alg_GCSE_WND.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import Python Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:37.059802700Z",
     "start_time": "2023-11-07T03:25:36.666584700Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "from tqdm import tqdm\n",
    "from tool_box.utils import NMSE\n",
    "# fix random seed\n",
    "\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:37.067552Z",
     "start_time": "2023-11-07T03:25:37.062040400Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.seed(3)\n",
    "N_RF = 1000\n",
    "SNR = 50 # dB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hide Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read Generated Channel Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:38.819018600Z",
     "start_time": "2023-11-07T03:25:37.066555800Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/4178796438.py:7: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  N_x = int(generate_channel_data['meta_data']['N_x'])\n",
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/4178796438.py:8: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  N_y = int(generate_channel_data['meta_data']['N_y'])\n",
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/4178796438.py:11: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  N = int(generate_channel_data['meta_data']['N'])\n"
     ]
    }
   ],
   "source": [
    "# read data from \\generate_channel_data\\generate_channel_data.mat\n",
    "import scipy.io as sio\n",
    "generate_channel_data = sio.loadmat('./data/generate_channel_data.mat')\n",
    "cluster_para = generate_channel_data['meta_data']['cluster_para']\n",
    "# xi = generate_channel_data['meta_data']['xi']\n",
    "f_c = generate_channel_data['meta_data']['f_c']\n",
    "N_x = int(generate_channel_data['meta_data']['N_x'])\n",
    "N_y = int(generate_channel_data['meta_data']['N_y'])\n",
    "lambda_c = generate_channel_data['meta_data']['lambda_c']\n",
    "delta = generate_channel_data['meta_data']['delta']\n",
    "N = int(generate_channel_data['meta_data']['N'])\n",
    "L_x = generate_channel_data['meta_data']['L_x']\n",
    "L_y = generate_channel_data['meta_data']['L_y']\n",
    "\n",
    "H_channel = np.mat(generate_channel_data['channel']['H_channel'][0][0])\n",
    "vec_H_a = np.mat(generate_channel_data['channel']['vec_H_a'][0][0])\n",
    "variance = np.mat(generate_channel_data['channel']['variance'][0][0])\n",
    "Psi = np.mat(generate_channel_data['channel']['Psi'][0][0])\n",
    "\n",
    "K = 0\n",
    "sparseness = 0.1\n",
    "\n",
    "\n",
    "sparse_power_threshold = 0.9\n",
    "eta = 0.15\n",
    "Geo_Approx_para = {\n",
    "    'epsilon_1' : 1e4,\n",
    "    'epsilon_2' : 1e-3,\n",
    "    'epsilon_3' : 1e3,\n",
    "    'epsilon_4' : 1e-2,\n",
    "    'K' : 0,   # the number of nonZero elements in the sparse vector\n",
    "    'tau' : 0.1,    # the 2K largest entries of the sparse vector\n",
    "    'alpha' : 5     # the biggest entry of the sparse vector\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:38.872778100Z",
     "start_time": "2023-11-07T03:25:38.817761500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l_x_abs_max =  32\n",
      "l_y_abs_max =  32\n",
      "M =  3205\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/2578872600.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  l_x_abs_max = int(L_x / lambda_c)\n",
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/2578872600.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  l_y_abs_max = int(L_y / lambda_c)\n"
     ]
    }
   ],
   "source": [
    "xi = []\n",
    "l_x_abs_max = int(L_x / lambda_c)\n",
    "l_y_abs_max = int(L_y / lambda_c)\n",
    "print(\"l_x_abs_max = \", l_x_abs_max)\n",
    "print(\"l_y_abs_max = \", l_y_abs_max)\n",
    "\n",
    "# for start with -l_x_abs_max to l_x_abs_max\n",
    "for l_x in range(-l_x_abs_max, l_x_abs_max + 1):\n",
    "    # for start with -l_y_abs_max to l_y_abs_max\n",
    "    for l_y in range(-l_y_abs_max, l_y_abs_max + 1):\n",
    "        k_x = 2 * np.pi * l_x / L_x\n",
    "        k_y = 2 * np.pi * l_y / L_y\n",
    "        if k_x ** 2 + k_y ** 2 < (2 * np.pi / lambda_c) ** 2:\n",
    "            xi.append((l_x, l_y))\n",
    "\n",
    "M = len(xi)\n",
    "print(\"M = \", M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:39.430238800Z",
     "start_time": "2023-11-07T03:25:38.870532100Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/3182367393.py:9: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  mat_H_a[l_x + l_x_abs_max, l_y + l_y_abs_max] = vec_H_a[l]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 4200x1500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the channel\n",
    "def visualize_wavenumber_domain_channel(vec_H_a):\n",
    "    mat_H_a = np.mat(np.zeros((2*l_x_abs_max+1, 2*l_y_abs_max+1), dtype=complex))\n",
    "    # all set to np.nan in mat_H_a\n",
    "    mat_H_a[:,:] = np.nan\n",
    "    for l, l_pair in enumerate(xi):\n",
    "        l_x = l_pair[0]\n",
    "        l_y = l_pair[1]\n",
    "        mat_H_a[l_x + l_x_abs_max, l_y + l_y_abs_max] = vec_H_a[l]\n",
    "\n",
    "    # plt.figure(figsize=(10, 10))\n",
    "    plt.imshow(np.abs(mat_H_a), extent=[-l_x_abs_max, l_x_abs_max, -l_y_abs_max, l_y_abs_max], cmap='hot')\n",
    "    plt.colorbar()\n",
    "    plt.title('Instantaneous channel in WD')\n",
    "    return mat_H_a\n",
    "    \n",
    "def visualize_wavenumber_variance(variance):\n",
    "    # plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(variance, cmap='jet', extent=[-l_x_abs_max, l_x_abs_max, -l_y_abs_max, l_y_abs_max])\n",
    "    plt.colorbar()\n",
    "    plt.title('Variance of the channel')\n",
    "\n",
    "def DFT_power_spectrum(H_channel):\n",
    "    # DFT power spectrum of the channel\n",
    "    DFT_power = np.abs(np.fft.fft2(np.reshape(H_channel, (N_x, N_y))))\n",
    "\n",
    "    # element shift according to the DFT power spectrum\n",
    "    DFT_power = np.fft.fftshift(DFT_power)\n",
    "    \n",
    "    # normalize the DFT power spectrum\n",
    "    DFT_power = DFT_power / np.max(DFT_power)\n",
    "\n",
    "    # visualize the DFT power spectrum\n",
    "    # plt.figure(figsize=(10, 10))\n",
    "    plt.imshow(DFT_power, cmap='jet', extent=[-np.pi/2, np.pi/2, -np.pi/2, np.pi/2])\n",
    "    plt.colorbar()\n",
    "    plt.title('normmalized DFT power spectrum')\n",
    "    return DFT_power\n",
    "\n",
    "plt.figure(figsize=(14, 5))\n",
    "# sub out of 3 in one row\n",
    "plt.subplot(131)\n",
    "visualize_wavenumber_domain_channel(vec_H_a)\n",
    "\n",
    "plt.subplot(132)\n",
    "visualize_wavenumber_variance(variance)\n",
    "\n",
    "plt.subplot(133)\n",
    "mat_H_DFT = DFT_power_spectrum(H_channel)\n",
    "vec_H_DFT = np.reshape(\n",
    "    np.array(\n",
    "        mat_H_DFT\n",
    "    ), (int(mat_H_DFT.shape[0] * mat_H_DFT.shape[1]), )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observation Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "{\\bf y} = {\\bf C} {\\bf \\Psi} {\\bf h}_a + {\\bf n}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.066782700Z",
     "start_time": "2023-11-07T03:25:39.431239100Z"
    }
   },
   "outputs": [],
   "source": [
    "# Measurement matrxi for compressed sensing, {N}_{RF} \\times N\n",
    "measurement_matrix = np.random.randn(N_RF, N) * 1 / np.sqrt(N)\n",
    "\n",
    "y = np.dot(\n",
    "    measurement_matrix,\n",
    "    np.dot(\n",
    "        Psi, vec_H_a\n",
    "    )\n",
    ")\n",
    "Phi = np.dot(measurement_matrix, Psi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Index-Wavenumber Remapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.072923Z",
     "start_time": "2023-11-07T03:25:41.067292500Z"
    }
   },
   "outputs": [],
   "source": [
    "def from_idx_to_l(idx):\n",
    "    return (idx[0] - l_x_abs_max, idx[1] - l_y_abs_max)\n",
    "\n",
    "def from_l_to_idx(l):\n",
    "    return (l[0] + l_x_abs_max, l[1] + l_y_abs_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph Edge Value Calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\eta_{l,l^\\prime} = 0.5 \\arcsin \\left( \\left( 1-m^8 \\right)^{-0.25} \\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "m = \\frac{(+1) \\times \\tilde{K} + (-1) \\times (N-\\tilde{K})}\n",
    "{N}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.094440500Z",
     "start_time": "2023-11-07T03:25:41.070891300Z"
    }
   },
   "outputs": [],
   "source": [
    "# Useless function\n",
    "def get_eta(sparseness):\n",
    "    m = 1 * sparseness + (-1) * (1 - sparseness)\n",
    "    eta = 0.5 * np.arcsin(\n",
    "        np.power(\n",
    "            1 - np.power(m, 8), -0.25\n",
    "        )\n",
    "    )\n",
    "    return eta\n",
    "# eta = get_eta(sparseness = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph 'Energy Function' Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{aligned}\n",
    "V_{l,l^\\prime}\\left(s_l, s_{l^\\prime}\\right) &= - \\eta_{l,l^\\prime} \\cdot \\left(s_l s_{l^\\prime} - 1 \\right) \\\\\n",
    "&= \\eta_{l,l^\\prime} \\cdot \\left(1 - s_l s_{l^\\prime} \\right)\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.094440500Z",
     "start_time": "2023-11-07T03:25:41.076696500Z"
    }
   },
   "outputs": [],
   "source": [
    "def func_V(s_l, s_l_prime):\n",
    "    return eta * (1 - s_l * s_l_prime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "D_l\\left( s_l \\right) = -\\log \\left( \\hat{p} \\left( h^{(j-1)}_{s,l} \\right) \\right)\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.094440500Z",
     "start_time": "2023-11-07T03:25:41.084058900Z"
    }
   },
   "outputs": [],
   "source": [
    "def func_D(h_sl, s_l, Geo_Approx_para):\n",
    "    tau = Geo_Approx_para['tau']\n",
    "    if s_l == 1:\n",
    "        if h_sl <= tau:\n",
    "            epsilon_4 = Geo_Approx_para['epsilon_4']\n",
    "            return np.log10(epsilon_4)\n",
    "        else:\n",
    "            epsilon_3 = Geo_Approx_para['epsilon_3']\n",
    "            return np.log10(epsilon_3)\n",
    "    elif s_l == -1:\n",
    "        if h_sl <= tau:\n",
    "            epsilon_1 = Geo_Approx_para['epsilon_1']\n",
    "            return np.log10(epsilon_1)\n",
    "        else:\n",
    "            epsilon_2 = Geo_Approx_para['epsilon_2']\n",
    "            return np.log10(epsilon_2)\n",
    "    else:\n",
    "        raise ValueError('s_l must be 1 or -1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.203628900Z",
     "start_time": "2023-11-07T03:25:41.091440300Z"
    }
   },
   "outputs": [],
   "source": [
    "myGraph = nx.grid_2d_graph(int(2*l_x_abs_max+1), int(2*l_y_abs_max+1))\n",
    "\n",
    "# traverse graph nodes, minus the absolute max of x and y\n",
    "node_set = myGraph.nodes()\n",
    "\n",
    "for l_x in range(-l_x_abs_max, l_x_abs_max+1):\n",
    "    for l_y in range(-l_y_abs_max, l_y_abs_max+1):\n",
    "        node_idx = from_l_to_idx((l_x, l_y))\n",
    "        \n",
    "        if (l_x, l_y) not in xi:\n",
    "            myGraph.remove_node(node_idx)\n",
    "            continue\n",
    "        else:\n",
    "            myGraph.nodes[node_idx]['l'] = (l_x, l_y)\n",
    "            myGraph.nodes[node_idx]['s'] = -1\n",
    "\n",
    "# add node 'a' and 'b'\n",
    "myGraph.add_node('a')   # node alpha\n",
    "myGraph.add_node('b')   # node beta\n",
    "\n",
    "# add edge between node and 'a', 'b'\n",
    "for l in xi:\n",
    "    node_idx = from_l_to_idx(l)\n",
    "    myGraph.add_edge('a', node_idx, weight = 0)\n",
    "    myGraph.add_edge('b', node_idx, weight = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### neighbor remapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.219811700Z",
     "start_time": "2023-11-07T03:25:41.203628900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-1, 0), (1, 0), (0, -1), (0, 1)]\n"
     ]
    }
   ],
   "source": [
    "def neighbor_l(Graph, l):\n",
    "    (l_x, l_y) = l\n",
    "    node_idx = (l_x + l_x_abs_max, l_y + l_y_abs_max)\n",
    "    res = []\n",
    "    for nei_node_idx in Graph.neighbors(node_idx):\n",
    "        if nei_node_idx == 'a' or nei_node_idx == 'b':\n",
    "            continue\n",
    "        else:\n",
    "            res.append(Graph.nodes[nei_node_idx]['l'])\n",
    "    return res\n",
    "print(neighbor_l(myGraph, (0, 0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the non Zero enries in the Sparse Channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.219811700Z",
     "start_time": "2023-11-07T03:25:41.207537700Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K = 415, \n",
      "N = 16641, \n",
      "2K = 830 \n",
      "tau = 0.012764218095888973, \n",
      "alpha = 2.8491992098074905, \n",
      "sparseness = 0.1294851794071763\n"
     ]
    }
   ],
   "source": [
    "def find_real_sparseness(vec_H_a, threshold=0.9):\n",
    "    vec_H_a_abs = np.abs(vec_H_a)\n",
    "    M = vec_H_a_abs.shape[0]\n",
    "    # sort the vector in descending order\n",
    "    vec_H_a_sorted = np.sort(np.reshape(np.array(vec_H_a_abs), (M,)), )\n",
    "    vec_H_a_sorted = np.flip(vec_H_a_sorted, axis=0)\n",
    "    overall_power = np.sum(vec_H_a_sorted)\n",
    "    \n",
    "    K = 0\n",
    "    for i, val in enumerate(vec_H_a_sorted):\n",
    "        if np.sum(vec_H_a_sorted[0:i]) >= threshold * overall_power:\n",
    "            K = i\n",
    "            break\n",
    "    tau = 0\n",
    "    if 2 * K >= M:\n",
    "        tau = vec_H_a_sorted[-1]\n",
    "    else:\n",
    "        tau = vec_H_a_sorted[2 * K]\n",
    "    alpha = vec_H_a_sorted[0]\n",
    "    return K, tau, alpha\n",
    "\n",
    "K, tau, alpha = find_real_sparseness(vec_H_a, threshold=sparse_power_threshold)\n",
    "sparseness = K / M\n",
    "print(\n",
    "    \"K = {}, \\nN = {}, \\n2K = {} \\ntau = {}, \\nalpha = {}, \\nsparseness = {}\".format(K, N, 2*K, tau, alpha, sparseness)\n",
    ")\n",
    "Geo_Approx_para['K'] = K\n",
    "Geo_Approx_para['tau'] = tau\n",
    "Geo_Approx_para['alpha'] = alpha    # alpha is the largest value in the vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.235982300Z",
     "start_time": "2023-11-07T03:25:41.216812300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K_DFT = 1128, \n",
      "N_DFT = 16641, \n",
      "2K_DFT = 2256 \n",
      "tau_DFT = 0.0022760345976456323, \n",
      "alpha_DFT = 1.0, \n",
      "sparseness_DFT = 0.06778438795745448\n"
     ]
    }
   ],
   "source": [
    "# print sparseness of vec_H_DFT\n",
    "K_DFT, tau_DFT, alpha_DFT = find_real_sparseness(vec_H_DFT, threshold=sparse_power_threshold)\n",
    "sparseness_DFT = K_DFT / len(vec_H_DFT)\n",
    "print(\n",
    "    \"K_DFT = {}, \\nN_DFT = {}, \\n2K_DFT = {} \\ntau_DFT = {}, \\nalpha_DFT = {}, \\nsparseness_DFT = {}\".format(K_DFT, len(vec_H_DFT), 2*K_DFT, tau_DFT, alpha_DFT, sparseness_DFT)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.235982300Z",
     "start_time": "2023-11-07T03:25:41.224927200Z"
    }
   },
   "outputs": [],
   "source": [
    "vec_H_a_abs = np.abs(vec_H_a)\n",
    "N = vec_H_a_abs.shape[0]\n",
    "# sort the vector in descending order\n",
    "vec_H_a_sortd = np.sort(np.reshape(np.array(vec_H_a_abs), (N,)), )\n",
    "vec_H_a_sortd = np.flip(vec_H_a_sortd, axis=0)\n",
    "overall_power = np.sum(vec_H_a_sortd)\n",
    "for i, val in enumerate(vec_H_a_sortd):\n",
    "    if np.sum(vec_H_a_sortd[0:i]) >= 0.95 * overall_power:\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Graph Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.249871600Z",
     "start_time": "2023-11-07T03:25:41.229647700Z"
    }
   },
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G.add_edge(\"x\", \"a\", capacity=3.0)\n",
    "G.add_edge(\"x\", \"b\", capacity=0.5)\n",
    "G.add_edge(\"a\", \"c\", capacity=3.0)\n",
    "G.add_edge(\"b\", \"c\", capacity=5.0)\n",
    "G.add_edge(\"b\", \"d\", capacity=4.0)\n",
    "G.add_edge(\"d\", \"e\", capacity=2.0)\n",
    "G.add_edge(\"c\", \"y\", capacity=2.0)\n",
    "G.add_edge(\"e\", \"y\", capacity=3.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.254871600Z",
     "start_time": "2023-11-07T03:25:41.235982300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('a', 'c'), ('x', 'b')]\n"
     ]
    }
   ],
   "source": [
    "cut_value, partition = nx.minimum_cut(G, \"x\", \"y\")\n",
    "reachable, non_reachable = partition\n",
    "\n",
    "cutset = set()\n",
    "for u, nbrs in ((n, G[n]) for n in reachable):\n",
    "    cutset.update((u, v) for v in nbrs if v in non_reachable)\n",
    "print(sorted(cutset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test the NumPy Matrix Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:41.254871600Z",
     "start_time": "2023-11-07T03:25:41.243067Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_mat = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "test_mat[0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test EMRF.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:42.318627800Z",
     "start_time": "2023-11-07T03:25:41.249871600Z"
    }
   },
   "outputs": [],
   "source": [
    "from tool_box.EMRF import EMRF\n",
    "# add noise\n",
    "noise = np.random.randn(*y.shape) + 1j * np.random.randn(*y.shape)\n",
    "noise = noise / np.linalg.norm(noise) * np.linalg.norm(y) / 10 ** (SNR / 20) \n",
    "y = y + noise\n",
    "\n",
    "test_EMRF = EMRF(\n",
    "    Graph = myGraph,\n",
    "    y = y,\n",
    "    Phi = Phi,\n",
    "    vec_H_a=vec_H_a,\n",
    "    xi = xi,\n",
    "    l_x_abs_max=l_x_abs_max,\n",
    "    l_y_abs_max=l_y_abs_max,\n",
    "    Geo_Approx_para=Geo_Approx_para,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:42.327604100Z",
     "start_time": "2023-11-07T03:25:42.320094700Z"
    }
   },
   "outputs": [],
   "source": [
    "def test_visual(path = \"default\"):\n",
    "    # subplots 1 row 2 columns\n",
    "    plt.figure(figsize=(14, 5))\n",
    "\n",
    "    # subplot 1\n",
    "    plt.subplot(1, 2, 1)\n",
    "    # visualize the support variable\n",
    "    visualize_wavenumber_domain_channel(test_EMRF.get_support() + 1)\n",
    "\n",
    "    # subplot 2\n",
    "    plt.subplot(1, 2, 2)\n",
    "    # visualize the hat_h\n",
    "    visualize_wavenumber_domain_channel(test_EMRF.hat_h_sl)\n",
    "    # save the figure\n",
    "    if path != \"default\":\n",
    "        plt.savefig(path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show Progress bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test $\\alpha-\\beta$-swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:48.094815900Z",
     "start_time": "2023-11-07T03:25:42.321603700Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/15 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 1/15 [00:00<00:04,  2.98it/s]/Users/brookbupt/Documents/GitHub/ICC-GXF/code/tool_box/EMRF.py:252: RuntimeWarning: divide by zero encountered in log10\n",
      "  return - np.log10(pdf)\n",
      "100%|██████████| 15/15 [00:07<00:00,  2.00it/s]\n"
     ]
    }
   ],
   "source": [
    "iter_num = 15\n",
    "# use tqdm\n",
    "for i in tqdm(range(iter_num)):\n",
    "    test_EMRF.alpha_beta_swap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:48.385163Z",
     "start_time": "2023-11-07T03:25:48.095817300Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMSE =  0.01668323001538505\n",
      "NMSE_v2 =  0.016532348676263516\n",
      "Converge NMSE =  0.01693646280032408\n",
      "Converge NMSE_v2 =  0.0167832011685664\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1920x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NMSE_list_v2 = test_EMRF.NMSE_list_v2\n",
    "NMSE_list = test_EMRF.NMSE_list\n",
    "# plot NMSE_list\n",
    "plt.figure()\n",
    "plt.subplot(211)\n",
    "plt.plot(NMSE_list)\n",
    "# set log scale\n",
    "plt.yscale('log')\n",
    "# grid on\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(NMSE_list_v2)\n",
    "# set log scale\n",
    "plt.yscale('log')\n",
    "# grid on  \n",
    "plt.grid(True)\n",
    "\n",
    "print(\"NMSE = \", np.min(NMSE_list))\n",
    "print(\"NMSE_v2 = \", np.min(NMSE_list_v2))\n",
    "\n",
    "# print the mean of the last 5 NMSE\n",
    "print(\"Converge NMSE = \", np.mean(NMSE_list[-5:]))\n",
    "print(\"Converge NMSE_v2 = \", np.mean(NMSE_list_v2[-5:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:49.214654400Z",
     "start_time": "2023-11-07T03:25:48.386163300Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/1438936305.py:6: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  spacing = int(lambda_c / delta)\n",
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/3182367393.py:9: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  mat_H_a[l_x + l_x_abs_max, l_y + l_y_abs_max] = vec_H_a[l]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 4200x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Saving results\n",
    "import os \n",
    "from scipy.io import savemat\n",
    "# create folder if not exist\n",
    "# folder = './data', with SNR and N_RF and spacing\n",
    "spacing = int(lambda_c / delta)\n",
    "folder = './data' + '/SNR_' + str(SNR) + '_Nx_' + str(N_x) + '_RF_' + str(N_RF) + '_spacing_' + str(spacing)\n",
    "if not os.path.exists(folder):\n",
    "    os.makedirs(folder)\n",
    "test_visual(path=folder + \"/RecoveryCompare.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:49.498814700Z",
     "start_time": "2023-11-07T03:25:49.216655Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n6/9zkr6zk96qz10ctnvn_mhc840000gn/T/ipykernel_32297/3182367393.py:9: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  mat_H_a[l_x + l_x_abs_max, l_y + l_y_abs_max] = vec_H_a[l]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1920x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "savemat(folder + '/alg_GCSE_WND.mat', {\n",
    "    'NMSE_list_v2' : NMSE_list_v2,\n",
    "    'NMSE_list' : NMSE_list,\n",
    "    'vec_H_a_recovered' : visualize_wavenumber_domain_channel(test_EMRF.hat_h_sl),\n",
    "    'meta_data' : {\n",
    "        # 'cluster_para' : cluster_para,\n",
    "        'f_c' : f_c,\n",
    "        'N_x' : N_x,\n",
    "        'N_y' : N_y,\n",
    "        'lambda_c' : lambda_c,\n",
    "        'delta' : delta,\n",
    "        'N' : N,\n",
    "        'N_RF' : N_RF,\n",
    "        'L_x' : L_x,\n",
    "        'L_y' : L_y,\n",
    "        'K' : K,\n",
    "        'sparseness' : sparseness,\n",
    "        'sparse_power_threshold' : sparse_power_threshold,\n",
    "        'eta' : eta,\n",
    "        'xi' : xi,\n",
    "        'l_x_abs_max' : l_x_abs_max,\n",
    "        'l_y_abs_max' : l_y_abs_max,\n",
    "        'M' : M,\n",
    "        'SNR' : SNR,\n",
    "        'iter_num' : iter_num\n",
    "    },\n",
    "}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:49.498814700Z",
     "start_time": "2023-11-07T03:25:49.492055600Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-11-07T03:25:49.498814700Z",
     "start_time": "2023-11-07T03:25:49.494307700Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}