changes

Encoding_Kelly.ipynb

@@ -423,7 +423,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,

Error_to_Image.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 626,
+   "execution_count": 33,
    "id": "dbef8759",
    "metadata": {
     "id": "dbef8759"
@@ -21,12 +21,13 @@
     "from numpy import linalg as la\n",
     "from scipy.stats import gaussian_kde\n",
     "import seaborn as sns\n",
-    "import pywt"
+    "import pywt\n",
+    "from collections import Counter"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 654,
+   "execution_count": 2,
    "id": "9ed20f84",
    "metadata": {
     "id": "9ed20f84"
@@ -99,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 647,
+   "execution_count": 3,
    "id": "ba2881d9",
    "metadata": {},
    "outputs": [],
@@ -111,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 655,
+   "execution_count": 4,
    "id": "11e95c34",
    "metadata": {},
    "outputs": [],
@@ -121,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 537,
+   "execution_count": 5,
    "id": "434e4d2f",
    "metadata": {},
    "outputs": [],
@@ -159,30 +160,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 606,
-   "id": "bf427edd",
+   "execution_count": 6,
+   "id": "4f4a5a35",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-19\n",
-      "[22286.]\n",
-      "22285.0\n",
-      "[22266.]\n",
-      "22266.999999999996\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "new_error = reconstruct(err, A)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 669,
-   "id": "5edcf208",
+   "execution_count": 7,
+   "id": "6d95ffce",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,8 +194,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 673,
-   "id": "953375c5",
+   "execution_count": 8,
+   "id": "1c848109",
    "metadata": {},
    "outputs": [
     {
@@ -304,22 +293,172 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 679,
-   "id": "90387cb9",
+   "execution_count": 9,
+   "id": "139938e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NodeTree(object):\n",
+    "    def __init__(self, left=None, right=None):\n",
+    "        self.left = left\n",
+    "        self.right = right\n",
+    "\n",
+    "    def children(self):\n",
+    "        return self.left, self.right\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return self.left, self.right\n",
+    "\n",
+    "\n",
+    "def huffman_code_tree(node, binString=''):\n",
+    "    '''\n",
+    "    Function to find Huffman Code\n",
+    "    '''\n",
+    "    if type(node) is str:\n",
+    "        return {node: binString}\n",
+    "    (l, r) = node.children()\n",
+    "    d = dict()\n",
+    "    d.update(huffman_code_tree(l, binString + '0'))\n",
+    "    d.update(huffman_code_tree(r, binString + '1'))\n",
+    "    return d\n",
+    "\n",
+    "\n",
+    "def make_tree(nodes):\n",
+    "    '''\n",
+    "    Function to make tree\n",
+    "    :param nodes: Nodes\n",
+    "    :return: Root of the tree\n",
+    "    '''\n",
+    "    while len(nodes) > 1:\n",
+    "        (key1, c1) = nodes[-1]\n",
+    "        (key2, c2) = nodes[-2]\n",
+    "        nodes = nodes[:-2]\n",
+    "        node = NodeTree(key1, key2)\n",
+    "        nodes.append((node, c1 + c2))\n",
+    "        nodes = sorted(nodes, key=lambda x: x[1], reverse=True)\n",
+    "    return nodes[0][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "id": "e477d0c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def enc_experiment(images, plot=True):\n",
+    "    origin, predict, diff, error, A = plot_hist(images, 2)\n",
+    "    image = Image.open(images[0])    #Open the image and read it as an Image object\n",
+    "    image = np.array(image)[1:,:]    #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
+    "    image = image.astype(int)\n",
+    "    new_error = np.copy(image)\n",
+    "    #new_error[1:-1,1:-1] = np.reshape(error[1:-1,1:-1],(510, 638))\n",
+    "    new_error[1:-1, 1:-1] = error[1:-1, 1:-1]\n",
+    "    keep = new_error[0,0]\n",
+    "    new_error[0,:] = new_error[0,:] - keep\n",
+    "    new_error[-1,:] = new_error[-1,:] - keep\n",
+    "    new_error[1:-1,0] = new_error[1:-1,0] - keep\n",
+    "    new_error[1:-1,-1] = new_error[1:-1,-1] - keep\n",
+    "    new_error[0,0] = keep\n",
+    "    new_error = np.ravel(new_error)\n",
+    "    if plot:\n",
+    "        plt.hist(new_error[1:],bins=100)\n",
+    "        plt.show()\n",
+    "    \n",
+    "    #ab_error = np.abs(new_error)\n",
+    "    #string = [str(i) for i in ab_error]\n",
+    "    string = [str(i) for i in new_error]\n",
+    "    #string = [str(i) for i in np.arange(0,5)] + [str(i) for i in np.arange(0,5)] + [str(i) for i in np.arange(0,2)]*2\n",
+    "    freq = dict(Counter(string))\n",
+    "    freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
+    "    \n",
+    "    node = make_tree(freq)\n",
+    "    encoding_dict = huffman_code_tree(node)\n",
+    "    #encoded = [\"1\"+encoding[str(-i)] if i < 0 else \"0\"+encoding[str(i)] for i in error]\n",
+    "    #print(time.time()-start)\n",
+    "    encoded = new_error.reshape((512,640)).copy().astype(str)\n",
+    "    \n",
+    "    #encoded = np.zeros_like(new_error.reshape((512,640))).astype(str)\n",
+    "    print(encoded)\n",
+    "    for i in range(encoded.shape[0]):\n",
+    "        for j in range(encoded.shape[1]):\n",
+    "            if i == 0 and j == 0:\n",
+    "                encoded[i][j] = encoded[i][j]\n",
+    "            else:\n",
+    "                #print(encoding_dict[encoded[i][j]])\n",
+    "                encoded[i][j] = encoding_dict[encoded[i][j]]\n",
+    "                #print(encoded[i][j])\n",
+    "    \n",
+    "    return encoding_dict, encoded, new_error.reshape((512,640))\n",
+    "    #print(encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "id": "fd8e96a5",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "15"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['22541' '-10' '14' ... '32' '48' '33']\n",
+      " ['7' '67' '-21' ... '-1' '-1' '77']\n",
+      " ['7' '-15' '-3' ... '10' '-45' '58']\n",
+      " ...\n",
+      " ['49' '82' '-2' ... '-64' '5' '151']\n",
+      " ['27' '-33' '18' ... '47' '-16' '208']\n",
+      " ['17' '0' '-5' ... '138' '207' '226']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "encode_dict, encoding, error = enc_experiment(images, plot=False)"
    ]
   },
-     "execution_count": 679,
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "id": "7ebd8dd8",
    "metadata": {},
-     "output_type": "execute_result"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11110111110\n",
+      "92\n",
+      "11110111110001\n"
+     ]
     }
    ],
-   "source": []
+   "source": [
+    "print(encoding[0][100])\n",
+    "print(error[0][100])\n",
+    "print(encode_dict['92'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6a579b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def decoder(A, encoded_matrix, encoding_dict):\n",
+    "    \"\"\"\n",
+    "    Function that accecpts the prediction matrix A for the linear system,\n",
+    "    the encoded matrix of error values, and the encoding dicitonary.\n",
+    "    \"\"\"\n",
+    "    error_matrix = encoded_matrix.copy()\n",
+    "    for i in range(error_matrix.shape[0]):\n",
+    "        for j in range(error_matrix.shape[1]):\n",
+    "            if i == 0 and j == 0:\n",
+    "                error_matrix[i][j] = encoded_matrix[i][j]\n",
+    "            else:\n",
+    "                error_matrix[i][j] = encoding_dict."
+   ]
   }
  ],
  "metadata": {
@@ -343,7 +482,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,