add files via upload

more-data.py

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+#!/usr/bin/env python3
+import os
+import math
+import sys
+
+import numpy as np
+from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
+
+# sample data: multiple measurements per dist
+# in meters
+distances = np.array([5.0, 10.0, 15.0, 18.5, 23.5])
+
+# measured path loss in db for each dist (rows:distances, columns:measurements)
+# example: 3 measurements per dist
+
+#measured_PL = np.array([], [], [], [], [])
+
+# i got these using scott's get_data script. thanks scott
+pinst10  = [-22.17792627372312,
+            -26.5983685883656,
+            -22.797848894157955,
+            -23.014822745152888,
+            -21.45208671888342]
+
+pinst15  = [-25.20854283824081,
+            -25.196382621588043,
+            -27.146880284259304,
+            -23.50194049443653,
+            -26.735523841588087]
+
+pinst185 = [-26.391715760893703,
+            -28.1335104958795,
+            -29.153177713518723,
+            -29.847186387317596,
+            -30.558908425543827]
+
+pinst235 = [-31.755831065931034,
+            -31.883960187854985,
+            -32.063876077498804,
+            -31.643081143951544,
+            -32.45807181713604]
+
+pinst5   = [-19.567076092915567,
+            -20.05316841156921,
+            -18.774664683716043,
+            -20.252929968934183,
+            -19.01518926375179]
+
+iterate  = 0
+cursor   = 0
+dist_cur = distances[cursor]
+
+# oh god
+measured_PL = [pinst5, pinst10, pinst15, pinst185, pinst235]
+
+# ref dist
+d0 = 5
+PL_d0 = np.mean(measured_PL[0])
+PL_d1 = np.mean(measured_PL[1])
+PL_d2 = np.mean(measured_PL[2])
+PL_d3 = np.mean(measured_PL[3])
+PL_d4 = np.mean(measured_PL[4])
+
+print(f"Reference distance: {d0}m")
+print(f"Mean Path-Loss at ref dist: about {PL_d0:.4f}")
+
+j = 1
+for i in [PL_d0, PL_d1, PL_d2, PL_d3, PL_d4]:
+    print(f"Computed Path loss #{j}: {i}")
+    j += 1
+
+# prepare data for linear regression
+# compute mean PL for each dist
+mean_PL = np.mean(measured_PL, axis=1)
+X = 10 * np.log10(distances / d0).reshape(-1, 1)
+Y = mean_PL - PL_d0 # relative path loss
+
+# fit linear regression
+model = LinearRegression()
+model.fit(X, Y)
+
+# extract path-loss exponent (slope)
+n = model.coef_[0]
+print(f'Estimated path-loss exponent n: {n:.2f}')
+
+# predicted path loss from model
+predicted_PL = PL_d0 + model.predict(X)
+
+# calc shadowing std. deviation using all measurements
+residuals = measured_PL = predicted_PL[:, np.newaxis] # residuals for all
+                                                      # measurements
+sigma = np.std(residuals)
+
+print(f'Shadowing standard deviation: {sigma:.2f} dB')
+
+# plot
+plt.figure(figsize=(8,6))
+plt.scatter(np.repeat(X, measured_PL.shape[1]), measured_PL.flatten(),
+            label='Measured PL')
+plt.plot(X, predicted_PL, color='red', label='Fitted line')
+plt.xlabel('10*log10(d/d0)')
+plt.ylabel('Path Loss (dB)')
+plt.title('Path Loss Experiment & Shadowing Estimation')
+plt.legend()
+plt.grid(True)
+plt.show()