Index: wflow-py/notebooks/BMI-Test.ipynb
===================================================================
diff -u -r77194d628eb5380f03db806ed483bb4d10ac2676 -rdcf2515c371c781e4f801f39367923bfc09cc6cb
--- wflow-py/notebooks/BMI-Test.ipynb	(.../BMI-Test.ipynb)	(revision 77194d628eb5380f03db806ed483bb4d10ac2676)
+++ wflow-py/notebooks/BMI-Test.ipynb	(.../BMI-Test.ipynb)	(revision dcf2515c371c781e4f801f39367923bfc09cc6cb)
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:1347e91b524e1aac68675a82975d501e1206afa60a4c0009de57f2e8e7f9b72c"
+  "signature": "sha256:3525720c71c3811aeb5020713a2ab21ef24cbe5734bc55a8124d9c8d705ac397"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -20,21 +20,40 @@
      "collapsed": false,
      "input": [
       "import wflow.wflow_bmi as bmi\n",
+      "%matplotlib\n",
       "reload(bmi)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "<module 'wflow.wflow_bmi' from 'C:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\wflow\\wflow_bmi.pyc'>"
+       "ename": "TypeError",
+       "evalue": "cannot return std::string from Unicode object",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[1;32m<ipython-input-11-10badc29f5f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mwflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwflow_bmi\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mbmi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'matplotlib'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbmi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36mmagic\u001b[1;34m(self, arg_s)\u001b[0m\n\u001b[0;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m' '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2205\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2206\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2207\u001b[0m     \u001b[1;31m#-------------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_line_magic\u001b[1;34m(self, magic_name, line)\u001b[0m\n\u001b[0;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2125\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2126\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2127\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\core\\magics\\pylab.pyc\u001b[0m in \u001b[0;36mmatplotlib\u001b[1;34m(self, line)\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\core\\magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m    191\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    194\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    195\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\core\\magics\\pylab.pyc\u001b[0m in \u001b[0;36mmatplotlib\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m     78\u001b[0m         \"\"\"\n\u001b[0;32m     79\u001b[0m         \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic_arguments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_argstring\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatplotlib\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 80\u001b[1;33m         \u001b[0mgui\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menable_matplotlib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     81\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_show_matplotlib_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36menable_matplotlib\u001b[1;34m(self, gui)\u001b[0m\n\u001b[0;32m   2941\u001b[0m                 \u001b[0mgui\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_gui_and_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpylab_gui_select\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2942\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2943\u001b[1;33m         \u001b[0mpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivate_matplotlib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2944\u001b[0m         \u001b[0mpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigure_inline_support\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2945\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\IPython\\core\\pylabtools.pyc\u001b[0m in \u001b[0;36mactivate_matplotlib\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m    285\u001b[0m     \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'backend'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 287\u001b[1;33m     \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    288\u001b[0m     \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mswitch_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstyle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_pylab_helpers\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minteractive\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\colorbar.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     32\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0martist\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmartist\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcbook\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollections\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     35\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontour\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcontour\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\collections.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0martist\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0martist\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0martist\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mallow_rasterization\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackend_bases\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mbackend_bases\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmpath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\backend_bases.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtight_bbox\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtight_bbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtextpath\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtextpath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcbook\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmplDeprecation\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\textpath.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPath\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfont_manager\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfont_manager\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mft2font\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mFT2Font\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mKERNING_DEFAULT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mLOAD_NO_HINTING\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mft2font\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mLOAD_TARGET_LIGHT\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1410\u001b[0m                 \u001b[0mverbose\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Using fontManager instance from %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0m_fmcache\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1411\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1412\u001b[1;33m             \u001b[0m_rebuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1413\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1414\u001b[0m         \u001b[0m_rebuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36m_rebuild\u001b[1;34m()\u001b[0m\n\u001b[0;32m   1395\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_rebuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1396\u001b[0m         \u001b[1;32mglobal\u001b[0m \u001b[0mfontManager\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1397\u001b[1;33m         \u001b[0mfontManager\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFontManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1398\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0m_fmcache\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1399\u001b[0m             \u001b[0mpickle_dump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfontManager\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_fmcache\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, size, weight)\u001b[0m\n\u001b[0;32m   1050\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefaultFont\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ttf'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mttffiles\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1052\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mttflist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreateFontList\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mttffiles\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1054\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mafmfiles\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfindSystemFonts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'afm'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;32mC:\\Users\\schelle\\AppData\\Local\\Continuum\\Anaconda\\lib\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36mcreateFontList\u001b[1;34m(fontfiles, fontext)\u001b[0m\n\u001b[0;32m    577\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m                 \u001b[0mfont\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mft2font\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFT2Font\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    580\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m                 \u001b[0mverbose\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Could not open font file %s\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mfpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+        "\u001b[1;31mTypeError\u001b[0m: cannot return std::string from Unicode object"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -61,7 +80,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "bmimodel.get_var_name(1)\n"
+      "bmimodel.get_var_count()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -71,7 +90,7 @@
        "output_type": "pyout",
        "prompt_number": 3,
        "text": [
-        "'SurfaceRunoff'"
+        "5"
        ]
       }
      ],
@@ -81,6 +100,30 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "for i in range(0,5):\n",
+      "    print bmimodel.get_var_name(i)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "IF\n",
+        "SurfaceRunoff\n",
+        "FirstZoneDepth\n",
+        "Latitude\n",
+        "Longitude\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "bmimodel.get_var_shape('SurfaceRunoff')"
      ],
      "language": "python",
@@ -107,23 +150,39 @@
      "metadata": {},
      "outputs": [
       {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "<matplotlib.image.AxesImage at 0xa68be48>"
+       "ename": "NameError",
+       "evalue": "name 'imshow' is not defined",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[1;32m<ipython-input-10-c2b68dc32b67>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbmimodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_var\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'FirstZoneDepth'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[1;31mNameError\u001b[0m: name 'imshow' is not defined"
        ]
-      },
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "imshow(bmimodel.get_var('Longitude'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
       {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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8EBUvsR9BMjJCq3DKDFz2Z31Ucm5ke/BOBFGkEBINwveiIligyR6zAKyy/44h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\nBwAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0xa667320>"
+       "ename": "NameError",
+       "evalue": "name 'imshow' is not defined",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[1;32m<ipython-input-9-a321813ef215>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbmimodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_var\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Longitude'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[1;31mNameError\u001b[0m: name 'imshow' is not defined"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 9
     },
     {
      "cell_type": "code",