import csv
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt

from .Bind import Bind
from .fit import fit_line
from .model import Linear1D
from .utils import readFileData, get_hist


class Process(object):
    """Process the experimental data according to the calibration results.

    Parameters
    ----------
    path : str
        coefficient file
    n : int, optional
        number of blocks, default 6
    m : int, optional
        number of binds, default 8
    """

    def __init__(self, path, n=6, m=8) -> None:
        self.n = n
        self.m = m

        self.binds = [[Bind(i, j) for j in range(m)] for i in range(n)]

        # Read Calibration Data
        pbar = tqdm(desc="Bind Initialization", total=n * m)
        data = list(csv.reader(open(path, "r")))
        data = np.array(data, dtype=np.float64)
        for i in range(n):
            for j in range(m):
                bind = self.binds[i][j]
                bind(data[j + i * m][2:])

                pbar.update(1)
        pbar.close()

    def read_data(self, file):
        """Read Process Data

        file : str
            task file path
        """
        # Read Data
        n, m = self.n, self.m
        total = len(open(file, "r").readlines()) * self.n * self.m
        file_list = csv.reader(open(file, "r"))

        self.pX = np.array([])
        self.eng = np.array([])

        pbar = tqdm(desc="Read Data", total=total)
        for row in file_list:
            ldata, rdata = readFileData(row[0], n, m)
            for i in range(n):
                for j in range(m):
                    bind = self.binds[i][j]

                    x = bind.predict_px(ldata[j + i * m], rdata[j + i * m]) + float(
                        row[1]
                    )
                    e = bind.predict_energy(ldata[j + i * m], rdata[j + i * m])
                    edge_l = 5 + 130 * i + float(row[1]) - 35
                    edge_r = edge_l + 65
                    idx = np.where((x >= edge_l) & (x <= edge_r))[0]

                    self.pX = np.hstack((self.pX, x[idx]))
                    self.eng = np.hstack((self.eng, e[idx]))

                    pbar.update(1)
        pbar.close()

    def energy_filter(self, lower, upper, sigma=5.0, maxiters=5):
        """Fit px - E line and do sigma clip iteratively.

        Parameters
        ----------
        lower/upper : float
            Upper and lower bounds on the initial filter
        sigma: float, optional
            The number of standard deviations to use for both the lower and upper clipping limit.
        maxiters: int or None, optional
            The maximum number of sigma-clipping iterations to perform or None to clip until convergence is achieved.
            If convergence is achieved prior to maxiters iterations, the clipping iterations will stop.
        """
        model = Linear1D()
        idx = np.where((self.eng >= lower) & (self.eng <= upper))[0]
        x, y = self.pX[idx], self.eng[idx]

        for i in range(maxiters):
            reg = fit_line(model, x, y)
            err = np.abs(y - reg(x))
            idx = np.where(err <= sigma * np.std(err))[0]
            if len(idx) == len(x):
                break
            x, y = x[idx], y[idx]

        self.pX_n = x
        self.eng_n = y
        self.reg = reg

    def draw_filter(self, path="filter.png"):
        """Draw the energy filter result

        Parameters
        ----------
        path : str, optional
            save path
        """        
        px_min = (np.min(self.pX_n) // 50) * 50
        px_max = (np.max(self.pX_n) // 50 + 1) * 50
        px_x = np.linspace(px_min, px_max, int(px_max - px_min))

        fig = plt.figure(figsize=(20, 8), dpi=200)
        ax = fig.add_subplot(1, 1, 1)

        ax.scatter(self.pX, self.eng, s=0.01, color="black")
        ax.scatter(self.pX_n, self.eng_n, s=0.01, color="orange")
        ax.plot(px_x, self.reg(px_x))

        ax.set_xlabel("x (mm)")
        ax.set_ylabel("Energy (MeV)")

        fig.savefig(path)
        plt.close()

    def draw_result(self, path="result.png"):
        """Draw the processing result

        Parameters
        ----------
        path : str, optional
            save path
        """
        fig = plt.figure(figsize=(20, 8), dpi=200)
        ax1 = fig.add_subplot(2, 1, 1)
        ax2 = fig.add_subplot(2, 1, 2)

        count, center = get_hist(self.pX_n, delta=0.1)
        ax1.scatter(self.pX_n, self.eng_n, s=0.05, color="k")
        ax2.step(center, count, where="post", color="k")

        px_min = (np.min(self.pX_n) // 50) * 50
        px_max = (np.max(self.pX_n) // 50 + 1) * 50
        ax1.set_xticks(np.arange(px_min, px_max, 50))
        ax2.set_xticks(np.arange(px_min, px_max, 50))

        ax1.set_xlabel("x (mm)")
        ax1.set_ylabel("Energy (MeV)")
        ax2.set_xlabel("x (mm)")
        ax2.set_ylabel("Count per bin")

        fig.savefig(path)
        plt.close()