def main():
drones = {}
drone_data = read_drone_data()
for index, drone in drone_data.iterrows():
if drone['drone_id'] is not np.nan:
drones[drone['drone_id']] = (drone['x_m'], drone['y_m'])
for window in TIME_WINDOWS:
files = get_files(window)
for file_name, method, output in files:
data = []
times = []
sourcemac = None
with open(file_name, 'r') as f:
reader = csv.DictReader(f, delimiter=';')
for row in reader:
data.append((float(row['x_m']), float(row['y_m'])))
times.append(float(row['timestamp']))
sourcemac = row['sourcemac']
for sigma in range(1, 51):
Bayesian_Philip()
def get_latex_table():
results = {}
for window in TIME_WINDOWS:
base_files = get_files(window)
for i, base_file in enumerate(base_files):
dist, std = get_performance.main(base_file[0], 'euc')
try:
results[base_file[1]].append(dist)
except KeyError:
results[base_file[1]] = [dist]
print('', end=" ")
for window in TIME_WINDOWS:
print('& ' + str(window), end=" ")
print('\\\\')
print('\\hline')
for key in results:
print(key, end=" ")
for value in results[key]:
print('& ' + str(round(value, 3)), end=" ")
print('\\\\')
print('\\hline')
def main():
pos_diff = ACTUAL_SECOND_POS[0] - ACTUAL_FIRST_POS[0]
time_diff = ACTUAL_SECOND_POS[1] - ACTUAL_FIRST_POS[1]
grad = pos_diff / time_diff
for window in TIME_WINDOWS:
files = get_files(window)
for file_name, method, output in files:
data = []
times = []
sourcemac = None
with open(file_name, 'r') as f:
reader = csv.DictReader(f, delimiter=';')
for row in reader:
data.append((float(row['x_m']), float(row['y_m'])))
times.append(float(row['timestamp']))
sourcemac = row['sourcemac']
act_init_pos = ACTUAL_FIRST_POS[0] + grad * (times[0] -
ACTUAL_FIRST_POS[1])
init_est_error = (np.array(data[0]) - act_init_pos)
P0 = np.array([[init_est_error[0]**2, 0],
[0, init_est_error[1]**2]])
for q_sigma in range(1, 501):
for r_sigma in range(21, 31):
kalman_filter = KalmanFilter(data[0], P0, times[0],
q_sigma, r_sigma)
results = kalman_filter.perform_kalman_steps(
data[1:], times[1:])
with open(
'/media/nickyz/Data/scriptie_data/results/kalman_filter/'
+ output + '_{}_{}.csv'.format(q_sigma, r_sigma),
'a') as f:
writer = csv.writer(f, delimiter=";")
writer.writerow(
["sourcemac", "timestamp", "x_m", "y_m"])
for i, result in enumerate(results):
writer.writerow([sourcemac, times[i]] +
list(result))
def main():
N = len(TIME_WINDOWS)
results = []
labels = ['Euclidean Distance', 'Method', 'Time Window']
methods = []
ps = []
prev_length = 0
for window in TIME_WINDOWS:
base_files = get_files(window)
methods = []
for i, base_file in enumerate(base_files):
dists = get_performance.main(base_file[0], 'euc', False)
methods.append(base_file[2])
# len_dif =
# prev_length = len()
for val in dists:
results.append([val, base_file[1], window])
# tri_shortness = len(part_results[0]) - len(part_results[-1])
#
# while tri_shortness > 0:
# part_results[-1].append(None)
# tri_shortness -= 1
#
# part_results.append([window for _ in part_results[0]])
#
# results += np.array(part_results).T.tolist()
df = pd.DataFrame.from_records(results, columns=labels)
g = sns.boxplot(x='Method',
y='Euclidean Distance',
hue='Time Window',
data=df)
g.set_xticklabels(labels=methods, rotation=30)
axes = g.axes
axes.set_ylim(0, 40)
plt.show()
def main():
for window in TIME_WINDOWS:
files = get_files(window)
for file_name, method, output in files:
data = []
times = []
sourcemac = None
with open(file_name, 'r') as f:
reader = csv.DictReader(f, delimiter=';')
for row in reader:
data.append((float(row['x_m']), float(row['y_m'])))
times.append(float(row['timestamp']))
sourcemac = row['sourcemac']
# for data_window in range(0, 41):
# med_filter = Median_filter(data_window)
# results = med_filter.get_series(data)
#
# with open('../results/med_filter/' + output + '_{}.csv'.format(data_window), 'a') as f:
# writer = csv.writer(f, delimiter=";")
# writer.writerow(["sourcemac", "timestamp", "x_m", "y_m"])
#
# for j, result in enumerate(results):
# writer.writerow([sourcemac, times[j]] + list(result))
#
# for data_window in range(0, 41):
# med_filter = Shifting_median_filter(data_window)
# results = med_filter.get_series(list(zip(times, data)))
#
# with open('../results/shift_med_filter/' + output + '_{}.csv'.format(data_window), 'a') as f:
# writer = csv.writer(f, delimiter=";")
# writer.writerow(["sourcemac", "timestamp", "x_m", "y_m"])
#
# for j, result in enumerate(results):
# writer.writerow([sourcemac, times[j]] + list(result))
# data_window = SIGMA_EST_WINDOWS[method][window]
# for sigma in range(251, 301):
# med_filter = Gaussian_median_filter(data_window, sigma)
# results = med_filter.get_series(list(zip(times, data)))
#
# with open('../results/gauss_med_filter/sigma_est/' + output + '_{}.csv'.format(sigma), 'a') as f:
# writer = csv.writer(f, delimiter=";")
# writer.writerow(["sourcemac", "timestamp", "x_m", "y_m"])
#
# for j, result in enumerate(results):
# writer.writerow([sourcemac, times[j]] + list(result))
sigma = WINDOW_EST_SIGMA[method][window]
for data_window in range(0, 41):
med_filter = Gaussian_median_filter(data_window, sigma)
results = med_filter.get_series(list(zip(times, data)))
with open('../results/gauss_med_filter/' + output + '_{}.csv'.format(data_window), 'a') as f:
writer = csv.writer(f, delimiter=";")
writer.writerow(["sourcemac", "timestamp", "x_m", "y_m"])
for j, result in enumerate(results):
writer.writerow([sourcemac, times[j]] + list(result))
def main():
for window in TIME_WINDOWS:
files = get_files(window)
for file_name, method, output in files:
data = []
times = []
sourcemac = None
with open(file_name, 'r') as f:
reader = csv.DictReader(f, delimiter=';')
for row in reader:
data.append((float(row['x_m']), float(row['y_m'])))
times.append(float(row['timestamp']))
sourcemac = row['sourcemac']
# for i in range(0, 101):
# alpha = i / 100
#
# exp_filter = Exp_mv_avg(alpha)
# results = exp_filter.get_series(data)
#
# with open('../results/exp_filter/' + output + '_{}.csv'.format(alpha), 'a') as f:
# writer = csv.writer(f, delimiter=";")
# writer.writerow(["sourcemac", "timestamp", "x_m", "y_m"])
#
# for j, result in enumerate(results):
# writer.writerow([sourcemac, times[j]] + list(result))
# for i in range(1001, 1501):
# tau = i / 10
#
# exp_filter = Irr_exp_mv_avg(tau)
# results = exp_filter.get_series(list(zip(times, data)))
#
# with open('../results/irr_exp_filter/' + output + '_{}.csv'.format(tau), 'a') as f:
# writer = csv.writer(f, delimiter=";")
# writer.writerow(["sourcemac", "timestamp", "x_m", "y_m"])
#
# for j, result in enumerate(results):
# writer.writerow([sourcemac, times[j]] + list(result))
# for i in range(0, 101, 5):
# alpha = i / 100
# for j in range(0, 101, 5):
# beta = j / 100
#
# exp_filter = Double_exp_smoothing(alpha, beta)
# results = exp_filter.get_series(data)
#
# with open('../results/dbl_exp_filter/' + output + '_{}_{}.csv'.format(alpha, beta), 'a') as f:
# writer = csv.writer(f, delimiter=";")
# writer.writerow(["sourcemac", "timestamp", "x_m", "y_m"])
#
# for k, result in enumerate(results):
# writer.writerow([sourcemac, times[k]] + list(result))
for i in range(31, 51):
tau_a = float(i)
for j in range(1, 31):
tau_b = float(j)
exp_filter = Irr_double_exp_smoothing(tau_a, tau_b)
results = exp_filter.get_series(list(zip(times, data)))
with open(
'/media/nickyz/Data/scriptie_data/results/dbl_irr_exp_filter/'
+ output + '_{}_{}.csv'.format(tau_a, tau_b),
'a') as f:
writer = csv.writer(f, delimiter=";")
writer.writerow(
["sourcemac", "timestamp", "x_m", "y_m"])
for k, result in enumerate(results):
writer.writerow([sourcemac, times[k]] +
list(result))