analysis: refactor python scripts

analysis/drone_peripheral_consumption_to_state.py

@@ -3,33 +3,55 @@
 from argparse import ArgumentParser
 from enum import Enum
 
-P = ArgumentParser(description='Given power law of a peripheral, convert a given CSV with peripheral power data to deduce its state.')
-P.add_argument('input_csv_filepath', type=str, help='Input CSV filepath containing peripheral power data.')
-P.add_argument('output_csv_filepath', type=str, help='Output CSV filepath to report peripheral state.')
-P.add_argument('off_state_power_W', type=float, help='Power consumption of the peripheral when in OFF state.')
-P.add_argument('on_state_power_W', type=float, help='Power consumption of the peripheral when in ON state.')
-P.add_argument('standby_state_power_W', type=float, help='Power consumption of the peripheral when in STANDBY state.')
+P = ArgumentParser(
+    description="Given power law of a peripheral, convert a given CSV with peripheral power data to deduce its state."
+)
+P.add_argument(
+    "input_csv_filepath",
+    type=str,
+    help="Input CSV filepath containing peripheral power data.",
+)
+P.add_argument(
+    "output_csv_filepath",
+    type=str,
+    help="Output CSV filepath to report peripheral state.",
+)
+P.add_argument(
+    "off_state_power_W",
+    type=float,
+    help="Power consumption of the peripheral when in OFF state.",
+)
+P.add_argument(
+    "on_state_power_W",
+    type=float,
+    help="Power consumption of the peripheral when in ON state.",
+)
+P.add_argument(
+    "standby_state_power_W",
+    type=float,
+    help="Power consumption of the peripheral when in STANDBY state.",
+)
 args = P.parse_args()
 
 peripheral_state = {
-  args.off_state_power_W: 'OFF',
-  args.on_state_power_W: 'ON',
-  args.standby_state_power_W: 'STANDBY'
+    args.off_state_power_W: "OFF",
+    args.on_state_power_W: "ON",
+    args.standby_state_power_W: "STANDBY",
 }
 
-inf = open(args.input_csv_filepath, 'r')
+inf = open(args.input_csv_filepath, "r")
 inr = csv.reader(inf)
-next(inr) # ignore header
+next(inr)  # ignore header
 
-outf = open(args.output_csv_filepath, 'w')
-outw = csv.writer(outf, lineterminator='\n')
-outw.writerow(['time','state'])
+outf = open(args.output_csv_filepath, "w")
+outw = csv.writer(outf, lineterminator="\n")
+outw.writerow(["time", "state"])
 
 for r in inr:
-  t = float(r[0])
-  p = float(r[1])
-  s = peripheral_state[p]
-  outw.writerow([t,s])
+    t = float(r[0])
+    p = float(r[1])
+    s = peripheral_state[p]
+    outw.writerow([t, s])
 
 inf.close()
 outf.close()

analysis/geosnr.py

@@ -1,61 +1,57 @@
-#%%
+# %%
 from pathlib import Path
 
 import pandas as pd
-import plotly.graph_objects as go
 import plotly.express as px
+import plotly.graph_objects as go
 
-#%% Helper functions
+
+# %% Helper functions
 def get_seed_name(x):
-  # param x: Path object
-  return int(x.name.split('-')[1].split('_')[1])
+    # param x: Path object
+    return int(x.name.split("-")[1].split("_")[1])
+
 
-#%%
-results_path = Path('../results')
-results = [x for x in results_path.iterdir() if x.is_dir() and x.name.startswith('ntn_hap-seed')]
+# %%
+results_path = Path("../results")
+results = [
+    x
+    for x in results_path.iterdir()
+    if x.is_dir() and x.name.startswith("ntn_hap-seed")
+]
 
 # Expected format: ntn_hap-seed_<seed>-<date>.<time>
 results.sort(key=get_seed_name)
 
-#%%
-traces = [x / 'ntn-snr-trace.txt' for x in results]
+# %%
+traces = [x / "ntn-snr-trace.txt" for x in results]
 
 # Read traces and store in a dataframe
 df = pd.DataFrame()
 
 for trace in traces:
-  temp_df = pd.read_csv(trace, sep=' ', header=None, names=['time', 'snr', 'propagationLoss'])
-  df = pd.concat((df, temp_df), axis=1)
+    temp_df = pd.read_csv(
+        trace, sep=" ", header=None, names=["time", "snr", "propagationLoss"]
+    )
+    df = pd.concat((df, temp_df), axis=1)
 
-mean_snr = df[['snr']].mean(axis=1)
-mean_propagation_loss = df[['propagationLoss']].mean(axis=1)
+mean_snr = df[["snr"]].mean(axis=1)
+mean_propagation_loss = df[["propagationLoss"]].mean(axis=1)
 
-#%%
+# %%
 # plot SNR
 fig = go.Figure()
-fig.add_trace(go.Scatter(
-  y=mean,
-  mode='lines',
-  name='SNR'
-))
-fig.update_layout(
-  title='SNR',
-  xaxis_title='Time (s)',
-  yaxis_title='SNR (dB)'
-)
+fig.add_trace(go.Scatter(y=mean, mode="lines", name="SNR"))
+fig.update_layout(title="SNR", xaxis_title="Time (s)", yaxis_title="SNR (dB)")
 fig.show()
 
-#%%
+# %%
 # plot propagation loss
 fig = go.Figure()
-fig.add_trace(go.Scatter(
-  y=mean_propagation_loss,
-  mode='lines',
-  name='PropagationLoss (dB)'
-))
+fig.add_trace(
+    go.Scatter(y=mean_propagation_loss, mode="lines", name="PropagationLoss (dB)")
+)
 fig.update_layout(
-  title='Propagation Loss',
-  xaxis_title='Time (s)',
-  yaxis_title='PropagationLoss (dB)'
+    title="Propagation Loss", xaxis_title="Time (s)", yaxis_title="PropagationLoss (dB)"
 )
-fig.show()
+fig.show()

analysis/get_rssi.py

@@ -6,31 +6,37 @@
 
 # TODO -- this script does not work, yet
 
-P = ArgumentParser(description='Retrieve RSSI measurements from summary XML file and export them in separate CSVs, '
-                               'one per drone.')
-P.add_argument('summary_filepath', type=str, help='Input summary XML file of the scenario.')
+P = ArgumentParser(
+    description="Retrieve RSSI measurements from summary XML file and export them in separate CSVs, "
+    "one per drone."
+)
+P.add_argument(
+    "summary_filepath", type=str, help="Input summary XML file of the scenario."
+)
 args = P.parse_args()
 
 base_path = PurePath(args.summary_filepath).parent
-output_prefix = 'rssi-drone'
+output_prefix = "rssi-drone"
 
 xmlroot = ET.parse(args.summary_filepath).getroot()
-zsp_addr = xmlroot.find('ZSPs').find('ZSP')
+zsp_addr = xmlroot.find("ZSPs").find("ZSP")
 print(zsp_addr)
-zsp_addr = zsp_addr.find('NetDevices').find('NetDevice').find('phy').find('address').text
+zsp_addr = (
+    zsp_addr.find("NetDevices").find("NetDevice").find("phy").find("address").text
+)
 
-drones = xmlroot.find('Drones').findall('Drone')
+drones = xmlroot.find("Drones").findall("Drone")
 i_drone = 0
 for d in drones:
-  for nd in d.find('NetDevices').findall('NetDevice'):
-    dtype = nd.find('type').text
-    if (dtype == 'ns3::WifiNetDevice'):
-      with open(f'{base_path}/{output_prefix}_{i_drone}.csv', 'w') as outf:
-        csvw = csv.writer(outf, lineterminator='\n')
-        csvw.writerow(['time', 'rssi'])
+    for nd in d.find("NetDevices").findall("NetDevice"):
+        dtype = nd.find("type").text
+        if dtype == "ns3::WifiNetDevice":
+            with open(f"{base_path}/{output_prefix}_{i_drone}.csv", "w") as outf:
+                csvw = csv.writer(outf, lineterminator="\n")
+                csvw.writerow(["time", "rssi"])
 
-        for s in nd.find('signal').findall('rssi'):
-          attrs = s.attrib
-          csvw.writerow([attrs['time'], attrs['value']])
+                for s in nd.find("signal").findall("rssi"):
+                    attrs = s.attrib
+                    csvw.writerow([attrs["time"], attrs["value"]])
 
-  i_drone += 1
+    i_drone += 1