start_to_end_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# SIMEX notebook for start-to-end single-particle imaging simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "# Import all SimEx modules\n",
    "from SimEx import *\n",
    "# Import all SimEx modules\n",
    "from SimEx.Calculators.WavePropagator import WavePropagator\n",
    "from SimEx.Calculators.GaussianPhotonSource import GaussianPhotonSource\n",
    "from SimEx.Parameters.WavePropagatorParameters import WavePropagatorParameters\n",
    "from SimEx.Parameters.GaussWavefrontParameters import GaussWavefrontParameters\n",
    "from SimEx.Analysis.XFELPhotonAnalysis import XFELPhotonAnalysis\n",
    "from SimEx.Utilities.Units import electronvolt, meter, joule, radian\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The SPB-SFX instrument layout at European XFEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture showing the SPB-SFX instrument layout at European XFEL](include/SPB-SFX_setup_811.jpg \"SPB-SFX instrument layout\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simplified beamline used in this simulation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture showing the simplified beamline for the simulation](include/s2e_spi_scheme.png \"Simplified beamline used in this simulation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Aquire XFEL source wavefront file from database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://in.xfel.eu/xpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2a: Propagation through beamline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import beamline for WPG "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prop import exfel_spb_kb_beamline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture showing the SPB-SFX beamline optical setup with distances](include/SPB-SFX_optics.png \"Optical distances in the SPB-SFX beamline\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Propagate SASE pulses to sample interaction point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "propagation_parameters = WavePropagatorParameters(beamline=exfel_spb_kb_beamline,\n",
    "                           backengine_mpicommand=\"mpirun -np24\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "propagator = WavePropagator(parameters=propagation_parameters,\n",
    "                                  input_path='FELsource_out_0000001.h5',\n",
    "                                  output_path='prop_out.h5'\n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           \n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propagator.backengine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel(R) MPI Library for Linux* OS, Version 2019 Update 6 Build 20191024 (id: 082ae5608)\r\n",
      "Copyright 2003-2019, Intel Corporation.\r\n"
     ]
    }
   ],
   "source": [
    "!mpirun --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "propagator.saveH5()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyse propagated wavefronts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Start initialization.\n",
      "\n",
      " Loading wavefront from prop_out.h5.\n",
      " ... done.\n",
      "\n",
      " Getting intensities.\n",
      " ... done.\n",
      " Data dimensions =  (42, 84, 651)\n",
      "\n",
      " Masking NANs.\n",
      " ... done.\n"
     ]
    }
   ],
   "source": [
    "prop_analysis=XFELPhotonAnalysis(input_path='prop_out.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot FEL spectrum before propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot FEL spectrum at source and after propagation\n",
    "prop_analysis.plotTotalPower(spectrum=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot FEL spectrum after propagation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'prop_analysis' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-630ef80c3394>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprop_analysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotTotalPower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspectrum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'prop_analysis' is not defined"
     ]
    }
   ],
   "source": [
    "prop_analysis.plotTotalPower(spectrum=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'source_analysis' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-f4fedbc47968>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Plot source and sample intensity map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msource_analysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotIntensityMap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'source_analysis' is not defined"
     ]
    }
   ],
   "source": [
    "# Plot source and sample intensity map\n",
    "source_analysis.plotIntensityMap()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_analysis.plotIntensityMap()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Photon-Matter interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'XMDYNPhotonMatterInteractor' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-a692563426f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m                 \"random_rotation\" : False}\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m photon_matter_interactor=XMDYNPhotonMatterInteractor(parameters=pmi_parameters,\n\u001b[0m\u001b[1;32m      5\u001b[0m                                                          \u001b[0minput_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'prop_out.h5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                                                          \u001b[0moutput_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pmi_out'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'XMDYNPhotonMatterInteractor' is not defined"
     ]
    }
   ],
   "source": [
    "pmi_parameters={\"number_of_trajectories\" : 1,\n",
    "                \"random_rotation\" : False}\n",
    "\n",
    "photon_matter_interactor=XMDYNPhotonMatterInteractor(parameters=pmi_parameters,\n",
    "                                                         input_path='prop_out.h5',\n",
    "                                                         output_path='pmi_out',\n",
    "                                                         sample_path='2nip.pdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "photon_matter_interactor.backengine()\n",
    "photon_matter_interactor.saveH5()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Scattering "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure Detector geometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DetectorPanel' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-0dee0688f9f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m panel = DetectorPanel(ranges={\"fast_scan_min\" : 0, \"fast_scan_max\" : 100,\n\u001b[0m\u001b[1;32m      2\u001b[0m                               \"slow_scan_min\" : 0, \"slow_scan_max\" : 100},\n\u001b[1;32m      3\u001b[0m                       \u001b[0mpixel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m220.0e-6\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmeter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                       \u001b[0menergy_response\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0melectronvolt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                       \u001b[0mdistance_from_interaction_plane\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.13\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmeter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'DetectorPanel' is not defined"
     ]
    }
   ],
   "source": [
    "panel = DetectorPanel(ranges={\"fast_scan_min\" : 0, \"fast_scan_max\" : 100,\n",
    "                              \"slow_scan_min\" : 0, \"slow_scan_max\" : 100},\n",
    "                      pixel_size=6*220.0e-6*meter,\n",
    "                      energy_response=1.0/electronvolt,\n",
    "                      distance_from_interaction_plane=0.13*meter,\n",
    "                      corners={\"x\" : -49, \"y\": -49},\n",
    "                      saturation_adu=1.e6,\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DetectorGeometry' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-2f8ab94a9bc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdetector_geometry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDetectorGeometry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpanels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpanel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'DetectorGeometry' is not defined"
     ]
    }
   ],
   "source": [
    "detector_geometry = DetectorGeometry(panels=panel,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure the Diffractor Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'SingFELPhotonDiffractorParameters' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-8c14124dd4ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m diffraction_parameters = SingFELPhotonDiffractorParameters(\n\u001b[0m\u001b[1;32m      2\u001b[0m                                                \u001b[0muniform_rotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                                                \u001b[0mslice_interval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m                                                \u001b[0mnumber_of_slices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                                                \u001b[0mnumber_of_diffraction_patterns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'SingFELPhotonDiffractorParameters' is not defined"
     ]
    }
   ],
   "source": [
    "diffraction_parameters = SingFELPhotonDiffractorParameters(\n",
    "                                               uniform_rotation=False,\n",
    "                                               slice_interval=100,\n",
    "                                               number_of_slices=100,\n",
    "                                               number_of_diffraction_patterns=50,\n",
    "                                               detector_geometry=detector_geometry,\n",
    "                                               forced_mpi_command='mpirun -np 50',\n",
    "                                              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize the Diffractor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffractor = SingFELPhotonDiffractor(parameters=diffraction_parameters,\n",
    "                                     input_path='pmi/',\n",
    "                                     output_path=\"diffr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the scattering simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffractor.backengine()\n",
    "diffractor.saveH5()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup analysis object "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spi_analysis = DiffractionAnalysis(diffractor.output_path,\n",
    "                                   pattern_indices=range(1,50),\n",
    "                                   poissonize=True,\n",
    "                                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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   ],
   "source": [
    "spi_analysislysis.animatePatterns(\"diffr_animated.gif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Wp8nA5tL3et71r959ovtnVxNzp9J7e2LnNZM57/rNI6zraV+5vuaO6v1Zj+zN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ac7f55cdb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spi_analysis.plotPattern(operation=numpy.sum,logscale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import wpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "wf = wpg.Wavefront()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "wf.load_hdf5('prop_out.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "wf_intensity = wf.get_intensity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, 84, 651)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf_intensity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ac8a66307f0>]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ht586GUvX3EwoCH0DoBfV0g00MLheNrEupXmttBzYJsOWeqlrL2tB6FKeN5B8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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ac8a6686b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot([numpy.log(wf_intensity[:,:,i].max()) for i in range(651)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAADKCAYAAAC11LviAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ac8a6d93940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(wf_intensity.shape[-1]):\n",
    "    if wf_intensity[:,:,i].max() < 1e14:\n",
    "        continue\n",
    "    imshow(wf_intensity[:,:,i], norm=Norm(vmin=1e15, vmax=1.5e18) )\n",
    "    savefig(\"include/wf_intensity/wf_intensity_{0:03d}.png\".format(i))\n",
    "    #imsave(\"include/wf_intensity/wf_intensity_{0:03d}.png\".format(i), wf_intensity[:,:,i], vmin=1e15, vmax=1.5e18 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import imshow, colorbar, imsave, plot, savefig\n",
    "from matplotlib.colors import Normalize as Norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42, 84, 651)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf_intensity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = numpy.ones((101,101))\n",
    "mask[40:60,40:60]=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "spi_analysis.mask = mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleanup previous run\n",
    "dirs_to_remove=['diffr, pmi']\n",
    "files_to_remove=['diffr.h5','5udc.pdb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "for d in dirs_to_remove:\n",
    "    if os.path.isdir(d):\n",
    "        shutil.rmtree(d)\n",
    "for f in files_to_remove:\n",
    "    if os.path.isfile(f):\n",
    "        os.remove(f)\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}