Viz 4 visualization

.py

def double_hist_and_scatter_plot(df):  

    # Auxilliary parameters for the plot, number of bins
    w = 2
    n = math.ceil((df['optical_power'].max() - df['optical_power'].min()) / w)

    # definitions for the axes limits and positions
    left, width = 0.1, 0.65
    bottom, height = 0.1, 0.65
    spacing = 0.005

    rect_scatter = [left, bottom, width, height]
    rect_histx = [left, bottom + height + spacing, width, 0.2]
    rect_histy = [left + width + spacing, bottom, 0.2, height]

    # Ranges for the optical receiving power. This will allow us to separate the "good" samples
    # from "critical" ones.
    normal_range = [0.0, -21.9]
    critical_range = [-22.0, -40]
    
    # Colors for the ranges, green for the "good" ones, and red for the "bad" ones
    normal_color = 'green'
    critical_color = 'red'

    # Lists of the ranges, to be able to iterate through them
    ranges = [normal_range, critical_range]
    colors = [normal_color, critical_color]

    # X axis values, in this case are the distances
    exes = []
    for range in ranges:
        x = df[(df['optical_power'] <= range[0]) & (df['optical_power'] >= range[1])]['distance']
        exes.append(x)
    
    # Y axis values for the plot, in this case are the Rx optical powers
        yses = []
        for range in ranges:
            y = df[(df['optical_power'] <= range[0]) & (df['optical_power'] >= range[1])]['optical_power']
            yses.append(y)

    # start with a rectangular Figure
    plt.figure(figsize=(30, 10))   

    # Creation of the axes, and some parameters for the plot
    ax_scatter = plt.axes(rect_scatter)
    ax_scatter.tick_params(direction='in', top=True, right=True, labelsize=20)
    ax_histx = plt.axes(rect_histx)
    ax_histx.tick_params(direction='in', labelbottom=False, labelsize=20)
    ax_histy = plt.axes(rect_histy)
    ax_histy.tick_params(direction='in', labelleft=False, labelsize=20, labelrotation=45)

    # the scatter plot:
    for (x, y, color) in zip(exes, yses, colors):
        ax_scatter.scatter(x, y, alpha=0.5, color= color)

    ax_scatter.set_xlabel('Distance (m)', fontsize=30)
    ax_scatter.set_ylabel('RX optical power (dBm)', fontsize=30)
    # Plot the threshold line
    ax_scatter.axhline(critical_range[0], color='red', lw=6)

    # The histogram plots, only for the power we will use color ranges, for the distance no
    for (x, y, color) in zip(exes, yses, colors):
        ax_histy.hist(y, bins=n, orientation='horizontal', color=color)
    ax_histx.hist(x, bins=n, color='green')
    plt.show()

    # CREACION E IMPRESION DE LA TABLA DE VALORES
    bins = ['Optimal', 'Critical']
    data = []
    for y in yses:
        data.append(y.count())
    
    table = pd.DataFrame(list(zip(bins, data)), columns=['Ranges', 'Quantity'])
    print(table.head(5))