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import matplotlib.pyplot as plt

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import numpy as np

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from sklearn.datasets import make_blobs

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from sklearn.preprocessing import (StandardScaler, MinMaxScaler, Normalizer,

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                                   RobustScaler)

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from .plot_helpers import cm2

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def plot_scaling():

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    X, y = make_blobs(n_samples=50, centers=2, random_state=4, cluster_std=1)

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    X += 3

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    plt.figure(figsize=(15, 8))

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    main_ax = plt.subplot2grid((2, 4), (0, 0), rowspan=2, colspan=2)

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    main_ax.scatter(X[:, 0], X[:, 1], c=y, cmap=cm2, s=60)

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    maxx = np.abs(X[:, 0]).max()

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    maxy = np.abs(X[:, 1]).max()

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    main_ax.set_xlim(-maxx + 1, maxx + 1)

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    main_ax.set_ylim(-maxy + 1, maxy + 1)

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    main_ax.set_title("Original Data")

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    other_axes = [plt.subplot2grid((2, 4), (i, j))

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                  for j in range(2, 4) for i in range(2)]

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    for ax, scaler in zip(other_axes, [StandardScaler(), RobustScaler(),

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                                       MinMaxScaler(), Normalizer(norm='l2')]):

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        X_ = scaler.fit_transform(X)

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        ax.scatter(X_[:, 0], X_[:, 1], c=y, cmap=cm2, s=60)

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        ax.set_xlim(-2, 2)

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        ax.set_ylim(-2, 2)

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        ax.set_title(type(scaler).__name__)

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    other_axes.append(main_ax)

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    for ax in other_axes:

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        ax.spines['left'].set_position('center')

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        ax.spines['right'].set_color('none')

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        ax.spines['bottom'].set_position('center')

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        ax.spines['top'].set_color('none')

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        ax.xaxis.set_ticks_position('bottom')

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        ax.yaxis.set_ticks_position('left')

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