Kelly Changes

1fcec44c · Kelly Chang · 1991ccfc · 1fcec44c · 1fcec44c · 1fcec44c
Commit 1fcec44c authored Mar 08, 2022 by Kelly Chang
5 changed files
--- a/.ipynb_checkpoints/Encoding_Kelly-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Encoding_Kelly-checkpoint.ipynb
 {
- "cells": [],
- "metadata": {},
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8868bc30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "from itertools import product\n",
+    "import os\n",
+    "import sys\n",
+    "from PIL import Image\n",
+    "from scipy.optimize import minimize,linprog\n",
+    "import time\n",
+    "import seaborn as sns\n",
+    "from sklearn.neighbors import KernelDensity\n",
+    "import pandas as pd\n",
+    "from collections import Counter\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "76317b02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def file_extractor(dirname=\"images\"):\n",
+    "    files = os.listdir(dirname)\n",
+    "    scenes = []\n",
+    "    for file in files:\n",
+    "        scenes.append(os.path.join(dirname, file))\n",
+    "    return scenes\n",
+    "\n",
+    "def image_extractor(scenes):\n",
+    "    image_folder = []\n",
+    "    for scene in scenes:\n",
+    "        files = os.listdir(scene)\n",
+    "        for file in files:\n",
+    "            image_folder.append(os.path.join(scene, file))\n",
+    "    images = []\n",
+    "    for folder in image_folder:\n",
+    "        ims = os.listdir(folder)\n",
+    "        for im in ims:\n",
+    "            if im[-4:] == \".jp4\" or im[-7:] == \"_6.tiff\":\n",
+    "                continue\n",
+    "            else:\n",
+    "                images.append(os.path.join(folder, im))\n",
+    "    return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor\n",
+    "\n",
+    "def im_distribution(images, num):\n",
+    "    \"\"\"\n",
+    "    Function that extracts tiff files from specific cameras and returns a list of all\n",
+    "    the tiff files corresponding to that camera. i.e. all pictures labeled \"_7.tiff\" or otherwise\n",
+    "    specified camera numbers.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but\n",
+    "        of specific tiff files that can be opened right away. This is the list that we iterate through and \n",
+    "        divide.\n",
+    "        \n",
+    "        num (str): a string designation for the camera number that we want to extract i.e. \"14\" for double digits\n",
+    "        of \"_1\" for single digits.\n",
+    "        \n",
+    "    Returns:\n",
+    "        tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted\n",
+    "        from the 'images' list that correspond to the given num.\n",
+    "    \"\"\"\n",
+    "    tiff = []\n",
+    "    for im in images:\n",
+    "        if im[-7:-6] == num:\n",
+    "            tiff.append(im)\n",
+    "    return tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "be1ff8a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_hist(tiff_list):\n",
+    "    \"\"\"\n",
+    "    This function is the leftovers from the first attempt to plot histograms.\n",
+    "    As it stands it needs some work in order to function again. We will\n",
+    "    fix this later. 1/25/22\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    image = tiff_list\n",
+    "    image = Image.open(image)    #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",
+    "    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
+    "    z0 = image[0:-2,0:-2]   # get all the first pixel for the entire image\n",
+    "    z1 = image[0:-2,1:-1]   # get all the second pixel for the entire image\n",
+    "    z2 = image[0:-2,2::]    # get all the third pixel for the entire image\n",
+    "    z3 = image[1:-1,0:-2]   # get all the forth pixel for the entire image\n",
+    "    # calculate the out put of the system of equation\n",
+    "    y0 = np.ravel(-z0+z2-z3)\n",
+    "    y1 = np.ravel(z0+z1+z2)\n",
+    "    y2 = np.ravel(-z0-z1-z2-z3)\n",
+    "    y = np.vstack((y0,y1,y2))\n",
+    "    # use numpy solver to solve the system of equations all at once\n",
+    "    predict = np.floor(np.linalg.solve(A,y)[-1])\n",
+    "    # flatten the neighbor pixlels and stack them together\n",
+    "    z0 = np.ravel(z0)\n",
+    "    z1 = np.ravel(z1)\n",
+    "    z2 = np.ravel(z2)\n",
+    "    z3 = np.ravel(z3)\n",
+    "    neighbor = np.vstack((z0,z1,z2,z3)).T\n",
+    "    # calculate the difference\n",
+    "    diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
+    "    \n",
+    "    # flatten the image to a vector\n",
+    "    image = np.ravel(image[1:-1,1:-1])\n",
+    "    error = image-predict\n",
+    "    \n",
+    "    return image, predict, diff, error, A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "8483903e",
+   "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": 86,
+   "id": "64a3a193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reconstruct(error, A):\n",
+    "    \"\"\"\n",
+    "    Function that reconstructs the original image\n",
+    "    from the error matrix and using the predictive\n",
+    "    algorithm developed in the encoding.\n",
+    "    \n",
+    "    Parameters:\n",
+    "        error (array): matrix of errors computed in encoding. Same \n",
+    "                       shape as the original image (512, 640) in this case\n",
+    "        A (array): Matrix used for the system of equations to create predictions\n",
+    "    Returns: \n",
+    "        image (array): The reconstructed image\n",
+    "    \"\"\"\n",
+    "    new_e = error.copy()\n",
+    "    rows, columns = new_e.shape\n",
+    "\n",
+    "    for r in range(1, rows-1):\n",
+    "        for c in range(1, columns-1):\n",
+    "            z0, z1, z2, z3 = new_e[r-1][c-1], new_e[r-1][c], new_e[r-1][c+1], new_e[r][c-1]\n",
+    "            y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))\n",
+    "\n",
+    "            '''if r == 345 and c == 421:\n",
+    "                print(new_e[r][c])\n",
+    "                print(np.linalg.solve(A,y)[-1])\n",
+    "                print(new_e[r][c] + np.linalg.solve(A,y)[-1])\n",
+    "                print(np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1]))\n",
+    "                \n",
+    "                y0 = np.ravel(-z0+z2-z3)\n",
+    "    y1 = np.ravel(z0+z1+z2)\n",
+    "    y2 = np.ravel(-z0-z1-z2-z3)\n",
+    "    y = np.vstack((y0,y1,y2))\n",
+    "    # use numpy solver to solve the system of equations all at once\n",
+    "    predict = np.floor(np.linalg.solve(A,y)[-1])\n",
+    "    # flatten the neighbor pixlels and stack them together\n",
+    "    z0 = np.ravel(z0)\n",
+    "    z1 = np.ravel(z1)\n",
+    "    z2 = np.ravel(z2)\n",
+    "    z3 = np.ravel(z3)\n",
+    "    neighbor = np.vstack((z0,z1,z2,z3)).T'''\n",
+    "        \n",
+    "            #Real solution that works, DO NOT DELETE\n",
+    "            print(new_e[r][c]+ np.floor(np.linalg.solve(A,y)[-1]))\n",
+    "            new_e[r][c] = new_e[r][c] + np.floor(np.linalg.solve(A,y)[-1])\n",
+    "            print(new_e[r][c])\n",
+    "            #new_e[r][c] = np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1])\n",
+    "            \n",
+    "    return new_e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "91879f19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "257\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-154.0\n",
+      "197.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "scenes = file_extractor()\n",
+    "images = image_extractor(scenes)\n",
+    "origin, predict, diff, error, A = plot_hist(images[0])\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",
+    "print(len(set(list(error))))\n",
+    "plt.hist(error,100)\n",
+    "plt.show()\n",
+    "print(min(error))\n",
+    "print(max(error))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "207b0bd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "458\n",
+      "{'61': '000000000', '87': '000000001000', '140': '000000001001000', '142': '000000001001001', '250': '000000001001010', '179': '000000001001011', '151': '000000001001100', '207': '0000000010011010', '141': '0000000010011011', '101': '00000000100111', '77': '00000000101', '69': '0000000011', '53': '00000001', '44': '0000001', '33': '000001', '20': '00001', '19': '00010', '18': '00011', '43': '0010000', '75': '00100010000', '84': '001000100010', '104': '00100010001100', '122': '001000100011010', '120': '001000100011011', '94': '0010001000111', '83': '001000100100', '153': '001000100101000', '130': '001000100101001', '143': '001000100101010', '127': '001000100101011', '93': '0010001001011', '115': '0010001001100000', '375': '0010001001100001', '366': '0010001001100010', '373': '0010001001100011', '377': '0010001001100100', '314': '0010001001100101', '402': '0010001001100110', '367': '0010001001100111', '133': '0010001001101000', '210': '0010001001101001', '334': '0010001001101010', '298': '0010001001101011', '176': '0010001001101100', '332': '0010001001101101', '335': '0010001001101110', '362': '0010001001101111', '371': '0010001001110000', '333': '0010001001110001', '251': '0010001001110010', '226': '0010001001110011', '247': '0010001001110100', '315': '0010001001110101', '304': '0010001001110110', '258': '0010001001110111', '306': '0010001001111000', '261': '0010001001111001', '289': '0010001001111010', '372': '0010001001111011', '239': '0010001001111100', '252': '0010001001111101', '224': '0010001001111110', '205': '0010001001111111', '60': '001000101', '52': '00100011', '32': '001001', '17': '00101', '51': '00110000', '139': '001100010000000', '149': '001100010000001', '132': '001100010000010', '154': '001100010000011', '163': '001100010000100', '162': '001100010000101', '281': '001100010000110', '346': '001100010000111', '384': '00110001000100000', '331': '00110001000100001', '383': '00110001000100010', '155': '00110001000100011', '354': '00110001000100100', '456': '00110001000100101', '229': '00110001000100110', '388': '00110001000100111', '294': '00110001000101000', '413': '00110001000101001', '211': '00110001000101010', '292': '00110001000101011', '376': '00110001000101100', '382': '00110001000101101', '387': '00110001000101110', '309': '00110001000101111', '158': '001100010001100', '159': '001100010001101', '313': '00110001000111000', '126': '00110001000111001', '327': '00110001000111010', '319': '00110001000111011', '302': '00110001000111100', '330': '00110001000111101', '385': '00110001000111110', '573': '00110001000111111', '76': '00110001001', '214': '0011000101000000', '259': '0011000101000001', '243': '0011000101000010', '255': '0011000101000011', '410': '0011000101000100', '399': '0011000101000101', '134': '0011000101000110', '270': '0011000101000111', '216': '0011000101001000', '236': '0011000101001001', '213': '0011000101001010', '196': '0011000101001011', '290': '0011000101001100', '231': '0011000101001101', '128': '0011000101001110', '193': '0011000101001111', '169': '0011000101010000', '178': '0011000101010001', '328': '0011000101010010', '160': '0011000101010011', '394': '0011000101010100', '336': '0011000101010101', '204': '0011000101010110', '227': '0011000101010111', '200': '0011000101011000', '212': '0011000101011001', '352': '0011000101011010', '147': '0011000101011011', '342': '0011000101011100', '308': '0011000101011101', '329': '0011000101011110', '379': '0011000101011111', '221': '00110001011000000', '267': '00110001011000001', '269': '001100010110000100', '177': '001100010110000101', '220': '001100010110000110', '202': '001100010110000111', '225': '00110001011000100', '185': '00110001011000101', '170': '00110001011000110', '198': '00110001011000111', '429': '001100010110010000', '206': '001100010110010001', '426': '001100010110010010', '438': '001100010110010011', '403': '001100010110010100', '424': '001100010110010101', '299': '001100010110010110', '325': '001100010110010111', '237': '001100010110011000', '152': '001100010110011001', '145': '001100010110011010', '230': '001100010110011011', '411': '001100010110011100', '286': '001100010110011101', '374': '001100010110011110', '469': '001100010110011111', '293': '00110001011010000', '406': '00110001011010001', '407': '00110001011010010', '421': '00110001011010011', '301': '00110001011010100', '275': '00110001011010101', '423': '00110001011010110', '395': '00110001011010111', '244': '00110001011011000', '337': '00110001011011001', '300': '00110001011011010', '233': '00110001011011011', '322': '00110001011011100', '400': '00110001011011101', '253': '00110001011011110', '361': '00110001011011111', '297': '001100010111000000', '390': '001100010111000001', '444': '001100010111000010', '242': '001100010111000011', '606': '001100010111000100', '498': '001100010111000101', '397': '001100010111000110', '532': '001100010111000111', '209': '001100010111001000', '283': '001100010111001001', '430': '001100010111001010', '351': '001100010111001011', '539': '001100010111001100', '530': '001100010111001101', '256': '001100010111001110', '491': '001100010111001111', '511': '001100010111010000', '570': '001100010111010001', '559': '001100010111010010', '478': '001100010111010011', '359': '001100010111010100', '22554': '001100010111010101', '512': '001100010111010110', '503': '001100010111010111', '474': '001100010111011000', '489': '001100010111011001', '404': '001100010111011010', '380': '001100010111011011', '519': '001100010111011100', '568': '001100010111011101', '515': '001100010111011110', '543': '001100010111011111', '439': '001100010111100000', '418': '001100010111100001', '419': '001100010111100010', '425': '001100010111100011', '454': '001100010111100100', '228': '001100010111100101', '447': '001100010111100110', '452': '001100010111100111', '414': '001100010111101000', '435': '001100010111101001', '416': '001100010111101010', '345': '001100010111101011', '401': '001100010111101100', '440': '001100010111101101', '409': '001100010111101110', '350': '001100010111101111', '494': '001100010111110000', '482': '001100010111110001', '502': '001100010111110010', '393': '001100010111110011', '422': '001100010111110100', '370': '001100010111110101', '461': '001100010111110110', '369': '001100010111110111', '398': '001100010111111000', '445': '001100010111111001', '203': '001100010111111010', '249': '001100010111111011', '405': '001100010111111100', '486': '001100010111111101', '467': '001100010111111110', '470': '001100010111111111', '68': '0011000110', '74': '00110001110', '91': '0011000111100', '114': '001100011110100', '307': '001100011110101', '199': '001100011110110', '110': '001100011110111', '89': '0011000111110', '109': '00110001111110', '105': '001100011111110', '150': '0011000111111110', '129': '0011000111111111', '42': '0011001', '31': '001101', '16': '00111', '15': '01000', '67': '0100100000', '66': '0100100001', '59': '010010001', '50': '01001001', '41': '0100101', '30': '010011', '14': '01010', '13': '01011', '12': '01100', '29': '011010', '49': '01101100', '73': '01101101000', '82': '011011010010', '88': '0110110100110', '90': '0110110100111', '264': '0110110101000000', '272': '0110110101000001', '181': '0110110101000010', '218': '0110110101000011', '368': '0110110101000100', '347': '0110110101000101', '186': '0110110101000110', '248': '0110110101000111', '365': '0110110101001000', '187': '0110110101001001', '165': '0110110101001010', '164': '0110110101001011', '305': '0110110101001100', '161': '0110110101001101', '271': '0110110101001110', '340': '0110110101001111', '386': '0110110101010000', '280': '0110110101010001', '112': '0110110101010010', '137': '0110110101010011', '100': '01101101010101', '326': '0110110101011000', '277': '0110110101011001', '288': '0110110101011010', '316': '0110110101011011', '148': '0110110101011100', '287': '0110110101011101', '131': '0110110101011110', '183': '0110110101011111', '81': '011011010110', '188': '011011010111000', '125': '011011010111001', '171': '0110110101110100', '166': '0110110101110101', '167': '011011010111011', '116': '011011010111100', '174': '011011010111101', '108': '011011010111110', '107': '011011010111111', '58': '011011011', '40': '0110111', '11': '01110', '10': '01111', '28': '100000', '39': '1000010', '57': '100001100', '65': '1000011010', '72': '10000110110', '70': '10000110111', '48': '10000111', '9': '10001', '7': '10010', '8': '10011', '27': '101000', '38': '1010010', '47': '10100110', '63': '1010011100', '71': '10100111010', '79': '101001110110', '97': '10100111011100', '103': '10100111011101', '96': '10100111011110', '138': '10100111011111000', '146': '10100111011111001', '282': '10100111011111010', '222': '10100111011111011', '118': '101001110111111', '56': '101001111', '6': '10101', '5': '10110', '0': '101110', '26': '101111', '3': '11000', '2': '11001', '1': '11010', '4': '11011', '25': '111000', '37': '1110010', '64': '1110011000', '119': '11100110010000000', '349': '11100110010000001', '364': '11100110010000010', '392': '11100110010000011', '358': '11100110010000100', '378': '11100110010000101', '197': '11100110010000110', '279': '11100110010000111', '323': '11100110010001000', '318': '11100110010001001', '360': '11100110010001010', '436': '11100110010001011', '217': '11100110010001100', '124': '11100110010001101', '311': '11100110010001110', '324': '11100110010001111', '274': '11100110010010000', '278': '11100110010010001', '310': '11100110010010010', '296': '11100110010010011', '353': '11100110010010100', '357': '11100110010010101', '262': '11100110010010110', '223': '11100110010010111', '303': '11100110010011000', '284': '11100110010011001', '396': '11100110010011010', '338': '11100110010011011', '135': '11100110010011100', '355': '11100110010011101', '234': '11100110010011110', '441': '11100110010011111', '195': '1110011001010000', '320': '1110011001010001', '257': '11100110010100100', '215': '11100110010100101', '254': '11100110010100110', '263': '11100110010100111', '157': '1110011001010100', '285': '1110011001010101', '172': '1110011001010110', '192': '1110011001010111', '381': '11100110010110000', '389': '11100110010110001', '321': '11100110010110010', '189': '11100110010110011', '415': '11100110010110100', '190': '11100110010110101', '232': '11100110010110110', '245': '11100110010110111', '180': '11100110010111000', '235': '11100110010111001', '273': '11100110010111010', '317': '11100110010111011', '240': '11100110010111100', '219': '11100110010111101', '182': '11100110010111110', '246': '11100110010111111', '113': '1110011001100000', '276': '1110011001100001', '265': '1110011001100010', '175': '1110011001100011', '121': '1110011001100100', '123': '1110011001100101', '144': '1110011001100110', '156': '1110011001100111', '184': '1110011001101000', '344': '1110011001101001', '266': '1110011001101010', '173': '1110011001101011', '348': '1110011001101100', '268': '1110011001101101', '363': '1110011001101110', '312': '1110011001101111', '80': '111001100111', '55': '111001101', '46': '11100111', '24': '111010', '36': '1110110', '54': '111011100', '62': '1110111010', '92': '11101110110000', '98': '11101110110001', '86': '1110111011001', '95': '11101110110100', '99': '11101110110101', '106': '111011101101100', '117': '111011101101101', '136': '1110111011011100', '241': '1110111011011101', '102': '111011101101111', '85': '1110111011100', '201': '1110111011101000', '238': '1110111011101001', '191': '1110111011101010', '208': '1110111011101011', '343': '1110111011101100', '111': '1110111011101101', '168': '1110111011101110', '339': '1110111011101111', '78': '111011101111', '45': '11101111', '23': '111100', '35': '1111010', '34': '1111011', '22': '111110', '21': '111111'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "scenes = file_extractor()\n",
+    "images = image_extractor(scenes)\n",
+    "start = time.time()\n",
+    "origin, predict, diff, error, A = plot_hist(images[0])\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,(510, 638))\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",
+    "plt.hist(new_error[1:],bins=100)\n",
+    "plt.show()\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",
+    "#print(freq)\n",
+    "freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
+    "node = make_tree(freq)\n",
+    "encoding = 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",
+    "print(len(encoding))\n",
+    "print(encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "14075c94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compress_rate(original, error, encoding):\n",
+    "    original = original.reshape(-1)\n",
+    "    error = error.reshape(-1)\n",
+    "    o_len = 0\n",
+    "    c_len = 0\n",
+    "    for i in range(0, len(original)):\n",
+    "        o_len += len(bin(original[i])[2:])\n",
+    "        c_len += len(encoding[str(abs(error[i]))])\n",
+    "        c_len += 1\n",
+    "        \n",
+    "    return c_len/o_len"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "b93c068b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4473590087890625"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compress_rate(image,new_error,encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "a8dc8674",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int('0100',2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "992dd8bb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.1"
+  }
+ },
 "nbformat": 4,
 "nbformat_minor": 5
 }
--- a/Encoding_Kelly.ipynb
+++ b/Encoding_Kelly.ipynb
--- a/Error_to_Image.ipynb
+++ b/Error_to_Image.ipynb
@@ -226,7 +226,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.9.1"
  }
 },
 "nbformat": 4,

--- a/multiple_compression_kelly.ipynb
+++ b/multiple_compression_kelly.ipynb
--- a/prediction_MSE_kelly.ipynb
+++ b/prediction_MSE_kelly.ipynb
@@ -2,7 +2,7 @@
 "cells": [
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 11,
   "id": "dbef8759",
   "metadata": {},
   "outputs": [],
@@ -19,7 +19,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
   "id": "b7a550e0",
   "metadata": {},
   "outputs": [],
@@ -74,7 +74,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
   "id": "9ed20f84",
   "metadata": {},
   "outputs": [],
@@ -102,7 +102,7 @@
    "    y2 = np.ravel(-z0-z1-z2-z3)\n",
    "    y = np.vstack((y0,y1,y2))\n",
    "    # use numpy solver to solve the system of equations all at once\n",
-    "    predict = np.linalg.solve(A,y)[-1]\n",
+    "    predict = np.floor(np.linalg.solve(A,y)[-1])\n",
    "    # flatten the neighbor pixlels and stack them together\n",
    "    z0 = np.ravel(z0)\n",
    "    z1 = np.ravel(z1)\n",
@@ -119,7 +119,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 14,
   "id": "8e3ef654",
   "metadata": {},
   "outputs": [],
@@ -152,7 +152,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
   "id": "fa65dcd6",
   "metadata": {},
   "outputs": [
@@ -160,10 +160,10 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Average Error First and Second Added: 20.017164930235474\n",
-      "Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
+      "Average Error First and Second Added: 20.017415790767718\n",
+      "Standard Deviaiton of Mean Errors: 0.1611803882535028\n",
      "Average Difference: 53.678648426455226\n",
-      "Average Time per Image for First: 0.049846380949020386\n"
+      "Average Time per Image for First: 0.07302114367485046\n"
     ]
    }
   ],
@@ -176,26 +176,113 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
   "id": "b7e88aab",
   "metadata": {},
   "outputs": [
    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[22643.5 22595.  22580.  ... 22923.  22984.  22937.5]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "(array([3.8620e+03, 2.0014e+04, 4.1788e+04, 4.4365e+04, 4.6890e+04,\n",
+       "        3.4932e+04, 3.0758e+04, 2.1131e+04, 1.8305e+04, 1.2642e+04,\n",
+       "        1.0859e+04, 7.4890e+03, 5.9740e+03, 5.3350e+03, 3.8500e+03,\n",
+       "        3.3960e+03, 2.3890e+03, 2.2130e+03, 1.5640e+03, 1.3960e+03,\n",
+       "        1.0520e+03, 9.5300e+02, 7.2300e+02, 6.6400e+02, 4.8700e+02,\n",
+       "        3.8800e+02, 3.5700e+02, 3.1300e+02, 2.5000e+02, 2.0000e+02,\n",
+       "        1.6800e+02, 1.2700e+02, 1.3000e+02, 6.9000e+01, 8.0000e+01,\n",
+       "        6.1000e+01, 6.2000e+01, 3.6000e+01, 2.4000e+01, 2.0000e+01,\n",
+       "        1.3000e+01, 1.3000e+01, 1.4000e+01, 1.0000e+01, 4.0000e+00,\n",
+       "        3.0000e+00, 3.0000e+00, 2.0000e+00, 0.0000e+00, 2.0000e+00]),\n",
+       " array([  0.  ,   8.46,  16.92,  25.38,  33.84,  42.3 ,  50.76,  59.22,\n",
+       "         67.68,  76.14,  84.6 ,  93.06, 101.52, 109.98, 118.44, 126.9 ,\n",
+       "        135.36, 143.82, 152.28, 160.74, 169.2 , 177.66, 186.12, 194.58,\n",
+       "        203.04, 211.5 , 219.96, 228.42, 236.88, 245.34, 253.8 , 262.26,\n",
+       "        270.72, 279.18, 287.64, 296.1 , 304.56, 313.02, 321.48, 329.94,\n",
+       "        338.4 , 346.86, 355.32, 363.78, 372.24, 380.7 , 389.16, 397.62,\n",
+       "        406.08, 414.54, 423.  ]),\n",
+       " <BarContainer object of 50 artists>)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(difference,bins=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "aaf16d98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fcf7a43fb80>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "error = np.abs(image-predict)\n",
+    "plt.scatter(error,np.sqrt(difference),alpha =0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3b78c8d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
    }
   ],
   "source": [
-    "print(predict)"
+    "type(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
-   "id": "7c55a3e1",
+   "id": "2a081505",
   "metadata": {},
   "outputs": [],
   "source": []