notebook_05_lhc_lossmap.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "33000148-5a1a-4be9-a862-0abe73f403cb",
   "metadata": {},
   "source": [
    "# LHC collimation study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8935728f-1568-454f-8899-83445db23632",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pathlib import Path\n",
    "\n",
    "import xobjects as xo\n",
    "import xtrack as xt\n",
    "import xcoll as xc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96a6ebe-e110-4872-8c99-210c223a26e1",
   "metadata": {},
   "source": [
    "### Import a line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e20c3d7b-fc99-4c0c-8b43-36a2ca347528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f218a66f92b7443ea25938dbe5990d85",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading line from dict:   0%|          | 0/102795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done loading line from dict.           \n"
     ]
    }
   ],
   "source": [
    "line = xt.Line.from_json(f'lhc_run3_b1.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03300695-0d08-48ae-a098-82cb094554c0",
   "metadata": {},
   "source": [
    "### Install and configure collimators using `xcoll`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cda8bb97-7bca-43bd-8b5a-ad6613994655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Installing tcp.6l3.b1           as EverestCollimator\n",
      "Installing tcsg.5l3.b1          as EverestCollimator\n",
      "Installing tcsg.4r3.b1          as EverestCollimator\n",
      "Installing tctph.4l5.b1         as EverestCollimator\n",
      "Installing tctpv.4l5.b1         as EverestCollimator\n",
      "Installing tcp.d6l7.b1          as EverestCollimator\n",
      "Installing tcp.c6l7.b1          as EverestCollimator\n",
      "Installing tcsg.b5l7.b1         as EverestCollimator\n",
      "Installing tcspm.b4l7.b1        as EverestCollimator\n",
      "Installing tctph.4l1.b1         as EverestCollimator\n",
      "Installing tctpv.4l1.b1         as EverestCollimator\n"
     ]
    }
   ],
   "source": [
    "coll_manager = xc.CollimatorManager.from_yaml('collimators_simplified.yaml', line=line, beam='1')\n",
    "coll_manager.install_everest_collimators(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656d6bb0-01ba-42cc-af38-4d600e6d7e82",
   "metadata": {},
   "source": [
    "#### Aperture sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92fba881-b370-4d5d-9cfc-bd9c6ad9eebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "095a5279b4584c178de3c65c23f01b90",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Checking aperture:   0%|          | 0/102742 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done checking aperture.           \n",
      "0 thin elements miss associated aperture (upstream):\n",
      "[]\n",
      "0 thick elements miss associated aperture (upstream or downstream):\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "df_with_coll = line.check_aperture()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3b7e0b-0e20-43ac-80b6-c140c15dbaff",
   "metadata": {},
   "source": [
    "#### Build tracker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c35ed22-7b98-43f2-8e46-c7000e28c93a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found suitable prebuilt kernel `default_xcoll`.\n"
     ]
    }
   ],
   "source": [
    "coll_manager.build_tracker()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "452cf615-f797-49a6-b945-ee78e7b6218c",
   "metadata": {},
   "source": [
    "#### Set collimators gaps (optics computed internally)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "215f2468-61ce-44fc-af30-48941bcf4867",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/giadarol/xsuite_packages/xcoll/xcoll/manager.py:515: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  self.colldb._optics = pd.concat([\n"
     ]
    }
   ],
   "source": [
    "coll_manager.set_openings()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ee7e06-605f-474f-87f0-0d24d02f50ff",
   "metadata": {},
   "source": [
    "#### Simulate impact of a pencil beam on the jaws of a given collimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c799519f-933b-4a07-9d34-e0d3b46f8bac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collimator tcp.c6l7.b1 is converging.\n"
     ]
    }
   ],
   "source": [
    "# Generate distribution\n",
    "tcp  = 'tcp.c6l7.b1'\n",
    "part = coll_manager.generate_pencil_on_collimator('tcp.c6l7.b1', num_particles=int(5e4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b55e028a-4914-4389-a244-5064675665a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n",
      "Done compiling ContextCpu kernels.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xtrack.tracker.Tracker at 0x2ce462390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enable multicore CPU parallelization\n",
    "line.discard_tracker()\n",
    "line.build_tracker(_context=xo.ContextCpu(omp_num_threads='auto'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8da19a05-04ef-486e-9952-e8a6e8d52e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d3e4cdf8e12349dea9f0ea03e691f4bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tracking:   0%|          | 0/30 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Track\n",
    "coll_manager.enable_scattering()\n",
    "line.track(part, num_turns=30, with_progress=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac2a123-302a-43ae-91da-b10eb4e9363d",
   "metadata": {},
   "source": [
    "#### Analysis of tracking results - loss maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3887d227-3064-4420-954c-bc4181e17c5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found suitable prebuilt kernel `default_xcoll`.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xtrack.tracker.Tracker at 0x2a71e2650>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Disable scattering and multi-core parallilization\n",
    "coll_manager.disable_scattering()\n",
    "line.discard_tracker()\n",
    "line.build_tracker(_context=xo.context_default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7f887ef-7c21-4af0-bb05-c12c4c10073b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing the aperture losses refinement.\n",
      "Found suitable prebuilt kernel `default_xcoll`.\n",
      "Found suitable prebuilt kernel `default_xcoll_backtrack_no_limit`.\n",
      "Found suitable prebuilt kernel `default_xcoll_no_limit`.\n"
     ]
    }
   ],
   "source": [
    "coll_manager.lossmap(part, file='mylossmap.json');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8460bb5e-df31-4dfd-9c08-779648678b7b",
   "metadata": {},
   "source": [
    "#### Plot (same tool as for measured loss-map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ed5b9b0-0057-402e-8b4c-bece282cb8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Packages pyspark or cern.nxcals are not installed. Data retrieval from NXCALS is not possible.\n",
      " If you are on SWAN and did not expect this warning, then restart and load the 'NXCals Pro' software stack.\n",
      "Maximum inefficiency in the DS is 0.000788.\n"
     ]
    }
   ],
   "source": [
    "import lossmaps as lm\n",
    "\n",
    "lmap = lm.SimulatedLossMap(lmtype=lm.LMType['B1H'], machine=lm.Machine.LHC, energy=6800, particle=lm.Particle.PROTON)\n",
    "lmap.load_data_json(json_files='mylossmap.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbac285b-b912-4900-9b1a-bcf61d1f6a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning, `show_layout` temporarily not supported.\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 2000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss map\n",
    "_ = lm.plot_lossmap(lmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "184cbed5-bc86-4ecc-98b1-a68061dfa7a7",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}