import numpy as np
import h5py as h5
import matplotlib.pyplot as plt
import sys

n_snaps = 11

# for the plotting
# n_radial_bins = int(sys.argv[1])

# some constants
OMEGA = 0.3  # Cosmological matter fraction at z = 0
PARSEC_IN_CGS = 3.0856776e18
KM_PER_SEC_IN_CGS = 1.0e5
CONST_G_CGS = 6.672e-8
h = 0.67777  # hubble parameter
gamma = 5.0 / 3.0
eta = 1.2349
H_0_cgs = 100.0 * h * KM_PER_SEC_IN_CGS / (1.0e6 * PARSEC_IN_CGS)

# read some header/parameter information from the first snapshot

filename = "Hydrostatic_0000.hdf5"
f = h5.File(filename, "r")
params = f["Parameters"]
unit_mass_cgs = float(params.attrs["InternalUnitSystem:UnitMass_in_cgs"])
unit_length_cgs = float(params.attrs["InternalUnitSystem:UnitLength_in_cgs"])
unit_velocity_cgs = float(params.attrs["InternalUnitSystem:UnitVelocity_in_cgs"])
unit_time_cgs = unit_length_cgs / unit_velocity_cgs
v_c = float(params.attrs["IsothermalPotential:vrot"])
v_c_cgs = v_c * unit_velocity_cgs
header = f["Header"]
N = header.attrs["NumPart_Total"][0]
box_centre = np.array(header.attrs["BoxSize"])

# calculate r_vir and M_vir from v_c
r_vir_cgs = v_c_cgs / (10.0 * H_0_cgs * np.sqrt(OMEGA))
M_vir_cgs = r_vir_cgs * v_c_cgs ** 2 / CONST_G_CGS

for i in range(n_snaps):

    filename = "Hydrostatic_%04d.hdf5" % i
    f = h5.File(filename, "r")
    coords_dset = f["PartType0/Coordinates"]
    coords = np.array(coords_dset)
    # translate coords by centre of box
    header = f["Header"]
    snap_time = header.attrs["Time"]
    snap_time_cgs = snap_time * unit_time_cgs
    coords[:, 0] -= box_centre[0] / 2.0
    coords[:, 1] -= box_centre[1] / 2.0
    coords[:, 2] -= box_centre[2] / 2.0
    radius = np.sqrt(coords[:, 0] ** 2 + coords[:, 1] ** 2 + coords[:, 2] ** 2)
    radius_cgs = radius * unit_length_cgs
    radius_over_virial_radius = radius_cgs / r_vir_cgs

    r = radius_over_virial_radius

    # bin_width = 1./n_radial_bins
    #     hist = np.histogram(r,bins = n_radial_bins)[0] # number of particles in each bin

    # #find the mass in each radial bin

    #     mass_dset = f["PartType0/Masses"]
    # #mass of each particles should be equal
    #     part_mass = np.array(mass_dset)[0]
    #     part_mass_cgs = part_mass * unit_mass_cgs
    #     part_mass_over_virial_mass = part_mass_cgs / M_vir_cgs

    #     mass_hist = hist * part_mass_over_virial_mass
    #     radial_bin_mids = np.linspace(bin_width/2.,1 - bin_width/2.,n_radial_bins)
    # #volume in each radial bin
    #     volume = 4.*np.pi * radial_bin_mids**2 * bin_width

    # #now divide hist by the volume so we have a density in each bin

    #     density = mass_hist / volume

    # read the densities

    density_dset = f["PartType0/Density"]
    density = np.array(density_dset)
    density_cgs = density * unit_mass_cgs / unit_length_cgs ** 3
    rho = density_cgs * r_vir_cgs ** 3 / M_vir_cgs

    t = np.linspace(0.01, 2.0, 1000)
    rho_analytic = t ** (-2) / (4.0 * np.pi)

    plt.plot(r, rho, "x", label="Numerical solution")
    plt.plot(t, rho_analytic, label="Analytic Solution")
    plt.legend(loc="upper right")
    plt.xlabel(r"$r / r_{vir}$")
    plt.ylabel(r"$\rho / (M_{vir} / r_{vir}^3)$")
    plt.title(
        r"$\mathrm{Time}= %.3g \, s \, , \, %d \, \, \mathrm{particles} \,,\, v_c = %.1f \, \mathrm{km / s}$"
        % (snap_time_cgs, N, v_c)
    )
    # plt.ylim((0.1,40))
    plt.xscale("log")
    plt.yscale("log")
    plot_filename = "density_profile_%03d.png" % i
    plt.savefig(plot_filename, format="png")
    plt.close()
