{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "262a5ec8-2553-4237-ab62-319b6ca22089", "metadata": {}, "source": [ "# Example-49: Advance (Computation of chromatic phase advances)" ] }, { "cell_type": "code", "execution_count": 1, "id": "3fdea51c-4c16-4ac5-b91d-e5b7822e6235", "metadata": {}, "outputs": [], "source": [ "# In this example chromatic phase advance are computed (derivatives of phase advance with respect to momentum deviation)" ] }, { "cell_type": "code", "execution_count": 2, "id": "6f9239a3-36b8-4015-8c71-6e90bbf677ce", "metadata": {}, "outputs": [], "source": [ "# Import\n", "\n", "from pprint import pprint\n", "\n", "import torch\n", "from torch import Tensor\n", "\n", "from pathlib import Path\n", "\n", "import matplotlib\n", "from matplotlib import pyplot as plt\n", "\n", "from model.library.corrector import Corrector\n", "from model.library.line import Line\n", "\n", "from model.command.util import chop\n", "from model.command.util import select\n", "\n", "from model.command.external import load_sdds\n", "from model.command.external import load_lattice\n", "\n", "from model.command.build import build\n", "\n", "from model.command.tune import tune\n", "from model.command.advance import advance\n", "from model.command.advance import chromatic_advance" ] }, { "cell_type": "code", "execution_count": 3, "id": "2f8614c5-6beb-4557-9b68-f6dadb4da157", "metadata": {}, "outputs": [], "source": [ "# Load ELEGANT twiss\n", "\n", "path = Path('ic.twiss')\n", "parameters, columns = load_sdds(path)\n", "\n", "nu_qx:Tensor = torch.tensor(parameters['nux'] % 1, dtype=torch.float64)\n", "nu_qy:Tensor = torch.tensor(parameters['nuy'] % 1, dtype=torch.float64)\n", "\n", "# Set phase advances from lattice start to each BPM\n", "\n", "kinds = select(columns, 'ElementType', keep=False)\n", "\n", "mu_qx = select(columns, 'psix', keep=False)\n", "mu_qy = select(columns, 'psiy' , keep=False)\n", "\n", "mu_qx:Tensor = torch.tensor([value for (key, value), kind in zip(mu_qx.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n", "mu_qy:Tensor = torch.tensor([value for (key, value), kind in zip(mu_qy.items(), kinds.values()) if kind == 'MONI'], dtype=torch.float64)\n", "\n", "positions = select(columns, 's', keep=False).items()\n", "positions = [value for (key, value), kind in zip(positions, kinds.values()) if kind == 'MONI']" ] }, { "cell_type": "code", "execution_count": 4, "id": "34fe012b-fcc3-4e49-a02b-388223fd09b4", "metadata": {}, "outputs": [], "source": [ "# Build and setup lattice\n", "\n", "# Load ELEGANT table\n", "\n", "path = Path('ic.lte')\n", "data = load_lattice(path)\n", "\n", "# Build ELEGANT table\n", "\n", "ring:Line = build('RING', 'ELEGANT', data)\n", "ring.flatten()\n", "\n", "# Merge drifts\n", "\n", "ring.merge()\n", "\n", "# Split BPMs\n", "\n", "ring.split((None, ['BPM'], None, None))\n", "\n", "# Roll lattice start\n", "\n", "ring.roll(1)\n", "\n", "# Set linear dipoles\n", "\n", "for element in ring:\n", " if element.__class__.__name__ == 'Dipole':\n", " element.linear = True\n", "\n", "# Split lattice into lines by BPMs\n", "\n", "ring.splice()\n", "\n", "# Set number of elements of different kinds\n", "\n", "nb = ring.describe['BPM']\n", "nq = ring.describe['Quadrupole']\n", "ns = ring.describe['Sextupole']" ] }, { "cell_type": "code", "execution_count": 5, "id": "df4803ee-d285-4a8b-9ca0-d9378e8f8e58", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "True\n" ] } ], "source": [ "# Compute tunes (fractional part)\n", "\n", "guess = torch.tensor(4*[0.0], dtype=torch.float64)\n", "nuqx, nuqy = tune(ring, [], alignment=False, matched=True, guess=guess, limit=8, epsilon=1.0E-9)\n", "\n", "# Compare with elegant\n", "\n", "print(torch.allclose(nu_qx, nuqx))\n", "print(torch.allclose(nu_qy, nuqy))" ] }, { "cell_type": "code", "execution_count": 6, "id": "b106825c-87f8-48ac-b138-4cd5d574c4bd", "metadata": {}, "outputs": [], "source": [ "# Compute chromatic phase advances\n", "\n", "dp = torch.tensor([0.0], dtype=torch.float64)\n", "dmuqxdp, dmuqydp = response = chromatic_advance(ring, [], alignment=False, matched=True, limit=16, epsilon=None).T" ] }, { "cell_type": "code", "execution_count": 7, "id": "4d99c3dd-8183-45a7-804f-eccba52743b7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor(0.0805, dtype=torch.float64)\n", "tensor(0.0013, dtype=torch.float64)\n" ] } ], "source": [ "# Test derivatives\n", "\n", "dp = torch.tensor([0.001], dtype=torch.float64)\n", "\n", "model = advance(ring, [0.0*dp], ('dp', None, None, None), alignment=False, matched=True, limit=16, epsilon=None).T\n", "error = advance(ring, [1.0*dp], ('dp', None, None, None), alignment=False, matched=True, limit=16, epsilon=None).T\n", "\n", "print((error - model).norm())\n", "print((error - (model + response * dp)).norm())" ] }, { "cell_type": "code", "execution_count": 8, "id": "519d0534-ec68-4001-8500-a911a8191def", "metadata": {}, "outputs": [ { "data": { "image/png": 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K/PjMzExlZmaefJyWlqaGDRvSfgooBjk50uTJ0tdf2+Zi8+fbqo9Atnix9R2V7AL5/vsJNgCgIHFx0uzZ1p5Qsk04x46VPvlECg72Bhp5PfKI9M471gO6c2eCDQAAULhduyzIWL7cugZIthCtd2+7V7v4Yu7Vztfu3RZurFwpxcd7j5cvnz/gqFjRvTECgFT09lOuhxpLly5VRkaGWrRooYSEBE2fPl379u3T5s2bFVZAw7+C9uCQRKgBXCDHkZ591ib5y5e3if6LL3Z7VKXj44+lp56yf4NrrrFJuuBgt0cFAL7h2DHptdek//7XzpNhYdK999p+S0WZWNi40VoaZmYSbAAAgPxOnJDWrLH70B9/9B5v0MDaSw0YwGbXxclxLDzyVHDk3Z+kQgULNnr3tqCjQgXXhgmgDPObUONUKSkpaty4sebMmaMRI0ac9jyVGkDJePddq8wICpKmT5euuMLtEZWu5culxx+3i7z+/aUJE2xjNQAoy775RpozxzYBl6S+faVRo6ya71zkDTY6dZIefZRgAwCAsiwhwaoyli6VUlPtWHCw1KOHhRkdO1KVUdIcx/bdWLVK+uwz+z/xqFhRuvxyCzg6d+a6DUDp8dtQQ5I6deqkvn37atasWWf92KL+RcuChQsLb/+waJGUmysNH17ao4I/WLNGevhhu6i55x7pxhvdHpE7Vq60ibbcXLt4+9vfrN8oAJQ1KSnSCy9In35qj+vUkcaMsVV75+uHH6QHH7Rg47LL7HzLCkAAAMqOnBxrdfzBB9K339r9p2RtKgcPtqr5mjXdHWNZ5Ti2f4mnRZVnQYtke250726bjHfqFJh7bgLwHUWd6/e56bqMjAzt2LFDt912m9tD8TvBwdKCBfZ+3mBj0SI7fvvt7owLvu3nn6WZM+0iZsgQ6Q9/cHtE7und20KMGTPsQi47W5o6lWADQNnhOFJsrAUaaWm2QvL3v5fuuEOqVOnCvna7dtITT1iwsX697eFEsAEAQOBLTpY++khaskQ6eNB7vFMn2yuja1eq5N0WFCQ1b25vd90lbdli1RurVtn/WWysvVWpYtU0vXtLl17KvTIA97heqTF+/HgNHjxYjRs31v79+zVt2jRt3LhRP//8s2rXrn3Wz6dSI7+8AcbQoac/BvJKSLDKjJQUu5B89FEuJiVbPTR1qvV37drV2nFRbgsg0CUkWKup9evtcZMm0gMPSC1bFu/32bTJgo3jx6nYAAAgUDmO9P33tlfGl19alYYkhYdLgwZZZUZUlLtjxNk5ji2EXLnSAo7kZO9zYWEWcFx1ldS+PQEHgOLhN+2nbrrpJq1Zs0bJycmqXbu2evTooZkzZ6pp06ZF+nxCjdM98YTtjVC7tqXoI0YQaOB06enWF33PHikmxjYJv9BVuIHEs4qYNikAAl1Ojm0C/tprds4rV04aNkz64x9L7uY0b7Bx6aVWMcg5FgAA/5eeLi1bZi2m8m5C3bat7ZXRsyfti/yV49hm7qtW2dtvv3mfq1ZNuvJKq+Bo147FkgDOn9+EGheKUON08+ZJ06bZC05IiG143K+fJedstAXJKhAeeMD6m0dGWghG79LTbdwoTZpkk27t20uPPUbwAyCwbNsmPfWUtHWrPW7fXho3TmrQoOS/N8EGAACBwXGkX36xIGPlSikry45XrmxzEdddZxWgCBy5uXYtt2qVtHq1dX/wiIiw8KpXL+mSS6xVOgAUFaFGGfbMM9LLL9uLSmamVLeuvdWuLfXpYxcVXFCUXY5jk/OffmoXmfPm8fNwJps326Tb0aNS69ZWCVWlitujAoALk5kpLVwovfOO3ZRWrSqNHGntIEpzAUTeYKNjR3t9ItgAUFoWLrTJtoKq2hctsvPj8OGlPSrAfxw7Jq1YYS2mtm/3Hm/WzPbK6NOHRWFlQU6OLZhcuVJas8b2ZfOoUcMbcLRty0JbAGdHqFFG5d1D47bbbHJgwQKbrKhe3ftxTZpYuNGnj4UdKDsWLLCfk5AQ6fHHrbUSziwuzipbMjKst/yTT1r/UADwR999Jz39tLR/vz3u1Uu67z676XTDjz9asHHsmNShgzRrFsEGgNJR2P6D7EsInNmuXRZkxMba4i/J9iDs3dvCjJYtmbwuq7KzbS+VlSulzz+3e2iPmjXturN3b6lVK35GABSMUKMMOtNF+WuvSd2720T22rX2QiPZi0j79hZw9OxpK/cRuJYts0oDySbpr77a3fH4k23bpPHjbdVJTIw0e7ZtcgcA/iI9XXrxRWnpUntcq5Y0Zox0+eXujkuyqrgJEwg2AJS+l1+WXnnFzoW3326rjV9/nUADONWJE9ZmaPFie932aNDA2ksNHMjCL+SXnS1t2GABxxdfSEeOeJ+LjLQ5qKuuklq0IOAA4EWoUQYVtXw6Pd0uRmJjre2DR/nydjHfr5/UuXPJbQ4Kd3z3nU0Y5eRIt9wi3Xmn2yPyPzt3Wq/5lBQpOtpWOuetgAIAX+Q4djM5b56dv4KCbPLhrrt8azHDqcHGY49JFSu6PSoAgSQlxfYQ2rZN2rLF/kxM9L4FBdkisH797JqvfXs2uwX275c+/NAWRaSm2rHgYKlHD6vK6NCBCWmc3YkT0rff2jXpl1/a9Z5H3breCo6YGH6egLKOUANFkphoPTCXL5fi473Hq1WzF5R+/SgLDAS//irde6+tjOjTR3roIf5Pz1d8vDR2rJScLDVqJM2ZwybrAHxXUpLttfX11/a4cWOr1Gvd2t1xFeannyzYOHqUYAPA+XMcu1bzBBhbt9rboUMFf3y9etJXX1mblNxcqV07O16tmk3c9uxp+/6w6AtlRU6OXTssXmwT0R61a0vXXitdcw33QDh/mZnegOOrr2xvNY+oKJuL6tVLatqUeQugLCLUwDlxHNvYKzbWNpD+7Tfvc/XqWbjRr5+VlsK/HD5sm78mJdnGXE8/LZUr5/ao/Nu+fRZsJCVJ9etbsBEZ6faoAMArN1d6/33pH/+wlXChodKtt0o33+z7rwF5g4327a0VFcEGgMI4jl2TeYILT4iR937GIyjI7meaN7e3mBjb0Pi996yNb0iIVXPExNi507MqXbI9Crt3t4Djsst8/1wKnI/kZOmjj6QlS6SDB+1YUJDUqZNVZXTpQvUSildmpgVoq1ZZq/TMTO9zDRp4A47oaAIOoKwg1MB5y8mxjZ1iY6U1a/Kn5i1bWrhx1VVSRIRrQ0QRHT8u3X+/3dg1aCC98IKtOMOFS0y0XvSJiVYuO2eOBYAA4LZdu2zfn19+scetW1t1RuPG7o7rXBBsACiI41grnLzto7Zutfa6pwoKki66yAIKT4jRtOnpbfdO3ZfQ83jYMKvYWL3a7onyhiSVK1vb3p49bbKXPYDgzxzHWhUvXmxtgXJz7Xh4uO3BOHgw9zkoHcePW7CxcqW0bp2UleV9rnFjb4sqf7qmBXDuCDVQLI4ftwub5cutPNDz0xIcbBfw/fpZSTYX8r4nN1eaMsXKOcPDLdCoX9/tUQWWpCSr2Ni3z0qx58yhmgmAe7KypH/9S3rzTVugULmy7Ztx3XX+ubItb7DRrp30+OMEG0BZkpsr7dmTv33Utm12TjhVaKit4vUEGDExFmCc7R7l1ECjsOO5ubbvz+rV9pac7P3YihWlbt0s4OjShfMU/EdamrRsme2XsXev93jbtlaVceWVVCTBPUePWsDx2WfSN9/YpuMe0dEWbvTu7b3/LuoeswB8H6EGit1vv9kLSmysrYzyqFRJuuIKqX9/638dHOzeGOE1b570v//Zhegzz/hu/3R/l5xswUZ8vFSjhgUbrBwBUNo2bZKeesomACVrkXL//Ra4+rOff7Yqk6NHpUsusWCjUiW3RwWguOXkSLt3528ftX17/opxj3LlLLDwhBfNm9sE1/lMvp7PJJjj2LnJE3AkJXmfq1DBgo1evezPU6tCALc5jlVyLl5sq+FPnLDjlSvb/fx119nvE+BLjhyxxbYrV0rr1+cPOJo1s3PuoUPWevVsITUA30eogRIVH297b8TGWvsdj5o1bSPqfv3Y1MlN//2v9Pzz9v60afYij5KTkiKNGyft3Glt2Z5+WmrSxO1RASgLMjKkl1+23teShav332+LDQLlNfiXX6Tx4wk2gEBx4oT066/520ft3Jm/zYhHhQo2YdWihTfAaNTIdzbsdhz7e6xebf3g894XlS8vde5sFRzduklVqrg2TEDHjtn9++LF0o4d3uMxMRZk9OnDayv8Q3q69MUXds7dsMFC8bySkqS775b++lcCDcBfEWqgVDiOtYeIjbXUPG8/24susnCjb182US5NX35pbaccx17Mb7rJ7RGVDWlpNum2bZsUFmbBRkyM26MCEMg+/1x69llvG5Rrr7V2U2Fh7o6rJBBsAP4pM9MCi7zto3btyr/K1qNy5fzto5o3lxo29J8qcMex6hJPwLFvn/e50FBr3duzp+3FEYjnafimnTulDz6w+3VP67by5W2PzOuusz0zA2URBMqetDS7Hl61yvaFyc21cDkxUapa1Rb7/PWv0ogRbo8UwLkg1ECpy862zZxiY20fB08pq2S9sPv1swv5qlXdG2Ogi4uTRo+2G8jBg20jay5SS09GhrVJiYuzn/Mnn5QuvtjtUQEINMnJFmZ8/rk9btDAJvzbtXN3XCWNYAPwbceO2aR+3j0wdu/2bjqcV1hY/vAiJsb2fguU61bHsfDGE3DEx3ufCwmRLr3U7ou6d7e974DilJVlm9svXmx7wXg0aGBBxsCBBGsIPCkpdm28cqW1FnQce025/HJrrXbttXRTAPwFoQZclZFhF1KxsdLGjd7joaH2otKvn/WZZeOx4pOYKN1zj+190rmz9NhjdtOE0nX0qPTgg3YDUbmyTbq1bev2qAAEAsexNlMvvWTnmpAQq8YbOtRWXZYFcXEWbBw5YufWxx+nZz3ghowMCy88Aca2bbanT0F3lhERFlx43mJipDp1AifAKIpff/XuwbFrl/d4cLDtSdizp7UNjIhwa4TwdUXZ/6V/f9v0e+lSKTXVngsJkXr0sI2/27cvW793KJsWLZL+/nf7HUhKsmqNunXtudatLdzo3dvaGwLwTYQa8BlJSdKKFdLy5XZB7xEWZns99OsntWnDBdaFyMiQ7r3XVsM1bSo99xyTPG46dkx66CHp+++lihUtYOrQwe1RAfBn8fHW1m7TJnvcsqVVhpXFFWd5g402baQnnuA1DyhJqan5w4utW6X9+wv+2Fq18ldgNG9ue+5xne8VH2+Lv1avtsoWj6Agq7jzBBw1a7o3RviewvYGWLjQ7v3q1rV7EI/ISJu8vfpqfpZQdpz6e/L669K8eVallJHh3X+jalVv9UZ0tLtjBnA6Qg34HMexnp6xsbZJmacHuGQXYX37WsDRqJF7Y/RH2dnShAk2gV6rljR/vlS7ttujQmamNHmytH69raCeOVO67DK3RwXA32RnS2++Kf3zn/Z+xYrSnXdKv/ud//SZLwkEG0DJOHw4f/uorVttgVJB6tbNH17ExEjVq5fueP3dvn3egGPLFu/xoCCrRvMEHFzbQ8o/YXvNNdKkSdL771toUbeu/dx07mwtprp0oWofZUthwZ/n+I03Wru/jz6SEhK8z7dubb8zPXtSvQH4CkIN+LTcXGtLtXy5XcjnXVXSvLmFG1ddZaWCKJzj2ETOJ59YX/F586xSA74hK0uaNk36+mtrvTZjhtStm9ujAuAvfv5Zmj3bW+XYpYvtlVSnjqvD8hlbtkjjxlmw0bq17WNEsAEUjeNIBw+eXoGRd9FRXvXr5w8vmjenJ39xS0z0Bhw//5z/udatbcLtyit5DSgrsrOtrfDhw/n/XLbMfk7S0+33uG5dq94cNMj2VKxXz+2RA+4oSou24cPt92bDBumDD6Qvv/Tu++Sp3rjuOqlx49IcOYBTEWrAb2Rm2otJbKz0zTfeF5WgIFvZ3r+/9QGtWNHdcfoiz6qD4GBrcdSli9sjwqmysy3M+PxzCzamTrUVdwBQmKNHpVdfld57z268IiKsxeBVV9HC5VRbtljFRkYGwQZQGMexCfO84cW2bbap6qmCgqxqOm8FRrNmUpUqpT7sMi0pya4dV6+2fdry3rG3bOkNOKKi3Bsjzl1OjrVzO3z49LDCc8zzlp5e+Nf54Qf7mQgLs3vBK69kr0rgfCQnW1C4ZIm9Tnq0bWutqajeANxBqAG/lJIirVxpAccvv3iPV6xoE8H9+kkdO1JKK9m/0WOP2fvjxtmLLnxTdrY0a5b02WcWQD30kE1OAsCp1q6V5s71tnsZMEC65x6JS5zCbd1qr4MEG4BNdO7bl7991LZt9vtxquBg6aKLvNUXLVrYPj2VKpX6sHEGycnegMMzme0RE2OTbj17Ws94lD7HsaAibzBRWFiRmpr//+9sQkKspVuNGt4/4+JsIWBEhAUZp7baAXDuHEf69lsLN/JWb4SF2SLbwYOp3gBKE6EG/N7evbb3Rmxs/s0Iq1eX+vSxgCMmpmyuWt240TaIzc6WbrpJuvtut0eEs8nNtVZhy5fbz+zEiXaBBACSTXzMm2fBvmTtI8aOZS+eotq61So20tMJNlB25ORIe/acXoGRt62rR2ioBRZ520dFR7MC1d/89pv0xRfSqlV2P+CZeJPs/9cTcDD5dmEcx4LAU4OJgsKK337L//9wNkFBFkjUqJE/rMj75jkWFpb/XvfUPQMK20MAwPlLTpY+/tgCjrx7Sl1yibd6o3x598YHlAWEGggYjmNVG7GxttI9Lc37XKNGFm706VN2+ofu3m1tSDIypF69rJ1RWQx2/JHjSE8/bZuTBQXZyuJrrnF7VADc5DhW9v7iizYhHxQk/fGP1vOXycZzs22bnVfT06VWrSzYoGUOAkV2tu2vkze82L7d2rieqnx5axnlCS9iYizACA0t9WGjBKWk2Iri1aul776zkMujcWNvwBEdzb2CZK+3x46dHlAUFlZkZ5/b1w8Pzx9QFBZWhIdbldS5OtsmyAQbQPHKzbXqjQ8/tErqvNUbAwZY9UajRu6OEQhUhBoISNnZ9sISG2sX8VlZ3ufatrWAo1evwN24MCXF2pAkJNhK1KefZtLL3ziO9Nxz0vvv2+P775eGDHFzRADcsm+fnce//94ex8RYFV5MjLvj8md5g42LL7aN1gk24Jaiblp6qqwsadeu/O2jduwoeJK1YkU7Z3jaR8XE2CQLrVrLlvR0b8Cxfn3+n5UGDbwBR7NmgRdwZGaevh9FQRtsHz5ccAh4JlWrnh5QFBRWhIeXfGh4vucTABfu0CGr3vjoo9OrNwYPtn1tqN4Aig+hBgLe0aPSmjUWcHz/vbc/aWio1LWrBRxduwbOi0tmpjRmjFWtREVJL7xgpcvwP45jq7Lffdce33OPdOON7o4JQOnJzrbf/4ULbfKyQgWbiLjxRiYiiwPBBnxFUVZW//GPVnHhqcDYutUqMvKuuveoUiV/+6jmzaX69c9v1TcC15Ej0ldfWcDxzTfSiRPe5+rV8wYcLVr4bsBx4kTBm2cXVFFRULu1M6lc+fR9KgoKKyIiAuc+EkDx8FRvfPCBVW945qCqVbPqjWuvpXoDKA6EGihTDh2SVqywgGPHDu/xKlWscqNfP0vRffXC/Wxyc6WHH7ZNAsPCpPnz2QzQ3zmO9Oqr0htv2OM775RuucXdMQEoeVu32iT79u32+NJLbe+MqCh3xxVoCDbgK/IGGH/4g/TMM9Jbb9nPZZUqUnx8wRsHV6vmDS48IUa9ev57LQt3HD0qff21BRzr1uWvVIiM9AYcrVrZz1ZJVgNkZ9tG2YUFFXnfL2hj+zMpX16qWfPsYUX16lbdBAAX6uBBaenS06s32rXzVm+UK+fe+AB/5lehxgsvvKDZs2crMTFR7dq107x589S5c+cifS6hBk61c6eFG59+amGHR2Sk1LevBRwXXeTa8M7L/Pm2qjc0VJozx1ptwf85jvTPf9pkh2Q3kMOHM2EBBKLjx6XXXpP+8x/73Q8Lk0aNkvr353e+pGzfboFRerrUsqUFG1Wruj0qlEVPPWVhxrFj9vtft669eVSvbqGFp31U8+ZS7dqcG1C8jh+3YGP1althfPy497latWwCLi3N7qGKum9Dbq4FFWeqqvC8n5p6buMNDS08oDi1qqJSJX5fALgjN9eq4jx7b+St3hg40AIOFqQC58ZvQo23335bQ4cO1UsvvaQuXbpo7ty5evfdd7VlyxZFRkae9fMJNVAYx5E2brSAY/VqW6nk0ayZTSRddZWt6vFl778vPfusvT95sm2KjsDy1lvS3/9u7//pT9Kf/8yNGRBI1q+3vTMSE+1xnz7SvffSQrA0EGzATY4jLVkiPf+8tatwHGs3d+ed+aswfP1aFIEnM9N+JlevtlZVee+T0tMtgLj1Vum666R//9tWInfqZD+zecOKlJSCK40KExx85mqKvMeqVuV6GIB/SUryVm8cPOg93r69hRtXXEH1BlAUfhNqdOnSRZ06ddLzzz8vScrNzVXDhg113333aeLEiWf9fEINFEVmpqXmsbG2QsnTpzgoyFp/9Osn9ehhPVZ9ydq10kMP2c0C7YkC23/+Y/ukSNae4p57uJED/F1qqlXaLV9ujyMjbYK9Sxd3x1XW7Nhh/+5paQQbKD0ZGRZmrlplgebRo1YpHBx8+mp3wE1ZWdKGDRZwfPml/ewmJtpbUFDB1UV5BQXZRtmFtXvK+2d4ONe3AAJfTo7NOy1ZYi0APbOu4eFWvXHttVRvAGfiF6FGVlaWKleurP/85z8aMmTIyePDhg1TSkqKFi9efNavQaiBc5WaajeYsbHSTz95j1eoYMFGv34WdISGujZESdYP/K9/tdLwq6+Wxo/nJiDQLV4szZ1r719/vXT//fyfA76ssN7jjiNNnGivM54JnBtukEaMsBYZKH15g40WLSzYCAtze1QIVL/8Is2YYZPCnj7bEyZIw4YV3sYH8AXZ2dJ331nA8fTT9vpVvrxVERcWVkRESCEhbo8cAHxTUpJVbnz8cf726B06eKs33J57AnxNUef6Xf3VOXTokHJyclSnTp18x+vUqaO4uLgCPyczM1OZeXY4S0tLK9ExIvCEh9uE8fXXS/v3W9/Y2Fhp717bbHzFCrs4v+oqCzhatCj9ieWkJGnSJAs0LrtMGjOGye2y4PrrrRz1qacs4Dhxwja6DQ52e2QAChIcnH9PHMkmMe+4wyrt6ta1G5bx421TYLinaVPbk2rsWGnLFumBBwg2UPwcR3r7bekf/7BVmsePW1XQqFHec4Tnz1PPHYAvCA2VOneW4uLsdSs01IKORo34WQWA8xEZ6V3IsG6d7b2xbp30/ff2FhHhrd6oX9/t0QL+xe/ywFmzZmn69OluDwMBIirKXlxuu80mOWJjpc8+s/6w//ufvTVoYOFG37728SXtyBFb4ZucLEVHSw8/THJfllx9tQUbs2bZao4TJ6QHH2QFHOCL8k5O5uZKVarY6uy9e+2mZNIk6Y9/5BzuK5o2tc2aPcHG+PEWIhNsoDikpNhr9zff2ONevSzYrFTp9Mlgz+Pc3NIcIVA0p1YTeR5LBBsAcL5CQqTLL7c3T/XGRx/ZvM+//21vHTta9UaPHtw/AEXhd+2nCqrUaNiwIe2nUGyys62vbGys9MUXth+HR+vWFnD06mUVHyXxvSdNsk1la9a0XuyRkcX/feD7Vq2SHnnEJjx69bK9VbiwAXxPaqo0ebLti5OZaSu1O3eWXn2VXrm+audOCzZSU23TW4INXKjvvpNmzrSNk8uXl+67T7rmGqps4X8Ka49G2zQAKH45Obbnxocf2qIIz+xsRIQ0aJBVb5TGwlrA1/jFnhqSbRTeuXNnzZs3T5JtFN6oUSPde++9bBQO1x09asFGbKwFHZ7flpAQ2+i1Xz+pWzfbj+NCOY5NrHz8sVSxovTcc1JMzIV/XfivL76Qpk+3sKt7d2naNKviAOCunBy78Vi61NpMZWdLP/xgE5iNGtkEJ5OZvi1vsBETY73jCTZwrnJybG+dN96w67jGje21Ojra7ZEB56ewvaIkCzZyc6Xhw0t7VAAQ+A4c8O69kZzsPX7ppVa90b07ixxRdvhNqPH2229r2LBhevnll9W5c2fNnTtX77zzjuLi4k7ba6MghBooLcnJ1poqNtY28faoXFnq2dMCjnbtzn//gzfesB7MQUHSY49JXbsWz7jh39atk6ZMsTZUXbpYa5vy5d0eFVA27d4tLVsmLV9uK7LzOnRIql3bJjZZyeofdu2yPasINnA+kpKsonLzZnt87bXSvfcWz0IXAABQNmVne6s3vv3Wu7C2enVv9Ua9eu6OEShpfhNqSNLzzz+v2bNnKzExUe3bt9dzzz2nLl26FOlzCTXghl9/9W4wnpTkPV6rlu290a+f1KRJ0b/eihXSo4/a+6NH24bRgMf69dbeJjPTVmrMnMmkCVBajhyxQHvpUumXX7zHIyLsXH/0qK2qOrX3OMGGf9i1yyo2UlIINlB0X34pPfGElJ5ui1vGj5d693Z7VAAAIJAkJnqrN/IuqLrsMqveuPxyqjcQmPwq1LgQhBpwk+NIP/5o4cbKlTb55dGkiU149eljq3elgku6N22Sxo2zjWUvu0z65z9L9a8AP/HDD7aB/PHjVhE0a5ZtPgqg+DmOtZBaulT6/HMpK8uOBwdbFd2gQfbnm2/SezwQEGygqE6ckF56Sfrf/+xxixbS1Kn0uwYAACUnO9ta3n74oS14zFu9cfXVto8X1RsIJIQaQCnLyrJWQbGx3h7rkrWTat/eAo69e20SzDPRtXevdM891s7q2DHbP2HYMFf/GvBhP/0kTZhgK8Nbt7ZVolWquD0qIHAkJFh7qWXL8lfhXXSRBRn9+tnNgwe9xwPHr79aK6qUFKlZMws2uKxEXnv3WgtITwvS//s/6c9/ZoUkAAAoPQkJ3uqN336zY0FB3uqNbt2kf/2LexT4N0INwEXp6dLq1RZwbNrkPV6+vLUp2L1buvNOa1/w3XdSWprdKI8Y4d6Y4R/i4qQHHpAyMqSWLaUnn2RFMXAhjh+38/XSpVYR5VGlilXaDRpkq7HZ+Dvw7d5tLSAJNnCq5culZ56x80V4uDRpku1zBQAA4IbsbOmrr7zVGx41alib3Lg46e67qSaHfyLUAHxEYqLtmbF8uRQf7z2WmGiTZOXKWYXGX/7i7jjhP7Zts/7daWk28fbUUzbJAqBoHMc29126VFq1yirlJDsnX3qpNHCg1KMHe9eURbt3W8XGb79JTZtasMH5tew6dkx69lnpk0/scfv20kMP2R5qAAAAviAhQVqyxKo3UlLs2IEDthDytttsvqmwtrmALyLUAHyM40jbt1v1xqef2kRacLB08cXWsx04F3l7wF90kTRnTv62OABOd/CgBczLllkrGY+oKAsyBgyQIiPdGx98Q95go0kTO78SbJQ927fbJMDevRZ4Dh8u3XqrXbsBAAD4muxs6wby4YfShg3exbTlyln18dChtjgjJMTtkQJnRqgB+LCFC6UXXrBWVMHBpOU4P/HxFmwkJ0sNG9rEG6tHgfyysqw0e+lS6dtvvRvrVawo9epl7aXatqW9FPIj2Ci7HEdavFiaP982Bq9VS5oyRbrkErdHBgAAUDT791v1xuTJdj0TFCS1a2fXs1dcYfdB7dsTcMA3EWoAPurUPob0NcSF2LfPgo2kJFtt/swzrDQHHMfatC1dau3/0tO9z11yiVVl9OolVark2hDhBwg2yp70dGn2bG8Fbbdu0oMP8v8OAAD8z6JF0muvWTvN5GSr1sh7TeMJOHr2lDp0IOCA7yDUAHxQYQEGwQYuRGKiBRsJCVKdOhZs1Kvn9qiA0peSYi3+li2Tdu70Hq9d21pLDRwo1a/v2vDgh+LjLdg4fNiCjaefts0XEXh++kmaMcMWCYSG2l5nN9xAFRcAAPA/BS2mfe01qXdvqWpVW8CRmur9+GrV8gccoaHujR0g1AB80MKF1m6qoOBi0SIpN9d6NgPnKilJGjfOen/Xrm0rihs0cHtUQMnLzpa++caqMtaulXJy7Hi5cnZhPmiQ1LEjffBx/gg2Apvj2OaZr71m12FRUdK0aVLz5m6PDAAA4NwVZTHtLbdIP/xge71+/rl3g3FJCguTevSwyvaOHQk4UPoINQCgjElOtmBj926pRg0LNho3dntUQMn49VcLMpYvz38R3rKlVWRcdZVdkAPFYc8eafRoCzaio+38SrDh/w4flh57zDbTlKQ+fazysXJld8cFAABwvs51MW1OjrRpkwUca9YUHHD07CldeikBB0oHoQYAlEEpKRZs7NxpE25PP20ri4FAkJFhe2QsWybFxXmPR0RI/ftbmBEd7drwEOD27LGKjeRk6aKLrNUfwYb/Wr9emjnTXjcrVJDuv9/OIbSbAgAAZVVurlVwrF5tb6cGHN27WwUHAQdKEqEGAJRRaWnS+PG2UXJYmAUbMTFujwo4P7m50nffWVXG559LJ07Y8ZAQqWtX6eqrpc6duahG6SDY8H/Z2dZq6q237HGTJtLUqVQ2AgAA5JWbm7+C47ffvM9VreoNOC67jHsxFC9CDQAowzIypAkTpF9+kapUkWbPli6+2O1RAUW3b59VZHzyiXTwoPd4dLTtk9GvH5PJcMfevdaKimDD/yQmSo88Iv38sz2+/npp5Eir1AAAAEDBcnOlH3/0BhyHD3ufq1Ilf8BRrpxbo0SgINQAgDLu6FFp4kS7+KhUSXr8cemSS9weFVC4Y8fsQnnZMlsV5FG1qvW6HzTINu+lPQzcljfYaNzYgo3q1d0eFc5kzRoL+DMy7Ob7gQesPzQAAACKLjdX2rzZG3AkJ3uf8wQcPXtKnToRcOD8EGoAAHTsmPTQQ9L339tK1FmzpA4d3B4V4OU4FrwtXWoXxseP2/GgIFvpM2iQXRiXL+/qMIHT7N1rragOHSLY8GWZmdKLL0qLF9vjVq2kKVOkunXdHRcAAIC/89zLefbgyBtwVK6cv4KD+zkUFaEGAECSTehMmSJ9+61dSDz6qK2aANyUlCQtX25hxv793uP161uQ0b+/VLu2e+MDiuLUYGPOHKlGDbdHBY/du6UZM6SdO+3xn/4k3XEHfZ8BAACKm+Pkr+A4dMj7XOXK0uWXW8DRqRMBB86MUAMAcFJWlvTww9LatTaZM326XVQApSkzU/rySwsyNmywC1/J2qP16mVhRps2tJeCf9m3z1pREWz4DsexNnbPPmvnnYgI6W9/I9AHAAAoDY5je5itWmUVHHn3SKxcWerWzVpUde7M3mY4HaEGACCf7Gxbsfr551JIiDR1qnTllW6PCoHOcaQtW2yCccUK62fv0a6dBRlXXmnBBuCv9u2zio2DB6VGjawVFcGGO44etWBpxQp73LGjBRo1a7o7LgAAgLLIE3B4WlQlJXmfq1TJAo5evQg44EWoAQA4TU6O9Nhj0mefScHBNtHTp4/bo0Ig+u03KTbWqjJ+/dV7PDJSGjhQGjBAiopybXhAsdu/3yo2PMHGnDlMpJe2rVstvN+3z17j7rjDWk4FB7s9MgAAADiO9Msv3gqOvAFHxYregKNLFwKOsoxQAwBQoNxc6cknpU8+sTY/Dz5oE8zAhcrOlr7+2oKMdessRJOsZ+oVV1hVRseOtJdC4Nq/3yo2kpKkhg2tYoNgo+Q5jvS//0kvvWTnochI20uqTRu3RwYAAICCOI4UF2cBx6pVpwccXbtawNG1KwFHWUOoAQAolOPYKuIlS2yCeexY6dpr3R4V/NXOndZeKjZWSknxHm/ZUrr6aql3b6lqVdeGB5Qqgo3SlZYmPfGE9NVX9rhHD2nCBCkszN1xAQAAoGg8LYs9AceBA97nKlTw7sHRtasFHghshBoAgDNyHGnePOm99+zxX/8q/e537o4J/iM93XrWL11qLV88qleX+ve3qozGjd0bH+CmhARrRZWUJDVoIM2dS7BREjZtkh55xDZpDw2VRo2Srr+eajAAAAB/5Qk4Vq+2gCMx0ftchQr5KzgIOAIToQYA4Kwcx9p1vPOOPR45Uvq//3N3TPBdubnS+vUWZHzxhbV5kWzj+csvt70yOne2yUWgrCPYKDm5udK//iUtXGivYw0aSNOmSc2auT0yAAAAFBfHsQV0nj04EhK8z1WoYHtveAKOSpXcGiWKG6EGAKBIHEd69VXpjTfs8YgR0q23ujsm+Ja9e6291Cef2IpojyZNrCKjb18pIsK14QE+i2Cj+CUnS48+Km3caI/797d/Y25kAQAAApfjSNu2eVtUnRpwdO5sAUe3blwX+jtCDQDAOVm0SFqwwN4fOlQaPpwWHmXZ0aO2Gubjj6XNm73Hw8IsxBg0yFZF8zMCnFlCgu2xceCABRvPPCPVquX2qPzTunXSrFlSaqq1GxgzxkINAAAAlB2OI23f7g049u/3Ple+vFVw9OxpAUflym6NEufLL0KNiy66SLt37853bNasWZo4cWKRvwahBgAUn3//W3r5ZZuE69ZNeu210yetFy2y1h/Dh7syRJQgx7Ee9UuX2sVhZqYdDwqylS8DB0rdu0vlyrk6TMDvJCZaNQHBxvnJzpZeecXbKrFZM2s31aCBu+MCAACAuxxH2rHD26Jq717vc+XKeVtUEXD4D78JNUaMGKE///nPJ4+FhYWpSpUqRf4ahBoAULz++19p8mSbhLv2WgsxPMGGp5rj9tutmgOBISnJ2kstW5a/jLdBA6vI6N+fCVjgQuUNNurXt2Cjdm23R+X7EhKkGTOkuDh7fMMN0t132yo8AAAAwMNxpJ07vRUcpwYcnhZVl19OwOHL/CbUGD16tEaPHn3eX4NQAwCK3wcfSA8+aJNwgwbZfhv//CeBRiDJzLTNvpculb77zi4AJbu4693b/t9btaK9FFCcEhOtZVJiohQVZXtsEGwUbuVK6amnrB1eWJi9LnXv7vaoAAAA4OscR9q1yxtw7NnjfS401AKOnj3t2vIc1tajFPhNqHH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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot and compare chromatic phase advances\n", "\n", "plt.figure(figsize=(16, 2))\n", "plt.errorbar(ring.locations().cpu().numpy(), dmuqxdp.cpu().numpy(), fmt='-', color='blue', marker='x', alpha=0.75)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "plt.figure(figsize=(16, 2))\n", "plt.errorbar(ring.locations().cpu().numpy(), dmuqydp.cpu().numpy(), fmt='-', color='blue', marker='x', alpha=0.75)\n", "plt.tight_layout()\n", "plt.show()" ] } ], "metadata": { "colab": { "collapsed_sections": [ "myt0_gMIOq7b", "5d97819c" ], "name": "03_frequency.ipynb", "provenance": [] }, "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": "3.12.1" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 5 }