################################################################################
# This file is part of SWIFT.
# Copyright (c) 2018 Matthieu Schaller (schaller@strw.leidenuniv.nl)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser 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 Lesser General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
################################################################################

# Computes the temperature evolution of the gas in a cosmological box

# Physical constants needed for internal energy to temperature conversion
k_in_J_K = 1.38064852e-23
mH_in_kg = 1.6737236e-27

# Number of snapshots generated
n_snapshots = 153
snapname = "santabarbara"

import matplotlib

matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import h5py

plt.style.use("../../../tools/stylesheets/mnras.mplstyle")

# Read the simulation data
sim = h5py.File("%s_0000.hdf5" % snapname, "r")
boxSize = sim["/Header"].attrs["BoxSize"][0]
time = sim["/Header"].attrs["Time"][0]
scheme = sim["/HydroScheme"].attrs["Scheme"][0]
kernel = sim["/HydroScheme"].attrs["Kernel function"][0]
neighbours = sim["/HydroScheme"].attrs["Kernel target N_ngb"][0]
eta = sim["/HydroScheme"].attrs["Kernel eta"][0]
alpha = sim["/HydroScheme"].attrs["Alpha viscosity"][0]
H_mass_fraction = sim["/HydroScheme"].attrs["Hydrogen mass fraction"][0]
H_transition_temp = sim["/HydroScheme"].attrs[
    "Hydrogen ionization transition temperature"
][0]
T_initial = sim["/HydroScheme"].attrs["Initial temperature"][0]
T_minimal = sim["/HydroScheme"].attrs["Minimal temperature"][0]
git = sim["Code"].attrs["Git Revision"]
cooling_model = sim["/SubgridScheme"].attrs["Cooling Model"].decode("utf-8")

if cooling_model == "Constant Lambda":
    Lambda = sim["/SubgridScheme"].attrs["Lambda/n_H^2 [cgs]"][0]

# Cosmological parameters
H_0 = sim["/Cosmology"].attrs["H0 [internal units]"][0]
gas_gamma = sim["/HydroScheme"].attrs["Adiabatic index"][0]

unit_length_in_cgs = sim["/Units"].attrs["Unit length in cgs (U_L)"]
unit_mass_in_cgs = sim["/Units"].attrs["Unit mass in cgs (U_M)"]
unit_time_in_cgs = sim["/Units"].attrs["Unit time in cgs (U_t)"]

unit_length_in_si = 0.01 * unit_length_in_cgs
unit_mass_in_si = 0.001 * unit_mass_in_cgs
unit_time_in_si = unit_time_in_cgs

# Primoridal mean molecular weight as a function of temperature
def mu(T, H_frac=H_mass_fraction, T_trans=H_transition_temp):
    if T > T_trans:
        return 4.0 / (8.0 - 5.0 * (1.0 - H_frac))
    else:
        return 4.0 / (1.0 + 3.0 * H_frac)


# Temperature of some primoridal gas with a given internal energy
def T(u, H_frac=H_mass_fraction, T_trans=H_transition_temp):
    T_over_mu = (gas_gamma - 1.0) * u * mH_in_kg / k_in_J_K
    ret = np.ones(np.size(u)) * T_trans

    # Enough energy to be ionized?
    mask_ionized = T_over_mu > (T_trans + 1) / mu(T_trans + 1, H_frac, T_trans)
    if np.sum(mask_ionized) > 0:
        ret[mask_ionized] = T_over_mu[mask_ionized] * mu(T_trans * 10, H_frac, T_trans)

    # Neutral gas?
    mask_neutral = T_over_mu < (T_trans - 1) / mu((T_trans - 1), H_frac, T_trans)
    if np.sum(mask_neutral) > 0:
        ret[mask_neutral] = T_over_mu[mask_neutral] * mu(0, H_frac, T_trans)

    return ret


z = np.zeros(n_snapshots)
a = np.zeros(n_snapshots)
T_mean = np.zeros(n_snapshots)
T_std = np.zeros(n_snapshots)
T_log_mean = np.zeros(n_snapshots)
T_log_std = np.zeros(n_snapshots)
T_median = np.zeros(n_snapshots)
T_min = np.zeros(n_snapshots)
T_max = np.zeros(n_snapshots)

# Loop over all the snapshots
for i in range(n_snapshots):
    sim = h5py.File("%s_%04d.hdf5" % (snapname, i), "r")

    z[i] = sim["/Cosmology"].attrs["Redshift"][0]
    a[i] = sim["/Cosmology"].attrs["Scale-factor"][0]

    u = sim["/PartType0/InternalEnergies"][:]

    # Compute the temperature
    u *= unit_length_in_si ** 2 / unit_time_in_si ** 2
    u /= a[i] ** (3 * (gas_gamma - 1.0))
    Temp = T(u)

    # Gather statistics
    T_median[i] = np.median(Temp)
    T_mean[i] = Temp.mean()
    T_std[i] = Temp.std()
    T_log_mean[i] = np.log10(Temp).mean()
    T_log_std[i] = np.log10(Temp).std()
    T_min[i] = Temp.min()
    T_max[i] = Temp.max()

# CMB evolution
a_evol = np.logspace(-3, 0, 60)
T_cmb = (1.0 / a_evol) ** 2 * 2.72

# Plot the interesting quantities
plt.figure()
plt.subplot(111, xscale="log", yscale="log")

plt.fill_between(a, T_mean - T_std, T_mean + T_std, color="C0", alpha=0.1)
plt.plot(a, T_max, ls="-.", color="C0", lw=1.0, label="${\\rm max}~T$")
plt.plot(a, T_min, ls=":", color="C0", lw=1.0, label="${\\rm min}~T$")
plt.plot(a, T_mean, color="C0", label="${\\rm mean}~T$", lw=1.5)
plt.fill_between(
    a,
    10 ** (T_log_mean - T_log_std),
    10 ** (T_log_mean + T_log_std),
    color="C1",
    alpha=0.1,
)
plt.plot(a, 10 ** T_log_mean, color="C1", label="${\\rm mean}~{\\rm log} T$", lw=1.5)
plt.plot(a, T_median, color="C2", label="${\\rm median}~T$", lw=1.5)

plt.legend(loc="upper left", frameon=False, handlelength=1.5)

# Cooling model
if cooling_model == "Constant Lambda":
    plt.text(
        1e-2,
        6e4,
        "$\Lambda_{\\rm const}/n_{\\rm H}^2 = %.1f\\times10^{%d}~[\\rm{cgs}]$"
        % (Lambda / 10.0 ** (int(log10(Lambda))), log10(Lambda)),
        fontsize=7,
    )
elif cooling_model == "EAGLE":
    plt.text(1e-2, 6e4, "EAGLE (Wiersma et al. 2009)")
elif cooling_model == b"Grackle":
    plt.text(1e-2, 6e4, "Grackle (Smith et al. 2016)")
else:
    plt.text(1e-2, 6e4, "No cooling")

# Expected lines
plt.plot(
    [1e-10, 1e10], [H_transition_temp, H_transition_temp], "k--", lw=0.5, alpha=0.7
)
plt.text(
    2.5e-2,
    H_transition_temp * 1.07,
    "$T_{\\rm HII\\rightarrow HI}$",
    va="bottom",
    alpha=0.7,
    fontsize=8,
)
plt.plot([1e-10, 1e10], [T_minimal, T_minimal], "k--", lw=0.5, alpha=0.7)
plt.text(1e-2, T_minimal * 0.8, "$T_{\\rm min}$", va="top", alpha=0.7, fontsize=8)
plt.plot(a_evol, T_cmb, "k--", lw=0.5, alpha=0.7)
plt.text(
    a_evol[20],
    T_cmb[20] * 0.55,
    "$(1+z)^2\\times T_{\\rm CMB,0}$",
    rotation=-34,
    alpha=0.7,
    fontsize=8,
    va="top",
    bbox=dict(facecolor="w", edgecolor="none", pad=1.0, alpha=0.9),
)


redshift_ticks = np.array([0.0, 1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0])
redshift_labels = ["$0$", "$1$", "$2$", "$5$", "$10$", "$20$", "$50$", "$100$"]
a_ticks = 1.0 / (redshift_ticks + 1.0)

plt.xticks(a_ticks, redshift_labels)
plt.minorticks_off()

plt.xlabel("${\\rm Redshift}~z$", labelpad=0)
plt.ylabel("${\\rm Temperature}~T~[{\\rm K}]$", labelpad=0)
plt.xlim(9e-3, 1.1)
plt.ylim(20, 2.5e7)

plt.tight_layout()

plt.savefig("Temperature_evolution.png", dpi=200)