{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test find_subpixel_position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1212603/1798919731.py:4: DeprecationWarning: Please use `gaussian_filter` from the `scipy.ndimage` namespace, the `scipy.ndimage.filters` namespace is deprecated.\n",
      "  from scipy.ndimage.filters import gaussian_filter\n"
     ]
    }
   ],
   "source": [
    "from openpiv.pyprocess import find_first_peak, find_second_peak, find_subpixel_peak_position\n",
    "from openpiv.pyprocess import extended_search_area_piv, correlate_windows\n",
    "from scipy.ndimage import shift\n",
    "from scipy.ndimage.filters import gaussian_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from test_process import create_pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ffb985d2a00>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = np.random.randn(32,32)\n",
    "a = gaussian_filter(a,.5)\n",
    "\n",
    "u0 = 2.8  # to the right\n",
    "v0 = -5.3 # downwards\n",
    "\n",
    "b = shift(a,(-v0, u0),mode='wrap')\n",
    "plt.imshow(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/user/Documents/repos/openpiv-python/openpiv/test/test_find_peaks.ipynb Cell 6\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/user/Documents/repos/openpiv-python/openpiv/test/test_find_peaks.ipynb#W5sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m u,v \u001b[39m=\u001b[39m extended_search_area_piv(a,b,window_size\u001b[39m=\u001b[39m\u001b[39m32\u001b[39m)\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "u,v = extended_search_area_piv(a,b,window_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00271353]] [[0.01545332]]\n"
     ]
    }
   ],
   "source": [
    "print(u-u0,v-v0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = correlate_windows(a,b)# + correlate_windows(c,d)\n",
    "from scipy.ndimage.filters import gaussian_filter\n",
    "\n",
    "corr = gaussian_filter(corr,1.5)\n",
    "\n",
    "x,y = np.meshgrid(np.arange(corr.shape[0]),np.arange(corr.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1, y1, p1 = find_first_peak(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2, y2, p2 = find_second_peak(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(i,j) = find_subpixel_peak_position(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f87925f5210>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.plot_surface(x,y,corr);\n",
    "ax.scatter(j,i,p1,'o',color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26,\n",
       " 28,\n",
       " 60.07128420968547,\n",
       " 23,\n",
       " 28,\n",
       " 20.830743171795604,\n",
       " 25.74341384564399,\n",
       " 28.189462289160172)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,y1,p1,x2,y2,p2,i,j "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.256586154356011"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32-i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.810537710839828"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32-j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.12 ('echopiv')",
   "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": "3.8.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "f83b0c3a4910470a1212112b1707d582432916ed4ba8aec962241a050aa18fae"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}