{
 "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": [],
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f87924e3cd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": [],
   "source": [
    "u,v = extended_search_area_piv(a,b,window_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00271353]] [[0.01545332]]\n"
     ]
    }
   ],
   "source": [
    "print(u-u0,v-v0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1, y1, p1 = find_first_peak(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2, y2, p2 = find_second_peak(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "(i,j) = find_subpixel_peak_position(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.256586154356011"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32-i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 [conda env:bospiv]",
   "language": "python",
   "name": "conda-env-bospiv-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": "3.7.9"
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