Anaconda Enterprise: Documentation

# histogram.py

    import numpy as np
    import scipy.special

    from bokeh.layouts import gridplot
    from bokeh.plotting import figure, show, output_file

    p1 = figure(title="Normal Distribution (μ=0, σ=0.5)",tools="save",
                background_fill_color="#E8DDCB")

    mu, sigma = 0, 0.5

    measured = np.random.normal(mu, sigma, 1000)
    hist, edges = np.histogram(measured, density=True, bins=50)

    x = np.linspace(-2, 2, 1000)
    pdf = 1/(sigma * np.sqrt(2*np.pi)) * np.exp(-(x-mu)**2 / (2*sigma**2))
    cdf = (1+scipy.special.erf((x-mu)/np.sqrt(2*sigma**2)))/2

    p1.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
            fill_color="#036564", line_color="#033649")
    p1.line(x, pdf, line_color="#D95B43", line_width=8, alpha=0.7, legend="PDF")
    p1.line(x, cdf, line_color="white", line_width=2, alpha=0.7, legend="CDF")

    p1.legend.location = "center_right"
    p1.legend.background_fill_color = "darkgrey"
    p1.xaxis.axis_label = 'x'
    p1.yaxis.axis_label = 'Pr(x)'

    p2 = figure(title="Log Normal Distribution (μ=0, σ=0.5)", tools="save",
                background_fill_color="#E8DDCB")

    mu, sigma = 0, 0.5

    measured = np.random.lognormal(mu, sigma, 1000)
    hist, edges = np.histogram(measured, density=True, bins=50)

    x = np.linspace(0.0001, 8.0, 1000)
    pdf = 1/(x* sigma * np.sqrt(2*np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2*sigma**2))
    cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2

    p2.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
            fill_color="#036564", line_color="#033649")
    p2.line(x, pdf, line_color="#D95B43", line_width=8, alpha=0.7, legend="PDF")
    p2.line(x, cdf, line_color="white", line_width=2, alpha=0.7, legend="CDF")

    p2.legend.location = "center_right"
    p2.legend.background_fill_color = "darkgrey"
    p2.xaxis.axis_label = 'x'
    p2.yaxis.axis_label = 'Pr(x)'

    p3 = figure(title="Gamma Distribution (k=1, θ=2)", tools="save",
                background_fill_color="#E8DDCB")

    k, theta = 1.0, 2.0

    measured = np.random.gamma(k, theta, 1000)
    hist, edges = np.histogram(measured, density=True, bins=50)

    x = np.linspace(0.0001, 20.0, 1000)
    pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))
    cdf = scipy.special.gammainc(k, x/theta) / scipy.special.gamma(k)

    p3.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
            fill_color="#036564", line_color="#033649")
    p3.line(x, pdf, line_color="#D95B43", line_width=8, alpha=0.7, legend="PDF")
    p3.line(x, cdf, line_color="white", line_width=2, alpha=0.7, legend="CDF")

    p3.legend.location = "center_right"
    p3.legend.background_fill_color = "darkgrey"
    p3.xaxis.axis_label = 'x'
    p3.yaxis.axis_label = 'Pr(x)'

    p4 = figure(title="Weibull Distribution (λ=1, k=1.25)", tools="save",
                background_fill_color="#E8DDCB")

    lam, k = 1, 1.25

    measured = lam*(-np.log(np.random.uniform(0, 1, 1000)))**(1/k)
    hist, edges = np.histogram(measured, density=True, bins=50)

    x = np.linspace(0.0001, 8, 1000)
    pdf = (k/lam)*(x/lam)**(k-1) * np.exp(-(x/lam)**k)
    cdf = 1 - np.exp(-(x/lam)**k)

    p4.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
           fill_color="#036564", line_color="#033649")
    p4.line(x, pdf, line_color="#D95B43", line_width=8, alpha=0.7, legend="PDF")
    p4.line(x, cdf, line_color="white", line_width=2, alpha=0.7, legend="CDF")

    p4.legend.location = "center_right"
    p4.legend.background_fill_color = "darkgrey"
    p4.xaxis.axis_label = 'x'
    p4.yaxis.axis_label = 'Pr(x)'

    output_file('histogram.html', title="histogram.py example")

    show(gridplot(p1,p2,p3,p4, ncols=2, plot_width=400, plot_height=400, toolbar_location=None))