add

.ipynb_checkpoints/prediction_MSE_Scout-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dbef8759",
+   "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\n",
+    "from time import time\n",
+    "from numpy import linalg as la"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b7a550e0",
+   "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:-5] == num:\n",
+    "            tiff.append(im)\n",
+    "    return tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9ed20f84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_hist(tiff_list, i):\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",
+    "\n",
+    "    image = tiff_list[i]\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",
+    "    row, col = image.shape\n",
+    "    \"\"\"predict = np.empty([row,col])     # create a empty matrix to update prediction\n",
+    "    predict[0,:] = np.copy(image[0,:])       # keep the first row from the image\n",
+    "    predict[:,0] = np.copy(image[:,0])      # keep the first columen from the image\n",
+    "    predict[-1,:] = np.copy(image[-1,:])       # keep the first row from the image\n",
+    "    predict[:,-1] = np.copy(image[:,-1])      # keep the first columen from the image\n",
+    "    diff = np.empty([row,col])\n",
+    "    diff[0,:] = np.zeros(col)       # keep the first row from the image\n",
+    "    diff[:,0] = np.zeros(row)\n",
+    "    diff[-1,:] = np.zeros(col)       # keep the first row from the image\n",
+    "    diff[:,-1] = np.zeros(row)\"\"\"\n",
+    "    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
+    "    z0 = image[0:-2,0:-2]\n",
+    "    z1 = image[0:-2,1:-1]\n",
+    "    z2 = image[0:-2,2::]\n",
+    "    z3 = image[1:-1,0:-2]\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",
+    "    predict = la.solve(A,y)[-1]\n",
+    "    #diff = [(np.max([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])-np.min([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])) for r in range(0,row-2) for c in range(0,col-2)]\n",
+    "    '''\n",
+    "    for r in range(1,row-1):                  # loop through the rth row\n",
+    "        for c in range(1,col-1):              # loop through the cth column\n",
+    "            actual_surrounding = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
+    "            #z = np.array([int(image[r-1,c-1]), int(image[r-1,c]), int(image[r-1,c+1]), int(image[r,c-1])])\n",
+    "            z = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
+    "            y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "            predict[r,c] = np.linalg.solve(A,y)[-1]\n",
+    "            diff[r,c] = (np.max(actual_surrounding)-np.min(actual_surrounding))\n",
+    "    predict = np.ravel(predict[1:-1,1:-1])\n",
+    "    diff = np.ravel(diff[1:-1,1:-1])'''\n",
+    "    image = np.ravel(image[1:-1,1:-1])\n",
+    "    return image, predict#, diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8e3ef654",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scenes = file_extractor()\n",
+    "images = image_extractor(scenes)\n",
+    "num_images = im_distribution(images, \"_9\")\n",
+    "error_mean = []\n",
+    "error_mean1 = []\n",
+    "diff_mean = []\n",
+    "times = []\n",
+    "times1 = []\n",
+    "all_error = []\n",
+    "for i in range(len(num_images)):\n",
+    "    \"\"\"start1 = time()\n",
+    "    image_1, predict_1, difference_1, x_s_1 = plot_hist(num_images, i, \"second\")\n",
+    "    stop1 = time()\n",
+    "    times1.append(stop1-start1)\n",
+    "    error1 = np.abs(image_1-predict_1)\n",
+    "    error_mean1.append(np.mean(np.ravel(error1)))\"\"\"\n",
+    "    start = time()\n",
+    "    image, predict = plot_hist(num_images, i)\n",
+    "    stop = time()\n",
+    "    times.append(stop-start)\n",
+    "    error = np.abs(image-predict)\n",
+    "    all_error.append(np.ravel(error))\n",
+    "    error_mean.append(np.mean(np.ravel(error)))\n",
+    "    #diff_mean.append(np.mean(np.ravel(difference)))\n",
+    "    \n",
+    "#image, predict, difference = plot_hist(images, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa65dcd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Error First and Second Added: 20.017164930235474\n",
+      "233.22663391021266\n",
+      "Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
+      "Average Time per Image for First: 0.023412495851516724\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Average Error First and Second Added: {np.mean(error_mean)}\")\n",
+    "print(np.std(image))\n",
+    "print(f\"Standard Deviaiton of Mean Errors: {np.sqrt(np.var(error_mean))}\")\n",
+    "#print(f\"Average Difference: {np.mean(diff_mean)}\")\n",
+    "print(f\"Average Time per Image for First: {np.mean(times)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f592fa32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]\n",
+      " [6 7 8]]\n",
+      "[4 5]\n",
+      "[4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.arange(9).reshape((3,3))\n",
+    "print(b)\n",
+    "print(b[1,1::])\n",
+    "print(b[1,1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dda442ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'difference' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10808/722042198.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdifference\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'font.size'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'difference' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAJDCAYAAAA8QNGHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUe0lEQVR4nO3dX4jld3nH8c/TXQP+qxGzik2ymJZo3AtTdIxStI2V1iQ3QfAiUQwNwhJqxMuEXuiFN/WiIGJ0WUII3piLGjSWaCgUTSGmzQZikjVEtpEm2whJVCwoNGzy9GKmMh1nM2cn59ndE18vODC/3/nOmQe+zPLe3zlzTnV3AACY8QdnegAAgFcysQUAMEhsAQAMElsAAIPEFgDAILEFADBox9iqqtuq6pmqevQk91dVfbmqjlXVw1X17uWPCQCwmha5snV7kite4v4rk1y8cTuY5GsvfywAgFeGHWOru+9N8ouXWHJ1kq/3uvuTnFtVb13WgAAAq2wZr9k6P8lTm46Pb5wDAPi9t3cJj1HbnNv2M4Cq6mDWn2rMa1/72vdccsklS/jxAACzHnzwwee6e99uvncZsXU8yYWbji9I8vR2C7v7cJLDSbK2ttZHjhxZwo8HAJhVVf+52+9dxtOIdyW5buOvEt+f5Ffd/bMlPC4AwMrb8cpWVX0jyeVJzquq40k+n+RVSdLdh5LcneSqJMeS/CbJ9VPDAgCsmh1jq7uv3eH+TvLppU0EAPAK4h3kAQAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABi0UGxV1RVV9XhVHauqm7e5/w1V9Z2q+lFVHa2q65c/KgDA6tkxtqpqT5JbklyZ5ECSa6vqwJZln07y4+6+NMnlSf6hqs5Z8qwAACtnkStblyU51t1PdPfzSe5IcvWWNZ3k9VVVSV6X5BdJTix1UgCAFbRIbJ2f5KlNx8c3zm32lSTvTPJ0kkeSfLa7X1zKhAAAK2yR2KptzvWW448keSjJHyX50yRfqao//J0HqjpYVUeq6sizzz57iqMCAKyeRWLreJILNx1fkPUrWJtdn+TOXncsyU+TXLL1gbr7cHevdffavn37djszAMDKWCS2HkhycVVdtPGi92uS3LVlzZNJPpwkVfWWJO9I8sQyBwUAWEV7d1rQ3Seq6sYk9yTZk+S27j5aVTds3H8oyReS3F5Vj2T9acebuvu5wbkBAFbCjrGVJN19d5K7t5w7tOnrp5P89XJHAwBYfd5BHgBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBC8VWVV1RVY9X1bGquvkkay6vqoeq6mhV/WC5YwIArKa9Oy2oqj1JbknyV0mOJ3mgqu7q7h9vWnNukq8muaK7n6yqNw/NCwCwUha5snVZkmPd/UR3P5/kjiRXb1nz8SR3dveTSdLdzyx3TACA1bRIbJ2f5KlNx8c3zm329iRvrKrvV9WDVXXdsgYEAFhlOz6NmKS2OdfbPM57knw4yauT/LCq7u/un/y/B6o6mORgkuzfv//UpwUAWDGLXNk6nuTCTccXJHl6mzXf6+5fd/dzSe5NcunWB+ruw9291t1r+/bt2+3MAAArY5HYeiDJxVV1UVWdk+SaJHdtWfPtJB+sqr1V9Zok70vy2HJHBQBYPTs+jdjdJ6rqxiT3JNmT5LbuPlpVN2zcf6i7H6uq7yV5OMmLSW7t7kcnBwcAWAXVvfXlV6fH2tpaHzly5Iz8bACAU1FVD3b32m6+1zvIAwAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAoIViq6quqKrHq+pYVd38EuveW1UvVNXHljciAMDq2jG2qmpPkluSXJnkQJJrq+rASdZ9Mck9yx4SAGBVLXJl67Ikx7r7ie5+PskdSa7eZt1nknwzyTNLnA8AYKUtElvnJ3lq0/HxjXO/VVXnJ/lokkPLGw0AYPUtElu1zbnecvylJDd19wsv+UBVB6vqSFUdefbZZxccEQBgde1dYM3xJBduOr4gydNb1qwluaOqkuS8JFdV1Ynu/tbmRd19OMnhJFlbW9sabAAArziLxNYDSS6uqouS/FeSa5J8fPOC7r7o/76uqtuT/NPW0AIA+H20Y2x194mqujHrf2W4J8lt3X20qm7YuN/rtAAATmKRK1vp7ruT3L3l3LaR1d1/8/LHAgB4ZfAO8gAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAwSGwBAAwSWwAAg8QWAMAgsQUAMEhsAQAMWii2quqKqnq8qo5V1c3b3P+Jqnp443ZfVV26/FEBAFbPjrFVVXuS3JLkyiQHklxbVQe2LPtpkr/o7ncl+UKSw8seFABgFS1yZeuyJMe6+4nufj7JHUmu3rygu+/r7l9uHN6f5ILljgkAsJoWia3zkzy16fj4xrmT+VSS776coQAAXin2LrCmtjnX2y6s+lDWY+sDJ7n/YJKDSbJ///4FRwQAWF2LXNk6nuTCTccXJHl666KqeleSW5Nc3d0/3+6Buvtwd69199q+fft2My8AwEpZJLYeSHJxVV1UVeckuSbJXZsXVNX+JHcm+WR3/2T5YwIArKYdn0bs7hNVdWOSe5LsSXJbdx+tqhs27j+U5HNJ3pTkq1WVJCe6e21ubACA1VDd2778atza2lofOXLkjPxsAIBTUVUP7vZCkneQBwAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGCQ2AIAGCS2AAAGiS0AgEFiCwBgkNgCABgktgAABoktAIBBYgsAYJDYAgAYJLYAAAaJLQCAQWILAGDQQrFVVVdU1eNVdayqbt7m/qqqL2/c/3BVvXv5owIArJ4dY6uq9iS5JcmVSQ4kubaqDmxZdmWSizduB5N8bclzAgCspEWubF2W5Fh3P9Hdzye5I8nVW9ZcneTrve7+JOdW1VuXPCsAwMpZJLbOT/LUpuPjG+dOdQ0AwO+dvQusqW3O9S7WpKoOZv1pxiT5n6p6dIGfz9npvCTPnekh2BV7t9rs32qzf6vrHbv9xkVi63iSCzcdX5Dk6V2sSXcfTnI4SarqSHevndK0nDXs3+qyd6vN/q02+7e6qurIbr93kacRH0hycVVdVFXnJLkmyV1b1tyV5LqNv0p8f5JfdffPdjsUAMArxY5Xtrr7RFXdmOSeJHuS3NbdR6vqho37DyW5O8lVSY4l+U2S6+dGBgBYHYs8jZjuvjvrQbX53KFNX3eST5/izz58ius5u9i/1WXvVpv9W232b3Xteu9qvZMAAJjg43oAAAaNx5aP+lldC+zdJzb27OGquq+qLj0Tc7K9nfZv07r3VtULVfWx0zkfL22R/auqy6vqoao6WlU/ON0zsr0F/u18Q1V9p6p+tLF3Xud8lqiq26rqmZO9NdWum6W7x25Zf0H9fyT54yTnJPlRkgNb1lyV5LtZf6+u9yf5t8mZ3Ja6d3+W5I0bX19p786e2yL7t2ndv2T9NZkfO9Nzuy2+f0nOTfLjJPs3jt98pud2W3jv/i7JFze+3pfkF0nOOdOzu3WS/HmSdyd59CT376pZpq9s+aif1bXj3nX3fd39y43D+7P+/mqcHRb53UuSzyT5ZpJnTudw7GiR/ft4kju7+8kk6W57eHZYZO86yeurqpK8LuuxdeL0jsl2uvverO/HyeyqWaZjy0f9rK5T3ZdPZb32OTvsuH9VdX6SjyY5FM42i/z+vT3JG6vq+1X1YFVdd9qm46UssndfSfLOrL/59yNJPtvdL56e8XiZdtUsC731w8uwtI/64bRbeF+q6kNZj60PjE7EqVhk/76U5KbufmH9P9icRRbZv71J3pPkw0leneSHVXV/d/9kejhe0iJ795EkDyX5yyR/kuSfq+pfu/u/h2fj5dtVs0zH1tI+6ofTbqF9qap3Jbk1yZXd/fPTNBs7W2T/1pLcsRFa5yW5qqpOdPe3TsuEvJRF/+18rrt/neTXVXVvkkuTiK0za5G9uz7J3/f6i4COVdVPk1yS5N9Pz4i8DLtqlumnEX3Uz+race+qan+SO5N80v+mzzo77l93X9Tdb+vutyX5xyR/K7TOGov82/ntJB+sqr1V9Zok70vy2Gmek9+1yN49mfUrkqmqt2T9A46fOK1Tslu7apbRK1vto35W1oJ797kkb0ry1Y2rIyfaB6yeFRbcP85Si+xfdz9WVd9L8nCSF5Pc2t3b/rk6p8+Cv3tfSHJ7VT2S9aelburu587Y0PxWVX0jyeVJzquq40k+n+RVyctrFu8gDwAwyDvIAwAMElsAAIPEFgDAILEFADBIbAEADBJbAACDxBYAwCCxBQAw6H8BU0gXwe5IAxEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (10,10))\n",
+    "ax = fig.add_subplot()\n",
+    "x = np.abs(predict-image)\n",
+    "y = difference\n",
+    "plt.plot(x,y,'o',alpha = 0.2)\n",
+    "plt.rcParams.update({'font.size': 20})\n",
+    "plt.xlabel(\"differnece to the true value\" )\n",
+    "plt.ylabel(\"differnece of min and max of true value of the surroundings\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58da6063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = Image.open(images[0])    #Open the image and read it as an Image object\n",
+    "image = np.array(image)[1:,:]\n",
+    "#z = np.array([image[1-1,1-1], image[1-1,1], image[1-1,1+1], image[1,1-1]])\n",
+    "z = np.array([22554,22552,22519,22561])\n",
+    "print(z)\n",
+    "'''A = np.array([[-3,0,1],[0,-3,3],[-1,-3,4]])\n",
+    "y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "a,b,c = np.linalg.solve(A,y)'''\n",
+    "A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
+    "y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "print(y)\n",
+    "a,b,c = np.linalg.solve(A,y)\n",
+    "print(a,b,c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2562feeb",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "i0 = (a*(-1) + b*(1) + c)\n",
+    "i1 = (a*(0) + b*(1) + c)\n",
+    "i2 = (a*(1) + b*(1) + c)\n",
+    "i3 = (a*(-1) + b*(0) + c)\n",
+    "print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "470cc137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 0\n",
+    "b = 2\n",
+    "c = 2\n",
+    "i0 = (a*(-1) + b*(1) + c)\n",
+    "i1 = (a*(0) + b*(1) + c)\n",
+    "i2 = (a*(1) + b*(1) + c)\n",
+    "i3 = (a*(-1) + b*(0) + c)\n",
+    "print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3292b395",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#z = np.hstack((image[0,:3], image[1,0]))\n",
+    "#x = np.array([-1,0,1,-1])\n",
+    "#y = np.array([-1,-1,-1,0])\n",
+    "A = np.array([[-3,0,1],[0,-3,3],[1,3,-4]])\n",
+    "#y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "y = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+    "print(np.linalg.solve(A,y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9687830",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "0.5**2 + 1.5**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e98eed4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y1= [0,1,2,3]\n",
+    "y2=[4,5,6,7]\n",
+    "y3=[8,9,10,11]\n",
+    "np.vstack((y1,y2,y3)).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7e88aab",
+   "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": "3.8.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/prediction_MSE_kelly-checkpoint.ipynb

@@ -152,7 +152,15 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD:.ipynb_checkpoints/prediction_MSE_kelly-checkpoint.ipynb
    "execution_count": 52,
+=======
+<<<<<<< HEAD
+   "execution_count": 31,
+=======
+   "execution_count": 43,
+>>>>>>> e4df997c1a14994e77600c8c4e3e1a2ec84ff59e
+>>>>>>> 2350ec9881b7954dc94449b36af098af82acbb95:.ipynb_checkpoints/prediction_MSE-checkpoint.ipynb
    "id": "fa65dcd6",
    "metadata": {},
    "outputs": [

prediction_MSE_Scout.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dbef8759",
+   "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\n",
+    "from time import time\n",
+    "from numpy import linalg as la"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b7a550e0",
+   "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:-5] == num:\n",
+    "            tiff.append(im)\n",
+    "    return tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9ed20f84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_hist(tiff_list, i):\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",
+    "\n",
+    "    image = tiff_list[i]\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",
+    "    row, col = image.shape\n",
+    "    \"\"\"predict = np.empty([row,col])     # create a empty matrix to update prediction\n",
+    "    predict[0,:] = np.copy(image[0,:])       # keep the first row from the image\n",
+    "    predict[:,0] = np.copy(image[:,0])      # keep the first columen from the image\n",
+    "    predict[-1,:] = np.copy(image[-1,:])       # keep the first row from the image\n",
+    "    predict[:,-1] = np.copy(image[:,-1])      # keep the first columen from the image\n",
+    "    diff = np.empty([row,col])\n",
+    "    diff[0,:] = np.zeros(col)       # keep the first row from the image\n",
+    "    diff[:,0] = np.zeros(row)\n",
+    "    diff[-1,:] = np.zeros(col)       # keep the first row from the image\n",
+    "    diff[:,-1] = np.zeros(row)\"\"\"\n",
+    "    A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
+    "    z0 = image[0:-2,0:-2]\n",
+    "    z1 = image[0:-2,1:-1]\n",
+    "    z2 = image[0:-2,2::]\n",
+    "    z3 = image[1:-1,0:-2]\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",
+    "    predict = la.solve(A,y)[-1]\n",
+    "    #diff = [(np.max([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])-np.min([z0[r,c],z1[r,c],z2[r,c],z3[r,c]])) for r in range(0,row-2) for c in range(0,col-2)]\n",
+    "    '''\n",
+    "    for r in range(1,row-1):                  # loop through the rth row\n",
+    "        for c in range(1,col-1):              # loop through the cth column\n",
+    "            actual_surrounding = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
+    "            #z = np.array([int(image[r-1,c-1]), int(image[r-1,c]), int(image[r-1,c+1]), int(image[r,c-1])])\n",
+    "            z = np.array([image[r-1,c-1], image[r-1,c], image[r-1,c+1], image[r,c-1]])\n",
+    "            y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "            predict[r,c] = np.linalg.solve(A,y)[-1]\n",
+    "            diff[r,c] = (np.max(actual_surrounding)-np.min(actual_surrounding))\n",
+    "    predict = np.ravel(predict[1:-1,1:-1])\n",
+    "    diff = np.ravel(diff[1:-1,1:-1])'''\n",
+    "    image = np.ravel(image[1:-1,1:-1])\n",
+    "    return image, predict#, diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8e3ef654",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scenes = file_extractor()\n",
+    "images = image_extractor(scenes)\n",
+    "num_images = im_distribution(images, \"_9\")\n",
+    "error_mean = []\n",
+    "error_mean1 = []\n",
+    "diff_mean = []\n",
+    "times = []\n",
+    "times1 = []\n",
+    "all_error = []\n",
+    "for i in range(len(num_images)):\n",
+    "    \"\"\"start1 = time()\n",
+    "    image_1, predict_1, difference_1, x_s_1 = plot_hist(num_images, i, \"second\")\n",
+    "    stop1 = time()\n",
+    "    times1.append(stop1-start1)\n",
+    "    error1 = np.abs(image_1-predict_1)\n",
+    "    error_mean1.append(np.mean(np.ravel(error1)))\"\"\"\n",
+    "    start = time()\n",
+    "    image, predict = plot_hist(num_images, i)\n",
+    "    stop = time()\n",
+    "    times.append(stop-start)\n",
+    "    error = np.abs(image-predict)\n",
+    "    all_error.append(np.ravel(error))\n",
+    "    error_mean.append(np.mean(np.ravel(error)))\n",
+    "    #diff_mean.append(np.mean(np.ravel(difference)))\n",
+    "    \n",
+    "#image, predict, difference = plot_hist(images, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fa65dcd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Error First and Second Added: 20.017164930235474\n",
+      "233.22663391021266\n",
+      "Standard Deviaiton of Mean Errors: 0.16101183692475135\n",
+      "Average Time per Image for First: 0.023412495851516724\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Average Error First and Second Added: {np.mean(error_mean)}\")\n",
+    "print(np.std(image))\n",
+    "print(f\"Standard Deviaiton of Mean Errors: {np.sqrt(np.var(error_mean))}\")\n",
+    "#print(f\"Average Difference: {np.mean(diff_mean)}\")\n",
+    "print(f\"Average Time per Image for First: {np.mean(times)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f592fa32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]\n",
+      " [6 7 8]]\n",
+      "[4 5]\n",
+      "[4 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = np.arange(9).reshape((3,3))\n",
+    "print(b)\n",
+    "print(b[1,1::])\n",
+    "print(b[1,1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "dda442ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'difference' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10808/722042198.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdifference\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'o'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'font.size'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'difference' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize = (10,10))\n",
+    "ax = fig.add_subplot()\n",
+    "x = np.abs(predict-image)\n",
+    "y = difference\n",
+    "plt.plot(x,y,'o',alpha = 0.2)\n",
+    "plt.rcParams.update({'font.size': 20})\n",
+    "plt.xlabel(\"differnece to the true value\" )\n",
+    "plt.ylabel(\"differnece of min and max of true value of the surroundings\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58da6063",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = Image.open(images[0])    #Open the image and read it as an Image object\n",
+    "image = np.array(image)[1:,:]\n",
+    "#z = np.array([image[1-1,1-1], image[1-1,1], image[1-1,1+1], image[1,1-1]])\n",
+    "z = np.array([22554,22552,22519,22561])\n",
+    "print(z)\n",
+    "'''A = np.array([[-3,0,1],[0,-3,3],[-1,-3,4]])\n",
+    "y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "a,b,c = np.linalg.solve(A,y)'''\n",
+    "A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
+    "y = np.array([-z[0]+z[2]-z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "print(y)\n",
+    "a,b,c = np.linalg.solve(A,y)\n",
+    "print(a,b,c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2562feeb",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "i0 = (a*(-1) + b*(1) + c)\n",
+    "i1 = (a*(0) + b*(1) + c)\n",
+    "i2 = (a*(1) + b*(1) + c)\n",
+    "i3 = (a*(-1) + b*(0) + c)\n",
+    "print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "470cc137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 0\n",
+    "b = 2\n",
+    "c = 2\n",
+    "i0 = (a*(-1) + b*(1) + c)\n",
+    "i1 = (a*(0) + b*(1) + c)\n",
+    "i2 = (a*(1) + b*(1) + c)\n",
+    "i3 = (a*(-1) + b*(0) + c)\n",
+    "print(sum([(i0-z[0])**2,(i1-z[1])**2,(i2-z[2])**2,(i3-z[3])**2]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3292b395",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#z = np.hstack((image[0,:3], image[1,0]))\n",
+    "#x = np.array([-1,0,1,-1])\n",
+    "#y = np.array([-1,-1,-1,0])\n",
+    "A = np.array([[-3,0,1],[0,-3,3],[1,3,-4]])\n",
+    "#y = np.array([z[0]-z[2]+z[3], z[0]+z[1]+z[2], -z[0]-z[1]-z[2]-z[3]])\n",
+    "y = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+    "print(np.linalg.solve(A,y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9687830",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "0.5**2 + 1.5**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e98eed4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y1= [0,1,2,3]\n",
+    "y2=[4,5,6,7]\n",
+    "y3=[8,9,10,11]\n",
+    "np.vstack((y1,y2,y3)).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7e88aab",
+   "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": "3.8.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

prediction_MSE_kelly.ipynb

@@ -199,7 +199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,