{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imageio in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (2.8.0)\n",
      "Requirement already satisfied: numpy in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (1.18.1)\n",
      "Requirement already satisfied: matplotlib in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (3.2.1)\n",
      "Requirement already satisfied: pillow in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (from imageio) (7.1.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (from matplotlib) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (from matplotlib) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: six>=1.5 in /Users/alexliberzon/miniconda3/envs/openpiv_lite_jupyter/lib/python3.7/site-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n",
      "Obtaining file:///Users/alexliberzon/Documents/repos/openpiv_python_lite\n",
      "Installing collected packages: openpiv-python-lite\n",
      "  Attempting uninstall: openpiv-python-lite\n",
      "    Found existing installation: openpiv-python-lite 0.1\n",
      "    Uninstalling openpiv-python-lite-0.1:\n",
      "      Successfully uninstalled openpiv-python-lite-0.1\n",
      "  Running setup.py develop for openpiv-python-lite\n",
      "Successfully installed openpiv-python-lite\n"
     ]
    }
   ],
   "source": [
    "!pip install imageio numpy matplotlib\n",
    "!pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openpiv_python_lite import extended_search_area_piv as piv, get_coordinates, random_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "frame_a = np.zeros((32, 32))\n",
    "frame_a = random_noise(frame_a)\n",
    "frame_b = np.roll(np.roll(frame_a, 3, axis=1), 2, axis=0)\n",
    "threshold = 0.1\n",
    "\n",
    "def test_piv_32():\n",
    "    \"\"\" test of the simplest PIV run 32 x 32 \"\"\"\n",
    "    u, v = piv(frame_a, frame_b, window_size=32)\n",
    "    assert(np.abs(u-3) < threshold)\n",
    "    assert(np.abs(v+2) < threshold)\n",
    "\n",
    "\n",
    "def test_piv_16_32():\n",
    "    \"\"\" test of the search area larger than the window \"\"\"\n",
    "    u, v = piv(frame_a, frame_b, window_size=16, search_area_size=32)\n",
    "    assert(np.abs(u[0,0]-3) < threshold)\n",
    "    assert(np.abs(v[0,0]+2) < threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_piv_32()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_piv_16_32()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imageio\n",
    "\n",
    "def test_piv():\n",
    "    \"\"\"\n",
    "    Simplest PIV run on the pair of images using default settings\n",
    "\n",
    "    piv(im1,im2) will create a tmp.vec file with the vector filed in pix/dt (dt=1) from \n",
    "    two images, im1,im2 provided as full path filenames (TIF is preferable, whatever imageio can read)\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    # if im1 is None and im2 is None:\n",
    "    im1 = ('./test/img/frame_a.tif')\n",
    "    im2 = ('./test/img/frame_b.tif')\n",
    "\n",
    "    frame_a = imageio.imread(im1)\n",
    "    frame_b = imageio.imread(im2)\n",
    "    \n",
    "    frame_a[0:32, 512-32:] = 255\n",
    "\n",
    "    print(frame_a[0,0])\n",
    "\n",
    "    u, v = piv(frame_a,frame_b,\n",
    "                                    window_size=32,overlap=16)\n",
    "    x, y = get_coordinates(image_size = frame_a.shape, \n",
    "                           window_size=32, overlap=16)\n",
    "\n",
    "    print(x[0], y[0], u[0], v[0])\n",
    "\n",
    "    return x, y, u, v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[ 16.  32.  48.  64.  80.  96. 112. 128. 144. 160. 176. 192. 208. 224.\n",
      " 240. 256. 272. 288. 304. 320. 336. 352. 368. 384. 400. 416. 432. 448.\n",
      " 464. 480. 496.] [16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16.\n",
      " 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16. 16.] [ 0.95938837  0.98346617  1.04784319  1.05886846  1.20761461  1.26742268\n",
      "  1.34640937  1.42034135  1.39168986  1.40575022  1.36677195  1.40086036\n",
      "  1.3123752   1.24080693  1.30784351  1.1512783   1.16767496  1.01316333\n",
      "  0.94193456  0.90701145  0.78900817  0.78919477  0.60782558  0.67340273\n",
      "  0.59532751  0.50765493  0.4711314   0.47280763  0.3971003  -4.14878738\n",
      "         nan] [ 7.98213358e-01  7.63942158e-01  7.38243280e-01  6.83750739e-01\n",
      "  6.35287937e-01  5.40945934e-01  4.83806352e-01  4.30068140e-01\n",
      "  2.87314701e-01  1.70037066e-01  6.34608020e-02 -3.12106990e-03\n",
      " -4.91035959e-02 -2.30341249e-01 -2.85272009e-01 -3.86972508e-01\n",
      " -4.49199516e-01 -5.06701844e-01 -5.31754472e-01 -5.82787049e-01\n",
      " -5.91071528e-01 -5.77511702e-01 -6.52894696e-01 -6.09258543e-01\n",
      " -5.51941830e-01 -6.86901207e-01 -6.55891684e-01 -6.25888334e-01\n",
      " -6.58582637e-01 -1.62875551e+01             nan]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alexliberzon/Documents/repos/openpiv_python_lite/openpiv_python_lite/pyprocess.py:265: RuntimeWarning: divide by zero encountered in log\n",
      "  subp_peak_position = (peak1_i + ((log(cl) - log(cr)) / (2 * log(cl) - 4 * log(c) + 2 * log(cr))),\n",
      "/Users/alexliberzon/Documents/repos/openpiv_python_lite/openpiv_python_lite/pyprocess.py:265: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  subp_peak_position = (peak1_i + ((log(cl) - log(cr)) / (2 * log(cl) - 4 * log(c) + 2 * log(cr))),\n",
      "/Users/alexliberzon/Documents/repos/openpiv_python_lite/openpiv_python_lite/pyprocess.py:266: RuntimeWarning: divide by zero encountered in log\n",
      "  peak1_j + ((log(cd) - log(cu)) / (2 * log(cd) - 4 * log(c) + 2 * log(cu))))\n",
      "/Users/alexliberzon/Documents/repos/openpiv_python_lite/openpiv_python_lite/pyprocess.py:266: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  peak1_j + ((log(cd) - log(cu)) / (2 * log(cd) - 4 * log(c) + 2 * log(cu))))\n"
     ]
    }
   ],
   "source": [
    "x,y,u,v = test_piv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.quiver.Quiver at 0x7fcf60f734d0>"
      ]
     },
     "execution_count": 9,
     "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": [
    "pylab.quiver(x,y,u,-v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:openpiv_lite_jupyter] *",
   "language": "python",
   "name": "conda-env-openpiv_lite_jupyter-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}