""" Plots the development of the biaxial order parameter over time

This script calculates the classical (nematic) order parameter based on the 
direction of the molecular bisector (although this can be changed using the 
method argument of the order_param() function). Th order function is caluclated
using the M-matrix method described by Eppenga et al., and described in the 
project report.

Plots the evolution of this order parameter over time, alongside the time
evolution of the volume fraction.
"""

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

file_root = "output_T_0.5_time_"  # two underscores to match typo in previous code
sampling_freq = 1  # only samples one in X files (must be integer)

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():
            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 order_param(data):
    """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 (first/last)
    Third index gives the component of the position (x,y,z)

    Method for calculation of Order Param given by Eppenga (1984)
    """
    method = "bisector"

    if method == "molecule":
        directors = data[:, 2, :] - data[:, 0, :]

    elif method == "arm":
        directors = data[:, 1, :] - data[:, 0, :]

    elif method == "bisector":
        midpoints = 0.5 * (data[:, 2, :] + data[:, 0, :])
        directors = data[:, 1, :] - midpoints

    norm_directors = directors / np.linalg.norm(directors, axis=1).reshape(
        -1, 1
    )  # reshape allows broadcasting
    M_matrix = np.zeros((3, 3))
    for i, j in np.ndindex(M_matrix.shape):
        M_matrix[i, j] = (
            np.sum(norm_directors[:, i] * norm_directors[:, j]) / N_molecules
        )
    number_of_nans = np.count_nonzero(np.isnan(M_matrix))
    if number_of_nans > 0:
        print("Warning: " + str(number_of_nans) + " NaN values removed")
    M_eigen = np.linalg.eigvals(np.nan_to_num(M_matrix))  # replaces nan with zero
    Q_eigen = (3 * M_eigen - 1) / 2
    return max(Q_eigen)  # largest eigenvalue corresponds to traditional order parameter


# READ MOLECULE POSITIONS

order_param_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/ First (0), Middle (1), or Last (2) atom,/ 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
            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
    order_param_values[i] = order_param(rod_positions)  # evaluate order param at time t
    print("T = " + str(time) + "/" + str(run_time))


# plt.plot(time_range, order_param_values)
# plt.plot(
#     time_range, uniform_filter1d(order_param_values, size=int(10)), linestyle="--",
# )
# plt.xlabel("Time (arbitrary units)")
# plt.ylabel("Order Parameter")
# plt.title("Evolution of Order Parameter")
# plt.savefig("order_plot.png")
# plt.show()

order_param_values[0] = order_param_values[1]  # remove error at time 0

fig, ax1 = plt.subplots()

color = "tab:red"
ax1.set_xlabel("Time (arbitrary units)")
ax1.set_ylabel("Order Parameter", color=color)
ax1.plot(time_range, uniform_filter1d(order_param_values, size=int(10)), color=color)
ax1.tick_params(axis="y", labelcolor=color)

ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis

color = "tab:blue"
ax2.set_ylabel(
    "Volume Fraction", color=color
)  # we already handled the x-label with ax1
ax2.plot(time_range, vol_frac(volume_values), color=color)
ax2.tick_params(axis="y", labelcolor=color)

plt.title("Evolution of Biaxial Order Parameter")
fig.tight_layout()  # otherwise the right y-label is slightly clipped
plt.savefig("order_and_volfrac_bisector.png")
plt.show()

