def run(self):
self.ping = os.popen('ping' + paramPing + self.host).read()
nqmWlan = calcNQM(getDelay(onlyTimes(self.ping)),
getJitter(onlyTimes(self.ping)),
getPacketsLoss(onlyPackets(self.ping)))
avg = mediaNQM(nqmWlan)
self.queue.put(avg)
config = json.load(f)
measurements = config["measurements"]
for measurement in measurements:
print("--------------------- PATH LOSS MODEL {} ---------------------".format(measurement))
output_fig_pgf = os.path.join(
input_path, 'path_loss_{}.{}'.format(measurement, OUTPUT_IMG_FORMAT))
CENTER = config[measurement]["center"]
#d0 = config[measurement]["d0"]
#pl0 = config[measurement]["pl0"]
input_file_path = os.path.join(
input_path, measurement, input_file_name)
df = pd.read_pickle(input_file_path)
df = util.onlyPackets(df)
d0 = 1
df = df[df.distance > d0]
df['distance_log'] = 10*np.log10(df.distance/d0)
# df[(df["distance"] < 105) & (df["distance"] > 100)]
results = smf.ols('pl_db ~ distance_log', data=df).fit()
print(print(results.summary()))
fig = plt.figure(figsize=(4, 3))
ax = fig.add_subplot(1, 1, 1)
slope, intercept, r_value, p_value, std_err = stats.linregress(
df['distance_log'], df['pl_db'])
input_file_name = "preprocessed_data.pkl"
with open(os.path.join(processing_path, "conf.json")) as config_file:
config_plot = json.load(config_file)["heatmap"]
grid_size = config_plot["grid_size"]
plot_snr = config_plot["plot_snr"]
plot_rss = config_plot["plot_rss"]
with open(os.path.join(path_to_measurements, "measurements.json")) as f:
config_measurement = json.load(f)
measurements = config_measurement["measurements"]
for measurement in measurements:
print("--------------------- HEATMAP {} ---------------------".
format(measurement))
#num_bins = config_measurement[measurement]["freq_samples_bins"]
input_file_path = os.path.join(input_path, measurement,
input_file_name)
for_map = pd.read_pickle(input_file_path)
for_map = util.onlyPackets(for_map)
#dist_in_bins = pd.cut(x=for_map.distance, bins=num_bins)
#rss_in_bins = pd.cut(x=for_map.rssi, bins=num_bins)
#
#ax = sns.heatmap(for_map.pivot_table("distance", "rssi"))
#plt.show()
#
#sns.distplot(for_map.rssi)
sns.jointplot(x=for_map.distance, y=for_map.rssi, kind="hex")
plt.show()
print("--------------------- DONE HEATMAP ---------------------")