{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 大作业编程基础参考:选题一（CSNS-RCS Simple Lattice）\n",
    "\n",
    "刘星光，2024年12月4日\n",
    "\n",
    "## RCS-R1布局示意图\n",
    "\n",
    "![RCS_r1](RCS_r1.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#0. 定义基本传输矩阵\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# 漂移段，3*3\n",
    "def D(l):\n",
    "    return np.array([[1,l,0],\n",
    "                    [0,1,0],\n",
    "                    [0,0,1]])\n",
    "\n",
    "# 四极磁铁：聚焦平面\n",
    "def QF(l,k):\n",
    "    sk=np.sqrt(k)\n",
    "    skl=sk*l\n",
    "    return np.array([[np.cos(skl),np.sin(skl)/sk,0],\n",
    "                     [-sk*np.sin(skl),np.cos(skl),0],\n",
    "                     [0,0,1]])\n",
    "\n",
    "# 四极磁铁：散焦平面\n",
    "def QD(l,k):\n",
    "    sk=np.sqrt(np.abs(k))\n",
    "    skl=sk*l\n",
    "    return np.array([[np.cosh(skl),np.sinh(skl)/sk,0],\n",
    "                     [sk*np.sinh(skl),np.cosh(skl),0],\n",
    "                     [0,0,1]])\n",
    "\n",
    "# 偏转磁铁（sector magnet）：推荐以偏转半径rho 和 偏转角度 theta 定义\n",
    "def SBend(r, a):\n",
    "    return np.array([[np.cos(a),    r*np.sin(a),    r*(1-np.cos(a))],\n",
    "                     [-np.sin(a)/r, np.cos(a),      np.sin(a)],\n",
    "                     [0,            0,              1]])\n",
    "# 注：无偏转的平面（比如y）按漂移段处理\n",
    "\n",
    "# 计算一系列矩阵的点积（dot product），注意点积的顺序\n",
    "def Mdot(ml):\n",
    "    mt = ml[0] # 储存结果的临时矩阵，先存上第一个\n",
    "    if len(ml) == 1: # 如果只有一个元件则直接返回\n",
    "        return mt\n",
    "    else:\n",
    "        # 依次对元件进行点积（注意顺序）\n",
    "        for i in range(1,len(ml)):\n",
    "            mt = np.dot(ml[i],mt)\n",
    "    return mt\n",
    "\n",
    "# 通过比对传输矩阵获得twiss参数 \n",
    "# cos 𝜑 + 𝛼 sin 𝜑       𝛽 sin 𝜑\n",
    "# −𝛾 sin 𝜑              cos 𝜑 − 𝛼 sin 𝜑\n",
    "def twiss_from_m(m):\n",
    "    mtr=m[0,0]+m[1,1] # 注：虽然定义为三阶用于计算含色散的函数，计算twiss参数比对时仅用2x2的矩阵的迹\n",
    "    if (mtr>2):\n",
    "        raise ValueError(\"二阶矩阵的迹应小于2才是稳定解\",mtr)\n",
    "    c=mtr*0.5 # cos项，等于迹的一半\n",
    "    # 计算其他项\n",
    "    s=np.sqrt(1-c**2)\n",
    "    beta=m[0,1]/s\n",
    "    alpha=(m[0,0]-c)/s\n",
    "    gamma=-(m[1,0])/s\n",
    "    return [beta,alpha,gamma]\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mx:\n",
      " [[-1.62512436  2.29407824  0.        ]\n",
      " [-1.92808155  2.10640491  0.        ]\n",
      " [ 0.          0.          1.        ]]\n",
      "My:\n",
      " [[ 1.32959105  0.98661757  0.        ]\n",
      " [-1.78724743 -0.57410864  0.        ]\n",
      " [ 0.          0.          1.        ]]\n",
      "注1：任意行列式应当为1\n",
      "det(Mx): 1.0000000000000013\n",
      "det(My): 1.0000000000000002\n",
      "注2：矩阵的迹应当小于2才是稳定解：\n",
      "Mx的迹： 0.48128055182485285\n",
      "My的迹： 0.7554824132816707\n"
     ]
    }
   ],
   "source": [
    "#1. 未验证函数的正确性，先计算个简单的FODO试试: \n",
    "\n",
    "# QF: QUAD, L = 0.4, K1 = 4.0;\n",
    "# D01: DRIFT, L = 0.5;\n",
    "# QD: QUAD, L= 0.4, K1 = -3.8; # 如果选4.0的话，x和y方向将完全对称，可以试试，这里略取不同以示两方向的差别\n",
    "# D02: DRIFT, L =0.5;\n",
    "\n",
    "# 以最直接的方式分别列出两方向的矩阵列表\n",
    "latx=[QF(0.4,4),D(0.5),QD(0.4,-3.8),D(0.5)]\n",
    "laty=[QD(0.4,-4),D(0.5),QF(0.4,3.8),D(0.5)]\n",
    "# 本别获取FODO两个方向的总矩阵\n",
    "Mx=Mdot(latx)\n",
    "My=Mdot(laty)\n",
    "\n",
    "# 输出两个方向的总矩阵确认和后续对比\n",
    "print(\"Mx:\\n\",Mx)\n",
    "print(\"My:\\n\",My)\n",
    "\n",
    "# det(M) 任意一个行列式（包括最后总的矩阵）的行列式应该为1 （推荐养成检查的好习惯）\n",
    "print(\"注1：任意行列式应当为1\")\n",
    "print(\"det(Mx):\",np.linalg.det(Mx))\n",
    "print(\"det(My):\",np.linalg.det(My))\n",
    "\n",
    "# 计算矩阵的迹 2cos(\\theta)，注意必须小于2才是稳定解\n",
    "print(\"注2：矩阵的迹应当小于2才是稳定解：\")\n",
    "print(\"Mx的迹：\",Mx[0,0]+Mx[1,1]) \n",
    "print(\"My的迹：\",My[0,0]+My[1,1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "传输矩阵：\n",
      "\n",
      "Mx:\n",
      " [[ 0.24064028  2.68849456  0.        ]\n",
      " [-0.35041628  0.24064028  0.        ]\n",
      " [ 0.          0.          1.        ]]\n",
      "My:\n",
      " [[ 0.37774121  0.80579039  0.        ]\n",
      " [-1.0639387   0.37774121  0.        ]\n",
      " [ 0.          0.          1.        ]]\n",
      "注1：任意行列式应当为1\n",
      "det(Mx): 0.9999999999999996\n",
      "det(My): 1.0000000000000004\n",
      "注2：矩阵的迹应当小于2才是稳定解：\n",
      "Trace Mx 0.48128055182485285\n",
      "Trace My 0.7554824132816704\n",
      "注3：同一个FODO，选择的起点不同，矩阵的形式不同（对应不同的twiss参数和相空间）\n",
      "𝛽x,𝛼x,𝛾x:  [2.7698895079255754, 0.0, 0.36102523120097996]\n",
      "𝛽y,𝛼y,𝛾y:  [0.8702674450516203, -1.1990599151012757e-16, 1.149072053293554]\n",
      "Twiss参数: X:\n",
      " 0 [2.7698895079255754, 0.0, 0.36102523120097996]\n",
      "Twiss参数: X:\n",
      " 1 [2.363532122421277, 1.9222511974864858, 1.986454773218174]\n",
      "Twiss参数: X:\n",
      " 2 [0.9378946182393347, 0.9290238108773984, 1.986454773218174]\n",
      "Twiss参数: X:\n",
      " 3 [0.7609647579248855, 1.429794181848416e-16, 1.3141213040232667]\n",
      "Twiss参数: X:\n",
      " 4 [0.9378946182393346, -0.9290238108773984, 1.986454773218174]\n",
      "Twiss参数: X:\n",
      " 5 [2.3635321224212764, -1.9222511974864855, 1.986454773218174]\n",
      "Twiss参数: Y:\n",
      " 0 [0.8702674450516203, -1.1990599151012757e-16, 1.149072053293554]\n",
      "Twiss参数: Y:\n",
      " 1 [1.0655639026729824, -1.0280141651771373, 1.9302578837790036]\n",
      "Twiss参数: Y:\n",
      " 2 [2.5761425387948704, -1.993143107066639, 1.9302578837790036]\n",
      "Twiss参数: Y:\n",
      " 3 [2.9962768420005688, 5.995299575506378e-17, 0.33374753159735265]\n",
      "Twiss参数: Y:\n",
      " 4 [2.576142538794871, 1.9931431070666394, 1.930257883779004]\n",
      "Twiss参数: Y:\n",
      " 5 [1.0655639026729824, 1.0280141651771375, 1.9302578837790039]\n"
     ]
    }
   ],
   "source": [
    "#2. 计算Twiss参数并作图\n",
    "# 一般最大最小值在四极磁铁的中点，因此对四极磁铁进行切分并排成对称布局，但用作tracking时没必要昨切分，当前以最简单直接的方法构建传输矩阵的列表\n",
    "latx=[QF(0.4*0.5,4),D(0.5),QD(0.4*0.5,-3.8),QD(0.4*0.5,-3.8),D(0.5),QF(0.4*0.5,4)]\n",
    "laty=[QD(0.4*0.5,-4),D(0.5),QF(0.4*0.5,3.8),QF(0.4*0.5,3.8),D(0.5),QD(0.4*0.5,-4)]\n",
    "Mx=Mdot(latx)\n",
    "My=Mdot(laty)\n",
    "# 输出两个方向的总矩阵确认和后续对比\n",
    "print(\"传输矩阵：\\n\")\n",
    "print(\"Mx:\\n\",Mx)\n",
    "print(\"My:\\n\",My)\n",
    "\n",
    "print(\"注1：任意行列式应当为1\")\n",
    "print(\"det(Mx):\",np.linalg.det(Mx))\n",
    "print(\"det(My):\",np.linalg.det(My))\n",
    "\n",
    "print(\"注2：矩阵的迹应当小于2才是稳定解：\")\n",
    "print(\"Trace Mx\",Mx[0,0]+Mx[1,1])\n",
    "print(\"Trace My\",My[0,0]+My[1,1])\n",
    "\n",
    "print(\"注3：同一个FODO，选择的起点不同，矩阵的形式不同（对应不同的twiss参数和相空间）\")\n",
    "\n",
    "# 输出twiss参数\n",
    "print(\"𝛽x,𝛼x,𝛾x: \",twiss_from_m(Mx))\n",
    "print(\"𝛽y,𝛼y,𝛾y: \",twiss_from_m(My))\n",
    "\n",
    "# 获取以任意原件为起点时的传输矩阵和twiss参数\n",
    "for i,m in enumerate(latx):\n",
    "    # print(i,m)\n",
    "    lt = latx[i:]+latx[:i]\n",
    "    print(\"Twiss参数: X:\\n\",i,twiss_from_m(Mdot(lt)))\n",
    "\n",
    "for i,m in enumerate(laty):\n",
    "    # print(i,m)\n",
    "    lt = laty[i:]+laty[:i]\n",
    "    print(\"Twiss参数: Y:\\n\",i,twiss_from_m(Mdot(lt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3. 一般情况：现在我们增加两个函数，让上述定义的函数更有用和具有一般性\n",
    "\n",
    "# 根据输入参数返回相应的传输矩阵，注意相应的处理要与lattice元件的定义格式相对应，可自行整理和定义\n",
    "def element_matrix(*args):\n",
    "    if args[0] == 'D':\n",
    "        l=args[1]\n",
    "        return [D(l),D(l),l]\n",
    "    elif args[0] == 'Q':\n",
    "        l=args[1]\n",
    "        k=args[2]\n",
    "        if k>0:\n",
    "            return [QF(l,k),QD(l,-k),l]\n",
    "        elif k<0:\n",
    "            return [QD(l,k),QF(l,-k),l]\n",
    "        else:\n",
    "            return [D(l),D(l),l] # if k=0, 按漂移段处理\n",
    "    elif args[0] == 'SBend':\n",
    "        l=args[1]\n",
    "        angle=args[2]\n",
    "        rho=l/angle\n",
    "        return [SBend(rho,angle),D(l),l]\n",
    "    else:\n",
    "        print('未定义元件类型!')\n",
    "        return None\n",
    "    \n",
    "# 返回x方向，y方向的传输矩阵及（各元件出口）位置坐标 [type,a1,a2,a3]\n",
    "def lat_matrices(lat):\n",
    "    ml=[element_matrix(e[0],*e[1:]) for e in lat]\n",
    "    mlx=list(map(lambda x: x[0],ml)) # 选择x矩阵列表，这里用了python的lambda函数，可以其他方法实现，抑或最开始x和y完全分开定义和计算\n",
    "    mly=list(map(lambda x: x[1],ml)) # 选择y矩阵列表\n",
    "    mls=list(map(lambda x: x[2],ml)) # 选择元件位置列表\n",
    "    return [mlx,mly,mls]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mx:\n",
      " [[ 0.24064028  2.68849456  0.        ]\n",
      " [-0.35041628  0.24064028  0.        ]\n",
      " [ 0.          0.          1.        ]] \n",
      "My:\n",
      " [[ 0.37774121  0.80579039  0.        ]\n",
      " [-1.0639387   0.37774121  0.        ]\n",
      " [ 0.          0.          1.        ]] [0.2, 0.5, 0.2, 0.2, 0.5, 0.2]\n"
     ]
    }
   ],
   "source": [
    "# e.g 以前述FODO为例\n",
    "\n",
    "# QF: QUAD, L = 0.4, K1 = 4.0;\n",
    "# D01: DRIFT, L = 0.5;\n",
    "# QD: QUAD, L= 0.4, K1 = -3.8; # choose different value so x- and y- directions are not symmetric\n",
    "# D02: DRIFT, L =0.5;\n",
    "\n",
    "# 这里假设每个元件都由四个值定义，保持同样的格式便于从外部文件读取（如果元件列表参数是从文件读入的话）\n",
    "lat = [[\"Q\",0.4*0.5,4.0],[\"D\",0.5,0,0],[\"Q\",0.4*0.5,-3.8],[\"Q\",0.4*0.5,-3.8],[\"D\",0.5,0,0],[\"Q\",0.4*0.5,4.0]]\n",
    "# 计算FODO的矩阵和元件长度列表\n",
    "[mlx,mly,mls]=lat_matrices(lat)\n",
    "print(\"Mx:\\n\",Mdot(mlx),\"\\nMy:\\n\",Mdot(mly),mls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sl:\n",
      " [0.  0.2 0.7 0.9 1.1 1.6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 依次以各元件为起点计算传输矩阵，再由矩阵对比的方式获得Twiss参数\n",
    "twiss_x=[]\n",
    "twiss_y=[]\n",
    "# 通过按格式定义的元件列表返回x，y方向的传输矩阵列表和元件长度列表\n",
    "[mlx,mly,mls]=lat_matrices(lat) # 实际这里我们只是为了存mls的值计算一下初始的排布各元件位置用于后续画图\n",
    "sl=np.cumsum(mls) #可以写个简单的循环计算元件位置坐标，这里用了numpy内置函数\n",
    "for i,l in enumerate(mls):\n",
    "    sl[i]-=l # 修正sl为元件入口处位置\n",
    "print(\"sl:\\n\",sl)\n",
    "\n",
    "for i,e in enumerate(lat):\n",
    "    lat_temp=lat[i:]+lat[:i] # 重新排元件顺序，以第i个元件为起始\n",
    "    # print(lat_temp)\n",
    "    [mlx,mly,mls]=lat_matrices(lat_temp)\n",
    "    [b,a,r]=twiss_from_m(Mdot(mlx))\n",
    "    twiss_x.append([sl[i],b,a,r])\n",
    "    [b,a,r]=twiss_from_m(Mdot(mly))\n",
    "    twiss_y.append([sl[i],b,a,r])\n",
    "\n",
    "# 通过作图确认twiss参数的beta函数\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(np.array(twiss_x)[:,0],np.array(twiss_x)[:,1],'-o')\n",
    "plt.plot(np.array(twiss_y)[:,0],np.array(twiss_y)[:,1],'-o')\n",
    "plt.xlabel('s [m]')\n",
    "plt.ylabel('beta')\n",
    "plt.title('Twiss parameters')\n",
    "plt.legend(['beta_x','beta_y'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sl:\n",
      " [ 0.    5.5   5.91  6.71  7.61  8.76  9.17 12.97 15.07 16.27 18.37 19.67\n",
      " 20.12 21.42 22.32 23.12 23.74 24.64 26.74 30.24 32.34 33.24 33.86 34.66\n",
      " 35.56 36.86 37.31 38.61 40.71 41.91 44.01 47.81 48.22 49.37 50.27 51.07\n",
      " 51.48]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4. 计算RCS——R1段twiss参数\n",
    "rcs_r1=[[\"D\",5.5,0],\n",
    "[\"Q\",0.41,0.7406566],\n",
    "[\"D\",0.8,0],\n",
    "[\"Q\",0.9,-0.579364096],\n",
    "[\"D\",1.15,0],\n",
    "[\"Q\",0.41,0.664196402],\n",
    "[\"D\",3.8,0],\n",
    "[\"SBend\",2.1,0.261799388],\n",
    "[\"D\",1.2,0],\n",
    "[\"SBend\",2.1,0.261799388],\n",
    "[\"D\",1.3,0],\n",
    "[\"Q\",0.45,0.570558026],\n",
    "[\"D\",1.3,0],\n",
    "[\"Q\",0.9,-0.579364096],\n",
    "[\"D\",0.8,0],\n",
    "[\"Q\",0.62,0.608457761],\n",
    "[\"D\",0.9,0],\n",
    "[\"SBend\",2.1,0.261799388],\n",
    "[\"D\",3.5,0],\n",
    "[\"SBend\",2.1,0.261799388],\n",
    "[\"D\",0.9,0],\n",
    "[\"Q\",0.62,0.608457761],\n",
    "[\"D\",0.8,0],\n",
    "[\"Q\",0.9,-0.579364096],\n",
    "[\"D\",1.3,0],\n",
    "[\"Q\",0.45,0.570558026],\n",
    "[\"D\",1.3,0],\n",
    "[\"SBend\",2.1,0.261799388],\n",
    "[\"D\",1.2,0],\n",
    "[\"SBend\",2.1,0.261799388],\n",
    "[\"D\",3.8,0],\n",
    "[\"Q\",0.41,0.664196402],\n",
    "[\"D\",1.15,0],\n",
    "[\"Q\",0.9,-0.579364096],\n",
    "[\"D\",0.8,0],\n",
    "[\"Q\",0.41,0.7406566],\n",
    "[\"D\",5.5,0]]\n",
    "\n",
    "# 依次以各元件为起点计算传输矩阵，再由矩阵对比的方式获得Twiss参数\n",
    "twiss_x=[]\n",
    "twiss_y=[]\n",
    "# caculate acuumulated values for mls, you can do it with a small loop if not with python and numpy\n",
    "[mlx,mly,mls]=lat_matrices(rcs_r1) # only mls would be use here\n",
    "\n",
    "sl=np.cumsum(mls) #可以写个简单的循环计算元件位置坐标，这里用了numpy内置函数\n",
    "for i,l in enumerate(mls):\n",
    "    sl[i]-=l # 修正sl为元件入口处位置\n",
    "print(\"sl:\\n\",sl)\n",
    "\n",
    "for i,e in enumerate(rcs_r1):\n",
    "    lat_temp=rcs_r1[i:]+rcs_r1[:i] # 重新排元件顺序，以第i个元件为起始\n",
    "    # print(lat_temp)\n",
    "    [mlx,mly,mls]=lat_matrices(lat_temp)\n",
    "    [b,a,r]=twiss_from_m(Mdot(mlx))\n",
    "    twiss_x.append([sl[i],b,a,r])\n",
    "    [b,a,r]=twiss_from_m(Mdot(mly))\n",
    "    twiss_y.append([sl[i],b,a,r])\n",
    "\n",
    "# 通过作图确认twiss参数的beta函数\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(np.array(twiss_x)[:,0],np.array(twiss_x)[:,1],'-o')\n",
    "plt.plot(np.array(twiss_y)[:,0],np.array(twiss_y)[:,1],'-o')\n",
    "plt.xlabel('s [m]')\n",
    "plt.ylabel('beta')\n",
    "plt.title('Twiss parameters')\n",
    "plt.legend(['beta_x','beta_y'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 关于单粒子追踪的提示（Hints on single particle tracking）\n",
    "\n",
    "- 本作业要求不包含纵向运动，也没有x与y方向的耦合，因此可以忽略D（s），可分别对x与y方向进行tracking\n",
    "- 初始粒子坐标可自行假设\n",
    "- 对于整圈的tracking，可直接使用整圈传输矩阵\n",
    "- 对于具体元件（element）后的粒子坐标，可根据需要逐个记录元件出口处的粒子坐标"
   ]
  }
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