fix

more-data.py

@@ -42,7 +42,7 @@ pinst235 = [14+31.755831065931034,
             14+32.45807181713604]
 
 pinst5   = [14+19.567076092915567,
-            14+-20.05316841156921,
+            14+20.05316841156921,
             14+18.774664683716043,
             14+20.252929968934183,
             14+19.01518926375179]
@@ -88,7 +88,7 @@ print(f'Estimated path-loss exponent n: {n:.2f}')
 predicted_PL = PL_d0 + model.predict(X)
 
 # calc shadowing std. deviation using all measurements
-residuals = measured_PL = predicted_PL[:, np.newaxis] # residuals for all
+residuals = predicted_PL[:, np.newaxis] # residuals for all
                                                       # measurements
 sigma = np.std(residuals)
 
@@ -96,12 +96,29 @@ 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(),
+#plt.scatter(np.repeat(X, measured_PL.shape[1]), measured_PL.flatten(),
-            label='Measured PL')
+#            label='Measured PL')
-plt.plot(X, predicted_PL, color='red', label='Fitted line')
+
+#for xyz in measured_PL:
+'''
+for xyz in range(5):
+    plt.scatter(np.repeat(X, measured_PL[xyz]),
+                measured_PL[xyz].flatten(),
+                label=f'Path Loss {xyz}')
+'''
+
+#plt.plot(X, predicted_PL, color='red', label='Fitted line')
+
+for xyz in range(5):
+    plt.plot(X, measured_PL[xyz], 'ro')
+
+plt.plot(X, predicted_PL, 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()
+
+print(measured_PL)