wifi-heatmap.py

from __future__ import division

import tabular as tb
import numpy as np
import matplotlib.mlab as ml
import matplotlib.pyplot as pp

from mpl_toolkits.axes_grid1 import AxesGrid
from scipy.interpolate import Rbf
from pylab import imread, imshow

layout = imread('input/Layout.png')

a = tb.tabarray(SVfile='input/mapping.csv')

s_beacons = ['2e:20', 'f6:70', '5b:30', '74:c0', 'f5:90', '16:a0']
g_beacons = ['14:a1', 'f6:71', '5b:31', '74:c1', 'f5:91', '16:a1']

grid_width = 797
grid_height = 530

image_width = 2544
image_height = 1691

num_x = image_width / 4
num_y = num_x / (image_width / image_height)

print "Resolution: %0.2f x %0.2f" % (num_x, num_y)

x = np.linspace(0, grid_width, num_x)
y = np.linspace(0, grid_height, num_y)

gx, gy = np.meshgrid(x, y)
gx, gy = gx.flatten(), gy.flatten()

levels = [-85, -80, -75, -70, -65, -60, -55, -50, -45, -40, -35, -30, -25]

interpolate = True

def add_inner_title(ax, title, loc, size=None, **kwargs):
    from matplotlib.offsetbox import AnchoredText
    from matplotlib.patheffects import withStroke

    if size is None:
        size = dict(size=pp.rcParams['legend.fontsize'])

    at = AnchoredText(title, loc=loc, prop=size,
                      pad=0., borderpad=0.5,
                      frameon=False, **kwargs)

    at.set_zorder(200)

    ax.add_artist(at)

    at.txt._text.set_path_effects([withStroke(foreground="w", linewidth=3)])

    return at

def grid_plots():
    f = pp.figure()

    f.suptitle("Individual AP RSSI")

    # Adjust the margins and padding
    f.subplots_adjust(hspace=0.1, wspace=0.1, left=0.05, right=0.95, top=0.85,
            bottom=0.15)

    # Create a grid of subplots using the AxesGrid helper
    image_grid = AxesGrid(f, 111, nrows_ncols=(2, 3), axes_pad=0.1,
            label_mode="1", share_all=True, cbar_location="right",
            cbar_mode="single", cbar_size="3%")

    for beacon, i in zip(s_beacons, xrange(len(s_beacons))):
        # Hide the axis labels
        image_grid[i].xaxis.set_visible(False)
        image_grid[i].yaxis.set_visible(False)

        if interpolate:
            # Interpolate the data
            rbf = Rbf(a['Drawing X'], a['Drawing Y'], a[beacon],
                    function='linear')

            z = rbf(gx, gy)
            z = z.reshape((num_y, num_x))

            # Render the interpolated data to the plot
            image = image_grid[i].imshow(z, vmin=-85, vmax=-25, extent=(0,
                image_width, image_height, 0), cmap='RdYlBu_r', alpha=1)

            #c = image_grid[i].contourf(z, levels, alpha=0.5)
            #c = image_grid[i].contour(z, levels, linewidths=5, alpha=0.5)
        else:
            z = ml.griddata(a['Drawing X'], a['Drawing Y'], a[beacon], x, y)

            c = image_grid[i].contourf(x, y, z, levels, alpha=0.5)

        image_grid[i].imshow(layout, interpolation='bicubic', zorder=100)

    # Setup the data for the colorbar and its ticks
    image_grid.cbar_axes[0].colorbar(image)
    image_grid.cbar_axes[0].set_yticks(levels)

    # Add inset titles to each subplot
    for ax, im_title in zip(image_grid, s_beacons):
        t = add_inner_title(ax, "Beacon %s" % im_title, loc=3)

        t.patch.set_alpha(0.5)

    pp.show()

def max_plot():
    # Get the maximum RSSI seen for each beacon
    max_rssi = [max(i) for i in a[s_beacons]]

    pp.title("Maximum RSSI seen for each beacon")

    if interpolate:
        # Interpolate the data
        rbf = Rbf(a['Drawing X'], a['Drawing Y'], max_rssi, function='linear')

        z = rbf(gx, gy)
        z = z.reshape((num_y, num_x))

        # Render the interpolated data to the plot
        image = pp.imshow(z, vmin=-85, vmax=-25, extent=(0,
            image_width, image_height, 0), cmap='RdYlBu_r', alpha=1)

        #pp.contourf(z, levels, alpha=0.5)
        #pp.contour(z, levels, linewidths=5, alpha=0.5)
    else:
        z = ml.griddata(a['Drawing X'], a['Drawing Y'], max_rssi, x, y)

        pp.contourf(x, y, z, levels, alpha=0.5)

    pp.colorbar(image)

    pp.imshow(layout, interpolation='bicubic', zorder=100)

    pp.show()

if __name__ == "__main__":
    grid_plots()
    max_plot()