import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import uniform_filter1d  # for rolling average
from phase_plot_future 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 "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 nematic_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)
    """
    directors = data[:, 1, :] - data[:, 0, :]  # director vector for each molecule
    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
        )
    M_eigen = np.linalg.eigvals(M_matrix)
    Q_eigen = (3 * M_eigen - 1) / 2
    return max(Q_eigen)  # largest eigenvalue corresponds to traditional order parameter


def smectic_order_param(data):
    """Input data in array of length Molecule Number 

    Input data will be rod_positions array which stores input data   
    First index gives molecule number
    Third index gives the component of the position of CoM (x,y,z)

    Method for calculation given by Polson (1997) 10.1103/PhysRevE.56.R6260
    """
    k = 2 * np.pi / 10  # divide by length of molecule
    exp_values = np.exp(1j * k * data)
    return np.abs(np.sum(exp_values)) / N_molecules


# READ MOLECULE POSITIONS

nem_order_param_values = np.zeros(len(time_range))
smec_order_param_values = np.full(len(time_range), np.nan)

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, 2, 3))
    """Indices are Molecule Number/ First (0) or Last (1) 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
            if int(particle_values[2]) == 1:  # first particle in molecule
                rod_positions[int(particle_values[1]) - 1, 0, :] = particle_values[3:6]
            if int(particle_values[2]) == 10:  # last particle in molecule
                rod_positions[int(particle_values[1]) - 1, 1, :] = particle_values[3:6]

    data_file.close()  # close data_file for time step t
    volume_values[i] = box_volume
    nem_order_param_values[i] = nematic_order_param(
        rod_positions
    )  # evaluate order param at time t
    smec_order_param_values[i] = smectic_order_param(
        rod_positions[:, 1]
    )  # use CoM to find smectic order param
    print("T = " + str(time) + "/" + str(run_time))


fig, ax1 = plt.subplots()

color = "tab:red"
ax1.set_xlabel("Time (arbitrary units)")
ax1.set_ylabel(r"Order Parameter ($S$)")
ax1.plot(
    time_range,
    uniform_filter1d(nem_order_param_values, size=int(10)),
    label=r"Nematic Order ($S_{n}$)",
    color="tab:blue",
)
ax1.plot(
    time_range,
    uniform_filter1d(smec_order_param_values, size=int(10)),
    label=r"Smectic Order ($S_{s}$)",
    color="tab:green",
)
ax1.tick_params(axis="y")

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

color = "tab:blue"
ax2.set_ylabel("Volume Fraction")  # we already handled the x-label with ax1
ax2.plot(
    time_range,
    vol_frac(volume_values),
    linestyle="--",
    label=r"Volume Fraction ($\phi$)",
    color="k",  # black
)
ax2.tick_params(axis="y")

fig.legend(loc=(0.48, 0.74))
fig.tight_layout()  # otherwise the right y-label is slightly clipped
plt.savefig("rigidrod_cryorderparam.png")
plt.show()

plt.plot(vol_frac(volume_values), nem_order_param_values, "bx", label="Nematic Order")
plt.plot(vol_frac(volume_values), smec_order_param_values, "gx", label="Smectic Order")
plt.ylabel("Order Parameter")
plt.xlabel("Volume Fraction")
plt.savefig("comb_order_vs_volfrac.png")
plt.show()

