RP-Dyn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is main.py\n",
      "\n",
      "B-field = 0.0\n",
      "   Theta = 0.0\n",
      "   Theta = 0.6981317007977318\n",
      "   Theta = 1.3962634015954636\n",
      "   Theta = 2.0943951023931953\n",
      "   Theta = 2.792526803190927\n",
      "   Theta = 3.490658503988659\n",
      "   Theta = 4.1887902047863905\n",
      "   Theta = 4.886921905584122\n",
      "   Theta = 5.585053606381854\n",
      "   Theta = 6.283185307179586\n",
      "B-field = 0.00011111111111111112\n",
      "   Theta = 0.0\n",
      "   Theta = 0.6981317007977318\n",
      "   Theta = 1.3962634015954636\n",
      "   Theta = 2.0943951023931953\n",
      "   Theta = 2.792526803190927\n",
      "   Theta = 3.490658503988659\n",
      "   Theta = 4.1887902047863905\n",
      "   Theta = 4.886921905584122\n",
      "   Theta = 5.585053606381854\n",
      "   Theta = 6.283185307179586\n",
      "B-field = 0.00022222222222222223\n",
      "   Theta = 0.0\n",
      "   Theta = 0.6981317007977318\n",
      "   Theta = 1.3962634015954636\n",
      "   Theta = 2.0943951023931953\n",
      "   Theta = 2.792526803190927\n",
      "   Theta = 3.490658503988659\n",
      "   Theta = 4.1887902047863905\n",
      "   Theta = 4.886921905584122\n",
      "   Theta = 5.585053606381854\n",
      "   Theta = 6.283185307179586\n",
      "B-field = 0.0003333333333333334\n",
      "   Theta = 0.0\n",
      "   Theta = 0.6981317007977318\n",
      "excepted\n",
      "   Theta = 1.3962634015954636\n",
      "excepted\n",
      "   Theta = 2.0943951023931953\n",
      "excepted\n",
      "   Theta = 2.792526803190927\n",
      "   Theta = 3.490658503988659\n",
      "   Theta = 4.1887902047863905\n",
      "excepted\n",
      "   Theta = 4.886921905584122\n",
      "excepted\n",
      "   Theta = 5.585053606381854\n",
      "excepted\n",
      "   Theta = 6.283185307179586\n",
      "B-field = 0.00044444444444444447\n",
      "   Theta = 0.0\n",
      "   Theta = 0.6981317007977318\n",
      "excepted\n",
      "   Theta = 1.3962634015954636\n",
      "excepted\n",
      "   Theta = 2.0943951023931953\n",
      "excepted\n",
      "   Theta = 2.792526803190927\n",
      "excepted\n",
      "   Theta = 3.490658503988659\n",
      "excepted\n",
      "   Theta = 4.1887902047863905\n",
      "excepted\n",
      "   Theta = 4.886921905584122\n",
      "excepted\n",
      "   Theta = 5.585053606381854\n",
      "excepted\n",
      "   Theta = 6.283185307179586\n",
      "B-field = 0.0005555555555555556\n",
      "   Theta = 0.0\n",
      "excepted\n",
      "   Theta = 0.6981317007977318\n",
      "excepted\n",
      "   Theta = 1.3962634015954636\n",
      "excepted\n",
      "   Theta = 2.0943951023931953\n",
      "excepted\n",
      "   Theta = 2.792526803190927\n",
      "excepted\n",
      "   Theta = 3.490658503988659\n",
      "excepted\n",
      "   Theta = 4.1887902047863905\n",
      "excepted\n",
      "   Theta = 4.886921905584122\n",
      "excepted\n",
      "   Theta = 5.585053606381854\n",
      "excepted\n",
      "   Theta = 6.283185307179586\n",
      "excepted\n",
      "B-field = 0.0006666666666666668\n",
      "   Theta = 0.0\n",
      "   Theta = 0.6981317007977318\n",
      "excepted\n",
      "   Theta = 1.3962634015954636\n",
      "excepted\n",
      "   Theta = 2.0943951023931953\n",
      "excepted\n",
      "   Theta = 2.792526803190927\n",
      "excepted\n",
      "   Theta = 3.490658503988659\n",
      "excepted\n",
      "   Theta = 4.1887902047863905\n",
      "excepted\n",
      "   Theta = 4.886921905584122\n",
      "excepted\n",
      "   Theta = 5.585053606381854\n",
      "excepted\n",
      "   Theta = 6.283185307179586\n",
      "B-field = 0.0007777777777777778\n",
      "   Theta = 0.0\n",
      "excepted\n",
      "   Theta = 0.6981317007977318\n",
      "excepted\n",
      "   Theta = 1.3962634015954636\n",
      "excepted\n",
      "   Theta = 2.0943951023931953\n",
      "excepted\n",
      "   Theta = 2.792526803190927\n",
      "excepted\n",
      "   Theta = 3.490658503988659\n",
      "excepted\n",
      "   Theta = 4.1887902047863905\n",
      "excepted\n",
      "   Theta = 4.886921905584122\n",
      "excepted\n",
      "   Theta = 5.585053606381854\n",
      "excepted\n",
      "   Theta = 6.283185307179586\n",
      "excepted\n",
      "B-field = 0.0008888888888888889\n",
      "   Theta = 0.0\n",
      "excepted\n",
      "   Theta = 0.6981317007977318\n",
      "excepted\n",
      "   Theta = 1.3962634015954636\n",
      "excepted\n",
      "   Theta = 2.0943951023931953\n",
      "excepted\n",
      "   Theta = 2.792526803190927\n",
      "excepted\n",
      "   Theta = 3.490658503988659\n",
      "excepted\n",
      "   Theta = 4.1887902047863905\n",
      "excepted\n",
      "   Theta = 4.886921905584122\n",
      "excepted\n",
      "   Theta = 5.585053606381854\n",
      "excepted\n",
      "   Theta = 6.283185307179586\n",
      "excepted\n",
      "B-field = 0.001\n",
      "   Theta = 0.0\n",
      "excepted\n",
      "   Theta = 0.6981317007977318\n",
      "excepted\n",
      "   Theta = 1.3962634015954636\n",
      "excepted\n",
      "   Theta = 2.0943951023931953\n",
      "excepted\n",
      "   Theta = 2.792526803190927\n",
      "excepted\n",
      "   Theta = 3.490658503988659\n",
      "excepted\n",
      "   Theta = 4.1887902047863905\n",
      "excepted\n",
      "   Theta = 4.886921905584122\n",
      "excepted\n",
      "   Theta = 5.585053606381854\n",
      "excepted\n",
      "   Theta = 6.283185307179586\n",
      "excepted\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "from spin_sim_src import *\n",
    "import pickle as pickle\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    print(\"This is main.py\\n\")\n",
    "\n",
    "    # run: python <filename>.py <unique_number> <tfinal> <timesteps> <B0_start> <B0_stop> <B_steps>\n",
    "    # <theta_start> <theta_stop> <theta_steps>\n",
    "    #\n",
    "    # theta is in units of pi\n",
    "    #\n",
    "    # for example: python cluster_do_colorplot.py 001 0.5e-3 500 0 1000e-6 100 0 1 100\n",
    "\n",
    "    # transform command line arguments into the parameters  8 1.0e-3 100 0.0 1000.e-6 10 0.0 2.0 10\n",
    "\n",
    "    unique_number = 8.\n",
    "    tfinal = 1.0e-3\n",
    "    timesteps = 100\n",
    "\n",
    "    B_start = 0.0\n",
    "    B_stop = 1000.0e-6\n",
    "    B_steps = 10\n",
    "\n",
    "    theta_start = 0.0 * np.pi\n",
    "    theta_stop = 2.0 * np.pi\n",
    "    theta_steps = 10\n",
    "\n",
    "    # B0 = 47e-6 #T, B field of Earth at Frankfurt\n",
    "    # theta = np.pi/4.0 #inclination of B field wrt to axis of RP, [0,pi]\n",
    "\n",
    "    phi = 0. * np.pi / 1.0  # azimuth of B field, , [0,2*pi]\n",
    "\n",
    "    mu0h = 9.274e-24 / 6.6e-34  # mu0 / h, 1/(T*s)\n",
    "    a = [0., 0., 0.]\n",
    "    a[0] = mu0h * 0.345e-4  # 0.345G given by PRE ; ax is now in Hz\n",
    "    a[1] = mu0h * 0.345e-4  # 0.345G given by PRE ; ay is now in Hz\n",
    "    a[2] = mu0h * 9e-4  # 9G given by PRE ; az is now in Hz\n",
    "    # \"Biologically feasible regime of HF strengths, 0.1 to 10G, see ref 26 in PRE\"\n",
    "\n",
    "    k = [0., 0., 0., 0.]\n",
    "    k[0] = 2e4  # Hz, rate of decay into |S>\n",
    "    k[1] = 2e4  # Hz, rate of decay into |T->\n",
    "    k[2] = 2e4  # Hz, rate of decay into |T0>\n",
    "    k[3] = 2e4  # Hz, rate of decay into |T+>\n",
    "\n",
    "    # time array to simulate\n",
    "    tinit = 0.0\n",
    "    # tfinal = 1.0e-3\n",
    "    # notimepoints = 1000.0\n",
    "\n",
    "    times = np.linspace(tinit, tfinal, timesteps)  # in seconds\n",
    "    timedetail = (tfinal - tinit) / timesteps\n",
    "    # time zero is creation of RP\n",
    "\n",
    "    num_states = 12\n",
    "\n",
    "    # preparing the initial density matrices for various scenarios\n",
    "    rho0_vectors, rho0_description = prep_ini_state()\n",
    "\n",
    "    # adding dimensions to rho0 to account for the shelving states\n",
    "    rho0 = rho_add_dim(total_num_states=num_states, rho0_vector=rho0_vectors[1])\n",
    "\n",
    "    # coupling to bath or decay operators\n",
    "    c_op_list = []\n",
    "\n",
    "    P1 = 1.0 / np.sqrt(2.0) * basis(num_states, 8) * (basis(num_states, 2).dag() - basis(num_states, 4).dag())\n",
    "    P2 = 1.0 / np.sqrt(2.0) * basis(num_states, 8) * (basis(num_states, 3).dag() - basis(num_states, 5).dag())\n",
    "    P3 = 1.0 / np.sqrt(2.0) * basis(num_states, 10) * (basis(num_states, 2).dag() + basis(num_states, 4).dag())\n",
    "    P4 = 1.0 / np.sqrt(2.0) * basis(num_states, 10) * (basis(num_states, 3).dag() + basis(num_states, 5).dag())\n",
    "    P5 = basis(num_states, 11) * basis(num_states, 0).dag()\n",
    "    P6 = basis(num_states, 11) * basis(num_states, 1).dag()\n",
    "    P7 = basis(num_states, 9) * basis(num_states, 6).dag()\n",
    "    P8 = basis(num_states, 9) * basis(num_states, 7).dag()\n",
    "\n",
    "    c_op_list.append(np.sqrt(k[0]) * P1)\n",
    "    c_op_list.append(np.sqrt(k[0]) * P2)\n",
    "    c_op_list.append(np.sqrt(k[1]) * P3)\n",
    "    c_op_list.append(np.sqrt(k[1]) * P4)\n",
    "    c_op_list.append(np.sqrt(k[2]) * P5)\n",
    "    c_op_list.append(np.sqrt(k[2]) * P6)\n",
    "    c_op_list.append(np.sqrt(k[3]) * P7)\n",
    "    c_op_list.append(np.sqrt(k[3]) * P8)\n",
    "\n",
    "    ex_op_list = []\n",
    "\n",
    "    # define the arrays to calculate the B-field and the theta angles\n",
    "    do_log_bfield_scan = False\n",
    "    if do_log_bfield_scan:\n",
    "        B0_arr = np.logspace(np.log10(B_start), np.log10(B_stop), B_steps)  # in T, 100 is usual\n",
    "    else:\n",
    "        B0_arr = np.linspace(B_start, B_stop, B_steps)  # in T, 100 is usual\n",
    "\n",
    "    theta_arr = np.linspace(theta_start, theta_stop, theta_steps)  # rad, 100 is usual\n",
    "\n",
    "    result_S = []\n",
    "    result_S_formula = []\n",
    "    # pops_tot = np.zeros([len(B0_arr), len(theta_arr), len(times), number_of_states])\n",
    "    pops_tot = np.zeros([len(B0_arr), len(theta_arr), num_states])\n",
    "\n",
    "    l0 = 0\n",
    "\n",
    "    for B0 in B0_arr:\n",
    "\n",
    "        l1 = 0\n",
    "\n",
    "        print(\"B-field = \" + str(B0))\n",
    "\n",
    "        for theta in theta_arr:\n",
    "            print(\"   Theta = \" + str(theta))\n",
    "\n",
    "            # building the Hamiltonian\n",
    "            h = build_Hamiltonian(a, B0, theta, phi)\n",
    "\n",
    "            # adding the shelving states as additional dimensions to the Hamiltonian\n",
    "            H = hamiltonian_add_dim(total_num_states=num_states, hamiltonian=h)\n",
    "\n",
    "            SYend, SYform, pops = time_evol(H, rho0, num_states, times, timesteps, timedetail, k, c_op_list, ex_op_list)\n",
    "            result_S.append(SYend)\n",
    "            result_S_formula.append(SYform)\n",
    "            # pops_tot[l0,l1,:,:] = pops\n",
    "            pops_tot[l0, l1, :] = pops[-1, :]  # only save the last time point\n",
    "\n",
    "            l1 += 1\n",
    "\n",
    "        l0 += 1\n",
    "\n",
    "    pickle.dump([result_S, result_S_formula, pops_tot, B0_arr, theta_arr, phi, a[0], a[1], a[2], k[0], k[1], tfinal,\n",
    "                 timedetail, rho0], open(\"Data_\" + rho0_description[1] + \".p\", \"wb\"))\n",
    "\n",
    "    print(\"Done.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "NameError",
     "evalue": "name 'ax' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-4549b7b5cf6d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[0mcbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabelsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfsizenb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'$\\rho_0$ = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mrho0_description\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m', $\\phi$ = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.1f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mphi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' rad, '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mr'tf = '\u001b[0m \u001b[0;34m+\u001b[0m  \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.2f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtfinal\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1e3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' ms, detail = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.2f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimedetail\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mr' $\\mu$s,'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n ax = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.3f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mmu0h\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1e4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' G , ay = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.3f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0may\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mmu0h\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1e4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' G, az = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.3f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maz\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mmu0h\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1e4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' G, '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mr'k$_s$ = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.1f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mks\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1000.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' kHz, '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'k$_t$ = '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{0:.1f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkt\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m1000.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' kHz'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfsizetit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'External static field B$_0$ ($\\mu$T)'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfsizepl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'Inclination $\\theta$ ($\\pi$)'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfsizepl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ax' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import sys\n",
    "#sys.path.append(\"/usr/bin\") # necessary for the tex fonts\n",
    "#sys.path.append(\"../Python modules/\") # necessary for the tex fonts\n",
    "\n",
    "fsizetit = 24\n",
    "fsizepl = 20\n",
    "sizex = 8\n",
    "sizey = 6\n",
    "fsizenb = 16\n",
    "dpi_no = 80\n",
    "lw = 2\n",
    "\n",
    "mu0h = 9.274e-24/6.6e-34 #mu0 / h, 1/(T*s)\n",
    "phi = 0.0\n",
    "###############################################################################\n",
    "\n",
    "data = pickle.load(open( \"Data_S, nuc. dep..p\", \"rb\" ))\n",
    "rho0_description = 'S, nuc. dep.'\n",
    "#data = pickle.load( open( \"Data_Elecs. dep., nuc. dep._complete.p\", \"rb\" ) ,encoding='latin1')\n",
    "#data = pickle.load( open( \"Data_Tzero, nuc. dep._complete.p\", \"rb\" ) ,encoding='latin1')\n",
    "#data = pickle.load( open( \"Data_Tminus, nuc. dep._complete.p\", \"rb\" ) ,encoding='latin1')\n",
    "#data = pickle.load( open( \"Data_Tplus, nuc. dep._complete.p\", \"rb\" ) ,encoding='latin1')\n",
    "\n",
    "#for k in [8,9,10,11]:\n",
    "#    plt.figure()\n",
    "#    plt.imshow(d[2][:, :, k])\n",
    "#    plt.colorbar()\n",
    "#    plt.title('State: ' + str(k))\n",
    "#\n",
    "#plt.show()\n",
    "\n",
    "\n",
    "result_S = 100*np.array(data[0]).reshape(len(data[3]),len(data[4]))\n",
    "result_S_formula = 100*np.array(data[1]).reshape(len(data[3]),len(data[4]))\n",
    "\n",
    "#fig 1 \n",
    "plt.figure(figsize=(sizex, sizey), dpi=dpi_no)\n",
    "plt.rc('text', usetex=False)\n",
    "plt.rc('font', family='serif')\n",
    "plt.rc('font', serif='Palatino')  \n",
    "plt.pcolor(data[4]/np.pi,data[3]*1e6,result_S,vmin=0, vmax=100) \n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.tick_params(labelsize=fsizepl)\n",
    "plt.title(r'$\\rho_0$ = ' + rho0_description + ', $\\phi$ = ' + str(\"{0:.1f}\".format(data[5])) + ' rad, ' + r'tf = ' +  str(\"{0:.2f}\".format(data[11]*1e3)) + ' ms, detail = ' + str(\"{0:.2f}\".format(data[12]*1e6)) + r' $\\mu$s,' + '\\n ax = ' + str(\"{0:.3f}\".format(data[6]/mu0h*1e4)) + ' G , ay = ' + str(\"{0:.3f}\".format(data[7]/mu0h*1e4)) + ' G, az = ' + str(\"{0:.3f}\".format(data[8]/mu0h*1e4)) + ' G, ' + r'k$_s$ = ' + str(\"{0:.1f}\".format(data[9]/1000.0)) + ' kHz, ' + 'k$_t$ = ' + str(\"{0:.1f}\".format(data[10]/1000.0)) + ' kHz', fontsize = fsizetit)\n",
    "plt.ylabel(r'External static field B$_0$ ($\\mu$T)',fontsize = fsizetit)\n",
    "plt.xlabel(r'Inclination $\\theta$ ($\\pi$)',fontsize = fsizetit)\n",
    "cbar.ax.set_ylabel(r'Singlet yield (\\%)',fontsize = fsizetit)\n",
    "cbar.set_ticks([0, 25, 50, 75, 100])\n",
    "plt.xticks([0.5, 1, 1.5],fontsize=fsizepl)\n",
    "plt.yticks([47, 250, 500,750, 1000],fontsize=fsizepl)\n",
    "plt.axhline(y=47, xmin=0, xmax=2*np.pi, linewidth=3, color = 'k')\n",
    "plt.ylim([data[3][0]*1e6,data[3][-1]*1e6])\n",
    "plt.axvline(x=0.98,linewidth=3, color = 'k')\n",
    "plt.axvline(x=1.02,linewidth=3, color = 'k')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "#fig 2 - to check if it's like fig 1\n",
    "plt.figure(figsize=(sizex, sizey), dpi=dpi_no)\n",
    "#    plt.rc('text', usetex=True)\n",
    "#    plt.rc('font', family='serif')\n",
    "#    plt.rc('font', serif='Palatino')  \n",
    "plt.pcolor(data[4]/np.pi,data[3]*1e6,result_S_formula) #,cmap='Greys') #, vmin=z_min, vmax=z_max)\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.tick_params(labelsize=fsizenb)\n",
    "plt.title(r'$\\rho_0$ = ' + rho0_description + ', $\\phi$ = ' + str(\"{0:.1f}\".format(phi)) + ' rad, ' + r'tf = ' +  str(\"{0:.2f}\".format(tfinal*1e3)) + ' ms, detail = ' + str(\"{0:.2f}\".format(timedetail*1e6)) + r' $\\mu$s,' + '\\n ax = ' + str(\"{0:.3f}\".format(ax/mu0h*1e4)) + ' G , ay = ' + str(\"{0:.3f}\".format(ay/mu0h*1e4)) + ' G, az = ' + str(\"{0:.3f}\".format(az/mu0h*1e4)) + ' G, ' + r'k$_s$ = ' + str(\"{0:.1f}\".format(ks/1000.0)) + ' kHz, ' + 'k$_t$ = ' + str(\"{0:.1f}\".format(kt/1000.0)) + ' kHz', fontsize = fsizetit)\n",
    "plt.ylabel(r'External static field B$_0$ ($\\mu$T)',fontsize = fsizepl)\n",
    "plt.xlabel(r'Inclination $\\theta$ ($\\pi$)',fontsize = fsizepl)\n",
    "cbar.ax.set_ylabel(r'Singlet yield (\\%)',fontsize = fsizepl)\n",
    "plt.xticks(fontsize=fsizenb)\n",
    "plt.yticks(fontsize=fsizenb)\n",
    "\n",
    "\n",
    "#fig 3\n",
    "plt.figure(figsize=(sizex, sizey), dpi=dpi_no)\n",
    "#    plt.rc('text', usetex=True)\n",
    "#    plt.rc('font', family='serif')\n",
    "#    plt.rc('font', serif='Palatino')  \n",
    "plt.plot(data[3]*1e6, np.nanmax(result_S, axis = 1)-np.nanmin(result_S, axis = 1)) #Sensitivity vs B0\n",
    "plt.legend()\n",
    "plt.xlabel(r'External static field B$_0$ ($\\mu$T)',fontsize = fsizepl)\n",
    "plt.ylabel(r'Sensitivity (\\%)',fontsize = fsizepl)\n",
    "plt.ylim([0,25])\n",
    "plt.xlim([0,1000])\n",
    "plt.xticks(fontsize=fsizenb)\n",
    "plt.yticks(fontsize=fsizenb)\n",
    "plt.title(r'$\\rho_0$ = ' + rho0_description + ', $\\phi$ = ' + str(\"{0:.1f}\".format(phi)) + ' rad, ' + r'tf = ' +  str(\"{0:.2f}\".format(tfinal*1e3)) + ' ms, detail = ' + str(\"{0:.1f}\".format(timedetail*1e6)) + r' $\\mu$s,' + '\\n ax = ' + str(\"{0:.3f}\".format(ax/mu0h*1e4)) + ' G , ay = ' + str(\"{0:.3f}\".format(ay/mu0h*1e4)) + ' G, az = ' + str(\"{0:.3f}\".format(az/mu0h*1e4)) + ' G, ' + r'k$_s$ = ' + str(\"{0:.1f}\".format(ks/1000.0)) + ' kHz, ' + 'k$_t$ = ' + str(\"{0:.1f}\".format(kt/1000.0)) + ' kHz', fontsize = fsizetit)\n",
    "\n",
    "\n",
    "#    plt.figure(figsize=(sizex, sizey), dpi=dpi_no)\n",
    "#    plt.rc('text', usetex=True)\n",
    "#    plt.rc('font', family='serif')\n",
    "#    plt.rc('font', serif='Palatino')  \n",
    "#    my_labels = ['uuu','uud','udu','udd','duu','dud','ddu','ddd','S recombined','T$_+$ recombined','T$_0$ recombined','T$_-$ recombined']\n",
    "#    for k in range(number_of_states):\n",
    "#        if k > 7:\n",
    "#            my_ls = '--'\n",
    "#            my_label = my_labels[k]\n",
    "#        else:\n",
    "#            my_label = my_labels[k]\n",
    "#            \n",
    "#            my_label = my_label.replace('u',r'$\\uparrow$')\n",
    "#            my_label = my_label.replace('d',r'$\\downarrow$')\n",
    "#    \n",
    "#            my_ls = '-'\n",
    "#    \n",
    "#        plt.plot(times*(1e6), pops_tot[:, k], label = my_label, ls = my_ls)\n",
    "#    plt.legend()\n",
    "#    plt.xlabel('Time after RP creation [$\\mu$s]')\n",
    "#    plt.ylabel('Populations')\n",
    "#    plt.ylim([0,0.52])\n",
    "\n",
    "multipage(rho0_description[k] + '.pdf')\n",
    "plt.close(\"all\")\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.37521745780854776, 0.4970480518506571, 0.40149895099525157, 0.27709466540892674, 0.32403716461857146, 0.4700376700819084, 0.4700376700819047, 0.3240371646183392, 0.27709466543872835, 0.4014989506593515, 0.4970479197493577, 0.5106214882125254, 0.4038938999791631, 0.28539521204721424, 0.32688992974786885, 0.4722972008212088, 0.4722972008212075, 0.3268899287789506, 0.2853952201403408, 0.40389389997932673, 0.5106213412963473, 0.49967507511740467, nan, nan, nan, 0.4727074406766203, 0.4727074406766102, nan, nan, nan, 0.49967507505170206, 0.49981354647082127, nan, nan, nan, nan, nan, nan, nan, nan, 0.49981354651118176, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.4999165329445018, nan, nan, nan, nan, nan, nan, nan, nan, 0.49991656619795993, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], [0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.36782191635139116, 0.489456631662846, 0.3946017898211904, 0.2728540232717541, 0.3191799904399761, 0.4625409308895069, 0.46254093088951365, 0.3191799904399788, 0.2728540232741105, 0.3946017898165577, 0.48945663166096165, 0.5289480702461624, 0.3965073528751224, 0.2812364372824934, 0.32123912887770595, 0.46367217524305543, 0.46367217524306564, 0.32123912887765127, 0.28123643728290226, 0.3965073530151691, 0.5289480701750721, 0.49088772779677325, nan, nan, nan, 0.4645091016179561, 0.46450910161792713, nan, nan, nan, 0.4908877277882736, 0.4912779037409156, nan, nan, nan, nan, nan, nan, nan, nan, 0.4912779032736602, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.49149104560731216, nan, nan, nan, nan, nan, nan, nan, nan, 0.4914910455747628, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan], array([[[ 0.00000000e+00,  1.12414151e-10,  5.15288406e-10, ...,\n",
      "          1.24787603e-01,  3.75207333e-01,  1.24787603e-01],\n",
      "        [ 0.00000000e+00,  1.12414151e-10,  5.15288406e-10, ...,\n",
      "          1.24787603e-01,  3.75207333e-01,  1.24787603e-01],\n",
      "        [ 0.00000000e+00,  1.12414151e-10,  5.15288406e-10, ...,\n",
      "          1.24787603e-01,  3.75207333e-01,  1.24787603e-01],\n",
      "        ...,\n",
      "        [ 0.00000000e+00,  1.12414151e-10,  5.15288406e-10, ...,\n",
      "          1.24787603e-01,  3.75207333e-01,  1.24787603e-01],\n",
      "        [ 0.00000000e+00,  1.12414151e-10,  5.15288406e-10, ...,\n",
      "          1.24787603e-01,  3.75207333e-01,  1.24787603e-01],\n",
      "        [ 0.00000000e+00,  1.12414151e-10,  5.15288406e-10, ...,\n",
      "          1.24787603e-01,  3.75207333e-01,  1.24787603e-01]],\n",
      "\n",
      "       [[ 0.00000000e+00,  1.89785843e-12,  5.15288406e-10, ...,\n",
      "          2.96296924e-03,  4.97026008e-01,  2.96296924e-03],\n",
      "        [-2.41559016e-06,  2.42517126e-06,  2.41610545e-06, ...,\n",
      "          1.02549864e-01,  3.93401320e-01,  1.02549864e-01],\n",
      "        [-7.03433740e-09,  7.47446530e-09,  7.54962581e-09, ...,\n",
      "          2.39132658e-01,  2.44640017e-01,  2.39132658e-01],\n",
      "        ...,\n",
      "        [-8.75629594e-09,  9.22194168e-09,  9.27158435e-09, ...,\n",
      "          2.39132658e-01,  2.44640017e-01,  2.39132658e-01],\n",
      "        [ 1.56473967e-06, -1.57060129e-06, -1.56422438e-06, ...,\n",
      "          1.02549864e-01,  3.93401320e-01,  1.02549864e-01],\n",
      "        [ 2.08298704e-40,  1.76249279e-12,  5.15288406e-10, ...,\n",
      "          2.96296924e-03,  4.97026140e-01,  2.96296924e-03]],\n",
      "\n",
      "       [[ 0.00000000e+00,  2.57377753e-12,  5.15288406e-10, ...,\n",
      "          7.54172672e-04,  4.87870164e-01,  7.54172672e-04],\n",
      "        [-4.26124581e-09,  4.54399649e-09,  4.77653421e-09, ...,\n",
      "          1.00277745e-01,  3.95550607e-01,  1.00277745e-01],\n",
      "        [-8.04053285e-09,  8.58172346e-09,  8.55582073e-09, ...,\n",
      "          2.35835804e-01,  2.42933177e-01,  2.35835804e-01],\n",
      "        ...,\n",
      "        [ 4.53484861e-06, -4.53815288e-06, -4.53433332e-06, ...,\n",
      "          2.35835805e-01,  2.42933169e-01,  2.35835805e-01],\n",
      "        [-1.14036247e-08,  1.17002861e-08,  1.19189131e-08, ...,\n",
      "          1.00277745e-01,  3.95550607e-01,  1.00277745e-01],\n",
      "        [-3.80218889e-40,  2.02024532e-12,  5.15288406e-10, ...,\n",
      "          7.54172672e-04,  4.87870311e-01,  7.54172672e-04]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        ...,\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan]],\n",
      "\n",
      "       [[            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        ...,\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan]],\n",
      "\n",
      "       [[            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        ...,\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan],\n",
      "        [            nan,             nan,             nan, ...,\n",
      "                     nan,             nan,             nan]]]), array([0.        , 0.00011111, 0.00022222, 0.00033333, 0.00044444,\n",
      "       0.00055556, 0.00066667, 0.00077778, 0.00088889, 0.001     ]), array([0.        , 0.6981317 , 1.3962634 , 2.0943951 , 2.7925268 ,\n",
      "       3.4906585 , 4.1887902 , 4.88692191, 5.58505361, 6.28318531]), 0.0, 484777.27272727276, 484777.27272727276, 12646363.636363639, 20000.0, 20000.0, 0.001, 1e-05, Quantum object: dims = [[12], [12]], shape = (12, 12), type = oper, isherm = True\n",
      "Qobj data =\n",
      "[[ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.25  0.   -0.25  0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.25  0.   -0.25  0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.   -0.25  0.    0.25  0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.   -0.25  0.    0.25  0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]\n",
      " [ 0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.    0.  ]]]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum object: dims = [[12], [12]], shape = (12, 12), type = oper, isherm = False\n",
      "Qobj data =\n",
      "[[ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.70710678  0.         -0.70710678  0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]\n",
      " [ 0.          0.          0.          0.          0.          0.\n",
      "   0.          0.          0.          0.          0.          0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from spin_sim_src import *\n",
    "\n",
    "num_states = 12\n",
    "\n",
    "P1 = 1.0 / np.sqrt(2.0) * basis(num_states, 8) * (basis(num_states, 2).dag() - basis(num_states, 4).dag())\n",
    "\n",
    "print(P1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\\begin{equation*}\\left(\\begin{array}{*{11}c}1.0\\\\0.0\\\\\\end{array}\\right)\\end{equation*}"
      ],
      "text/plain": [
       "Quantum object: dims = [[2], [1]], shape = (2, 1), type = ket\n",
       "Qobj data =\n",
       "[[1.]\n",
       " [0.]]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basis(2,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B values 50\n",
      "Geomagnetic field =  4.7e-05\n",
      "ks =  20000\n",
      "kt =  20000\n",
      "dt =  1.0003334444814938e-07\n",
      "B0 =  1e-08 Done!\n",
      "Sensitivity =  0.0009021498777093129\n",
      "B0 =  1.2648552168552958e-08 Done!\n",
      "Sensitivity =  0.0014382303655455786\n",
      "B0 =  1.5998587196060583e-08 Done!\n",
      "Sensitivity =  0.002288043627136749\n",
      "B0 =  2.0235896477251575e-08 Done!\n",
      "Sensitivity =  0.003627821007178622\n",
      "B0 =  2.5595479226995356e-08 Done!\n",
      "Sensitivity =  0.005721650589656069\n",
      "B0 =  3.2374575428176436e-08 Done!\n",
      "Sensitivity =  0.008948688773996183\n",
      "B0 =  4.0949150623804256e-08 Done!\n",
      "Sensitivity =  0.013813782593987523\n",
      "B0 =  5.1794746792312124e-08 Done!\n",
      "Sensitivity =  0.02089862814917709\n",
      "B0 =  6.551285568595509e-08 Done!\n",
      "Sensitivity =  0.03067760101131034\n",
      "B0 =  8.286427728546843e-08 Done!\n",
      "Sensitivity =  0.04313644880681111\n",
      "B0 =  1.0481131341546857e-07 Done!\n",
      "Sensitivity =  0.057355337460004596\n",
      "B0 =  1.3257113655901095e-07 Done!\n",
      "Sensitivity =  0.0717280565391995\n",
      "B0 =  1.6768329368110082e-07 Done!\n",
      "Sensitivity =  0.08518173300929688\n",
      "B0 =  2.1209508879201903e-07 Done!\n",
      "Sensitivity =  0.0969525222176259\n",
      "B0 =  2.682695795279726e-07 Done!\n",
      "Sensitivity =  0.10557921339686416\n",
      "B0 =  3.3932217718953295e-07 Done!\n",
      "Sensitivity =  0.11221739743167197\n",
      "B0 =  4.2919342601287785e-07 Done!\n",
      "Sensitivity =  0.1166048406267432\n",
      "B0 =  5.428675439323858e-07 Done!\n",
      "Sensitivity =  0.11957406010246441\n",
      "B0 =  6.866488450043002e-07 Done!\n",
      "Sensitivity =  0.12160189947542344\n",
      "B0 =  8.685113737513528e-07 Done!\n",
      "Sensitivity =  0.12285778748872933\n",
      "B0 =  1.0985411419875583e-06 Done!\n",
      "Sensitivity =  0.12383195498181032\n",
      "B0 =  1.3894954943731374e-06 Done!\n",
      "Sensitivity =  0.12467159109991499\n",
      "B0 =  1.757510624854793e-06 Done!\n",
      "Sensitivity =  0.12544943824467092\n",
      "B0 =  2.2229964825261954e-06 Done!\n",
      "Sensitivity =  0.12638635870370876\n",
      "B0 =  2.8117686979742304e-06 Done!\n",
      "Sensitivity =  0.12767356863588203\n",
      "B0 =  3.5564803062231285e-06 Done!\n",
      "Sensitivity =  0.1295769829882926\n",
      "B0 =  4.498432668969443e-06 Done!\n",
      "Sensitivity =  0.13243312300664412\n",
      "B0 =  5.689866029018299e-06 Done!\n",
      "Sensitivity =  0.13668327372960215\n",
      "B0 =  7.1968567300115205e-06 Done!\n",
      "Sensitivity =  0.1428396294963107\n",
      "B0 =  9.102981779915217e-06 Done!\n",
      "Sensitivity =  0.15135033704827786\n",
      "B0 =  1.1513953993264481e-05 Done!\n",
      "Sensitivity =  0.16235577595413492\n",
      "B0 =  1.4563484775012443e-05 Done!\n",
      "Sensitivity =  0.17533249636129278\n",
      "B0 =  1.8420699693267162e-05 Done!\n",
      "Sensitivity =  0.1888016045329376\n",
      "B0 =  2.3299518105153718e-05 Done!\n",
      "Sensitivity =  0.2024669070522595\n",
      "B0 =  2.94705170255181e-05 Done!\n",
      "Sensitivity =  0.2132603127913837\n",
      "B0 =  3.7275937203149415e-05 Done!\n",
      "Sensitivity =  0.22064098149958322\n",
      "B0 =  4.714866363457394e-05 Done!\n",
      "Sensitivity =  0.2240889835855694\n",
      "B0 =  5.9636233165946426e-05 Done!\n",
      "Sensitivity =  0.2232950238183133\n",
      "B0 =  7.543120063354622e-05 Done!\n",
      "Sensitivity =  0.21788208067080445\n",
      "B0 =  9.540954763499944e-05 Done!\n",
      "Sensitivity =  0.20730069064876205\n",
      "B0 =  0.00012067926406393288 Done!\n",
      "Sensitivity =  0.19105416727189334\n",
      "B0 =  0.00015264179671752333 Done!\n",
      "Sensitivity =  0.16916977873313882\n",
      "B0 =  0.00019306977288832496 Done!\n",
      "Sensitivity =  0.1427454804768309\n",
      "B0 =  0.000244205309454865 Done!\n",
      "Sensitivity =  0.11394467034441469\n",
      "B0 =  0.0003088843596477485 Done!\n",
      "Sensitivity =  0.08603683248626298\n",
      "B0 =  0.0003906939937054621 Done!\n",
      "Sensitivity =  0.061863673199174796\n",
      "B0 =  0.0004941713361323838 Done!\n",
      "Sensitivity =  0.04268277793011582\n",
      "B0 =  0.0006250551925273976 Done!\n",
      "Sensitivity =  0.02853089527735958\n",
      "B0 =  0.0007906043210907702 Done!\n",
      "Sensitivity =  0.01863624319224183\n",
      "B0 =  0.001 Done!\n",
      "Sensitivity =  0.011984510729186326\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Dynamics of radical pair subjected to Geomagnetic field\n",
    "#\n",
    "from qutip import *\n",
    "from scipy import *\n",
    "from pylab import *\n",
    "import math\n",
    "import csv\n",
    "\n",
    "\n",
    "# problem parameters:\n",
    "pi=math.pi\n",
    "muB = 5.788*10**-5  # Bohr Magneton in eV/Tesla\n",
    "g=2 # g-factor for electron\n",
    "gama=muB*g # in eV/Tesla\n",
    "gama=gama*0.1519756*10**16 # in sec-1/Tesla (Converted using natural units, by taking hbar=1)\n",
    "#B0=47*10**-6 # Geomagnetic field in Frankfurt (Tesla)\n",
    "#B0=46*10**-6 # Geomagnetic field in Frankfurt (Tesla)\n",
    "Brf=150*10**-9 # Disturbing RF field of strength 150 nanoTesla\n",
    "w = 2*pi*1.316*10**6      # Frequency of externally applied RF field\n",
    "sqrt2=math.sqrt(2)\n",
    "\n",
    "# Initial State\n",
    "up=basis(2,0)\n",
    "down=basis(2,1)\n",
    "singlet=(tensor(up,down)-tensor(down,up))/sqrt2 # Initial State of the radical pair\n",
    "trip0=(tensor(up,down)+tensor(down,up))/sqrt2 # Triplet state with zero spin\n",
    "tripu= tensor(up,up) # Tirplet state with spin +1\n",
    "tripd=tensor(down,down) # Triplet state with spin -1\n",
    "\n",
    "# Initial State\n",
    "e_dm=singlet*singlet.dag()\n",
    "nuc_dm=0.5*qeye(2)\n",
    "rho=tensor(nuc_dm,e_dm)\n",
    "rho0=Qobj(pad(rho.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "\n",
    "# For Local Coherence\n",
    "elec_dm=singlet*singlet.dag()\n",
    "elec_array=elec_dm.full()\n",
    "elec_array[1][1]=0   # Making diagonal elements of density matrix to zero\n",
    "elec_array[2][2]=0\n",
    "elec_offd=Qobj(elec_array)\n",
    "nuc_dm=0.5*qeye(2)\n",
    "rho_d=tensor(nuc_dm,elec_offd)\n",
    "rho0_offd=Qobj(pad(rho_d.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "\n",
    "# Defining Hamiltonian operators\n",
    "S1x=tensor(qeye(2),sigmax(),qeye(2))\n",
    "S2x=tensor(qeye(2),qeye(2),sigmax())\n",
    "S1x_10=Qobj(pad(S1x.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0))) # pad() function increases the size of array of S1x from  \n",
    "#8x8 to 10x10 and put 0s at the newly created places\n",
    "S2x_10=Qobj(pad(S2x.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "Sx=S1x_10+S2x_10\n",
    "S1y=tensor(qeye(2),sigmay(),qeye(2))\n",
    "S2y=tensor(qeye(2),qeye(2),sigmay())\n",
    "S1y_10=Qobj(pad(S1y.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "S2y_10=Qobj(pad(S2y.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "Sy=S1y_10+S2y_10\n",
    "S1z=tensor(qeye(2),sigmaz(),qeye(2))\n",
    "S2z=tensor(qeye(2),qeye(2),sigmaz())\n",
    "S1z_10=Qobj(pad(S1z.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "S2z_10=Qobj(pad(S2z.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "Sz=S1z_10+S2z_10\n",
    "#ISx8=tensor(sigmax(),sigmax(),qeye(2))\n",
    "ISx8=tensor(sigmax(),qeye(2),sigmax())\n",
    "#ISy8=tensor(sigmay(),sigmay(),qeye(2))\n",
    "ISy8=tensor(sigmay(),qeye(2),sigmay())\n",
    "#ISz8=tensor(sigmaz(),sigmaz(),qeye(2))\n",
    "ISz8=tensor(sigmaz(),qeye(2),sigmaz())\n",
    "ISx=Qobj(pad(ISx8.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "ISy=Qobj(pad(ISy8.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "ISz=Qobj(pad(ISz8.full(), ((0, 2), (0, 2)), 'constant', constant_values=(0)))\n",
    "\n",
    "# Geomagnetic field angle (in radian)\n",
    "theta_list=linspace(0.0, pi/2, 25)\n",
    "#theta_list=linspace(pi/2, pi, 1)\n",
    "noa=len(theta_list)\n",
    "\n",
    "# Defining Zeeman Field Window\n",
    "#B0=[0,10**-6,20**-6,30**-6,40**-6,50**-6,60**-6,70**-6,80**-6,90**-6,100**-6]\n",
    "#B=logspace(log10(float(0.01)),log10(100),100)\n",
    "B=logspace(log10(float(0.01)),log10(1000),50)\n",
    "B0=[x*10**-6 for x in B]\n",
    "loB=len(B0)\n",
    "print('B values',loB)\n",
    "# Magnetic field made zero for calculating coherence\n",
    "#B0_coh=0\n",
    "\n",
    "\n",
    "# Hyperfine Coupling Constants (in eV)\n",
    "B_geo=47*10**-6\n",
    "print('Geomagnetic field = ', B_geo)\n",
    "#a=logspace(log10(10**-2),log10(10**2),200)\n",
    "#a=logspace(log10(5),log10(10**2),20)\n",
    "#a=10\n",
    "#ax=gama*B_geo*4\n",
    "#ay=gama*B_geo*3\n",
    "#az=0\n",
    "#a=logspace(log10(float(1.00)),log10(20),1)\n",
    "#az=[x*gama*B0 for x in a]\n",
    "#loa=len(az)\n",
    "#print('No of hyperfines = ', loa)\n",
    "\n",
    "#ax=0.100*5*10**-9\n",
    "#ay=0.100*5*10**-9\n",
    "#az=1.3*10**-8\n",
    "#ax=ax*0.1519756*10**16 # in per-sec\n",
    "#ay=ay*0.1519756*10**16 # in per-sec\n",
    "#az=az*0.1519756*10**16 # in per-sec\n",
    "ax=0.3455 # In Gauss\n",
    "ay=0.3455 # In Gauss\n",
    "az=9.0 # In Gauss\n",
    "\n",
    "ax=ax*10**-4 # in Tesla\n",
    "ay=ay*10**-4 # in Tesla\n",
    "az=az*10**-4 # in Tesla\n",
    "\n",
    "ax=ax*muB # in eV\n",
    "ay=ay*muB # in eV\n",
    "az=az*muB # in eV\n",
    "\n",
    "ax=ax*0.1519756*10**16 # in per-sec\n",
    "ay=ay*0.1519756*10**16 # in per-sec\n",
    "az=az*0.1519756*10**16 # in per-sec\n",
    "\n",
    "# Decay rates [These three rates are simulated]\n",
    "#k=5*10**4\n",
    "ks=2*10**4\n",
    "kt=2*10**4\n",
    "k=2*10**4\n",
    "#lok=len(k)\n",
    "print('ks = ', ks)\n",
    "print('kt = ', kt)\n",
    "\n",
    "# Time steps in Integration. It would take 6/k time to decay the population by almost 99%\n",
    "tlist=[]\n",
    "tlist = linspace(0.0, float(6)/k, 3000) # Defining time instants for simulation\n",
    "noe=len(tlist)\n",
    "dt=tlist[1]-tlist[0]\n",
    "print(\"dt = \", dt)\n",
    "\n",
    "# Defining Projection operators    \n",
    "\n",
    "P=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "\n",
    "P1=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P1[8][1]=1/sqrt2\n",
    "P1[8][2]=-1/sqrt2\n",
    "P1=sqrt(ks)*Qobj(P1)\n",
    "\n",
    "P2=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P2[8][5]=1/sqrt2\n",
    "P2[8][6]=-1/sqrt2\n",
    "P2=sqrt(ks)*Qobj(P2)\n",
    "\n",
    "P3=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P3[9][1]=1/sqrt2\n",
    "P3[9][2]=1/sqrt2\n",
    "P3=sqrt(kt)*Qobj(P3)\n",
    "\n",
    "P4=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P4[9][5]=1/sqrt2\n",
    "P4[9][6]=1/sqrt2\n",
    "P4=sqrt(kt)*Qobj(P4)\n",
    "\n",
    "P5=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P5[9][0]=1\n",
    "P5=sqrt(kt)*Qobj(P5)\n",
    "\n",
    "P6=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P6[9][3]=1\n",
    "P6=sqrt(kt)*Qobj(P6)\n",
    "\n",
    "P7=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P7[9][4]=1\n",
    "P7=sqrt(kt)*Qobj(P7)\n",
    "\n",
    "P8=[[0 for x in range(10)] for x in range(10)] # 10x10 array containing only 0s\n",
    "P8[9][7]=1\n",
    "P8=sqrt(kt)*Qobj(P8)\n",
    "\n",
    "# Defining Collapse Operators\n",
    "c_op_list = [P1,P2,P3,P4,P5,P6,P7,P8]\n",
    "\n",
    "# Variables\n",
    "sensitivity=[]\n",
    "coherence=[]\n",
    "\n",
    "for lb in range(0,loB):  # Loop over various hyperfine constant values\n",
    "    singlet_yield=[] # Storing Singlet yield for\n",
    "    singlet_yield_coh=[] # Defining Singlet Yield variable\n",
    "    triplet_yield=[] # Defining Triplet Yield variable\n",
    "    Cohr=0\n",
    "    for th in range(0,noa): # Loop over various angle values\n",
    "        Bx=B0[lb]*sin(theta_list[th])\n",
    "        By=0\n",
    "        Bz=B0[lb]*cos(theta_list[th])\n",
    "        #Brfx = Brf*sin(3*pi/2+theta_list[th])\n",
    "        #Brfy = 0\n",
    "        #Brfz = Brf*cos(3*pi/2+theta_list[th])\n",
    "\n",
    "        H0 = 0.5*gama*Bx*Sx + 0.5*gama*By*Sy + 0.5*gama*Bz*Sz + 0.25*ax*ISx + 0.25*ay*ISy + 0.25*az*ISz\n",
    "        #H0 = gama*Bx*Sx/2 + gama*By*Sy/2 + gama*Bz*Sz/2 + ax*ISx/4 + ay*ISy/4 + az*ISz/4\n",
    "        #H0 = Bx*Sx/2 + By*Sy/2 + Bz*Sz/2 + ax*ISx/4 + ay*ISy/4 + az*ISz/4\n",
    "\n",
    "        #H1 = gama*Brfx*Sx + gama*Brfy*Sy + gama*Brfz*Sz\n",
    "        #args={'w':w}\n",
    "        #H=[H0,[H1,'cos(w*t)']]\n",
    "            # Running the Master Equation solver\n",
    "        opts = Odeoptions(nsteps=5000)\n",
    "\n",
    "        result = mesolve(H0, rho0, tlist, c_op_list, [],options=opts)\n",
    "        #result_coh = mesolve(H0_coh, rho0_offd, tlist, c_op_list, [],options=opts)\n",
    "        #print('theta = ', theta_list[th],\"Done\")\n",
    "        #result = mesolve(H, rho0, tlist, c_op_list, [], args=args, options=None) # Hamiltonian with RF field also.\n",
    "        #rh_ss=steadystate(H, c_op_list,maxiter=500, tol=1e-06, method='mesolve')\n",
    "\n",
    "            # Output states of the differential equation solver\n",
    "        rh=[]\n",
    "        rh_coh=[]\n",
    "        rh_static=[]\n",
    "        rh=result.states\n",
    "        singlet_yield.append(abs(rh[noe-1][8][0][8]))\n",
    "        #singlet_yield_coh.append(abs(rh_coh[noe-1][8][0][8]))\n",
    "        triplet_yield.append(abs(rh[noe-1][9][0][9]))\n",
    "\n",
    "    print('B0 = ', B0[lb], 'Done!')\n",
    "    Sens=max(singlet_yield)-min(singlet_yield)\n",
    "    print('Sensitivity = ', Sens)\n",
    "    sensitivity.append(Sens)\n",
    "    #Cohr=max(singlet_yield_coh)\n",
    "    #coherence.append(Cohr)\n",
    "\n",
    "# Saving data to a file\n",
    "ar_data=array([B,sensitivity])\n",
    "fl = open('data/FW_ax0.3455G_ay0.3455G_az8.5G_k2104.csv', 'w')\n",
    "writer = csv.writer(fl)\n",
    "writer.writerow(['Magnetic Field (microTesla)', 'Sensitivity']) #if needed\n",
    "for values in ar_data:\n",
    "    writer.writerow(values)\n",
    "fl.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4987236180540427, 0.49866139435010387, 0.49847442350116383, 0.4981690440737438, 0.4977543033739633, 0.49724088174430664, 0.49664107098610977, 0.49597174049536336, 0.4952478114042023, 0.4944848269378422, 0.4937001489129288, 0.4929073955806722, 0.49212201497682906, 0.49135789596151164, 0.49062540020032247, 0.4899356199721108, 0.4892971650337425, 0.48871767246260023, 0.4882057095198194, 0.48776341060647876, 0.48739956147615493, 0.4871129710607031, 0.48699577453130943, 0.48682476003825315, 0.48673910732485637]\n"
     ]
    }
   ],
   "source": [
    "print(singlet_yield)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.49879762976929704, 0.49885985347325507, 0.49904682432218334, 0.49935220374953837, 0.49976694444947184, 0.5002803660791693, 0.5008801768372824, 0.5015495073280507, 0.5022734364191693, 0.5030364208854192, 0.5038210989103414, 0.5046138522426173, 0.5053992328464966, 0.5061633518618909, 0.5068958476231012, 0.5075856278512315, 0.5082240827896596, 0.5088035753607086, 0.5093155383035495, 0.5097578372169674, 0.5101216863471961, 0.5104082767625356, 0.5105254732921013, 0.5106964877851156, 0.5107821404984392]\n"
     ]
    }
   ],
   "source": [
    "print(triplet_yield)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SSqSCjDi1DQkG973zJTg8OrgrySouIjEVTWHJIHII7HAKgNTyxRGpeMNPaUOCGf/vH4spcOfxy7upuIjEUDR703qgTQnLuxE5RCYS964/uTX/c24H3l2wnltemcOBvIKwI4lUG9EUlneA68ysSdEFZtYLuIbIeRaRKuG6H2Xx6/M68P7CDSouIjEUTWH5LZAHzAXuJzJ8yxAzexX4hMh8LA/GPKFIBRp6Uha/Pf9EPli0gdtencvBfBUXkfKKZj6W9UBvYDowjMjYYFcDlxKZOfJkd99aESFFKtKQvq341bkdeG/heu587XPyVFxEyiXa+VhWAwPNrC5wPJHiskwFRaq6YT/KIq+ggPve+ZKkBNMd+iLlcERDurj7DmBmjLOIhGr4KW04mO88/P4SkhITeOhiDbkvciQ0VphIITf3b8vB/AIe/edSkhJMk4WJHIHDnmMxswIzyzOzlEKv80t55EWzcTM728yWmNkyM7u3hPV6Bp8/qFDb7Wa2wMwWmtkdxbznp2bmwThmImV2++ntuKV/W8bNXM2v3l6AezTTEolIST2WPxO58iu/yOuYMLNE4EngTCAXmGlmb7v7omLWexB4v1BbR+AGIAc4ALxnZv9w96XB8hbB5+q+GomamfFfPz6OgwUFPDN5BUkJCfz6vA6YqeciUhYlzcdyrZnVcvf8Q69jvO0cIif+VwCY2ThgILCoyHq3AhOAnoXa2gPT3H1P8N7JwIXAQ8HyR4Cfoftq5AiZGfeefQJ5+c7oT1eSnBiZ9ljFRaR0pV1uPN/MTq6gbTcDVhd6nRu0fcfMmhEpGKOKvHcBcIqZNTSzmsAAoEXwnvOBNe4+r6SNm9lwM5tlZrM2bdpUvm8i1ZKZ8d8/ac+QPsfy3L9X8tD7S3RYTKQMSjt5XxP4l5k9CvzS3ffHcNvF/elXdK99FLjH3fML/6Xo7ovN7EFgErALmAfkBUXml0TmiymRuz8LPAuQnZ2tfy2kWGbGb84/kYMFztMfLyc5wbjrx8eHHUskrpVWWDoATxGZY+UcM7vG3WfHaNu5BL2MQHMid+8Xlg2MC4pKBjDAzPLcfaK7jwZGA5jZfcHntQGygHnBe5oDc8wsJ7jBUyRqZsb/G9iR/Hzn8Y+WkZSYwG2ntws7lkjcKrGwuPs24HIzewN4GphqZvcD/3vo3Es5zATamVkWsAYYDFxRZPtZh342s7HA3919YvC6kbtvNLOWwEVAnyBvo0Lv+RrIdvfN5cwqR7mEBOP+izpxsKCAP076iqREY2S/tmHHEolLZbqPxd3fDE6QPw38N3CBmRXXc3F3v66Mn5lnZrcQudorERjj7gvNbESwvOh5laImBPPCHARuDoqKSIVJSDAeHtSF/ALnofeWkJyQwA2ntA47lkjcsWhORppZNpFRjg93b4i7e2IsglWm7OxsnzVrVtgxpIrIyy/g9tc+5x/z1/Hb809kSN9WYUcSCYWZzXb37KLtZeqxBPeS/Aq4F9hB5JDV1JgmFKkikhITePSyrhzIK+DXby8kJSmBy3Nahh1LJG6UWliCmxH/DHQF/g7c4O4bKjqYSDxLTkzgiSu6ceNLs/nFX78gJTGBi3s0DzuWSFwo8T6WYJiVmUBr4Dp3P19FRSSiRlIio67qwUltMrh7/Dz+Nq/oRY0iR6fSbpC8D/gM6OTuL1RCHpEqJTU5kWev6UF2qwbc8drnvLdAV7WLlFZYbnP3M4J5WESkGDVTkhhzbU86N0/n1lfn8NGX6tTL0a3EwuLuT1RWEJGqrHaNJMYOzeGEY+oy4uU5fPKVhgmSo1c0c96LSAnS05J56bocWmfUYvhLs5i6fEvYkURCocIiEkP1aqbwl+t70aJ+Ta57cSazv9Gs3XL0UWERibGGtWvwl+t70bhuKteOmcm81d+GHUmkUqmwiFSARnVTeeWGXtSrlczVo6ezcO32sCOJVJoyFxYza2lmaSUsTwsGhBQRoEl6Gq9c35vaNZK46vnpLFm/M+xIIpUimh7LSiKTbh3O+cE6IhJo0aAmr9zQm5SkBK58fjrLNu4KO5JIhYumsJQ2J2sCP5yoS+So1yqjFn+5vjfgXPHcNFZu3h12JJEKFe05lpIKR3tAZylFitG2UW3+cn1v8goixWXVlj1hRxKpMKWNFTbEzD4ys4+Cpv8+9LrI43PgF8C/KjyxSBV1/DF1ePm6Xuw9mM/lz00jd5uKi1RPpfVY6hGZ6jeLSG8ls9DrQ49WweeMAUZWVFCR6qBD07q8fF0vdu47yBXPTWfd9r1hRxKJuTJP9GVmBcBV7v5KxUaqfJroSyrb56u/5ernp9Owdgqv3diHxnVTw44kErXDTfRV5nMs7p5QHYuKSBi6tqjH2GE92bRzP5c/N41NO/eHHUkkZqK+QdLMsszsejP7pZm1CtpSgvtcUmIdUKS66nFsA14YmsO6b/dxxXPT2LJLxUWqh6gKi5k9CHwFPAv8L5EJwABSgUXoHItIVHKyGjD62mxWb9vDlc9PZ9vuA2FHEim3aO68vxG4G3gS+DGF7mtx9x3A28B5sQ4oUt31bZPBc9dks2Lzbq4aPZ3tew6GHUmkXKLpsYwE/urudwBzi1k+Hzg+JqlEjjInt8vkmat7sHTDLq4ZM50d+1RcpOqKprAcB0wqYfkmIKN8cUSOXv2Pb8RTV3Zn4dodDBkzg13788KOJHJEoiks+4BaJSw/Ft15L1IuZ3RozBNXdGN+7naGvjCD3SouUgVFU1hmcJhBKM0sFbga+CyajZvZ2Wa2xMyWmdm9JazX08zyzWxQobbbzWyBmS00szsKtT9sZl+a2Xwz+6uZ1Ysmk0jYzu7YhMcGd2X2N9u47sWZ7Dmg4iJVSzSF5WGgj5m9BHQO2o4xs7OAj4HmwO/L+mFmlkjkQoBzgA7A5WbW4TDrPQi8X6itI3ADkAN0Ac41s3bB4klAR3fvTOQKtp9H8R1F4sK5nZvyx0u7MmPlVoaNVXGRqiWaGyT/CdwEDAL+GTS/BLxD5B/3G9x9ahTbzgGWufsKdz8AjAMGFrPercAEYGOhtvbANHff4+55wGSC3pS7fxC0AUwjUvBEqpwLujX7rrgMfUHFRaqOqO5jcfdniYwPdgfwNPAM8FOgrbuPjXLbzYDVhV7nBm3fMbNmRArGqCLvXQCcYmYNzawmMABoUcw2hgHvFrdxMxtuZrPMbNamTZuijC5SOS7o1oxHLuvKzK+3cu0LM3XORaqEpGjf4O7rgT/FYNvFze9SdOCyR4F73D3f7D+ru/vi4GbNScAuYB7wvT3OzH4ZtP2luI0HRfJZiIwVdoTfQaTCDezaDDPjjnFzGfrCTF4Y2pNaNaLedUUqTZhz3ufy/V5Gc2BtkXWygXFm9jWRQ3BPmdkFAO4+2t27u/spwFZg6aE3mdkQ4FzgSi/rKJsicez8Lk15bHA3Zq/axrUv6FJkiW+H/bPHzMYcwee5u19XxnVnAu3MLAtYAwwGrijyYVmF8owF/u7uE4PXjdx9o5m1BC4C+gTtZwP3AKe6uya8kGrjvC5NMYPbx33OkDEzGDu0J3VSk8OOJfIDJfWnrz2Cz3OgTIXF3fPM7BYiV3slAmPcfaGZjQiWFz2vUtQEM2sIHARudvdtQfsTQA1gUnD4bJq7j4j+q4jEn3M7NyXBjFtfncuQMTN4cViOiovEnTLPx1KdaT4WqWre/WIdt746l07N03lxWA51VVwkBOWej0VE4sc5nZrwxBXd+CJ3O9eMnqGxxSSuqLCIVFFnd2zCE1d0Z8Ga7Vw9egbb96q4SHwo8zWLZTiZ78BeYBUwyd2LGwFZRGLo7I7H8NSV3bn5lTlcM3o6f76uF+lpOiwm4Yp2zvtDKxe9B6VouxO5k/4ad88vb8iKpnMsUtVNWrSBkX+ZTfsmdXlpWC/Sa6q4SMWLxTmWTGA28AbQC6gHpAO9gfHALCJ35fcMXg8Gfla+2CJSFmd2aMzTV/Zg8bodXDl6Gls1E6WEKJrC8ntgo7sPdveZ7r7D3Xe6+wx3vwzYDPzG3WcHrz8DrqqI0CLyQ2d0aMyzV2fz1YZdDH52Kht37As7khyloiks5xEZcPJw/sH3pyZ+m0gPRkQqSf8TGjH22p7kbtvLpc9MJXeb7hGWyhdNYUkFmpawvHmwziG7KTJ+l4hUvL5tM3jpul5s2X2AS0dNZeXm3WFHkqNMNIVlCnCrmfUuusDM+gC3BOsc0onvj14sIpWkx7H1efWG3uzLK+CSUVNZsn5n2JHkKBJNYflp8PyZmU01s7HBYyrwKZErwX4K380oeRrwt5imFZEy69gsndeG9ybB4LJnp/JF7vawI8lRIpqJvuYDPYhMutURuCZ4dAzaegbr4O773P0Edz/sdMMiUvHaNa7DGyP6UCsliSuem8bMr7eGHUmOAtFO9LXM3S8lcplxEyLnXNLd/VJ3/6oiAopI+RzbsBZvjOhDZp0aXDN6Bp8u3Rx2JKnmjmhIF3cvcPcN7r7e3QtiHUpEYqtpvTReu7EPxzasybCxM/nnog1hR5JqLOpp6MzsOKAt0JBiZoF09z/HIJeIxFhmnRqMG96bIWNmMOLl2TxyWVfO61LShZ4iRyaascIaAy8CZx5qKmY1B1RYROJUvZopvHx9L64bO4vbxs1l74F8Lu3ZovQ3ikQhmh7LE0SKytPAR8CWCkkkIhWqTmoyLw7LYfhLs/jZhPnsOZDHtSfpXmaJnWgKy5nAKHe/paLCiEjlSEtJ5Pkh2dz6ylx+87dF7NiXx62ntSWYdVWkXKI5eZ8AzKuoICJSuWokJfLkld25qFsz/jjpK/574gLyCzSjrJRfND2WfwNdKiqIiFS+5MQE/nBpFxrVTWXU5OVs3rWfxwZ3IzU5MexoUoVF02O5C7jQzC6uqDAiUvnMjHvPOYFfn9eBDxZt4Krnp/PtHg27L0cumh7L08Au4HUzWwusAIpO4uXufnqswolI5Rl6UhaZdWpw12vzuGTUVF4clkPTemlhx5IqKJoeS2sgmcjUw3lASyLD4hd+tI51QBGpPOd2bsrYYT1Zv30fFz01RYNXyhGJZqywVu6eVdqjIsOKSMXr2yaD10f0ocCdS0ZNYfoK3Vkg0TmiIV1EpHpr36Qub47sS0adGlw9ZgbvfrEu7EhShYRaWMzsbDNbYmbLzOywIyGbWU8zyzezQYXabjezBWa20MzuKNTewMwmmdnS4Ll+RX8Pkeqoef2aTBjRl45N6zLylTm8NPXrsCNJFXHYk/dm9hGRIVrOcve84HVpynzy3swSgSeJ3HiZC8w0s7fdfVEx6z0IvF+orSNwA5ADHADeM7N/uPtS4F7gQ3d/IChW9wL3lCWTiHxf/Vop/OX63tz66hz+562FrN+xj5/++HjdSCklKqnH0prICXkr8rqkRzQn73OAZe6+wt0PAOOAgcWsdyuR+V42FmprD0xz9z3ungdMBi4Mlg0kMqYZwfMFUWQSkSLSUhIZdVUPBvdswZP/Ws7Pxs/nYL4GNZfDO2yPxd1blfQ6Bprx/amLc4FehVcws2ZECsZpQM9CixYAvzOzhsBeYAAwK1jW2N3XBZnXmVmj4jZuZsOB4QAtW7Ys95cRqc6SEhO4/6JONK6bymMfLmXzrv386Yru1K4R9QDpchQI8xzL4UZHLuxR4B53/979Mu6+mMjhsUnAe0SGmsmLZuPu/qy7Z7t7dmZmZjRvFTkqmRl3nnkcv7uwI5O/2sSgp6eQu21P2LEkDh1xYTGzJDPra2aXmNmJR/ARuUDh8bqbA2uLrJMNjDOzr4FBwFNmdgGAu4929+7ufgqwFVgavGeDmTUJMjbh+4fQRKScrux1LGOH5rDm270MfOIzTXcsP1BiYTGzfmb2uJkdU6Q9C5hNZPywccB8MxsT5bZnAu3MLMvMUoDBwNuFVwjujWkVHIYbD4x094lBhkbBc0vgIuDV4G1vA0OCn4cAb0WZS0RKccpxmUy8+STqpiVzxXPTeGPW6tLfJEeN0nos1wID3X19kfYXgU7AFOARYBEwxMyGUEbBSfdbiFzttRh43d0XmtkIMxtRho+YYGaLgL8BN7v7tqD9AeBMM1tK5IqzB8qaSUTKrk1mbSaOPIleWQ25e/x8fvePRRodWQAw9y0xlZEAAA9iSURBVMP/j2BmC4F/FZ6DxcxOIFJIPnH3fkFbGjAXWFMVxwrLzs72WbNmlb6iiPzAwfwC/u/vi/jz1G847YRGPDa4K3VSk8OOJZXAzGa7e3bR9tJ6LE2Ar4q09SNykv35Qw3uvhd4BehcvpgiUtUkJybwvwM78n8XRE7qX/z0FFZt0Un9o1lphaUGkct5Czt02e/kIu2rgfRYhBKRqufq3sfy0rAcNuzYz8AnP2Waxhg7apVWWFYBRa/4+hGw0d2Lnq2rCXwbq2AiUvX0bZvBWzefRINaKVz1/HRenbEq7EgSgtIKy7+Ba8ysE4CZXQi0A94tZt1OwJrYxhORqqZVRi3eHHkSfdtm8PM3v+C3f1tInu7UP6qUVljuJ3I47HMz20jkkt8DwB8KrxSM53U+8GlFhBSRqiU9LZkxQ7IZdlIWL3z2NcNenMWOfQfDjiWVpMTC4u4rgVOBd4AtRHoq/dx9YZFV+wfLdc+IiACRYWB+dV4HHrioE1OWbeaCJz5j8bodYceSSlDi5cZHC11uLFKxZqzcyi2vzGH73oP89vwTuaxnC42QXA0c6eXGIiLllpPVgHduP5mcrAbc++YX3PX6PHbvj2p4P6lCVFhEpFJk1K7B2KE53HXmcUz8fA3nP/EpS9bvDDuWVAAVFhGpNIkJxm2nt+Mv1/Vi+948Bj75qcYZq4ZUWESk0vVtm8E7t/+Ibi3qc/f4+fz0jXnsPZBf+hulSlBhEZFQNKqTysvX9+K209sxYU4uA5/8lGUbdWisOlBhEZHQJCYYd515HH8elsOWXQc470+f8eac3LBjSTmpsIhI6E5ul8k7t59Mp+bp3PX6PO4ZP599B3VorKpSYRGRuNC4biqvXN+Lm/u34bVZq7ngyc90aKyKUmERkbiRlJjA3WedwNihPdm4cz8DHv+UUZOXawKxKkaFRUTiTr/jG/H+HafQ//hMHnj3Sy5+eop6L1WICouIxKXMOjUYdVUPHr+8G99s2c2Axz/l6Y+Xa6TkKkCFRUTilplxfpemfHDnqZx2fCMefO9LLh41laUb1HuJZyosIhL3MuvU4OmruvOny7uxastufqLeS1xTYRGRKsHMOO9Q7+UE9V7imQqLiFQph3ovT1zRjdVb9/CTxz/lqY+XqfcSR1RYRKTKMTPO7dyUD+48hdPbN+Kh95Zw8dNT1HuJEyosIlJlZdSuwdNX9eDJK7qzettefvL4pzzw7pfs0lwvoQq1sJjZ2Wa2xMyWmdm9JazX08zyzWxQobY7zWyhmS0ws1fNLDVo72pm08zsczObZWY5lfFdRCQ8P+nchA/uPIXzujRl1OTl9Hv4Y16fuZoC3VgZitAKi5klAk8C5wAdgMvNrMNh1nsQeL9QWzPgNiDb3TsCicDgYPFDwG/dvSvwq+C1iFRzGbVr8IdLu/DWzSfRskEaP5swn4FPfsbMr7eGHe2oE2aPJQdY5u4r3P0AMA4YWMx6twITgI1F2pOANDNLAmoCa4N2B+oGP6cXaheRo0CXFvWYcFNfHhvclc279nPJqKnc8soc1ny7N+xoR42kELfdDCg8dVwu0KvwCkHP5ELgNKDnoXZ3X2NmvwdWAXuBD9z9g2DxHcD7wfIEoG9xGzez4cBwgJYtW8bi+4hInDAzBnZtxpkdGjNq8gqembycSYs2cOOpbRhxamtqpoT5T1/1F2aPxYppK3pA9FHgHnf/3vjZZlafSO8mC2gK1DKzq4LFNwF3unsL4E5gdHEbd/dn3T3b3bMzMzPL8TVEJF7VTEnirjOP46Of9uPHJx7D4x8u5bTfT2bi3DW46/xLRQmzsOQCLQq9bs4PD1tlA+PM7GtgEPCUmV0AnAGsdPdN7n4QeJP/9EyGBK8B3iByyE1EjmLN6qXxp8u78caIPmTWqcEdr33OxU9PYd7qb8OOVi2FWVhmAu3MLMvMUoicfH+78ArunuXurdy9FTAeGOnuE4kcAuttZjXNzIDTgcXB29YCpwY/nwYsrfivIiJVQc9WDXjr5pN4aFBnVm3dy8AnP+P2cXN1/0uMhXag0d3zzOwWIld7JQJj3H2hmY0Ilo8q4b3TzWw8MAfIA+YCzwaLbwAeC07q7yM4jyIiApCQYFya3YJzOh7DUx8vZ+xnX/PW52s5s0NjRvZrQ7eW9cOOWOWZjjNCdna2z5o1K+wYIhKCrbsPMHbK17w45Wu27z1I3zYNGdmvLSe1bUjkgIgcjpnNdvfsH7SrsKiwiAjs2p/Hq9NX8dy/V7Bx5346N09nZL82/LjDMSQkqMAUR4WlBCosInLI/rx8JsxewzOfLOebLXtok1mLEae24YJuzUhO1ChYhamwlECFRUSKyssv4J0F63nqX8v4cv1OmtVL44aTs7isZ0vSUhLDjhcXVFhKoMIiIofj7ny8ZBNPfbyMmV9vo2GtFAbntODi7s1pnVk77HihUmEpgQqLiJTFjJVbeWbycv61ZCMFDt1b1uPiHs05t3NT0tOSw45X6VRYSqDCIiLR2LBjHxPnrmHCnFy+2rCLlKQEftyhMRf3aM7JbTNIOkrOxaiwlECFRUSOhLvzxZrtTJidy1vz1vLtnoM0qlODC7s14+IezTmucZ2wI1YoFZYSqLCISHntz8vnX19uZPzsNXy8ZCN5BU7n5ulc3L0553dpSv1aKWFHjDkVlhKosIhILG3etZ+3Pl/L+Nm5LF63gwSDFg1q0jqjFq0za9M6sxatM2rTJrMWmXVqVNkbMVVYSqDCIiIVZdHaHby3cD3LN+5i+aZdrNy8m/15Bd8tr1MjiazMWrTOqEWbzNrfFZ6sjFqkJsf3Zc2HKyyalEBEpAJ1aFqXDk3rfve6oMBZu30vKzbtZsWmXazYvJsVm3YzY+VWJn7+nwHekxKMDk3r0r1lfbq1rEf3lvVpXj+tSvRu1GNBPRYRiQ97DuSxMig0i9btYM4325ifu529ByNTUmXWqUH3oMh0P7Y+nZqlh9qrUY9FRCTO1UxJ4sSm6ZzYNJ3zujQFIiMAfLl+J3NXbWPOqm+Zs2ob7y/cAPywV9OuUR2a1kslPS051J6NeiyoxyIiVcvmXfuZGxSZor0agLTkRJqkp3JMeipN0tNoWi/yc9P0tO+e66Yllbv4qMciIlJNZNSuwZkdGnNmh8bAf3o132zZw7rte1m3fR/rt+9j7fa9TFm+mQ079lFQpA+RlpxIk3qp3HdhJ3q3bhjTfCosIiJVXFJiAh2bpdOxWXqxy/PyC9i0az9rv40UnEPFZ932vdSrGfuhaFRYRESquaTEBJqkp9EkPa1Stnd0DGgjIiKVRoVFRERiSoVFRERiSoVFRERiSoVFRERiSoVFRERiSoVFRERiSoVFRERiSmOFAWa2CfjmCN+eAWyOYZxYUrboxWsuULYjFa/Z4jUXlD3bse6eWbRRhaWczGxWcYOwxQNli1685gJlO1Lxmi1ec0H5s+lQmIiIxJQKi4iIxJQKS/k9G3aAEihb9OI1FyjbkYrXbPGaC8qZTedYREQkptRjERGRmFJhERGRmFJhKSMzO9vMlpjZMjO7t5jlZmaPB8vnm1n3OMp2ZZBpvplNMbMu8ZCr0Ho9zSzfzAZVRq6yZjOzfmb2uZktNLPJ8ZLNzNLN7G9mNi/INrSSco0xs41mtuAwy8PcB0rLFso+UJZshdar1P2gLLmOeB9wdz1KeQCJwHKgNZACzAM6FFlnAPAuYEBvYHocZesL1A9+PqcyspUlV6H1PgLeAQbF0e+sHrAIaBm8bhRH2X4BPBj8nAlsBVIqIdspQHdgwWGWh7IPlDFbpe8DZc1W6L97Ze8Hpf3OjngfUI+lbHKAZe6+wt0PAOOAgUXWGQj82SOmAfXMrEk8ZHP3Ke6+LXg5DWgeD7kCtwITgI2VkCmabFcAb7r7KgB3r6x8ZcnmQB0zM6A2kcKSV9HB3P2TYFuHE9Y+UGq2kPaBQ9su7fcGIewHZch1xPuACkvZNANWF3qdG7RFu05FiHa71xH5q7KilZrLzJoBFwKjKiFPYWX5nR0H1Dezj81stpldE0fZngDaA2uBL4Db3b2gcuKVKKx9IFqVtQ+USYj7QWmOeB9IqsBQ1YkV01b0Ou2yrFMRyrxdM+tPZKf6UYUmCjZXTFvRXI8C97h7fuSP70pTlmxJQA/gdCANmGpm09z9qzjIdhbwOXAa0AaYZGb/dvcdFZytNGHtA2VWyftAWYW1H5TmiPcBFZayyQVaFHrdnMhfi9GuUxHKtF0z6ww8D5zj7lviJFc2MC7YmTKAAWaW5+4T4yBbLrDZ3XcDu83sE6ALUNGFpSzZhgIPeOTA9zIzWwmcAMyo4GylCWsfKJMQ9oGyCms/KM2R7wOVdQKrKj+IFOAVQBb/OaF6YpF1fsL3T1zOiKNsLYFlQN94+p0VWX8slXfSsiy/s/bAh8G6NYEFQMc4yfY08Jvg58bAGiCjkn53rTj8yd5Q9oEyZqv0faCs2YqsV2n7QRl+Z0e8D6jHUgbunmdmtwDvE7l6Y4y7LzSzEcHyUUSu5hhA5H/ePUT+qoyXbL8CGgJPBX8V5XkFj6paxlyhKEs2d19sZu8B84EC4Hl3L/Fy0crKBvwfMNbMviDyj/g97l7hw6+b2atAPyDDzHKBXwPJhXKFsg+UMVul7wNRZAtFabnKsw9oSBcREYkpXRUmIiIxpcIiIiIxpcIiIiIxpcIiIiIxpcIiIiIxpcIiEqfM7KxgOI1dZrbJzJ4ws9Swc4mURoVFJA6Z2X8B7wHrgDuBvwE3A4+FmUukLHQfi0icMbMzgA+An7n77wu1vwf0BzI9/HHBRA5LPRaROGJmCUR6JXOBPxRZ/DGRYV46VnIskahoSBeR+HIW0AG41n94OOFA8JxeuZFEoqPCIhJfLgPygX+bWUaRZY2D552VG0kkOjrHIhJHzOwbIiPxlqSZu8fNcPQiRamwiMSJoIeyCfgr8FQxq7wO7Hf3SpnuV+RI6VCYSPxoHTzPdPd/Fl5gZllAfeCVSk8lEiVdFSYSP2oHz8WdQxkUPL9WSVlEjpgKi0j8OHRvSt3CjWaWAtwELAH+UdmhRKKlwiISPxYRmXnxrCLtvyMyhext7p5f2aFEoqVzLCJxwt33mNnzwG1m9jIwGTgHuBC4290/CDWgSBnpqjCROBIc9noIuBKoCcwG7nP390INJhIFFRYREYkpnWMREZGYUmEREZGYUmEREZGYUmEREZGYUmEREZGYUmEREZGYUmEREZGYUmEREZGYUmEREZGY+v/t+6ywiL1uawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "figure()\n",
    "#ylim([0.00,0.20])\n",
    "plt.plot(theta_list, singlet_yield)\n",
    "#plot(B,sensitivity,'r')\n",
    "xlabel(r\"$\\theta$\",fontsize=18)\n",
    "ylabel('Singlet Yield', fontsize=18 )\n",
    "savefig('Figures/SY_ax0.3455G_ay0.3455G_az8.5G_k2104.eps')\n",
    "savefig('Figures/SY_ax0.3455G_ay0.3455G_az8.5G_k2104.pdf')\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure()\n",
    "#ylim([0.00,0.20])\n",
    "plt.plot(theta_list, triplet_yield)\n",
    "#plot(B,sensitivity,'r')\n",
    "xlabel(r\"$\\theta$\",fontsize=18)\n",
    "ylabel('Triplet Yield', fontsize=18 )\n",
    "savefig('Figures/TY_ax0.3455G_ay0.3455G_az8.5G_k2104.eps')\n",
    "savefig('Figures/TY_ax0.3455G_ay0.3455G_az8.5G_k2104.pdf')\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Plotting Sensitivity/Singlet Yield\n",
    "figure()\n",
    "#ylim([0.00,0.20])\n",
    "semilogx(B,sensitivity,'g')\n",
    "#plot(B,sensitivity,'r')\n",
    "xlabel(\"B ($\\mu$T)\",fontsize=18)\n",
    "ylabel('Sensitivity', fontsize=18 )\n",
    "savefig('Figures/FW_ax0.3455G_ay0.3455G_az8.5G_k2104.eps')\n",
    "savefig('Figures/FW_ax0.3455G_ay0.3455G_az8.5G_k2104.pdf')\n",
    "\n",
    "figure()\n",
    "plot(B,sensitivity,'g')\n",
    "xlabel(\"B ($\\mu$T)\",fontsize=18)\n",
    "ylabel('Sensitivity', fontsize=18 )\n",
    "savefig('Figures/FW_ax0.3455G_ay0.3455G_az8.5G_k2104_Linear.eps')\n",
    "savefig('Figures/FW_ax0.3455G_ay0.3455G_az8.5G_k2104_Linear.pdf')\n",
    "\n",
    "show()"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}