00-intro.ipynb

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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "PyNE Tutorial\n",
      "=============\n",
      "\n",
      "Welcome to the PyNE tutorial! \n",
      "\n",
      "![PyNE Tree](https://raw.github.com/pyne/pyne/staging/img/pyne_icon_big.png)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Is PyNE Installed?\n",
      "\n",
      "You can verify that PyNE is successfully installed by running the following: \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pyne import data\n",
      "print data.atomic_mass('U235')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "235.043930131\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Have you cloned the tutorial repo?\n",
      "\n",
      "```bash\n",
      "$ git clone https://github.com/pyne/tutorial.git\n",
      "```\n",
      "\n",
      "----------------"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# What is PyNE?\n",
      "\n",
      "As out tagline says, PyNE is the open source nuclear engineering toolkit.  \n",
      "\n",
      "* PyNE is intended as a library of composable tools that is used to build nuclear science and engineering applications.\n",
      "* It is permissively licensed (2-clause BSD)\n",
      "* It supports both a C++ and a Python API.\n",
      "* The name 'PyNE' is a bit of a misnomer since most of the code base is in C++ but most daily usage happens in Python.\n",
      "* The v0.3 code base was just released!\n",
      "* As an organization, PyNE was born in April 2011.  However, core parts of PyNE have existed since 2007."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# What are the goals of PyNE?\n",
      "\n",
      "To help nuclear engineers:\n",
      "\n",
      "* be more productive,\n",
      "* have the *best* solvers,\n",
      "* have a beautiful API,\n",
      "* write really great code,\n",
      "* and teach the next generation.\n",
      "\n",
      "And of course, to perform neat tricks like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from pyne import mesh, material \n",
      "from pyne.xs.channels import sigma_t\n",
      "from yt.config import ytcfg; ytcfg[\"yt\",\"suppressStreamLogging\"] = \"True\"\n",
      "from yt.mods import *\n",
      "m = mesh.Mesh(structured_coords=[np.linspace(0.0, 1.0, 101), np.linspace(0.0, 1.0, 101), [0.0, 1.0]], structured=True)\n",
      "fuel = material.from_atom_frac({'U235': 0.045, 'U238': 0.955, 'O16': 2.0}, mass=1.0, density=10.7)\n",
      "cool = material.from_atom_frac({'H1': 2.0, 'O16': 1.0}, mass=1.0, density=1.0)\n",
      "for i, mat, ve in m:\n",
      "    coord = m.mesh.getVtxCoords(ve)\n",
      "    m.mats[i] = fuel if (coord[0]**2 + coord[1]**2) <= 0.5**2 else cool\n",
      "m.sigma_t = mesh.ComputedTag(lambda mesh, i: sigma_t(mesh.mats[i], group_struct=[10.0, 1e-6], phi_g=[1.0])[0])\n",
      "m.u_mass = mesh.ComputedTag(lambda mesh, i: max(mesh.mats[i]['U':'Np'].mass, 0.0))\n",
      "SlicePlot(PyneMoabHex8StaticOutput(m), 'z', 'sigma_t', origin='native').display()\n",
      "print \"Pu Content of Mesh:\", sum(m.u_mass[:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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\"><br>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x5f15590>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Pu Content of Mesh: "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1732.10345314\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# What should you do with PyNE?\n",
      "\n",
      "As a **user** you should do your work or research with PyNE.  Even if you have your own software which looks and behaves similar to some aspects of PyNE, using PyNE will mean that you no longer have to develop AND maintain that functionality.\n",
      "\n",
      "As a **developer** you should be selfish.  Contribute to PyNE in ways that support the work that you are doing. If a feature you want is not in PyNE right now, chances are that other people want to see that feature too!  This will help your future self as much as future other people."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# What goes into PyNE?\n",
      "\n",
      "Anything that is not export controllable, proprietary, or under HIPPA restrictions!  (If you have questions, ask.)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Contact PyNE\n",
      "\n",
      "**Website:** http://pynesim.org/\n",
      "\n",
      "**User's Mailing List:** pyne-users@googlegroups.com\n",
      "\n",
      "**Developer's List:** pyne-dev@googlegroups.com"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Questions?"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}