#!/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  = [14+22.17792627372312,
            14+26.5983685883656,
            14+22.797848894157955,
            14+23.014822745152888,
            14+21.45208671888342]

pinst15  = [14+25.20854283824081,
            14+25.196382621588043,
            14+27.146880284259304,
            14+23.50194049443653,
            14+26.735523841588087]

pinst185 = [14+26.391715760893703,
            14+28.1335104958795,
            14+29.153177713518723,
            14+29.847186387317596,
            14+30.558908425543827]

pinst235 = [14+31.755831065931034,
            14+31.883960187854985,
            14+32.063876077498804,
            14+31.643081143951544,
            14+32.45807181713604]

pinst5   = [14+19.567076092915567,
            14+20.05316841156921,
            14+18.774664683716043,
            14+20.252929968934183,
            14+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 = 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')

#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)