b001_13.py

b001_13.py

import seaborn as sns

sns.set_style("darkgrid", {"grid.color": ".6", "grid.linestyle": ":"})


def radar_histogram(ax, df):
    """
    Input: 
        df with at least 2 columns distance_km and bearing_deg.
    Output: radar histogram plot.
    """
    # Figures parameter
    directions = 40
    
    bottom = 4
    height_scale = 8
    
    # bearing: degrees from nort pole clockwise
    bearing_bins = np.linspace(0, 360, directions+1, endpoint=False)
    # angle visualisation: rad from east counterclockwise
    theta = - 1 * np.linspace(0, 2 * np.pi, directions, endpoint=False) + np.pi/2
    width = (2*np.pi) / directions
    
    # data binning
    se_bins = pd.cut(df["bearing_deg"].to_numpy(), bearing_bins)
    np_bins = se_bins.value_counts().to_numpy()
    bins =  height_scale * np.array(np_bins) / np.max(np_bins)
    
    # Uncomment to debug figure:
    # bins = range(directions)
    
    # plotting    
    ax_bars = ax.bar(theta, bins, width=width, bottom=bottom, color="blue")

    ax.set_yticklabels([])
    ax.set_xticks(np.linspace(0, 2 * np.pi, 8, endpoint=False))
    ax.set_xticklabels(['E', '', 'N', '', 'W', '', 'S', ''])
    ax.grid(False)

    return ax

def radar_histogram_3_levels(ax, df):
    """
    Input: 
        df with at least 2 columns distance_km and bearing_deg.
    Output: radar histogram plot.
    """
    # Figures parameter
    directions = 40
    height_scale = 2

    bottom_inner = 2
    bottom_betw = 5
    bottom_outer = 8
    
    # bearing: degrees from nort pole clockwise
    bearing_bins = np.linspace(0, 360, directions+1, endpoint=False)
    # angle visualisation: rad from east counterclockwise
    theta = - 1 * np.linspace(0, 2 * np.pi, directions, endpoint=False) + np.pi/2
    width = (2*np.pi) / directions
    
    # data binning
    
    df_inner = df[df["distance_km"] <= 10]
    se_bins_inner = pd.cut(df_inner["bearing_deg"].to_numpy(), bearing_bins)
    np_bins_inner = se_bins_inner.value_counts().to_numpy()
    bins_inner =  height_scale * np.array(np_bins_inner) / np.max(np_bins_inner)
    
    df_betw = df[(df["distance_km"] > 10) & (df["distance_km"] <= 20)]
    se_bins_betw = pd.cut(df_betw["bearing_deg"].to_numpy(), bearing_bins)
    np_bins_betw = se_bins_betw.value_counts().to_numpy()
    bins_betw =  height_scale * np.array(np_bins_betw) / np.max(np_bins_betw)
    
    df_outer = df[df["distance_km"] > 20]
    se_bins_outer = pd.cut(df_outer["bearing_deg"].to_numpy(), bearing_bins)
    np_bins_outer = se_bins_outer.value_counts().to_numpy()
    bins_outer =  height_scale * np.array(np_bins_outer) / np.max(np_bins_outer)
    
    # plotting
    
    ax_bars_inner = ax.bar(theta, bins_inner, width=width, bottom=bottom_inner, color="blue")
    ax_bars_betw = ax.bar(theta, bins_betw, width=width, bottom=bottom_betw, color="blue")
    ax_bars_outer = ax.bar(theta, bins_outer, width=width, bottom=bottom_outer, color="blue")

    
    ax.set_yticklabels([])
    # uncomment to add values on radius axis
    # ax.set_yticks(np.arange(0,10,1.0))
    # ax.set_yticklabels(['', '', '', '<=10Km ', '', '', '>10Km\n <=20Km', '', '', '>20Km'])

    ax.set_xticks(np.linspace(0, 2 * np.pi, 8, endpoint=False))
    ax.set_xticklabels(['E', '', 'N', '', 'W', '', 'S', ''])
    ax.grid(False)

    return ax


fig = plt.figure(figsize=(12, 12))

ax11 = fig.add_subplot(221, polar=True)
ax11 = radar_histogram(ax11, df_commuters_st_luke_office)
ax11.set_title("Saint Luke Office", y=1.08, x=1.1)

ax12 = fig.add_subplot(222, polar=True)
ax12 = radar_histogram_3_levels(ax12, df_commuters_st_luke_office)

ax21 = fig.add_subplot(223, polar=True)
ax21 = radar_histogram(ax21, df_commuters_albert_road)
ax21.set_title("Albert Road Office", y=1.08, x=1.1)

ax22 = fig.add_subplot(224, polar=True)
ax22 = radar_histogram_3_levels(ax22, df_commuters_albert_road)

plt.plot()