim, pdf=False):
    cat = open_csiborg(nsim)
    mpart = 4.38304044e+09

    with plt.style.context(utils.mplstyle):
        plt.figure()
        plt.scatter(cat["totpartmass"], cat["lagpatch_ncells"], s=0.25,
                    rasterized=True)
        plt.xscale("log")
        plt.yscale("log")
        for n in [1, 10, 100]:
            plt.axvline(n * 512 * mpart, c="black", ls="--", zorder=0, lw=0.8)
        plt.xlabel(r"$M_{\rm tot} / M_\odot$")
        plt.ylabel(r"$N_{\rm cells}$")

        for ext in ["png"] if pdf is False else ["png", "pdf"]:
            fout = join(utils.fout, f"init_mass_vs_ncells_{nsim}.{ext}")
            print(f"Saving to `{fout}`.")
            plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
        plt.close()


###############################################################################
#                              HMF plot                                       #
###############################################################################


def process_counts(counts):
    mean = numpy.mean(counts, axis=0)
    std = numpy.std(counts, axis=0)
    return mean, std


def plot_hmf(pdf=False):
    print("Plotting the HMF...", flush=True)
    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)

    csiborg_nsims = paths.get_ics("csiborg")
    print("Loading CSiBORG halo counts.", flush=True)
    for i, nsim in enumerate(tqdm(csiborg_nsims)):
        data = numpy.load(paths.halo_counts("csiborg", nsim))
        if i == 0:
            bins = data["bins"]
            csiborg_counts = numpy.full((len(csiborg_nsims), len(bins) - 1),
                                        numpy.nan, dtype=numpy.float32)
        csiborg_counts[i, :] = data["counts"]
    csiborg_counts /= numpy.diff(bins).reshape(1, -1)

    print("Loading Quijote halo counts.", flush=True)
    quijote_nsims = paths.get_ics("quijote")
    for i, nsim in enumerate(tqdm(quijote_nsims)):
        data = numpy.load(paths.halo_counts("quijote", nsim))
        if i == 0:
            bins = data["bins"]
            nmax = data["counts"].shape[0]
            quijote_counts = numpy.full(
                (len(quijote_nsims) * nmax, len(bins) - 1), numpy.nan,
                dtype=numpy.float32)
        quijote_counts[i * nmax:(i + 1) * nmax, :] = data["counts"]
    quijote_counts /= numpy.diff(bins).reshape(1, -1)

    x = 10**(0.5 * (bins[1:] + bins[:-1]))
    # Edit lower limits
    csiborg_counts[:, x < 1e12] = numpy.nan
    quijote_counts[:, x < 8e12] = numpy.nan
    # Edit upper limits
    csiborg_counts[:, x > 4e15] = numpy.nan
    quijote_counts[:, x > 4e15] = numpy.nan

    with plt.style.context(utils.mplstyle):
        cols = plt.rcParams["axes.prop_cycle"].by_key()["color"]
        fig, ax = plt.subplots(nrows=2, sharex=True,
                               figsize=(3.5, 2.625 * 1.25),
                               gridspec_kw={"height_ratios": [1, 0.65]})
        fig.subplots_adjust(hspace=0, wspace=0)

        mean_csiborg, std_csiborg = process_counts(csiborg_counts)
        ax[0].plot(x, mean_csiborg, label="CSiBORG")
        ax[0].fill_between(x, mean_csiborg - std_csiborg,
                           mean_csiborg + std_csiborg, alpha=0.5)

        mean_quijote, std_quijote = process_counts(quijote_counts)
        ax[0].plot(x, mean_quijote, label="Quijote")
        ax[0].fill_between(x, mean_quijote - std_quijote,
                           mean_quijote + std_quijote, alpha=0.5)

        log_y = numpy.log10(mean_csiborg / mean_quijote)
        err = numpy.sqrt((std_csiborg / mean_csiborg / numpy.log(10))**2
                         + (std_quijote / mean_quijote / numpy.log(10))**2)

        ax[1].plot(x, 10**log_y, c=cols[2])
        ax[1].fill_between(x, 10**(log_y - err), 10**(log_y + err), alpha=0.5,
                           color=cols[2])
        ax[1].axhline(1, color="k", ls=plt.rcParams["lines.linestyle"],
                      lw=0.5 * plt.rcParams["lines.linewidth"], zorder=0)
        ax[0].set_ylabel(r"$\frac{\mathrm{d} n}{\mathrm{d}\log M_{\rm h}}~\mathrm{dex}^{-1}$")  # noqa
        ax[1].set_xlabel(r"$M_{\rm h}$ [$M_\odot$]")
        ax[1].set_ylabel(r"$\mathrm{CSiBORG} / \mathrm{Quijote}$")

        ax[0].set_xscale("log")
        ax[0].set_yscale("log")
        ax[1].set_yscale("log")
        ax[0].legend()

        fig.tight_layout(h_pad=0, w_pad=0)
        for ext in ["png"] if pdf is False else ["png", "pdf"]:
            fout = join(utils.fout, f"hmf_comparison.{ext}")
            print(f"Saving to `{fout}`.")
            fig.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
        plt.close()


@cache_to_disk(7)
def load_field(kind, nsim, grid, MAS, in_rsp=False):
    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
    return numpy.load(paths.field(kind, MAS, grid, nsim, in_rsp=in_rsp))


###############################################################################
#                             Projected field                                 #
###############################################################################


def plot_projected_field(kind, nsim, grid, in_rsp, MAS="PCS",
                         highres_only=True, pdf=False):
    print(f"Plotting projected field `{kind}`. ", flush=True)
    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
    nsnap = max(paths.get_snapshots(nsim))
    box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)

    if kind == "overdensity":
        field = load_field("density", nsim, grid, MAS=MAS, in_rsp=in_rsp)
        density_gen = csiborgtools.field.DensityField(box, MAS)
        field = density_gen.overdensity_field(field) + 2
    else:
        field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=in_rsp)

    if highres_only:
        csiborgtools.field.fill_outside(field, numpy.nan, rmax=155.5,
                                        boxsize=677.7)
        # start = field.shape[0] // 4
        start = round(field.shape[0] * 0.27)
        end = round(field.shape[0] * 0.73)
        # end = field.shape[0] - start
        field = field[start:end, start:end, start:end]

    labels = [r"$y-z$", r"$x-z$", r"$x-y$"]
    with plt.style.context(utils.mplstyle):
        fig, ax = plt.subplots(figsize=(3.5 * 2, 2.625), ncols=3, sharey=True,
                               sharex=True)
        fig.subplots_adjust(hspace=0, wspace=0)
        for i in range(3):
            img = numpy.nanmean(field, axis=i)
            if i == 0:
                vmin, vmax = numpy.nanpercentile(img, [1, 99])
                im = ax[i].imshow(numpy.nanmean(field, axis=i), vmin=vmin,
                                  vmax=vmax)
            else:
                ax[i].imshow(numpy.nanmean(field, axis=i), vmin=vmin,
                             vmax=vmax)
            ax[i].set_title(labels[i])
        cbar_ax = fig.add_axes([1.0, 0.1, 0.025, 0.8])
        fig.colorbar(im, cax=cbar_ax, label="Mean projected field")

        fig.tight_layout(h_pad=0, w_pad=0)
        for ext in ["png"] if pdf is False else ["png", "pdf"]:
            fout = join(utils.fout, f"field_{kind}_{nsim}_rsp{in_rsp}.{ext}")
            print(f"Saving to `{fout}`.")
            fig.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
        plt.close()

###############################################################################
#                             Sky distribution                                #
###############################################################################


def get_sky_label(kind, volume_weight):
    if volume_weight:
        if kind == "density":
            label = r"$\log \int_{0}^{R} r^2 \rho(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$"  # noqa
        if kind == "overdensity":
            label = r"$\log \int_{0}^{R} r^2 \left[\delta(r, \mathrm{RA}, \mathrm{dec}) + 2\right] \mathrm{d} r$"  # noqa
        elif kind == "potential":
            label = r"$\int_{0}^{R} r^2 \phi(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$"  # noqa
        elif kind == "radvel":
            label = r"$\int_{0}^{R} r^2 v_r(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$"  # noqa
        else:
            label = None
    else:
        if kind == "density":
            label = r"$\log \int_{0}^{R} \rho(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$"  # noqa
        if kind == "overdensity":
            label = r"$\log \int_{0}^{R} \left[\delta(r, \mathrm{RA}, \mathrm{dec}) + 2\right] \mathrm{d} r$"  # noqa
        elif kind == "potential":
            label = r"$\int_{0}^{R} \phi(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$"  # noqa
        elif kind == "radvel":
            label = r"$\int_{0}^{R} v_r(r, \mathrm{RA}, \mathrm{dec}) \mathrm{d} r$"  # noqa
        else:
            label = None
    return label


def plot_sky_distribution(kind, nsim, grid, nside, MAS="PCS", plot_groups=True,
                          dmin=0, dmax=220, plot_halos=None,
                          volume_weight=True, pdf=False):
    """
    NOTE: add distance for groups.
    """
    paths = csiborgtools.read.Paths(**csiborgtools.paths_glamdring)
    nsnap = max(paths.get_snapshots(nsim))
    box = csiborgtools.read.CSiBORGBox(nsnap, nsim, paths)

    if kind == "overdensity":
        field = load_field("density", nsim, grid, MAS=MAS, in_rsp=False)
        density_gen = csiborgtools.field.DensityField(box, MAS)
        field = density_gen.overdensity_field(field) + 2
    else:
        field = load_field(kind, nsim, grid, MAS=MAS, in_rsp=False)

    angpos = csiborgtools.field.nside2radec(nside)
    dist = numpy.linspace(dmin, dmax, 500)
    out = csiborgtools.field.make_sky(field, angpos=angpos, dist=dist, box=box,
                                      volume_weight=volume_weight)

    with plt.style.context(utils.mplstyle):
        label = get_sky_label(kind, volume_weight)
        if kind in ["density", "overdensity"]:
            out = numpy.log10(out)
        healpy.mollview(out, fig=0, title="", unit=label)

        if plot_halos is not None:
            bounds = {"dist": (dmin, dmax),
                      "totpartmass": (plot_halos, None)}
            cat = csiborgtools.read.HaloCatalogue(nsim, paths, bounds=bounds)
            X = cat.position(cartesian=False)
            healpy.projscatter(numpy.deg2rad(X[:, 2] + 90),
                               numpy.deg2rad(X[:, 1]),
                               s=1, c="red", label="CSiBORG haloes")

        if plot_groups:
            groups = csiborgtools.read.TwoMPPGroups(fpath="/mnt/extraspace/rstiskalek/catalogs/2M++_group_catalog.dat")  # noqa
            healpy.projscatter(numpy.deg2rad(groups["DEC"] + 90),
                               numpy.deg2rad(groups["RA"]), s=1, c="blue",
                               label="2M++ groups")

        if plot_halos is not None or plot_groups:
            plt.legend(markerscale=10)

        for ext in ["png"] if pdf is False else ["png", "pdf"]:
            fout = join(utils.fout, f"sky_{kind}_{nsim}_from_{dmin}_to_{dmax}_vol{volume_weight}.{ext}")  # noqa
            print(f"Saving to `{fout}`.")
            plt.savefig(fout, dpi=utils.dpi, bbox_inches="tight")
        plt.close()


###############################################################################
#                        Command line interface                               #
###############################################################################


if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument('-c', '--clean', action='store_true')
    args = parser.parse_args()

    cached_funcs = ["load_field"]
    if args.clean:
        for func in cached_funcs:
            print(f"Cleaning cache for function {func}.")
            delete_disk_caches_for_function(func)

    # plot_mass_vs_occupancy(7444)
    # plot_mass_vs_normcells(7444 + 24 * 4, pdf=False)
    # plot_mass_vs_ncells(7444, pdf=True)
    # plot_hmf(pdf=True)
    # plot_sky_distribution("radvel", 7444, 256, nside=64,
    #                       plot_groups=False, dmin=50, dmax=100,
    #                       plot_halos=5e13, volume_weight=False)

    plot_projected_field("potential", 7444, 256, in_rsp=True)