H_angle_xsuite.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9bb243bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pkruyt/cernbox/xsuite-env/xtrack/xtrack/progress_indicator.py:95: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import xtrack as xt\n",
    "import xobjects as xo\n",
    "import xpart as xp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "602a6bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lattice parameters for ELENA at 100 keV\n",
    "# from https://acc-models.web.cern.ch/acc-models/elena/scenarios/highenergy/highenergy.tfs\n",
    "qx = 2.36168984503\n",
    "qy = 1.38992572490\n",
    "circumference = 30.40531277976 #m\n",
    "\n",
    "# relativistic factors\n",
    "gamma_rel = 1.0001066 # at 100 keV\n",
    "\n",
    "# optics at e-cooler (approximate), in m\n",
    "beta_x = 1.7\n",
    "beta_y = 2.7\n",
    "D_x = 1\n",
    "\n",
    "# electron cooler parameters\n",
    "current = 0.001 # A current\n",
    "length = 1 # m cooler length\n",
    "radius_e_beam = 25*1e-3 #m radius of the electron beam\n",
    "temp_perp = 100e-3 # <E> [eV] = kb*T\n",
    "temp_long =  1e-3 # <E> [eV]\n",
    "magnetic_field = 0.010 # 100 Gauss in ELENA\n",
    "# idea is to study magnetic field imperfections\n",
    "magnetic_field_ratio_list = [0,5e-4,1e-3,5e-3] #Iterate over different values of the magnetic field quality to see effect on cooling performance.\n",
    "#magnetic_field_ratio is the ratio of transverse componenet of magnetic field and the longitudinal component. In the ideal case, the ratio is 0.\n",
    "\n",
    "# some initial beam parameters\n",
    "emittance = 5e-6\n",
    "dp_p = 2e-3 \n",
    "\n",
    "# simulation parameters: simulate 10 s of cooling, and take data once every 10 ms\n",
    "max_time_s = 10\n",
    "int_time_s = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "588164be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# some constants, and simple computations\n",
    "clight = 299792458.0\n",
    "mass0 = 938.27208816*1e6 #ev/c^2\n",
    "\n",
    "beta_rel = np.sqrt(gamma_rel**2 - 1)/gamma_rel\n",
    "p0c = mass0*beta_rel*gamma_rel #eV/c\n",
    "T_per_turn = circumference/(clight*beta_rel)\n",
    "\n",
    "# compute length of simulation, as well as sample interval, in turns\n",
    "num_turns = int(max_time_s/T_per_turn)\n",
    "save_interval = int(int_time_s/T_per_turn)\n",
    "\n",
    "# compute initial beam parameters\n",
    "x_init = np.sqrt(beta_x*emittance)\n",
    "y_init = np.sqrt(beta_y*emittance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/10 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
    },
    {
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      " 10%|█         | 1/10 [01:18<11:46, 78.55s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
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    {
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      " 20%|██        | 2/10 [02:34<10:14, 76.76s/it]"
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    {
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     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
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      " 30%|███       | 3/10 [03:49<08:51, 75.99s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 40%|████      | 4/10 [05:01<07:28, 74.75s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
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      " 50%|█████     | 5/10 [06:17<06:14, 74.90s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      " 60%|██████    | 6/10 [07:32<05:00, 75.14s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
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     "output_type": "stream",
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      " 70%|███████   | 7/10 [08:46<03:44, 74.84s/it]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
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      " 80%|████████  | 8/10 [10:03<02:30, 75.35s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
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      " 90%|█████████ | 9/10 [11:18<01:15, 75.39s/it]"
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    {
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     "output_type": "stream",
     "text": [
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No longitudinal focusing found in the line. Assuming coasting beam uniformly distributed along the line - EXPERIMENTAL!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n",
      "Compiling ContextCpu kernels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "EXPERIMENTAL FEATURE: coasting beam.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done compiling ContextCpu kernels.\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [12:34<00:00, 75.43s/it]\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "n_part = 1000\n",
    "\n",
    "angle_list=np.linspace(-4e-3,4e-3,10)\n",
    "# angle_list=np.linspace(-40e-3,40e-3,50)\n",
    "\n",
    "final_emittance_x=[]\n",
    "final_emittance_x_normalised=[]\n",
    "sigma_x_list=[]\n",
    "\n",
    "final_emittance_y=[]\n",
    "final_emittance_y_normalised=[]\n",
    "sigma_y_list=[]\n",
    "\n",
    "# simulation parameters: simulate 10 s of cooling, and take data once every 10 ms\n",
    "max_time_s = 2.4\n",
    "int_time_s = 0.01\n",
    "\n",
    "# compute length of simulation, as well as sample interval, in turns\n",
    "num_turns = int(max_time_s/T_per_turn)\n",
    "save_interval = int(int_time_s/T_per_turn)\n",
    "\n",
    "\n",
    "#plot some overall values\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.rcParams.update({'font.size': 25})\n",
    "for angle in tqdm(angle_list):\n",
    "        # Define the whole machine\n",
    "        arc = xt.LineSegmentMap(\n",
    "                qx=qx, qy=qx,\n",
    "                dqx=0, dqy=0,\n",
    "                length=circumference,\n",
    "                betx=beta_x,\n",
    "                bety=beta_y,\n",
    "                dx=D_x)\n",
    "\n",
    "        electron_cooler = xt.ElectronCooler(\n",
    "                length=length,\n",
    "                radius_e_beam=radius_e_beam,\n",
    "                current=current,\n",
    "                temp_perp=temp_perp,\n",
    "                temp_long=temp_long,\n",
    "                magnetic_field=magnetic_field, \n",
    "                magnetic_field_ratio=1e-3,\n",
    "                offset_px=angle,\n",
    "                space_charge=0)\n",
    "\n",
    "        # create a monitor object, to reduce holded data\n",
    "        monitor = xt.ParticlesMonitor(start_at_turn=0, stop_at_turn=1,\n",
    "                                n_repetitions=int(num_turns/save_interval),\n",
    "                                repetition_period=save_interval,\n",
    "                                num_particles=n_part)\n",
    "\n",
    "        line = xt.Line(\n",
    "                elements=[monitor, electron_cooler, arc])\n",
    "        line.particle_ref = xp.Particles(mass0=mass0, q0=1, p0c=p0c)\n",
    "        line.build_tracker()\n",
    "\n",
    "        # create desired beam\n",
    "        bunch_intensity = None\n",
    "        beta_gamma = line.particle_ref._beta0*line.particle_ref._gamma0\n",
    "        gemitt_x = 10e-6\n",
    "        gemitt_y = 5e-6\n",
    "        nemitt_x = gemitt_x*beta_gamma\n",
    "        nemitt_y = gemitt_y*beta_gamma\n",
    "        sigma_dp = 1e-3\n",
    "        particles = xp.generate_matched_gaussian_bunch(\n",
    "                num_particles=n_part, total_intensity_particles=bunch_intensity,\n",
    "                nemitt_x=nemitt_x, nemitt_y=nemitt_y, sigma_dp=sigma_dp,\n",
    "                coasting=True,\n",
    "                line=line)\n",
    "\n",
    "        # just track all particles, and keep turn-by-turn data (memory expensive!)\n",
    "        line.track(particles, num_turns=num_turns,\n",
    "                turn_by_turn_monitor=False)\n",
    "\n",
    "        # extract relevant values\n",
    "        x = monitor.x[:,:,0]\n",
    "        px = monitor.px[:,:,0]\n",
    "        y = monitor.y[:,:,0]\n",
    "        py = monitor.py[:,:,0]\n",
    "        delta = monitor.delta[:,:,0]\n",
    "        time = monitor.at_turn[:, 0, 0] * T_per_turn\n",
    "\n",
    "        # compute actions. for x, remove the dp/p contribution:\n",
    "        action_x = ((x-D_x*delta)**2/beta_x + beta_x*px**2)\n",
    "        # for y, simple compute:\n",
    "        action_y = (y**2/beta_y + beta_y*py**2)\n",
    "\n",
    "        emittance_x_normalised=np.mean(action_x, axis=1)/gemitt_x/2\n",
    "        emittance_x=np.mean(action_x, axis=1)\n",
    "        sigma_x=np.std(x*1e3, axis=1)\n",
    "        \n",
    "        emittance_y_normalised=np.mean(action_y, axis=1)/gemitt_y/2\n",
    "        emittance_y=np.mean(action_y, axis=1)\n",
    "        sigma_y=np.std(y*1e3, axis=1)\n",
    "        \n",
    "\n",
    "        #plt.plot(time, np.std(delta, axis=1)/sigma_dp, label=f'RMS dp/p (init={sigma_dp})')\n",
    "\n",
    "        #(WARNING! FACTOR 2 TO BE UNDERSTOOD!)\n",
    "        #plt.plot(time, np.mean(action_x, axis=1)/gemitt_x/2, label=f'RMS $\\epsilon_x$ (init={gemitt_x})')\n",
    "        #plt.plot(time, np.mean(action_y, axis=1)/gemitt_y/2, label=f'RMS $\\epsilon_y$ (init={gemitt_y})')\n",
    "\n",
    "        #plt.plot(time,emittance_x , label=r'$\\theta$='f'{angle}')\n",
    "\n",
    "        final_emittance_x_normalised.append(emittance_x_normalised[-1])       \n",
    "        final_emittance_x.append(emittance_x[-1])\n",
    "        sigma_x_list.append(sigma_x[-1])\n",
    "\n",
    "        final_emittance_y_normalised.append(emittance_y_normalised[-1])       \n",
    "        final_emittance_y.append(emittance_y[-1])\n",
    "        sigma_y_list.append(sigma_y[-1])\n",
    "                #final_emittance_y.append(np.mean(action_y, axis=1)/gemitt_y/2)\n",
    "\n",
    "# sigma_x_list = [float(value) * 1e3 for value in sigma_x_list]\n",
    "# sigma_y_list = [float(value) * 1e3 for value in sigma_y_list]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "197830f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize=(12, 8))\n",
    "# plt.rcParams.update({'font.size': 25})\n",
    "# plt.plot(angle_list,final_emittance_x_normalised)\n",
    "# plt.xlabel(r'$\\theta$')\n",
    "# plt.ylabel('final emittance')\n",
    "# plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
    "# plt.tight_layout()\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.rcParams.update({'font.size': 25})\n",
    "plt.scatter(angle_list*1e3,sigma_y_list,label='Vertical')\n",
    "plt.scatter(angle_list*1e3,sigma_x_list,label='Horizontal')\n",
    "plt.xlabel(r'$\\theta_x$ [mrad]')\n",
    "plt.ylabel(r'profile $\\sigma$ [mm]')\n",
    "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
    "plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.savefig('H_angle_Xsuite.png', format='png', dpi=300)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "b3c9a0a5475e728b39e61ac58d244ad936924f9cf82746515a9faeb1f1a0b703"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}