examples/generate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9b4d5f5e",
   "metadata": {},
   "source": [
    "# Simulate overfocused data\n",
    "\n",
    "This notebook generates a file `fourdstem_overfocused.npy` with a dataset that coarsely simulates the pinhole camera projection in strongly over- or underfocused 4D STEM. Underfocused data is generated if the specified overfocus is negative.\n",
    "\n",
    "This dataset can then be used to test the interactive adjustment routine in the `stem_overfocus.ipynb` notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64ec82d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from microscope_calibration.util.stem_overfocus_sim import smiley, project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77039125",
   "metadata": {},
   "source": [
    "## Set parameters for simulation\n",
    "\n",
    "The interactive adjustment should then reach equivalent parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a26e070d",
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 64\n",
    "\n",
    "sim_params = {\n",
    "    'overfocus': 0.001,  # m\n",
    "    'scan_pixel_size': 0.000001,  # m\n",
    "    'camera_length': 0.15,  # m\n",
    "    'detector_pixel_size': 0.000050,  # m\n",
    "    'scan_rotation': 37,\n",
    "    'flip_y': False,\n",
    "    'cy': size//2,\n",
    "    'cx': size//2,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786e2a09",
   "metadata": {},
   "source": [
    "## Generate a test object\n",
    "\n",
    "This is a comic-like face with low symmetry to make it easy for humans to identify the orientation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d623219f",
   "metadata": {},
   "outputs": [],
   "source": [
    "obj = smiley(size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab02fd5a",
   "metadata": {},
   "source": [
    "## Show the test object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81dfea81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb3c96364d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "axes.imshow(obj)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fec2061",
   "metadata": {},
   "source": [
    "## Project to simulated 4D STEM dataset using a reference ray tracing implementation\n",
    "\n",
    "This uses a simple model to trace the rays from the pixels of a supposed detector through the object to the supposed focus point above or below the specimen as a function of scan position. It serves as a reference for the more complex TEMGYM Basic implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eaf9e402",
   "metadata": {},
   "outputs": [],
   "source": [
    "fourdstem_overfocused = project(obj, detector_shape=(size, size), scan_shape=(size, size), sim_params=sim_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f97e491",
   "metadata": {},
   "source": [
    "## Show a single simulated detector frame at the center of the scan\n",
    "\n",
    "Note how this is a magnified and rotated image containing the \"nose\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02563bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb3c290d310>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XD+f+r2dzA3K9qepHxURdVXbL38t1dZe5nltl27ZpIWPBjg78877vMwVHRESDExsQERFZwQZERERWsAEREZEVQyqEoHtRT6J7oU+6qOflBT1AvqhninQRWbwgPBAaF5GzRp8Ux2/PeM3QwYT6clybZ3Or6kp1UViXTs0dOpUijte+I4+bENnuXehFVVeqGtKhW2/ZUfXi+CRfrOtjUSkNdHo2t6quVDXUX92tAexe9AuGEIiIaHBiAyIiIivYgIiIyAo2ICIisoINiIiIrIi0fQAq/9+3b0FkVN941rbfrPfs+XTSSreNlZMzuuk4VdIkZXptyJipBFPQwndcJ620plJ+DU2klQ4pwkQmEkyjw2PE8UrobeqnQzepJNUVoFdbUc16aTfHQL11noiT7zCQglNRpd1UDnWGJix166rHCYrjuT75Rdzb6b625iUeFsfdpuD6i5+AiIjICjYgIiKygg2IiIisYAMiIiIr2ICIiMiKQZuC84qXaSVVOu7Oshtcz62bYNJNK5ngZVpJlY57dMJG13NLCSZAnWJSpZW8JKWVTCWVpNo6+XqqkbnDFD9WUjquM/HMv65/b54ojmcn6aXgJKq6Oi/KzOKLUjpOlYw73Jlm5DlN4ycgIiKygg2IiIisYAMiIiIr2ICIiMgKNiAiIrJi0O6ImnfFj0LWglPZuv7Xnh2PlCoxlShZe+zSfj+2uj5BHPcfllN9JgSSvUslZU+rFMenJlYZmf+bSW/0+7HpkXJicESY38ixSIpbxng2t6quVDWkQ7fewrvk8fY072pr0Rd3eTa3qq5UNaRDt95anEDI2Eut41wfh8p9u7/c78cG2zpQccv/5Y6oREQ0OLEBERGRFWxARERkBRsQERFZodWACgsLMXPmTMTHx2Ps2LG49tprUVZW1ucxHR0dWLZsGUaPHo0RI0Zg8eLFqK2Vl5EhIqLPL6214EpKSrBs2TLMnDkT3d3d+N73vocrrrgCBw8eRFzcx+uArVy5En/5y1+wceNGJCQkYPny5Vi0aBHeeKP/qSSV6Gp5baUrr/payNhfXvq96+cDgHOjekLGDit21jRFJ60UyGkXx3XSSqqkUky1/O8TEwmm8qqx4ripFJxEN6kkpYwAvbSSao6r4o6L4yZSTHljj4njf67PdT23iqqGVKTa0q6rZPm1/fPB3JCxRefv1Ztb4a1AhjieHil/P01Q1ZDEVF3tbc0MGVO9htLr3V9aDWjz5s19/v+pp57C2LFjUVpaii9+8YtobGzEk08+iWeeeQbz588HAGzYsAGTJk3Czp07MWfOnAEfKBERDS+urgE1NjYCAJKSkgAApaWl6OrqQn5+fu9jcnJykJmZiR07dohzBAIBNDU19bkREdHwN+AGFAwGsWLFCsydOxdTpkwBANTU1MDn8yExMbHPY1NSUlBTUyPOU1hYiISEhN5bRob8EZeIiIaXATegZcuWYf/+/SguLnZ1AAUFBWhsbOy9VVbKfyFPRETDy4A2pFu+fDleeuklbN++HePG/eviVmpqKjo7O9HQ0NDnU1BtbS1SU+UNrvx+P/z+0Iu60bXtiIwY+MVuKZgAqMMJOhf6TPHyYrEqnBCzz/3SPdrhBMXFYi9JF4tNXSj2slZ0LiJLF4q9plqeSVUTOpR1NVWuZR2qC+WqC+u5cRWun3MwUdXVj+u+4Hpu6TUMtHShqB9fq1U1juNg+fLl2LRpE1599VWMHz++z/0zZsxAVFQUtm7d2jtWVlaGiooK5OXl6TwVERENc1qfgJYtW4ZnnnkGzz//POLj43uv6yQkJCAmJgYJCQm4+eabsWrVKiQlJWHkyJG44447kJeXxwQcERH1odWA1q5dCwCYN29en/ENGzbgP//zPwEAjz76KMLDw7F48WIEAgEsWLAAa9asMXKwREQ0fGg1oP7s3BAdHY2ioiIUFfXnN4BERPR5xbXgiIjIikG7Id3UbzyACF/fDenG7O7/H6mGH68Tx//77RdcHd/pmEiUqCiXu6h3v2maboIpGCWPq5J3Jqye6d33TZUQMrEhnW5irqpb/nH87cm5ro9F5c/bZ3s2t6q2VDWkQ6fe0pIbxfGl40vcH4jCDSM+9GxuQK4tVf3o0qm3fQ3pIWPdrQG8dtU6bkhHRESDExsQERFZwQZERERWsAEREZEVbEBERGTFsEjBqRJvOnTScbpJExMJJilpAgDl77pfPdzLpBJgJq2kYiLFlOOrFsdzfQNaKrEPVQrORFpJt65UNSQxUVcA4GuQayus2/3cqrrSrSGJbl2pakhioq4AICIs9LU91Clv2qlLqi2d+mEKjoiIBjU2ICIisoINiIiIrGADIiIiK9iAiIjIiiGVgoutl3djjP/7Uc+O52e7n/dsblWK6UynlbxMKqkkXVzj3eSQU0w6SSXATFpJSioB5tJKkpVHr/NsblW9qWpIxURtdcXLb10p02vdT67wi4nFns2tqjdVDUlM1VV5V3LI2JrKy/r99UzBERHRoMYGREREVrABERGRFWxARERkBRsQERFZMWhTcLO+/H8RGRX92V+goErG/b93Xh7wnJ/QTZpIiRJAL1Wi64PtWZ7NrUowqVJJOnQTTNUfJojjz178a9fHojLD7/NsblVtqWpIh069ffBRknzHgXjXx6HSHePdW5GqriaNMpOYu23sa/1+bEZklzg+NiLOyLFIXmiN9Wxuqa6YgiMiokGNDYiIiKxgAyIiIivYgIiIyAozOyMNQt3Z/V/OxhTdC8W3Z8gXLo1cLJ7cLI8buIgc7uESPSqqsIHKV17/PyFjusEE1cXiup7QcVMXkLMi5R/JcvlQjFDWkMRQXUV0SGNh4mMDo9yHE06ckM/RVAhBoqoflbqeVnFcp7bagp3ieH6MPP5Ke2K/51b5YvKRkLGO6C70J5bBT0BERGQFGxAREVnBBkRERFawARERkRVsQEREZMWgTcGdzIlAhD+iz9jogz3iY2NqhEiNh7xOKknpuLv3LjYzuZRiUiSYpKSS11RppXCf/L3XISXjAGDHvF+5nls3waRKK3lJSisBwO8/muV+ckU6LqLUferSf0ovHded7GFkUGFn+zkhYxnxh43MraqtEWFRrufOj2kQx6V03P5295tffho/ARERkRVsQEREZAUbEBERWcEGREREVrABERGRFYN2Q7rJ/+dBRPj6bkg35i05DWLCc8+u92xu1XpLJlIlvy/TSzB11MeEjMV94G0YsjXbu8SXKh0XVjPwzQw/8Y8bftrvx44Kl5+vy3Gf3lN57ORUz+ZW1ZVUPwOhU3OOHIJD27ne1dV38rZ4Nvd1inScqoZ06NZbvSKN+YeGGa6Oo6OlCw/k/Y0b0hER0eDEBkRERFawARERkRVsQEREZAUbEBERWTFo14KTfHihvK6WTjruLxt/q7gnQhw1kWLK9deJ416srfQJnbRSa5a8xaluOk6VVoo94gsZM5VgiomT5+mA+0SRik5aKSrMTF1JaaWbEkvFx7pNMAHA1LQqcXx3feiaZ6ao6kdFqitAr7YSk1vE8d+UzxXHv5X9Rr/nVjnYKa+PNzfauzXsVGk3Fam2TNTVp/ETEBERWcEGREREVrABERGRFWxARERkhdZV5rVr12Lt2rX45z//CQCYPHky7rvvPixcuBAA0NHRgbvuugvFxcUIBAJYsGAB1qxZg5SUFOMH/u+kcMKbBb80Mrd0EVl1AVn3Qp8JXl4sVoUTYivcZ1d0LyDHjWp3/Zy6vvSz/xLH9/yX+9pShROqe9yfp244YW/TONfPqasrPnQFsMgWzRSCglRbvjknjcytE06Y7D9h5Dl1xIbLP1cw8N6kqqvvVFwTMtbV2r/n0/oENG7cODz00EMoLS3Fnj17MH/+fFxzzTU4cOAAAGDlypV48cUXsXHjRpSUlKCqqgqLFi3SeQoiIvqc0Pqn7NVXX93n/x944AGsXbsWO3fuxLhx4/Dkk0/imWeewfz58wEAGzZswKRJk7Bz507MmTPH3FETEdGQN+BrQD09PSguLkZrayvy8vJQWlqKrq4u5Ofn9z4mJycHmZmZ2LFjh3KeQCCApqamPjciIhr+tBvQvn37MGLECPj9ftx2223YtGkTzj//fNTU1MDn8yExMbHP41NSUlBTU6Ocr7CwEAkJCb23jAzv/jiTiIgGD+0GNHHiROzduxe7du3C0qVLsWTJEhw8eHDAB1BQUIDGxsbeW2Vl5YDnIiKiocP1hnT5+fk455xzcP311+Pyyy/HqVOn+nwKysrKwooVK7By5cp+zXe6DemiPwq6OVQAwPafFmk9XkorVXTLy3eYIqVKTNm9LzQd5zspJ7J06aSYukfIZRc1ybtfwUZsT/BsblUyTpV206VTc0e75A3A1lRfZuRYJO+UnOfZ3Kq6UtWQjtgpp7Qenz36Q3H89rTXXB+LyrwY9+97Kqq6UtVQf7U29+DL09/3fkO6YDCIQCCAGTNmICoqClu3bu29r6ysDBUVFcjLy3P7NERENMxopeAKCgqwcOFCZGZmorm5Gc888wy2bduGLVu2ICEhATfffDNWrVqFpKQkjBw5EnfccQfy8vKYgCMiohBaDaiurg5f//rXUV1djYSEBEybNg1btmzBl770JQDAo48+ivDwcCxevLjPH6ISERF9mlYDevLJJ097f3R0NIqKilBUpHedhYiIPn+4FhwREVkxaDekax8NRHxq36/oj7x7Pp20UmbkCHFcNx2nSppIiRpTCabE9NCUWdvJUUbmVtFJK3Udkl8T3XRc60lhQ74p8vpUI/cr1s/ScPmypeL49jVPuJ5bRTeppEpq6dRW+UdjxHFVmqxtv7e15RVV2k1Feg11k3ETouQar5CXZFS+D+lIi5A3rjzq3d54ffATEBERWcEGREREVrABERGRFWxARERkBRsQERFZMWhTcF5R7ThpIlGimmNbu/s+r5tgUqWVhipVOq4zxX1cp0kzHZfwT0UsyUNSWslUUkmqrZX7v2Jkbp10nKkdUXU0VCmShJopOInqZ/Onmc+7nhuQU7eq9yDVLs628RMQERFZwQZERERWsAEREZEVbEBERGQFGxAREVnhekdU0z7ZEXXC9x5ERHT0Z38BgL994yeeHY+UKjGVKHmjI6rfjz0QOEsc/035XCPHIuncmeTZ3Kr14UZON7PgX31N/9dJi6qTvw+pb3qXHNpatNazuVV1paohHbr11lAvp7Jij7hff0+lLdO7lOLMqe+L47kjj7ue+6bEUq3HJ4eHvoamduCVPNF4dr8f297Sjbtn/t37HVGJiIgGgg2IiIisYAMiIiIr2ICIiMiKIbUUz9f/Y6s4/oeGGSFjuhf0VNqCocu0eHmhD9C7WPyt7DfEcZ2LxaoLxThXXqLGxAXkqOYzv+yKKmygUjNL/j7rhBNGHDopjl8z/3px/PlX/9TvuVXO9zWL4wcCrqdWUtaQQptQW7p1FaaIT8V9EPq21pplJpiwrzpdHDcRQlCRwgYqqoCU7nvWqWBHyNh18YfFx25sztGa+9/xExAREVnBBkRERFawARERkRVsQEREZAUbEBERWTFol+L5/o4rED1CL7XUH6p0nE7SxFQKTkqaAO5SJZ/lpzsWeDa3KsWkSitJYi6uN3IsjS3CMk5H44zMLTn3afcbmJ2OlI5T1Y+Kibra25wpjr+yf5LruVXiyr1btkeVjotOljeuVPnaxDddH8uUmEpxPD+mwfXcKi2OoV0NP6W5OYjzJtVyKR4iIhqc2ICIiMgKNiAiIrKCDYiIiKxgAyIiIisGbQrunN8VICK2b5JJJ2kynBIlgDrBpEol6dBNMEXWy+lE/ynv1nfrzG3xbG5VOi7uhPvzSXulTuvxwX/Kdfu7I6+6PhaV71dd4dncqtpS1ZAOnXrrUe1tOVleN8+Eh3P/17O5Afm9LFYjzXs6dT2t/X5sZXfo97KlOYj5U48zBUdERIMTGxAREVnBBkRERFawARERkRVsQEREZMWQ2hFVRZV4k7zSniiO66TjVEmTWMjjOokSlTkx74vjJlJwKrpJpcAoOVCpk1ZSrQUXo3i8uOabpq5Rih1OT3j346FKu6l8/dz5IWO6yTgprQQAt419LWRsXd1lWnOrnHWWvCNsbX2KkfklysSb5EC8PK6ZjssaHXqeayrl1/D2jNDXeyA+6A5dx26Sz7t181T14wY/ARERkRVsQEREZAUbEBERWcEGREREVgypEML2+nPF8SkZehd0Japwwpfj2lzPPTZCXupFFU7w4mLf6Xh9oVgKJyRO/sjI3Akj5E3ZpHBC1yn3gQVdbeeMEsejNUMIEimYAAAPHP6767mlYAKgDiccOuVdqEAl6OW7lyKckPXFD1xPrRtOyI4ys0mjjtHhobGfSsib97nBT0BERGQFGxAREVnBBkRERFawARERkRVsQEREZIWrDekeeughFBQU4M4778Rjjz0GAOjo6MBdd92F4uJiBAIBLFiwAGvWrEFKSv9SMp9sSHfZS7chMs4/0EPzNFEyyRer9fgeJyiO7+00nyr5xJ1lN3g2d+078vcyqtn9Bm5xc/S+P+2dcmKwtVZOHpqQ9aJ3ezhGb31XHP/L0R2u59apt8OdaeL42mOXuj4OlZOvp3o2t6NIzHUmyj+burKn9T/VODWxShz/ZtIbRo5Fcl6UdwlQqa4835Bu9+7d+PWvf41p06b1GV+5ciVefPFFbNy4ESUlJaiqqsKiRYsG+jRERDRMDagBtbS04MYbb8T69esxatS//s6hsbERTz75JH7+859j/vz5mDFjBjZs2IB//OMf2Llzp7GDJiKioW9ADWjZsmW48sorkZ+f32e8tLQUXV1dfcZzcnKQmZmJHTvkXyEEAgE0NTX1uRER0fCn/bfExcXFeOutt7B79+6Q+2pqauDz+ZCYmNhnPCUlBTU1NeJ8hYWF+NGPfqR7GERENMRpfQKqrKzEnXfeiT/+8Y+IjjZzUaugoACNjY29t8pK90uUEBHR4Kf1Cai0tBR1dXW48MILe8d6enqwfft2/OpXv8KWLVvQ2dmJhoaGPp+CamtrkZoqJ1z8fj/8/oGn3VT+3jxRHM9O8m5dJVXaTSXXJ7/8JtJKS8eXiOMmUkzBs+T113BYtW2ce6q0m0pcSug6e7rJuOha+ftTOyt0LOXNLq25Vf70/jbFPeZ/Rj6hqiGJqbqqrk8IHcxpFx/rN1BXEfLUQKLrqZVUaTeV356cK47rpOPSI+UkaosTEMdHhLmvq3OjQjd0bIrq33uhVgO6/PLLsW/fvj5j3/jGN5CTk4O7774bGRkZiIqKwtatW7F48WIAQFlZGSoqKpCXl6fzVERENMxpNaD4+HhMmTKlz1hcXBxGjx7dO37zzTdj1apVSEpKwsiRI3HHHXcgLy8Pc+bMMXfUREQ05Blf0PzRRx9FeHg4Fi9e3OcPUYmIiP6d6wa0bdu2Pv8fHR2NoqIiFBUVuZ2aiIiGMa4FR0REVgzaHVHfPzAO4Z+KeqvWW9JNm7jVGJQjNSYSJYCcjituGWNkbinFpEowiUklj+mm3XRIyTgA6HnX/XnWzpKPW5WOe/6Jx10/py4prQQAhzvdz61Kx923+8uu5w5opuPCzQQStZRXjQ0ZM/W+pErH/WDsP1zPrZOOUz3WDX4CIiIiK9iAiIjICjYgIiKygg2IiIisYAMiIiIrXO2I6oVPdkTNXH8vwmP7puAWnb/Xs+c1kShRUaXjTKRKXmodp/X4va2ZIWN/Ppjr+jhOJybWQMxKU3urz/Ucvvf7vwbZD776J3H8qrjjro/DBlVdSfUzEFo1Vy///MRUe/fv5/apqsXj3FO9j+XGVbieW7fevHpvamoO4uycau92RCUiInKDDYiIiKxgAyIiIivYgIiIyIpBuxSPRHXhUiecoLrQp7roOlQvIutcLFa9ftrhBMXF4nZhM7WYzGa9uRXCws58hkYVOJCYqiudZZ5MhFsu9MvLXpkKIYgU9aPSniZveqYTTggqVn5SLfOjWhZIx4668eJ47nj3IQQV3WXCuBQPERENa2xARERkBRsQERFZwQZERERWsAEREZEVQyoFpyKltVbPfMHI3FKKSZVgMrUhnQ4v00rKdNz22a7nbq+IF8dV6TgbabfRc2o8m1uVjrthxIeu59ZdXqWq+8y/ttnpdSFj5fUZRuaW0nH+ejP/1tZJx6UlNxp5Th1HuiLE8Vz3K1Mp6+q9ro6QsZZ+1hQ/ARERkRVsQEREZAUbEBERWcEGREREVrABERGRFUNqQzoTqZKl40u0Hp/jqw4Zy/V5Gx6UUiWm/Pbk3JCxfQ3pRuYuf1cjxTRaTmTFjjC/3tQnEmK8e11VdSXVz0Do1FxEmPzvykOdbUaORbLy6HWeza2qK1+D+38/h3XrPb4rXn67TJle6/pYVH4xsdizuVV1paqh/mpqDmLUeUe5IR0REQ1ObEBERGQFGxAREVnBBkRERFawARERkRWDdi248OpohEf3TcHBw7WVdNJKezvl6IxuOk6VNJnkiw0ZM5VguiS+LGTMVApOSZF4k7S1yOtN6abjOt5LCB1D6BhgJsG0rSFHHM8ZayYFJ9FNKkl1BejVVnlXsjh+e8Zr4viaysv6PbeK76xW+Y4GeT1BE1RpN5Xad1JCxnTratIo+fHr6uTX8Lax8muu46OgvMPr2Ig413P3Bz8BERGRFWxARERkBRsQERFZwQZERERWDNoQgle8vFisCifM8LvfDUr3ArLqYrGXVBeLOzvcl5kqnBBeFS2O65AuIAPqi8iqi8Veki4Wm7pQLNXWC61yvenSCSd88FGSkefUEa65FI8OVV3Nm/eukfmlcIIqmJAR2WXkOU3jJyAiIrKCDYiIiKxgAyIiIivYgIiIyAo2ICIismLQbkh39uoHQpfiUTCVKpFIqRJTiRKdFFNbsFMcf6U90cixSO7eu9izuVXJuJgDMUbmD4zqf1l3J8vfz/wph4wci+SB9L95NreqrlQ1pEO33va3y5vJ/b5slutjUYko9W6JHlVdqWpIh2695cZXhIxdF3/Y9XGojAiL6vdjm5qDOCvnODekIyKiwYkNiIiIrGADIiIiK9iAiIjICjYgIiKyQisF98Mf/hA/+tGP+oxNnDgRhw9/nLzo6OjAXXfdheLiYgQCASxYsABr1qxBSoq8JpLkdCk4naSJqQSTl0mTwZJWUiWVVEwkmDrq5bRb3AdmlieU0komkkqAXm1J9XM6JmprVLicHu1yelzPraor3RqS6NaVqoYkpurKCZPH2851/zOrqivdGpLo1pWqhiRSXXmWgps8eTKqq6t7b6+//nrvfStXrsSLL76IjRs3oqSkBFVVVVi0aJHuUxAR0eeA9j8LIiMjkZqaGjLe2NiIJ598Es888wzmz58PANiwYQMmTZqEnTt3Ys6cOeJ8gUAAgcC/tltuamrSPSQiIhqCtD8BlZeXIz09HRMmTMCNN96IioqPPx6Wlpaiq6sL+fn5vY/NyclBZmYmduzYoZyvsLAQCQkJvbeMDPcf5YmIaPDTakCzZ8/GU089hc2bN2Pt2rU4duwYLrnkEjQ3N6OmpgY+nw+JiYl9viYlJQU1NTXKOQsKCtDY2Nh7q6ysHNCJEBHR0KL1K7iFCxf2/ve0adMwe/ZsZGVl4dlnn0VMzMCWUPH7/fD75c3GiIho+HIVDUlMTMR5552HI0eO4Etf+hI6OzvR0NDQ51NQbW2teM3os3SP7kJ4TMSAj+2V/ZPEcS+TJrpMpJJU8mMaxPHHTk51PffXJr4pjqtSTDppJVN8DaFxpW5Dm8RKtfWdvC1G5t7YLO/YK6WYdJJKpuT668RxEyk4VV2tf+NS13O3Zslbn6rScaq0m5f21MivoYn3JlVd3ZrwT9dzR4WFvk9HhfXvBXT1d0AtLS14//33kZaWhhkzZiAqKgpbt27tvb+srAwVFRXIy8tz8zRERDQMaX0C+s53voOrr74aWVlZqKqqwv3334+IiAh89atfRUJCAm6++WasWrUKSUlJGDlyJO644w7k5eUpE3BERPT5pdWAjh8/jq9+9av46KOPMGbMGFx88cXYuXMnxowZAwB49NFHER4ejsWLF/f5Q1QiIqJP02pAxcXFp70/OjoaRUVFKCoqcnVQREQ0/HEtOCIismLQ7oiafdeDiPD3TfnorLeUmNwijn8r+w1Xx3c6JhIlKqrEXL2BdeP+0DBD6/F7m8aJ47v3neP6WFRiK8ys5SVR1ZWqhnTo1ttk/wlxfG60mXXsJNU97Z7NraotVQ3p0Kk330k5URvZ4l3czTfnpGdzA3JtqepHl069XXVW6Pe42+nCNjzPHVGJiGhwYgMiIiIr2ICIiMgKNiAiIrLCuyu7Z5DOxeLflM8Vx3UuFqsu9L3RESWOm7iAHBvuk+8wEEJQ0b1QPHPq++K4iYvF3SPkrIyJi8i+Gvn7BkNL90h0LxZLtaVbV9KSKQCQGTkiZKyi230AAwC+EFcujpsIIaioakhiqq6kebr3jxIfGzvllNbcKiUnzwsZm5xmJoQgkcIGbvETEBERWcEGREREVrABERGRFWxARERkBRsQERFZMaRScF6mlVTpuEenPOt6bt10nCqt5BWvk0pSOu6dktAEz0DopJhUj/WSlFQCzKSVVHU1Lyboem4pGQeo03FHu9TLrXglMb1JHG87KafPdHhZK22a6bjs0R96diwqXi7P9O/4CYiIiKxgAyIiIivYgIiIyAo2ICIisoINiIiIrBi0G9JNuO8BhEdHf/YXKHiZKLk97TWtx0+IktM6qqSRCdvavfu3xZrqy8Tx8o/GuJ5blRBSUa3Z5WWKafql73k2t6q2VDWkQ6feVBsgqpJ3Jqzc/xXP5lbVlakN6XTqrTNJfm1Vayma8NPM5z2b+5bMi0PGuCEdERENamxARERkBRsQERFZwQZERERWsAEREZEVQ2otOB0NVYrkhYfrKukmlVTraplIK82N9i7FdGmSnAIzkYJT0U0rmVgLTpVWknZ4NZVg+kdrtjg+IbHUyPwSVQ1JVOsX6tbVgcBZIWOqXYlV6zTq6EyVjzvyiGKnYQNU9aOi2jlYp7ZyRx4Xx//QIO9mepOBuoqYeG7ImNMTAOQlJvvgJyAiIrKCDYiIiKxgAyIiIivYgIiIyAo2ICIismLQpuAiWsMQ0d03yaRKMemmTdzyOqkkpePSImKMzC2lmFQJJimp5DUv00qqJF1bZrfruXUTTKq0kpfagp3iuIkdeFXpuCcaz3Y9t246rqHeuzUWVaKaQ2urM8nM3KraumVuieu5ddJxt33pP10/36fxExAREVnBBkRERFawARERkRVsQEREZMWg3ZDuvFUPIsLfd0O61iz3F4tVTFzQU1Etd5Ec7v7Cuu4F5FPBjpCxjc05ro/jdH66Y4Fnc8cqwglhBqpap96ik9vF8a9NfNP9gSisSNrn2dyqupLqZyB0am5vc6Y4/sr+SUaORRJX7t0SPaq6UtWQDt16e+5n88Xx5J3ulizr7glga/mj3JCOiIgGJzYgIiKygg2IiIisYAMiIiIr2ICIiMiKQbsUjyTuA/lwTaSVfl82Sxw3kWLaGxgrjufHNLieW0UnrXRd/GFxXDcdp0or5U85FDJmKsHUmSjH3fyn9Daw06GTVjJVV1NiKkPGXmlPFB9roq5MLM+jS1U/KlJdAXq1FVkvL0MVGOVdXUXXKt52k11PraRKu6nUzwndXNJtMk7CT0BERGQFGxAREVnBBkRERFawARERkRXaDejEiRO46aabMHr0aMTExGDq1KnYs2dP7/2O4+C+++5DWloaYmJikJ+fj/LycqMHTUREQ5/WWnCnTp3CBRdcgMsuuwxLly7FmDFjUF5ejnPOOQfnnPPxpkkPP/wwCgsL8fTTT2P8+PG49957sW/fPhw8eBDR0dGf8QynXwtOR8+M5gF/7WdRJZikpNLpmEgrxSrWk6vraXU9t8r3q67wbG5VgkmVVlIxkVbqUZXfZO9q6+Hc//VsblW9qWpIYqquKrtDv5/r6i4zMrdk27Zpns0NyPWmrB8VA3XVUy5vxpdY5npqpdEb3wkZ63Y68Wpb8WeuBacVw3744YeRkZGBDRs29I6NHz++978dx8Fjjz2GH/zgB7jmmmsAAL/73e+QkpKC5557DjfccIPO0xER0TCm9Su4F154ARdddBGuu+46jB07FhdccAHWr1/fe/+xY8dQU1OD/Pz83rGEhATMnj0bO3bsEOcMBAJoamrqcyMiouFPqwEdPXoUa9euRXZ2NrZs2YKlS5fi29/+Np5++mkAQE1NDQAgJSWlz9elpKT03vdphYWFSEhI6L1lZGQM5DyIiGiI0WpAwWAQF154IR588EFccMEFuPXWW3HLLbdg3bp1Az6AgoICNDY29t4qK/WuoxAR0dCk1YDS0tJw/vnn9xmbNGkSKioqAACpqakAgNra2j6Pqa2t7b3v0/x+P0aOHNnnRkREw59WCGHu3LkoK+sbp3jvvfeQlZUF4ONAQmpqKrZu3Yrc3FwAH6fadu3ahaVLl7o+WK1UyYF4eVwzaZI1+mTImG7aTUW1lteX49pczz02Ik4cl1JMUiLpdG4b+5o4rpNiOnQqRRzXTbupSGt5RbYbWh9Oqi1FXUn1czprKuXX8PYM+TWXZEfVi+Ox4bFaxyJR1VVpoNP13Lp1paohScr0WnG89p3+zwEAUc1yDWkn3iSK96wen/vtfRsmyuOqdFzsh/1fX7N93uSQse6uDuBvn/21Wg1o5cqV+MIXvoAHH3wQX/nKV/Dmm2/iiSeewBNPPAEACAsLw4oVK/DjH/8Y2dnZvTHs9PR0XHvttTpPRUREw5xWA5o5cyY2bdqEgoICrF69GuPHj8djjz2GG2+8sfcx3/3ud9Ha2opbb70VDQ0NuPjii7F58+Z+/Q0QERF9fmhvx3DVVVfhqquuUt4fFhaG1atXY/Xq1a4OjIiIhjeuBUdERFYM2g3pgpFA2Bk8Op2LxSYuFAPqi8WHhOu5k3zuLyADwOjwmJCxSvT/guNADKaLxRLHQJ11npAvzkMzhKBDVT8qhzrlcItObfU4QXE81ye/iHs73dfWvER5w0SdutKlUz8AECacpm5ddWTqBTmiatwHdtrS5POMNb/3nIifgIiIyAo2ICIisoINiIiIrGADIiIiK9iAiIjIikGbgvOKl2klVTru0QkbXc+tm2BSpZW85GVaSZWOO/m6vMagDinBBKhTTJ2JZ/61/Xtz6Foq2Ul6KTgVqbbOizLzh+M66bjDnWlGnlNH8KwO+Y7DoWlRXaq6ap/gftkiAOhK7QoZUyXjfE2GlqEyjJ+AiIjICjYgIiKygg2IiIisYAMiIiIrBl0IwXE+3vsiGAi9OOgYWDEm2CFfQO5uDbifXKGl2buL1k0+eW6dEEJLp5nja++Sv0FevrY9Qp2Yoqo3VQ1JTJ17IDL0gnNLlId15eHcgFxzqvpRMfHaBtvk+ukJeHfRPthuJoQgzt3RI47rnk93V2i9aX1998ev6yfv5yphzmc94gw7fvw4MjIybB8GERG5VFlZiXHjxinvH3QNKBgMoqqqCvHx8WhubkZGRgYqKyuH9VbdTU1NPM9h4vNwjgDPc7gxfZ6O46C5uRnp6ekID1df6Rl0v4ILDw/v7ZhhYR9/bBw5cuSw/uZ/guc5fHwezhHgeQ43Js8zISHhMx/DEAIREVnBBkRERFYM6gbk9/tx//33w+/32z4UT/E8h4/PwzkCPM/hxtZ5DroQAhERfT4M6k9AREQ0fLEBERGRFWxARERkBRsQERFZwQZERERWDOoGVFRUhLPPPhvR0dGYPXs23nzzTduH5Mr27dtx9dVXIz09HWFhYXjuuef63O84Du677z6kpaUhJiYG+fn5KC8vt3OwA1RYWIiZM2ciPj4eY8eOxbXXXouysrI+j+no6MCyZcswevRojBgxAosXL0Ztrbzj6WC1du1aTJs2rfcvx/Py8vDXv/619/7hcI6f9tBDDyEsLAwrVqzoHRsO5/nDH/4QYWFhfW45OTm99w+Hc/zEiRMncNNNN2H06NGIiYnB1KlTsWfPnt77z/R70KBtQH/605+watUq3H///Xjrrbcwffp0LFiwAHV1dbYPbcBaW1sxffp0FBUViff/5Cc/weOPP45169Zh165diIuLw4IFC9DR4d2Kz6aVlJRg2bJl2LlzJ15++WV0dXXhiiuuQGtra+9jVq5ciRdffBEbN25ESUkJqqqqsGjRIotHrW/cuHF46KGHUFpaij179mD+/Pm45pprcODAAQDD4xz/3e7du/HrX/8a06ZN6zM+XM5z8uTJqK6u7r29/vrrvfcNl3M8deoU5s6di6ioKPz1r3/FwYMH8bOf/QyjRo3qfcwZfw9yBqlZs2Y5y5Yt6/3/np4eJz093SksLLR4VOYAcDZt2tT7/8Fg0ElNTXUeeeSR3rGGhgbH7/c7//3f/23hCM2oq6tzADglJSWO43x8TlFRUc7GjRt7H3Po0CEHgLNjxw5bh2nEqFGjnN/85jfD7hybm5ud7Oxs5+WXX3YuvfRS584773QcZ/h8L++//35n+vTp4n3D5Rwdx3Huvvtu5+KLL1beb+M9aFB+Aurs7ERpaSny8/N7x8LDw5Gfn48dO3ZYPDLvHDt2DDU1NX3OOSEhAbNnzx7S59zY2AgASEpKAgCUlpaiq6urz3nm5OQgMzNzyJ5nT08PiouL0drairy8vGF3jsuWLcOVV17Z53yA4fW9LC8vR3p6OiZMmIAbb7wRFRUVAIbXOb7wwgu46KKLcN1112Hs2LG44IILsH79+t77bbwHDcoGVF9fj56eHqSkpPQZT0lJQU1NjaWj8tYn5zWczjkYDGLFihWYO3cupkyZAuDj8/T5fEhMTOzz2KF4nvv27cOIESPg9/tx2223YdOmTTj//POH1TkWFxfjrbfeQmFhYch9w+U8Z8+ejaeeegqbN2/G2rVrcezYMVxyySVobm4eNucIAEePHsXatWuRnZ2NLVu2YOnSpfj2t7+Np59+GoCd96BBtx0DDR/Lli3D/v37+/w+fTiZOHEi9u7di8bGRvzP//wPlixZgpKSEtuHZUxlZSXuvPNOvPzyy4iOjrZ9OJ5ZuHBh739PmzYNs2fPRlZWFp599lnExMRYPDKzgsEgLrroIjz44IMAgAsuuAD79+/HunXrsGTJEivHNCg/ASUnJyMiIiIkaVJbW4vU1FRLR+WtT85ruJzz8uXL8dJLL+G1117rsyNiamoqOjs70dDQ0OfxQ/E8fT4fzj33XMyYMQOFhYWYPn06fvGLXwybcywtLUVdXR0uvPBCREZGIjIyEiUlJXj88ccRGRmJlJSUYXGen5aYmIjzzjsPR44cGTbfSwBIS0vD+eef32ds0qRJvb9utPEeNCgbkM/nw4wZM7B169besWAwiK1btyIvL8/ikXln/PjxSE1N7XPOTU1N2LVr15A6Z8dxsHz5cmzatAmvvvoqxo8f3+f+GTNmICoqqs95lpWVoaKiYkidpyQYDCIQCAybc7z88suxb98+7N27t/d20UUX4cYbb+z97+Fwnp/W0tKC999/H2lpacPmewkAc+fODfmTiPfeew9ZWVkALL0HeRJtMKC4uNjx+/3OU0895Rw8eNC59dZbncTERKempsb2oQ1Yc3Oz8/bbbztvv/22A8D5+c9/7rz99tvOBx984DiO4zz00ENOYmKi8/zzzzvvvvuuc8011zjjx4932tvbLR95/y1dutRJSEhwtm3b5lRXV/fe2traeh9z2223OZmZmc6rr77q7Nmzx8nLy3Py8vIsHrW+e+65xykpKXGOHTvmvPvuu84999zjhIWFOX/7298cxxke5yj59xSc4wyP87zrrrucbdu2OceOHXPeeOMNJz8/30lOTnbq6uocxxke5+g4jvPmm286kZGRzgMPPOCUl5c7f/zjH53Y2FjnD3/4Q+9jzvR70KBtQI7jOL/85S+dzMxMx+fzObNmzXJ27txp+5Bcee211xwAIbclS5Y4jvNxDPLee+91UlJSHL/f71x++eVOWVmZ3YPWJJ0fAGfDhg29j2lvb3duv/12Z9SoUU5sbKzzH//xH051dbW9gx6Ab37zm05WVpbj8/mcMWPGOJdffnlv83Gc4XGOkk83oOFwntdff72Tlpbm+Hw+56yzznKuv/5658iRI733D4dz/MSLL77oTJkyxfH7/U5OTo7zxBNP9Ln/TL8HcT8gIiKyYlBeAyIiouGPDYiIiKxgAyIiIivYgIiIyAo2ICIisoINiIiIrGADIiIiK9iAiIjICjYgIiKygg2IiIisYAMiIiIr/n/3DnFpkmWMZAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "axes.imshow(fourdstem_overfocused[size//2, size//2, :, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7227396f",
   "metadata": {},
   "source": [
    "## Show the trace of the central detector pixel\n",
    "\n",
    "This plots the value of the pixel at the center of the simulated detector as a function of scan position. This reproduces the simulated object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "256436c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb3c2497f10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots()\n",
    "axes.imshow(fourdstem_overfocused[:, :, size//2, size//2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d4cbce",
   "metadata": {},
   "source": [
    "## Save to disk\n",
    "\n",
    "This file can then be opened in the `stem_overfocus.ipynb` notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5105a58d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"fourdstem_overfocused.npy\", fourdstem_overfocused)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6dd7bd7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "kernelspec": {
   "display_name": "Python (calib311)",
   "language": "python",
   "name": "calib311"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}