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

#for the plotting
max_r = float(sys.argv[1]) #in units of the virial radius 
n_radial_bins = int(sys.argv[2])
n_snaps = int(sys.argv[3])

#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
CONST_m_H_CGS = 1.67e-24
h = 0.67777 # hubble parameter
gamma = 5./3.
eta = 1.2349
H_0_cgs = 100. * h * KM_PER_SEC_IN_CGS / (1.0e6 * PARSEC_IN_CGS)

#read some header/parameter information from the first snapshot

filename = "CoolingHalo_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
lambda_cgs = float(params.attrs["LambdaCooling:lambda_cgs"])
X_H = float(params.attrs["LambdaCooling:hydrogen_mass_abundance"])
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. * 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 = "CoolingHalo_%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.
    coords[:,1] -= box_centre[1]/2.
    coords[:,2] -= box_centre[2]/2.
    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_edges = np.linspace(0.,max_r,n_radial_bins+1)
    bin_width = bin_edges[1] - bin_edges[0]
    hist = np.histogram(r,bins = bin_edges)[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.,max_r - 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(10./n_radial_bins,10.0,1000)
    rho_analytic = t**(-2)/(4.*np.pi)

    #calculate cooling radius

    r_cool_over_r_vir = np.sqrt((2.*(gamma - 1.)*lambda_cgs*M_vir_cgs*X_H**2)/(4.*np.pi*CONST_m_H_CGS**2*v_c_cgs**2*r_vir_cgs**3))*np.sqrt(snap_time_cgs)

    #initial analytic density profile
    
    if (i == 0):
        r_0 = radial_bin_mids[0]
        rho_0 = density[0]

        rho_analytic_init = rho_0 * (radial_bin_mids/r_0)**(-2)
    plt.plot(radial_bin_mids,density,'ko',label = "Average density of shell")
    plt.plot(radial_bin_mids,rho_analytic_init,label = "Initial analytic density profile")
    plt.xlabel(r"$r / r_{vir}$")
    plt.ylabel(r"$(\rho / \rho_{init})$")
    plt.title(r"$\mathrm{Time}= %.3g \, s \, , \, %d \, \, \mathrm{particles} \,,\, v_c = %.1f \, \mathrm{km / s}$" %(snap_time_cgs,N,v_c))
    #plt.ylim((1.e-2,1.e1))
    plt.plot((r_cool_over_r_vir,r_cool_over_r_vir),(1.0e-4,1.0e4),'r',label = "Cooling radius")
    plt.xlim((radial_bin_mids[0],max_r))
    plt.ylim((1.0e-4,1.0e4))
    #plt.plot((0,max_r),(1,1))
    #plt.xscale('log')
    plt.yscale('log')
    plt.legend(loc = "upper right")
    plot_filename = "density_profile_%03d.png" %i
    plt.savefig(plot_filename,format = "png")
    plt.close()

