import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
from scipy.ndimage import uniform_filter1d  # for rolling average
from phase_plot import vol_frac

file_root = "output_T_0.5_time_"
sampling_freq = 50  # only samples one in X files (must be integer) #30
plotting_freq = 1  # only plots on in X of the sampled distributions

plt.rcParams.update({"font.size": 13})  # for figures to go into latex at halfwidth

# READ PARAMETER VALUES FROM LOG FILE

file_name = "log.lammps"
log_file = open(file_name, "r")
mix_steps_values = []

for i, line in enumerate(log_file):
    """For loop iteratres over every line in file to find the required variables.

    However, because the dump_interval occurs last (in the main body not the preamble)
    we break at this point to avoid reading the whole file unnecessarily."""

    if "variable N" in line:  # to extract independant variable value of N
        for t in line.split():  # separate by whitespace
            try:
                N_molecules = int(t)
            except ValueError:
                pass  # any non-floats in this line are ignored

    if "variable len" in line:  # to extract length of molecule
        for t in line.split():  # separate by whitespace
            try:
                mol_length = int(t)
            except ValueError:
                pass  # any non-floats in this line are ignored

    if "dump" and "all custom" in line:  # to extract time interval for dump
        for t in line.split():  # separate by whitespace
            try:
                dump_interval = int(t)
                # interval is the last integer in that line
            except ValueError:
                pass
        break  # got all data, break from for loop

    if "variable mix_steps" in line:  # to extract mix_steps (for shrinking)
        run_num = 0  # counts number of runs
        for t in line.split():
            try:
                mix_steps_values.append(int(t))
                run_num += 1
            except ValueError:
                pass

    if "variable run_steps" in line:  # to extract run_steps (for equilibration)
        for t in line.split():
            try:
                equilibrium_time = int(t)  # time per run
            except ValueError:
                pass
        # this comes up first in file so no searching variable here

log_file.close()

tot_mix_time = sum(mix_steps_values)
run_time = tot_mix_time + run_num * equilibrium_time
time_range = range(0, int(run_time), int(dump_interval * sampling_freq))
print(
    "N_molecules, run_time, dump_interval = "
    + str((N_molecules, run_time, dump_interval))
)


# time_range = range(0, 3300000, 100000)  # FOR SIMPLICITY IN TESTING


def angle_dist(data, remove_split_mol=True):
    """Input data in array of size Molecule Number x 3 x 3

    Input data will be rod_positions array which stores input data   
    First index gives molecule number
    Second index gives particle number within molecule (1st/mid/last)
    Third index gives the component of the position (x,y,z)

    Allows option to remove molecules that are split across the boundaries of the box

    """

    if remove_split_mol:
        molecules_removed = 0
        # where molecule end-to-end length is greater than expected, replace with nans
        for i in range(N_molecules):
            if np.linalg.norm(data[i, 2, :] - data[i, 0, :]) > (mol_length + 0.5):
                data[i, :, :].fill(np.nan)
                molecules_removed += 1
                # remove data for molecules that are longer than expected (ie 10 units)
                # this is due to them spanning the edges of the simulation region
        print("Number of molecules removed is : " + str(molecules_removed))

    rod_1 = data[:, 1, :] - data[:, 0, :]  # director vector for first end of molecule
    norm_rod_1 = rod_1 / np.linalg.norm(rod_1, axis=1).reshape(-1, 1)
    rod_2 = data[:, 2, :] - data[:, 1, :]  # director vector for second end of molecule
    norm_rod_2 = rod_2 / np.linalg.norm(rod_2, axis=1).reshape(-1, 1)

    angle_values = np.sum(norm_rod_1 * norm_rod_2, axis=1)
    angle_values = angle_values[~np.isnan(angle_values)]  # remove nans
    return angle_values


# READ MOLECULE POSITIONS

angle_mean_values = np.zeros(len(time_range))
volume_values = np.full(len(time_range), np.nan)  # new array of NaN
for i, time in enumerate(time_range):  # interate over dump files
    data_file = open(file_root + str(time) + ".dump", "r")
    extract_atom_data = False  # start of file doesn't contain particle values
    extract_box_data = False  # start of file doesn't contain box dimension

    box_volume = 1
    rod_positions = np.zeros((N_molecules, 3, 3))
    """Indices are Molecule Number; Atom number 1st/mid/last ; Positional coord index"""

    for line in data_file:
        if "ITEM: BOX" in line:  # to start reading volume data
            extract_box_data = True
            extract_atom_data = False
            continue  # don't attempt to read this line

        if "ITEM: ATOMS" in line:  # to start reading particle data
            extract_box_data = False
            extract_atom_data = True
            continue

        if extract_box_data and not extract_atom_data:
            # evaluate before particle values
            # each line gives box max and min in a single axis
            box_dimension = []
            for d in line.split():  # separate by whitespace
                try:
                    box_dimension.append(float(d))
                except ValueError:
                    pass  # any non-floats in this line are ignored
            box_volume *= box_dimension[1] - box_dimension[0]
            # multiply box volume by length of this dimension of box

        if extract_atom_data and not extract_box_data:
            # evaluate after box dimension collection
            # each line is in the form "id mol type x y z vx vy vz"
            particle_values = []
            for t in line.split():  # separate by whitespace
                try:
                    particle_values.append(float(t))
                except ValueError:
                    pass  # any non-floats in this line are ignored

            # # Save positional coordatinates of end particles - REGULAR
            # if int(particle_values[2]) == 1:  # first particle
            #     rod_positions[int(particle_values[1]) - 1, 0, :] = particle_values[3:6]
            # if int(particle_values[2]) == int((mol_length + 1) / 2):  # central particle
            #     rod_positions[int(particle_values[1]) - 1, 1, :] = particle_values[3:6]
            # if int(particle_values[2]) == mol_length:  # last particle
            #     rod_positions[int(particle_values[1]) - 1, 2, :] = particle_values[3:6]

            # Save positional coordatinates of end particles - CLOSE
            centre = (mol_length + 1) / 2  # for odd length molecules only
            if int(particle_values[2]) == int(centre - 1):
                rod_positions[int(particle_values[1]) - 1, 0, :] = particle_values[3:6]
            if int(particle_values[2]) == int(centre):  # central particle
                rod_positions[int(particle_values[1]) - 1, 1, :] = particle_values[3:6]
            if int(particle_values[2]) == int(centre + 1):
                rod_positions[int(particle_values[1]) - 1, 2, :] = particle_values[3:6]

    data_file.close()  # close data_file for time step t
    volume_values[i] = box_volume

    angle_data = angle_dist(rod_positions)
    angle_mean_values[i] = np.mean(angle_data)  # evaluate order param at time t

    tot_plot_num = len(time_range) // plotting_freq
    colors = plt.cm.cividis(np.linspace(0, 1, tot_plot_num))
    if i % plotting_freq == 0 and time != 0:
        print(time)
        sns.kdeplot(
            angle_data,
            color=colors[i // plotting_freq - 1],
            bw_adjust=0.5,  # adjusts smoothing (default is 1)
            # gridsize=50,
            alpha=1,  # adjusts transparency
        )

    print("T = " + str(time) + "/" + str(run_time))

sm = plt.cm.ScalarMappable(cmap=cm.cividis, norm=plt.Normalize(vmin=0, vmax=run_time))
cbar = plt.colorbar(sm)
cbar.ax.set_ylabel("Number of Time Steps", rotation=270, labelpad=15)

plt.title("Evolution of angle distribution")
plt.xlabel(r"Mean Angle ($cos(\theta)$)")
plt.ylabel("Normalised Frequency")
plt.savefig("angle_dist.png")
plt.show()

plt.plot(time_range, angle_mean_values)
plt.plot(
    time_range, uniform_filter1d(angle_mean_values, size=int(10)), linestyle="--",
)
plt.xlabel("Time (arbitrary units)")
plt.ylabel(r"Mean Angle ($cos(\theta)$)")
plt.title("Evolution of Mean Angle")
plt.savefig("angle_mean.png")
plt.show()
