2025-01-13 Final focus offset error study.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "375a1e2f-07e3-4df8-9e7f-3147475d90ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from UTILITY_quickstart import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe9bab62-7a9e-4926-a166-98de5e00f361",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Base Tao lattice\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    }
   ],
   "source": [
    "#Start from golden lattice\n",
    "tao = initializeTao(\n",
    "    loadDefaultLatticeTF = False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65632d9a-dda0-4343-b700-cf8ff8ec5b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = {\"quadOffset\": [], \"beamOffset\": []}\n",
    "step = 50e-6\n",
    "for quadOffset in np.arange(-200e-6, 200e-6 + step, step):\n",
    "    setXOffset(tao, \"Q5FF\", quadOffset)\n",
    "    beamOffset = getMatrix(tao,\"MFFF\",\"PENT\",order=0)[0]\n",
    "\n",
    "    \n",
    "    results[\"quadOffset\"].append(quadOffset)\n",
    "    results[\"beamOffset\"].append(beamOffset)\n",
    "    #print([L2PhaseSet, P[\"mean_energy\"]])\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"quadOffset\"]), \n",
    "    1e6 * np.array(results[\"beamOffset\"])\n",
    ")\n",
    "\n",
    "# Adding title and labels\n",
    "plt.xlabel('Q5FF offset [um]')\n",
    "plt.ylabel('Beam offset at PENT [um]')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a385416-65e1-4219-8e72-606109a3f86f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Show all cases\n",
    "\n",
    "# quadNameList = [\"Q5FF\", \"Q4FF\", \"Q3FF\", \"Q2FF\", \"Q1FF\", \"Q0FF\"]\n",
    "\n",
    "\n",
    "# for quad in quadNameList: \n",
    "#     #Start from golden lattice\n",
    "#     tao = initializeTao(\n",
    "#         loadDefaultLatticeTF = False\n",
    "#     )\n",
    "    \n",
    "    \n",
    "#     results = {\"quadOffset\": [], \"beamOffset\": []}\n",
    "#     step = 50e-6\n",
    "#     for quadOffset in np.arange(0, 200e-6 + step, step):\n",
    "#         setXOffset(tao, quad, quadOffset)\n",
    "#         beamOffset = np.sqrt( getMatrix(tao,\"MFFF\",\"PENT\",order=0)[0] ** 2 + getMatrix(tao,\"MFFF\",\"PENT\",order=0)[2] ** 2 ) \n",
    "    \n",
    "        \n",
    "#         results[\"quadOffset\"].append(quadOffset)\n",
    "#         results[\"beamOffset\"].append(beamOffset)\n",
    "#         #print([L2PhaseSet, P[\"mean_energy\"]])\n",
    "\n",
    "    \n",
    "#     # plt.scatter(\n",
    "#     #     1e6 * np.array(results[\"quadOffset\"]), \n",
    "#     #     1e6 * np.array(results[\"beamOffset\"])\n",
    "#     # )\n",
    "    \n",
    "#     # # Adding title and labels\n",
    "#     # plt.xlabel(f'{quad} offset [um]')\n",
    "#     # plt.ylabel('Beam offset at PENT [um]')\n",
    "\n",
    "#     print(f\"\"\"{quad} : {results['beamOffset'][-1] / results['quadOffset'][-1]}\"\"\")\n",
    "    \n",
    "#     # Display the plot\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a6b8f88-03f8-445e-a1ed-d0b2e26ad405",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a44ece58-6115-4731-ba19-dbae8f6d1145",
   "metadata": {},
   "source": [
    "# Automating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35d5fdba-7470-48e2-ad85-4e81802214f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Overwriting lattice with setLattice() defaults\n",
      "No defaults file provided to setLattice(). Using setLattice_configs/defaults.yml\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'beta_a': 0.50649204966748,\n",
       " 'alpha_a': 0.0466683356262582,\n",
       " 'gamma_a': 1.9786646882376,\n",
       " 'phi_a': 73.0782192378143,\n",
       " 'eta_a': -5.53984755575948e-06,\n",
       " 'etap_a': -1.05998552673305e-05,\n",
       " 'beta_b': 0.500627846338041,\n",
       " 'alpha_b': -0.120964597934503,\n",
       " 'gamma_b': 2.02671993053367,\n",
       " 'phi_b': 60.5521347355512,\n",
       " 'eta_b': -2.11809896530596e-18,\n",
       " 'etap_b': -2.52217362743306e-18,\n",
       " 'c_mat11': 1.24354101578855e-16,\n",
       " 'c_mat12': -2.15263057112982e-17,\n",
       " 'c_mat21': -3.33737707176956e-17,\n",
       " 'c_mat22': 4.78887427948486e-18,\n",
       " 'gamma_c': 1.0}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load a default configuration\n",
    "importedDefaultSettings = loadConfig(\"setLattice_configs/defaults.yml\")\n",
    "\n",
    "#Initialize tao object\n",
    "tao = initializeTao(\n",
    "    #inputBeamFilePathSuffix = importedDefaultSettings[\"inputBeamFilePathSuffix\"],\n",
    "    #numMacroParticles = 1e4,\n",
    "    #csrTF = False\n",
    ")\n",
    "\n",
    "#Set lattice to the loaded config\n",
    "setLattice(tao, **importedDefaultSettings)\n",
    "\n",
    "tao.twiss_at_s(ele = \"PENT\", s_offset = 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2d1c103-6fe4-429e-ba1c-3260a2962730",
   "metadata": {},
   "outputs": [],
   "source": [
    "allMagnetConfigs = []\n",
    "\n",
    "for offset in [-0.5, 0, 0.5]:\n",
    "    allMagnetConfigs.append(\n",
    "        finalFocusSolver(tao, \n",
    "                     ele = \"PENT\",\n",
    "                     s_offset = offset,\n",
    "                     targetBetaX = 0.5,\n",
    "                     targetAlphaX = 0.0,\n",
    "                     targetBetaY = 0.5,\n",
    "                     targetAlphaY = 0,\n",
    "                     verbose = True\n",
    "                     )\n",
    "    )\n",
    "\n",
    "allMagnetConfigs = { \"upstream\" : allMagnetConfigs[0], \"PENT\" : allMagnetConfigs[1], \"downstream\" : allMagnetConfigs[2] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f0d6eda5-1f11-43fc-a0a8-7b6f1bcf87b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.96241376955297\n",
      "4.81389973521349\n",
      "5.80778927904348\n",
      "5.44785872122574\n",
      "2.91441357093688\n",
      "2.58783501822881\n"
     ]
    }
   ],
   "source": [
    "setLattice(tao, ** ( importedDefaultSettings | allMagnetConfigs[\"downstream\"] ))\n",
    "quadNameList = [\"Q5FF\", \"Q4FF\", \"Q3FF\", \"Q2FF\", \"Q1FF\", \"Q0FF\"]\n",
    "for quad in quadNameList:\n",
    "    #print(tao.ele_gen_attribs(quad)[\"K1\"])\n",
    "    print(getMatrix(tao, quad, \"PENT\")[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af68c2fc-369a-4efc-bfe8-ac84ddd105b2",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Monte Carlo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fb26c5fb-1aa0-4b41-8cc5-06112b483345",
   "metadata": {},
   "outputs": [],
   "source": [
    "# quadNameList = [\"Q5FF\", \"Q4FF\", \"Q3FF\", \"Q2FF\", \"Q1FF\", \"Q0FF\"]\n",
    "# #quadErrorSigma = 10e-6 \n",
    "\n",
    "\n",
    "# results = pd.DataFrame()\n",
    "\n",
    "\n",
    "# #for quadErrorSigma in [1e-6, 2e-6, 5e-6, 10e-6, 20e-6, 50e-6, 100e-6, 200e-6, 500e-6]:\n",
    "# for quadErrorSigma in [50e-6]:\n",
    "#     for iteration in np.arange(20):\n",
    "\n",
    "#         #Prevent recalculation until changes are made\n",
    "#         tao.cmd(\"set global lattice_calc_on = F\")\n",
    "        \n",
    "#         for quad in quadNameList:\n",
    "#             setXOffset(tao, quad, quadErrorSigma * np.random.normal())\n",
    "#             setYOffset(tao, quad, quadErrorSigma * np.random.normal())\n",
    "            \n",
    "#         #Reenable lattice calculations\n",
    "#         tao.cmd(\"set global lattice_calc_on = T\")\n",
    "\n",
    "#         for magnetConfigKey in allMagnetConfigs.keys():\n",
    "#             magnetConfig = allMagnetConfigs[magnetConfigKey]\n",
    "            \n",
    "#             setLattice(tao, **(importedDefaultSettings | magnetConfig ))\n",
    "            \n",
    "#             newLine = { \"quadErrorSigma\" : quadErrorSigma, \"iteration\": iteration, \"magnetConfigKey\" : magnetConfigKey } \n",
    "\n",
    "#             for evalEle in [\"M5FF\", \"IPOTR1\", \"PENT\", \"IPOTR2\", \"M0EX\"]:\n",
    "\n",
    "#                 newLine[f\"{evalEle}_xCenter\"] = getMatrix(tao, \"MFFF\", evalEle, order=0)[0]\n",
    "#                 newLine[f\"{evalEle}_yCenter\"] = getMatrix(tao, \"MFFF\", evalEle, order=0)[2]\n",
    "        \n",
    "        \n",
    "#             # Convert newLine to a DataFrame before concatenating\n",
    "#             newLine_df = pd.DataFrame([newLine])\n",
    "#             results = pd.concat([results, newLine_df], ignore_index=True)\n",
    "\n",
    "# # Save results to JSON\n",
    "# results.to_json('results.json', orient='records')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0707b01-eea9-4cc3-8cb0-5d77c50f9d88",
   "metadata": {},
   "source": [
    "## Single magnet sensitivities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "217c9244-2f73-4102-afe1-173701888e8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Base Tao lattice\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    }
   ],
   "source": [
    "quadNameList = [\"Q5FF\", \"Q4FF\", \"Q3FF\", \"Q2FF\", \"Q1FF\", \"Q0FF\"]\n",
    "#quadErrorSigma = 10e-6 \n",
    "\n",
    "\n",
    "results = pd.DataFrame()\n",
    "\n",
    "#     #Start from golden lattice\n",
    "tao = initializeTao(\n",
    "    loadDefaultLatticeTF = False\n",
    ")\n",
    "\n",
    "#for quadErrorSigma in [1e-6, 2e-6, 5e-6, 10e-6, 20e-6, 50e-6, 100e-6, 200e-6, 500e-6]:\n",
    "for quad in quadNameList:\n",
    "    for axis in [\"X\", \"Y\"]: \n",
    "        \n",
    "        #Prevent recalculation until changes are made\n",
    "        tao.cmd(\"set global lattice_calc_on = F\")\n",
    "        \n",
    "        # for quad in quadNameList:\n",
    "        #     setXOffset(tao, quad, quadErrorSigma * np.random.normal())\n",
    "        #     setYOffset(tao, quad, quadErrorSigma * np.random.normal())\n",
    "\n",
    "\n",
    "        #Clean start each loop (remember that setLattice() doesn't check for these offsets!!)\n",
    "        for quadInnerLoop in quadNameList:\n",
    "            setXOffset(tao, quadInnerLoop, 0)\n",
    "            setYOffset(tao, quadInnerLoop, 0)\n",
    "\n",
    "        tao.cmd(f\"set ele {quad} {axis}_OFFSET = {50e-6}\")\n",
    "        \n",
    "        #Reenable lattice calculations\n",
    "        tao.cmd(\"set global lattice_calc_on = T\")\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "        for magnetConfigKey in allMagnetConfigs.keys():\n",
    "            magnetConfig = allMagnetConfigs[magnetConfigKey]\n",
    "            \n",
    "            setLattice(tao, **(importedDefaultSettings | magnetConfig ))\n",
    "            \n",
    "            newLine = { \"quad\" : quad, \"axis\": axis, \"magnetConfigKey\" : magnetConfigKey } \n",
    "\n",
    "            #for evalEle in [\"M5FF\", \"IPOTR1\", \"PENT\", \"IPOTR2\", \"M0EX\"]:\n",
    "            for evalEle in [\"PENT\"]:\n",
    "\n",
    "                newLine[f\"{evalEle}_xCenter\"] = tao.ele_orbit(\"PENT\")['x']\n",
    "                newLine[f\"{evalEle}_yCenter\"] = tao.ele_orbit(\"PENT\")['y']\n",
    "        \n",
    "        \n",
    "            # Convert newLine to a DataFrame before concatenating\n",
    "            newLine_df = pd.DataFrame([newLine])\n",
    "            results = pd.concat([results, newLine_df], ignore_index=True)\n",
    "\n",
    "# Save results to JSON\n",
    "results.to_json('results.json', orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "005d0a20-2429-4eab-8102-ba99d2d3a093",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5e8384b1-6c1f-4dbb-8b6a-030408a10684",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Printing R-matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e38dfe60-d12d-43ac-aa76-ad18600ccdb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Base Tao lattice\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Waist location is upstream\n",
      "Q5FF\n"
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     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
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    }
   ],
   "source": [
    "#     #Start from golden lattice\n",
    "tao = initializeTao(\n",
    "    loadDefaultLatticeTF = False\n",
    ")\n",
    "\n",
    "print(\"\\n\\n\\n\")\n",
    "for magnetConfigKey in allMagnetConfigs.keys():\n",
    "    magnetConfig = allMagnetConfigs[magnetConfigKey]\n",
    "\n",
    "    print(f\"Waist location is {magnetConfigKey}\")\n",
    "    setLattice(tao, **(importedDefaultSettings | magnetConfig ))\n",
    "\n",
    "    for quad in quadNameList:\n",
    "        print(quad)\n",
    "        rawMat6 = tao.ele_mat6(quad)\n",
    "        df = pd.DataFrame.from_dict(rawMat6, orient='index', columns=['1', '2', '3', '4', '5', '6'])\n",
    "        displayMatrix(df)\n",
    "\n",
    "    print(\"\\n\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff632a47-c434-4ae9-9a6a-07404e51dcbd",
   "metadata": {},
   "source": [
    "## Sensitivity to incoming beam errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c33bf34d-c843-47c2-a643-5d8ab971a750",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Overwriting lattice with setLattice() defaults\n",
      "No defaults file provided to setLattice(). Using setLattice_configs/defaults.yml\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'beta_a': 0.50649204966748,\n",
       " 'alpha_a': 0.0466683356262582,\n",
       " 'gamma_a': 1.9786646882376,\n",
       " 'phi_a': 73.0782192378143,\n",
       " 'eta_a': -5.53984755575948e-06,\n",
       " 'etap_a': -1.05998552673305e-05,\n",
       " 'beta_b': 0.500627846338041,\n",
       " 'alpha_b': -0.120964597934503,\n",
       " 'gamma_b': 2.02671993053367,\n",
       " 'phi_b': 60.5521347355512,\n",
       " 'eta_b': -2.11809896530596e-18,\n",
       " 'etap_b': -2.52217362743306e-18,\n",
       " 'c_mat11': 1.24354101578855e-16,\n",
       " 'c_mat12': -2.15263057112982e-17,\n",
       " 'c_mat21': -3.33737707176956e-17,\n",
       " 'c_mat22': 4.78887427948486e-18,\n",
       " 'gamma_c': 1.0}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load a default configuration\n",
    "importedDefaultSettings = loadConfig(\"setLattice_configs/defaults.yml\")\n",
    "\n",
    "#Initialize tao object\n",
    "tao = initializeTao(\n",
    "    #inputBeamFilePathSuffix = importedDefaultSettings[\"inputBeamFilePathSuffix\"],\n",
    "    #numMacroParticles = 1e4,\n",
    "    #csrTF = False\n",
    ")\n",
    "\n",
    "#Set lattice to the loaded config\n",
    "setLattice(tao, **importedDefaultSettings)\n",
    "\n",
    "tao.twiss_at_s(ele = \"PENT\", s_offset = 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62eb9895-ab61-4178-b70a-8865c34235c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = {\"incomingError\": [], \"beamOffset\": []}\n",
    "step = 50e-6\n",
    "for incomingError in np.arange(-200e-6, 200e-6 + step, step):\n",
    "    #setXOffset(tao, \"Q5FF\", quadOffset)\n",
    "\n",
    "    tao.cmd(f\"set ele patch_sector20 x_offset = {incomingError}\")\n",
    "    beamOffset = tao.ele_orbit(\"PENT\")['x']\n",
    "    \n",
    "    results[\"incomingError\"].append(incomingError)\n",
    "    results[\"beamOffset\"].append(beamOffset)\n",
    "    #print([L2PhaseSet, P[\"mean_energy\"]])\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffset\"])\n",
    ")\n",
    "\n",
    "# Adding title and labels\n",
    "plt.xlabel('Incoming error [um]')\n",
    "plt.ylabel('Beam offset at PENT [um]')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5502cff5-ebb1-4208-b6fd-9f6c410024f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Results:\n",
      "Optimal Parameters: [ -67.76985175  -79.7311106    98.00236006  132.62333463 -244.00418159\n",
      "  130.61315049]\n",
      "Objective Function Value at Optimal Parameters: 0.00015590334323612473\n",
      "Number of Iterations: 710\n",
      "Converged: True\n",
      "Optimization Results:\n",
      "Optimal Parameters: [ -72.10008027  -81.35361869   99.27429833  126.46448734 -234.61674012\n",
      "  126.33646397]\n",
      "Objective Function Value at Optimal Parameters: 4.577586581124526e-07\n",
      "Number of Iterations: 686\n",
      "Converged: True\n",
      "Optimization Results:\n",
      "Optimal Parameters: [ -75.89008909  -83.0248896    99.54732097  121.83115775 -226.68150937\n",
      "  121.69834842]\n",
      "Objective Function Value at Optimal Parameters: 0.0001352163591527429\n",
      "Number of Iterations: 730\n",
      "Converged: True\n"
     ]
    }
   ],
   "source": [
    "allMagnetConfigs = []\n",
    "\n",
    "for offset in [-0.5, 0, 0.5]:\n",
    "    allMagnetConfigs.append(\n",
    "        finalFocusSolver(tao, \n",
    "                     ele = \"PENT\",\n",
    "                     s_offset = offset,\n",
    "                     targetBetaX = 0.5,\n",
    "                     targetAlphaX = 0.0,\n",
    "                     targetBetaY = 0.5,\n",
    "                     targetAlphaY = 0,\n",
    "                     verbose = True\n",
    "                     )\n",
    "    )\n",
    "\n",
    "allMagnetConfigs = { \"upstream\" : allMagnetConfigs[0], \"PENT\" : allMagnetConfigs[1], \"downstream\" : allMagnetConfigs[2] }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc6ccb82-3593-4660-b1ea-7cf39954a51e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Overwriting lattice with setLattice() defaults\n",
      "No defaults file provided to setLattice(). Using setLattice_configs/defaults.yml\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Initialize tao object\n",
    "tao = initializeTao(\n",
    "    #inputBeamFilePathSuffix = importedDefaultSettings[\"inputBeamFilePathSuffix\"],\n",
    "    #numMacroParticles = 1e4,\n",
    "    #csrTF = False\n",
    ")\n",
    "\n",
    "#Set lattice to the loaded config\n",
    "setLattice(tao, **importedDefaultSettings)\n",
    "\n",
    "\n",
    "results = {\"incomingError\": [], \"beamOffsetUpstream\": [], \"beamOffsetPENT\": [], \"beamOffsetDownstream\": []}\n",
    "axis = \"y\"\n",
    "\n",
    "step = 50e-6\n",
    "for incomingError in np.arange(-200e-6, 200e-6 + step, step):\n",
    "    #setXOffset(tao, \"Q5FF\", quadOffset)\n",
    "\n",
    "    tao.cmd(f\"set ele patch_sector20 {axis}_offset = {incomingError}\")\n",
    "\n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"upstream\"] ))\n",
    "    beamOffsetUpstream = tao.ele_orbit(\"PENT\")[axis]\n",
    "\n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"PENT\"] ))\n",
    "    beamOffsetPENT = tao.ele_orbit(\"PENT\")[axis]\n",
    "    \n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"downstream\"] ))\n",
    "    beamOffsetDownstream = tao.ele_orbit(\"PENT\")[axis]\n",
    "    \n",
    "    results[\"incomingError\"].append(incomingError)\n",
    "    results[\"beamOffsetUpstream\"].append(beamOffsetUpstream)\n",
    "    results[\"beamOffsetPENT\"].append(beamOffsetPENT)\n",
    "    results[\"beamOffsetDownstream\"].append(beamOffsetDownstream)\n",
    "    #print([L2PhaseSet, P[\"mean_energy\"]])\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetUpstream\"]),\n",
    "    label = \"waist = PENT - 50 cm\"\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetPENT\"]),\n",
    "    label = \"waist = PENT\"\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetDownstream\"]),\n",
    "    label = \"waist = PENT + 50 cm\"\n",
    ")\n",
    "\n",
    "# Adding title and labels\n",
    "plt.xlabel(f'Incoming {axis} error [um]')\n",
    "plt.ylabel(f'Beam {axis} offset at PENT [um]')\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3db800dc-e8a1-4452-b34d-e67557ffe4d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Overwriting lattice with setLattice() defaults\n",
      "No defaults file provided to setLattice(). Using setLattice_configs/defaults.yml\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Initialize tao object\n",
    "tao = initializeTao(\n",
    "    #inputBeamFilePathSuffix = importedDefaultSettings[\"inputBeamFilePathSuffix\"],\n",
    "    #numMacroParticles = 1e4,\n",
    "    #csrTF = False\n",
    ")\n",
    "\n",
    "#Set lattice to the loaded config\n",
    "setLattice(tao, **importedDefaultSettings)\n",
    "\n",
    "\n",
    "results = {\"incomingError\": [], \"beamOffsetUpstream\": [], \"beamOffsetPENT\": [], \"beamOffsetDownstream\": []}\n",
    "axis = \"x\"\n",
    "\n",
    "step = 5e-6\n",
    "for incomingError in np.arange(-20e-6, 20e-6 + step, step):\n",
    "    #setXOffset(tao, \"Q5FF\", quadOffset)\n",
    "\n",
    "    tao.cmd(f\"set ele patch_sector20 {axis}_pitch = {incomingError}\")\n",
    "\n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"upstream\"] ))\n",
    "    beamOffsetUpstream = tao.ele_orbit(\"PENT\")[axis]\n",
    "\n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"PENT\"] ))\n",
    "    beamOffsetPENT = tao.ele_orbit(\"PENT\")[axis]\n",
    "    \n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"downstream\"] ))\n",
    "    beamOffsetDownstream = tao.ele_orbit(\"PENT\")[axis]\n",
    "    \n",
    "    results[\"incomingError\"].append(incomingError)\n",
    "    results[\"beamOffsetUpstream\"].append(beamOffsetUpstream)\n",
    "    results[\"beamOffsetPENT\"].append(beamOffsetPENT)\n",
    "    results[\"beamOffsetDownstream\"].append(beamOffsetDownstream)\n",
    "    #print([L2PhaseSet, P[\"mean_energy\"]])\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetUpstream\"]),\n",
    "    label = \"waist = PENT - 50 cm\"\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetPENT\"]),\n",
    "    label = \"waist = PENT\"\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    1e6 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetDownstream\"]),\n",
    "    label = \"waist = PENT + 50 cm\"\n",
    ")\n",
    "\n",
    "# Adding title and labels\n",
    "plt.xlabel(f\"Incoming {axis}' error [urad]\")\n",
    "plt.ylabel(f'Beam {axis} offset at PENT [um]')\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6bca381c-3868-4660-940e-ecbf725dea46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Environment set to:  /Users/nmajik/Documents/SLAC/FACET2-Bmad-PyTao\n",
      "Tracking to end\n",
      "CSR off\n",
      "Overwriting lattice with setLattice() defaults\n",
      "No defaults file provided to setLattice(). Using setLattice_configs/defaults.yml\n",
      "WARNING! No beam file is specified!\n",
      "Number of macro particles defined by input file\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Initialize tao object\n",
    "tao = initializeTao(\n",
    "    #inputBeamFilePathSuffix = importedDefaultSettings[\"inputBeamFilePathSuffix\"],\n",
    "    #numMacroParticles = 1e4,\n",
    "    #csrTF = False\n",
    ")\n",
    "\n",
    "#Set lattice to the loaded config\n",
    "setLattice(tao, **importedDefaultSettings)\n",
    "\n",
    "\n",
    "results = {\"incomingError\": [], \"beamOffsetUpstream\": [], \"beamOffsetPENT\": [], \"beamOffsetDownstream\": []}\n",
    "axis = \"y\"\n",
    "\n",
    "step = 10e6\n",
    "for incomingError in np.arange(9.9e9, 10.1e9 + step, step):\n",
    "    #setXOffset(tao, \"Q5FF\", quadOffset)\n",
    "\n",
    "    tao.cmd(f\"set ele patch_sector20 e_tot_set = {incomingError}\")\n",
    "\n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"upstream\"] ))\n",
    "    beamOffsetUpstream = tao.ele_orbit(\"PENT\")[axis]\n",
    "\n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"PENT\"] ))\n",
    "    beamOffsetPENT = tao.ele_orbit(\"PENT\")[axis]\n",
    "    \n",
    "    setLattice(tao, **(importedDefaultSettings | allMagnetConfigs[\"downstream\"] ))\n",
    "    beamOffsetDownstream = tao.ele_orbit(\"PENT\")[axis]\n",
    "    \n",
    "    results[\"incomingError\"].append(incomingError)\n",
    "    results[\"beamOffsetUpstream\"].append(beamOffsetUpstream)\n",
    "    results[\"beamOffsetPENT\"].append(beamOffsetPENT)\n",
    "    results[\"beamOffsetDownstream\"].append(beamOffsetDownstream)\n",
    "    #print([L2PhaseSet, P[\"mean_energy\"]])\n",
    "\n",
    "plt.scatter(\n",
    "    1e-9 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetUpstream\"]),\n",
    "    label = \"waist = PENT - 50 cm\"\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    1e-9 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetPENT\"]),\n",
    "    label = \"waist = PENT\"\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    1e-9 * np.array(results[\"incomingError\"]), \n",
    "    1e6 * np.array(results[\"beamOffsetDownstream\"]),\n",
    "    label = \"waist = PENT + 50 cm\"\n",
    ")\n",
    "\n",
    "# Adding title and labels\n",
    "plt.xlabel(f\"Incoming energy [GeV]\")\n",
    "plt.ylabel(f'Beam {axis} offset at PENT [um]')\n",
    "\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ead7fb95-abc0-47a8-98c5-0fdfa09a1381",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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