def fit_all():
"""
Plots from heterogenity experiment
"""
# Plotting the histograms of the different phones on every ap
for ap in CURVES['aps']:
plt.figure()
for mac in CURVES['macs']:
# _, x1, _ = get_rss_fluctuation(
# CURVES['1m_start'], CURVES['5m_end'], ap, mac)
# _, x2, _ = get_rss_fluctuation(
# CURVES['7m_start'], CURVES['11m_end'], ap, mac)
# X = x1 + x2
_, X, _ = get_rss_fluctuation(CURVES['7m_start'], CURVES['7m_end'],
ap, mac)
# Apply Kalman filter
kalman = KalmanFilter(0.01, 0.1)
filtered_X = []
for p in X:
filtered_X.append(kalman.filter(int(p)))
X = filtered_X
# Data Histogram
# plt.figure()
# plt.hist(X, bins=30, label=mac, histtype='step', density=True)
# plt.xlabel('RSSI (dBm)')
# plt.ylabel('Frequency')
# plt.legend()
# plt.title('Collective RSSI for ap %d' % ap)
# Kernel Density Estimation
X = np.array(X)
# bandwidths = 10 ** np.linspace(-1, 1, 100)
# grid = GridSearchCV(KernelDensity(kernel='gaussian'),
# {'bandwidth': bandwidths},
# cv=LeaveOneOut().get_n_splits(X))
# grid.fit(X[:, None])
# bandwidth = grid.best_params_['bandwidth']
# print(grid.best_params_)
bandwidth = 3.0
X_d = np.linspace(-90, -10, X.shape[0])
kde = KernelDensity(bandwidth=bandwidth,
kernel='gaussian').fit(X[:, None])
logprob = kde.score_samples(X_d[:, None])
plt.fill_between(X_d,
np.exp(logprob),
alpha=0.2,
label=TRILATERATION['macs'][mac])
plt.xlabel('RSSI (dBm)')
plt.ylabel('Probability Density')
plt.legend()
# plt.title('Kernel estimation for ap %d with bandwidth=%.2f' %
# (ap, bandwidth))
plt.show()
# Plotting the histograms of the different aps on every phone
for mac in CURVES['macs']:
plt.figure()
for ap in CURVES['aps']:
# _, x1, _ = get_rss_fluctuation(
# CURVES['1m_start'], CURVES['5m_end'], ap, mac)
# _, x2, _ = get_rss_fluctuation(
# CURVES['7m_start'], CURVES['11m_end'], ap, mac)
# X = x1 + x2
_, X, _ = get_rss_fluctuation(CURVES['7m_start'], CURVES['7m_end'],
ap, mac)
# Apply Kalman filter
kalman = KalmanFilter(0.01, 0.1)
filtered_X = []
for p in X:
filtered_X.append(kalman.filter(int(p)))
X = filtered_X
# Data Histogram
# plt.hist(X, bins=30, label=ap, histtype='step', stacked=True)
# plt.xlabel('RSSI (dB)')
# plt.ylabel('Frequency')
# plt.legend()
# plt.title('Collective RSSI for phone %s' % mac)
# Kernel Density Estimation
X = np.array(X)
# bandwidths = 10 ** np.linspace(-1, 1, 100)
# grid = GridSearchCV(KernelDensity(kernel='gaussian'),
# {'bandwidth': bandwidths},
# cv=LeaveOneOut().get_n_splits(X))
# grid.fit(X[:, None])
# bandwidth = grid.best_params_['bandwidth']
# print(grid.best_params_)
bandwidth = 3.0
X_d = np.linspace(-90, -10, X.shape[0])
kde = KernelDensity(bandwidth=bandwidth,
kernel='gaussian').fit(X[:, None])
logprob = kde.score_samples(X_d[:, None])
plt.fill_between(X_d,
np.exp(logprob),
alpha=0.2,
label='AP ' + str(ap))
plt.xlabel('RSSI (dBm)')
plt.ylabel('Probability Density')
plt.legend()
# plt.title('Kernel estimation for device %s with bandwidth=%.2f' %
# (mac, bandwidth))
plt.show()
def fit():
"""
Plots from curve fitting experiment
"""
# Parse data from config file
t = []
X = []
for i in range(0, 13):
t_temp, X_temp, _ = get_rss_fluctuation(CURVE[str(i) + 'm_start'],
CURVE[str(i) + 'm_end'],
CURVE['ap'], CURVE['mac'])
t = t + t_temp
X = X + X_temp
print(len(t), len(X))
# Apply Kalman filter
kalman = KalmanFilter(0.01, 0.1)
filtered_X = []
for p in X:
filtered_X.append(kalman.filter(int(p)))
time_ranges = []
for i in range(13):
start_timestamp = convert_date_to_secs(CURVE[str(i) + 'm_start'])
end_timestamp = convert_date_to_secs(CURVE[str(i) + 'm_end'])
time_ranges.append((start_timestamp, end_timestamp))
# Compute average RSSI for every distance
data = []
hist_data = []
avgs = []
medians = []
i = 0
for s, e in time_ranges:
single_data = []
for p in t:
if p in range(s, e):
single_data.append(X[t.index(p)])
data = data + list(zip(single_data, [i] * len(single_data)))
hist_data.append(single_data)
i += 1
medians.append(median(single_data))
avgs.append(round(mean(single_data), 1))
print(len(X))
print(len(data))
lengths = [len(x) for x in hist_data]
# print(lengths)
print(sum(lengths))
ticks = [0]
for i, l in enumerate(lengths):
# print(i, l)
ticks.append(sum(lengths[:i + 1]))
# del ticks[-1]
print(ticks)
# Plot raw data
plt.xticks(ticks, range(0, 14))
plt.plot(range(0, len(X)), X)
plt.ylabel('RSSI (dBm)')
plt.xlabel('Distance (m)')
plt.figure()
plt.hist(hist_data[:6], bins=30, label=range(0, 6), density=True)
plt.xlabel('RSSI (dBm)')
plt.ylabel('Probability Density')
plt.legend()
# plt.title('RSSI frequencies measured in the first 6 meters')
plt.figure()
plt.hist(X, bins=20, density=True, histtype='step')
plt.xlabel('RSSI (dB)')
plt.ylabel('Frequency')
bandwidth = 2
X = np.array(X)
X_d = np.linspace(-75, -25, X.shape[0])
kde = KernelDensity(bandwidth=bandwidth, kernel='gaussian').fit(X[:, None])
logprob = kde.score_samples(X_d[:, None])
plt.fill_between(X_d, np.exp(logprob), alpha=0.2, color='b')
plt.xlabel('RSSI (dBm)')
plt.ylabel('Probability Density')
# plt.legend(loc='upper right')
# Plot collective RSSI histogram
# plt.figure()
# plt.hist(X, bins=30)
# plt.xlabel('RSSI')
# plt.ylabel('Frequency')
# Plot averaged data
# plt.figure()
# plt.plot(avgs, range(0, len(avgs)))
plt.figure()
x, y = zip(*sorted(data, key=lambda x: x[0]))
plt.scatter(y, x)
popt, _ = curve_fit(log_dist_func, x, y)
# Fit curve
x, y = zip(*sorted(zip(avgs, range(0, len(avgs)))))
popt, _ = curve_fit(log_dist_func, x, y)
# Plot curve
print('Collective RSSI averages: %s' % avgs)
x_fit = np.linspace(0, -100, 100)
plt.plot(log_dist_func(x_fit, *popt),
x_fit,
'g--',
label='fit: $PL_0$=-%5.3f, $\gamma$=%5.3f' % tuple(popt))
plt.ylabel('RSSI (dBm)')
plt.xlabel('Distance (m)')
plt.xlim(-1, 25)
plt.ylim(-80, 0)
plt.legend(loc='upper right')
plt.show()
def fit_multiple():
"""
Comprehensive plots from heterogentiy experiment
"""
# Parse data from config file
distances = range(1, 12, 2)
all_avgs = []
all_medians = []
for mac in CURVES['macs']:
for ap in CURVES['aps']:
t1, x1, _ = get_rss_fluctuation(CURVES['1m_start'],
CURVES['5m_end'], ap, mac)
t2, x2, _ = get_rss_fluctuation(CURVES['7m_start'],
CURVES['11m_end'], ap, mac)
t = t1 + t2
X = x1 + x2
# Apply Kalman filter
kalman = KalmanFilter(0.01, 0.1)
filtered_X = []
for p in X:
filtered_X.append(kalman.filter(int(p)))
# Plot raw data
# plt.figure(figsize=(12.0, 8.0))
# plt.plot(range(0, len(X)), X)
# plt.xlabel('Sample')
# plt.ylabel('RSSI (dBm)')
# plt.ylim(-75, -25)
# # plt.title('Raw data from ap %s for %s' %
# # (ap, TRILATERATION['macs'][mac]))
# plt.show()
# Plot filtered data
# plt.figure()
# plt.plot(filtered_X, range(0, len(X)))
# plt.xlabel('RSSI (dBm)')
# plt.ylabel('Sample')
# plt.xlim(-80, -20)
# plt.title('Filtered data from ap %s for %s' %
# (ap, TRILATERATION['macs'][mac]))
time_ranges = []
for i in distances:
start_timestamp = convert_date_to_secs(CURVES[str(i) +
'm_start'])
end_timestamp = convert_date_to_secs(CURVES[str(i) + 'm_end'])
time_ranges.append((start_timestamp, end_timestamp))
# Compute average RSSI for every distance
data = []
hist_data = []
avgs = []
medians = []
i = 1
for s, e in time_ranges:
single_data = []
for p in t:
if p in range(s, e):
single_data.append(X[t.index(p)])
data = data + list(zip(single_data, [i] * len(single_data)))
hist_data.append(single_data)
i += 2
medians.append(median(single_data))
avgs.append(round(mean(single_data), 1))
# all_data.append(data)
all_avgs.append(avgs)
all_medians.append(medians)
# # Color-coded histogram
# plt.figure(figsize=(12.0, 8.0))
# plt.hist(hist_data, bins=30, label=distances)
# plt.xlabel('RSSI (dBm)')
# plt.ylabel('Frequency')
# plt.legend(loc='upper right')
# plt.xlim(-75, -25)
# # plt.title('RSSI frequencies as detected by ap %s from %s at different distances' %
# # (ap, TRILATERATION['macs'][mac]))
# # Plot collective RSSI histogram
# plt.figure()
# plt.hist(filtered_X, bins=30,
# density=True, histtype='step')
# plt.xlabel('RSSI (dB)')
# plt.ylabel('Frequency')
# # plt.title('Collective RSSI from ap %s for %s' %
# # (ap, TRILATERATION['macs'][mac]))
# # # Kernel Density Estimation
# X = np.array(filtered_X)
# # bandwidths = 10 ** np.linspace(-1, 1, 100)
# # grid = GridSearchCV(KernelDensity(kernel='gaussian'),
# # {'bandwidth': bandwidths},
# # cv=LeaveOneOut.get_n_splits(X))
# # grid.fit(X[:, None])
# # bandwidth = grid.best_params_['bandwidth']
# bandwidth = 1.5
# X_d = np.linspace(-75, -25, X.shape[0])
# kde = KernelDensity(
# bandwidth=bandwidth, kernel='gaussian').fit(X[:, None])
# logprob = kde.score_samples(X_d[:, None])
# plt.fill_between(X_d, np.exp(logprob), alpha=0.2, color='b')
# plt.xlabel('RSSI (dBm)')
# plt.ylabel('Probability Density')
# # plt.legend(loc='upper right')
# # plt.title('Gaussian kernel estimation of RSSI data from ap %d' % (ap))
# # # Plot averaged data
# # plt.figure()
# # plt.plot(avgs, distances, label='averaged RSSI')
# # # Fit curve
# # popt, _ = curve_fit(log_dist_func, avgs, distances)
# # # Plot curve
# # avgs.sort()
# # print('RSSI averages for ap %s and device %s: %s' %
# # (ap, TRILATERATION['macs'][mac], avgs))
# # plt.plot(avgs, log_dist_func(avgs, *popt), 'g--',
# # label='fit: $d_0$=-%5.3f, $\gamma$=%5.3f' % tuple(popt))
# # plt.xlabel('RSSI (dBm)')
# # plt.ylabel('Distance (m)')
# # # plt.title('Curve fit for ap %s and device %s' %
# # # (ap, TRILATERATION['macs'][mac]))
# # plt.ylim(0, 12)
# # plt.legend(loc='upper right')
# plt.show()
avged_avgs = []
for i in np.array(all_medians).T:
avged_avgs.append(mean(i))
plt.figure(figsize=(12.0, 8.0))
r, _ = zip(*data)
plt.hist(r, bins=300, density=True)
plt.xlim(-100, 0)
plt.figure(figsize=(12.0, 8.0))
# plt.plot(avged_avgs, distances, label='mean RSSI')
# x, y = zip(*sorted(zip(avged_avgs, distances)))
x, y = zip(*sorted(data, key=lambda x: x[0]))
plt.scatter(y, x)
popt, _ = curve_fit(log_dist_func, x, y)
# Plot curve
print('Collective RSSI averages: %s' % avged_avgs)
x_fit = np.linspace(0, -100, 100)
plt.plot(log_dist_func(x_fit, *popt),
x_fit,
'g--',
label='fit: $d_0$=-%5.3f, $\gamma$=%5.3f' % tuple(popt))
plt.ylabel('RSSI (dBm)')
plt.xlabel('Distance (m)')
# plt.title('Ultimate curve fit')
plt.xlim(0, 12)
plt.ylim(-90, 0)
plt.legend(loc='upper right')
plt.show()
