Newton Excel Bach, not (just) an Excel Blog

import matplotlib.pyplot as plt
from matplotlib import colors

import pyxll
from pyxll import xl_func, xl_arg, xl_return
from pyxll import  plot

@xl_func
def MPLTute_1():
    # Create the figure
    fig = plt.subplots()[0]
    # Plot the data
    plt.plot([1,2,3,4])
    plt.plot([0,2,4,6])
    plt.ylabel('some numbers')
    # Display the figure in Excel
    plot(fig)

@xl_func
@xl_arg('data','float[]')
def MPLTute_1a(data):
    fig, ax = plt.subplots()
    ax.plot(data)
    ax.set(ylabel='some numbers')
    plot(fig)

@xl_func
@xl_arg('data','float[][]')
def MPLTute_2(data):
    fig, ax = plt.subplots()
    ax.plot(data[0], data[1], 'ro')
    ax.set_xbound(0, 6)
    ax.set_ybound(0, 20)
    ax.set(ylabel='some numbers')
    plot(fig)

@xl_func
def MPLTute_3():
    t = np.arange(0., 5., 0.2)
    fig, ax = plt.subplots()
    ax.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
    plot(fig)

@xl_func
@xl_arg('props','dict<str, var>')
def MPLTute_4(props):
    x = np.arange(0., 5., 0.2)
    y = x**2
    fig, ax = plt.subplots()
    ax.plot(x, y, **props)
    plot(fig)

@xl_func
def MPLTute_5():
    np.random.seed(19680801)
    mu, sigma = 100, 15
    x = mu + sigma * np.random.randn(10000)
    
    fig, ax = plt.subplots()
    # the histogram of the data
    # Changed from the tutorial example: "n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)" 
    # because use of normed now raises an error.
    n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)
 
    plt.xlabel('Smarts')
    plt.ylabel('Probability')
    plt.title('Histogram of IQ')
    plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
    plt.axis([40, 160, 0, 0.03])
    plt.grid(True)
    
    plot(fig)

@xl_func
def MPLTute_6():
    fig, ax = plt.subplots()
    t = np.arange(0.0, 5.0, 0.01)
    s = np.cos(2*np.pi*t)
    line, = plt.plot(t, s, lw=2)
    plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
    arrowprops=dict(facecolor='black', shrink=0.05),)
    plt.ylim(-2,2)

    plot(fig)

@xl_func
@xl_arg('scaletype', 'int')
def MPLTute_7(scaletype):
    fig, ax = plt.subplots()
    from matplotlib.ticker import NullFormatter # useful for `logit` scale
    # Fixing random state for reproducibility
    np.random.seed(19680801)
    # make up some data in the interval ]0, 1[
    y = np.random.normal(loc=0.5, scale=0.4, size=1000)
    y = y[(y > 0) & (y < 1)]
    y.sort()
    x = np.arange(len(y))
    # plot with selected axes scale
    if scaletype == 1:
        # plt.figure(1)
        # # linear
        # plt.subplot(221)
        plt.plot(x, y)
        plt.yscale('linear')
        plt.title('linear')
        plt.grid(True)
        # log
    elif scaletype == 2:
        # plt.subplot(222)
        plt.plot(x, y)
        plt.yscale('log')
        plt.title('log')
        plt.grid(True)
        # symmetric log
    elif scaletype == 3:
        # plt.subplot(223)
        plt.plot(x, y - y.mean())
        plt.yscale('symlog', linthreshy=0.01)
        plt.title('symlog')
        plt.grid(True)
        # logit
    elif scaletype == 4:
        # plt.subplot(224)
        plt.plot(x, y)
        plt.yscale('logit')
        plt.title('logit')
        plt.grid(True)
    else:
        return 'scaletype must be between 1 and 4'
    # Format the minor tick labels of the y-axis into empty strings with
    # `NullFormatter`, to avoid cumbering the axis with too many labels.
    plt.gca().yaxis.set_minor_formatter(NullFormatter())
    # Adjust the subplot layout, because the logit one may take more space
    # than usual, due to y-tick labels like "1 - 10^{-3}"
    # Not required, Excel version returns only 1 chart
    # plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
    # wspace=0.35)

    plot(fig)


@xl_func
def MPLTute_7a():
    # Multiplot version
    fig, ax = plt.subplots()
    from matplotlib.ticker import NullFormatter # useful for `logit` scale
    # Fixing random state for reproducibility
    np.random.seed(19680801)
    # make up some data in the interval ]0, 1[
    y = np.random.normal(loc=0.5, scale=0.4, size=1000)
    y = y[(y > 0) & (y < 1)]
    y.sort()
    x = np.arange(len(y))
    # plot with various axes scales
    
    fig = plt.figure(1)
    # # linear
    plt.subplot(221)
    plt.plot(x, y)
    plt.yscale('linear')
    plt.title('linear')
    plt.grid(True)
    # log
    plt.subplot(222)
    plt.plot(x, y)
    plt.yscale('log')
    plt.title('log')
    plt.grid(True)
    # symmetric log
    plt.subplot(223)
    plt.plot(x, y - y.mean())
    plt.yscale('symlog', linthreshy=0.01)
    plt.title('symlog')
    plt.grid(True)
    # logit
    plt.subplot(224)
    plt.plot(x, y)
    plt.yscale('logit')
    plt.title('logit')
    plt.grid(True)

    # Format the minor tick labels of the y-axis into empty strings with
    # `NullFormatter`, to avoid cumbering the axis with too many labels.
    plt.gca().yaxis.set_minor_formatter(NullFormatter())
    # Adjust the subplot layout, because the logit one may take more space
    # than usual, due to y-tick labels like "1 - 10^{-3}"
    plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
    wspace=0.35)

    plot(fig)