{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "80145653",
   "metadata": {},
   "source": [
    "# Reading IAEA phase space\n",
    "\n",
    "The international Atomic Energy Agency (IAEA) maintains a [database of phase spaces](https://www-nds.iaea.org/phsp/photon1/) from different medical accelerators, as described in [this report](https://inis.iaea.org/collection/NCLCollectionStore/_Public/37/073/37073778.pdf?r=1). \n",
    "\n",
    "In principle, this is great: a standardised phase space format that everyone can adhere to! \n",
    "In reality, this is not so much a format as a loose fraternity of data. This mean it is very difficult to provide standardised methods to read these files; although we provide a DataLoader `Load_IAEA`, it is very likely that this will not work 'out of the box' for arbitrary data, and will require some fine tuning. Therefore, the point of this example is to demonstrate the process you would go through to read an IAEA phase space file into this code.\n",
    "\n",
    "## The IAEA format\n",
    "\n",
    "All IAEA phase spaces consist of two files:\n",
    "\n",
    "1. A header file, which is ASCII (human readable) encoded. This contains a lot of information, some of which is essential to how to read the second file:\n",
    "2. The phasespace file (typical extension `.phsp` of `.IAEAphsp`. This is a file in binary format: to read it correctly, we need to understand exactly what order different quantities are described and how many bytes of data are used to describe them. In principle, such information should be available in the header!\n",
    "\n",
    "The IAEA report provides the following table describing their phase space format:\n",
    "\n",
    "![](../docsrc/IAEA_phase_space.png)\n",
    "\n",
    "However, any of these quantities can also be described as constants, which means that the byte order of an IAEA file is essentially arbitrary.\n",
    "In principle, it should be possible to dervice this information from the header file, but it's not yet clear to me whether they are consistent enough to always allow that in practice.\n",
    "\n",
    "## Reading an IAEA phase space file\n",
    "\n",
    "With all this said, how does one actually go about reading an IAEA phase space?\n",
    "In this case, I will read in a small section of a varian phase space file which contained the following information in the header:\n",
    "\n",
    "```\n",
    "Format of the phase space file:\n",
    "Type (1 byte); Energy (4 bytes); X position (4); Y position (4); Component of\n",
    "momentum direction in X (4); Component of momentum direction vector in Y (4)\n",
    "```\n",
    "\n",
    "and\n",
    "\n",
    "```\n",
    "$RECORD_CONSTANT:\n",
    "     26.7       // Constant Z\n",
    "     1.0000     // Constant Weight\n",
    "```\n",
    "\n",
    "With these two pieces of information, we can specify the order and types of the data, as well as the constants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1da6d71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brendan/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:440: UserWarning: this data has negative energy in it, what does that even mean. forcing all energy to positive\n",
      "  warnings.warn('this data has negative energy in it, what does that even mean. forcing all energy to positive')\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '../') # not necessary when code is installed\n",
    "from ParticlePhaseSpace import PhaseSpace, DataLoaders\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "# get data file:\n",
    "file_name = Path(r'/home/brendan/Dropbox (Sydney Uni)/abstracts,presentations etc/temp/TrueBeam_v2_6X_00.phsp')\n",
    "# file_name = Path(r'/home/brendan/Downloads/Varian_TrueBeam6MV_01.phsp')\n",
    "# read in\n",
    "data_schema = np.dtype([\n",
    "               ('particle type', 'i1'),\n",
    "               ('Ek', 'f4'),\n",
    "               ('x', 'f4'),\n",
    "               ('y', 'f4'),\n",
    "               ('Cosine X', 'f4'),\n",
    "               ('Cosine Y', 'f4')])\n",
    "\n",
    "constants = {'z': np.float32(26.7), 'weight': np.int8(1)}\n",
    "\n",
    "ps_data = DataLoaders.Load_IAEA(data_schema=data_schema, constants=constants, input_data=file_name, n_records=int(1e5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802bc644",
   "metadata": {},
   "source": [
    "In this case, we were able to figure out the format of the data in the header, and specify this to the data loader.\n",
    "\n",
    "A few other notes here:\n",
    "\n",
    "- we specified `n_records=1e5`; this controls how many rows of the data get read in. Since this files can be very large it is not always desirable to read them in one 'chunk'.\n",
    "- we got a warning about negative energy. As the warning says: the energy in this phase space is negative. I don't know why that is; it physically makes no sense so I force the energy to positive.\n",
    "\n",
    "Now that we've loaded the data let's take a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40bb73db",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PS = PhaseSpace(ps_data)\n",
    "# again, because this data can be quite large we can manually delete the loader now we've finished with it\n",
    "del ps_data\n",
    "PS.plot.particle_positions_hist_2D(log_scale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c83973c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PS.plot.energy_hist_1D()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b8fdc73",
   "metadata": {},
   "source": [
    "This looks about right, although I suspect that the spatial coordinates should actually be in cm. The header does not specify what units are used, but the XY intensity plot suggest a beam spread between +- 4 mm, which seem to small. To change the units, we would just have to specify what units to use at read in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97db2036",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brendan/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:440: UserWarning: this data has negative energy in it, what does that even mean. forcing all energy to positive\n",
      "  warnings.warn('this data has negative energy in it, what does that even mean. forcing all energy to positive')\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ParticlePhaseSpace import ParticlePhaseSpaceUnits\n",
    "available_units = ParticlePhaseSpaceUnits()\n",
    "\n",
    "ps_data = DataLoaders.Load_IAEA(data_schema=data_schema,\n",
    "                                constants=constants, input_data=file_name, n_records=int(1e5),\n",
    "                               units=available_units('cm_MeV'))\n",
    "PS = PhaseSpace(ps_data)\n",
    "# again, because this data can be quite large we can manually delete the loader now we've finished with it\n",
    "del ps_data\n",
    "PS.plot.particle_positions_hist_2D(log_scale=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbae20fe",
   "metadata": {},
   "source": [
    "## When things don't work quite so well...\n",
    "\n",
    "To take another example; look at the TrueBeam data available on [this database](https://www-nds.iaea.org/phsp/photon1/).\n",
    "\n",
    "In this instance, the header does not nicely specify what is in the the phase space data, so we have to guess. This is what the header does tell us:\n",
    "\n",
    "```PERL\n",
    "$RECORD_CONTENTS:\n",
    "    1     // X is stored ?\n",
    "    1     // Y is stored ?\n",
    "    1     // Z is stored ?\n",
    "    1     // U is stored ?\n",
    "    1     // V is stored ?\n",
    "    1     // W is stored ?\n",
    "    0     // Weight is stored ?\n",
    "    0     // Extra floats stored ?\n",
    "    0     // Extra longs stored ?\n",
    "\n",
    "$RECORD_CONSTANT:\n",
    "     1.0000     // Constant Weight\n",
    "```\n",
    "\n",
    "Based on this, and the info on IAEA phase space format in the table above, I would guess that the data scheme looks something like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce5d4b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_schema = np.dtype([\n",
    "                       ('x', 'f4'),\n",
    "                       ('y', 'f4'),\n",
    "                       ('z', 'f4'),\n",
    "                       ('Cosine X', 'f4'),\n",
    "                       ('Cosine Y', 'f4'),\n",
    "                       ('Ek', 'f4'),\n",
    "                       ('particle type', 'i2')])\n",
    "\n",
    "constants = {'weight': np.int8(1)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49802568",
   "metadata": {},
   "source": [
    "let's see how this goes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "68c952aa",
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "specified data schema has differeny byte length to that indicated in header.\nheader specifies 25,\nschema specifies 26",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m file_name \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/brendan/Downloads/Varian_TrueBeam6MV_01.phsp\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m ps_data \u001b[38;5;241m=\u001b[39m \u001b[43mDataLoaders\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLoad_IAEA\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_schema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_schema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstants\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                                \u001b[49m\u001b[43minput_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_records\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1e5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mavailable_units\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcm_MeV\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:397\u001b[0m, in \u001b[0;36mLoad_IAEA.__init__\u001b[0;34m(self, data_schema, constants, input_data, n_records, offset, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_records \u001b[38;5;241m=\u001b[39m n_records\n\u001b[1;32m    396\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offset \u001b[38;5;241m=\u001b[39m offset\n\u001b[0;32m--> 397\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minput_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:53\u001b[0m, in \u001b[0;36m_DataLoadersBase.__init__\u001b[0;34m(self, input_data, particle_type, units)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_data \u001b[38;5;241m=\u001b[39m input_data\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_input_data()\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_import_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_loaded_data()\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:413\u001b[0m, in \u001b[0;36mLoad_IAEA._import_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_header_to_dict()\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_required_info_present()\n\u001b[0;32m--> 413\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_record_length_versus_data_schema\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    414\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m quantity \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCosine X\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCosine Y\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEk\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[1;32m    415\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m quantity \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_schema\u001b[38;5;241m.\u001b[39mfields:\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:554\u001b[0m, in \u001b[0;36mLoad_IAEA._check_record_length_versus_data_schema\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    552\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRECORD_LENGTH\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_header_dict\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    553\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_schema\u001b[38;5;241m.\u001b[39mitemsize \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_header_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRECORD_LENGTH\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[0;32m--> 554\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspecified data schema has differeny byte length to that indicated in header.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    555\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mheader specifies \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_header_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRECORD_LENGTH\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    556\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mschema specifies \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_schema\u001b[38;5;241m.\u001b[39mitemsize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mException\u001b[0m: specified data schema has differeny byte length to that indicated in header.\nheader specifies 25,\nschema specifies 26"
     ]
    }
   ],
   "source": [
    "file_name = Path(r'/home/brendan/Downloads/Varian_TrueBeam6MV_01.phsp')\n",
    "ps_data = DataLoaders.Load_IAEA(data_schema=data_schema, constants=constants,\n",
    "                                input_data=file_name, n_records=int(1e5), units=available_units('cm_MeV'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42eac209",
   "metadata": {},
   "source": [
    "So, this is an interesting error. There is a field called `$RECORD_LENGTH` in the header which specifies how many bytes are in each entry of data. Of course, the number of bytes specified by our data_scheme specification should match this, and the code checks that it does before trying anything else.\n",
    "\n",
    "In this case, it would appear that our specified data scheme is one byte longer than that specified in the header. Now, we know from the previous file that we loaded that varian has been known to use a 1 byte integer instead of a 2 byte integer (as specified by the IAEA, which could explain this discrepancy. So let's try using a 1 byte integer instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38956186",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unknown particle code -74",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 11\u001b[0m\n\u001b[1;32m      1\u001b[0m data_schema \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype([\n\u001b[1;32m      2\u001b[0m                        (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf4\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m      3\u001b[0m                        (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf4\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      7\u001b[0m                        (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEk\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf4\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m      8\u001b[0m                        (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparticle type\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi1\u001b[39m\u001b[38;5;124m'\u001b[39m)])\n\u001b[1;32m     10\u001b[0m constants \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39mint8(\u001b[38;5;241m1\u001b[39m)}\n\u001b[0;32m---> 11\u001b[0m ps_data \u001b[38;5;241m=\u001b[39m \u001b[43mDataLoaders\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLoad_IAEA\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_schema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_schema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstants\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[43m                                \u001b[49m\u001b[43minput_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_records\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1e5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mavailable_units\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcm_MeV\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:397\u001b[0m, in \u001b[0;36mLoad_IAEA.__init__\u001b[0;34m(self, data_schema, constants, input_data, n_records, offset, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_records \u001b[38;5;241m=\u001b[39m n_records\n\u001b[1;32m    396\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offset \u001b[38;5;241m=\u001b[39m offset\n\u001b[0;32m--> 397\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minput_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:53\u001b[0m, in \u001b[0;36m_DataLoadersBase.__init__\u001b[0;34m(self, input_data, particle_type, units)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_data \u001b[38;5;241m=\u001b[39m input_data\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_input_data()\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_import_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_loaded_data()\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:429\u001b[0m, in \u001b[0;36mLoad_IAEA._import_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    425\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_columns[constant]] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mSeries(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constants[constant] \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m    426\u001b[0m                                                            np\u001b[38;5;241m.\u001b[39mones(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcategory\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    428\u001b[0m \u001b[38;5;66;03m# OK; add the fields I assume is not in the data\u001b[39;00m\n\u001b[0;32m--> 429\u001b[0m particle_types_pdg \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iaea_types_to_pdg\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mparticle type\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    430\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_n_particles_in_header(particle_types_pdg)\n\u001b[1;32m    431\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_columns[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparticle type\u001b[39m\u001b[38;5;124m'\u001b[39m]] \u001b[38;5;241m=\u001b[39m particle_types_pdg\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:477\u001b[0m, in \u001b[0;36mLoad_IAEA._iaea_types_to_pdg\u001b[0;34m(self, varian_types)\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01min\u001b[39;00m np\u001b[38;5;241m.\u001b[39munique(varian_types):\n\u001b[1;32m    476\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m]:\n\u001b[0;32m--> 477\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munknown particle code \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    478\u001b[0m pdg_types[varian_types\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m22\u001b[39m  \u001b[38;5;66;03m# gammas\u001b[39;00m\n\u001b[1;32m    479\u001b[0m pdg_types[varian_types \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m11\u001b[39m  \u001b[38;5;66;03m# electrons\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: unknown particle code -74"
     ]
    }
   ],
   "source": [
    "data_schema = np.dtype([\n",
    "                       ('x', 'f4'),\n",
    "                       ('y', 'f4'),\n",
    "                       ('z', 'f4'),\n",
    "                       ('Cosine X', 'f4'),\n",
    "                       ('Cosine Y', 'f4'),\n",
    "                       ('Ek', 'f4'),\n",
    "                       ('particle type', 'i1')])\n",
    "\n",
    "constants = {'weight': np.int8(1)}\n",
    "ps_data = DataLoaders.Load_IAEA(data_schema=data_schema, constants=constants,\n",
    "                                input_data=file_name, n_records=int(1e5), units=available_units('cm_MeV'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "714e302a",
   "metadata": {},
   "source": [
    "OK, doing this at least the byte number is correct, but now we have an unidentified type of particle. This indicates that we very likely did not read in the particle type correctly. We also know from last time that varian specified the particle type first; let's try that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9e43e0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/brendan/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:440: UserWarning: this data has negative energy in it, what does that even mean. forcing all energy to positive\n",
      "  warnings.warn('this data has negative energy in it, what does that even mean. forcing all energy to positive')\n"
     ]
    },
    {
     "ename": "Exception",
     "evalue": "failed to calculate momentums from topas data. Possible solution is to increasethe value of momentum_precision_factor, currently set to  2.00e-03and failed data has value  2.71e+01",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mException\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 13\u001b[0m\n\u001b[1;32m      1\u001b[0m data_schema \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdtype([\n\u001b[1;32m      2\u001b[0m                        (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparticle type\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi1\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m      3\u001b[0m                        (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf4\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      8\u001b[0m                        (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEk\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mf4\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m      9\u001b[0m                        ])\n\u001b[1;32m     11\u001b[0m constants \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m: np\u001b[38;5;241m.\u001b[39mint8(\u001b[38;5;241m1\u001b[39m)}\n\u001b[0;32m---> 13\u001b[0m ps_data \u001b[38;5;241m=\u001b[39m \u001b[43mDataLoaders\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLoad_IAEA\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_schema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_schema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconstants\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconstants\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m                                \u001b[49m\u001b[43minput_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfile_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_records\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1e5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mavailable_units\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcm_MeV\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:397\u001b[0m, in \u001b[0;36mLoad_IAEA.__init__\u001b[0;34m(self, data_schema, constants, input_data, n_records, offset, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_records \u001b[38;5;241m=\u001b[39m n_records\n\u001b[1;32m    396\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_offset \u001b[38;5;241m=\u001b[39m offset\n\u001b[0;32m--> 397\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minput_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:53\u001b[0m, in \u001b[0;36m_DataLoadersBase.__init__\u001b[0;34m(self, input_data, particle_type, units)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_input_data \u001b[38;5;241m=\u001b[39m input_data\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_input_data()\n\u001b[0;32m---> 53\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_import_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_loaded_data()\n",
      "File \u001b[0;32m~/Documents/python/ParticlePhaseSpace/examples/../ParticlePhaseSpace/DataLoaders.py:458\u001b[0m, in \u001b[0;36mLoad_IAEA._import_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    456\u001b[0m             temp[location] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    457\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 458\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfailed to calculate momentums from topas data. Possible solution is to increase\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    459\u001b[0m                             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe value of momentum_precision_factor, currently set to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmomentum_precision_factor\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m 1.2e\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    460\u001b[0m                             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mand failed data has value \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrelative_difference\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m 1.2e\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    461\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_negative_locations\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m d\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m entries returned invalid pz values and were set to zero.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    462\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mWe will now check that momentum and energy are consistent to within \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    463\u001b[0m                   \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_energy_consistency_check_cutoff\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m 1.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_units\u001b[38;5;241m.\u001b[39menergy\u001b[38;5;241m.\u001b[39mlabel\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    465\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_columns[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpz\u001b[39m\u001b[38;5;124m'\u001b[39m]] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mSeries(np\u001b[38;5;241m.\u001b[39msqrt(temp), dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mfloat32)\n",
      "\u001b[0;31mException\u001b[0m: failed to calculate momentums from topas data. Possible solution is to increasethe value of momentum_precision_factor, currently set to  2.00e-03and failed data has value  2.71e+01"
     ]
    }
   ],
   "source": [
    "data_schema = np.dtype([\n",
    "                       ('particle type', 'i1'),\n",
    "                       ('x', 'f4'),\n",
    "                       ('y', 'f4'),\n",
    "                       ('z', 'f4'),\n",
    "                       ('Cosine X', 'f4'),\n",
    "                       ('Cosine Y', 'f4'),\n",
    "                       ('Ek', 'f4'),\n",
    "                       ])\n",
    "\n",
    "constants = {'weight': np.int8(1)}\n",
    "\n",
    "ps_data = DataLoaders.Load_IAEA(data_schema=data_schema, constants=constants,\n",
    "                                input_data=file_name, n_records=int(1e5), units=available_units('cm_MeV'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894a0d7b",
   "metadata": {},
   "source": [
    "Getting closer! we read in the particles correctly, but the conversion to momentum failed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb9b0c9",
   "metadata": {},
   "source": [
    "At this point, i basically did a lot of debugging. I was pretty sure that the data consisted of 1 1-byte integer and 6 4-byte floats. I also new for instance what the distribution of energy, x, and y should look like. So basically, I swapped around the order of the columns until all these distributions started to look right, ending up with the following schema:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39a8cc8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ParticlePhaseSpace import PhaseSpace, DataLoaders\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "# get data file:\n",
    "file_name = Path(r'/home/brendan/Dropbox (Sydney Uni)/abstracts,presentations etc/temp/TrueBeam_v2_6X_00.phsp')\n",
    "file_name = Path(r'/home/brendan/Downloads/Varian_TrueBeam6MV_01.phsp')\n",
    "\n",
    "data_schema = np.dtype([\n",
    "                       ('particle type', 'i1'),\n",
    "                       ('Ek', 'f4'),\n",
    "                       ('x', 'f4'),\n",
    "                       ('y', 'f4'),\n",
    "                       ('z', 'f4'),\n",
    "                       ('Cosine X', 'f4'),\n",
    "                       ('Cosine Y', 'f4')\n",
    "                       ])\n",
    "\n",
    "constants = {'weight': np.int8(1)}\n",
    "\n",
    "ps_data = DataLoaders.Load_IAEA(data_schema=data_schema, constants=constants,\n",
    "                                input_data=file_name, n_records=int(1e5), units=available_units('cm_MeV'))\n",
    "PS = PhaseSpace(ps_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "091a2faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PS.plot.energy_hist_1D()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ddce5a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PS.plot.position_hist_1D()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bff2a63",
   "metadata": {},
   "source": [
    "OK, so this read in appears to have worked; all the consistency checks are passing and the data all looks as expected for a 6MV beam.\n",
    "Note however, that the units look incorrect\n",
    "\n",
    "## Next steps\n",
    "\n",
    "What would be nice is to be able to develop an automated IAEA phase space reader. However, at this point it appears that the information is frequently ordered in arbitrary ways that are not actually explained in the header. For the most recent data, I only managed to figure out how to read it after a period of trial and error. It really does not appear that files in the IAEA database are following the IAEA format at all..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f2b6047",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}