Enrich_relations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Render our plots inline\n",
    "%matplotlib inline\n",
    "%pylab inline  \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "\n",
    "# General Plotting Parameters\n",
    "mpl.rcParams['figure.figsize'] = (8,5)\n",
    "mpl.rcParams['lines.linewidth'] = 2.5\n",
    "mpl.rcParams['font.weight'] = 'bold'\n",
    "mpl.rcParams['axes.linewidth'] = 1.5\n",
    "mpl.rcParams['font.size'] = 14.\n",
    "mpl.rcParams['legend.fontsize'] = 12.\n",
    "mpl.rcParams['axes.labelsize'] = 12.\n",
    "mpl.rcParams['xtick.labelsize'] = 10.\n",
    "mpl.rcParams['ytick.labelsize'] = 10.\n",
    "mpl.rcParams['xtick.minor.pad'] = 4\n",
    "mpl.rcParams['xtick.direction'] = 'out'\n",
    "mpl.rcParams['ytick.direction'] = 'out'\n",
    "\n",
    "#Git says this is patched, but it doesn't work from Pip --upgrade 26-mar-2015\n",
    "#mpl.rcParams['xtick.minor.visible'] = True  \n",
    "\n",
    "# These are the \"Tableau 20\" colors as RGB.  \n",
    "tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14),\n",
    "             (255, 187, 120), (44, 160, 44), (152, 223, 138),\n",
    "              (148, 103, 189),\n",
    "             (197, 176, 213), (140, 86, 75), (196, 156, 148),  \n",
    "             (227, 119, 194), (247, 182, 210), (127, 127, 127),\n",
    "             (199, 199, 199), (188, 189, 34), (219, 219, 141),\n",
    "             (23, 190, 207), (158, 218, 229),(214, 39, 40), (255, 152, 150)]  \n",
    "    \n",
    "# Scale the RGB values to the [0, 1] range,\n",
    "# which is the format matplotlib accepts.  \n",
    "for i in range(len(tableau20)): \n",
    "    r, g, b = tableau20[i]  \n",
    "    tableau20[i] = (r / 255., g / 255., b / 255.)  \n",
    "\n",
    "plot_dir = '/Users/mbmcgarry/git/data_analysis/enrich_calcs/' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import calc_enrich\n",
    "reload(calc_enrich)\n",
    "\n",
    "# Standard 2 (P2-Type) parameters (Glaser 2009)\n",
    "d = 0.15  # m \n",
    "Z = 1.0   # m\n",
    "F_m_hrs = 15*60*60/(1e3) # grams/hr  \n",
    "T = 320.0# K\n",
    "cut = 0.5\n",
    "v_a = 485\n",
    "#omega = 485/(d/2)\n",
    "#v_a = omega * (d/2.0)\n",
    "\n",
    "# Estimates for IR-1 cascade \n",
    "Fc_month = 739 #kg/month\n",
    "Pc_month = 77 #kg/month\n",
    "Nfc = 0.0071\n",
    "Npc = 0.035\n",
    "Nwc = 0.001\n",
    "\n",
    "F_m = F_m_hrs/(60*60*1000.0)\n",
    "Fc = Fc_month/(30.4*24*60*60)\n",
    "Pc = Pc_month/(30.4*24*60*60)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x113fe9ad0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11334de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from calc_enrich import calc_del_U\n",
    "from calc_enrich import alpha_by_swu\n",
    "from calc_enrich import N_product_by_alpha\n",
    "\n",
    "S2_alpha, S2_del_U, del_U_yr = calc_del_U(v_a, Z, d, F_m, T, cut)\n",
    "\n",
    "dels = []\n",
    "alpha_dels = []\n",
    "\n",
    "for i in range(100):\n",
    "    del_U = S2_del_U*(3-(0.1*i))\n",
    "    dels.append(del_U)\n",
    "    alpha_dels.append(alpha_by_swu(del_U, F_m, cut))\n",
    "\n",
    "scaled_dels = [x*1e7 for x in dels]\n",
    "\n",
    "plt.scatter(scaled_dels, alpha_dels)\n",
    "plt.xlabel('sep. potential (1e7 kg/s)')\n",
    "plt.ylabel('sep. factor (alpha)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111694850>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "SZK6mCBIkqQuJgiSJKnL/wcxRXNOVxDOiQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114016f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ts = []\n",
    "alpha_Ts = []\n",
    "\n",
    "for i in range(30):\n",
    "    T_i = 240+ (5*i)\n",
    "    alpha_i, del_U_i, del_U_yr = calc_del_U(v_a, Z, d, F_m, T_i, cut, eff=1.0)\n",
    "    Ts.append(T_i)\n",
    "    alpha_Ts.append(alpha_i)\n",
    "    \n",
    "plt.scatter(Ts,alpha_Ts)\n",
    "plt.xlabel('T (K)')\n",
    "plt.ylabel('sep. factor (alpha)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1126173d0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "Acr9ng9cPMfaJfXAQX4k9SQi3gPclpnv72Hf1wOPyszJP/+T1ADDX5KklvG2vyRJLWP4S5LUMoa/\n",
       "JEktY/hLktQyhr8kSS1j+EuS1DKGvyRJLWP4S5LUMv8fnbkXyNEwX1oAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d87410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Zs = []\n",
    "alpha_Zs = []\n",
    "\n",
    "for i in range(30):\n",
    "    Z_i = 0.1 + (0.1*i)\n",
    "    alpha_i, del_U_i, del_U_yr = calc_del_U(v_a, Z_i, d, F_m, T, cut, eff=1.0)\n",
    "    Zs.append(Z_i)\n",
    "    alpha_Zs.append(alpha_i)\n",
    "    \n",
    "plt.scatter(Zs,alpha_Zs)\n",
    "plt.xlabel('Z (m)')\n",
    "plt.ylabel('sep. factor (alpha)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1147c68d0>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "SdozSd4I3FlViz3Wt9jnXg68tKpO7VD3+2l6H55VVd9dVqCSAJO+pDFI8kLgnl2M0Jc0RCZ9SZJm\n",
       "hPf0JUmaESZ9SZJmhElfkqQZYdKXJGlGmPQlSZoRJn1JkmbE/wfPVc8kYhWqKwAAAABJRU5ErkJg\n",
       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f3cb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vs = []\n",
    "alpha_vs = []\n",
    "\n",
    "v_min = 200\n",
    "for i in range(40):\n",
    "    v_i = v_min + (20*i)\n",
    "    alpha_i, del_U_i, del_U_yr = calc_del_U(v_i, Z, d, F_m, T, cut, eff=1.0)\n",
    "    vs.append(v_i)\n",
    "    alpha_vs.append(alpha_i)\n",
    "    \n",
    "plt.scatter(vs,alpha_vs)\n",
    "plt.xlabel('v (m/s)')\n",
    "plt.xlim([v_min, max(vs)*1.1])\n",
    "plt.ylabel('sep. factor (alpha)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1939942860967817e-07"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114084c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from calc_enrich import machines_per_cascade\n",
    "\n",
    "alpha_ms = []\n",
    "n_mach = []\n",
    "\n",
    "for i in range(30):\n",
    "    del_U_i = S2_del_U*(3-(0.1*i))\n",
    "    alpha_i = alpha_by_swu(del_U_i, F_m, cut)\n",
    "    n_cf = machines_per_cascade(del_U_i, Npc, Nwc, Fc, Pc)\n",
    "\n",
    "    dels.append(del_U_i)\n",
    "    alpha_ms.append(alpha_i)\n",
    "    n_mach.append(n_cf)\n",
    "\n",
    "plt.scatter(alpha_ms, n_mach)\n",
    "plt.xlabel('sep. factor')\n",
    "plt.ylabel('machines/cascade')\n",
    "\n",
    "S2_del_U\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1147c6fd0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "Ukf8f9bhFc1liZjzAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b21fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fs = []\n",
    "alpha_fs = []\n",
    "print F_m\n",
    "for i in range(1,50):\n",
    "    Fmi = (F_m*(0.1*i))\n",
    "    Fs.append(Fmi)\n",
    "    alpha_i, del_U_i, del_U_yr = calc_del_U(v_a, Z, d, Fmi, T, cut)\n",
    "    alpha_fs.append(alpha_by_swu(del_U_i, Fmi, cut))\n",
    "    \n",
    "scaled_fs = [x*1e6 for x in Fs]\n",
    "    \n",
    "plt.scatter(scaled_fs, alpha_fs)\n",
    "plt.xlabel('flow rate (1e-6 kg/s)')\n",
    "plt.ylabel('sep. factor (alpha)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013700244194341928"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "aJAkSZ0YNEiSpE4MGiRJUicGDZIkqZP/BeECTn1DJZaQAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b60b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cuts = []\n",
    "alpha_cuts = []\n",
    "assays = []\n",
    "Nf = 0.007\n",
    "\n",
    "for i in range(1,50):\n",
    "    cut_i = i*0.02\n",
    "    cuts.append(cut_i)\n",
    "    alpha_i, del_U_i, del_U_yr = calc_del_U(v_a, Z, d, F_m, T, cut_i)\n",
    "    alpha_cuts.append(alpha_by_swu(del_U_i, F_m, cut_i))\n",
    "    assay_i = N_product_by_alpha(alpha_i,Nf)\n",
    "    assays.append(assay_i)\n",
    "scaled_assays = [x*y for x,y in zip(cuts, assays)]\n",
    "scaled_cuts = [x*y for x,y in zip(cuts, alpha_cuts)]\n",
    "    \n",
    "plt.scatter(cuts, assays)\n",
    "plt.xlim([-0.1,1])\n",
    "plt.ylim([min(assays)*0.9,max(assays)*1.1])\n",
    "plt.xlabel('cut (P/F)')\n",
    "plt.ylabel('Product Assay')\n",
    "\n",
    "max(assays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114df6ad0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "kCRJnZg0SJKkTkwaJElSJyYNkiSpE5MGSZLUiUmDJEnq5P8D+ZdknfuBcwoAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1125e6f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(cuts, scaled_assays)\n",
    "plt.xlim([-0.1,1])\n",
    "plt.ylim([min(scaled_assays)*0.9,max(scaled_assays)*1.1])\n",
    "plt.xlabel('cut (P/F)')\n",
    "plt.ylabel('cut * assay')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114cc5590>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "P/cnlLQYUb8HT5IWLiK+RTGq4QdztDkSWJ+Zz52tTVv7DwG3ZuaFXQpTUguTA0kDFxH/Cdg3M68Z\n",
       "dCySTA4kSVIbaw4kSVKFyYEkSaowOZAkSRUmB5IkqcLkQJIkVZgcSJKkiv8PiOKZXrWUzI4AAAAA\n",
       "SUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114856cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(cuts, scaled_cuts)\n",
    "plt.xlim([-0.1,1])\n",
    "plt.xlabel('cut (P/F)')\n",
    "plt.ylabel('cut * alpha')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150ea290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps = 1\n",
    "plt.figure(figsize=(10, 12))\n",
    "\n",
    "plt.subplot(4, 2, 1)    \n",
    "plt.scatter(scaled_dels, alpha_dels)\n",
    "plt.xlabel('sep. potential (1e7 kg/s)')\n",
    "plt.ylabel('sep. factor (alpha)')\n",
    "\n",
    "plt.subplot(4, 2, 2)    \n",
    "plt.scatter(Ts,alpha_Ts)\n",
    "plt.xlabel('T (K)')\n",
    "plt.ylabel('sep. factor (alpha)')\n",
    "\n",
    "plt.subplot(4, 2, 3)    \n",
    "plt.scatter(Zs,alpha_Zs)\n",
    "plt.xlabel('Z (m)')\n",
    "plt.ylabel('sep. factor (alpha)')\n",
    "\n",
    "plt.subplot(4, 2, 4)    \n",
    "plt.scatter(vs,alpha_vs)\n",
    "plt.xlabel('v (m/s)')\n",
    "plt.xlim([v_min, max(vs)*1.1])\n",
    "plt.ylabel('sep. factor (alpha)')\n",
    "\n",
    "plt.subplot(4, 2, 5)    \n",
    "plt.scatter(alpha_ms, n_mach)\n",
    "plt.xlabel('sep. factor')\n",
    "plt.ylabel('machines/cascade')\n",
    "\n",
    "plt.subplot(4, 2, 6)    \n",
    "plt.scatter(scaled_fs, alpha_fs)\n",
    "plt.xlabel('flow rate (1e-6 kg/s)')\n",
    "plt.ylabel('sep. factor (alpha)')\n",
    "\n",
    "plt.subplot(4, 2, 7)    \n",
    "plt.scatter(cuts, assays)\n",
    "plt.xlim([-0.1,1])\n",
    "plt.ylim([min(assays)*0.9,max(assays)*1.1])\n",
    "plt.xlabel('cut (P/F)')\n",
    "plt.ylabel('product assay')\n",
    "\n",
    "plt.subplot(4, 2, 8)    \n",
    "plt.scatter(cuts, scaled_assays)\n",
    "plt.ylim([min(scaled_assays)*0.9,max(scaled_assays)*1.1])\n",
    "plt.xlabel('cut (P/F)')\n",
    "plt.ylabel('cut * product assay')\n",
    "\n",
    "plt.tight_layout()\n",
    "if ps == 1:\n",
    "    savefig(plot_dir + 'enrich_relations.png')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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