16_fusion_spectra.ipynb

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    "#  Producing Fusion Spectra for the Neutron Generator\n",
    "\n",
    "This note produces a spectrum of neutron enegries at the aftre either DT or TT fusion.\n",
    "\n",
    "In order to calculate the neutron energy the following steps are taken:\n",
    "\n",
    "1.) We sample the input velocities of the deuterons, or a triton for DT fusion from Maxwell-BOltzmann distirbutions, where the average energy is asusmed to be realte dto the applied HV of the neutron generator\n",
    "\n",
    "2.) We sample a random angle to to determine the collision angle between the two particle.\n",
    "\n",
    "3.) We sample a second random angle which is the emission angle of the neutron, and determine the nuetron energy from this angle sand simple scattering kinematics\n",
    "\n",
    "4.) We transform the neutron energy back into the lab frame."
   ]
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   "source": [
    "import numpy as np\n",
    "import root_numpy as rn\n",
    "import ROOT as ROOT\n",
    "import datetime\n",
    "from pprint import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "from root_numpy import root2array, tree2array\n",
    "import pandas as pd\n",
    "from numpy.polynomial import legendre  \n",
    "import matplotlib.gridspec as gridspec\n",
    "%matplotlib inline\n",
    "plt.rc('font', size=16)\n",
    "plt.rcParams['figure.figsize'] = (10.0, 7.0)"
   ]
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    "##Lookup table of Legendre coefficients in COM frame for paramterization of differential cross-sections\n",
    "\n",
    "##Deuterium-deuterium Fusion\n",
    "A_d = [[0.025, 0.819, 0, 0.181, 0, 0], \n",
    "       [0.125, 0.785, 0, 0.215, 0, 0],\n",
    "       [0.310, 0.758, 0, 0.241, 0, 0.001],\n",
    "       [0.520, 0.737, 0, 0.261, 0, 0.002],\n",
    "       [0.780, 0.719, 0, 0.279, 0, 0.002],\n",
    "       [1.060, 0.705, 0, 0.292, 0, 0.003],\n",
    "       [1.350, 0.692, 0, 0.304, 0, 0.004],\n",
    "       [1.660, 0.681, 0, 0.314, 0, 0.005],\n",
    "       [2.000, 0.671, 0, 0.323, 0, 0.006]]\n",
    "\n",
    "##Deuterium-tritium Fusion\n",
    "A_t = [[4.325, 1], \n",
    "       [19.6, 1],\n",
    "       [52.9, 1],\n",
    "       [106, 1],\n",
    "       [175, 1],\n",
    "       [250, 1],\n",
    "       [315, 1],\n",
    "       [367, 1],\n",
    "       [394, 1]]\n"
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vq8teGevtlfi9BCDoYrFYcvVktiA2Ozurzs7OrEGsu7tbzc3NDE8CeSCsbYO1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\nAAAQYIQ1AACAACOsAQAABNj/D7VD2pO0RdAPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd639ec62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Energies=[0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "particles=['deuteron', 'deuteron']\n",
    "products=['helium3', 'neutron']\n",
    "mass=[m_deuteron/1000, m_deuteron/1000]\n",
    "product_mass=[m_helium3/1000, m_neutron/1000]\n",
    "Q=3.269 #MeV\n",
    "for index in range(len(A_d)):\n",
    "    E_a=Energies[index]\n",
    "    leg_coefficients = A_t[index][1:]\n",
    "    cross_section = A_t[index][0]\n",
    "    cos=np.linspace(-1, 1, 200)\n",
    "    sin=np.sqrt(1-cos**2)\n",
    "    beta_factor = np.sqrt(E_a*(E_a+2*mass[1]))/(mass[1]+mass[0]+E_a)\n",
    "    gamma_factor = np.sqrt((mass[1]*product_mass[1]*E_a)/\n",
    "                           (product_mass[0]*(product_mass[1]+product_mass[0])*Q+\n",
    "                           product_mass[0]*(product_mass[1]+product_mass[0]-mass[1])*E_a))\n",
    "    dtheta_dthetaprime = np.sqrt(1-gamma_factor**2*sin**2)/(gamma_factor*cos+np.sqrt(1-gamma_factor**2*sin**2))**2\n",
    "    cos_prime = (beta_factor-cos)/(beta_factor*cos-1)   \n",
    "    #Weighting factor using Liskien1982\n",
    "    weight_prime = legendre.legval(cos_prime, leg_coefficients)*cross_section\n",
    "    weight = weight_prime/dtheta_dthetaprime\n",
    "    #weight_prime = legendre.legval(cos, leg_coefficients)*cross_section\n",
    "    plt.plot(cos, weight)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##Produce Maxwell-Boltzmann Distribution with average energy equal to applied HV \n",
    "def gen_distribution(E_ave):\n",
    "    prob_d=[]\n",
    "    prob_t=[]\n",
    "#E_ave=40 #keV\n",
    "    m_neutron = 939576 #keV\n",
    "    m_deuteron = 1875633 #keV\n",
    "    m_triton=2808421 #keV\n",
    "    m_helium3=2809388 #keV\n",
    "    m_helium4=3728400 #keV\n",
    "    num_div=10000\n",
    "    max_v = 0.01\n",
    "    v=np.linspace(0, max_v, num_div)\n",
    "    kT_B = 2*E_ave/3\n",
    "    MB_tv = (m_triton/(2*np.pi*kT_B))**(3/2)*4*np.pi*v**2*np.exp(-0.5*m_triton*v**2/kT_B)\n",
    "    MB_dv = (m_deuteron/(2*np.pi*kT_B))**(3/2)*4*np.pi*v**2*np.exp(-0.5*m_deuteron*v**2/kT_B)\n",
    "    prob_d=(MB_dv*(max_v/num_div))\n",
    "    prob_t=(MB_tv*(max_v/num_div))\n",
    "    prob_d = prob_d/np.sum(prob_d) \n",
    "    prob_t = prob_t/np.sum(prob_t)\n",
    "    return (v, prob_d, prob_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##Produce Energy Histogram\n",
    "def gen_spectrum(detector_pos, E_ave):\n",
    "    #Generate velocity distributions\n",
    "    velocity_dist=gen_distribution(E_ave)\n",
    "\n",
    "    #particles=['deuteron', 'deuteron']\n",
    "    #products=['helium3', 'neutron']\n",
    "    #mass=[m_deuteron/1000, m_deuteron/1000]\n",
    "    #product_mass=[m_helium3/1000, m_neutron/1000]\n",
    "    tritium_abundance=0.0005\n",
    "    emission_prob = []\n",
    "    energies_lab=[]\n",
    "    energies_boost=[]\n",
    "    phi_angles=[]\n",
    "    neutron_v=[]\n",
    "    neutron_v_comps=[]\n",
    "    for i in range(0, 1000000):\n",
    "        particles=['deuteron', 'deuteron']\n",
    "        products=['helium3', 'neutron']\n",
    "        mass=[m_deuteron/1000, m_deuteron/1000]\n",
    "        product_mass=[m_helium3/1000, m_neutron/1000]\n",
    "    \n",
    "        #Pick the particle energies from appropriate MB distirbutions\n",
    "        velocities=np.random.choice(velocity_dist[0], 2, p=velocity_dist[1]) \n",
    "        Q=3.269 #MeV\n",
    "        \n",
    "        #Roll the dice for a DT fusion event\n",
    "        p=np.random.ranf()\n",
    "        if p<tritium_abundance:\n",
    "            particles[0]='triton'\n",
    "            mass[0]=m_triton/1000\n",
    "            velocities[0]=np.random.choice(v, p=prob_t) \n",
    "            products[0]='helium4'\n",
    "            product_mass[0]=m_helium4/1000\n",
    "            Q=17.59 #MeV\n",
    "    \n",
    "        #Pick two random angles (in terms of cos(x) between -1 and 1:\n",
    "        #0.) Collision angle in lab frame, to determine incident energy \n",
    "        #1.) Polar Emission angle of neutron in collision frame, to determine cross section and neutron energy\n",
    "        #2.) Azimuthal Emission Angle in collision frame as transforming back to lab frame breaks symmetry\n",
    "        angles=np.random.ranf(3)\n",
    " \n",
    "        #Define co-ordinate systme such that particle[0] is moving along \"x-axis\" (x)\n",
    "        #Determine incident particle energy in frame where particle[0] is stationary\n",
    "        #All cross section data are beam on target, so to avoid mistakes we transform to \n",
    "        #co-ordinates where the triton/deuteron is stationary, and a deuteron is incident\n",
    "        cos0=2*angles[0]-1\n",
    "        sin0=np.sqrt(1-cos0**2)\n",
    "        v01_x = velocities[1]*cos0#+velocities[0]\n",
    "        v01_z = velocities[1]*sin0\n",
    "        E_a=0.5*mass[1]*(v01_x**2+v01_z**2)  #Incident energy of deuteron in stationary frame   \n",
    "    \n",
    "        #From kinematics determine energy of emitted neutron using angle[1], where angle[1] is relative to\n",
    "        #z-coordinate of collision (z')\n",
    "        cos1 = 2*angles[1]-1\n",
    "        cos1 = detector_pos*cos1 ######Stuff for Cylindrical check\n",
    "        sin1 = np.sqrt(1-cos1**2)\n",
    "        r_factor=np.sqrt(mass[1]*product_mass[1]*E_a)/(product_mass[1]+product_mass[0])*cos1\n",
    "        s_factor=(E_a*(product_mass[0]-mass[1])+product_mass[0]*Q)/(product_mass[1]+product_mass[0])\n",
    "        E_neutron = np.power(r_factor + np.sqrt(r_factor**2+s_factor),2)\n",
    "    \n",
    "        #Based on incident particle energy, determine appropriate coefficients from look-up table\n",
    "        #A_x[i][0] is the cross section, A-x[i][1:] is the coefficients\n",
    "        if particles[0]=='deuteron':\n",
    "            leg_coefficients = A_d[np.clip(np.rint(E_a/0.010).astype(int)-2, 0, 8)][1:]\n",
    "            cross_section = A_d[np.clip(np.rint(E_a/0.010).astype(int)-2, 0, 8)][0]\n",
    "        elif particles[0]=='triton':\n",
    "            leg_coefficients = A_t[np.clip(np.rint(E_a/0.010).astype(int)-2, 0, 8)][1:]\n",
    "            cross_section = A_t[np.clip(np.rint(E_a/0.010).astype(int)-2, 0, 8)][0]\n",
    "    \n",
    "        #Determine relative cross section (compared to 90degrees) \n",
    "        #Cross section parametrization in center-of-mass frame\n",
    "        #Some factors for converting between center-of-mass frame, and stationary target frame\n",
    "        beta_factor = np.sqrt(E_a*(E_a+2*mass[1]))/(mass[1]+mass[0]+E_a)\n",
    "        gamma_factor = np.sqrt((mass[1]*product_mass[1]*E_a)/\n",
    "                           (product_mass[0]*(product_mass[1]+product_mass[0])*Q+\n",
    "                           product_mass[0]*(product_mass[1]+product_mass[0]-mass[1])*E_a))\n",
    "        dtheta_dthetaprime = np.sqrt(1-gamma_factor**2*sin1**2)/(gamma_factor*cos1+np.sqrt(1-gamma_factor**2*sin1**2))**2\n",
    "        cos1_prime = (beta_factor-cos1)/(beta_factor*cos1-1)   \n",
    "        #Weighting factor using Liskien1982\n",
    "        weight_prime = legendre.legval(cos1_prime, leg_coefficients)*cross_section\n",
    "        weight = weight_prime/dtheta_dthetaprime\n",
    "        \n",
    "        #Convert emitted neutron energy back into lab frame\n",
    "        #Theta_neutron is neutron emission angle relative to velocity[0] \n",
    "        theta0_prime=np.arctan(v01_z/v01_x)\n",
    "        theta1=np.arccos(cos1)\n",
    "        theta_neutron=np.pi-theta0_prime-theta1\n",
    "    \n",
    "        #Determine rotation matrix to transform from collision x'-z' co-ordinates, to lab x-z co-ordinates\n",
    "        theta_rot = 0.5*(theta0_prime-theta_neutron-theta1)\n",
    "        c, s = np.cos(-theta_rot), np.sin(-theta_rot)\n",
    "        R = np.matrix([[c, -s], [s, c]])\n",
    "    \n",
    "        #Determine z'-x' components of neutron energy (y' and y chosen to be common)\n",
    "        #This is a polar angle so it can ranmge from 0 to 2pi\n",
    "        phi2 = 2*np.pi*angles[2]\n",
    "        cos2 = np.cos(phi2)\n",
    "        sin2 = np.sin(phi2)\n",
    "        v_neutron_prime = np.sqrt(2*E_neutron/product_mass[1])\n",
    "        v_neutron_xprime = v_neutron_prime*sin1*cos2\n",
    "        v_neutron_zprime = v_neutron_prime*cos1\n",
    "        v_neutron_y = v_neutron_prime*sin1*sin2\n",
    "        v_vector_prime = [v_neutron_zprime, v_neutron_xprime]\n",
    "    \n",
    "        #Transfrom to unprime (lab) co-ordinates and boost\n",
    "        v_vector = np.dot(R, v_vector_prime)\n",
    "        v_neutron_x = v_vector.item(1)+velocities[0]\n",
    "        v_neutron_z = v_vector.item(0)\n",
    "        v_neutron_lab2 = v_neutron_x**2+v_neutron_y**2+v_neutron_z**2\n",
    "        E_neutron_boost = 0.5*product_mass[1]*v_neutron_lab2    \n",
    "       \n",
    "   \n",
    "        theta0_prime=np.arctan(v01_z/v01_x)\n",
    "        theta1=np.arccos(cos1)\n",
    "        theta_neutron=np.pi-theta0_prime-theta1\n",
    "        v_neutron = np.sqrt(2*E_neutron/product_mass[1])\n",
    "        v_neutron_labx = v_neutron*np.cos(theta_neutron)#+velocities[0]\n",
    "        v_neutron_lab2 = v_neutron_labx**2+(v_neutron*np.sin(theta_neutron))**2\n",
    "        E_neutron_lab = 0.5*product_mass[1]*v_neutron_lab2   \n",
    "    \n",
    "        energies_lab.append(E_neutron_lab)\n",
    "        emission_prob.append(weight)\n",
    "        energies_boost.append(E_neutron_boost)\n",
    "   \n",
    "    return (energies_lab, emission_prob)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_spectra(emission_prob, energies_lab):\n",
    "    numrows=2\n",
    "    numcols=2\n",
    "    binwidth=0.01\n",
    "    norm=[emission_prob[i]*binwidth for i in range(0, len(energies_lab))]\n",
    "    norm_factor=np.sum(norm)\n",
    "    fig = plt.figure(figsize=(15,10*numrows))\n",
    "    fig.suptitle(\"Figure 1: Energy spectrum of neutrons produced from DD-fusion of %s keV deuterons\" %E_ave, size=15)\n",
    "    gridspec.GridSpec(numrows, numcols)\n",
    "    \n",
    "    plt.subplot2grid((numrows,numcols), (0,0), colspan=2)\n",
    "    plt.hist(energies_lab, bins= 1400, normed=True, weights=emission_prob)\n",
    "    ax0 = plt.gca()\n",
    "    ax0.set_ylabel(\"Probability\")\n",
    "    ax0.set_xlabel(\"Energy [MeV]\")\n",
    "\n",
    "    plt.subplot2grid((numrows,numcols), (1,0))\n",
    "    (n_dd, bins_dd, pathces_dd) = plt.hist(energies_lab, bins= 200, normed=True, range=[1.5, 3.5], weights=emission_prob)\n",
    "    ax1 = plt.gca()\n",
    "    ax1.set_ylabel(\"Probability\")\n",
    "    ax1.set_xlabel(\"Energy [MeV]\")\n",
    "\n",
    "    plt.subplot2grid((numrows,numcols), (1,1))\n",
    "    (n_dt, bins_dt, pathces_dt) = plt.hist(energies_lab, bins= 400, normed=True, range=[12,16], weights=emission_prob)\n",
    "    ax2.set_ylabel(\"Probability\")\n",
    "    ax2.set_xlabel(\"Energy [MeV]\")\n",
    "\n",
    "    return (plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_dd_spectra(emission_prob, energies_lab, colour, angle):\n",
    "    numrows=1\n",
    "    numcols=1\n",
    "    binwidth=0.01\n",
    "    norm=[emission_prob[i]*binwidth for i in range(0, len(energies_lab))]\n",
    "    norm_factor=np.sum(norm)\n",
    "    fig = plt.figure(figsize=(7, 5*numrows))\n",
    "    fig.suptitle(\"Figure 1: Energy spectrum of neutrons at %s polar angle (%s keV)\" %(angle, E_ave) , size=15)\n",
    "    gridspec.GridSpec(numrows, numcols)\n",
    "    \n",
    "    plt.subplot2grid((numrows,numcols), (0,0))\n",
    "    (n_dd, bins_dd, pathces_dd) = plt.hist(energies_lab, bins= 200, normed=True, range=[1.5, 3.5], color=colour, weights=emission_prob)\n",
    "    ax0 = plt.gca()\n",
    "    ax0.set_ylabel(\"Probability\")\n",
    "    ax0.set_xlabel(\"Energy [MeV]\")\n",
    "\n",
    "    return (plt, n_dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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cu0sCZkk6WdIb27xcMzMzK2l3EHAssANwnaSbJX1C0kptzoOZmZkBioj2LlAa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd63e08a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mZmY1qaNi4OROz8PMzMz6z3UCzMzMGsovCzIzM2sovyzIzMysofyyIDMzs4by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\nZmbWUP8fdTm6nnOP5BQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd63df23cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X48VK1ueWkruEoutIjffytqO8IdgJpGL3/YE/RcS/Gs1kRNwfEV8jRar5i4lIDXTSl3QQ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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd643361b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A24ANgWuAk4CFmelPS0qSpIGZZGqVzDyM8gojSZKkCfHpckmSJLXOJFOSJEmt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\n8+KeJeR+ojMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd645c4cc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tkwRIOhw4NSKmZP/3JSLiqP6mGRHHAccNS4Bm1qcJTEASq7x5FR6f+nizwzGz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd63f3c77b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+Ntxo4MPA1RGxqNY08kGAmZmVS/Hib+LEie0rTIt1dBAg6SLgLuBOYD6wBfAF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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd643cfee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RPwWuA74FXAPsCHwQ+F1m7jzJvMuAY4FjukZdnJl395nHmke1zppHafaw5nE0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\n9v8BZ+/taEUxUJwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd642e4e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xk0dJkiQ1ZvIoSZKkxkweJUmS1JjJ4xCLiAMj4jsRcVNE3B0R10bERyNiswbz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6458367f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HAWZmZhXlIMDMzKyiHASYmZlVlIMAMzOzinIQYGZmVlEOAszMzCqqtCBA0svL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd63d6a7470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gwMzMrKIcBJiZmVWUgwAzM7OKchBgZmZWUaUFAZJeXNa0zczMbPTKLAmYI+k0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\nRf1/UxtK7p5AibEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd646f8bf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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i/MzMzGwISgsCImImsCHwAeAR4EfAw5J+LOlNZc3XzMzMWlNqxcCIWBIR50fE\nLsCrge8B04EbJf1J0v6SVikzD2ZmZlbfaLYOeA54GvgnIGBN4CTgXknbjmI+zMzMjFEIAiS9U9Jp\nwMPAUUAf8KaI2BR4HXAv8JOy82FmZmbLW6msCUv6FHAQsBlwB/A54LSIeL6WJiLukTQLuLysfJiZ\nmVl9ZZYEHA/cDuwQEa+PiB/mA4Cce0glBGZmbeFPCpu1prSSAKA3Ih4fLFFEPAzMKjEfZlYxtU8K\nT506tdNZMetqZZYEXCdpi3oDJL1e0n0lztvMzMwGUWYQsCHQqPnfKsCrSpy3mZmZDaLs1gHRoP8b\ngbklz9vMzMyaaGudAEmfAT6TdQZwgaRFhWQrA+sCZ7Vz3mZmZjY07a4YeB/Lmvt9DPgz8GQhzQuk\nVgMntnneZmZmNgRtDQIi4jzgPABJAEdFxP3tnIeZmZm1R2lNBCPi42VN28zMzEau3XUCvgKcGBGP\nZP83ExFxdDvnb2ZmZq1rd0nATOD3pE8HzxwkbQAOAszMzDqk3XUCJtT738zMzLqPL9RmZmYV5SDA\nzMysotpdMXAJjd8SWBQRUeYHjMzMzKyJdl+Ej6L1IMDMzMw6qN0VA2e2c3pmZmZWHtcJMDMzqyi/\nLMhsCHp7e+nv7+90NszM2sIvCzIbgv7+fvr6+pg6dWqns2JmNmJ+WZCZmVlF+UJtZmZWUaW305f0\nPmB7YG3gaeDKiPh92fM1MzOz5koLAiStDZwPbAO8AMwF1gQOl3QdMD0ini5r/mZmZtZcmY8Dvge8\nDtgTeGlErAe8FPhQ1v+7Jc7bzIyenh4k0dvb2+msmHWlMoOAXYHDI+I3EbEYICIWR8Q5wBeA6a1M\nRNL6kr4v6VpJ8yQtkdTSES1pFUnHSXpE0vxsGtsNe4nMbEwZGBigr6/PzTrNGigzCFgM3Ntg2D3Z\n8Fa8hlSa8DRwFUN7LfHJwAHAl4BdgEeBiyW9cQjTMDMzG5fKDALOA2Y0GDYjGz6oiLgyItaLiPcD\nv2515pK2APYGDo2IkyOij/QoYg7pGwdmZmaV1u43Bu6Q67wA+G9JrwTOAZ4E1iXd1b8V+HQ7513H\ndGARcHatR0QslnQW8HlJPRExUHIezMzMula7WwdcRiquV+7vZOADddKeA0xs8/zzNgPuj4iFhf63\nASuTHjPcUeL8zczMulq7g4Buepfq2sAzdfo/nRtuZmZWWe1+bfCV7ZyemZmZlaf0NwZ20DNAvaaE\ntRKAhi9L9871AAAVQUlEQVQqmjlz5tL/p0yZwpQpU9qZLzMz62KzZ89m9uzZnc7GqCg1CJC0OXAg\nsCmwamFwRMS0Emd/G7C7pFUL9QI2J1UYbNR8cbkgwMzMqqV48zdr1qzOZaZkpTURlDQNuBF4D7AT\n6ZXBGwBTSJXyVNa8MxeQKgAubaYoaSKpmeDFbhlgZmZVV+Z7AmYBZwJbkC74B0bEJsC2pBcFHdPq\nhCTtIWkPYMtsWjtn/bbPhvdKelHSl2rjRMRfgF8C35F0QNZ88ZfAhsCR7VhAMzOzsazMxwFvJgUC\ntTf8TQSIiGslHQV8DXhHi9P6VW46Afww+/9KYAdSYFD75e0PHAscTSqJuAXYKSJuGeKymJmZjTtl\nBgEC5kXEEkkLgPVywx4A3tDqhCKiaYlFRDxInXcORMQLwGHZz8wqqvYhocmTJzNnzpxOZ8esa5T5\nOOBu4FXZ/38B9lMyAfgE9dvwm5m1nT8kZFZfmUHA70jP/wG+DuxOapb3DPAR4DslztvMzMwGUdrj\ngIg4Ivf/BZLeRaqp30OqnX9+WfM2MzOzwY3ay4Ii4hrgmtGan5mZmTVXehAg6d+A7Ulv6nsauDIi\n7ix7vmZmZtZcaUGApFWBU0gv58k33QtJvwQ+ntXeNzMzsw4os2Lg8aTKgJ8jvcN/Uvb38Kz/cSXO\n28zMzAZR5uOAvYD/ioh8K4CHgG9JCuAI4P+WOH8zMzNrosySgFWAPzcYdgPpvf5mZmbWIWUGAZcB\n724wbBpwRYnzNjMzs0G09XGApI1znd8GTpP0MuAc4ElgXWBP4APAvu2ct5mZmQ1Nu+sE3MuyD/1A\nahXwGeDQQj9IH/9Z4X3/ZmZmNjraHQR8vM3TM+sKvb29fu+8mY07bQ0CIuLUdk7PrFv09/fT19fH\n1KlTO50VM7O2GY03BgrYjGVvDLw9IqL5WGZmZla2MlsHIOlA4FHgVmB29vcRSQeUOV8zs3p6enqQ\nRG9vb6ezYtYVSgsCJO0DnEB6J8BHgR2zvzcAJ0jau6x5m5nVMzAwQF9fn+t3mGXKfBxwOHBiRBxU\n6H+mpBOAzwNnljh/MzMza6LMxwGbAmc0GHZGNtzMzMw6pMwg4HlggwbDNsiGm5mZWYeUGQRcBHxd\n0pb5npLeDBybDTczM7MOKTMIOBxYAPxJ0j2SrpF0D+mjQi9kw83MzKxDSgsCIuIx4E3Ap4GbgPnZ\n308Bb46Ix8uat5mZmQ2ulNYBknqAnYFbIuIHwA/KmI+ZmZkNXyklARExAJwN+I0cZmZmXarMOgH3\nAWuVOH0zMzMbgTKDgG8CX5K0ZonzMDMzs2Eq842BOwDrAQ9Kupr08aC8iIiPlTh/MzMza6LMIGA7\nYIB08d+8znB/SdDMzKyDymodsBKwFTA3IhaWMQ8zMzMbmbbWCZA0UdK3gbnAw8Bzks6R5AqCZmZm\nXabdJQGHA4cCFwM3AhsBM4CfAnu2eV5mZmY2Au0OAj4G/DAiPlXrIeky4ARJL4sIfzTIzMysS7S7\nieBGwK8L/c4GJgKvavO8zMzMbATaHQT0kD4alFerGLhym+dlZmZmI1BG64D1JW2c656Y6z83nzAi\n7ith/mZmZtaCMoKA4uOAmnPr9JtYp5+ZmZmNgnYHAR9v8/TMzMysJG0NAiLi1HZOz8ysDD09PUhi\n8uTJzJkzp9PZMeuYMj8gZGbWlQYGBujr66O/v7/TWTHrKAcBZmZmFeUgwMzMrKIcBJiZmVWUgwCz\nJnp7e5HU6WyYmZXCQYBZE/39/fT19XU6G2ZmpXAQYGZmVlEOAszMzCrKQYCZmVlFOQgwMzOrKAcB\nZmZmFeUgwMzMrKIcBJiZmVWUgwAzM7OKchBgZmZWUQ4CzMzMKspBgJmZWUU5CDAzM6uorg8CJG0g\n6deS5kp6VtI5kia3OO6SOr/Fkt5Ydr7NrPv19PQgid7e3k5nxawjVup0BpqRNAnoAxYA+2a9jwWu\nkPTGiFjQwmROBk4o9Lu7fbk0s7FqYGCAvr4+pk6d2umsmHVEVwcBwEHAhsAmEXE/gKS/AvcABwPf\naWEaj0TE9aXl0MzMbIzq9scBuwJ/rAUAABHxAHANsFunMmVmZjYedHsQsDnwtzr9bwM2a3Eah0h6\nQdI8SZdL2rZ92TMzMxu7uj0IWBt4pk7/p4G1Whj/ROBAYDtgP2A1Un2C7duWQzMzszGq2+sEjEhE\nHJTrvF7S74BbgaOBd3UmV2ZmZt2h24OAZ6h/x9+ohKCpiFgg6XzgP5ulmzlz5tL/p0yZwpQpU4Y6\nKzMzG6Nmz57N7NmzO52NUdHtQcBtpHoBRZsBt5c103wQYGZm1VK8+Zs1a1bnMlOybq8TcD6wlaQN\naz2y/98JnDfUiUlajdSq4E9tyZ2ZmdkY1u1BwE+BB4DzJE2XNB04F3iQ3AuAJPVKelHSl3L9DpX0\n39l475S0F3AVsAHwxVFdCjMzsy7U1UFARMwHdiC94e804HTg78C0bFiNcr+avwNvB04BZgPfB/qB\nd0bEtaVn3szMrMt1e50AIuIhYMYgaR4EJhb6XQBcUGLWzMzMxrSuLgkwMzOz8jgIMDMzqygHAWZ1\n9Pb2ImnwhGZmY5iDALM6+vv76evr63Q2zMxK5SDAzMysohwEmJmZVZSDADMzs4pyEGBmZlZRDgLM\nzMwqykGAmZlZRTkIMDMzqygHAWZWeT09PfT29nY6G2ajzkGAmVXewMAA/f39nc6G2ahzEGBmZlZR\nDgLMzMwqykGAmZlZRTkIMDMzqygHAWZmZhXlIMDMzKyiHASYmZlVlIMAMzOzinIQYGZmVlEOAszM\nzCrKQYCZmVlFOQgwMyN9REiSPyRkleIgwMyM9BGhvr4+f0jIKsVBgJmZWUU5CDAzM6soBwFmZmYV\n5SDAzMysohwEmJmZVZSDADMzs4pyEGBmZlZRDgLMzMwqykGAWYHfGGdmVeEgwKzAb4wzs6pwEGBm\nZlZRDgLMzMwqykGAmZlZRTkIMDMzqygHAWZmZhXlIMDMzKyiHASYmeX09PQgye+LsEpwEGBmljMw\nMEBfX5/fF2GV4CDAzMysohwEmJmZVZSDADMzs4pyEGBmZlZRDgLMzMwqykGAmZlZRTkIMDMzqygH\nAWZmZhXlIMDMzKyiHASYmZlVlIMAMzOzinIQYGZmVlFdHwRI2kDSryXNlfSspHMkTW5x3FUkHSfp\nEUnzJV0rabuy82xmZjYWdHUQIGkS0AdsAuwLfBR4LXBFNmwwJwMHAF8CdgEeBS6W9MZycmxmZjZ2\nrNTpDAziIGBDYJOIuB9A0l+Be4CDge80GlHSFsDewP4RcVrW7yrgNuAoYPdSc25mZtblurokANgV\n+GMtAACIiAeAa4DdBhl3OrAIODs37mLgLGAnST1tz62ZjRs9PT1Iore3t9NZMStNtwcBmwN/q9P/\nNmCzQcbdDLg/IhbWGXdl4DUjz56VYfbs2R2Zb29vL5I6Mm/rPgMDA/T19dHf39/RfHTqeLBq6PYg\nYG3gmTr9nwbWGsG4teHWhTp10uvv76evr68j8zZrxEGAlanbgwAzMzMrSbcHAc9Q/46/0V1+q+PC\nshKBFfz4xz9uKXNmZmZjmSKi03loSNLlQE9EbF/o3wcQEVObjPtl4IvAmvl6AZJmAp8HVo+IgTrj\nde8KMTOzjoiIcVlhqNubCJ4PHCdpw6xVAJI2BN4JHD7IuBcAs4AZwOnZuBOBDwEX1wsAYPxuaDMz\ns6JuLwlYDfgLsAD4ctb7KOAlwBYRMT9L1wvcB8yMiGNy458J7EgKGO4HDgF2BraOiFtGaznMzMy6\nUVfXCcgu8jsAdwOnke7o/w5MqwUAGeV+efsDpwBHAxcC6wM7OQAwMzPr8iAAICIeiogZEbFmRKwR\nEXtExJxCmgcjYmJEHF3o/0JEHAa8DTgJCOD3kpZkpQeDytIWf4v96uGhkbSXpAslPSZpkaQ5kr4r\nafUWxvU3INpghNvAx0GbSNpJ0lWSnpA0IOkpSedJekML4/pYaJMRbodxczx09eOAdpH0LtKbAm8E\nJpIeEWxUDCYajLuE9A2CEwqDbq3zIiJrQNJ1wEPAb4EHgdcDXwXuiohtBhn3DOB9wGGkxzqfzLq3\niohby8z3eDLCbeDjoE0kfZi07q8H/gFsQKrEvCHw+oh4qMm4PhbaZITbYfwcDxFRqR/pg0KLgd4W\n0y8Bjup0vsf6D3h5nX4zsm0xpcl4W2TbYL9cv4nAncC5nV6usfQb7jbI0vk4KHfbvCpbx4c2SeNj\noQu2Q5Zu3BwPXf84wMaHiHiqTu9bSPU41m8yqr8B0SYj2AZWvrnZ32bnZB8L5WtlO4wrlVnQETpE\n0guS5km6XNK2nc7QOLEDqZ7GHU3S+BsQ5WplG9T4OGgjSRMkTZT0KuDHwGOkC3ojPhZKMIztUDMu\njgcHAYM7ETgQ2A7YD1gNuELS9k3HsqYkrU96j8OlEXFTk6T+BkRJhrANwMdBGf4EDJCe7b8T2DEi\nHmmS3sdCOYa6HWAcHQ+VqBiYJ+kAUmWOlioG1hl/EnAr8EhEvKvd+asCSS8BrgT+BXhHswNO0sXA\ny6JQcU3SNOASYPuIuKbM/I5HQ9kGDcb3cTBCkjYFXgr0Ap8DNga2iYj7GqT3sVCCoW6HBtMYs8eD\nSwKGKCIWkN5k+LZO52UskrQq6Z0NG5Le2TDYxWfY34Cw+oaxDVbg42DkIuKuiLgxIn4LvIdUye+I\nJqP4WCjBMLZDvWmM2ePBQYCNGkkrAb8B3gq8LyJub2G024CNsgtX3uakSlL3tjeX49swt4GVLCLm\nkV6KtmGTZD4WStbidhhXHAQMUfYq491Iz5GsRZImAGcCU4DpEXFDi6NeQKr0NCM3rUG/AWErGsE2\nqDctHwdtJGkt0sX87ibJfCyUrMXtUG+8MXs8dPsHhNpG0h7Zv1uSmkTtLOlJ4MmIuEp1vj8g6VBS\nu9E+4ClgMuklHRuQKoNY634I7AEcCyyQ9I7csIci4uF62yAi/iLpl8B3JK3Msm9AbAjsPZoLMA4M\naxv4OGgvpW+a3AzcRPouyibAZ0htz7+dpfGxULLhbodxdzx0+kUFo/UjbdjFdX5XxLKXRCwGvpwb\nZ1fgGtKGHgCeJL1t7a2dXp6x9iOdsOqt/8XAVxptg6z/KsDxwCPAfOA6YLtOL9NY+w13G/g4aPt2\n+ALpwvM8qRh/DvAz4DW5ND4WunQ7jLfjoXKtA8zMzCxxnQAzM7OKchBgZmZWUQ4CzMzMKspBgJmZ\nWUU5CDAzM6soBwFmZmYV5SDAzMysohwEmI2QpI9JWtLgN24/6iJpdraMiyWdnOv/rtzyv7vOeK/K\nDf/EEOd5s6S/NRm+cTbdr2TdP83N66qhzMusCirz2mCzkgWwJ/Bwof+LHcjLaAnSG9f+E/hHneHP\nAfsClxX670d6S9tLhzHPU4FvSXpzRNxcZ/jHSG8HPTXrPhb4KfDjYczLbNxzSYBZ+9wSEdcXfjeN\nZgayd8qPpuci4oaIuL/OsN8AH8i+tZ63L/DrYc7vDNJrXBu9o30f4JqIeBAgIh6IiOtJAYmZFTgI\nMBslkvbPiqXfIennkp6VNFfSiZJWKaSdJOkbku6T9EL29whJyqWpFbt/QNIJkp4AHssN31vSHZIW\nSLpF0q6S+iRdkQ3/l2zan6qT15mS5klaY5iLG6QgIIAP5qa7DbAxcDrpQ17F+W4h6XxJT0uaL+kP\nkrZdOtGIJ4HfA3tlX0XMj7tdNu2fDTPPZpXjIMCsfSZKKv7yF7rahzp+BvwV2AU4Gtg/+wss/Tzs\nJaS73a8D04DvAV8Cvllnvt8jfQVtT9JdNpKmAz8nFdfvDBwDfIP0pbSUmYjHgXOBg/ITyy6unwDO\niohnh7oScuaTAoF9c/32I318ZYWSA0lvyYZNytK9H3gUuEzSm3NJTwVeAexUmMS+2Tx/NYI8m1WK\n6wSYtYeAu+r0vxCYXuj3s4j4Rvb/H7K74xnA4Vm/jwDbAO+IiD/n0k0EjpX0jYjIP4O/MiI+XZjH\nkcCNEbHP0gxKtwF/K+TzR8AVkt4ZEddk/d4PrA/8T/NFbslpwKWS/hV4mrScn2uQ9jjSd9x3iogl\nWb8rJN0CfJllJQoXAHNJgcJF2bKtQgqCzo2If7Yh32aV4JIAs/YIYDdgy8Lv0Drpflfodxvpoluz\nE+lieHO+VIFUwW5lYKvC+OfmOyS9BHgT6fOmy2YccTuFQCUirgTuAA7O9T4YuDUibmi0sC1QNv0+\nUmXJfUifYF2VOnfqklYFts+GKbfcKwGXZsNqeV4EnAVMl/SyrPduwBosqxBoZi1wSYBZ+9wWEfe1\nkG5eoXsxMDHX/QpgU9K3yosCeHmhX7EZ4pqki3C9Gvv1+v0YOE7Sp4HVSUHIIXVzPjw/J921Pwic\nHxHPS1q7kGZt0jo4hlSjv2hJoftUUquEGcDJ2fQfZcWWCGbWhIMAs+7zFHAn6e55hcpzwAODjD+X\nFCysU2fYOqSLZd5ppLoH+5MuxvOAX7Sc28GdBvwXsBkrPhqpmUu60H+LdJdfb7mXiojrJd0F7Cvp\nAmBH4NsREc3GM7PlOQgw6z6/B3YHnoqIOYOkXeGiFxHzJN0MfAD4Wq2/pM1JJQyPFtI/L+kM4D9I\nbfd/0Ybn6kvzFRF3SfoBKQC5uG7iiPmSrgbeGBGfb3Eep5JKDr5AKkU4bWRZNqseBwFm7SHgzZLW\nrTPshlxFt1acQborv0rSN4FbgR7SBXxn4EMRsTA333pmAedK+gXpZTnrAjNJAUC9vPyI1EoggJ8M\nIa+NLJeviPi/LYzzWeBKSecDpwCPA2uR6lasFBFfLqQ/nRQEHArclNV5MLMhcBBg1h4BnN1g2Lqs\n+Ny+3vjpn4gXJe1EusP9JLAR6c2Dd5FKCRbVG2+5iUVcIGkfUiuB3wH3kork/wtYodlfRNwq6R5g\nbkT8ZZC8tqKVYvnl0kTEzZLeRsrzj0mPJp4GbiQFKRTSP5y982AarhBoNizyIzSzapD0ClL7/GMj\n4quFYa8l1UM4ICJ+1uL0+rJ/3w0s6cbn8dl7GmotKyZExPaDjGJWKW4iaDYOSVpV0nclTZe0jaSP\nkF5ANB84KZdufUlTgBOAR4Azhzir7UklEycNlrBDTiDlb7tOZ8SsG7kkwGwcktQDnAO8ndSk8Dng\nKuCL+Wfnko4EvkIqBTg4Iv4whHm8Fqi10/9HC5UYR52kyaTHMQDPR8Q9ncyPWbdxEGBmZlZRfhxg\nZmZWUQ4CzMzMKspBgJmZWUU5CDAzM6soBwFmZmYV5SDAzMysov4/CCooUeSS8foAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd63f0084a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colours=['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w', '0.25', '0.5', '0.75']\n",
    "for pos in range(0, 11):\n",
    "    output=gen_spectrum(0.1*pos, 40)\n",
    "    plot_output = plot_dd_spectra(output[1], output[0], colours[pos], 0.1*pos)\n",
    "    plot_output[0].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "f=open('dd_fusion_%dkeV.dat' %E_ave, 'w')\n",
    "#print (f)\n",
    "f.write('unit: MeV\\n')\n",
    "f.write('spectrum:\\n')\n",
    "for entry in range(len(bins_dd)-1):\n",
    "    f.write('%f %f\\n' %(bins_dd[entry], n_dd[entry]))\n",
    "f.write('%f 0' %bins_dd[len(bins_dd)-1])\n",
    "f.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f2=open('dt_fusion_%dkeV.dat' %E_ave, 'w')\n",
    "#print (f)\n",
    "f2.write('unit: MeV\\n')\n",
    "f2.write('spectrum:\\n')\n",
    "for entry in range(len(bins_dt)-1):\n",
    "    #print ('%f %f' %(bins_dt[entry], n_dt[entry]))\n",
    "    f2.write('%f %f\\n' %(bins_dt[entry], n_dt[entry]))\n",
    "f2.write('%f 0' %bins_dt[len(bins_dt)-1])\n",
    "f2.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}