def plot_heatmap(network, network_mean_rssi):
fig, ax = plt.subplots() # note we must use plt.subplots, not plt.subplot
i1netmode.plot_atmosphere(ax)
cm = plt.cm.get_cmap("hot")
my_cmap = cm(np.linspace(0, 1, len(network_mean_rssi)))
rects = []
simple_plot = True
if simple_plot:
sc = plt.scatter(x, y, c=network_mean_rssi, s=250, marker="s", cmap=cm)
plt.colorbar(sc)
else: # better plot but freaking messy
for i in range(len(x)):
if i <= 13:
rect = Rectangle((x[i] - 0.5, y[i] - 1), 1, 2.5, color=my_cmap[i])
else:
rect = Rectangle((x[i] - 0.5, y[i] - 0.5), 1, 1, color=my_cmap[i])
rects.append(rect)
ax.add_patch(rect)
minx = min(network_mean_rssi)
maxx = max(network_mean_rssi)
N = len(network_mean_rssi)
norm = mpl.colors.Normalize(vmin=minx, vmax=maxx)
sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)
sm.set_array([])
plt.colorbar(sm)
plt.title("RSSI Map of {}".format(network))
# plt.show()
plt.draw()
plt.waitforbuttonpress(0) # this will wait for indefinite time
plt.close(fig)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pprint
from sklearn.neighbors import KNeighborsClassifier as kNN
from i0positions import Positions
import i1netmode
plt.ion()
fig, ax = plt.subplots() # note we must use plt.subplots, not plt.subplot
i1netmode.plot_atmosphere(ax)
dataset = pd.read_csv("data/i2processed_average_positions.csv", header=[0, 1, 2])
features = np.asarray(dataset.iloc[:, 3:])
labels = np.asarray(dataset["Relative Position"])
# MACs of networks in dataset
networks = [z for (_, _, z) in list(dataset)[3:]]
# If we want only selected wifis in dataset:
network_names = ["ap", "hub0", "hub1", "hub2"]
networks = [z for (x, _, z) in list(dataset)[3:] if x in network_names]
features = np.asarray(dataset[network_names])
found_networks = [0] * len(networks)
# a = dataset.hist()
# plt.plot(a)