SecretColorsExample.py

"""
Rohit Suratekar
September 2018
Examples of newly developed library SecretColors
https://pypi.org/project/SecretColors/
"""
import matplotlib
import matplotlib.pylab as plt
import numpy as np

from SecretColors.palette import Palette

ibm = Palette("ibm")


def test_scatter():
    x_data = np.random.randint(0, 100, 300)
    y_data = np.random.randint(0, 100, 300)
    z_data = np.random.randint(0, 100, 300)
    plt.figure(figsize=(9, 6))

    plt.subplot(131)
    plt.scatter(x_data, y_data, c=z_data, cmap="Blues")
    plt.title('Default')
    plt.colorbar()
    plt.subplot(132)
    plt.title('SecretColors (default)')
    plt.scatter(x_data, y_data, c=z_data, cmap=ibm.cmap_of(matplotlib,
                                                           ibm.cerulean()))

    plt.colorbar()

    plt.subplot(133)
    plt.scatter(x_data, y_data, c=z_data, cmap=ibm.cmap_of(matplotlib,
                                                           ibm.cerulean(),
                                                           start_grade=30))
    plt.title('SecretColors (graded)')
    plt.colorbar()
    plt.show()


def test_bar():
    x_data = np.random.random(8)
    ind = range(len(x_data))
    labels = ['data_' + str(x) for x in ind]
    plt.figure(figsize=(9, 6))

    cmap = matplotlib.cm.get_cmap('Reds')
    plt.subplot(121)
    colors = [cmap(x) for x in x_data]
    plt.bar(ind, x_data, color=colors)
    plt.xticks(ind, labels, rotation=90)
    plt.title("Default")

    plt.subplot(122)
    new_colors = ibm.normalized_colors_for(
        matplotlib, x_data, ibm.red(), upper_bound=1.2)
    plt.bar(ind, x_data, color=new_colors)
    plt.xticks(ind, labels, rotation=90)
    plt.title("SecretColors")

    plt.show()


def test_bar2():
    x_data = np.random.random(10)
    ind = range(len(x_data))
    labels = ['data_' + str(x) for x in ind]
    plt.figure(figsize=(9, 6))

    plt.subplot(121)
    for i in ind:
        plt.bar(i, x_data[i])
    plt.xticks(ind, labels, rotation=90)
    plt.title("Default")

    plt.subplot(122)
    for i in ind:
        plt.bar(i, x_data[i], color=ibm.base(x_data)[i])
    plt.xticks(ind, labels, rotation=90)
    plt.title("SecretColors")

    plt.show()


def test_lines():
    all_data = []
    for d in range(4):
        data = np.random.random(30) + d * 0.8
        all_data.append(data)
    time = range(len(all_data[0]))
    plt.figure(figsize=(9, 6))

    plt.subplot(121)
    colors = ["r", "g", "b", "y"]
    i = 0
    for data in all_data:
        plt.plot(time, data, linewidth=4, color=colors[i])
        i += 1
    plt.title("Default")

    plt.subplot(122)
    new_colors = [ibm.red(), ibm.green(), ibm.blue(), ibm.yellow()]
    i = 0
    for data in all_data:
        plt.plot(time, data, linewidth=4, color=new_colors[i])
        i += 1
    plt.title("SecretColors")

    plt.show()


def test_heatmap():
    a = np.random.random((16, 16))

    plt.subplot(121)
    plt.imshow(a, cmap='Greens', interpolation='nearest')

    plt.subplot(122)
    plt.imshow(a, cmap=ibm.cmap_of(matplotlib, ibm.green()),
               interpolation='nearest')

    plt.show()


def test_distribution():
    mu, sigma = 100, 15
    mu2, sigma2 = 150, 20
    x = mu + sigma * np.random.randn(10000)
    y = mu2 + sigma2 * np.random.randn(10000)

    plt.subplot(121)
    plt.hist(x, 30, alpha=0.8)
    plt.hist(y, 30, alpha=0.8)

    plt.subplot(122)
    plt.hist(x, 30, alpha=0.8, color=ibm.cerulean())
    plt.hist(y, 30, alpha=0.8, color=ibm.orange())

    plt.show()


if __name__ == "__main__":
    test_scatter()