basic_tutorial.ipynb

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    "<img align=\"left\" src=\"./logo.png\" width=300>\n",
    "\n",
    "# `Phaser`: BCDI Phase retrieval in Python\n",
    "\n",
    "## Created by: Siddharth Maddali\n",
    "### Argonne National Laboratory\n",
    "<a href=\"https://doi.org/10.5281/zenodo.4305131\" style=\"float: left;\"><img src=\"https://zenodo.org/badge/DOI/10.5281/zenodo.4305131.svg\" alt=\"DOI\"></a>"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "# Initial setup"
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "\n",
    "# import custom modules\n",
    "import Phaser as ph\n",
    "import ExperimentalGeometry as exp # currently implements the motors at 34-ID-C\n",
    "import TilePlot as tp\n",
    "import PlotLikeMatlab as plm\n",
    "\n",
    "# plotting \n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "\n",
    "# Module to read Matlab .mat files\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# 'inline': non-interactive plot\n",
    "# 'notebook': interactive plot (Jupyter notebook)\n",
    "# 'widget': interactive plot (Jupyter lab)\n",
    "%matplotlib inline"
   ]
  },
  {
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   "source": [
    "## Loading data set into Python"
   ]
  },
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     "name": "stdout",
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     "text": [
      "dict_keys(['__header__', '__version__', '__globals__', 'crystal_4', 'intens_coherent', 'intens_partcoh', 'signal', 'pcc_parameters', 'covariance_matrix'])\n",
      "Array size =  (128, 128, 70)\n"
     ]
    }
   ],
   "source": [
    "dataset = sio.loadmat( 'data.mat' )\n",
    "print( dataset.keys() )\n",
    "    # NOTE: if you opened this file in Matlab,\n",
    "    # you'd see only the 'data' variable in the workspace.\n",
    "    \n",
    "data = dataset[ 'intens_coherent' ] # the 3D data is now a numpy array.\n",
    "data = np.random.poisson( 3.e4 * data / data.max() )\n",
    "print( 'Array size = ', data.shape )"
   ]
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   "source": [
    "## Pre-processing the dataset\n",
    "\n",
    "Assumes that the following are already done:\n",
    "   1. Stray scattering removed\n",
    "   1. White-field correction done\n",
    "   1. Background/hot pixels taken care of"
   ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array size =  (128, 128, 70)\n",
      "Bragg peak initially at:  [64, 64, 35]\n",
      "Bragg peak now at:  [64, 64, 35]\n"
     ]
    }
   ],
   "source": [
    "# If necessary, trim the dataset to even dimensions \n",
    "# by removing the last image in the stack. Typically \n",
    "# this is not necessary for in-plane dimensions since \n",
    "# detectors are usually even-pixeled.\n",
    "\n",
    "# data = data[:,:,:-1]\n",
    "print( 'Array size = ', data.shape )\n",
    "\n",
    "maxHere = [ n[0] for n in np.where( data==data.max() ) ]\n",
    "print( 'Bragg peak initially at: ', maxHere )\n",
    "\n",
    "# Now centering Bragg peak in the array. If this is not done, you will see \n",
    "# a phase ramp in the final reconstruction.\n",
    "for n in [ 0, 1, 2 ]: \n",
    "    data = np.roll( data, data.shape[n]//2 - maxHere[n], axis=n )\n",
    "    \n",
    "maxHereNow = [ n[0] for n in np.where( data==data.max() ) ]\n",
    "print( 'Bragg peak now at: ', maxHereNow )"
   ]
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   "source": [
    "## Creating initial support for phase retrieval\n",
    "   - This gets updated with a shrinkwrap algorithm\n",
    "   - Initial support should never be bigger than half of the array size along each axis."
   ]
  },
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   "source": [
    "shp = data.shape\n",
    "supInit = np.zeros( shp )\n",
    "supInit[ #   // means integer division in Python3, as opposed to /, the usual floating point division\n",
    "    ( shp[0]//2 - shp[0]//6 ):( shp[0]//2 + shp[0]//6 ), \n",
    "    ( shp[1]//2 - shp[1]//6 ):( shp[1]//2 + shp[1]//6 ), \n",
    "    ( shp[2]//2 - shp[2]//6 ):( shp[2]//2 + shp[2]//6 )\n",
    "] = 1."
   ]
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   "source": [
    "# CPU tutorial \n",
    "## Create a phase retrieval solver object for CPU"
   ]
  },
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   "execution_count": 6,
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     "start_time": "2020-02-24T21:05:12.962624Z"
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   "source": [
    "PR = ph.Phaser( modulus=np.sqrt( data ) ) # support initialized inside"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Shrinkwrap\n",
    "Implemented in the method `PR.Shrinkwrap( sigma, thresh )`. \n",
    "   - Object modulus is convolved with a Gaussian (std deviation `sigma`)\n",
    "   - Thresholded to fraction `thresh` of the maximum value."
   ]
  },
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   "source": [
    "## Example recipe for phase retrieval\n",
    "   - 150 iterations of error reduction (ER), with support-updating every 30 iterations\n",
    "   - 300 iterations of hybrid input-output (HIO)\n",
    "   - 100 iterations of solvent-flipping (SF) with support-update every 25 iterations\n",
    "   - 300 iterations of hybrid input-output (HIO)\n",
    "   - 450 iterations of ER again, with support-updating every 90 iterations"
   ]
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     "text": [
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.36it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.87it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.62it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.80it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.75it/s]\n",
      "HIO: 100%|██████████| 300/300 [00:45<00:00,  6.56it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  7.04it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  7.10it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  6.83it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  6.35it/s]\n",
      "HIO: 100%|██████████| 300/300 [00:50<00:00,  5.99it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.72it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.77it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.77it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.83it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.87it/s]\n"
     ]
    }
   ],
   "source": [
    "sigma = np.linspace( 5., 3., 5 )    #\n",
    "for sig in sigma:                   #  150 iters. of error reduction\n",
    "    PR.ER( 30, show_progress=True ) #  with shrinkwrap every 30 iters.\n",
    "    PR.Shrinkwrap( sig, 0.1 )       #\n",
    "    \n",
    "PR.HIO( 300, show_progress=True )   #  300 iterations of hybrid I/O\n",
    "\n",
    "sigma = np.linspace( 3., 2., 4 )\n",
    "for sig in sigma:                   #\n",
    "    PR.SF( 25, show_progress=True ) #  100 iterations of solvent flipping, \n",
    "    PR.Shrinkwrap( sig, 0.1 )       #  shrinkwrap every 25 iterations.\n",
    "    \n",
    "PR.HIO( 300, show_progress=True )   #  300 iterations of hybrid I/O\n",
    "\n",
    "sigma = np.linspace( 2., 1., 5 )\n",
    "for sig in sigma:                   #\n",
    "    PR.ER( 90, show_progress=True ) #  450 iterations of error reduction, \n",
    "    PR.Shrinkwrap( sig, 0.1 )       #  shrinkwrap every 90 iterations."
   ]
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   "source": [
    "## Extracting image and support from black box"
   ]
  },
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     "end_time": "2020-02-24T21:12:41.987838Z",
     "start_time": "2020-02-24T21:12:41.889923Z"
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   "outputs": [],
   "source": [
    "PR.Retrieve() # centers the compact object in array, and smooths it\n",
    "img = PR.finalImage\n",
    "sup = PR.finalSupport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the result\n",
    "\n",
    "### Scatterer amplitude"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, im, ax = tp.TilePlot( \n",
    "    ( \n",
    "        np.absolute( img[64,29:99,:] ), \n",
    "        np.absolute( img[29:99,64,:] ), \n",
    "        np.absolute( img[29:99,29:99,35] )                                         \n",
    "    ),\n",
    "    ( 1, 3 )\n",
    ")\n",
    "\n",
    "ax[0].set_xlabel( 'YZ' )\n",
    "ax[1].set_xlabel( 'ZX' )\n",
    "ax[2].set_xlabel( 'XY' )\n",
    "\n",
    "# fig.savefig( 'images/scattererAmp.jpg' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-25T23:09:43.062219Z",
     "start_time": "2019-09-25T23:09:42.539057Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Scatterer support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 409.19735294117646, 'XY')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, im, ax = tp.TilePlot( \n",
    "    ( \n",
    "        sup[64,29:99,:], \n",
    "        sup[29:99,64,:], \n",
    "        sup[29:99,29:99,35]                                         \n",
    "    ),\n",
    "    ( 1, 3 )\n",
    ")\n",
    "\n",
    "ax[0].set_xlabel( 'YZ' )\n",
    "ax[1].set_xlabel( 'ZX' )\n",
    "ax[2].set_xlabel( 'XY' )\n",
    "\n",
    "# fig.savefig( 'images/scattererSup.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-25T23:09:44.772463Z",
     "start_time": "2019-09-25T23:09:44.410410Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Scatterer phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 409.19735294117646, 'XY')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, im, ax = tp.TilePlot( \n",
    "    ( \n",
    "        np.angle( img[64,29:99,:] ), \n",
    "        np.angle( img[29:99,64,:] ), \n",
    "        np.angle( img[29:99,29:99,35] )                                         \n",
    "    ),\n",
    "    ( 1, 3 )\n",
    ")\n",
    "\n",
    "ax[0].set_xlabel( 'YZ' )\n",
    "ax[1].set_xlabel( 'ZX' )\n",
    "ax[2].set_xlabel( 'XY' )\n",
    "\n",
    "# fig.savefig( 'images/scattererPh.jpg' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-25T23:09:49.060030Z",
     "start_time": "2019-09-25T23:09:48.549285Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Reconstruction error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error')"
      ]
     },
     "execution_count": 12,
     "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": [
    "plt.figure()\n",
    "plt.semilogy( PR.Error() )\n",
    "plt.grid()\n",
    "plt.xlabel( 'Iteration (n)' )\n",
    "plt.ylabel( 'Error' )\n",
    "\n",
    "# plt.savefig( 'images/reconErr.jpg' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Isosurface plotting\n",
    "After the recipe has run and the object array is retrieved as shown above, its isosurface can be plotted with Matlab-like Python functions defined in the `PlotLikeMatlab` module. \n",
    "Any 3D rendering in Bragg phase retrieval should be aware of the sampling step sizes and directions in real space, that are implied by each of the three dimensions of the \n",
    "The `ExperimentalGeometry` module is needed to correctly resolve the 3D rendering of the object. \n",
    "\n",
    "For the purpose of demonstration, the phased object obtained above is assigned arbitrary experimental parameters below. \n",
    "In reality, these have to match the actual scan parameters from the measurement.\n",
    "This example is customized to the 34-ID-C experimental end station at the Adcanced Photon Source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = { # these are 34-ID-C parameters\n",
    "    'lam':1.378e-10, # wavelength in meters\n",
    "    'pix':55.e-6, # pixel pitch, meters\n",
    "    'arm':0.65, # sample-detector distance, meters\n",
    "    'gamma':45., # elevation, degrees\n",
    "    'delta':25., # azimuth, dgrees\n",
    "    'dtheta':0.01, # rocking step, degrees\n",
    "    'recipSpaceSteps':shp # object dimensions, pixels\n",
    "}\n",
    "geom = exp.ScatteringGeometry( **params )\n",
    "Bdet = geom.detectorFrame\n",
    "Breal, Brecip = tuple( Bdet.T @ this for this in geom.getSamplingBases() )\n",
    "    # real-space and Fourier-space sampling bases are now in the detector frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorbar = {\n",
    "    'title':'Phase (rad)', \n",
    "    'vertical':True, \n",
    "    'interactive':True, \n",
    "    'label_font_size':20, \n",
    "    'title_font_size':25, \n",
    "    'font_family':'times', \n",
    "    'color':[ 0., 0., 0. ]\n",
    "}\n",
    "\n",
    "isol = plm.isosurface( \n",
    "    data=img, \n",
    "#     data=np.conj( np.flip( img, axis=[ 0, 1, 2 ] ) ), # use this for the mirror twin solution\n",
    "    isoval=0.5,\n",
    "    Br=Breal, \n",
    "    colorbar=colorbar\n",
    ")\n",
    "isol.set_background( [ 0.5, 0.5, 0.5 ] )\n",
    "# plm.savefig3d( isol, 'images/isol' ) # saves as pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phased object: isosurface\n",
    "\n",
    "<img src=\"images/isol.png\" width=600>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the different shape of the object above, compared to the pixelated reconstructions. \n",
    "This is simply a demonstration in the use of `Breal`. \n",
    "In a reconstruction with real-world data, a similar isosurface with the properly calculated `Breal` is guaranteed to be rendered correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Recipe strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "`Phaser` can also run an entire phase retrieval recipe by parsing \"recipe strings\", which are simply Python strings that encode a phase retrieval recipe. \n",
    "See `RecipeParser.py` documentation for more details.\n",
    "\n",
    "   - Recipe string for 30 iterations of ER is simply:<br/>`'ER:30'`.  \n",
    "   \n",
    "   - Recipe string for 100 ER followed by 25 HIO, followed by 40 SF:<br/>`'ER:100+HIO:25+SF:40'`\n",
    "   \n",
    "   - Shrinkwrap recipe string format:<br/>`'SR:<sigma>:<thresh>'`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Generating the recipe string for the CPU recipe above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-24T21:15:06.748529Z",
     "start_time": "2020-02-24T21:15:06.699718Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ER:30+SR:5.00:0.1+ER:30+SR:4.50:0.1+ER:30+SR:4.00:0.1+ER:30+SR:3.50:0.1+ER:30+SR:3.00:0.1+HIO:300+SF:25+SR:3.00:0.1+SF:25+SR:2.67:0.1+SF:25+SR:2.33:0.1+SF:25+SR:2.00:0.1+HIO:300+ER:90+SR:2.00:0.1+ER:90+SR:1.75:0.1+ER:90+SR:1.50:0.1+ER:90+SR:1.25:0.1+ER:90+SR:1.00:0.1\n"
     ]
    }
   ],
   "source": [
    "recipestr1 = '+'.join( [ 'ER:30+SR:%.2f:0.1'%sig for sig in np.linspace( 5., 3., 5 ) ] )\n",
    "recipestr2 = '+'.join( [ 'SF:25+SR:%.2f:0.1'%sig for sig in np.linspace( 3., 2., 4 ) ] )\n",
    "recipestr3 = '+'.join( [ 'ER:90+SR:%.2f:0.1'%sig for sig in np.linspace( 2., 1., 5 ) ] )\n",
    "recipestr = '+HIO:300+'.join( [ recipestr1, recipestr2, recipestr3 ] )\n",
    "print( recipestr )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Running the recipe using the string\n",
    "\n",
    "Here, starting with a new `Phaser` object and use the `runRecipe` method..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-24T21:20:21.576807Z",
     "start_time": "2020-02-24T21:15:12.564823Z"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.67it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.69it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.71it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.74it/s]\n",
      " ER: 100%|██████████| 30/30 [00:05<00:00,  5.75it/s]\n",
      "HIO: 100%|██████████| 300/300 [00:50<00:00,  5.93it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  6.53it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  6.39it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  6.35it/s]\n",
      " SF: 100%|██████████| 25/25 [00:03<00:00,  6.51it/s]\n",
      "HIO: 100%|██████████| 300/300 [00:49<00:00,  6.01it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.78it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.82it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.84it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.85it/s]\n",
      " ER: 100%|██████████| 90/90 [00:15<00:00,  5.85it/s]\n"
     ]
    }
   ],
   "source": [
    "PR_alt = ph.Phaser( modulus=np.sqrt( data ) )\n",
    "PR_alt.runRecipe( recipestr, show_progress=True )\n",
    "PR.Retrieve() \n",
    "img = PR.finalImage\n",
    "sup = PR.finalSupport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "... and so on, to get the same result!\n",
    "\n",
    "   - Recipe strings can be used in parallelized phase retrieval.\n",
    "   - Disabling the progress bars in the above example (`show_progress=False`) will likely speed up the whole recipe.\n",
    "   - Recipe strings can work with CPU as well as GPU without loss of generality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# GPU tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-24T21:21:55.131605Z",
     "start_time": "2020-02-24T21:21:12.669719Z"
    }
   },
   "outputs": [],
   "source": [
    "PR2 = ph.Phaser( modulus=np.sqrt( data ), gpu=True ).gpusolver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Recipe for GPU phase retrieval (identical to earlier CPU recipe)\n",
    "   Notice how **obscenely faster** the same recipe runs on the GPU!\n",
    "   In my case, this was a single [**NVidia GeForce GTX 1650**](https://www.nvidia.com/en-us/geforce/graphics-cards/gtx-1650/) with 4 MiB memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-24T21:23:16.036347Z",
     "start_time": "2020-02-24T21:22:58.824126Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " ER: 100%|██████████| 30/30 [00:00<00:00, 117.66it/s]\n",
      " ER: 100%|██████████| 30/30 [00:00<00:00, 334.29it/s]\n",
      " ER: 100%|██████████| 30/30 [00:00<00:00, 345.83it/s]\n",
      " ER: 100%|██████████| 30/30 [00:00<00:00, 341.42it/s]\n",
      " ER: 100%|██████████| 30/30 [00:00<00:00, 334.90it/s]\n",
      "HIO: 100%|██████████| 300/300 [00:00<00:00, 307.20it/s]\n",
      " SF: 100%|██████████| 25/25 [00:00<00:00, 392.62it/s]\n",
      " SF: 100%|██████████| 25/25 [00:00<00:00, 383.11it/s]\n",
      " SF: 100%|██████████| 25/25 [00:00<00:00, 396.17it/s]\n",
      " SF: 100%|██████████| 25/25 [00:00<00:00, 399.89it/s]\n",
      "HIO: 100%|██████████| 300/300 [00:00<00:00, 313.50it/s]\n",
      " ER: 100%|██████████| 90/90 [00:00<00:00, 346.28it/s]\n",
      " ER: 100%|██████████| 90/90 [00:00<00:00, 340.75it/s]\n",
      " ER: 100%|██████████| 90/90 [00:00<00:00, 360.89it/s]\n",
      " ER: 100%|██████████| 90/90 [00:00<00:00, 344.63it/s]\n",
      " ER: 100%|██████████| 90/90 [00:00<00:00, 330.79it/s]\n"
     ]
    }
   ],
   "source": [
    "sigma = np.linspace( 5., 3., 5 )    #\n",
    "for sig in sigma:                   #  150 iters. of error reduction\n",
    "    PR2.ER( 30, show_progress=True ) #  with shrinkwrap every 30 iters.\n",
    "    PR2.Shrinkwrap( sig, 0.1 )       #\n",
    "    \n",
    "PR2.HIO( 300, show_progress=True )   #  300 iterations of hybrid I/O\n",
    "\n",
    "sigma = np.linspace( 3., 2., 4 )\n",
    "for sig in sigma:                   #\n",
    "    PR2.SF( 25, show_progress=True ) #  100 iterations of solvent flipping, \n",
    "    PR2.Shrinkwrap( sig, 0.1 )       #  shrinkwrap every 25 iterations.\n",
    "    \n",
    "PR2.HIO( 300, show_progress=True )   #  300 iterations of hybrid I/O\n",
    "\n",
    "sigma = np.linspace( 2., 1., 5 )\n",
    "for sig in sigma:                   #\n",
    "    PR2.ER( 90, show_progress=True ) #  450 iterations of error reduction, \n",
    "    PR2.Shrinkwrap( sig, 0.1 )       #  shrinkwrap every 90 iterations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Extracting image and support from black box `PR2`\n",
    "Same way as for the CPU recipe: \n",
    "```python\n",
    "PR2.Retrieve()\n",
    "img = PR2.finalImage\n",
    "sup = PR2.finalSupport\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parallelization on CPU/GPU\n",
    "\n",
    "   - Implemented using MPI bindings for Python (`mpi4py`).\n",
    "   - Uses recipe strings for efficient parallelization.\n",
    "   - See Python script `Guided.py` for a basic CPU tutorial and `Guided_GPU.py` for the corresponding GPU tutorial.\n",
    "   - Usage: \n",
    "       - Install `mpi4py`: instructions [here](https://mpi4py.readthedocs.io/en/stable/install.html).\n",
    "       - Run the script from the Bash (not Python!) command line; for example, to parallelize on 4 processors:\n",
    "       ```bash\n",
    "       $ mpirun -n 4 python Guided.py\n",
    "        ```\n",
    "   - The genetic algorithms implemented are described in [A. Ulvestad et al. _Sci Rep_ 7, 9920 (2017)](https://doi.org/10.1038/s41598-017-09582-7).\n",
    "\n",
    "## CAUTION\n",
    "\n",
    "   1. There seems to be significant overhead introduced when scaling up to multiple processors; this can be offset somewhat by keeping the phase retrieval recipe itself short and simple."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial coherence correction (PCC)\n",
    "For a partially coherent beam, Phaser allows for an estimate of the partial coherence function (PCF) in addition to executing the recipe.\n",
    "The PCF is modeled as a 6-parameter Gaussian blurring function in 3D (based on the analysis presented in [Maddali et al., _Phys. Rev. Applied_ **14**, 024085 (2020)](https://link.aps.org/doi/10.1103/PhysRevApplied.14.024085)). \n",
    "The inferred diffraction signal is obtained by convolving the coherent diffraction intensity with the PCF.\n",
    "\n",
    "Currently the partial coherence correction is implemented only on the GPU.\n",
    "A synthetic partially coherent dataset is generated and reconstructed for this demonstration. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCC implementation\n",
    "\n",
    "A full tutorial is available [here](https://nbviewer.jupyter.org/github/siddharth-maddali/Phaser/blob/master/partial_coherence.ipynb). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Acknowledgements\n",
    "\n",
    "   - **Matthew Wilkin** (Dept. of Materials Science and Engg., Carnegie Mellon University) <br/>\n",
    "   for finding bugs through continued usage and even fixing them."
   ]
  }
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