full-vs-raman-peaks-not-rays-noise.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d17f7c4",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2023-10-16T16:42:30.336528Z",
     "iopub.status.busy": "2023-10-16T16:42:30.335871Z",
     "iopub.status.idle": "2023-10-16T16:42:32.025777Z",
     "shell.execute_reply": "2023-10-16T16:42:32.024704Z"
    },
    "papermill": {
     "duration": 1.6973,
     "end_time": "2023-10-16T16:42:32.028457",
     "exception": false,
     "start_time": "2023-10-16T16:42:30.331157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20/2734862977.py:105: RuntimeWarning: divide by zero encountered in divide\n",
      "  cosmic_ray = cosmic_ray_amplitude / ((self.wavelengths - self.wavelengths[position]) ** 2)\n",
      "/tmp/ipykernel_20/2734862977.py:108: RuntimeWarning: invalid value encountered in divide\n",
      "  cosmic_ray = cosmic_ray / cosmic_ray.max()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 1. INPUT PARAMETERS\n",
    "class SyntheticRamanSpectrum:\n",
    "    def __init__(self, pixels, poly_order_range, num_peaks_range, snr_range, cosmic_ray_prob, num_spectra):\n",
    "        self.pixels = pixels\n",
    "        self.poly_order_range = poly_order_range\n",
    "        self.num_peaks_range = num_peaks_range\n",
    "        self.snr_range = snr_range\n",
    "        self.cosmic_ray_prob = cosmic_ray_prob\n",
    "        self.num_spectra = num_spectra\n",
    "        self.wavelengths = np.linspace(-1, 1, self.pixels)\n",
    "        self.full_spectra_list = []\n",
    "        self.raman_spectra_list = []\n",
    "\n",
    "    # 2. CHEBYSHEV BASELINE    \n",
    "    def chebyshev_polynomial(self, x, *coefficients):\n",
    "        order = len(coefficients)\n",
    "        chebyshev_vals = np.zeros_like(x)\n",
    "    \n",
    "        for i in range(order):\n",
    "            chebyshev_vals += coefficients[i] * np.cos(i * np.arccos(np.array(x)))\n",
    "     \n",
    "        return chebyshev_vals\n",
    "    \n",
    "    def generate_spectrum(self):\n",
    "        # Randomly select polynomial order for Chebyshev baseline\n",
    "        poly_order = np.random.randint(self.poly_order_range[-1], self.poly_order_range[1] + 1)\n",
    "\n",
    "        # Initialize an empty array to store the Chebyshev coefficients\n",
    "        cheby_baseline_coefficients = np.random.uniform(-1, 1, poly_order + 1)\n",
    "\n",
    "        # Generate Chebyshev coefficients iteratively\n",
    "        for i in range(poly_order + 1):\n",
    "            cheby_baseline_coefficients[i] = np.random.uniform(-1, 1)\n",
    "\n",
    "        # Generate Chebyshev baseline\n",
    "        self.cheby_baseline = self.chebyshev_polynomial(self.wavelengths, *cheby_baseline_coefficients)\n",
    "    \n",
    "    # 3. LORENTZIAN PEAKS\n",
    "    def add_lorentzian_peaks(self):\n",
    "        # Define parameters for Lorentzian peaks\n",
    "        num_peaks = np.random.randint(self.num_peaks_range[0], self.num_peaks_range[1] + 1)\n",
    "        peak_amplitudes = np.random.uniform(0, 1, num_peaks)\n",
    "        peak_positions = np.random.uniform(-1, 1, num_peaks)\n",
    "        peak_width = 1 / (self.pixels * 0.1)\n",
    "\n",
    "        # Initialize peaks array\n",
    "        peaks = np.zeros_like(self.wavelengths)\n",
    "\n",
    "        # Add Lorentzian peaks to spectrum\n",
    "        for i in range(num_peaks):\n",
    "            peaks += peak_amplitudes[i] * (peak_width ** 2) / ((self.wavelengths - peak_positions[i]) ** 2 + peak_width ** 2)\n",
    "\n",
    "        # Update peaks attribute\n",
    "        self.peaks = peaks\n",
    "\n",
    "        # Add peaks to Chebyshev baseline\n",
    "        self.peaky_cheby = self.peaks + self.cheby_baseline\n",
    "    \n",
    "    # 4. NOISE GENERATION\n",
    "    def add_gaussian_noise(self, noise_amplitude_range=(0, 1)):\n",
    "        # Calculate signal power\n",
    "        signal_power = np.mean(np.abs(self.cheby_baseline + self.peaks) ** 2)\n",
    "\n",
    "        # Calculate noise power based on SNR\n",
    "        noise_power = signal_power / self.snr_range\n",
    "\n",
    "        # Calculate standard deviation of noise\n",
    "        noise_std = np.sqrt(noise_power)\n",
    "\n",
    "        # Generate random noise amplitudes within specified range\n",
    "        min_amp, max_amp = noise_amplitude_range\n",
    "        noise_amplitudes = np.random.uniform(min_amp, max_amp)\n",
    "\n",
    "        # Generate Gaussian noise samples\n",
    "        noise = np.random.normal(0, noise_std, len(self.wavelengths))\n",
    "\n",
    "        # Scale noise by noise amplitudes\n",
    "        total_noise = noise * noise_amplitudes\n",
    "        \n",
    "        # Add noise to peaky cheby spectra\n",
    "        self.noisy_peaky_cheby = self.peaky_cheby + total_noise\n",
    "\n",
    "    # 5. COSMIC RAYS\n",
    "    # Generate Cosmic Rays \n",
    "    # Lorentzian peak with a width of 0, y = 1 / (x - position)^2  \n",
    "    # Cosmic ray amplitudes: randomized between 0.8 & L2-norm of complete signal\n",
    "\n",
    "    def add_cosmic_rays(self, num_cosmic_rays):\n",
    "        cosmic_rays_spectrum = self.noisy_peaky_cheby.copy()\n",
    "    \n",
    "        # Calculate L2-norm of complete signal\n",
    "        l2_norm = np.linalg.norm(cosmic_rays_spectrum)\n",
    "    \n",
    "        for _ in range(num_cosmic_rays):\n",
    "            # Generate random amplitude between 0.8 and the L2-norm of the complete signal\n",
    "            cosmic_ray_amplitude = np.random.uniform(0.8, l2_norm)\n",
    "        \n",
    "            # Randomly select position for cosmic ray\n",
    "            position = np.random.randint(0, len(cosmic_rays_spectrum))\n",
    "        \n",
    "            # Create cosmic ray using y = 1 / (x - position)^2  \n",
    "            cosmic_ray = cosmic_ray_amplitude / ((self.wavelengths - self.wavelengths[position]) ** 2)\n",
    "\n",
    "            # Normalize cosmic ray\n",
    "            cosmic_ray = cosmic_ray / cosmic_ray.max()\n",
    "\n",
    "            # Add cosmic ray to spectrum\n",
    "            cosmic_rays_spectrum += cosmic_ray\n",
    "\n",
    "        # Update full synthetic spectrum with cosmic rays\n",
    "        full_spectrum = cosmic_rays_spectrum + self.noisy_peaky_cheby\n",
    "        self.full_spectra_list.append(full_spectrum)\n",
    "\n",
    "        # Update the Raman spectrum (only peaks)\n",
    "        raman_spectrum = self.peaks\n",
    "        self.raman_spectra_list.append(raman_spectrum)\n",
    "\n",
    "# Parameters\n",
    "pixels = 1024\n",
    "poly_order_range = (2, 5)\n",
    "num_peaks_range = (3, 6)\n",
    "snr_range = 20\n",
    "cosmic_ray_prob = 0.1\n",
    "num_spectra = 5\n",
    "\n",
    "synthetic_data_generator = SyntheticRamanSpectrum(pixels, poly_order_range, num_peaks_range, snr_range, cosmic_ray_prob, num_spectra)\n",
    "\n",
    "# Generate synthetic spectra\n",
    "for _ in range(num_spectra):\n",
    "    # Randomly sample parameter values within specified ranges\n",
    "    synthetic_data_generator.generate_spectrum()\n",
    "    synthetic_data_generator.add_lorentzian_peaks()\n",
    "    synthetic_data_generator.add_gaussian_noise()\n",
    "    \n",
    "    # Generate a random number of cosmic rays between 0 and 5\n",
    "    num_cosmic_rays = np.random.randint(0, 6)\n",
    "    synthetic_data_generator.add_cosmic_rays(num_cosmic_rays)\n",
    "\n",
    "full_spectra = synthetic_data_generator.full_spectra_list    # full_spectra = full synthetic measured spectra\n",
    "raman_spectra = synthetic_data_generator.raman_spectra_list  # raman_spectra = only Raman signals\n",
    "\n",
    "# Visualize results\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "num_plots = min(5, num_spectra)\n",
    "\n",
    "# Plot full synthetic spectra\n",
    "for i in range(num_plots):\n",
    "    plt.subplot(2, num_plots, i + 1)\n",
    "    plt.plot(synthetic_data_generator.wavelengths, full_spectra[i])\n",
    "    plt.title('Full Spectrum')\n",
    "    plt.xlabel('Wavelength')\n",
    "    plt.ylabel('Intensity')\n",
    "\n",
    "# Plot Raman spectra\n",
    "for i in range(num_plots):\n",
    "    plt.subplot(2, num_plots, i + num_plots + 1)\n",
    "    plt.plot(synthetic_data_generator.wavelengths, raman_spectra[i])\n",
    "    plt.title('Raman Spectrum')\n",
    "    plt.xlabel('Wavelength')\n",
    "    plt.ylabel('Intensity')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "198748f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-16T16:42:32.041076Z",
     "iopub.status.busy": "2023-10-16T16:42:32.040413Z",
     "iopub.status.idle": "2023-10-16T16:42:32.048531Z",
     "shell.execute_reply": "2023-10-16T16:42:32.047779Z"
    },
    "papermill": {
     "duration": 0.016294,
     "end_time": "2023-10-16T16:42:32.050988",
     "exception": false,
     "start_time": "2023-10-16T16:42:32.034694",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNR (linear scale): 100.0\n"
     ]
    }
   ],
   "source": [
    "# A) CALCULATE SD from SNR for ZERO-MEAN GAUSSIAN DISTRIBUTION\n",
    "# A.1) Convert SNR to Linear Scale (SNR linear = 10^(SNR_target/10))\n",
    "\n",
    "# Given SNR in dB\n",
    "SNR_dB = snr_range\n",
    "\n",
    "# Convert SNR to linear scale\n",
    "SNR_linear = 10 ** (SNR_dB / 10)\n",
    "\n",
    "print(f\"SNR (linear scale): {SNR_linear}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a619c8a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-16T16:42:32.062238Z",
     "iopub.status.busy": "2023-10-16T16:42:32.061525Z",
     "iopub.status.idle": "2023-10-16T16:42:32.067432Z",
     "shell.execute_reply": "2023-10-16T16:42:32.066416Z"
    },
    "papermill": {
     "duration": 0.013666,
     "end_time": "2023-10-16T16:42:32.069442",
     "exception": false,
     "start_time": "2023-10-16T16:42:32.055776",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance: 0.01\n"
     ]
    }
   ],
   "source": [
    "# A.2) Calculate Variance (V = 1/SNR linear)\n",
    "\n",
    "# Variance using formula\n",
    "variance = 1 / SNR_linear\n",
    "\n",
    "print(f\"Variance: {variance}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "703ab180",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-16T16:42:32.079928Z",
     "iopub.status.busy": "2023-10-16T16:42:32.079599Z",
     "iopub.status.idle": "2023-10-16T16:42:32.084835Z",
     "shell.execute_reply": "2023-10-16T16:42:32.083663Z"
    },
    "papermill": {
     "duration": 0.013284,
     "end_time": "2023-10-16T16:42:32.087195",
     "exception": false,
     "start_time": "2023-10-16T16:42:32.073911",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard Deviation: 0.1\n"
     ]
    }
   ],
   "source": [
    "# A.3) Calculate Standard Deviation (σ = √Variance)\n",
    "import numpy as np\n",
    "\n",
    "# Calculate the standard deviation\n",
    "std_deviation = np.sqrt(variance)\n",
    "\n",
    "print(f\"Standard Deviation: {std_deviation}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f65b35b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-16T16:42:32.098475Z",
     "iopub.status.busy": "2023-10-16T16:42:32.097804Z",
     "iopub.status.idle": "2023-10-16T16:42:32.104798Z",
     "shell.execute_reply": "2023-10-16T16:42:32.103834Z"
    },
    "papermill": {
     "duration": 0.014693,
     "end_time": "2023-10-16T16:42:32.106616",
     "exception": false,
     "start_time": "2023-10-16T16:42:32.091923",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.        , -0.97979798, -0.95959596, -0.93939394, -0.91919192,\n",
       "       -0.8989899 , -0.87878788, -0.85858586, -0.83838384, -0.81818182,\n",
       "       -0.7979798 , -0.77777778, -0.75757576, -0.73737374, -0.71717172,\n",
       "       -0.6969697 , -0.67676768, -0.65656566, -0.63636364, -0.61616162,\n",
       "       -0.5959596 , -0.57575758, -0.55555556, -0.53535354, -0.51515152,\n",
       "       -0.49494949, -0.47474747, -0.45454545, -0.43434343, -0.41414141,\n",
       "       -0.39393939, -0.37373737, -0.35353535, -0.33333333, -0.31313131,\n",
       "       -0.29292929, -0.27272727, -0.25252525, -0.23232323, -0.21212121,\n",
       "       -0.19191919, -0.17171717, -0.15151515, -0.13131313, -0.11111111,\n",
       "       -0.09090909, -0.07070707, -0.05050505, -0.03030303, -0.01010101,\n",
       "        0.01010101,  0.03030303,  0.05050505,  0.07070707,  0.09090909,\n",
       "        0.11111111,  0.13131313,  0.15151515,  0.17171717,  0.19191919,\n",
       "        0.21212121,  0.23232323,  0.25252525,  0.27272727,  0.29292929,\n",
       "        0.31313131,  0.33333333,  0.35353535,  0.37373737,  0.39393939,\n",
       "        0.41414141,  0.43434343,  0.45454545,  0.47474747,  0.49494949,\n",
       "        0.51515152,  0.53535354,  0.55555556,  0.57575758,  0.5959596 ,\n",
       "        0.61616162,  0.63636364,  0.65656566,  0.67676768,  0.6969697 ,\n",
       "        0.71717172,  0.73737374,  0.75757576,  0.77777778,  0.7979798 ,\n",
       "        0.81818182,  0.83838384,  0.85858586,  0.87878788,  0.8989899 ,\n",
       "        0.91919192,  0.93939394,  0.95959596,  0.97979798,  1.        ])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B) GENERATE CHEBYSHEV POLYNOMIALS \n",
    "# B.1) Define Parameters\n",
    "\n",
    "# Set maximum polynomial order n\n",
    "n = 5\n",
    "\n",
    "# Create an array of x values (-1 to 1) to evaluate polynomials\n",
    "x_values = np.linspace(-1, 1, 100)\n",
    "\n",
    "x_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "406cfd1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-16T16:42:32.117314Z",
     "iopub.status.busy": "2023-10-16T16:42:32.116960Z",
     "iopub.status.idle": "2023-10-16T16:42:32.125288Z",
     "shell.execute_reply": "2023-10-16T16:42:32.124181Z"
    },
    "papermill": {
     "duration": 0.015988,
     "end_time": "2023-10-16T16:42:32.127280",
     "exception": false,
     "start_time": "2023-10-16T16:42:32.111292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.        , -0.97979798, -0.95959596, -0.93939394, -0.91919192,\n",
       "       -0.8989899 , -0.87878788, -0.85858586, -0.83838384, -0.81818182,\n",
       "       -0.7979798 , -0.77777778, -0.75757576, -0.73737374, -0.71717172,\n",
       "       -0.6969697 , -0.67676768, -0.65656566, -0.63636364, -0.61616162,\n",
       "       -0.5959596 , -0.57575758, -0.55555556, -0.53535354, -0.51515152,\n",
       "       -0.49494949, -0.47474747, -0.45454545, -0.43434343, -0.41414141,\n",
       "       -0.39393939, -0.37373737, -0.35353535, -0.33333333, -0.31313131,\n",
       "       -0.29292929, -0.27272727, -0.25252525, -0.23232323, -0.21212121,\n",
       "       -0.19191919, -0.17171717, -0.15151515, -0.13131313, -0.11111111,\n",
       "       -0.09090909, -0.07070707, -0.05050505, -0.03030303, -0.01010101,\n",
       "        0.01010101,  0.03030303,  0.05050505,  0.07070707,  0.09090909,\n",
       "        0.11111111,  0.13131313,  0.15151515,  0.17171717,  0.19191919,\n",
       "        0.21212121,  0.23232323,  0.25252525,  0.27272727,  0.29292929,\n",
       "        0.31313131,  0.33333333,  0.35353535,  0.37373737,  0.39393939,\n",
       "        0.41414141,  0.43434343,  0.45454545,  0.47474747,  0.49494949,\n",
       "        0.51515152,  0.53535354,  0.55555556,  0.57575758,  0.5959596 ,\n",
       "        0.61616162,  0.63636364,  0.65656566,  0.67676768,  0.6969697 ,\n",
       "        0.71717172,  0.73737374,  0.75757576,  0.77777778,  0.7979798 ,\n",
       "        0.81818182,  0.83838384,  0.85858586,  0.87878788,  0.8989899 ,\n",
       "        0.91919192,  0.93939394,  0.95959596,  0.97979798,  1.        ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B.2) Initialize Matrix\n",
    "\n",
    "# Create an empty matrix to store Chebyshev polynomials\n",
    "chebyshev_matrix = np.zeros((n + 1, len(x_values)))\n",
    "\n",
    "# Initialize first two rows of matrix\n",
    "chebyshev_matrix[0] = np.ones(len(x_values))  # T0(x) = 1\n",
    "chebyshev_matrix[1] = x_values                # T1(x) = x\n",
    "\n",
    "chebyshev_matrix\n",
    "chebyshev_matrix[0]\n",
    "chebyshev_matrix[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea39ce47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-16T16:42:32.138318Z",
     "iopub.status.busy": "2023-10-16T16:42:32.137992Z",
     "iopub.status.idle": "2023-10-16T16:42:32.150555Z",
     "shell.execute_reply": "2023-10-16T16:42:32.149405Z"
    },
    "papermill": {
     "duration": 0.02075,
     "end_time": "2023-10-16T16:42:32.152789",
     "exception": false,
     "start_time": "2023-10-16T16:42:32.132039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ,\n",
       "         1.        ,  1.        ,  1.        ,  1.        ,  1.        ],\n",
       "       [-1.        , -0.97979798, -0.95959596, -0.93939394, -0.91919192,\n",
       "        -0.8989899 , -0.87878788, -0.85858586, -0.83838384, -0.81818182,\n",
       "        -0.7979798 , -0.77777778, -0.75757576, -0.73737374, -0.71717172,\n",
       "        -0.6969697 , -0.67676768, -0.65656566, -0.63636364, -0.61616162,\n",
       "        -0.5959596 , -0.57575758, -0.55555556, -0.53535354, -0.51515152,\n",
       "        -0.49494949, -0.47474747, -0.45454545, -0.43434343, -0.41414141,\n",
       "        -0.39393939, -0.37373737, -0.35353535, -0.33333333, -0.31313131,\n",
       "        -0.29292929, -0.27272727, -0.25252525, -0.23232323, -0.21212121,\n",
       "        -0.19191919, -0.17171717, -0.15151515, -0.13131313, -0.11111111,\n",
       "        -0.09090909, -0.07070707, -0.05050505, -0.03030303, -0.01010101,\n",
       "         0.01010101,  0.03030303,  0.05050505,  0.07070707,  0.09090909,\n",
       "         0.11111111,  0.13131313,  0.15151515,  0.17171717,  0.19191919,\n",
       "         0.21212121,  0.23232323,  0.25252525,  0.27272727,  0.29292929,\n",
       "         0.31313131,  0.33333333,  0.35353535,  0.37373737,  0.39393939,\n",
       "         0.41414141,  0.43434343,  0.45454545,  0.47474747,  0.49494949,\n",
       "         0.51515152,  0.53535354,  0.55555556,  0.57575758,  0.5959596 ,\n",
       "         0.61616162,  0.63636364,  0.65656566,  0.67676768,  0.6969697 ,\n",
       "         0.71717172,  0.73737374,  0.75757576,  0.77777778,  0.7979798 ,\n",
       "         0.81818182,  0.83838384,  0.85858586,  0.87878788,  0.8989899 ,\n",
       "         0.91919192,  0.93939394,  0.95959596,  0.97979798,  1.        ],\n",
       "       [ 1.        ,  0.92000816,  0.84164881,  0.76492195,  0.68982757,\n",
       "         0.61636568,  0.54453627,  0.47433935,  0.40577492,  0.33884298,\n",
       "         0.27354352,  0.20987654,  0.14784206,  0.08744006,  0.02867054,\n",
       "        -0.02846648, -0.08397102, -0.13784308, -0.19008264, -0.24068973,\n",
       "        -0.28966432, -0.33700643, -0.38271605, -0.42679318, -0.46923783,\n",
       "        -0.51004999, -0.54922967, -0.58677686, -0.62269156, -0.65697378,\n",
       "        -0.68962351, -0.72064075, -0.75002551, -0.77777778, -0.80389756,\n",
       "        -0.82838486, -0.85123967, -0.87246199, -0.89205183, -0.91000918,\n",
       "        -0.92633405, -0.94102643, -0.95408632, -0.96551372, -0.97530864,\n",
       "        -0.98347107, -0.99000102, -0.99489848, -0.99816345, -0.99979594,\n",
       "        -0.99979594, -0.99816345, -0.99489848, -0.99000102, -0.98347107,\n",
       "        -0.97530864, -0.96551372, -0.95408632, -0.94102643, -0.92633405,\n",
       "        -0.91000918, -0.89205183, -0.87246199, -0.85123967, -0.82838486,\n",
       "        -0.80389756, -0.77777778, -0.75002551, -0.72064075, -0.68962351,\n",
       "        -0.65697378, -0.62269156, -0.58677686, -0.54922967, -0.51004999,\n",
       "        -0.46923783, -0.42679318, -0.38271605, -0.33700643, -0.28966432,\n",
       "        -0.24068973, -0.19008264, -0.13784308, -0.08397102, -0.02846648,\n",
       "         0.02867054,  0.08744006,  0.14784206,  0.20987654,  0.27354352,\n",
       "         0.33884298,  0.40577492,  0.47433935,  0.54453627,  0.61636568,\n",
       "         0.68982757,  0.76492195,  0.84164881,  0.92000816,  1.        ],\n",
       "       [-1.        , -0.8230463 , -0.65568964, -0.49773214, -0.34897593,\n",
       "        -0.20922314, -0.07827587,  0.04406374,  0.15799357,  0.2637115 ,\n",
       "         0.3614154 ,  0.45130316,  0.53357264,  0.60842173,  0.67604831,\n",
       "         0.73665025,  0.79042543,  0.83757172,  0.878287  ,  0.91276916,\n",
       "         0.94121606,  0.96382558,  0.98079561,  0.99232402,  0.99860868,\n",
       "         0.99984747,  0.99623827,  0.98797896,  0.97526742,  0.95830151,\n",
       "         0.93727913,  0.91239814,  0.88385642,  0.85185185,  0.81658231,\n",
       "         0.77824567,  0.73703982,  0.69316262,  0.64681196,  0.59818571,\n",
       "         0.54748176,  0.49489796,  0.44063222,  0.38488239,  0.32784636,\n",
       "         0.26972201,  0.21070721,  0.15099985,  0.09079779,  0.03029891,\n",
       "        -0.03029891, -0.09079779, -0.15099985, -0.21070721, -0.26972201,\n",
       "        -0.32784636, -0.38488239, -0.44063222, -0.49489796, -0.54748176,\n",
       "        -0.59818571, -0.64681196, -0.69316262, -0.73703982, -0.77824567,\n",
       "        -0.81658231, -0.85185185, -0.88385642, -0.91239814, -0.93727913,\n",
       "        -0.95830151, -0.97526742, -0.98797896, -0.99623827, -0.99984747,\n",
       "        -0.99860868, -0.99232402, -0.98079561, -0.96382558, -0.94121606,\n",
       "        -0.91276916, -0.878287  , -0.83757172, -0.79042543, -0.73665025,\n",
       "        -0.67604831, -0.60842173, -0.53357264, -0.45130316, -0.3614154 ,\n",
       "        -0.2637115 , -0.15799357, -0.04406374,  0.07827587,  0.20922314,\n",
       "         0.34897593,  0.49773214,  0.65568964,  0.8230463 ,  1.        ],\n",
       "       [ 1.        ,  0.69283004,  0.41674544,  0.17021117, -0.04827585,\n",
       "        -0.2401867 , -0.4069605 , -0.55000436, -0.67069343, -0.77037088,\n",
       "        -0.85034789, -0.91190367, -0.95628545, -0.98470847, -0.998356  ,\n",
       "        -0.99837932, -0.98589773, -0.96199857, -0.92773718, -0.88413691,\n",
       "        -0.83218916, -0.77285334, -0.70705685, -0.63569516, -0.55963171,\n",
       "        -0.47969801, -0.39669354, -0.31138583, -0.22451044, -0.13677091,\n",
       "        -0.04883883,  0.03864618,  0.12507652,  0.20987654,  0.29250258,\n",
       "         0.37244295,  0.44921795,  0.52237986,  0.59151294,  0.65623343,\n",
       "         0.71618954,  0.77106147,  0.8205614 ,  0.8644335 ,  0.90245389,\n",
       "         0.93443071,  0.96020404,  0.97964597,  0.99266056,  0.99918384,\n",
       "         0.99918384,  0.99266056,  0.97964597,  0.96020404,  0.93443071,\n",
       "         0.90245389,  0.8644335 ,  0.8205614 ,  0.77106147,  0.71618954,\n",
       "         0.65623343,  0.59151294,  0.52237986,  0.44921795,  0.37244295,\n",
       "         0.29250258,  0.20987654,  0.12507652,  0.03864618, -0.04883883,\n",
       "        -0.13677091, -0.22451044, -0.31138583, -0.39669354, -0.47969801,\n",
       "        -0.55963171, -0.63569516, -0.70705685, -0.77285334, -0.83218916,\n",
       "        -0.88413691, -0.92773718, -0.96199857, -0.98589773, -0.99837932,\n",
       "        -0.998356  , -0.98470847, -0.95628545, -0.91190367, -0.85034789,\n",
       "        -0.77037088, -0.67069343, -0.55000436, -0.4069605 , -0.2401867 ,\n",
       "        -0.04827585,  0.17021117,  0.41674544,  0.69283004,  1.        ],\n",
       "       [-1.        , -0.53462064, -0.14412485,  0.17794146,  0.43772548,\n",
       "         0.64107398,  0.79353978,  0.90038819,  0.96660349,  0.99689539,\n",
       "         0.99570548,  0.96721367,  0.91534471,  0.8437746 ,  0.75593706,\n",
       "         0.65503001,  0.54402201,  0.42565873,  0.3024694 ,  0.1767733 ,\n",
       "         0.05068618, -0.07387326, -0.19517689, -0.31168072, -0.42201843,\n",
       "        -0.5249949 , -0.61957976, -0.70490093, -0.78023815, -0.84501652,\n",
       "        -0.89880005, -0.94128518, -0.97229437, -0.99176955, -0.99976574,\n",
       "        -0.99644457, -0.98206779, -0.95699084, -0.92165636, -0.87658777,\n",
       "        -0.82238279, -0.75970695, -0.68928719, -0.61190533, -0.52839167,\n",
       "        -0.43961851, -0.34649364, -0.24995398, -0.15095903, -0.05048444,\n",
       "         0.05048444,  0.15095903,  0.24995398,  0.34649364,  0.43961851,\n",
       "         0.52839167,  0.61190533,  0.68928719,  0.75970695,  0.82238279,\n",
       "         0.87658777,  0.92165636,  0.95699084,  0.98206779,  0.99644457,\n",
       "         0.99976574,  0.99176955,  0.97229437,  0.94128518,  0.89880005,\n",
       "         0.84501652,  0.78023815,  0.70490093,  0.61957976,  0.5249949 ,\n",
       "         0.42201843,  0.31168072,  0.19517689,  0.07387326, -0.05068618,\n",
       "        -0.1767733 , -0.3024694 , -0.42565873, -0.54402201, -0.65503001,\n",
       "        -0.75593706, -0.8437746 , -0.91534471, -0.96721367, -0.99570548,\n",
       "        -0.99689539, -0.96660349, -0.90038819, -0.79353978, -0.64107398,\n",
       "        -0.43772548, -0.17794146,  0.14412485,  0.53462064,  1.        ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B.3) Generate Polynomials\n",
    "\n",
    "# Loop from i = 2 to n\n",
    "for i in range(2, n + 1):\n",
    "    # Compute i-th Chebyshev polynomial using recursive formula\n",
    "    ti_minus_1 = chebyshev_matrix[i - 1]  # Ti-1(x)\n",
    "    ti_minus_2 = chebyshev_matrix[i - 2]  # Ti-2(x)\n",
    "    \n",
    "    ti = 2 * x_values * ti_minus_1 - ti_minus_2     # Ti(x) = 2 * x * Ti-1(x) - Ti-2(x)\n",
    "    \n",
    "    # Store computed polynomial values in i-th row of matrix\n",
    "    chebyshev_matrix[i] = ti\n",
    "    \n",
    "chebyshev_matrix"
   ]
  }
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