AdaPS_multifocal_generation_AXIAL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multifocal generation with Adam optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This optimization model is non-convex. We select the Adam optimizer, a stochastic optimization algorithm to find a satisfactory solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Some preparations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-25T11:03:34.221124Z",
     "start_time": "2019-09-25T11:03:34.212794Z"
    }
   },
   "source": [
    "This notebook has been tested to be executable in the environment listed as below: \n",
    "- ubuntu 16.04 \n",
    "- python 3.6\n",
    "- tensorflow-gpu 1.4.0 \n",
    "- NVIDIA GPU + CUDA 8.0\n",
    "\n",
    "Please check your environment before run this notebook. If your environment is not consistent, we suggest you configure a same envoronment to avoid some annoying errors caused by version incompatibility of Tensorflow platform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Set up the forward model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import special\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-25T11:04:34.592359Z",
     "start_time": "2019-09-25T11:04:34.587877Z"
    }
   },
   "source": [
    "### Set the parameters about your optical system here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input parameters\n",
    "N_pupil = 800 # pixel number of pupil\n",
    "N_focal = 100 # pixel number of focal\n",
    "N_spot = 8 # pixel number of z\n",
    "N_depth = 500 # for final confirm\n",
    "\n",
    "spot_distance = 50e-6 # spot interval\n",
    "pixel_rho = 0.5e-5 # pixel size in pupil plane\n",
    "pixel_r = 0.2e-6 # pixel size in focal plane\n",
    "pixel_z = 1e-6 # pixel size along z-axis\n",
    "bias = 0e-6 # bias term\n",
    "sum_area = 5e-6 #  window size, for calculate the foci intensity in the focal plane\n",
    "\n",
    "# physical parameter\n",
    "f = 7.32e-3 # focal length of the objective\n",
    "wavelength = 920e-9 # wave length\n",
    "NA = 0.5\n",
    "n_obj= 1.33 # refractive index\n",
    "w0 = 5e-3 # incident beam waist radius, \n",
    "scat_length = 180e-6 # scattering depth\n",
    "\n",
    "k0 = 2 * math.pi / wavelength\n",
    "r_obj = NA /n_obj * f # diameter of back pupil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800,)\n",
      "(800,)\n",
      "(500,)\n"
     ]
    }
   ],
   "source": [
    "# coordinate\n",
    "rho_index = np.array(range(0, N_pupil)) * pixel_rho # coordinate of pupil\n",
    "r_index = np.array(range(0,N_pupil)) * pixel_r # coordinate of focal\n",
    "depth_array = (np.array(range(1,N_depth + 1)) - N_depth / 2) * pixel_z\n",
    "\n",
    "# test\n",
    "print(rho_index.shape)\n",
    "print(r_index.shape)\n",
    "print(depth_array.shape) # note it's column vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spot dpeth [-1.75e-04 -1.25e-04 -7.50e-05 -2.50e-05  2.50e-05  7.50e-05  1.25e-04\n",
      "  1.75e-04]\n",
      "[ 75. 124. 175. 225. 275. 325. 375. 425.]\n",
      "[0.57375342 0.32919299 0.1888756  0.10836802 0.06217652 0.03567399\n",
      " 0.02046808 0.01174363]\n"
     ]
    }
   ],
   "source": [
    "# spot position and intensity\n",
    "depth_ind = np.zeros([N_spot]); # depth index in the original array\n",
    "intensity_weight = np.zeros([N_spot]); # intensity weight of each foci\n",
    "spot_depth_indx = np.zeros([N_spot]); # depth of each foci\n",
    "if np.mod(N_spot, 2) == 0:\n",
    "    for i in range(0, N_spot):\n",
    "        spot_depth_indx[i] = spot_distance * (i + 1 - N_spot / 2 - 0.5) + bias\n",
    "        depth_ind[i] = np.int(spot_depth_indx[i] / pixel_z + N_depth / 2)\n",
    "        intensity_weight[i] = np.exp(-2 * spot_distance * (i + 1) / scat_length) # note: for two-photon, the intensity would be doubled\n",
    "else:\n",
    "    for i in range(0, N_spot):\n",
    "        spot_depth_indx[i] = spot_distance * (i + 1 - (N_spot + 1) / 2) + bias\n",
    "        depth_ind[i] = np.int(spot_depth_indx[i] / pixel_z + N_depth / 2)\n",
    "        intensity_weight[i] = np.exp(-2 * spot_distance * (i + 1) / scat_length)\n",
    "        \n",
    "# print spot location\n",
    "print('spot dpeth in micro', spot_depth_indx*1e6)\n",
    "print('spot dpeth in index', depth_ind)\n",
    "print('intensity weights', intensity_weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-25T11:06:05.725513Z",
     "start_time": "2019-09-25T11:06:05.720283Z"
    }
   },
   "source": [
    "### Define energy distribution of the input beam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'pupil plane')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define input beam\n",
    "gaussian_mask = np.exp(-np.power(rho_index / w0, 2));\n",
    "pupil =np.ones(N_pupil) * gaussian_mask;\n",
    "pupil_cut_ind = (np.abs(rho_index- r_obj)).argmin(); # find the nearest value in the rho coordinate\n",
    "pupil[pupil_cut_ind:] = 0;\n",
    "\n",
    "plt.plot(rho_index * 1e3, pupil)\n",
    "plt.xlabel('rho in mm')\n",
    "plt.title('pupil plane')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# summing area\n",
    "focal_sum_ind = (np.abs(r_index- sum_area)).argmin(); # find the nearest value in the rho coordinate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(800, 800)\n",
      "(25, 800)\n"
     ]
    }
   ],
   "source": [
    "# forward matrix\n",
    "H_matrix = special.jv(0, -k0 * np.matmul(r_index.reshape(-1, 1), rho_index.reshape(1, -1)) / f)\n",
    "print(H_matrix.shape)\n",
    "simplfied_matrix = H_matrix[:focal_sum_ind, :]\n",
    "print(simplfied_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the forward model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'z axis um')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test forward model\n",
    "Phi = np.zeros(N_pupil)\n",
    "buf1 = 2 * math.pi * rho_index * np.exp(1j * Phi) *  pupil # size: (N_pupil,)\n",
    "buf1_exp = np.tile(\n",
    "    buf1.reshape(1, -1), \n",
    "    (N_depth, 1)\n",
    ") # size: [N_depth, N_pupil];\n",
    "buf2 = np.exp(\n",
    "    1j * k0 * \n",
    "    np.matmul(depth_array.reshape(-1, 1), (np.power(rho_index, 2)).reshape(1, -1))\n",
    "    / 2 / f/f) # size: [N_depth, N_pupil];\n",
    "buf3 = buf2 * buf1_exp # size: [N_spot, N_pupil];\n",
    "\n",
    "h_psf = np.matmul(buf3, np.transpose(H_matrix)) # size: [N_spot, focal_sum_ind];\n",
    "\n",
    "axial_intensity = np.sum(np.power(np.abs(h_psf), 4), 1)# should divided the guided intensity\n",
    "lateral_intensity = np.power(np.abs(h_psf[np.int(N_depth / 2 - 1),]), 4)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(r_index * 1e6, lateral_intensity)\n",
    "plt.title('lateral intensity distribution')\n",
    "plt.xlabel('r um')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.plot(depth_array * 1e6, axial_intensity)\n",
    "plt.title('axial intensity distribution')\n",
    "plt.xlabel('z axis um')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Run optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\smile\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compl_exp_tf(phase, dtype=tf.complex64, name='complex_exp'):\n",
    "    \"\"\"Complex exponent via euler's formula, since Cuda doesn't have a GPU kernel for that.\n",
    "    Casts to *dtype*.\n",
    "    \"\"\"\n",
    "    phase = tf.cast(phase, tf.float64)\n",
    "    return tf.add(tf.cast(tf.cos(phase), dtype=dtype),\n",
    "                  1.j * tf.cast(tf.sin(phase), dtype=dtype),\n",
    "                  name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define phase as a variable\n",
    "# should be initialized by a spherical function\n",
    "phase_spherical = np.angle(np.exp(1j * k0 * bias * np.power(rho_index[:pupil_cut_ind], 2) / 2 / f/ f)) / 2 / np.pi\n",
    "x = tf.Variable(initial_value=phase_spherical, name='Phi', dtype=tf.float32, constraint=lambda t: tf.clip_by_value(t, 0, 1)) # valid part in pupil plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# some variables that needs not to be changed\n",
    "tf_rho_index = tf.constant(rho_index)\n",
    "tf_pupil = tf.constant(pupil)\n",
    "tf_simplfied_matrix = tf.cast(tf.constant(np.transpose(simplfied_matrix)), tf.complex64)\n",
    "\n",
    "buf2 = np.exp(\n",
    "    1j * k0 * \n",
    "    np.matmul(spot_depth_indx.reshape(-1, 1), (np.power(rho_index, 2)).reshape(1, -1))\n",
    "    / 2 / f/f) # size: [N_spot, N_pupil];\n",
    "tf_buf2 = tf.constant(buf2, dtype=tf.complex64)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Shape:0' shape=(1,) dtype=int32>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# phase\n",
    "# tf.shape(x)\n",
    "# tf.cast(x, tf.float64)\n",
    "# tf.cast(tf.zeros([N_pupil - pupil_cut_ind]), tf.float64)\n",
    "tf_Phi = tf.concat([x * 2 * np.pi, tf.zeros([N_pupil - pupil_cut_ind])], 0)\n",
    "tf.shape(tf_Phi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Shape_1:0' shape=(1,) dtype=int32>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculation buf1\n",
    "tf_buf11 =tf.multiply(tf_rho_index, tf_pupil)\n",
    "tf_buf12 = tf.cast(tf_buf11, tf.complex64)\n",
    "tf_buf1 = 2 * np.pi * tf.multiply(tf_buf12, compl_exp_tf(tf_Phi)) # size: (N_pupil,)\n",
    "\n",
    "tf.shape(tf_buf1) # size: (N_pupil,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Shape_2:0' shape=(2,) dtype=int32>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculation buf1_exp\n",
    "tf_buf1_exp = tf.matmul(tf.reshape(tf.cast(tf.ones([N_spot, 1]), tf.complex64), [-1, 1]), tf.reshape(tf_buf1, [1, -1])) # size: [N_spot, N_pupil], broadcast;\n",
    "tf.shape(tf_buf1_exp) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate buf3\n",
    "tf_buf3 = tf.multiply(tf_buf2, tf_buf1_exp) # size: [N_spot, N_pupil];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate PSF\n",
    "tf_h_psf = tf.matmul(tf_buf3, tf_simplfied_matrix) # size: [N_spot, focal_sum_ind];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define axial intensity\n",
    "tf_axial_intensity = tf.reduce_sum(tf.pow(tf.abs(tf_h_psf), 4), axis=1)# so here its 2P intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define lambda\n",
    "lambda_1 = 1\n",
    "lambda_2 = 10\n",
    "lambda_3 = 1e3\n",
    "n_round = 300000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define learning rate\n",
    "ada_learning_rate = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define loss\n",
    "loss_1 = -tf.reduce_sum(tf_axial_intensity)\n",
    "loss_2 = tf.reduce_max(tf_axial_intensity * intensity_weight) / tf.reduce_min(tf_axial_intensity * intensity_weight)\n",
    "loss_3 = tf.keras.backend.std(tf_axial_intensity * intensity_weight)\n",
    "# loss_3 = -tf.reduce_sum(tf.slice(tf_axial_intensity, [-1], [1]) # last element\n",
    "# tf.shape(loss_1)\n",
    "# tf.shape(loss_2)\n",
    "loss = tf.add(\n",
    "    tf.add(tf.cast(lambda_1 * loss_1, tf.float64), \n",
    "                     tf.cast(lambda_2 * loss_2, tf.float64)), \n",
    "                     tf.cast(lambda_3 * loss_3, tf.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\smile\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:189: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "starting at cost:  123566.09169316292\n",
      "step: 30000 cost: -70.50001776218414 loss1: -81.39184 loss2: 1.0066525 loss3: 0.0008252958\n",
      "step: 60000 cost: -106.17712378501892 loss1: -117.16078 loss2: 1.0058278 loss3: 0.00092538\n",
      "step: 90000 cost: -107.55162966251373 loss1: -118.61158 loss2: 1.0056214 loss3: 0.0010037359\n",
      "step: 120000 cost: -107.89044517278671 loss1: -118.62717 loss2: 1.0043657 loss3: 0.0006930643\n",
      "step: 150000 cost: -107.44330847263336 loss1: -118.6698 loss2: 1.0061777 loss3: 0.0011647142\n",
      "step: 180000 cost: -108.14122194051743 loss1: -118.67059 loss2: 1.002825 loss3: 0.00050112157\n",
      "step: 210000 cost: -107.6758326292038 loss1: -118.69222 loss2: 1.0053195 loss3: 0.000963195\n",
      "step: 240000 cost: -107.73123961687088 loss1: -118.705124 loss2: 1.0047783 loss3: 0.0009261014\n",
      "step: 270000 cost: -107.76016062498093 loss1: -118.69316 loss2: 1.00493 loss3: 0.0008837002\n"
     ]
    }
   ],
   "source": [
    "## optimization1\n",
    "optimizer = tf.train.AdamOptimizer(ada_learning_rate)\n",
    "train = optimizer.minimize(loss)\n",
    "init = tf.initialize_all_variables()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    print(\"starting at\", \"cost: \", sess.run(loss))\n",
    "    loss_array =np.array([])\n",
    "    for i in range(1, n_round):\n",
    "        sess.run(train)\n",
    "        loss_array = np.append(loss_array, sess.run(loss))\n",
    "        if not np.mod(i, np.int(n_round / 10)):\n",
    "            print(\"step:\", i, \"cost:\", sess.run(loss), \"loss1:\", \n",
    "                  sess.run(loss_1), \"loss2:\", sess.run(loss_2), \"loss3:\", sess.run(loss_3))\n",
    "\n",
    "    output_phase = sess.run(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Evaludation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'rad')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Phi = np.zeros(N_pupil)\n",
    "Phi[:pupil_cut_ind] = output_phase\n",
    "buf1 = 2 * math.pi * rho_index * np.exp(1j * Phi * 2 * np.pi) *  pupil # size: (N_pupil,)\n",
    "buf1_exp = np.tile(\n",
    "    buf1.reshape(1, -1), \n",
    "    (N_depth, 1)\n",
    ") # size: [N_depth, N_pupil];\n",
    "buf2 = np.exp(\n",
    "    1j * k0 * \n",
    "    np.matmul(depth_array.reshape(-1, 1), (np.power(rho_index, 2)).reshape(1, -1))\n",
    "    / 2 / f/f) # size: [N_depth, N_pupil];\n",
    "buf3 = buf2 * buf1_exp # size: [N_spot, N_pupil];\n",
    "\n",
    "h_psf = np.matmul(buf3, np.transpose(H_matrix)) # size: [N_spot, focal_sum_ind];\n",
    "\n",
    "axial_intensity = np.sum(np.power(np.abs(h_psf), 4), 1)# should divided the guided intensity\n",
    "lateral_intensity = np.power(np.abs(h_psf[np.int(N_depth / 2 - 1),]), 4) # still 2P\n",
    "\n",
    "fig1 = plt.figure(1)\n",
    "plt.plot(depth_array * 1e6, axial_intensity)\n",
    "plt.hold\n",
    "plt.plot(depth_array[depth_ind.astype(np.int16) - 1] * 1e6, axial_intensity[depth_ind[0].astype(np.int16) - 1] / intensity_weight * intensity_weight[0])\n",
    "plt.title('Optimized axial intensity distribution')\n",
    "plt.xlabel('z axis um')\n",
    "\n",
    "fig2 = plt.figure(2)\n",
    "plt.plot(rho_index * 1e3, Phi * 2 * np.pi)\n",
    "plt.title('optimized phase')\n",
    "plt.xlabel('r distnace mm')\n",
    "plt.ylabel('rad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## generate 2d phase\n",
    "phase_2d = np.zeros([2 * pupil_cut_ind, 2 * pupil_cut_ind])\n",
    "for theta_index in range(0, pupil_cut_ind * 2): # azimuth angle\n",
    "    theta = theta_index / (pupil_cut_ind * 2) * 2 * np.pi\n",
    "    for r_ind in range(0, pupil_cut_ind): # radius\n",
    "        i = np.max([\n",
    "            np.min(\n",
    "                [np.int(r_ind * np.cos(theta) + pupil_cut_ind), 2 * pupil_cut_ind]\n",
    "            ), \n",
    "            0])\n",
    "        j = np.max([\n",
    "            np.min(\n",
    "                [np.int(r_ind * np.sin(theta) + pupil_cut_ind), 2 * pupil_cut_ind]\n",
    "            ), \n",
    "            0])\n",
    "        phase_2d[i, j] = Phi[r_ind]\n",
    "phase_2d *= 2 *np.pi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 1099.5, 1099.5, -0.5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot phase mask\n",
    "fig3 = plt.figure(3)\n",
    "plt.imshow(phase_2d, cmap='hsv')\n",
    "plt.title('optimized 2d phase')\n",
    "plt.colorbar()\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build target folder\n",
    "target_dir = \"Ada_results/simulation/foci_{0}/dis_{1:.0f}um/\" .format(N_spot, spot_distance * 1e6)\n",
    "if not os.path.exists(target_dir):\n",
    "    os.makedirs(target_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the phase pattern\n",
    "sio.savemat('{0}/1d_phase_cut.mat'.format(target_dir), {'phase_cut': (output_phase * 2 * np.pi)})\n",
    "sio.savemat('{0}/1d_phase_full.mat'.format(target_dir), {'phase_full': (Phi * 2 * np.pi)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save simulation parameters\n",
    "with open(\"{0}/Parameters.txt\".format(target_dir), \"w\") as text_file:\n",
    "    text_file.write(\"N_pupil: %.0f \\n\" % N_pupil)\n",
    "    text_file.write(\"N_focal: %.0f \\n\" % N_focal)\n",
    "    text_file.write(\"pixel_rho: %.2f um \\n\" % (pixel_rho*1e6))\n",
    "    text_file.write(\"pixel_r: %.2f \\n\" % (pixel_r*1e6))\n",
    "    text_file.write(\"f: %.2f mm \\n\" % (f * 1e3))\n",
    "    text_file.write(\"wavelength: %.0f \\n\" % (wavelength * 1e9))\n",
    "    text_file.write(\"NA: %.2f \\n\" % NA)\n",
    "    text_file.write(\"n_obj: %.2f \\n\" % n_obj)\n",
    "    text_file.write(\"input_w0: %.2f mm \\n\" % (w0 * 1e3))\n",
    "    text_file.write(\"lambda_loss_energy: %.2f \\n\" % lambda_1)\n",
    "    text_file.write(\"lambda_loss_max_min: %.2f \\n\" % lambda_2)\n",
    "    text_file.write(\"ada_learning_rate: %.5f \\n\" % ada_learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save figures\n",
    "fig1.savefig('{0}/axial_distribution.png'.format(target_dir), dpi=220)\n",
    "fig2.savefig('{0}/1d_phase.png'.format(target_dir), dpi=220)\n",
    "fig3.savefig('{0}/2d_phase.png'.format(target_dir), dpi=220, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.75e-04, -1.25e-04, -7.50e-05, -2.50e-05,  2.50e-05,  7.50e-05,\n",
       "        1.25e-04,  1.75e-04])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spot_depth_indx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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