import numpy as np
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture

def file_filter(file):
    filter_data = []
    fit_data = np.array([])

    data = np.loadtxt(file, dtype=np.uint16)

    model = GaussianMixture(n_components=2)
    model.fit(data)

    ny = model.predict(data)
    for i in np.unique(ny):
        idx = np.where(ny == i)[0]
        fit_data = idx if len(idx) > len(fit_data) else fit_data
        filter_data.append(data[idx])

    return filter_data, data[fit_data]

def draw_filter(data, title):
    fig = plt.figure(figsize=(8, 8))
    ax = fig.add_subplot(1, 1, 1)
    for cluster in data:
        ax.scatter(cluster[:, 0], cluster[:, 1], s=0.1)
    fig.savefig(title, facecolor='w', transparent=False)
    plt.close()

def get_hist(data, delta=1, maxN=50):
    step = delta
    edge = np.arange(data.min(), data.max() + 1, step)
    count, _ = np.histogram(data, bins=edge)
    while count.max() <= maxN:
        step += delta
        edge = np.arange(data.min(), data.max() + 1, step)
        count, _ = np.histogram(data, bins=edge)
    return count, (edge[1:] + edge[:-1]) / 2