Thesis Idea Figures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some thesis idea figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import matplotlib.patches as patches\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from numpy import array as arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def spherical_y(x,r):\n",
    "    # x^2+y^2=r^2\n",
    "    # y=sqrt(r^2-x^2)\n",
    "    return np.sqrt(r**2-x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mountColor = \"#353535\"\n",
    "mountFillColor='#252525'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laser_2(ax, xi, yi, thickness, length, laserargs={}, orientation='hor'):\n",
    "    if orientation=='hor':\n",
    "        xpts = [xi, xi+length]\n",
    "        yp1 = [yi-thickness/2, yi-thickness/2]\n",
    "        yp2 = [yi+thickness/2, yi+thickness/2]\n",
    "        laser(ax, xpts, yp1, yp2, laserargs)\n",
    "        return xi+length, yi\n",
    "    else:\n",
    "        xpts = [xi-thickness/2, xi+thickness/2]\n",
    "        yp1 = [yi, yi]\n",
    "        yp2 = [yi+length, yi+length]\n",
    "        laser(ax, xpts, yp1, yp2, laserargs)\n",
    "        return xi, yi+length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laser(ax, x, y1, y2, laserargs):\n",
    "    ax.plot(x, y1, **laserargs)\n",
    "    ax.plot(x, y2, **laserargs)\n",
    "    ax.fill_between(x,y1,y2, **laserargs)\n",
    "    return x[-1], y1[-1], y2[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laserx(ax, y, x1, x2, laserargs):\n",
    "    ax.plot(x1, y, **laserargs)\n",
    "    ax.plot(x2, y, **laserargs)\n",
    "    ax.fill_betweenx(y, x1, x2, **laserargs)\n",
    "    return y[-1], x1[-1], x2[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rect(ax, x,y,width,height, plotArgs={}, fillArgs={}, orientation='vert'):\n",
    "    if orientation=='vert':\n",
    "        left = x\n",
    "        right = x+width\n",
    "        top = y + height/2\n",
    "        bot = y - height/2\n",
    "        # make a box\n",
    "        xpts = [left,right,right,left,left] \n",
    "        ypts = [bot,bot,top,top,bot]\n",
    "        ax.plot(xpts, ypts, **plotArgs);\n",
    "        ax.fill_between([left, right], [bot, bot], [top, top],**fillArgs)\n",
    "    else:\n",
    "        left = x-width/2\n",
    "        right = x+width/2\n",
    "        top = y + height\n",
    "        bot = y\n",
    "        # make a box\n",
    "        xpts = [left,right,right,left,left] \n",
    "        ypts = [bot,bot,top,top,bot]\n",
    "        ax.plot(xpts, ypts, **plotArgs);\n",
    "        ax.fill_between([left, right], [bot, bot], [top, top],**fillArgs)        \n",
    "    return xpts, ypts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aom(ax, x, y, Hoffset, height, width, borderArgs={}, fillArgs={}, text='', fontargs={}):\n",
    "    xpts, ypts = rect(ax, x, y+Hoffset, width, height, plotArgs=borderArgs, fillArgs=fillArgs)\n",
    "    plt.text(x+width/10, y+Hoffset-0.4*height, text, **fontargs)\n",
    "    return x+width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relay(ax, xp, yp, lt, relayFL, lensargs={}, laserargs={}, includeFocusing=True, fractionAround=1, fillArgs=None):    \n",
    "    # laser through relay between AOMs\n",
    "    fa = fractionAround\n",
    "    if includeFocusing:\n",
    "        laser(ax,[xp,xp+relayFL*fa, xp+(2+fa)*relayFL, xp+(2+2*fa)*relayFL], [yp+lt/2,yp+lt/2, yp-lt/2, yp-lt/2],\n",
    "                 [yp-lt/2,yp-lt/2, yp+lt/2, yp+lt/2], laserargs)\n",
    "    else:\n",
    "        laser(ax,[xp,xp+fa*relayFL], [yp+lt/2,yp+lt/2], [yp-lt/2,yp-lt/2], laserargs)\n",
    "        laser(ax,[xp+(2+fa)*relayFL, xp+(2+2*fa)*relayFL], [yp-lt/2, yp-lt/2], [yp+lt/2, yp+lt/2], laserargs)\n",
    "    lens(ax, xp+fa*relayFL, yp, 0.1, abs(relayFL)/2, lensargs, fillargs=fillArgs);\n",
    "    lens(ax, xp+(2+fa)*relayFL, yp, 0.1, abs(relayFL)/2, lensargs, fillargs=fillArgs);\n",
    "    xp += (2+2*fa)*relayFL \n",
    "    return xp, yp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotate(x,y, theta):\n",
    "    return [x * np.cos(theta) - y*np.sin(theta), x * np.sin(theta) + y*np.cos(theta)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mirror(ax, x, y, theta, size=1, plotArgs={'color':'c'}):\n",
    "    \"\"\"\n",
    "    orientation takes from 0-7\n",
    "    \"\"\"\n",
    "    off = size/(2*np.sqrt(2))\n",
    "    mOff1 = rotate( -size/2, 0, theta)\n",
    "    mOff2 = rotate(  size/2, 0, theta)\n",
    "    ax.plot([x+mOff1[0], x+mOff2[0]], [y+mOff1[1], y+mOff2[1]],linewidth=3, **plotArgs)\n",
    "    numHash=10\n",
    "    for num in range(numHash):\n",
    "        spacing = size/(numHash-1)\n",
    "        mb1 = rotate(-size/2+num*spacing, 0, theta)\n",
    "        mb2 = rotate(-size/2+(num+1)*spacing, size/10, theta)\n",
    "        ax.plot([x+mb1[0],x+mb2[0]], [y+mb1[1], y+mb2[1]], **plotArgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def waveplate(ax, x, y, plotArgs={}, fillArgs={}, size=0.5, theta=0):\n",
    "    rect2(ax, x, y, 0.1, size, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quarterWaveplate(ax, x, y, size=0.5, theta=0):\n",
    "    waveplate(ax, x, y, plotArgs={'color':'#D7BE69'}, fillArgs={'color':'#D7BE69', 'alpha':0.4}, theta=theta, size=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def halfWaveplate(ax, x, y, size=0.5, theta=0):\n",
    "    waveplate(ax, x, y, plotArgs={'color':'#FFA500'}, fillArgs={'color':'#FFA500', 'alpha':0.6}, theta=theta, size=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def iris(ax, x, y, orientation='vert', outerSize=1, innerSize=0.1, plotArgs={'color':mountColor}, fillArgs={'color':mountFillColor}):\n",
    "    halfIrisSize=(outerSize-innerSize)/2\n",
    "    th = 0.05\n",
    "    if orientation=='vert':\n",
    "        rect(ax, x, y+(outerSize+innerSize)/4, th, halfIrisSize, plotArgs=plotArgs, fillArgs=fillArgs)\n",
    "        rect(ax, x, y-(outerSize+innerSize)/4, th, halfIrisSize, plotArgs=plotArgs, fillArgs=fillArgs)\n",
    "    else:\n",
    "        rect(ax, x-(outerSize+innerSize)/4, y, halfIrisSize, 0.05, plotArgs=plotArgs, fillArgs=fillArgs, orientation='hor')\n",
    "        rect(ax, x+(outerSize+innerSize)/4, y, halfIrisSize, 0.05, plotArgs=plotArgs, fillArgs=fillArgs, orientation='hor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def window(ax, x, y, theta, size=1, thickness=0.2, plotArgs={'color':'w', 'alpha':0.1}, fillArgs={'color':'w', 'alpha':0.1}):\n",
    "    points = [[-size/2, -thickness/2], [size/2,-thickness/2], [size/2,thickness/2], [-size/2, thickness/2], [-size/2, -thickness/2]]\n",
    "    rPoints = [rotate(p[0], p[1],  theta) for p in points]\n",
    "    shPoints = np.array([[x + p[0], y+p[1]] for p in rPoints ])\n",
    "    ax.plot(shPoints[:,0], shPoints[:,1], **plotArgs)\n",
    "    ax.fill(shPoints[:,0], shPoints[:,1], **fillArgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def focusingTweezerArray(ax, xp, yp, initW, laserargs={}, nt=3, spacing=0.2, focalLength=1, distanceBeyondF=0.3,orientation='vert', offset=0):\n",
    "    #laserargs['alpha']/=nt\n",
    "    if orientation=='vert':\n",
    "        xl = xp-initW/2\n",
    "        xr = xp + initW/2\n",
    "        for n in range(nt):\n",
    "            dxl = - spacing*(n-(nt-1)/2) + initW/2 + offset\n",
    "            dxl = dxl + dxl*distanceBeyondF/focalLength\n",
    "            dxr = spacing*(n-(nt-1)/2) + initW/2 - offset\n",
    "            dxr = dxr + dxr*distanceBeyondF/focalLength\n",
    "            yf = yp + focalLength + distanceBeyondF\n",
    "            laserx(ax, [yp, yf], [xl, xl + dxl ],[xr, xr-dxr], laserargs)\n",
    "    else:\n",
    "        yt = yp + initW/2\n",
    "        yb = yp - initW/2\n",
    "        for n in range(nt):\n",
    "            dyt = - spacing*(n-(nt-1)/2) + initW/2 + offset\n",
    "            dyt = dyt + dyt*distanceBeyondF / focalLength\n",
    "            dyb = spacing*(n-(nt-1)/2) + initW/2 - offset\n",
    "            dyb = dyb + dyb*distanceBeyondF / focalLength\n",
    "            \n",
    "            xf = xp + focalLength + distanceBeyondF\n",
    "            laser(ax, [xp, xf], [yb, yb + dyb ],[yt, yt-dyt], laserargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cell(ax, x, y, h=1, wt=0.25, lensfillArgs=None, lensPlotArgs=None):\n",
    "    w=(1+2/np.sqrt(2))*h\n",
    "    lt = 0.06\n",
    "    cellColor='w'\n",
    "    window(ax, x, y+h/2+wt/2, 0, size=w, thickness=wt, plotArgs=lensPlotArgs, fillArgs=lensfillArgs)\n",
    "    window(ax, x, y-h/2-wt/2, 0, size=w, thickness=wt, plotArgs=lensPlotArgs, fillArgs=lensfillArgs)\n",
    "    window(ax, x-w/2-wt/2, y, np.pi/2, size=1, thickness=wt, plotArgs=lensPlotArgs, fillArgs=lensfillArgs)\n",
    "    window(ax, x+w/2+wt/2, y, np.pi/2, size=1, thickness=wt, plotArgs=lensPlotArgs, fillArgs=lensfillArgs)\n",
    "    ax.add_artist(plt.Circle((x,y), h/2-lt/2, color=cellColor, alpha=0.2, lw=3, fill=False))\n",
    "    ax.add_artist(patches.Ellipse((x-h/2-h/(2*np.sqrt(2)),y), h/np.sqrt(2)-lt, h-lt, color=cellColor, alpha=0.2, lw=3, fill=False))\n",
    "    ax.add_artist(patches.Ellipse((x+h/2+h/(2*np.sqrt(2)),y), h/np.sqrt(2)-lt, h-lt, color=cellColor, alpha=0.2, lw=3, fill=False))\n",
    "    ax.plot([x-h/2, x-h/2], [y-h/2, y+h/2], linestyle='-', color=cellColor, alpha=0.2)\n",
    "    ax.plot([x+h/2, x+h/2], [y-h/2, y+h/2], linestyle='-', color=cellColor, alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(ax, x, y, size=0.75, text='',fontArgs={}, orientation='hor', fillArgs={'color':'#AAAAAA'}, textYOff=0):\n",
    "    # High-NA lens\n",
    "    w, h = arr((4,3))*size\n",
    "    fx, fy = arr((0.85, 0.2))\n",
    "    if orientation=='hor':\n",
    "        plt.text(x+w/10, y+0.4*h+textYOff, text, **fontArgs)\n",
    "        ax.fill([x, x+fx*w, x+w, x+w,x+fx*w,x,x], [y-h/2,y-h/2,y-h/2+fy*h,y+h/2-fy*h,y+h/2,y+h/2,y-h/2], **fillArgs)    \n",
    "        return x+w, y\n",
    "    else:\n",
    "        plt.text(x-0.32*w, y+h/10+textYOff, text, **fontArgs)\n",
    "        ax.fill([x-h/2, x-h/2, x-h/2+fy*h, x+h/2-fy*h, x+h/2, x+h/2, x-h/2], [y, y+fx*w, y+w, y+w,y+fx*w,y,y], **fillArgs)    \n",
    "        return x, y+w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cube(ax, x, y, size=1, plotArgs={}, fillArgs={}, text='PBS', fontargs={'color':'w'}, textXAdjust=0, theta=0):\n",
    "    xpts, ypts = rect2(ax, x, y, size, size, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    plt.text(x+size/20-0.4*size+textXAdjust, y-0.4*size, text, **fontargs)\n",
    "    divPts = [[-size/2, -size/2], [+size/2, +size/2]]\n",
    "    rDivPts = np.array([rotate(p[0],p[1],theta) for p in divPts])\n",
    "    ax.plot(x+rDivPts[:,0], y+rDivPts[:,1], **plotArgs)\n",
    "    return x + size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deformableMirror(ax, x, y, fillArgs={'color':mountFillColor}, textArgs={'color':'w'}):\n",
    "    mirror(ax, x, y, 3*np.pi/2, size=0.2)\n",
    "    rect(ax, x+0.05, y, 0.25, 1.5, fillArgs=fillArgs)\n",
    "    cable2(ax, x+0.3, y, 2, vScale=0.05, cableThickness=0.75, extralines=10, cablePlotArgs={'color':'#B0B0B0', 'alpha':0.5}, cableFillArgs={'color':'#B0B0B0', 'alpha':0.5})\n",
    "    ax.text(x+0.35, y+0.5, 'Deformable\\nMirror', **textArgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cable2(ax, x, y, length, vScale=0.25, freq=6, cableThickness=0.1, cablePlotArgs={'color':'b', 'alpha':0.5}, cableFillArgs={'color':'b', 'alpha':0.5}, extralines=0, theta=0, xOff=0):\n",
    "    xpts = np.linspace(xOff,xOff+length,100)\n",
    "    ypts = np.sin(freq*xpts)*vScale+cableThickness/2\n",
    "    ypts2 = ypts-cableThickness\n",
    "    pts1 = np.array([rotate(x,y,theta) for x,y in zip(xpts,ypts)])\n",
    "    pts2 = np.array([rotate(x,y,theta) for x,y in zip(xpts,ypts2)])\n",
    "    ax.plot(x+pts1[:,0],y+pts1[:,1], **cablePlotArgs)\n",
    "    ax.plot(x+pts2[:,0],y+pts2[:,1], **cablePlotArgs)\n",
    "    ptsAll=np.concatenate((pts1,np.array(list(reversed(pts2)))),axis=0)\n",
    "    for num in range(extralines):\n",
    "        subPts = np.array([rotate(x,y,theta) for x,y in zip(xpts,ypts2 + (num+1)*cableThickness/(extralines+1))])\n",
    "        ax.plot(x+subPts[:,0],y+subPts[:,1], **cablePlotArgs)\n",
    "    ax.fill(x+ptsAll[:,0], y+ptsAll[:,1], **cableFillArgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fiberLauncher2(ax, x, y, plotArgs={'color':mountColor}, fillArgs={'color':mountFillColor}, orientation='left', \n",
    "                   theta=0, thickness=0.3, fiberLength=3, cableFillArgs={'color':'b', 'alpha':0.5, 'hatch':'/'}, offset=True):\n",
    "    offP = rotate(thickness/2,0, theta ) if offset else [0,0]\n",
    "    cable2(ax, x + offP[0], y + offP[1], fiberLength, vScale=0.1, cableFillArgs=cableFillArgs, cablePlotArgs={'color':'w','alpha':0.5}, freq=3,theta=theta, xOff=thickness/2)\n",
    "    rect2(ax, x + offP[0], y + offP[1], thickness, 1, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rect2(ax, x,y,width,height, plotArgs={}, fillArgs={}, theta=0):\n",
    "    left = -width/2\n",
    "    right = +width/2\n",
    "    top = +height/2\n",
    "    bot = -height/2\n",
    "    # make a box\n",
    "    xpts = [left,right,right,left,left] \n",
    "    ypts = [bot,bot,top,top,bot]\n",
    "    pts = np.array([rotate(x,y,theta) for x,y in zip(xpts,ypts)])\n",
    "    ax.plot(x+pts[:,0], y+pts[:,1], **plotArgs);\n",
    "    ax.fill(x+pts[:,0], y+pts[:,1], **fillArgs);\n",
    "    return xpts, ypts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lens(ax, xp,yp,w,r, lensargs, fillargs=None, theta=0):\n",
    "    if fillargs == None:\n",
    "        fillargs = lensargs\n",
    "    xpts = np.linspace(-r, -r+w/2, 10);\n",
    "    ypts = spherical_y(xpts, abs(r))\n",
    "    xpts2 = np.linspace(r-w/2, r, 10);\n",
    "    ypts2 = spherical_y(xpts2, r)\n",
    "    x_ = arr(list(xpts+r) + list(xpts2-r+w))\n",
    "    y_ = arr(list(ypts) + list(ypts2))\n",
    "    allPts = []\n",
    "    for x, y in zip(x_, y_):\n",
    "        rp = rotate(x,y, theta)\n",
    "        allPts.append([xp+rp[0],yp+rp[1]])\n",
    "    for p in list(reversed(list(zip(x_, y_)))):\n",
    "        rp = rotate(p[0],-p[1],theta)\n",
    "        allPts.append([xp+rp[0],yp+rp[1]])\n",
    "    allPts = np.array(allPts)\n",
    "    ax.plot(allPts[:,0], allPts[:,1], **lensargs)\n",
    "    ax.fill(allPts[:,0], allPts[:,1], **fillargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lens2(ax, x,y,w,r, lensargs, fillargs=None):\n",
    "    if fillargs == None:\n",
    "        fillargs = lensargs\n",
    "    xpts = np.linspace(-r, -r+w/2, 100);\n",
    "    ypts = spherical_y(xpts, r)\n",
    "    xpts2 = np.linspace(r-w/2, r, 100);\n",
    "    ypts2 = spherical_y(xpts2, r)\n",
    "    x_ = arr(list(xpts+r) + list(xpts2-r+w))\n",
    "    y_ = arr(list(ypts) + list(ypts2))\n",
    "    ax.plot(x+x_,y+y_,\n",
    "             x+x_,y-y_,\n",
    "             **lensargs)\n",
    "    ax.fill_between(x+x_,y+y_,y-y_, **fillargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laserPts(ax, pts, laserargs={}, fillargs=None, theta=0):\n",
    "    if fillargs is None:\n",
    "        fillargs=laserargs\n",
    "    ax.plot(pts[:,0], pts[:,1], **laserargs, zorder=-10)\n",
    "    ax.fill(pts[:,0], pts[:,1], **laserargs, zorder=-10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laserZ(ax, xi, yi, thickness, length, laserargs={},fillargs=None,theta=0, endSl=0, startSl=0):\n",
    "    endSlant=endSl*thickness/2\n",
    "    startSlant=startSl*thickness/2\n",
    "    xpts = [startSlant, length+endSlant, length-endSlant, -startSlant, startSlant]\n",
    "    th = thickness\n",
    "    ypts = [-th/2, -th/2,th/2,+th/2,-th/2]\n",
    "    pts = np.array([[x,y] for x,y in zip(xpts,ypts)])\n",
    "    rpts = np.array([rotate(pt[0],pt[1],theta) for pt in pts])\n",
    "    finPts = np.array([[xi+p[0], yi+p[1]] for p in rpts])\n",
    "    laserPts(ax, finPts, laserargs, fillargs, theta=theta)\n",
    "    centerOff = rotate(length, 0,theta)\n",
    "    return [xi+centerOff[0],yi+centerOff[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "def baslerCamera(ax, x, y, fontargs={'color':'w'}):\n",
    "    rect(ax, x, y, 2, 1, {'color':'k'}, {'color':'k'})\n",
    "    ax.text(x,y-0.45,'Basler Camera',**fontargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "def photodetector(ax, x, y, plotArgs={'color':mountColor}, fillArgs={'color':mountFillColor}, textArgs={'color':'w'}, theta=0, xOff=0):\n",
    "    bodyTh=0.3\n",
    "    offsetP = rotate(0, 0.05+bodyTh/2, theta)\n",
    "    rect2(ax, x+offsetP[0], y+offsetP[1], 1, bodyTh, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta);\n",
    "    rect2(ax, x, y, 0.25, 0.05, plotArgs={'color':'b', 'alpha':0.5}, fillArgs={'color':'b', 'alpha':0.5},theta=theta)\n",
    "    ax.text(x+xOff, y+0.1, 'PD', **textArgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gtPolarizer(ax, x, y, size, text=\"Glan-Taylor\\nPolarizer\", plotArgs={}, fillArgs={}, fontargs={'color':'w'}, textXAdjust=0, textYAdjust=0, theta=0):\n",
    "    \"\"\"\n",
    "    Beam is reflected at ~68 degrees up from init. implies angle of surface is 34 degrees from normal. \n",
    "    cos(34 degrees) = (size/2) / (L/2) -> L = size / cos(34)\n",
    "    \"\"\"\n",
    "    rect2(ax, x, y, 1.4*size, size, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    plt.text(x+textXAdjust, y+textYAdjust, text, **fontargs)\n",
    "    L = size / np.cos(34 * np.pi/180)\n",
    "    divPts = [[-L/2,0],[L/2,0]]\n",
    "    r1DivPoints = np.array([rotate(p[0],p[1],(90-34)*np.pi/180) for p in divPts])\n",
    "    r2DivPoints = np.array([rotate(p[0],p[1],theta) for p in r1DivPoints])\n",
    "    ax.plot(x+r2DivPoints[:,0], y+r2DivPoints[:,1], **plotArgs)\n",
    "    return x + size    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweezer Rail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function copy.deepcopy(x, memo=None, _nil=[])>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy.deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tweezerRail( textColor='w', aomBorderColor='#353535', aomFill='#252525', lensargs = {\"color\":'k', 'linewidth':0.5}, \n",
    "                 figScale=10, baseFontSize=12, lensFillArgs=None,  \n",
    "                figBoxScale = 10, bgColor='#111111', invTextColor='w', \n",
    "                laserColors={\"Tweezer\":\"#FF00008A\", \"Tweezer-Pickoff\":\"#FF000070\", '795':'#FFFF0070', \n",
    "                             \"Sidemot\":'#FF00FFAA', 'Imaging':'#FFFF0070', '780':'#FF000070', 'Mot':'g'}):\n",
    "    ### SETUP\n",
    "    fig, ax = plt.subplots(figsize=(figScale,figScale))\n",
    "    fig.set_facecolor(bgColor)\n",
    "    ax.axis(\"off\")\n",
    "    # Invisible boundary to prevent auto-zoom\n",
    "    xoff = -0.25*figBoxScale\n",
    "    x1, x2 = xoff, 3*figBoxScale+xoff\n",
    "    yoff = -1.1*figBoxScale\n",
    "    y1, y2 = yoff, 3*figBoxScale+yoff\n",
    "    ax.plot([x1,x2,x2,x1,x1], [y1,y1,y2,y2,y1], color='w');\n",
    "\n",
    "    stdTextArg={'color':textColor,'fontsize':baseFontSize*figScale/20}\n",
    "    invTextArg={'color':invTextColor,'fontsize':baseFontSize*figScale/20}\n",
    "    \n",
    "    ### TWEEZER RAIL\n",
    "    aomThickness=1\n",
    "    lt=0.06\n",
    "    xp, yp = 1.5, 0\n",
    "    launcherTh=0.3\n",
    "    fiberLauncher2(ax, xp, yp, thickness=launcherTh, cableFillArgs={'color':'w','alpha':0.5,'hatch':'/'})\n",
    "    halfWaveplate(ax, xp-0.7, yp)\n",
    "    tweezerLaser = {\"color\":laserColors[\"Tweezer\"]}\n",
    "    xp,yp=laser_2(ax, xp, yp, lt, -1.5, laserargs=tweezerLaser)\n",
    "    cube(ax, xp, yp, plotArgs=lensargs, fillArgs=lensFillArgs, size=0.75, fontargs=stdTextArg, theta=np.pi/2)\n",
    "    xp,yp=laser_2(ax, xp, yp, lt, 1.5, orientation='vert', laserargs=tweezerLaser)\n",
    "    mirror(ax, xp, yp, np.pi/4, 1)\n",
    "    xp,yp=laser_2(ax, xp, yp, lt, 2.5, laserargs=tweezerLaser)\n",
    "    deformableMirror(ax,xp,yp, textArgs=stdTextArg)\n",
    "    quarterWaveplate(ax, xp-0.5, yp)\n",
    "    xp,yp=laser_2(ax, xp, yp, lt, -2.5, laserargs=tweezerLaser)\n",
    "    xp,yp=laser_2(ax, xp, yp, lt, -3, orientation='vert', laserargs=tweezerLaser)\n",
    "    mirror(ax, xp, yp, 3*np.pi/4, 1)\n",
    "    # laser before aoms\n",
    "    px_h_aom, py = 0,-1.5\n",
    "    xp,_=laser_2(ax, xp, yp, lt, 1, laserargs=tweezerLaser)\n",
    "    # Vert AOM\n",
    "    xp=aom(ax, xp, yp, 0, aomThickness,aomThickness, borderArgs={'color':aomBorderColor}, fillArgs={'color':aomFill},\n",
    "          text='Vertical\\nAOM', fontargs=invTextArg)\n",
    "    halfWaveplate(ax, xp+0.5, yp)\n",
    "    ### Relay between aoms\n",
    "    xp, yp = relay(ax, xp, yp, lt, 2, lensargs=lensargs, laserargs=tweezerLaser, fillArgs=lensFillArgs )\n",
    "    # Hor AOM\n",
    "    xp=aom(ax, xp, 0, -1, 2*aomThickness,aomThickness, borderArgs={'color':aomBorderColor}, fillArgs={'color':aomFill},\n",
    "           text='Horizontal\\n AOM', fontargs={**invTextArg,'rotation':90})\n",
    "    xp, yp = laser_2(ax,xp, yp, lt, 1, laserargs=tweezerLaser)\n",
    "    mirror(ax, xp, yp, 7*np.pi/4, 1)\n",
    "    xp, yp = laser_2(ax,xp, yp, lt, -2, orientation='vert', laserargs=tweezerLaser)\n",
    "    mirror(ax, xp, yp, 5*np.pi/4, 1)\n",
    "    filterRelayFL=-4\n",
    "    focusingTweezerArray(ax, xp-2, yp,lt, focalLength=filterRelayFL, laserargs=tweezerLaser, orientation='hor', distanceBeyondF=filterRelayFL, spacing=0.012, offset=0)\n",
    "    focusingTweezerArray(ax, xp-2, yp,lt, focalLength=filterRelayFL, laserargs=tweezerLaser, orientation='hor', distanceBeyondF=0, spacing=0.012, offset=-0.1, nt=1)\n",
    "    xp, yp = relay(ax, xp, yp, lt, filterRelayFL, lensargs=lensargs, laserargs=tweezerLaser, includeFocusing=False, fractionAround=0.5, fillArgs=lensFillArgs )\n",
    "    \n",
    "    iris(ax, xp+6, yp)\n",
    "    mirror(ax, xp, yp, 1*np.pi/4, 1)\n",
    "    xp, yp = laser_2(ax,xp, yp, lt, -2.5, orientation='vert', laserargs=tweezerLaser)\n",
    "    # intensity servo pickoff cube\n",
    "    xis, yis = xp, yp+0.75\n",
    "    smallCubeAdjust=-0.4\n",
    "    cube(ax, xis, yis,0.25, plotArgs=lensargs, fillArgs=lensFillArgs, fontargs=stdTextArg, textXAdjust=smallCubeAdjust, theta=np.pi/2);\n",
    "    halfWaveplate(ax, xis, yis+0.25, theta=np.pi/2)\n",
    "    tweezerPickoff = copy(tweezerLaser)\n",
    "    tweezerPickoff['color'] = laserColors[\"Tweezer-Pickoff\"]\n",
    "    cube(ax, xis, yis+0.5,0.25, plotArgs=lensargs, fillArgs=lensFillArgs, fontargs=stdTextArg, textXAdjust=smallCubeAdjust, theta=np.pi/2);\n",
    "    xis, yis = laser_2(ax, xis, yis, lt, 1.5, laserargs=tweezerPickoff)\n",
    "    mirror(ax, xis, yis, 5*np.pi/4, size=0.5)\n",
    "    xis, yis = laser_2(ax, xis, yis, lt, 0.4, orientation='vert', laserargs=tweezerPickoff)\n",
    "    photodetector(ax, xis, yis, textArgs=invTextArg)\n",
    "    # back to main line\n",
    "    mirror(ax, xp, yp, 3*np.pi/4, 1)\n",
    "    xp, yp = laser_2(ax,xp, yp, lt, 1, laserargs=tweezerLaser)\n",
    "    # telescope glc\n",
    "    glcFL=0.55\n",
    "    bigAchFL= 20*glcFL\n",
    "    # laser through telescope\n",
    "    y_l = 20*lt\n",
    "    px, py = (5.5,-1/2)\n",
    "    extraDistance=1.5\n",
    "    laser(ax, [xp, xp+glcFL, xp+glcFL+bigAchFL,xp+glcFL+bigAchFL+extraDistance, xp+glcFL+bigAchFL+extraDistance+y_l], \n",
    "                     [yp+lt/2,yp+lt/2,yp-y_l/2, yp-y_l/2, yp+y_l/2], \n",
    "                     [yp-lt/2,yp-lt/2,yp+y_l/2, yp+y_l/2, yp+y_l/2], tweezerLaser)\n",
    "    objective(ax, xp+glcFL/2, yp, size=0.1, text='GLC Lens', fontArgs=stdTextArg, textYOff=-0.5)\n",
    "    xp = xp+glcFL+bigAchFL+extraDistance+y_l/2\n",
    "    mirror(ax, xp, yp, 5*np.pi/4, 2)\n",
    "    lens(ax,xp-extraDistance-y_l/2,yp,0.2,5, lensargs, fillargs=lensFillArgs)\n",
    "    yp += y_l/2\n",
    "    cube(ax, xp, yp+1.35, size=2, plotArgs=lensargs, fillArgs=lensFillArgs, fontargs=stdTextArg)\n",
    "    # pickoff\n",
    "    xpk=xp\n",
    "    ypk=yp+1.35\n",
    "    pickoffL=1\n",
    "    laser(ax, [xpk-y_l/2,xpk+y_l/2, xpk+y_l/2+pickoffL], [ypk-y_l/2,ypk-y_l/2,ypk-y_l/2],[ypk-y_l/2,ypk+y_l/2,ypk+y_l/2],tweezerPickoff)\n",
    "    \n",
    "    xp, yp=laser_2(ax, xp,yp, y_l, 6.5, orientation='vert',laserargs=tweezerLaser)\n",
    "    halfWaveplate(ax, xp, yp-0.5, size=2, theta=np.pi/2)\n",
    "    iris(ax, xp, yp-1, orientation='hor', outerSize=2, innerSize=30/24)\n",
    "    window(ax, xp, yp-2.1, 3*np.pi/4, size=2.5, plotArgs={'color':'c','alpha':0.2 },fillArgs={'color':'c','alpha':0.2 })\n",
    "    xim, yim = xp, yp\n",
    "    _, yp=objective(ax, xp, yp, text='Sill Objective Lens\\n'+r'NA$\\approx 0.64$', orientation='vert', fontArgs=stdTextArg)\n",
    "    cellCenterOffset=0.85\n",
    "    cell(ax, xp, yp+cellCenterOffset, lensfillArgs=lensFillArgs, lensPlotArgs=lensargs)\n",
    "    # lasers after sill (loop for each tweezer)\n",
    "    focusingTweezerArray(ax, xp, yp,y_l, focalLength=cellCenterOffset,laserargs=tweezerLaser, spacing=0.1)\n",
    "    # imaging path... (or start of)\n",
    "    imagingLight = copy(tweezerLaser)\n",
    "    imagingLight[\"color\"] = laserColors[\"Imaging\"]\n",
    "    _, _, yim = laser(ax, [xim-y_l/2, xim+y_l/2], [yim,yim], [yim-2.1+y_l/2 + 0.1*np.sqrt(2), yim-2.1-y_l/2 + 0.1*np.sqrt(2)], laserargs=imagingLight )\n",
    "    topOfDichroic = yim+y_l\n",
    "    laser780=copy(tweezerLaser)\n",
    "    laser780['color'] = laserColors['780']\n",
    "    xim, _, yim = laser(ax, [xim-y_l/2, xim+y_l/2, xim+y_l/2+2], [topOfDichroic,yim,yim], [topOfDichroic,topOfDichroic,topOfDichroic], laserargs=laser780 )\n",
    "    yim -= y_l/2\n",
    "    lens(ax, xim-1, yim, 0.2, 5, lensargs=lensargs, fillargs=lensFillArgs)\n",
    "    ### AXIAL PATH\n",
    "    axialLaser = copy(laser780)\n",
    "    axialLaser['color'] = laserColors['Axial']\n",
    "    xax, yax = 10,15\n",
    "    fiberLauncher2(ax, xax, yax, orientation='right', theta=np.pi, offset=True)\n",
    "    xax,yax = laser_2(ax, xax, yax, lt, xp-xax, laserargs=axialLaser)\n",
    "    mirror(ax, xax, yax, theta=7*np.pi/4)\n",
    "    xax,yax = laser_2(ax, xax, yax, lt, yp-yax+4, laserargs=axialLaser, orientation='vert')\n",
    "    halfWaveplate(ax, xax, yax+2.3, theta=np.pi/2)\n",
    "    cube(ax, xax, yax+3.3, size=0.5, plotArgs=axialLaser, fillArgs=lensFillArgs, fontargs=stdTextArg, textXAdjust=1.5*smallCubeAdjust, theta=np.pi/2)\n",
    "    bigCubePos = yax+1.3\n",
    "    laserZ(ax, xax, bigCubePos, lt, 1, axialLaser )\n",
    "    photodetector(ax, xax+1, bigCubePos, textArgs=invTextArg, theta=3*np.pi/2, xOff=0.4)\n",
    "    bcs = 1.5\n",
    "    cube(ax, xax, bigCubePos, size=bcs, plotArgs=lensargs, fillArgs=lensFillArgs, fontargs=stdTextArg, theta=np.pi/2)\n",
    "    lens(ax, xax, yax, 0.2, 1, lensargs,theta=np.pi/2, fillargs=lensFillArgs)\n",
    "    axFL=yp-yax+0.5\n",
    "    focusingTweezerArray(ax, xax, yax, lt, axialLaser, nt=1, focalLength=axFL)\n",
    "    motLight = copy(axialLaser)\n",
    "    motLight['color']=laserColors['Mot']\n",
    "    ### MOT Path\n",
    "    focusingTweezerArray(ax, xax, yax, 1, motLight, nt=1, focalLength=axFL)\n",
    "    #laser(ax, [xax-0.325, xax+0.325], [yax, yax], [bigCubePos+bcs/2,bigCubePos-bcs/2],laser780 )\n",
    "    motW = 0.75\n",
    "    xax, yax = laserZ(ax, xax, yax, motW, bigCubePos-yax, motLight, theta=np.pi/2, endSl=-1)\n",
    "    xax, yax = laserZ(ax, xax, yax, motW, 3, motLight, startSl=1, theta=2*np.pi/2, endSl=1)\n",
    "    mirror(ax, xax, yax, 3*np.pi/4, size=2)\n",
    "    xax, yax = laserZ(ax, xax, yax, motW, 2, motLight, startSl=-1, theta=np.pi/2, endSl=-1)\n",
    "    mirror(ax, xax, yax, 7*np.pi/4, size=2)\n",
    "    xax, yax = laserZ(ax, xax, yax, motW, 3, motLight, startSl=1, theta=np.pi)\n",
    "    lens(ax, xax, yax, 0.1, 1, lensargs, fillargs=lensFillArgs)\n",
    "    focusingTweezerArray(ax, xax, yax, motW, motLight, nt=1, focalLength=-1, distanceBeyondF=0, orientation='hor')\n",
    "    baslerCamera(ax, xax-3, yax, fontargs=invTextArg)\n",
    "    ### EO Path\n",
    "    eox, eoy = 0, 4\n",
    "    eoW = 0.06\n",
    "    eoLaser = copy(laser780)\n",
    "    eoLaser['color'] = laserColors['Eom']\n",
    "    fiberLauncher2(ax, eox, eoy, theta=-np.pi/2, fiberLength=1)    \n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, 2, eoLaser, theta=np.pi/2, endSl=1)\n",
    "    mirror(ax, eox, eoy, np.pi/4)\n",
    "    cube(ax, eox+1, eoy, size=0.5, plotArgs=lensargs, fillArgs=lensFillArgs, fontargs=stdTextArg)\n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, 2, eoLaser, startSl=-1, endSl=1)\n",
    "    mirror(ax, eox, eoy, -np.pi/4)\n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, eoy-(yp+0.85), eoLaser, startSl=-1, endSl=1, theta=-np.pi/2)\n",
    "    mirror(ax, eox, eoy, 3*np.pi/4)\n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, 1, eoLaser, startSl=-1)\n",
    "    halfWaveplate(ax, eox+1, eoy)\n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, 3.5, eoLaser, startSl=-1)    \n",
    "    gtPolarizer(ax, eox, eoy, 0.5, plotArgs=lensargs, fillArgs=lensFillArgs, textYAdjust=-0.8, textXAdjust=-0.5, fontargs=stdTextArg)\n",
    "    eoxp, eoyp = eox, eoy\n",
    "    eoxp, eoyp = laserZ(ax, eoxp, eoyp, eoW, 3.2, eoLaser, startSl=-1, theta=(90-68)*np.pi/180+np.pi/2)\n",
    "    mirror(ax, eoxp, eoyp, -34*np.pi/180 )\n",
    "    eoxp, eoyp = laserZ(ax, eoxp, eoyp, eoW, 3, eoLaser, startSl=-1, theta=np.pi)\n",
    "    photodetector(ax, eoxp, eoyp, theta=np.pi/2, textArgs=invTextArg)\n",
    "    \n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, 1.5, eoLaser, startSl=-1)\n",
    "    halfWaveplate(ax, eox, eoy)\n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, 1, eoLaser, startSl=-1)\n",
    "    quarterWaveplate(ax, eox, eoy)\n",
    "    eox, eoy = laserZ(ax, eox, eoy, eoW, 2.5, eoLaser, startSl=-1)\n",
    "    lens(ax, eox, eoy, 0.2, 1, lensargs, fillargs=lensFillArgs)\n",
    "    focusingTweezerArray(ax, eox, eoy, eoW, eoLaser, nt=1, focalLength=xp-eox, orientation='hor', distanceBeyondF=0)\n",
    "    \n",
    "    ### sidemot beam path\n",
    "    smx, smy = 20, 1\n",
    "    smw = 0.1\n",
    "    smLaser = laser780\n",
    "    smLaser['color'] = laserColors['Sidemot']\n",
    "    fiberLauncher2(ax, smx, smy, theta=np.pi/4, fiberLength=3)\n",
    "    smx, smy = laserZ(ax, smx, smy, smw, 1, theta=5*np.pi/4, laserargs=smLaser)\n",
    "    mirror(ax, smx, smy, theta=np.pi)\n",
    "    smx, smy = laserZ(ax, smx, smy, smw, 2, theta=3*np.pi/4, laserargs=smLaser)\n",
    "    cube(ax, smx, smy, 0.5, plotArgs=lensargs, fillArgs=lensFillArgs, theta=np.pi/4);\n",
    "    smpx, smpy = smx, smy\n",
    "    smpx, smpy=laserZ(ax, smpx, smpy, smw, 1, smLaser, theta=np.pi/4, startSl=1)\n",
    "    photodetector(ax, smpx, smpy, theta=7*np.pi/4, textArgs=invTextArg)\n",
    "    smx, smy = laserZ(ax, smx, smy, smw, 1, theta=3*np.pi/4, laserargs=smLaser)\n",
    "    quarterWaveplate(ax, smx, smy, theta=3*np.pi/4);\n",
    "    smx, smy = laserZ(ax, smx, smy, smw, 6.5, theta=3*np.pi/4, laserargs=smLaser)\n",
    "    quarterWaveplate(ax, smx, smy, theta=3*np.pi/4);\n",
    "    smx, smy = laserZ(ax, smx, smy, smw, 0.5, theta=3*np.pi/4, laserargs=smLaser)\n",
    "    mirror(ax, smx, smy, theta=np.pi/4)\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figScale=25\n",
    "tweezerRail(lensargs={'color':'w', 'alpha':0.2}, figScale=figScale, lensFillArgs={'color':'#808080', 'alpha':0.3}, baseFontSize=8, bgColor='w', textColor='k',\n",
    "            laserColors={\"Tweezer\":\"#FF00008A\", \"Tweezer-Pickoff\":\"#FF000070\", '795':'#FFFF0070', \n",
    "                         \"Sidemot\":'#FF00FFAA', 'Imaging':'#FFFF0070', '780':'#FF000070', 'Mot':'#00FF0090', 'Axial':'#0000FFA0', \"Eom\":'#11111111'});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figScale=25\n",
    "tweezerRail(lensargs={'color':'w', 'alpha':0.2}, figScale=figScale, lensFillArgs={'color':'#A0A0A0', 'alpha':0.3}, baseFontSize=8);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LGM Laser Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "electronicsCableColor='#303030'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dbrHead(ax, x, y, plotArgs={'color':mountColor}, fillArgs={'color':mountFillColor}, theta=0, heatSinkEmph={'color':'#505050'}):\n",
    "    sizeScale=1\n",
    "    rect2(ax, x, y, sizeScale*1.3, sizeScale*1, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    for xoff in sizeScale*np.array([0, -0.2, -0.4]):\n",
    "        offset1 = rotate(xoff, 0, theta)\n",
    "        rect2(ax, x+offset1[0], y+offset1[1], 0.01*sizeScale, 1*sizeScale, plotArgs={'color':'none'}, fillArgs=heatSinkEmph, theta=theta)\n",
    "    cableHeadOffset = rotate(-0.7*sizeScale, 0, theta)\n",
    "    cableHeadTh = 0.2*sizeScale\n",
    "    rect2(ax, x-cableHeadOffset[0], y+cableHeadOffset[1], cableHeadTh, 0.3*sizeScale, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    cableOffset = rotate(-0.7*sizeScale-cableHeadTh/2, 0, theta)\n",
    "    cable2(ax, x + cableOffset[0], y + cableOffset[1], 1*sizeScale, theta=theta+np.pi, vScale=0.1, \n",
    "           cableFillArgs={'color':electronicsCableColor}, cablePlotArgs={'color':electronicsCableColor});\n",
    "    fox, foy = rotate(sizeScale*0.65, 0, theta)\n",
    "    return x + fox, y+foy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isolator(ax, x, y, plotArgs={'color':mountColor}, fillArgs={'color':mountFillColor}, theta=0):\n",
    "    ss=1\n",
    "    isoOffX, isoOffY = rotate(ss*1, 0, theta)\n",
    "    rect2(ax, x+isoOffX, y+isoOffY, ss*2, ss*1, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    isoArrowPx1, isoArrowPy1 = rotate(1.5*ss, 0, theta)\n",
    "    isoArrowPx2, isoArrowPy2 = rotate(0.5*ss, 0, theta)\n",
    "    plt.annotate(\"\", xy=(x+isoArrowPx1, y+isoArrowPy1), xytext=(x+isoArrowPx2, y+isoArrowPy2), arrowprops=dict(arrowstyle=\"->\", color='w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prismPair(ax, x, y, theta=0, lensargs={}, fillargs=None):\n",
    "    if fillargs == None:\n",
    "        fillargs = lensargs\n",
    "    scale = 0.5\n",
    "    prism1Pts = scale*np.array([[-0.35, -0.6], [-0.35, +0.0], [+0.55, -0.3]])\n",
    "    rPrism1Pts = np.array([rotate(p[0],p[1], theta) for p in prism1Pts])\n",
    "    \n",
    "    prism2Pts = scale*np.array([[0.35, 0.], [0.35, +0.6], [-0.55, 0.3]])\n",
    "    rPrism2Pts = np.array([rotate(p[0],p[1], theta) for p in prism2Pts])\n",
    "    \n",
    "    ax.plot(x+rPrism1Pts[:,0], y+rPrism1Pts[:,1], **lensargs)\n",
    "    ax.fill(x+rPrism1Pts[:,0], y+rPrism1Pts[:,1], **fillargs)    \n",
    "    \n",
    "    ax.plot(x+rPrism2Pts[:,0], y+rPrism2Pts[:,1], **lensargs)\n",
    "    ax.fill(x+rPrism2Pts[:,0], y+rPrism2Pts[:,1], **fillargs)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1109,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eom(ax, x, y, plotArgs={'color':mountColor}, fillArgs={'color':mountFillColor}, crystalArgs={}, theta=0):\n",
    "    ss=1\n",
    "    eoOffX, eoOffY = rotate(ss*0.5, 0, theta)\n",
    "    rect2(ax, x+eoOffX, y+eoOffY, ss*2, ss*0.5, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    rect2(ax, x, y, ss*0.2, ss*0.5, plotArgs=crystalArgs, fillArgs=crystalArgs, theta=theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1268,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rbCell(ax, x, y, plotArgs={}, fillArgs={}, theta=0):\n",
    "    ss=1\n",
    "    rect2(ax, x, y, ss*1, ss*3.5, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    xpts = np.linspace(0.5, 0.75, 20)\n",
    "    ypts1 = xpts**2\n",
    "    ypts2 = 0.5*xpts**2+0.25\n",
    "    pts = [[x,y] for x, y in zip(xpts, ypts1)]\n",
    "    pts2 = [[x,y] for x, y in zip(list(reversed(xpts)), list(reversed(ypts2)))]\n",
    "    allPts =  pts + pts2\n",
    "    rAllPts = np.array([rotate(p[0],p[1],theta) for p in allPts])\n",
    "    ax.plot(x+rAllPts[:,0], y+rAllPts[:,1], **plotArgs)\n",
    "    ax.fill(x+rAllPts[:,0], y+rAllPts[:,1], **fillArgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1293,
   "metadata": {},
   "outputs": [],
   "source": [
    "def taBox(ax, x, y, plotArgs={'color':mountColor}, fillArgs={'color':mountFillColor}, glassArgs={}, theta=0):\n",
    "    ss=1\n",
    "    boxS=3\n",
    "    winTh=0.2\n",
    "    taChipSize=0.5\n",
    "    # wins\n",
    "    rect2(ax, x+winTh/2, y, ss*winTh, ss*1, plotArgs=glassArgs, fillArgs=glassArgs, theta=theta)\n",
    "    rect2(ax, x+boxS*ss+3*winTh/2, y, ss*winTh, ss*1, plotArgs=glassArgs, fillArgs=glassArgs, theta=theta)\n",
    "    # box\n",
    "    rect2(ax, x+boxS*ss/2+winTh, y, ss*boxS, ss*boxS, plotArgs=plotArgs, fillArgs=fillArgs, theta=theta)\n",
    "    # cutout\n",
    "    ax.add_artist(plt.Circle((x+boxS*ss/2+winTh, y), 1, color='#050505'))\n",
    "    #\n",
    "    rect2(ax, x+boxS*ss/2+winTh, y, ss*taChipSize*1.5, ss*taChipSize, plotArgs={'color':'y', 'alpha':1}, fillArgs={'color':'#ffd100', 'alpha':1, 'zorder':5}, theta=theta)\n",
    "    x0 = x+boxS*ss/2+winTh\n",
    "    xpoints = [x0-ss*taChipSize*0.75, x0, x0+ss*taChipSize*0.75, x0+ss*taChipSize*0.75, x0, x0-ss*taChipSize*0.75,]\n",
    "    ypoints = [y+ss*taChipSize/10, y+ss*taChipSize/10, y+ss*taChipSize/2.1, y-ss*taChipSize/2.1, y-ss*taChipSize/10, y-ss*taChipSize/10]\n",
    "    plt.fill(xpoints, ypoints, color='#996515',zorder=10)\n",
    "    return x+boxS*ss+2*winTh, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def D1LaserSetup():\n",
    "    figScale, figBoxScale = 15,10\n",
    "    bgColor='#111111'\n",
    "    lensargs={'color':'w', 'alpha':0.2}\n",
    "    lensFillArgs={'color':'#A0A0A0', 'alpha':0.3}\n",
    "    ### SETUP\n",
    "    stretch = 2\n",
    "    fig, ax = plt.subplots(figsize=(stretch*figScale,figScale))\n",
    "    fig.set_facecolor(bgColor)\n",
    "    ax.axis(\"off\")\n",
    "    # Invisible boundary to prevent auto-zoom\n",
    "    xoff = -0.25*figBoxScale\n",
    "    x1, x2 = xoff, 3*stretch*figBoxScale+xoff\n",
    "    yoff = -0.5*figBoxScale\n",
    "    y1, y2 = yoff, 3*figBoxScale+yoff\n",
    "    ax.plot([x1,x2,x2,x1,x1], [y1,y1,y2,y2,y1], color='w');1\n",
    "\n",
    "    ### Spectroscopy Setup\n",
    "    ssLaserArgs = {\"color\":'r', 'linewidth':0.5, 'alpha':0.3}\n",
    "    ssTh=0.1\n",
    "    specHx, specHy = 0,5\n",
    "    specHx, specHy = dbrHead(ax, specHx, specHy, theta=np.pi/2)\n",
    "    prismPair(ax, specHx, specHy+0.8, lensargs=lensargs, fillargs=lensFillArgs)\n",
    "    specHx, specHy = laserZ(ax, specHx, specHy, ssTh, 5, ssLaserArgs, theta=np.pi/2 )\n",
    "    isolator(ax, specHx, specHy-3, theta=np.pi/2)\n",
    "    specHx, specHy = laserZ(ax, specHx, specHy, ssTh, 3, ssLaserArgs, theta=np.pi/2 )\n",
    "    isolator(ax, specHx, specHy-3, theta=np.pi/2)\n",
    "    mirror(ax, specHx, specHy, theta=np.pi/4)\n",
    "    specHx, specHy = laserZ(ax, specHx, specHy, ssTh, 2, ssLaserArgs, theta=0 )\n",
    "    mirror(ax, specHx, specHy, theta=-np.pi/4)\n",
    "    specHx, specHy = laserZ(ax, specHx, specHy, ssTh, 1, ssLaserArgs, theta=-np.pi/2 )\n",
    "    fiberLauncher2(ax, specHx, specHy, theta=-np.pi/2, fiberLength=1)\n",
    "    ### sat spec line\n",
    "    expandedLW = 0.35\n",
    "    ssx, ssy = 4, 12\n",
    "    fiberLauncher2(ax, ssx, ssy, theta=np.pi/2, fiberLength=1)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, ssTh, 1, ssLaserArgs, theta=-np.pi/2 )\n",
    "    halfWaveplate(ax, ssx, ssy, theta=np.pi/2)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, ssTh, 1, ssLaserArgs, theta=-np.pi/2 )\n",
    "    cube(ax, ssx, ssy, 0.5, plotArgs=lensargs, fillArgs=lensFillArgs, textXAdjust=-1, theta=np.pi/2)\n",
    "    \n",
    "    ### pickoff for offset lock\n",
    "    sspoffx, sspoffy = ssx, ssy\n",
    "    sspoffx, sspoffy = laserZ(ax, sspoffx, sspoffy, ssTh, 3, ssLaserArgs, theta=0 )\n",
    "    mirror(ax, sspoffx, sspoffy, theta=-np.pi/4)\n",
    "    sspoffx, sspoffy = laserZ(ax, sspoffx, sspoffy, ssTh, 2, ssLaserArgs, theta=-np.pi/2 )\n",
    "    mirror(ax, sspoffx, sspoffy, theta=3*np.pi/4)\n",
    "    sspoffx, sspoffy = laserZ(ax, sspoffx, sspoffy, ssTh, 3, ssLaserArgs, theta=0 )\n",
    "    cube(ax, sspoffx, sspoffy, 0.5, plotArgs=lensargs, fillArgs=lensFillArgs, text='NPBS', textXAdjust=0.5)\n",
    "    sspoffx, sspoffy = laserZ(ax, sspoffx, sspoffy, ssTh, 1, ssLaserArgs, theta=np.pi/2 )\n",
    "    photodetector(ax, sspoffx, sspoffy)\n",
    "\n",
    "    # back to sat spec\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, ssTh, 1, ssLaserArgs, theta=-np.pi/2 )\n",
    "    halfWaveplate(ax, ssx, ssy, theta=np.pi/2)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, ssTh, 1, ssLaserArgs, theta=-np.pi/2 )\n",
    "    eom(ax, ssx, ssy, crystalArgs=lensargs)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, ssTh, 6, ssLaserArgs, theta=-np.pi/2 )\n",
    "    lens(ax, ssx, ssy, 0.2, 0.5, lensargs, theta=np.pi/2)\n",
    "    focusingTweezerArray( ax, ssx, ssy, ssTh, focalLength=-.5, laserargs=ssLaserArgs, orientation='vert', \n",
    "                          distanceBeyondF=0, nt=1 )\n",
    "    ssy -= 0.5\n",
    "    mirror(ax, ssx, ssy, theta=3*np.pi/4)\n",
    "    ssx += 8\n",
    "    focusingTweezerArray( ax, ssx, ssy, expandedLW, focalLength=-8, laserargs=ssLaserArgs, orientation='hor', \n",
    "                          distanceBeyondF=0, nt=1 )\n",
    "\n",
    "    lens(ax, ssx, ssy, 0.1, 1, lensargs, theta=0)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 1, ssLaserArgs, theta=0 )\n",
    "    mirror(ax, ssx, ssy, theta=5*np.pi/4)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 2, ssLaserArgs, theta=np.pi/2 )\n",
    "    mirror(ax, ssx, ssy, theta=-np.pi/4)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 1, ssLaserArgs, theta=np.pi )\n",
    "    halfWaveplate(ax,ssx, ssy)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 1, ssLaserArgs, theta=np.pi, endSl=1 )\n",
    "    \n",
    "    cube(ax, ssx, ssy, 1, plotArgs=lensargs, fillArgs=lensFillArgs, textXAdjust=-1, theta=np.pi/2)\n",
    "    sspumpx, sspumpy = ssx, ssy\n",
    "    sspumpx, sspumpy = laserZ(ax, sspumpx, sspumpy, expandedLW, 2, ssLaserArgs, theta=np.pi/2, endSl=-1, startSl=-1 )\n",
    "    mirror(ax, sspumpx, sspumpy, theta=-np.pi/4)\n",
    "    sspumpx, sspumpy = laserZ(ax, sspumpx, sspumpy, expandedLW, 5, ssLaserArgs, theta=np.pi, endSl=-1, startSl=1)\n",
    "    mirror(ax, sspumpx, sspumpy, theta=np.pi/4)\n",
    "    sspumpx, sspumpy = laserZ(ax, sspumpx, sspumpy, expandedLW, 2, ssLaserArgs, theta=-np.pi/2, endSl=-1, startSl=1 )\n",
    "  \n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 5, ssLaserArgs, theta=np.pi,endSl=1, startSl=1 )\n",
    "    rbCell(ax, ssx+2.5, ssy, plotArgs=lensargs, fillArgs=lensFillArgs, theta=np.pi/2)\n",
    "    \n",
    "    cube(ax, ssx, ssy, 1, plotArgs=lensargs, fillArgs=lensFillArgs, textXAdjust=-1, theta=np.pi/2)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 4, ssLaserArgs, theta=np.pi, startSl=1 )\n",
    "    mirror(ax, ssx, ssy, theta=3*np.pi/4)\n",
    "    ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 1, ssLaserArgs, theta=np.pi/2 )\n",
    "    lens(ax, ssx, ssy, 0.2, 0.3, lensargs, theta=np.pi/2)\n",
    "    focusingTweezerArray(ax, ssx, ssy, expandedLW, focalLength=0.5, laserargs=ssLaserArgs, orientation='vert', \n",
    "                         distanceBeyondF=0, nt=1)\n",
    "    ssy += 0.5\n",
    "    #ssx, ssy = laserZ(ax, ssx, ssy, expandedLW, 0.5, ssLaserArgs, theta=np.pi/2 )\n",
    "    photodetector(ax, ssx, ssy)\n",
    "    \n",
    "    ### active laser head\n",
    "    workHx, workHy = 17,11\n",
    "    workHx, workHy = dbrHead(ax, workHx, workHy, theta=np.pi/2)\n",
    "    prismPair(ax, workHx, workHy+0.8, lensargs=lensargs, fillargs=lensFillArgs)\n",
    "    workHx, workHy = laserZ(ax, workHx, workHy, ssTh, 2, ssLaserArgs, theta=np.pi/2 )\n",
    "    mirror(ax, workHx, workHy, theta=-np.pi/4)\n",
    "    workHx, workHy = laserZ(ax, workHx, workHy, ssTh, 7, ssLaserArgs, theta=np.pi )\n",
    "    isolator(ax, workHx+6, workHy, theta=np.pi)\n",
    "    isolator(ax, workHx+3, workHy, theta=np.pi)\n",
    "    mirror(ax, workHx, workHy, theta=np.pi/4)\n",
    "    workHx, workHy = laserZ(ax, workHx, workHy, ssTh, 2, ssLaserArgs, theta=-np.pi/2 )\n",
    "    mirror(ax, workHx, workHy, theta=-5*np.pi/4)\n",
    "    workHx, workHy = laserZ(ax, workHx, workHy, ssTh, 1, ssLaserArgs, theta=0 )\n",
    "    fiberLauncher2(ax, workHx, workHy, theta=0, fiberLength=1)\n",
    "    \n",
    "    ### work laser splitting\n",
    "    workSx, workSy = workHx+4, workHy-2\n",
    "    fiberLauncher2(ax, workSx, workSy, theta=np.pi/2, fiberLength=1)\n",
    "    workSx, workSy = laserZ(ax, workSx, workSy, ssTh, 1, ssLaserArgs, theta=-np.pi/2 )\n",
    "    halfWaveplate(ax, workSx, workSy, theta=np.pi/2)\n",
    "    workSx, workSy = laserZ(ax, workSx, workSy, ssTh, 2, ssLaserArgs, theta=-np.pi/2 )\n",
    "    cube(ax, workSx, workSy, size=0.5, plotArgs=lensargs, fillArgs=lensFillArgs )\n",
    "    workOffx, workOffy = workSx, workSy\n",
    "    \n",
    "    workOffx, workOffy = laserZ(ax, workOffx, workOffy, ssTh, 5, ssLaserArgs, theta=-np.pi )\n",
    "    halfWaveplate(ax, workOffx+4, workOffy)\n",
    "    mirror(ax, workOffx, workOffy, theta=3*np.pi/4)\n",
    "    workOffx, workOffy = laserZ(ax, workOffx, workOffy, ssTh, 1.5, ssLaserArgs, theta=np.pi/2 )\n",
    "    \n",
    "    workSx, workSy = laserZ(ax, workSx, workSy, ssTh, 4, ssLaserArgs, theta=-np.pi/2 )\n",
    "    isolator(ax, workSx, workSy+3, theta=-np.pi/2)\n",
    "    mirror(ax, workSx, workSy, theta=3*np.pi/4)\n",
    "    workSx, workSy = laserZ(ax, workSx, workSy, ssTh, 2, ssLaserArgs, theta=0 )\n",
    "    mirror(ax, workSx, workSy, theta=5*np.pi/4)\n",
    "    workSx, workSy = laserZ(ax, workSx, workSy, ssTh,2, ssLaserArgs, theta=np.pi/2 )\n",
    "    fiberLauncher2(ax, workSx, workSy, theta=np.pi/2, fiberLength=1)\n",
    "    \n",
    "    ### 3W TA and splitting section\n",
    "    tax, tay = 24, 14\n",
    "    fiberLauncher2(ax, tax, tay, theta=np.pi)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 1, ssLaserArgs)\n",
    "    mirror(ax, tax, tay, theta=-np.pi/4)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 2, ssLaserArgs, theta=-np.pi/2)\n",
    "    mirror(ax, tax, tay, theta=3*np.pi/4)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 1, ssLaserArgs)\n",
    "    prismPair(ax, tax, tay, theta=np.pi/2, lensargs=lensargs)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 1, ssLaserArgs)\n",
    "    tax, tay = taBox(ax, tax, tay, glassArgs=lensargs)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 4, ssLaserArgs)\n",
    "    lens(ax, tax, tay, 0.1, 1, lensargs)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 1, ssLaserArgs)\n",
    "    mirror(ax, tax, tay, np.pi/4)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 3, ssLaserArgs, theta=-np.pi/2)\n",
    "    mirror(ax, tax, tay, np.pi/4)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 7, ssLaserArgs, theta=np.pi)\n",
    "    isolator(ax, tax+6, tay, theta=np.pi)\n",
    "    isolator(ax, tax+3, tay, theta=np.pi)\n",
    "    cube(ax, tax, tay, size=0.5, plotArgs=lensargs, fillArgs=lensFillArgs)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 1, ssLaserArgs, theta=np.pi)\n",
    "    lens(ax, tax, tay, 0.1, 1, lensargs)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 2, ssLaserArgs, theta=np.pi)\n",
    "    mirror(ax, tax, tay, np.pi/4)\n",
    "    tax, tay = laserZ(ax, tax, tay, ssTh, 2, ssLaserArgs, theta=-np.pi/2)\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nETxO9ffGk71e3/3ud3PZZZelWq1meHj4pMJQshiYJicnT+o5lvyaNRq58j/8h8zX69n1qlclSZq9vSk1m5m7/vrMX3NN2v/3/07tm99c2teFZag8OpreW2/N+DvfmcaWLUmzmUZ/fxorV6bZ0ZHa178uMAEAACxjAhOFzV95Zcp796bZ03M8LpXn5vJbt96al33jG/lqrdbiCU/MwPe+l+vf+tbc/v73Z6a3d3G7lwsuSLlaTamvr9XjQcs0+/szf+21aXZ2Pu344KOP5nff/vacNz6ekVP0dX/wJD//5ptvzp133plf/uVfzq/+6q/mnnvuybZt25ZkthMx0Gic0uc/2et111135f3vf39uvPHG/Pqv/3ruv//+/O7v/m5Lt8k7Jdes0chr3/e+fGtuLl/+qZ9KkjT7+lLfuDGlo0fT7Opa+teEZao8Pp7ed74zE7fckvbPfS7tn/tcq0cCAADgNBGYeN4m3/zmxZVLPT1Z+P6VS1NTue7223PgO9/JV2u1fKyjo8WTnrg37NmT69/2tvyfO+/MTH9/UqmkMTiY8sGDGXvPe1IeH0/PXXe1ekw4Lerr1mXqzW9O7etfT5LFe60ds/6hh3LNbbflvrm5DJzCr/vBcjknm64mJyfz4Q9/OH/xF3+Rl770pUsy14k61d8fl+J6zc3N5c///M/z8Y9/PK94xStafg+mU3nNfumDH8y+RiM7fvqnkyTNFSvSbGvLzL/8l5l9zWvS/j//Z9r+6Z9O2evDclGenEzvzTendIojOgAAAGcWgYnnbeGFL0z5wIGnrVzK9HR+7pZbUv3ud1s73BJasWtXXv32t+fv7757MTKVSmkMDKQyNZXm7Gyrx4PTprliRRYuvzzNSuVpxysPP5wbbrklo2fZ18Pk5GS+9KUvtXqMs8bc3Fw+//nPt3qMU6rcaOTqP/zDdM/P58Gf/dnFg52daaxenfLc3FN/10GLVSqV9PT0POPxVkfgJ53Ncamvry+9vb2tHuOsMj4+nrGxsVaPAQAAtJjAxI808Xu/t7hyqbc3CwMDx99wK01MpOdd78r6hx8+6+659KP07d2b1//7f5+Pvfe9aQwMLG6Xt2lTSmNjGf3AB1IeGyu5u38AACAASURBVEvvrbe2ekw4Jerr1mXyrW9N9ctfXvz4hS88/lj1gQfSc9ddaZueTpbZ1z3nplKzmVf95/+cR5vNzL72tUmS5sqVqXd3Z/pXfiUzr399Ov7yL9P2ta+1eFLOVePj46lWqyk9Q/Cs1+uZPcti/5mot7c37e3taZzFkex0qlQqqVarAhMAACAw8aPVt25d3Dbo+1cuTU6m553vTHXnztYOdwqt2L8/Pdu2ZfzOO9NcsyYplRZvBH/gQErj460eD06ZZl/f4tf9D/xUfOX++9Nz990pzc21aDI4NUpJOj/ykWRhIbM///OLB2u1NDo6Uh0a8meelhobG/NG/mnQaDQyMTHR6jHOCj09Pc8YPAEAgHOPwMSP1OztzcLatU+tXDp6NL233ZbKrl0tnuzUqxw6lL6bbsr47bencf75SbmcxsUXp3HhhTn64Q+nPDqavt///VaPCUuq0dW1+L+XXHL8WO2++9L9vveltLDQqrHglCol6fzzP09pfj4zr3/94sH+/iysWJHJrVtTmppK51/8Rdr+4R9aOicAAADAmcL+Rjyn+SuuWFy5VD3WIkdH03vLLedEXHpSeWQkvbfcktL+/YsHSqWkrS2l4eGUz+AVXIODg6lUKunr68vKlStbPQ5niWaplNmf+7mnHat+7WviEueEUpLO//E/0vHf/ttTB8vlNJvNlHfuTMkqEgAAAIDjBCaeVf388zNx001PrVwaHk7fzTensndviyc7/cpHjqTvpptSfuKJxQOlUhoveEHmX/3qHPnYxzL6oQ+1dsAf0Nvbm1/8xQ/kn/2zP82LX/wneeMb70ilUmn1WJwFZn7hF7JwxRXHP6596UvpEZc4x3T+5V+m86MfferAypVZeMUrMrFtW45+5COZu+661g0HAAAAcIYQmHhW5X370vbZzyZJSocPL65cGhpq8VStUx4dTe+tt6Z8bPVWZceOtH/yk2m/557Uvv71Fk/3dFu2vCSNxtps3/54LrpoVR5/fFfqP3A/HXgm7f/rf6V0LCLXvvjFdH/wgym56TnnoI5Pfzqdf/Inix/MzKTtr/867Z/9bNruvTflgwdbOxwAAADAGcA9mHhWpSRdf/ZnKU9MpO1LX0rl0KFWj9Ry5fHx9G7bltnXvjYdH//4GfvG+9atr8iRI7MplxtZubKaf/on9wzh+SmPj6dv27bM/ot/kY5PfOKM/TMOp0PHZz+b0uxsKjt2pLpjR6vHAQAAADijCEw8p1KSzk99qtVjnFHKExPp/O//vdVjPKu2trZccMGVue++g7nkkg1JRrNjx/2tHouzSHl0NJ0f/3irxziuUqmkp6en1WOclEqlctpWES6H65Wc3mv2XNo/97lWjwAAAABwRrJFHiwzmza9IOXyQB59dFcuvvj87N//QCYnJ1s9FpyQ8fHxLCyD+z/V6/XMzs6e8tdZLtcrOX3XDAAAAIATYwUTLDObN78i09P1HDkylfPP78y9936t1SPBCRsbG8vY2FirxzhruF4AAAAAnC5WMMEys3nzVdmx42g2b16bZC7bt/9jq0cCAAAAAGCZsYIJlpGBgbXp7NyYHTseyebNg5me3pVDhw60eiwAAJ5Ds9nMwsJC5ubmsrCwkGazmUajkVKplFKplEqlkra2trS1taVUKrV6XAAAgCQCEywrGza8KElbnnjiUK6//tLs2vX5Vo8EAMAPaDabmZ2dzeTkZGZmZjI/P388HDWbzR/6/d//WLVaTVtbW7q6utLd3S04AQAALSMwwTIyOHhZxsYW36Do6all794HWz0SAADH1Ov1TExMZGxsLI1G42kx6ZnC0jM9trCwkIWFhUxPT2dkZCQ9PT3p6+tLrVY7pbMDAAD8IIEJlpHzzrske/aM57zzViSp58CB77Z6JACAc169Xs/hw4czNTWV5Llj0vP15HOMj49nYmIibW1tWb16ddra2k76uQEAAJ4PgQmWiVqtlr6+83Po0JEMDKxKMprh4UOtHgsA4JzVbDYzNTWV4eHhJYlKz/U6s7OzGRoaSm9vb/r7+22dBwAAnHLlVg8ALI2VK1em2ezJyMho+vt7Mz5+IAsLC60eCwDgnLSwsJADBw6c8rj0/ZrNZsbHx7Nnz57MzMycltcEAADOXQITLBM9PSuTtGVsbDK9vZ0ZH7d6CeD5qlQqGRgYyPr161s9CrAMzMzMZO/evZmZmTltcelJzWYz9Xo9Bw4cyOjo6Gl9bQAA4NxiizxYJjo7VyZpZnp6Pl1dlUxMjLV6JIAzWrlazYp167L+vPPy0xdemJ6enkxOTuZv/uZv0mg0Wj0ecJaamprKoUOHTntY+kHNZjNHjx5NvV63ZR4AAHBKCEywTFQqHUmS+flGarVy5udnWzwRwJmnWq1m7dq1Wb9+fTYPDGSmqysdCwuZmZjI9PR0enp6vAkLnLAzJS496ckt85rNZlatWuX7GwAAsKQEJn5IX19fent7n/fv/06jkfljvz6YZLD8/HZeHB8fz9jYqV1lc9FFFyVJpqenn9fvP5PPBYAT09bWdjwqrV+/Ph0dHZmbm8v0yEgO7tmT7tnZHGhry6pVq1o9KnAWm56ePqPi0pOazWYmJiZSLpfT39/f6nEAAIBlRGDih/T29qa9vf15bw905Ac+rj2Pz6lUKqlWq6ctytRqz2eqs+Ncnk2jsbhiqVYrZ2GhkWr1+Z0zwHJ26aWX5uKLL05nZ2dmZ2dz5MiR7Nq1K/Pz8xmcm8uCn+YHlkC9Xj8j49KTms1mxsbG0tHRkc7OzlaPAwAALBMCE8+o0WhkYmLilD3/6dqCaHp6OrVabVmcy48yPX00SSkdHbVMTTXS0dHX6pEAWm5sbCzbt29PV1dXarXaGfvmL3B2Gx4ePuPv3dZsNnPo0KFs2LAh5ee5Sh8AAOC5CEywTExMHE0yn76+royPT2fjxrWtHgmg5fbu3Zu9e/cmSTo7O7Ny5cqcd955WbFiRapzc6nMzCSz7lkH54pVq1Y94zZxJ/PDQpOTk5mZmTmZsU6bRqORkZGRDAwMnPBzVKvVVCqVbN269WnHjxw5ksOHD5/siCxD0zfemOqDD6b20EOtHgUAgCUmMMEycfTo0ZRKE1m1akWOHFnIZZdtTKVSSb1eb/VoAGeE+fn5DA0NZWhoKB0dHbm0oyPN889PbeXK9FWraWtra/WIwCnW39+fK6+88mnbJz/yyCMnvNq9Xq9neHj4rFodOTU1lampqXR1dZ3Q51cqlVQqlVx11VXHj7W3t2d4eDif+cxnlmpMlolmtZqZG29Mx8KCwAQAsAwJTLBMzM3NZWxsX9as2ZhHHqklacvq1Wty8OCBVo8G0HI9PT257rrrMjk5eTwyze7fn0MHDya1WravW5eLL744lUql1aPCadWs1VKan2/1GKdVrVbLt7/97eMfn8zWdqOjo2dVXEoWt8o7fPhwOjs7T3jlVr1ef9o13LBhQ1auXLlUI7JMTP3Kr6T66KNJktLUVIunAQDgVBCYYBk5cOCxnH/+K/KFL3wuyVVZt+5igQkgi1s69fb2plqtZt26dbn88suz8cCBVPbuTXXPnhw8eDBHjx5NR0eHlZ8sa81yOWk0UkrS6O7OxO23p/a1r6XzE59o9WhnnUajkfHx8VaPcULq9Xrm5ubS3t7e6lFYpprVamZvuCGNL3whSdJYvbrFEwEAcCoITCx7lUolPT09z+v3dnZ2pqurKyMjI+nv78/s7GymfsRP251J29ANDX0nF100kXp9NFNT/z979x1nV1ngf/xzyq1z79RMZtIgnUBCAElAiKBRVEQUEGKFVVxWWVzdXReFVSCUCAKylg1FERGJ/BZdEFREFyISejEQQgLplUmml9vLOef3xyRDhiQkmXamfN+vFy8yz7n3nO/cTJKZ+z3P8xQZN+5oVq582u9YIiKDgud5bN++nUKhQCwW44h4nJrjjqPkmGP4YEcHdXV17Nixw++YIv0qdfnlFI49FiObxQuFIBDAmTwZbJvw//t/9HwnopHnQN8jDmae59He3s7o0dqzU/pe6pJLsFetAqBw0kkA5D7xCQLLl2uZPBEREZFhRgWTDGuJRALbtg96+Q/DMAiHw9i79uIwDOOAbx44jkOunzeILy0tJR6PH/BxTU1refbZ65k4McMzz7xEOJxj3Lhxh3StRCJBR0dHT6OKiAx6nueRSCRob26mwzQZVVJCcPRoZs2axaxZs2hra6Ouro5t27YN2dkJIvsTePpprC1bcMvLKRx3HF5lJQDZBQvwAgEiv/qVSqaD4HkebW1tQ255vD2l02mKxSK2rR8Jhyvbtpk6dSrr1q0bsBviPMsif9ppnQU2wO79DfN5jGx2QDKIiIiIyMDRTxMyrHV0dBxSWVJWVsbpp/+AP/95C+FwBXPnFrn11gt8f/MgHo8TCoUOYo8AD9NspaQkRbFYJBgsP6TlnizLwrZtFUwiMmJ4nkc+kWBbLkcgEKCyspJ4PM6kSZNob29XwSTDTmjZMgASV1zRWS55Huy6ESd39tlg20R+8QvfS6bp06dTLBbZuHGjz0n2rVAoDJoZ7L2RSqUoKyvzO4b0k8MPP5wTTjiBdDrN1q1b+/16yf/4DwIrVgBQeO97u8aN9nZiV1+NvWVLv2cQERERkYGlgklkD+3t7SSTWxk/voZ167bynvfMorp69KDYx8h1XZLJ5AEft23bNrLZLLZt09LSQrFYPOhrxGKxHm/2LCIyFJnBIHY4TFkwSCaT4Y033qC9vZ10Oo1pmn7HE+k3occeI7BqFU5VFYU5c/B2LZWWO/NMPNsmeuedGD7dYGNZFrNnz6a+vn7QFky9mb1+xhku8+cf6Kahg+CC1RJmxZsu9zzcs/Nls1kVTMNYdXU1FRUVjB49ut8LJs+yKMybB7v/7QwEOv/f0UH8yiuxtm/v1+uLiIiIiD9UMIm8Q13dm4wbN5knn2wBbGpqpg+KgulgZXctPXEoxZKIyEgSDAYJhUIEHAfSaVIbNrA8k+n6+3O3A88aFRm6gi+8AEDiqqu6yqXd8qefDrZN9I47MPrwz0FFRQWh3ctm7UMul6O1tZXq6mrKysro6OjANM1B+Wcxm832eIb7/PkuXz3VI2z17Nr5vMHrK8KsWB7FcQzOnp3nnoczPTpXfy/zLP4aO3Ys2WyW2trafr1O4sorCbz8MgCFE07oGjeam4lfdRWW9jcUERERGbZUMIm8Q1PTRqZNC5BOF8nlHKqrpwBP+R1LZEQ60P5j2jNMDlVpaSktLS289dZbTG9ooD6fp9p1yYbDfkcT8UX497/HfeEFiqNHUzzhBNxdezfmTzuts2RavLhPrhMIBPjQhz5ERUXFPgsj0zRZs2YNy5YtY8KECRQKBVzXpby8nJaWlj7J0Jd6W8z0pFzK5QxeeyXCylcjeIBT7JwpYvVisqXrujiOg2X1sO2SQSscDlNaWkpDQwNlZWUEg0Hy+XyfX8czTYrHHQeFQufArq8lo6WF+BVXYNUPnRv1REREROTQqWASeYeWls2ASWVlmKamPBUVY/2OJDJivdv+Y9ozTA5FoVBg9erVbNmypWu5UTebfXspH5ERKvDqqwDkFy7sKpd2y3/gA3iBAM73v9+5V1MvFAoFmpubCYVCvPnmm3sdr6qqYuXKldi2zaRJk2hqaiKTyVBbWzvoCibXdQd0png2Y/Dq8girV0bxPA/H6bu/twzDIJ/PE4lE+uycMjhEo1Fs26a5uZmqqirC4XCfF0wd119P4PnnASjOmdM1bu7cSWzhQqzGxj69noiIiIgMPiqYRN6hvb0BKBKPR+noyDJmTI3fkUQGlGdZFGfOJPDaa35HAfa//5j2DJNDkUqlWLVqld8xRAatyP334/71rxTHj6dwwgm4hx8OQGHePB69/HLm3Hwz9PLN6a1btzJlyhRc1+1244Bt26TTaVpbWxk7diylpaVs2rSJQqFATU0Na9aswXGcXl27LzmOg2EYPV4iDzqXuTvgY3bNWHpjdQQ8cBwD6Nt/9zzPo1AoqGAahiKRCJ7nkcvl8DyPSCTSpzfleKaJM2MGRirVObDrhg2joYH4lVdiNjf32bVEREREZPBSwSR9YsGCBTzyyCOk02m/o/RaOp3CMHJEo2HS6RyRSLnfkUT6lReJkJ87FyOTgVyO7Fln4Rx3HJGf/Yzwn//sdzwRERkA9ptvwptvkrvmmq5yabdN8+YRtizOuPZaeMdeZYcisGEDx86dy6R4nOQeMxsi0SjNGzYwP5XCqKmhJpWieteNBSXFIkdEIiSbmnp83T1V53LUFIu83otz9HZPKDMR4J47SzngPRIG4IHr9u/NFL0pyvZ0dLHI+b34+pC+NcrzeF8+T20qxWHFIlHHobWPfn/uu/VWpi5bxgvQuTzeLua2bcSvvhqztbVPriMiIiIig58KJum16upqrrvuOo444ggWLVrkd5xeKxQKgEMwGCCfzxMMDo59OSzLIhaL9es1bFt/JYxEbnU16X/7t73GM1/5Cpgm4T/9yYdUIiLih8gvf4lXWUlh8mSKc+fiTJ0KwBvvfS83LlrErEWLsHs6kymfp2LbNkaNH0/rHoVRMRhkS0sLH7EsWqdO5a2mJlp3tS+ZXA5n7Fga+2iZPMs0mdzLc/S2kDGKJpblUSwOjiU6+6pgKgEatezooFE1bhxxz6PZdak0DNpCoT75/XFNk+ZJk7Da2zsHdp3T3L6d+FVXYe4eFxEREZERQT8BSK/90z/9E8lkkk9+8pNEo1G/4/Ra5w/ZHoZh4LoMiiW4EonEgK71LyOLWVdH6de+RuyKKwj8+c+wx12nmYsuInP22T6mExGRgWRv2kTg73/HmT27q1za7c33vIdlCxdSDIV6fP7GzZsJRaOYlgWAFQhQyGTIJBKEa2sJx2J07DG7KZtMUl5TgzWIboLpi+8NXc/Asrx3/c80+6b4kZEpk0gAYOwqgNw+WGbyDz//OSu/8AUAGo49tmv88PXrOedb31K5JCIiIjICDZ6f1GRIqq6u5uyzz6auro7Gxka+8IUvcOedd/odq1csywJMHMfFti2KxYLfkejo6DjkNdPPz2ZpNE2qXZcl4cExC0sGJ6NYxNqxAy8UonD66Xsdz/7DP4BlEXngAR/SiYiIH6K33YZXVkZ+5kyKxx+PM2MGAA3HHMNTixZx9hVXEMxkDvm81vbtBFMpxldWkmhoIFpSQuOGDVS7LuakSYQzGeK5XLfnxIDDysq6LavXU5WuS6qX5+htweSU5Th5Ghxo4lAuZ/L6igjFIv0626mvbqbygOpeLh8ofSfU0kIWqLIsYp5HeS6H0YvfH88wSNXW0nbkkd3Gx27YwD9edhmNmQzoZw4RERGREUcFk/TKRRddxO9+9zvmzp3Lb3/7W6666iqWLFlCpgdvOAwW4XAYCJLN5giHK8nl2vyOJDIgrK1bKbvwQpxRo8ifdhqFOXPwqqoAyH7hC3ihEJH77uvj7cVFRGQwsnbsgB07SF9wQVe5tFv9kUfy8+99j/iiRRg92H9zfV0dhx9+OK+3tVEVDvNCMkmhtBRnxgxaWlrYGQx2e3yJ45CaPJkVu2Zk9MaUUIj39nI2lGVZvVtWzoSjjj64vXCOeU+atW+Gefn5KIWiQbHQt0WTYRi7bq7qvRdsm1+rYBg0RgPpUIg3Sko4KhDgQcOgpYe/P213303wb38DYMfs2V3jY1av5r1XX83qXI7q3kcWERERkSFIBZP0yuuvv87TTz/N3Llz2bZtG7fccgvmEF97vaSkFM8LkkymicUipFIb/I4kMiAM18Vob8etqiL/0Y/udTx33nkYhkH417/2Id2hs22bcDiMaZp4nkexWCSbzfbZXhMiIiNB7JZb8GIx8nPmUDj2WJxZswBwZsyg47rriC9ciJlMHtI5t27dyowZM4hGo6TTaVKpFBMmTKCsrAzLsqjadXPDbrtLkJ07d1JfX99nn1tPWZaFaZq4AzBbx7LgyJlZjjgyy/q1IV56roRcvm+LplAvljyUwSuVSuF5HmVlZRSLxR7dAOgFg3j5PF48TuEdRbO1Zg2fvPJK2t8x41BERERERhYVTNIrf/jDH7p9/MAwWEKrvHw8AG1tGSorQzQ17fQ5kcjAsjdupPzTn8YZM4bcmWdSOOYYvNGjAcieey5uOIx3660+p3xbKBRi7NjDqa09klGjJlNZOZ7S0hoCgRgQoHO7QQ9w8LwCqVQH7e1baGnZRn39WnbseJOmpkYVTyIi+2C2tEBLC4WLL95rJpM7aRLJq68mds01mIcwu2jHjh2kUikOP/xwnnrqKQDa29t55ZVX9rtcm+d5FAr+L1u8WzAYJJs9uFlIfcE0YfqMHFOn59i4PsSLz5WQzfZN0WQPov2tpO9ks1lSqRRjxoyhvb29R1+v7T//OfZTT4Fp4h5xRNe4/dprxG64oXOZzCF+c6GIiIiI9I5+mhB5h1GjJlMoOKTTBcrLA6xZs9XvSCIDzigWwbbJf+ADEAh0O5b/+Md50nU56mc/w/CxlAmHw8Tjcc4550dAOZ7n0dZWoLExw4YNSZLJJrLZPI7j7Lr7vZRIZBKlpTZVVdVMmTKPWbNsoEChsJNt21awdetyNm5cTnu7lsYUEdlTfNEivGiU3CmnUDjmmM6ZTKaJM3kyHddfT+kVV2C2tx/UuQqFAlu3bmXatGm0trYCnftNvvDCC/35KfSpcDjc44LpmGN6/m+nacLU6TmmTMuxaUOQF58robzS6fH5AoFAn+3BJIOL4zg0NDRw7LHHsnbt2kO+kcYDvEgEZ9q0buPWa68Ru/56jHy+D9OKiIiIyFClgknkHWpqprJzZ46amhIMw6ChYY3fkUR8YW/aRMVnPoMzejSZz3yG4pFH4tXWArDyE58gEQ5z4k9+4mtGz/NYtqyFbdveoLExRWlpiMrKOOXlcWKxKFVVncstuW6QfH4U2ew2GhubWbOmnebmFIYB48ZVcdhhNUya9BEmT/4E8+enaWtby8aNL7Fp0/Ns374Jx+n5m3ciIsOBkU5jpNMUTjwRZ/r0bse8ceNI7F4ub1dhdCCbNm0iHo9TLBb7I26/C4VCGIbR49mveReCvZj4YRgweWqeSVPytCWhc8buoQtrz6RhraGhgVQqRUNDwyE/1wuHwbJwp07tGgu89BIlN9/ceSOSiIiIiAgqmES6sSyLmpojeOmlFsaPr8EwEuzYsdnnVCL+8kpKKL7nPZ1vNOxh84c/TK3nceYtt0A/LRO0ynXZ31uV+XyeZDLJ6NERPvSh4xg9OoRpmhhGDkiTzyfI5TooFgsYRgDbricajWGa4/G8IwCDTKZIXV2a7dsbefjhDaTTeSZPrmXq1EkcffRsjjvuyzjODp544hZWrlzeL5+j9I3S0lLi8fghPWeV61IAGoBxh7DETyKRoKOj49ACigwT8e98B8Jhsh/7GMVZsygefXTn8lnjx9Nxww2Ufve7mM3NBzxPfX09y5YtIz9EZ0H0Zt+ij3ykZ2VQXzMMg2g06ncM6Ufbtm1jx44dvPXWW4f0PM8wSH/lK93GbJVLIiIiIrIPKphE9jBmzARMs5otW/7Oe95zBK2tG0in037HEvGVvWkT5RdeiFNdTfqCC3CmTMEbMwaA5z/yEdKhEDNuuQWzHzY7f7fdNqLRKGVlZRxzjEtd3TJefnkNjY1raWnZQVtb237vii8pKaG8vIKqqsnU1Exn7NgjmTp1Kp43hWzWYdOmdt54Ywt/+UsjVVVxJk+eTlPT0HwDdCSJx+OEQiHcQ/g63LO8PNi3ey3LwrZtFUwyYhmuC+k0xWOPpThzZrdj3ujRZD73OUoWLz7geRzHGdLfY5mmSTweH9J/F1iW1auiTAa/jo4OHnnkkUP6txGgOGsWhQ98oOvjwFNPUfKTn2BoRreIiIiIvIMKJpE9TJw4F9c1qKtr45xzYqxe/YrfkUQGDa+kBHfyZAiHwXHAsgB47f3vp9WyOOnmmzEH8I2HdDpNLpdj8eLzD2mJolQqRSqV4q23tgPLAIhEIhx22BEcfvhcJk48nqOOOgLPO5odOzKsX18kl1PBNBS4rksymezXa8RiMe1XIgLEr7wSzzAgHCb33veSufhirI0bid55p9/RBkxpaSmJRKLHy+T5yTAMysvL9ffZCHCo5RJAYOVKwr/8JdkvfYnA009T8uMfd5bLIiIiIiLvoIJJZA+TJs1h27YUNTWlBAI2mzcPnc2mRfqbvXkzZf/yLzhjxpD+8peJ1tbSMW4cANve9z7MQICPf+97WH24dMq77Rjgui6u6/bJG3uZTIY1a15lzZpXgTuprq5h8uS5TJw4l1NOmU4+X0ZLS68vIyIyrBieB5kM4SeewKqvx964ESOX8zvWgLFtm1AoRLaflontb1oeT95N5Pe/x962DXvFCpVLIiIiIrJfKphEdonH41RXz2Tlyp1MmTINx2lh69b1fscSGXS8YBCvrIwkdO69tGtvpi0nnsjPFi4kdtNNGIV3W9zu4I0zzR5uW947jY31NDb+kRde+COhUGhI3p0uIjKQAqtX+x3hoMRiMSKRCOPHj+8a2759e4/PV15eTn19/ZD7dyIej2Mewt5ze7J2zWDe8zXUUnvDU+AVreYgIiIiIu+uZz9ViAxDkycfh+fFePPNdqZNm8zWrc/sdw8XkZHM3rKF0m9/m9j3v4+1aRPGzp1dx4rHH0/iiivwgkEfE/atXC43ZDehFxGRvSWTScrLy7v+M00Tp4dLvIbDYaLR6JBaas6yLMrLy3t9nj1fw0gkQl1dXR+kExERERGRoUQzmER2mTLlZFpaPAxjOhUVSf7+92f8jiQyuFkWRqGAmUjgRqN4paUAOEcfTfK73yV2ww0YQ3TZIBERGZ5WrFjBihUruo2NGzeOQKDn82WrqqrIZrM9LqkGkmEYjB49usezl6BzD0SAX//6130VS0REREREhijNcJzPjQAAIABJREFUYBIBgsEgEybM5c03g8yYkQRa2LhxxQGfJzKSWVu3Er/6aqK33YbR2orR2Nh1rHj00XQsXIgXifiYUEREpP+Zpkl1dfWgn8VkGAalpaVazk5ERERERPqMCiYRYOLEyRhGLevXb2batNHs3PkaqVTK71giQ4JRLGLt2IFVV9etZHKPOILEwoW42kRcRESGuXA4TDweH9Qlk23bfbI0noiIiIiIyG4qmESAlpYmli+/kcbGy3njje+zfLmW/BA5WNZbbxG76SYi994LgNHa2nXMmT6dxLXX4sZifsUTEREZEBUVFYN2PybbtqmtrR2U2UREREREZOjSHkwiQFNTE8uW/R7ThJUrt/kdR2RIMrJZAitX4kUiOOPH406YAIAXjUIoBMmkzwlFRET6j2EYjBo1isbGRjKZDJ7n+R0J6CyXxowZg2VZfkcREREREZFhRgWT9Il8Pk8+n/c7hoj4yKqro2Tx4q6Pcx/6ENnzziO2cCFmc7OPyURERAaGYRhUV1fT0tJCMpn0tWQyDKNr5pLKJRERERER6Q8qmKRPfOUrX6Gjo8PvGCIyiISWLiX41FMYKp9FRGQEMQyDyspKAoEAra2tvpRMhmFQUlJCZWUlpqlV0UVEREREpH+oYJI+oXJJRPalt+VSJBIBILaPPZwsy8JxnF6dX0REpD8YhkFpaSnRaJSmpiZyudyAFU2WZVFdXU04HB6Q64mIiIiIyMilgklERIYkx3HI5XJ+xxAREdkv27apqakhlUrRvGu52P4qmgzDIB6PU15erllLIiIiIiIyIFQwiQxxX5yX49w5e88SqXFdCkAA+LRZ6HbsgZeD3PNMaGACivTC+vXr/Y4gIiLSK4ZhEIvFiEajpFIp2tvbcRynT4omwzC6iqV4PI5t68c7EREREREZOPoJRGSIO3dOnrmTiqTfMZEjCHiAAYzC7RqPhWHKaEcFk4iIiMgAMk2zqwjK5XJ0dHSQTqe7jh9M4WQYRtdjQ6EQZWVlRCKRrnEREREREZGBpIJJZBhI5+DhV4Ldxk4sFLHxyBgGy/e4m/X0owuE7YHfbFpEhodRo0bR1NRELBbDcRwymYzfkbpEIhEOP/zwfr1GY2PjgO2jIiLDVygUorq6Gs/zcByHfD5PLpcjm81SLBa7/T1jGAamaRIOhwmFQgSDQQKBgEolERERERHxnQomkWEgFu4sjvYU8TxcwMRjtPH2sdoyvTEqIj0Ti8X44hd/zGOPpYnFYsydu5PFiy/FcRy/o4mIDEmGYWDbNrZtE41G/Y4jIiIiIiJySFQwiQxxD7wcZMpoZ69ZSTZvL5EXQqWSiPTepEmzcd0JbNz4LOeeO4Vt21YOqnIpk8mwatWqfr1GLBbr1/OLiIiIiIiIiAwVKphEhrh7ngntcz+l87NZGk2TatdlSTjsQzIRGW4mTz6Z9vYcrpulstLi1Vef8TuSiIiIiIiIiIj4xPQ7gIiIiAx+tm0zceJc1q5tYPr08RhGgo0bl/sdS0REREREREREfKIZTCIiInJAEyfOwDRHs27dC5xyymwaGlaQSCT8jtVNIBBg5syZ/XqNxsZGCoXCgR8oIiIiIiIiIjLMqWASERGRA5o8+SRyOY/GxiTjx0d54YUX/I60l0KhoD2YREREREREREQGiJbIExERkQOaOPF4Nm5sY+LEagyjyKZNL/odSUREREREREREfKQZTLJPlmX1613atq0vPRGRoaKysopYbBIbN65l0qSxZLPb2Lmzzu9YIiIiIiIiIiLiI81gkr0kEgmKxaLfMUREZJCYMOFoPC/Eli2NTJxYzrZtK/yOJCIiIiIiIiIiPtM0EtlLR0cHHR0dB/3487NZGs3OrrLadVkSDvdXNBER8cG4cbNIJgs4jkdpaYDly1/zO9I+BQIBZs6c2a/XaGxspFAo9Os1RERERERERESGAhVMIiIi8q5qa6fx1lspamvLAIedO9f4HWmfCoUCq1at6tdr9OfysSIiIiIiIiIiQ4kKJhEREdkv27YpK5vAihWt1NRUYhgdNDU1+B1LRKTPRCIRQAXywbIsC8dx/I4hIiIiIiKDgPZgEhERkf0qLy/HMGK0tLRTXh4nkajXEnEiIiOY4zjkcjm/Y4iIiIiIyCCgGUwiMuw51dUUTj6Z0MMPY/gdRmSIicXK8bwQHR1JSkujJBJv+B2px4LBIMFg8F0fk8/nyefzA5RIRAaD9evX+x1BRERERERkSFLBJCLDmltVReL66/GqqijMmEHoL3/BzGQwEgmsujq/44kMepFIGeCRyRSJRi2SyQ6/I+2XZVnvusSVZVkHPEckEnnXEsq29a2TiIiIiIiIiAioYBKRYS77qU/hVVUBUDzxRIonngiAtXkzpd/8pp/RRIYE244CkM87BAImhcLgXBYpkUhg2zaGceB5ivvbO+RgCigREREREREREemkgklEhrXI3XfjjBpFce7crjFz82bsF1+kcNxxGO3t2Bs3+phQZGjxPM/vCPvU0dFBR8e7z64aN24cgUCAZDK5z+O7Zz9t3ry5r+OJiIiIiIiIiAw7KphEhjDDMDj++OMxDIOWlpZux6pzOSzTpNJ1mRIKdTvW2tq61+OHK6NYJHbTTaT+7d8ozJsHgDtxIrmJE8kB1saNlF56qb8hRQYx1+2csWTbJsWiSyDw7nsYiYiIiIiIiIjIyKCCSWQI8zyPSCTC8ccfT0dHB+3t7V3HKhyHkGEQ9TyO3WPZp3A4TEtLC48++qgfkX1hOA4lP/whqWKRwvvf3zVubt+OvXw5+XnzMFpbCaxe7WNKkcEpk2kHDMJhm0zGIxTa/x5HQ4FlWZSUlOw1bhiG9lcSERERERERETkEeidFZIirq6vjyCOPpLW1lebmZpqbmwGozedpNwzKPI8399iwfsKECft8c3W4M1yXkv/+b9KOQ/6DHwTAHT+e3Hnndc5kWr+ewLe/7W9IkUEolWoDipSWRunocJkwYazfkXps9z5NezIMA8/zBu3SfyIiIiIiIiIig5UKJpFhoLm5mfb2dqqqqjAMg6amJr8jDUqG6xK99VYoFMh/9KNvj+/cib1qFbkPfxizuZnA8uU+phQZXNra2jCMJOXlY2htLWXmzGpM08R1Xb+jHbI992kqLy+nrKysq1xyXZctW7b4nFBEREREREREZOhQwSQyTNTX1+N5HpWVlZimCdu3+x1pUDI8j+hPf4pRLJL7+McB8GpryZ11FgDW2rUqmET2kMvlSCTqGTXqWDZuXIFhTKSysoqmpka/o/VKW1sb8XgcwzAwDAPLsojFYiSTSb+jiYiIiIiIiIgMCabfAURGisbGRp588kmeeeYZ7r77biKRSLfxZcuW8cQTT3DCCScAncs23XDDDTzzzDM8/fTTPP744xx22GHveo2Ghgba2to678yvqen3z2moMoDIXXcRevjh7uNNTVjr15M96yzyJ53kTziRQaihYS3jx6doaHgLCDBmzDS/I/WJztlZBtC5p11ZWZnPiUREREREREREhg7NYBIZIJlMhve///0A/PSnP+XCCy/ktttu6zb+wQ9+kCuvvJJPfOITnHPOOYwZM4b3ve99eJ7H2LFjSaVSB7xOQ0MDrutSUlHBcZ5HYMcOyOe7jlfl89jhMJFstn8+0SHE++lPeT6T4eXPfrbz41GjyH/sY2AYWGvWEHzuOZ8TigwOO3a8weTJZ5LJFMlkiowZM4uVK5/1O1avJRIJKioqumYx2baNbdsUi0W/o4mIiIiIiIiIDHoqmER88NxzzzFz5sy9xuPxOG1tbQDU1tayc+fOro3n6+rqDvr8TU1NlGUyTKmspAlob3x7KaugYRAyDBpNTWAEmLJkCdlikdfPP79zwDAItbbibdtG5nOfw6yvJ/TXv/obUqSflJaWEo/HD/i4xsbVPPvsTUyalOaZZ14mGMwybty4Q7pWIpHo2v9oMCkWi9i23TWTKRwOa5k8EREREREREZGDoIJJZIBZlsVpp53G0qVLAYhEIjz55JOEQiFqa2s5a9deQA899BB/+tOfOOmkk1i2bBm/+c1vWLly5UFf543WVlygctQoDrcs2nbsACDuedieR7Xr9vnnNlSNvu8+Zmaz3H/RRQDkKipg/vyumUwqmGS4isfjhEIh3AP8feB5HqbZSjyeplgsEgqVEQ6HcRznoK5jWRa2bQ/KgslxHGz77W+H9vy1iIiIiIiIiIjsn95FERkgu4sk6JzBtGTJEqD70nlz587l9ttv5+STT6auro4TTjiBU089lVNOOYWHHnqICy+8kGXLlh30NV9sa2OcYbB27FgSwSA7duxgQjBIiWXxQDjc95/kEHb+gw9yLPDqrpIJy4JkErO5mfRFF2Fu3074z3/2NaNIf3Bd96Bm7OzcuZNEIkEwGKS9vZ3sISyzGYvFumYIDTbmO2ZzHqhsExERERERERGRTiqYRAbInkXS/rz00ktUVlYyatQompqayOfzPP744zz++OM0NjZyxhlnHFLBBJBsaaHBthk9evReb6RKdzMeegizWGT5xRd3DsRiFE48ESOXw9q4UQWTjGiJRAKA/B57ug0Hey6PBxxScSYiIiIiIiIiMpLp3WaRQWTatGlYlkVLSwuzZ8+mtrYWAMMwOOqoo9i+fXuPztvW1kZDQwMlJSXU1tYO2pkEg8H0P/6R+T/5CeyexRAI4Nk2FAqkvvENsmef7W9AEekz4XC4q3j3PA/XdYddgSYiIiIiIiIi0l80g0nEZ3sunWcYBpdccgmu61JdXc2PfvQjQqEQAMuXL+fOO+/c5znC4TATJkzoNlaVzxM0DOKex4RgEOi8U7+8vJzKykoMw8DzvH78zIauWX/+My8YBumvfQ1ME8JhikcfjdHejheJ+B1PRPpIZWUlnudhGAaGYXTN0hIRERERERERkQNTwSQyQA477LB9jldXV+9zfOnSpSxduvSA521tbaWlpYWSkpJu43Y4TMgwsD2PEssCOvcWyWQyRKNRYrGY3kx9F6EnnsAoFkl94xud+zEFg3hlZbhlZSQvvxxr0yYi99/vd0wR6aFoNEogEADoKtvb2tr8jCQiIiIiIiIiMqSoYBIZ4lpaWnj00Uf3Go9kszSaJtWuywPhcLdjgUCAQqEwUBGHrOBTT0GxSOqb3+wqmZzJk/Hq6zHSab/jiUgPGYZBVVVVt9lLra2tmtUpIiIiIiIiInIIVDDJXkpLS4nH4wf9+FWuy+6qogEYZx7c1l6JRIKOjo5DDyi9pnLp4AWfew5uuonUpZdCIADBIO6YMRQdh8S112KtW0f03nv9jinSY5ZlEYvF+vUatj24vt2orKzstveS4zj690hERERERERE5BANrnd8ZFCIx+OEQiFc1z2ox7e+4+PAQTzHsixs29YbejIkBF96CePGG0ledhkEAhgtLZjNzRAOg/ZkkiEskUhg2zaGYfgdZcDYtr3XkqINDQ0+pRERERERERERGbpUMMk+ua5LMpnst/PHYrER9YamDH2B5cuJXX89+fe/n+jixRgHWcCKDGYdHR2HXPSfv8fym0vesfzmUFBVVdX1a8/zyGazmtUpIiIiIiIiItIDKphERA5SYMUKAitW+B1DRHrINE1CoVC3Gxyam5t9TCQy8hSOOgqvpITgSy/5HWVEONSlrwezyK5Z4+vXr++3a+j16h+5+fOxNm7E3rLF7ygiIiIi0sdUMImIiMiIsOfsWc/zKBaLOI7jcyqRkSV3+uk4EyeqYBogh7r09Uin16vvebZN+utfJ/zrX6tgEhERERmGVDCJiIjIiBAOh/E8D8MwMAyDTCbjdySREaNw7LG4ZWV4kQhGNut3nBGlv5e+HiixWGxArqPXq+9kzzgDa/t2AAz9mysiIiIyLKlgEhERkRHBNM1uHxeLRZ+SiIw8ufnzcaZMwYtG9UazyAjgWRaZiy4i+NhjnR9Hoz4nEhEREZH+oIJJRER6vOfAq6++yrp167Asi02bNvHd736XbDbbNW7bNo7j8Pvf/557772Xjo4OOjo6+uEzEDkwz/O6fWxZlk9JREaO/Ny5uFVVeNEoXjze+V95OdkzzyT8xz/6HU9E+kHmvPOwNm0CID9vHgDZs84i+PzzXTOaRERERGR4UMEkIiI93nMgl8vx+c9/HoBFixbx2c9+ll//+tfdxisqKrj++uspLy/ntttuU8Ekvslms4TDYaCzbNr9axHpP/l583CmTsWLxfD2uJHBGT8eDzD8i3ZAlZWVFAoFEomE31FEhgzPNMl+/vMEly7tHNg9cykYxK2qUsEkIiIiMsyoYBIREaBnew54ntf1nOeff54jjjiCZDLZbTyZTPKd73yHBx54gNtvv73Pc4scrGQySVlZWdceTMFgENM0tZm7SD/Iz5uHU1uLV1KCW1YGJSVdx0KPPkrk5z8f1OWSYRicfvrppFIpHn744QG/fjgcJqu9qmSISV9wAfa6dcDbM5cAyOWI3XADgdde8ymZiIiIiPQXFUwiItJrlmVx6qmn8tRTT+3z+LZt2zBNk8rKSjZv3jyw4UR2cRyHQqFAIBDAMDrf2q6oqKC5udnnZCLDT2HuXIrTp+OWlHQvl/7wByJ33z2oyyXonNkbiUQwTZNgMEg+n+/xuSorK6moqDjox9fU1GBZFi+99JJKJhkyXNMkd845uH/7W+fA7lnCuRyx664jsHq1b9lEREREpP+oYBIRkR4Lh8Ndd3a//PLL/O///u9+H7v7DX0RPzU1NTFmzJiu/ZhKSkpob2+nWCz6nExkeMjNn48zbhxuNIpbWvr28lhA6MEHiSxZMujLJYDq6moMwyAcDlNeXk5DQ0OPz1VRUcGMGTMOat+30tJSSktLaWhoULkkQ8ZLX/sak1euBKBw0klvH0iniS9ahP3mmz4lExEREZH+poJJRER6LJvNctZZZx3wcRMmTMBxHFpaWgYglcj+FQqFruUbC4UCkUikq2wSkd4rzp5NccYM3FisW7kUvv9+wvffPyTKJYDy8nLy+TyBQICKiopeFUzQOdN3w4YN7/qYUaNG4TgO6XRapbcMGa5psuFjH8PY/ec9FOr8fzpN/Oqrsdev9y+ciIiIiPQ7FUwiItKvKioquOaaa/jNb37jdxQRgG5FZyKR8DGJyPCR+8hHKE6ciBuLde65tHt5LCB8331E3mWG62BUXl5OJpPB8zxKS0v7/XrV1dWUlJTQ1tZGOBymZI9lBUUGq79861uUrVgBwIaTT+4aNxIJYtdcg71xo1/RRERERGSAqGASEZE+t3vpPNu2cRyHhx56iN/+9rd+xxIRkX5SnDGDwlFH4cVi3cqlyD33EN61lOpQEo/HyWazmKZJdI+ZWP1hd7nU2tpKe3s7tbW1/Xq9ngoEApx77rlks1keeughv+MMepZl8fGPf5yysjLuvfdev+P0Oc8wWDt/PuMDgc6Pd/2fRILYVVdhb9niYzoRERERGSgqmKRPHH/88bzyyiu4rut3lBHni/NynDtn742na1yXAhAAPm0Wuh174OUg9zwTGpiAMiREIhEAYrHYfh+TTh9JJjO129hRR90EfG7XOdYTjb4BwNy5c/d6vmVZOI7TR4lFRGQwyJ55JsWpU/FiMbyysreXxwIid91F+JFHfEzXc4FAgGw2S7FYJBgM9um5lyxZwtatW7Esi+bmZn73u99RX1/PbbfdxtatWwkEAuRyOXbu3MmLL76IYRhcf/31nHrqqXieRzab5ctf/jJbt27t01z7s7tYuvjii1m3bh033XTTgFx3qNpdLF1yySU0Nzdz4403+h2pzyX/8z+xly8HYPt739s1brS2El+4EGv7dr+iiYiIiMgAU8EkvRaPx1myZAl33HEHP/7xj/2OM+KcOyfP3ElF0rnu40HAAwxgFG8Xf7EwTBntqGCSQ9befiqOEwf2vUm541R0FUz7Pu6Qy+X2e1xERIYeZ/J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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D1LaserSetup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}