Repository: projectmesa/mesa

Issue:
Passing dictionary argument to BatchRunner
Not sure if it is an issue and not intended behaviour but nevertheless decided to notify about it.

When you pass a dictionary value into batch runner it only takes keys and not values. So you'll not get a cartesian product of your values. For example, if you pass something like this:

``` python
class MoneyModel(Model):

    def __init__(self, n, grid_params):  # grid_params is intended to be a dictionary
        super().__init__(self)
        ...


def main():
    ...
    n_iters, n_steps = 5, 200
    params = {
        "grid_params": {"height": 10, "width": 10},
        "n": range(10, 500, 10)}
    batch = BatchRunner(MoneyModel, params,
        iterations=n_iters, max_steps=n_steps,
        model_reporters={"Gini": compute_gini})
    batch.run_all()
```

You'll get issue in there: https://github.com/projectmesa/mesa/blob/master/mesa/batchrunner.py#L71, i.e. variable `kwargs` will be equal to `{'grid_params': 'width', 'n': 10}` (or any other dictionary key) instead of original value. 


Discussion:

Patch:
diff --git a/docs/tutorials/intro_tutorial.ipynb b/docs/tutorials/intro_tutorial.ipynb
index 63b79978..3127a7fd 100644
--- a/docs/tutorials/intro_tutorial.ipynb
+++ b/docs/tutorials/intro_tutorial.ipynb
@@ -183,9 +183,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "empty_model = MoneyModel(10)\n",
@@ -235,9 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class MoneyAgent(Agent):\n",
@@ -294,15 +290,13 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 4.,  0.,  0.,  0.,  0.,  2.,  0.,  0.,  0.,  4.]),\n",
-       " array([ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8,  2. ]),\n",
+       "(array([ 4.,  0.,  5.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),\n",
+       " array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
@@ -312,9 +306,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1DBDXpIaZshLUsMMeUlq2P8BrOKkBgdtOTYAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x106cd7b38>"
+       "<matplotlib.figure.Figure at 0x110782ba8>"
       ]
      },
      "metadata": {},
@@ -350,16 +344,14 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 437.,  303.,  144.,   75.,   28.,    9.,    4.]),\n",
-       " array([0, 1, 2, 3, 4, 5, 6, 7]),\n",
-       " <a list of 7 Patch objects>)"
+       "(array([ 429.,  306.,  160.,   63.,   26.,   16.]),\n",
+       " array([0, 1, 2, 3, 4, 5, 6]),\n",
+       " <a list of 6 Patch objects>)"
       ]
      },
      "execution_count": 8,
@@ -368,9 +360,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10cee86d8>"
+       "<matplotlib.figure.Figure at 0x11901ae80>"
       ]
      },
      "metadata": {},
@@ -602,14 +594,12 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x10fb446a0>"
+       "<matplotlib.colorbar.Colorbar at 0x119326b00>"
       ]
      },
      "execution_count": 13,
@@ -618,9 +608,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAScAAAEACAYAAADx87DPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAScAAAD8CAYAAAA11GIZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkf4fThnIruhslcgAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fa4b860>"
+       "<matplotlib.figure.Figure at 0x1191a07b8>"
       ]
      },
      "metadata": {},
@@ -737,9 +727,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "model = MoneyModel(50, 10, 10)\n",
@@ -757,14 +745,12 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10fa4b278>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1192d1518>"
       ]
      },
      "execution_count": 16,
@@ -773,9 +759,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xmc1fP+B/DXu41SIlEqTSil3CK0iGsQxtYipbiKixK5\nbn6Xyg/1u2Qvy+Wiy5W9KCpXiMshbUabpGlTmVal0qpmef/+eJ8xZ2bO8j1nvmf5nvN6Ph7n0fl+\nz+d8vp/5NvM+n/NZRVVBRETeVCXZBSAiotgxiBMReRiDOBGRhzGIExF5GIM4EZGHMYgTEXmYoyAu\nIjkikiciK0RkWJDXDxeRaSKySESWiMj1rpeUiIgqkEjjxEWkCoAVAC4AsBFALoC+qpoXkGYEgMNV\ndYSI1AewHEADVS2MW8mJiMhRTbwDgJWquk5VCwBMANC9XBoFUMf/vA6AXxjAiYjiz0kQbwwgP+B4\nvf9coGcBtBaRjQAWA7jDneIREVE4bnVsXgxgoao2AnAagOdEpLZLeRMRUQjVHKTZAKBpwHET/7lA\nNwB4GABUdbWIrAHQCsC3gYlEhAu1EBHFQFUl2HknNfFcAM1FJEtEagDoC2BauTTrAHQFABFpAOAk\nAD+GKAgf/sfIkSOTXoZUefBe8H7wfoR+hBOxJq6qRSIyBMAMWNB/WVWXicgge1nHAXgQwHgR+c7/\ntrtVdXukvImIqHKcNKdAVT8G0LLcuRcDnm+CtYsTEVECccZmEmVnZye7CCmD96Is3o+yeD9CizjZ\nx9WLiWgir0dElA5EBBqiY9NRc0q8NWvWDOvWrUt2MVJCVlYW1q5dm+xiEJFHpERN3P8pk7BypDLe\nCyIqL1xNnG3iREQexiBORORhDOJERB7GIO6CwYMHY/To0a6nJSKKhB2bDk2YMAFPPfUUvv/+e9Su\nXRvHH388+vfvj8GDB7t6HS/cCyJKLHZsVtKYMWMwdOhQDBs2DFu2bMHmzZvxwgsvYPbs2SgoKEh2\n8YgogzGIR7Br1y6MHDkSzz//PHr27InDDjsMANCuXTu8/vrrqF69Om644Qbcf//9AIAvv/wSxx13\nHMaOHYsGDRqgcePGGD9+/O/5BaYlIqosBvEI5syZg4MHD6Jbt26O37N582bs3r0bGzduxEsvvYTb\nbrsNv/76axxLSUSZyjNBXKTyj1hs27YN9evXR5UqpbeqS5cuOPLII1GrVi18/fXXFd5To0YN3Hff\nfahatSouueQS1K5dG8uXL4/1RyciCiklpt07kay+vqOOOgrbtm1DcXHx74F81qxZAICmTZuiuLg4\n6HsCg36tWrWwZ8+exBSYiDKKZ2riydK5c2cccsghmDp1aoXXOIqEiJKNQTyCunXr4v7778ett96K\nyZMnY8+ePVBVLFq0CPv27Ut28YjIRSNHAvPnJ7sU0WEQd+Cuu+7C2LFj8dhjj6Fhw4Zo2LAhBg8e\njMceewydO3eO+H6JtUGeiBJm61bg4YeBp59Odkmiw8k+KYb3gig5xo4FPv0UmDMHWL8eqF072SUq\nxck+RBQS6wx2D15+GRgxAvjjH4HJk5NdIuccBXERyRGRPBFZISLDgrz+NxFZKCILRGSJiBSKyBHu\nF5eI3DR2LHD99ckuRfzdfjuwbFno1+fOBQoKgHPOAfr3B159NXFlq6yIzSkiUgXACgAXANgIIBdA\nX1XNC5H+cgB/VdWuQV5jc0oEvBeUKN98A1x2mQWvrVuB6tWTXaL4yM8HTjgBaN0amDcPOPTQimlu\nuglo0QIYNgw4cABo1AhYsADIykp8eYOpbHNKBwArVXWdqhYAmACge5j0/QC8HX0xiShRdu0C+vUD\nXnjBAtzcuckuUfx88IH9rC1aAMOHV3x9925rPhkwwI4POQTo0wd4443EljNWToJ4YwD5Acfr/ecq\nEJGaAHIAeKhFiSjzDBkCXHAB0KsXcPHFwIwZyS3Prl1AvObDTZsGdOsG/OtfwPvvA9Onl339nXeA\nc88FGjYsPTdgAPDaa97oL3C7Y/MKAF+r6k6X8yUil7zxBpCbCzz5pB1fdBHwySfu5F1UBHz7bfTv\n69PHAunu3e6Uo8Tu3cDs2fYzHnmk/ew33ggsXQps2GCPf/3LzgXq2NEC+Lx5wfOdPBkIsuJGUjiZ\ndr8BQNOA4yb+c8H0RYSmlFGjRv3+PDs7G9nZ2cjKyuJYar+sVGmEo7Q0Zw4wdKgNpfMvyImzzgLy\n8oBffgGOOir2vHfsAK69FvjoI2DJEuCUU5y97+OPgTVrbFTIVVcB//mPe+3zM2YAnTsDhx9ux+ec\nA9x5pwX1EscfD1xySdn3iVhtfNQo4MUXS9vGd++2TtJZs4BffwXGjQN69HCnrIF8Ph98Pp+zxKoa\n9gGgKoBVALIA1ACwCMDJQdLVBfALgJph8lIiSo68PNUGDVSnT6/42mWXqU6YEHveixernnii6l//\nqvr3v6v+6U/O3ldQoNqmjerUqfb8iitU+/dXLS6OvSyBBgxQffbZ2N67a5fq3Xer1quneu21qpMm\nqbZooXrjjap79qh++61qw4aq//63O2UNxx87g8fVUC9o2eCbA2A5gJUAhvvPDQIwMCDNAABvRcgn\n/j8tEVWwcaNqs2ahA84zz6j++c9lzy1apPrjj+HzPXDA3lu/vuobb9i5nTst8EV6r6rqCy+onnde\nadDeu1e1UyfVgQPtwyYvT/W33yLnE0xhoZVr7drY3l9i507VRx9Vbd++4gddXp5qVpbqAw+o7t9f\nueuEU+kg7taDQZzIPStXqt57b+R0v/6qetppFmhCyctTbdy4NJhu3my1zKws1Q0bKqYvLFR97TX7\nYMjJUV2ypOzrw4er3npr5HI1bKi6YEHZ81u3qg4ZonrRRarNm6vWrGm13mjNnKnarl3074vWTz+p\nXn65/SyjR6tu3+7+NRjEiYKYP1+1e3fVe+6xr/JeM2KE/QXPnBk6zYEDql27qg4aFL6JorjYAvbS\npapFRaoXX2wfEA8/rNq2rdVGS+TmWnA86yzVL78Mnt/mzapHHKG6aVPoaw4frnr99WF/RFW1gP7E\nE5HTlXf33c4+5NyyZIk139Srp/rFF+7mzSBOFGDZMtXevVWPPVb16actyHXtajVArygutlrqX/6i\n2rlz8ABdVGRtud27W805koEDVceOVR0zxvIsKLB8b7tN9fzzreY8YoTqMcdY00mkduvbblMdNqzi\n+U2brN27SRPV9esjl+uVV1T79YucrrxWrVS/+Sb691XWJ5+oNmoU/gMsWgzi5JrCQtUHH7Q/eLc6\nn+KtoEB12jQLeK1bqx51lNUw9+yx1wsLLdhkZVnt3AsWLbKmjMJCqym/917FNMOGWTDeu9dZnpMm\nqZ58surRR5dtzy4sVO3ZU7V2bdUrr3QenNassVrp3LmqCxfaY+xYu/93320dh058953qSSc5S1ti\n+XL7kC4qiu59brnvPmvrd/Lh6QSDOLkiP1/13HNVs7Ptj/3tt5NdImcef9xGQDz0kNXMQv1hvfKK\nBcRkW7VKdcqU8GnuvVf1b3+z5x99pNqyZWmT0IEDVmNu2VJ12zbn192xQ7VaNdW33qr42r591nQS\n7Qf3/fdb00vJo0cP+yYUjYIC1Vq17JuAU3fdpXrnndFdx02FhRbE77svdJpoPmAYxKnSPvrIvkaP\nHm2/oPPm2XC1LVuSXbLwiovta/XXX0dOu3ev6qGHuld7itVNN1ln3oABwWurxcUWoOfNKz0+7zzV\nF1+0DsC2ba2jbePG6K/900+VKnrcdO6s6vM5S7t3r41KWbUqvmWKZNMma1Z57jkrS8kH4OLF1s5/\nxBGq11xjH7qRhAviXIqWItq3z1a6mzgRuOceoGpVoEMHW+3t9tuTXbrw5s0DiottQksktWoBxxwD\nrFsX/3KFcuAA8N57tvhStWpA+/YVd5pZutT+T848045FgEcfBe6+G7j0Uvt32jTg2GOjv/5xx1X+\nZ4iH0093PhN0wgSbcXniifEtUyQNG9r/5SefANnZQL16tgjXZZfZpKp582ypgR497P8zlBUrIlwo\nVHSPxwOsiXvSP/5hnWPl7dtnbZWTJye+TE4NHGjNKE5ddJHqhx/GrzyRvP++6h//WHo8caLVKmfM\nKD03cqTq0KEV3ztxorudaanEaedmcbENpww2oSnZtmyxb0+B3/QKCqyT96yzQg9N/NOf2JwSkyVL\nrD0v2V+tk+3gQevwmzs3+OuzZlkHktPOs0Tau1f1yCOdjYAocfvtNjojWXr3tmaRQDNnWiAvGUrY\nurXq7NmJL1syOe3cnD3bZo4mq0MzFkVF1unetWvF13bvVq1bl80pMfnsM9tv75prgIMHk12a5Hn7\nbVuqtGPH4K+fdZZ9RXRrASU3vfeelbtx0DU3g2vVCli+PH5lCmfXLruPvXqVPX/22cCbbwJXXmn/\n/vpr6P+PdHXyybZl2q5d4dM9+yxw221AFQ9FtipVgCeeAH74wdacCTRlCtClS4T3x69o3rZ2LfB/\n/2cB/Ior4rdMZioZMQLo2RPY6V+DsrgYeOQROx9Oz562xGeqeeUV4IYbontPy5a2GFQyTJliK/kF\nW4TqootsIaYBAyzIeylIuaFaNaBtW2DhwtBptmyxZWa9uFNR9erAoEHAc8+VPf/GG8B114V/b4b9\nKji3Zo3Vyt59F2jSBLjwQtsBJV298Yb9rI0aWYfZ998DU6faSnddK+zRVFb37sCHH6bW/Vm7Fli8\n2NaRjkYya+JvvWXf/ELp2dNWCPyf/0lcmVLJ6adX7OQtsW8fcO+9QO/etuSsFw0caIMHSipRmzZZ\n52ek32EG8RDWrgWaNbMawEsv2b9TpiS7VPGxcKEtT/r++1YTGDkSOO88CxYjRtjoh3CaNLGRAF99\nlZjyOvHqq0DfvsG34gqnUSNg797SP6RE2bLFdteJ9Ad74YVA06bh06SrYEG8oMC+obRoYUvhBqx0\n7TkNG9qSuOPH2/GECVZBqlUrwhtDNZbH4wGPdGwWF6vWqWOTH0q8/bZNPU43W7fazL+JE8ueX7DA\n1ttw2kH00EM2zTrZiottZbyjj7Z1QGJx+umhO3Lj5ZlnnC/fmqkWL7bx8SXy8+34wgttPZd0MGuW\nLadQVGSrJn72mZ0HR6dEZ9s26xEOdOCATW7Jy0tOmeLlssuCr28RrWXLbBW8ZI4K2L/f1npu3dqm\nXcfq2mtVx493r1yRbNhgIyo+/jhx1/Sikpmbu3ZZBatNG1siNp2UDJEcM8YmCpWMjgsXxNmcEsTa\ntbbbR6AaNYA//9k2lk0Xa9ZYm9sDD1Q+r1atgDp1Ytuayw3bt9vOMLt22c900kmx55XIzs1Vq2z0\nyc03216XFFq1asAf/mDNTj16WF/NXXclu1TuErHRNXffbf0jVatGfg+DeBBr1lh7eHkDBwKvvx5+\ndpWXvP22dQS5tRVWjx7JGaVy8KBt69Wli3UM1a5dufwS1bm5aJF98IwYAQwbFv/rpYPTT7ct4Bo0\nAMaOjdxf40X9+tmw2P79naVnEA8iWE0csMDeqZMFCq9TtTHH117rXp7JGGqoajWXOnVsrK0bf9St\nWsW3Jv7TT9Z5fNFFwDPPWC2cnOnaFTjjDOu4TtdhlrVqAT/+aN86nEjT21A5a9YED+IAcMstwPPP\nJ7Y88bBkiY3C6NzZvTzPOMPG0ydynPWTT9rO7W++6eyrpxPNm9vvQGFh6bl162wUxIwZ1gQSywSw\nRYtsbZPTTrPmny++sG8Q5FzPnjYWPNpRR14Tze8yg3gQoZpTABsC9PPPwDffJLRIrnvzTfva5mZt\npkoVoE8fG68bGADjZdo0YMwY+7eyTSiBata0xaPWrCk9N2iQfct47DGrQTdoYB/mxcXO8x061Ga4\n5ucD//gH0KaNe2WmDBaqx1PLjirJAZAHYAWAYSHSZANYCOB7AF+ESJOA/t3KO/nkinsGBnrtNdtP\nz+0tmBKlqEj1uONsPQq37d9vey727m3rrsTLnDm2nkjJcqxuy8lR/eADez5/vo28Cdyw94cfVDt2\ntCVgV6+OnN/q1Tbs0cmyo0TloTKjU0SkCoBnAVwMoA2AfiLSqlyaugCeA3C5qp4CoLcrnzBJoFo6\n0SeU666zDs6+fa1zxT6f3LN/v7v5lTdrFlC3rvM2t2gceqjVWPfutZp+PGZxrlhhnajjx9uSuPEQ\n2C7+8MPAnXcChxxS+vrJJ9t9vPRSK0PnzqWPe++tmN+rr9r9qFEjPuWlDBYqumtp7bkTgI8Cjoej\nXG0cwGAAf3eQV0I+tSpj0yar4TmxZo0NyI+0q3c0tmyxMepXX22TGeLhlltse7J4+u03G4N+3XXu\n5rtpk+rxx6u+9JK7+Zb3/PM25jwvz34fdu8OnTY/31bPmz3bVho89tiy27wVFdlKkAsXxrfMlL5Q\nyXHijQHkBxyv958LdBKAeiLyhYjkikiEJVtSV6iRKcE0a2adU6+/bivLueHBB4Grr7axyqeearXA\nAwfcyRuwDrl337VvEfF0yCHAO+8AH3xga0DEauZMG4bXpYs9zjzTFji68UbXihpUyTDDxx4DhgwJ\n3+bepElpLfzss4H77is7ZPCLL2w9j1NPjW+ZKTNVczGf9gDOB3AYgDkiMkdVV5VPOCpgcYPs7Gxk\nZ2e7VAR3hOvUDObwwy3IfPRR5QPj6tW2CNKyZcDRR9s40RtuAIqKgn9Fj8Unn1iAiuZnjFWtWrYC\n5KRJse0AtGqVjWN//HFbDhew5pr27d0tZzCtWtlokqVLrRzRuOkmGzXz6ae21kksqylSZvP5fPD5\nfM4Sh6qia9nmlI81fHPKMAAjA45fAtArSF6J+e5RCQ89ZDtxR2PcONW+fSt/7auvVn3ggbLnJk1S\nveKKyuddolevipsOxNOHH9r+iNHavt3WxUhkWQMVF6sefnjsm+2++65Nn96xw5rHtm51t3yUWVDJ\n5pRcAM1FJEtEagDoC2BauTRTAZwtIlVFpBaAjgCWOfsYSS3R1sQBq21+/HHlNo/49ltbBXDo0LLn\nTz3VaoTR+vLLih2u27db7bBPn9jLGa0LL7SOyLVrQ6fZs8eG3g0dCsyebfexd28bzjlwYMKKWoYI\n8Pe/xz6tu1cvmwnbpw9wwQVA/frulo+oRMQgrqpFAIYAmAFgKYAJqrpMRAaJyEB/mjwAnwD4DsBc\nAONU9Yf4FTt+wk30CaVhQxut4PTbD2BNLx072izDNWusDXXkSFu/O9Dxx9uyqNu3O8/7559tY9bp\n08uenzgRyMkBjjjCeV6VVb26BbR33gmd5r//tSajI46woF2/vrWpP/FE4soZzB132P9tLESsPf3T\nT9mUQnEWqooejwc80JzSvLmtyBetRx5xPkpl2TLVY46xzVxvvtlGP7Rsaau0BXP22aqff+68LB9/\nbF/h27Uru6pgx47J2QT488+taSGUm25SffLJ0uPVq8uOyfayadNC/78SOYUwzSmibg9yDkNENJHX\ni1ZRkdWEd+6MflpvXp41Hfz0U+T1O265xWp4JX28hYU2NrxOneDpb7/dOvbKN7WE8vDDwLZt1jxz\n1132lX75cqud5+fbanCJVFQEHHecjdJo2bLsa8XFNrrjq69sujsRVSQiUNWgkYXT7gNs2gTUqxfb\nugytWtlojAULwqfbts2aNQYPLj1XrVroAA4A7dpF1y6+YIGt9vbQQzbcrbAQeO01W9oy0QEcsHUg\n+vQJvnDYwoU2wocBnCg2DOIBYunUDNS9e+Qt3F54wdqIGzRwnm+0nZsLF9owvK5dbbux8eNtLPuA\nAc7zcFvfvrb0bfkvYv/5D3D55ckpE1E6YBAPEEunZqAePWxz4VAOHLA9LJ02i5Ro08ZGeDiZ9LNz\np+3X2KKFNeuMHg389a+2g3rbttFd100dOwK//WajTwIxiBNVDoN4gEhrpkTSsaONDHn8cWs2Ke+t\nt6xpJNrV62rWtI2If3Aw3mfRIgvWJUtZnnWWjUgZNCi6a7pNxGaj3npr6XoqmzbZRJouXZJbNiIv\nS0ILaepas6ZyAaVqVRvW99RT1sabk2NjhEuWe33iCeDpp2PLu6RJ5bTTwqdbsKDijMZ33kmNBfSv\nucba5p980rafmj7dtiRza2chokzEIO732282euLWWyuXT/v2Fqh27ADeeMP2Ayxx1VU2giUWwdrF\nVSuOhFm4EDj//LLnUiGAA1bW55+3Vf969bKmlF69kl0qIm/jEEO/0aNt1mQy9oh04rPPbEPjL7+0\n44ICa74ZPdpmNpZo08Y2fEjlxZYee8xmuM6fb9tQHXVUsktElNrCDTHMiCD+5ptld2np2tX2yiyx\nfr21Vefmli60lGq2brXOyh07rEb73HM2zvy002zLMMDW8D76aOvcTOV1qwsKbDXCOnVslUIiCi+j\nx4kfPGhTuffvtyaTPXuAbt3KDgUcNszGbadqAAcsONeubXs97txp63pMnw4sXly6ecF33wGtW6d2\nAAesDfzdd60DmIgqJ+3bxBctspEdo0eXnuvd24a17dgBnHSSzRYcNy55ZXSqZNLPrFn2QXTmmbbs\n6T//abumB+vUTFUtWtiDiCon7YP4nDkVd3Q//XRbrOrii23s9ZgxFReeSkWnngq89x7w4Ye2zjVg\nU/jbtbMPqYUL7WcjosyR9s0pwYI4YGt4zJxpzSj9+iW+XLE49VSbeTl0aOnqescdB5x3no2E8VJN\nnIjckfYdm1lZ1vFXfuElL8rPt2GKX3xh67SUKBkauW4d8MsvNjmIiNJHxo5O2bjRdnTfti3yyoJe\npgqccoqNB1+yJNmlISK3hQviad0mXtKUks4BHLCfb8QIG6lCRJklrWvif/ub7TL+v/+bsEsSEbku\nY8eJh+rUJCJKF2lbEz940GrhW7bYJBkiIq+qdE1cRHJEJE9EVojIsCCvnysiO0Vkgf9xb2ULXVkL\nF9pkEgZwIkpnETs2RaQKgGcBXABgI4BcEZmqtsN9oK9UtVscyhgTNqUQUSZwUhPvAGClqq5T1QIA\nEwB0D5IupcaAMIgTUSZwEsQbA8gPOF7vP1deZxFZJCIfikhrV0pXCQziRJQJ3BonPh9AU1XdJyKX\nAJgC4KRgCUeNGvX78+zsbGRnZ7tUhFLr19uqhdxBnYi8yOfzwefzOUobcXSKiHQCMEpVc/zHwwGo\nqj4a5j1rAJyuqtvLnU/I6JRJk4BXXwU++CDulyIiirvKjk7JBdBcRLJEpAaAvgCmlbtAg4DnHWAf\nDtuRJPPmld30gYgoXUUM4qpaBGAIgBkAlgKYoKrLRGSQiAz0J7tKRL4XkYUAngJwddxK7MDcubZ1\nGRFRuku7yT4FBTbJZ8MGoG7duF6KiCghMmra/fff2/KzDOBElAnSLoizKYWIMknaBfF58xjEiShz\npF0QnzuXI1OIKHOkVcfmjh1A06b2b7W03u6CiDJJxnRs5ubabu8M4ESUKdIqiLNTk4gyTVoFcXZq\nElGmSZs2cVXg6KOB774DGjWKyyWIiJIiLdvEf/vNat1vvWUBfPVqoGZNBnAiyiye7QJcsADYtg14\n6CFg4kTgnHPYlEJEmcezNfE5c4BLLwXmzwfatgWGD+f4cCLKPJ5tE7/qKuDKK4FrrrHjlSuBhg2B\nOnVcyZ6IKGWEaxP3ZBBXBZo0Ab7+Gjj+eBcKRkSUwtKuYzM/HygqApo1S3ZJiIiSy5NBfPZs2wRZ\ngn4uERFlDk8Gce5kT0RkGMSJiDzMcx2b+/cD9evbGPGaNV0qGBFRCqt0x6aI5IhInoisEJFhYdKd\nKSIFInJlrIWNZP58oHVrBnAiIsBBEBeRKgCeBXAxgDYA+olIqxDpHgHwiduFDMSmFCKiUk5q4h0A\nrFTVdapaAGACgO5B0t0OYBKAn10sXwUM4kREpZwE8cYA8gOO1/vP/U5EGgHooarPA4jbwD9VBnEi\nokBuLYD1FIDAtvKQgXzUqFG/P8/OzkZ2drbji6xbZ/9mZUVXOCIiL/H5fPD5fI7SRhydIiKdAIxS\n1Rz/8XAAqqqPBqT5seQpgPoA9gIYqKrTyuVVqdEpEyYA77wDvPdezFkQEXlOuNEpTmriuQCai0gW\ngE0A+gLoF5hAVU8IuNgrAD4oH8DdkJsLnHmm27kSEXlXxDZxVS0CMATADABLAUxQ1WUiMkhEBgZ7\ni8tl/N38+bYRMhERGc9M9ikuBo48EvjxR+Coo1wuGBFRCkuLVQxXrbIgzgBORFTKM0GcTSlERBUx\niBMReRiDOBGRh3miY7OkU3P1alvBkIgok3i+Y3P1auCIIxjAiYjK80QQZ1MKEVFwDOJERB7GIE5E\n5GEp37Gpap2aK1cCRx8dp4IREaUwT3dsrl4NHH44AzgRUTApH8TZlEJEFBqDOBGRh6V0EJ8/H5gy\nhWuIExGFkpJBfPlyoHdvoFs3YOhQ4MILk10iIqLUlHJBfPVqoFMn4IwzbETK4MFAlZQrJRFRanBr\no2TXzJwJ5OQAw4ZFTktElOlSro7LfTSJiJxjECci8jBHQVxEckQkT0RWiEiFhg4R6SYii0VkoYh8\nIyJdYinMwYPA998D7dvH8m4ioswTsU1cRKoAeBbABQA2AsgVkamqmheQ7DNVneZP/wcA7wA4OdrC\nfPcd0Lw5cNhh0b6TiCgzOamJdwCwUlXXqWoBgAkAugcmUNV9AYe1ARTHUphvvmFTChFRNJwE8cYA\n8gOO1/vPlSEiPURkGYAPAPw5lsKwPZyIKDquDTFU1SkApojI2QAeBBB0is6oUaN+f56dnY3s7Ozf\nj3NzgSFD3CoREZE3+Xw++Hw+R2kjLkUrIp0AjFLVHP/xcACqqo+Gec9qAGeq6vZy50MuRbtnD9Cg\nAbBjB1CjhqOyExFlhMouRZsLoLmIZIlIDQB9AUwrd4ETA563B1CjfACPZMEC4JRTGMCJiKIRsTlF\nVYtEZAiAGbCg/7KqLhORQfayjgPQS0T6AzgIYD+APtEWJDcX6NAh2ncREWW2lNnZp29f4NJLgf79\nE1YcIiJP8MTOPhyZQkQUvZQI4tu2AVu3Ai1bJrskRETekhJB/NtvbfceLjlLRBSdlAibc+eyKYWI\nKBZJD+Lhr48+AAAIQElEQVSFhcD48baTDxERRSfpQXzaNKBRI9bEiYhikfQg/vTTwB13JLsURETe\nlNQgvmiR7al55ZXJLAURkXclNYg/8wxw661A9erJLAURkXclbcbm1q3ASSfZjvb16yesCEREnpOS\nMzZffBHo1YsBnIioMpJWEz/hBGDyZOC00xJ2eSIiTwpXE09aED/0UGD7dqBWrYRdnojIk1KuOaWg\nwCb51KyZjKsTEaWPpATxPXuA2rUBCfq5QkRETiUliO/eDdSpk4wrExGlFwZxIiIPS1oQr107GVcm\nIkovrIkTEXmYoyAuIjkikiciK0RkWJDXrxGRxf7H1yLyh3D5MYgTEbkjYhAXkSoAngVwMYA2APqJ\nSKtyyX4E8EdVbQfgQQD/CpcngzgRkTuc1MQ7AFipqutUtQDABADdAxOo6lxV/dV/OBdA43AZMogT\nEbnDSRBvDCA/4Hg9wgfpmwB8FC5DBnEiIndUczMzETkPwA0Azg6VZtSoUfjvf235WZ8vG9nZ2W4W\ngYjI83w+H3w+n6O0EddOEZFOAEapao7/eDgAVdVHy6VrC2AygBxVXR0iL1VV/OUvwIknckcfIiIn\nKrt2Si6A5iKSJSI1APQFMK3cBZrCAvh1oQJ4IDanEBG5I2JziqoWicgQADNgQf9lVV0mIoPsZR0H\n4D4A9QD8U0QEQIGqdgiVJyf7EBG5w1GbuKp+DKBluXMvBjy/GcDNTi/KmjgRkTs4Y5OIyMMYxImI\nPIxBnIjIw5K2KQSDOBFR5bEmTkTkYQkP4gcPAsXFwCGHJPrKRETpJ+FBvKQWzv01iYgqLylBnBN9\niIjckbSaOBERVR6DOBGRhzGIExF5GIM4EZGHJTyIc6IPEZF7WBMnIvIwBnEiIg9jECci8jBO9iEi\n8jDWxImIPMxREBeRHBHJE5EVIjIsyOstRWS2iPwmIneGy4tBnIjIPRH32BSRKgCeBXABgI0AckVk\nqqrmBST7BcDtAHpEyo9BnIjIPU5q4h0ArFTVdapaAGACgO6BCVR1m6rOB1AYKTOOEycico+TIN4Y\nQH7A8Xr/uZiwJk5E5B52bBIReVjENnEAGwA0DThu4j8Xk59/HoV//hOoVQvIzs5GdnZ2rFkREaUl\nn88Hn8/nKK2oavgEIlUBLId1bG4C8A2Afqq6LEjakQD2qOqYEHlptWqKvXuBGjUclY+IKOOJCFQ1\n6H5oEYO4P4McAE/Dml9eVtVHRGQQAFXVcSLSAMC3AOoAKAawB0BrVd1TLh+tXl1x8GDlfiAiokxS\n6SDuYkG0Xj3FL78k7JJERJ4XLognvGOTnZpERO5hECci8jAGcSIiD2MQJyLyMAZxIiIPYxAnIvIw\nBnEiIg9LeBDnrj5ERO5hTZyIyMMYxImIPIxBnIjIwxjEiYg8jEGciMjDGMSJiDyMQZyIyMMYxImI\nPIyTfYiIPIw1cSIiD0t4EK9ePdFXJCJKX46CuIjkiEieiKwQkWEh0jwjIitFZJGInOpuMYmIKJiI\nQVxEqgB4FsDFANoA6CcircqluQTAiaraAsAgAC/Eoaxpx+fzJbsIKYP3oizej7J4P0JzUhPvAGCl\nqq5T1QIAEwB0L5emO4DXAEBV5wGoKyINXC1pGuIvZinei7J4P8ri/QjNSRBvDCA/4Hi9/1y4NBuC\npCEiIpclvGOTiIjcI6oaPoFIJwCjVDXHfzwcgKrqowFpXgDwhapO9B/nAThXVbeUyyv8xYiIKChV\nlWDnqzl4by6A5iKSBWATgL4A+pVLMw3AbQAm+oP+zvIBPFwhiIgoNhGDuKoWicgQADNgzS8vq+oy\nERlkL+s4VZ0uIpeKyCoAewHcEN9iExER4KA5hYiIUlfCOjadTBhKVyLSREQ+F5GlIrJERP7iP3+k\niMwQkeUi8omI1E12WRNJRKqIyAIRmeY/zsj7ISJ1ReRdEVnm/x3pmKn3AgBEZKiIfC8i34nImyJS\nI5PvRyQJCeJOJgyluUIAd6pqGwCdAdzm//mHA/hMVVsC+BzAiCSWMRnuAPBDwHGm3o+nAUxX1ZMB\ntAOQhwy9FyLSCMDtANqraltYk28/ZOj9cCJRNXEnE4bSlqpuVtVF/ud7ACwD0AR2D171J3sVQI/k\nlDDxRKQJgEsBvBRwOuPuh4gcDuAcVX0FAFS1UFV/RQbeiwBVARwmItUA1ITNO8nk+xFWooK4kwlD\nGUFEmgE4FcBcAA1KRvGo6mYAxySvZAn3JIC7AAR2ymTi/TgewDYRecXftDRORGohM+8FVHUjgDEA\nfoIF719V9TNk6P1wgpN9EkhEagOYBOAOf428fK9yRvQyi8hlALb4v52EG3aaCfejGoD2AJ5T1faw\n0V3Dkbm/G0fAat1ZABrBauTXIkPvhxOJCuIbADQNOG7iP5cx/F8NJwF4XVWn+k9vKVljRkQaAvg5\nWeVLsC4AuonIjwDeBnC+iLwOYHMG3o/1APJV9Vv/8WRYUM/U342uAH5U1e2qWgTgfQBnIXPvR0SJ\nCuK/TxgSkRqwCUPTEnTtVPFvAD+o6tMB56YBuN7/fACAqeXflI5U9R5VbaqqJ8B+Fz5X1esAfIAM\nux/+JoJ8ETnJf+oCAEuRob8bsGaUTiJyqIgI7H78gMy9HxElbJy4iOTAeuFLJgw9kpALpwAR6QLg\nKwBLYF8DFcA9AL4B8A6A4wCsA9BHVXcmq5zJICLnAvgfVe0mIvWQgfdDRNrBOnirA/gRNlmuKjLw\nXgCAiIyEfbgXAFgI4CYAdZCh9yMSTvYhIvIwdmwSEXkYgzgRkYcxiBMReRiDOBGRhzGIExF5GIM4\nEZGHMYgTEXkYgzgRkYf9Px+XBpify8XiAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAf0/IYYG4Jtr1ywAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fa6c400>"
+       "<matplotlib.figure.Figure at 0x11934cdd8>"
       ]
      },
      "metadata": {},
@@ -797,14 +783,25 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -874,14 +871,12 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10fa5a978>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11af58c18>"
       ]
      },
      "execution_count": 18,
@@ -890,9 +885,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwhziklSYQ1ySCvs/3hkyICCoKsoAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110e7d6d8>"
+       "<matplotlib.figure.Figure at 0x119132898>"
       ]
      },
      "metadata": {},
@@ -914,14 +909,12 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10f311438>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11ae5a588>"
       ]
      },
      "execution_count": 19,
@@ -930,9 +923,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEPCAYAAAC0r/QVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nThm2LLy88wAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlB+FtkxWaQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110eadc50>"
+       "<matplotlib.figure.Figure at 0x11afe9a20>"
       ]
      },
      "metadata": {},
@@ -1009,7 +1002,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We instantiate a BatchRunner with a model class to run, and a dictionary mapping parameters to values for them to take. If any of these parameters are assigned more than one value, as a list or an iterator, the BatchRunner will know to run all the combinations of these values and the other ones. The BatchRunner also takes an argument for how many model instantiations to create and run at each combination of parameter values, and how many steps to run each instantiation for. Finally, like the DataCollector, it takes dictionaries of model- and agent-level reporters to collect. Unlike the DataCollector, it won't collect the data every step of the model, but only at the end of each run.\n",
+    "We instantiate a BatchRunner with a model class to run, and two dictionaries: one of the fixed parameters (mapping model arguments to values) and one of varying parameters (mapping each parameter name to a sequence of values for it to take).  The BatchRunner also takes an argument for how many model instantiations to create and run at each combination of parameter values, and how many steps to run each instantiation for. Finally, like the DataCollector, it takes dictionaries of model- and agent-level reporters to collect. Unlike the DataCollector, it won't collect the data every step of the model, but only at the end of each run.\n",
     "\n",
     "In the following example, we hold the height and width fixed, and vary the number of agents. We tell the BatchRunner to run 5 instantiations of the model with each number of agents, and to run each for 100 steps. We have it collect the final Gini coefficient value.\n",
     "\n",
@@ -1019,17 +1012,24 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "245it [00:37,  6.58it/s]\n"
+     ]
+    }
+   ],
    "source": [
-    "parameters = {\"width\": 10,\n",
-    "              \"height\": 10,  \n",
-    "              \"N\": range(10, 500, 10)}\n",
+    "fixed_params = {\"width\": 10,\n",
+    "               \"height\": 10}\n",
+    "variable_params = {\"N\": range(10, 500, 10)}\n",
     "\n",
     "batch_run = BatchRunner(MoneyModel, \n",
-    "                        parameters, \n",
+    "                        variable_params,\n",
+    "                        fixed_params,\n",
     "                        iterations=5, \n",
     "                        max_steps=100,\n",
     "                        model_reporters={\"Gini\": compute_gini})\n",
@@ -1046,14 +1046,12 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x114ab80f0>"
+       "<matplotlib.collections.PathCollection at 0x11b28bac8>"
       ]
      },
      "execution_count": 23,
@@ -1062,9 +1060,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x111145518>"
+       "<matplotlib.figure.Figure at 0x11f4bb048>"
       ]
      },
      "metadata": {},
@@ -1107,9 +1105,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [mesa_dev]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [mesa_dev]"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1121,9 +1119,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1+"
+   "version": "3.5.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/docs/tutorials/intro_tutorial.rst b/docs/tutorials/intro_tutorial.rst
index 95de4112..9b3ff29c 100644
--- a/docs/tutorials/intro_tutorial.rst
+++ b/docs/tutorials/intro_tutorial.rst
@@ -804,16 +804,15 @@ indefinitely in ``__init__``.
             self.running = True
             # ...
 
-We instantiate a BatchRunner with a model class to run, and a dictionary
-mapping parameters to values for them to take. If any of these
-parameters are assigned more than one value, as a list or an iterator,
-the BatchRunner will know to run all the combinations of these values
-and the other ones. The BatchRunner also takes an argument for how many
-model instantiations to create and run at each combination of parameter
-values, and how many steps to run each instantiation for. Finally, like
-the DataCollector, it takes dictionaries of model- and agent-level
-reporters to collect. Unlike the DataCollector, it won't collect the
-data every step of the model, but only at the end of each run.
+We instantiate a BatchRunner with a model class to run, and two dictionaries: 
+one of the fixed parameters (mapping model arguments to values) and one of
+varying parameters (mapping each parameter name to a sequence of values for it
+to take). The BatchRunner also takes an argument for how many model
+instantiations to create and run at each combination of parameter values, and 
+how many steps to run each instantiation for. Finally, like the DataCollector, 
+it takes dictionaries of model- and agent-level reporters to collect. Unlike
+the DataCollector, it won't collect the data every step of the model, but only 
+at the end of each run.
 
 In the following example, we hold the height and width fixed, and vary
 the number of agents. We tell the BatchRunner to run 5 instantiations of
@@ -827,12 +826,13 @@ Now, we can set up and run the BatchRunner:
     # run.py
     from mesa.batchrunner import BatchRunner
 
-    parameters = {"width": 10,
-                  "height": 10,  
-                  "N": range(10, 500, 10)}
+    fixed_params = {"width": 10,
+                    "height": 10}  
+    variable_params = {"N": range(10, 500, 10)}
     
     batch_run = BatchRunner(MoneyModel, 
-                            parameters, 
+                            fixed_parameters=fixed_params,
+                            variable_parameters=variable_params, 
                             iterations=5, 
                             max_steps=100,
                             model_reporters={"Gini": compute_gini})
diff --git a/examples/Schelling/analysis.ipynb b/examples/Schelling/analysis.ipynb
index 38f07ae9..f60068a9 100644
--- a/examples/Schelling/analysis.ipynb
+++ b/examples/Schelling/analysis.ipynb
@@ -17,10 +17,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -38,9 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -56,10 +54,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -84,9 +80,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -95,15 +91,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -114,7 +121,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>66</td>\n",
+       "      <td>61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -122,15 +129,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>67</td>\n",
+       "      <td>69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>66</td>\n",
+       "      <td>71</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>66</td>\n",
+       "      <td>71</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -138,14 +145,14 @@
       ],
       "text/plain": [
        "   happy\n",
-       "0     66\n",
+       "0     61\n",
        "1     67\n",
-       "2     67\n",
-       "3     66\n",
-       "4     66"
+       "2     69\n",
+       "3     71\n",
+       "4     71"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,26 +170,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10672dd30>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x110917ac8>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDmkAAAAASUVORK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1066d15f8>"
+       "<matplotlib.figure.Figure at 0x110917400>"
       ]
      },
      "metadata": {},
@@ -202,9 +207,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -213,15 +218,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -241,28 +257,28 @@
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
        "      <th>(0, 0)</th>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>(0, 1)</th>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>(0, 2)</th>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(0, 3)</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(0, 4)</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>(0, 5)</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -271,14 +287,14 @@
       "text/plain": [
        "              x  y\n",
        "Step AgentID      \n",
-       "0    (0, 0)   1  5\n",
-       "     (0, 1)   4  7\n",
-       "     (0, 2)   6  0\n",
-       "     (0, 3)   2  1\n",
-       "     (0, 4)   2  2"
+       "0    (0, 0)   2  5\n",
+       "     (0, 2)   8  4\n",
+       "     (0, 3)   5  4\n",
+       "     (0, 4)   4  9\n",
+       "     (0, 5)   1  0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -300,10 +316,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from mesa.batchrunner import BatchRunner"
@@ -311,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -342,21 +356,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "parameters = {\"height\": 10, \"width\": 10, \"density\": 0.8, \"minority_pc\": 0.2, \n",
-    "              \"homophily\": range(1,9)}"
+    "fixed_params = {\"height\": 10, \"width\": 10, \"density\": 0.8, \"minority_pc\": 0.2} \n",
+    "variable_parms = {\"homophily\": range(1,9)}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -365,33 +379,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "param_sweep = BatchRunner(SchellingModel, parameters, iterations=10, \n",
+    "param_sweep = BatchRunner(SchellingModel, \n",
+    "                          variable_parameters=variable_parms, fixed_parameters=fixed_params,\n",
+    "                          iterations=10, \n",
     "                          max_steps=200,\n",
     "                          model_reporters=model_reporters)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "80it [00:02, 27.26it/s] \n"
+     ]
+    }
+   ],
    "source": [
     "param_sweep.run_all()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -400,16 +422,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEACAYAAACj0I2EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nl6QG/RdKeUiSHYeeDwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1082ba400>"
+       "<matplotlib.figure.Figure at 0x112f4d128>"
       ]
      },
      "metadata": {},
@@ -424,9 +444,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [mesa_dev]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [mesa_dev]"
   },
   "language_info": {
    "codemirror_mode": {
@@ -438,9 +458,13 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.3"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/examples/forest_fire/Forest Fire Model.ipynb b/examples/forest_fire/Forest Fire Model.ipynb
index 59e4d847..56332b83 100644
--- a/examples/forest_fire/Forest Fire Model.ipynb
+++ b/examples/forest_fire/Forest Fire Model.ipynb
@@ -24,9 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import random\n",
@@ -59,9 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class TreeCell(Agent):\n",
@@ -117,9 +113,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "class ForestFire(Model):\n",
@@ -193,9 +187,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fire = ForestFire(100, 100, 0.6)"
@@ -211,9 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fire.run_model()"
@@ -227,17 +217,13 @@
     "\n",
     "But... so what? This code doesn't include a visualization, after all. \n",
     "\n",
-    "**TODO: Add a MatPlotLib visualization**\n",
-    "\n",
     "Remember the data collector? Now we can put the data it collected into a pandas DataFrame:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "results = fire.dc.get_model_vars_dataframe()"
@@ -253,14 +239,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10a022710>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x119756a90>"
       ]
      },
      "execution_count": 7,
@@ -269,9 +253,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAUAIIXqp/w8TtkNp4lrx8wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a019eb8>"
+       "<matplotlib.figure.Figure at 0x119756320>"
       ]
      },
      "metadata": {},
@@ -294,14 +278,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x10a4ddc50>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11c105a20>"
       ]
      },
      "execution_count": 8,
@@ -310,9 +292,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nj4josB9MSBmuscOXqgAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x109efdbe0>"
+       "<matplotlib.figure.Figure at 0x11c0ffdd8>"
       ]
      },
      "metadata": {},
@@ -342,23 +324,19 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "param_set = dict(height=50, # Height and width are constant\n",
-    "                 width=50,\n",
-    "                 # Vary density from 0.01 to 1, in 0.01 increments:\n",
-    "                 density=np.linspace(0,1,101)[1:])"
+    "fixed_params = dict(height=50, # Height and width are constant\n",
+    "                 width=50)\n",
+    "# Vary density from 0.01 to 1, in 0.01 increments:\n",
+    "variable_params = dict(density=np.linspace(0,1,101)[1:])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# At the end of each model run, calculate the fraction of trees which are Burned Out\n",
@@ -369,13 +347,12 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Create the batch runner\n",
-    "param_run = BatchRunner(ForestFire, param_set, model_reporters=model_reporter)"
+    "param_run = BatchRunner(ForestFire, variable_parameters=variable_params, \n",
+    "                        fixed_parameters=fixed_params, model_reporters=model_reporter)"
    ]
   },
   {
@@ -388,15 +365,13 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 100/100 [00:08<00:00,  4.78it/s]\n"
+      "100it [00:06,  6.45it/s]\n"
      ]
     }
    ],
@@ -414,9 +389,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "df = param_run.get_model_vars_dataframe()"
@@ -425,77 +398,76 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>BurnedOut</th>\n",
-       "      <th>Run</th>\n",
        "      <th>density</th>\n",
-       "      <th>height</th>\n",
-       "      <th>width</th>\n",
+       "      <th>Run</th>\n",
+       "      <th>BurnedOut</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td> 0.933628</td>\n",
-       "      <td> 63</td>\n",
-       "      <td> 0.64</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>6</th>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td> 1.000000</td>\n",
-       "      <td> 86</td>\n",
-       "      <td> 0.87</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>98</th>\n",
+       "      <td>0.02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td> 0.793289</td>\n",
-       "      <td> 58</td>\n",
-       "      <td> 0.59</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>73</th>\n",
+       "      <td>0.03</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.030303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td> 1.000000</td>\n",
-       "      <td> 81</td>\n",
-       "      <td> 0.82</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>75</th>\n",
+       "      <td>0.04</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.021053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td> 0.052219</td>\n",
-       "      <td> 30</td>\n",
-       "      <td> 0.31</td>\n",
-       "      <td> 50</td>\n",
-       "      <td> 50</td>\n",
+       "      <th>81</th>\n",
+       "      <td>0.05</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.030303</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   BurnedOut  Run  density  height  width\n",
-       "0   0.933628   63     0.64      50     50\n",
-       "1   1.000000   86     0.87      50     50\n",
-       "2   0.793289   58     0.59      50     50\n",
-       "3   1.000000   81     0.82      50     50\n",
-       "4   0.052219   30     0.31      50     50"
+       "    density  Run  BurnedOut\n",
+       "6      0.01    0   0.000000\n",
+       "98     0.02    1   0.000000\n",
+       "73     0.03    2   0.030303\n",
+       "75     0.04    3   0.021053\n",
+       "81     0.05    4   0.030303"
       ]
      },
      "execution_count": 14,
@@ -517,9 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -533,9 +503,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGvxJREFUeJzt3X+MXWWdx/H3dwqzGbNhsY6pK0hAra52kQxErOLuXCMz\nd2STYtvNIorbBaWsWUDNKAWbwCQrS1iZ2IAJ0Iowu0S7G6HruGFnGFlutSyytkKppUW6SsIPbZyi\nxnUnaZv57h/33Llnzv0x595zf809n1dy0/vjOec8czrzued+n+eca+6OiIikR0+7OyAiIq2l4BcR\nSRkFv4hIyij4RURSRsEvIpIyCn4RkZRJHPxm9g0zO2pmByq8/gkz229mz5rZE2b2nqTbFBGR+jXi\niP9+YKTK6z8D/tzd3wP8PbC9AdsUEZE6JQ5+d/8B8Osqrz/p7r8NHj4FnJl0myIiUr9W1/g/BTzS\n4m2KiEjIKa3akJl9CLgKuKhV2xQRkVItCf5gQHcHMOLuJWUhM9MFg0RE6uDuVusyTS/1mNlZwMPA\nFe5+pFI7d9fNnVtuuaXtfeiUm/aF9oX2RfVbvRIf8ZvZt4BBoN/MXgJuAU4Nwvxe4Gbg9cDdZgZw\nwt0vTLpdERGpT+Lgd/fLl3j908Cnk25HREQaQ2fudphMJtPuLnQM7Ysi7Ysi7YvkLEmdqGGdMPNO\n6IeIyHJiZngnDu6KiEhnUfCLiKSMgl9EJGUU/CIiKaPgFxFJGQW/iEjKKPhFRFJGwS8ikjIKfhGR\nlFHwi4ikjIJfRCRlFPwiIimj4BcRSRkFv4hIyij4RURSRsEvIpIyCn4RkZRR8IuIpIyCX0QkZRT8\nIiIpo+AXEUmZRMFvZt8ws6NmdqBKmzvN7AUz229mA0m2JyIiyZ2ScPn7gbuAfyr3opldArzd3Veb\n2fuAu4G1CbcpIrLI9PQ04+PbARgd3Uw2m23aOsLtBgfPZ/fuH5fcHx3dDFC2XbXX6mlXD3P3uhcG\nMLOzge+6+7llXrsHeNzd/yV4fBgYdPejkXaetB8i0lkaEcbV1gf5UJydPcbBg/s5fnwbAL29n2PN\nmvPo739D7PAcHDyfW2+9i7m520vWUa0dXA9cHdzfAdwZLP9F4ETQpwOxXyuu71x6ez8HnMrx41+p\n0m4cd7da92Wzg/+7wG3u/l/B4+8BW9x9X6Sdgl9kmSp3BDw7e5SDB38ahBb09W1h69brKobuUke5\n5cK9GIoAXwAeDO5fAdxRoV35oO7p+Tzz81cFy02H1nGgSjuACWAyuL8O2BR6/h7gSWBjja9NAg8B\n7wf+NnitUruH6wr+pKWeOKKdKpvwY2NjC/czmQyZTKZ5PRKRmlQ6ep+enmb9+k0LR8AzM4VgfRX4\nCoWgmpuDm28eZX5+HDjAzMw/UgjT4jLnsnv3xwgf5Ybb5cP9TUCW48fvoRiKBduDf+9YeL58u0JQ\n37nw/Pw85MO4sJ7COjZWadcOueD2DPB83WtpdvC/Arwl9PjM4LkS4eAXkc4RDffduz+2UAaZnT0W\nPB8N1jeXrGd+fjXlwrS4zB2RoC7XbjtQqWT0ah0/XVFPzwvMz08suZ5iO1j8CeL6hTbFcs4EcE7s\n14rrm6C39zDwRY4fJ9TuzuD+V4N2h+v4SZsf/JPAtcBOM1sL/CZa3xeRzjY+vj0U7tMcP34KTz99\nJQA9PaMVltpMvlxC0K5QIknqVUpDMR+ma9a8A4CDB8PPL25XKajzpajPs3v3JLOzK0LrOKdiO4DB\nwRtCJasbFp4fHf1nIFyyivdacX0/Z3R055LtZmZq34OQsMZvZt8CBoF+4ChwC3AqgLvfG7T5GjAC\n/B640t1LhqJV4xfpXMPDG5mZKdSXo7XmL9DT8w3m578aPF48OBkeIC0OitY3oFkI9/7+VSVjAdHy\nU7lBYKg+thAefK40c6cRg9SNZGbtGdxtBAW/SPMknV2zuNQTrZlPMDCwg/7+VUB9YVrLNMhOCt1O\noOAXkRLR+nxf3xZ27ZqoK/zzUydLZ+vUsz5pDAW/iJRYXKYBmGBoaJJHH30IqK+k0ej5+VK/eoO/\nFdM5RaQDVZ6KeS579myqeCSfzWYV9sucgl+kC4VLM729xVktfX1bGB3NT0VcPFunID+tcm4u/7oC\nvjsp+EW6QLRkE738wMDA/cElDFSPFwW/yLIXLdk89lhhznzh7FXo7y/W9QtGRzezZ88m5uYKzxRP\nHgp/MpDuo+AXWeaiJZu4lxXIZrPs2jUROUGocPKQPhl0MwW/SBcKX1ag2tF7dKB269aWdE/aTMEv\nssxFSzbRywro6F2iNI9fpAtobn066QQuEVmS3iC6S73Bry9bF1lGpqenGR7eyPDwRqanp2tedv36\nTczMrGNmZh3r12+quI4k25FlwN3bfst3Q0SqmZqa8r6+VQ4PODzgfX2rfGpqKvbyQ0MbgmU9uD3g\nQ0MbGr4daZ0gO2vOXA3uiiwT0WmbzTq7tlXbkfZR8IukRLnZPzpJK50U/CLLRNLgjp6wVWmap94g\nup9m9YgsI62alaPZP8uDpnOKpIzCWRT8IinSqG/WkuVNwS+SIkt9s5akg07gEhGRWDSrR2QZ0swb\nSUKlHpEOUM9ArQZ3pW01fjMbAbYBK4Cvu/vtkdf7gQeBN5H/hHGHuz8QaaPgl9TSQK3Uqy3Bb2Yr\ngOeBi4FXgB8Bl7v7oVCbMeAP3P2m4E3geWCVu58MtVHwS2ppoFbq1a7B3QuBI+7+orufAHYCl0ba\n/AI4Lbh/GnAsHPoiUmrfvv26MqY0TdLgPwN4KfT45eC5sB3AGjN7FdgPfDbhNkW6yujoZvr6tgAT\nwe16Xnvto0teOlmkXkln9cSpz3wJeMbdM2b2NmDGzM5z99+FG42NjS3cz2QyZDKZhF0TWR7C19DZ\nt28/r712NXAHoCtjymK5XI5cLpd4PUlr/GuBMXcfCR7fBMyHB3jN7BHgVnd/Inj8GLDF3feG2qjG\nL4Lq/VKbemv8SY/49wKrzexs4FXgMuDySJvD5Ad/nzCzVcA7gZ8l3K5IV9L8fGmFRkzn/AjF6Zz3\nufttZnYNgLvfG8zkuR84i/yYwm3u/s3IOnTELxLQ/HyJS9fqERFJGV2rR0REYlHwi4ikjIJfRCRl\nFPwiIimj4BcRSRkFv4hIyij4RURSRsEvIpIyCn4RkZRR8IuIpIyCX0QkZRT8IiIpo+AXEUkZBb+I\nSMoo+EVEUkbBLyKSMgp+EZGUUfCLiKSMgl9EJGUU/CIiKaPgFxFJGQW/iEjKKPhFRFImcfCb2YiZ\nHTazF8xsS4U2GTN72sx+Yma5pNsUEZH6mbvXv7DZCuB54GLgFeBHwOXufijU5nTgCSDr7i+bWb+7\nz0bW40n6ISKSRmaGu1utyyU94r8QOOLuL7r7CWAncGmkzceBh9z9ZYBo6IuISGslDf4zgJdCj18O\nngtbDaw0s8fNbK+ZfTLhNkVEJIFTEi4fpz5zKnA+8GHgdcCTZvZDd38h3GhsbGzhfiaTIZPJJOya\niEh3yeVy5HK5xOtJWuNfC4y5+0jw+CZg3t1vD7XZAvS5+1jw+OvAlLt/O9RGNX4RkRq1q8a/F1ht\nZmebWS9wGTAZafMd4INmtsLMXge8D3gu4XZFRKROiUo97n7SzK4FpoEVwH3ufsjMrglev9fdD5vZ\nFPAsMA/scHcFv4hImyQq9TSsEyr1iIjUrF2lHhERWWYU/CIiKaPgFxFJGQW/iEjKKPhFRFJGwS8i\nkjIKfhGRlFHwi7TI9PQ0w8MbGR7eyPT0dLu7IymmE7hEWmB6epr16zcxN5e/jFVf3xZ27Zogm822\nuWeynOkELpEONj6+PQj9TUD+DWB8fPuSy+lTgjRD0ssyi0iTRD8l7NmzSZ8SpCEU/CItMDq6mT17\nNjE3l3/c17eF0dGJqsss/pQAc3P55xT8kpRKPSItkM1m2bVrgqGhSYaGJtm69TrGx7erhCNtocFd\nkRaLO9CrAWFZSr2Duwp+kRYbHt7IzMw6CiUcyH8SePTRh0raTk9PLwwCj45uVujLIvUGv2r8Ih0s\nm80q7KXhFPwiLVbPQK9II6nUI9IGKuFII6jGLyKSMjpzV0REYlHwi4ikjIJfRCRlFPwiIimTOPjN\nbMTMDpvZC2a2pUq795rZSTPbkHSbIiJSv0TBb2YrgK8BI8C7gcvN7F0V2t0OTAE1j0CLiEjjJD3i\nvxA44u4vuvsJYCdwaZl21wHfBn6VcHsiIpJQ0uA/A3gp9Pjl4LkFZnYG+TeDu4OnNGFfRKSNkl6y\nIU6IbwNudHc3M6NCqWdsbGzhfiaTIZPJJOyaiEh3yeVy5HK5xOtJdOauma0Fxtx9JHh8EzDv7reH\n2vyMYtj3A/8HXO3uk6E2OnNXRKRG7Tpzdy+w2szONrNe4DJgMtzA3d/q7ue4+znk6/yfCYe+yHKg\n776VbpKo1OPuJ83sWmAaWAHc5+6HzOya4PV7G9BHkbbSd99Kt9FF2kSWsNQXp+hKm9Iu+iIWkTbQ\npwFZjhT8Ikuo9sUp4+Pbg9DPfxqYm8s/p+CXTqZr9YgsIZvNsmtXvrwzNDS55BH9vn37NQgsHU01\nfpEEoqUeuB64GjiXvr4tKvtIU+kbuETapDC4u2/ffl577aPAHcEriweBRRpN38Al0ibZbJZHH32I\nCy44Dzi33d0RWZIGd0UapNogsEgnUalHpIE0p19aSTV+EZGUUY1fRERiUfCLiKSMgl9EJGUU/CIi\nKaPgFxFJGQW/SIi+cEXSQNM5RQLR6+7oWjvS6TSPXyShpb5wRaTTaB6/iIjEouAXCYyObqavbwsw\nAUwE19rZXLatxgJkOVOpRyQkzrV2NBYgnUI1fpEW0ViAdArV+EVEJBZdj1+kRrruvix3iUs9ZjYC\nbANWAF9399sjr38CuAEw4HfAZ9z92UgblXpkWdF196UTtKXGb2YrgOeBi4FXgB8Bl7v7oVCb9wPP\nuftvgzeJMXdfG1mPgl9EpEbtqvFfCBxx9xfd/QSwE7g03MDdn3T33wYPnwLOTLhNERFJIGnwnwG8\nFHr8cvBcJZ8CHkm4TZFlQ/P9pRMlHdyNXZ8xsw8BVwEXlXt9bGxs4X4mkyGTySTsmkgySev40fn+\ne/Zs0nx/SSSXy5HL5RKvJ2mNfy35mv1I8PgmYL7MAO97gIeBEXc/UmY9qvFLR2nESVqa7y/N1q4a\n/15gtZmdbWa9wGXAZKRjZ5EP/SvKhb5IJxof3x6E/iYg/wZQOPoXWe4SlXrc/aSZXQtMk5/OeZ+7\nHzKza4LX7wVuBl4P3G1mACfc/cJk3RbpfJrvL51Kl2wQKaNR1+PRfH9pJl2rR6TBFNrS6RT8IjEp\n0KVbKPhFYtAllaWb1Bv8ukibdI1KR/Lh52dnj4Vm68DcXH4Gj4Jf0kTBL12h0slSwKLne3pG29ZH\nkU6h4JeOF6cmv3jeffFIPn+/+Pz8/AF6ej7P/Hx+OU2xlDRS8EtHix7J7979SdaseQf9/avqHJg9\nl/POezf9/fnzDAcHr2N8fDvj49s10Cvp4e5tv+W7Id1oamrKh4Y2+NDQBp+amqq53dDQBocHHDy4\nPeCw1uEB7+tbtdB2amrK+/pWBa8XX6v0fLVlRJaLIDtrz9x6Fmr0TcHfneIGa7V25YN/w8L9oaEN\ni9ZT7s2jljeV8PpEOl29wa9SjzRNpbp7tJxSrV30sgfwBeDBstvLZrOLZvIMD28E8uMCujCaSJG+\nbF3aphDO+/btr9gmm82ya1f+qpYDA/fT23sS+CUwEQzMbi673vXrNzEzs46ZmXWsX7+p7LXwR0c3\n09e3BZiouj6RrlPPx4RG31CppytFSzi9vW/0gYGLfGhog3/5y18OvTbqcFqsWnucMYNaSjhxxyBE\nOhGq8Us1jQi4etZRWGZgYNB7e09fCPeenjdEwnnUV658W0MCWLV7SQsFfxdLGtqNmL2SdB2lYby2\naeGs2TqSFgr+LtWIEGvEEXDSdZQuP+o9Pa9vWjirhCNpUG/wp2ZWTydekTHcp8HB89m9+8fA4v4t\nNTOmET9XI75bNrx8od/h9ZV+KcmDbN06yu7dk0G7xl4oLTzDR0Qi6nm3aPSN4Ii/WUdpnfjRf3Gf\nKg9uVjvSbsQ8+dIB2NN9YGBwYQC28P+xeDC28glSvb2ne2/vGytuq5H/vzqql7RjuZd66g3nRszy\naGWAFLa1cuXbgsD34ISkxf0rzH6JDorW8qYQ/pnincQ05dC/sK38m9HownbDbwSV+9C82n10P3ba\nm7lIq9Ub/B1T6ol7sk9YpSsy1vIRv/RaMB9jzZrz6O9/Q01ljziXBB4cPJ9bb70r2NY68icjDZVZ\n2wH273+O+fmrAejt/SIDAzuC69Ms/fNV2i+Fk5jCJzfNzh4LLbkduIPC/0HeJHAHc3Owe/dkS06E\nSnJRNpV3RGKo592i0TegrsHDuEfyAwMXVSw/VDvireVTR5wySOkUxsJ1ZxaXesq1K7cvKm23lvLQ\n4tJM6dH6UpdHiH4iqVbqiSPukbymbIp0wRF/6eBfssvlRo96e3s/x8DA/cGRfKWj5sVHvHGPIuNf\nEviekmVXrvwVF1zwcwYHb1gY6Jyd/VOefnrpn7FwVmvxE0X+SpP5M2HXxerr8eMEnyYmmZ1dwcGD\nX+T48ULr64GrKZ7Vmv//KN234U8kOxe2A7UP2sY9km/074tImnRM8FcKsWqXyy33xz84eN3CZQDm\n5q4gHHD9/ZOLyh3j49uZnT1Gb+/ngrB7tWQbs7PHFl3zpdK3OsVzUcm14L/5zWIwbt3KwrrzwVps\nFw7daBkkm81Gwvgc8qFdunw5/f2rSvYLELwZ/Rj4+aIAL/fmEd63QNNLLtHfl0bPChLpavV8TGj0\njcg8/loG7sKDltGZJ/myzVSMckf+UgLRklBv7xsjZYxK7U4vWa5cu0oDpEv9XHEuI1xunny5M2E7\n5byAaj+3Bm1F4qFds3qAEeAw8AKwpUKbO4PX9wMDZV5f9MPUGyxxr90edzbMwMBFFdZXWguvNAsn\nPD2ymZciaOX1aZodzpqmKRJPvcGfqNRjZiuArwEXA68APzKzSXc/FGpzCfB2d19tZu8D7gbW1ral\nA+zbt5/h4Y0VTxCqJF9Dn4xdCgif+FMo8Sz25rLLFcolw8MbOX58G9XKIM1QS8076clNzS6z6OQr\nkSar592icAPeD0yFHt8I3Bhpcw9wWejxYWBVpM2id7FqJzdFyy/1fKNSve2KpaPKs3/aWQbRkbJI\nutCOUg/wl8CO0OMrgLsibb4LfCD0+HvABZE2JT/Q4hOdypVbygdr0q/6q9QubglHZRARaZV6gz/p\nrB6P2c6WWm5sbGzhfiaTWfi4Pzy8kZmZ+B2KWyaop93iGTU7yy6vMoiINEsulyOXyyVej+XfNOpc\n2GwtMObuI8Hjm4B5d7891OYeIOfuO4PHh4FBdz8aauOV+lFuzjicCOro+Vp2rWfrioh0AzPD3aMH\n1ksvlzD4TwGeBz5MfhL8fwOXe+ng7rXufknwRrHN3ddG1lMx+CHe1R9FRNKmLcEfbPgjwDZgBXCf\nu99mZtcAuPu9QZuvkZ/2+XvgSnf/cWQdVYNfRERKtS34G0HBLyJSu3qDv6cZnRERkc6l4BcRSRkF\nv4hIyij4RURSRsEvIpIyCn4RkZRR8IuIpIyCX0QkZRT8IiIpo+AXEUkZBb+ISMoo+EVEUkbBLyKS\nMgp+EZGUUfCLiKSMgl9EJGUU/CIiKaPgFxFJGQW/iEjKKPhFRFJGwS8ikjIKfhGRlKk7+M1spZnN\nmNlPzexRMzu9TJu3mNnjZnbQzH5iZtcn666IiCSV5Ij/RmDG3d8BPBY8jjoBfN7d1wBrgb8zs3cl\n2GbXy+Vy7e5Cx9C+KNK+KNK+SC5J8K8DJoL7E8BHow3c/Zfu/kxw/3+BQ8CbE2yz6+mXukj7okj7\nokj7Irkkwb/K3Y8G948Cq6o1NrOzgQHgqQTbFBGRhE6p9qKZzQBvKvPS1vADd3cz8yrr+UPg28Bn\ngyN/ERFpE3OvmNfVFzQ7DGTc/Zdm9sfA4+7+J2XanQr8O/Af7r6twrrq64SISMq5u9W6TNUj/iVM\nApuA24N//y3awMwMuA94rlLoQ30dFxGR+iQ54l8J/CtwFvAi8Ffu/hszezOww93/wsw+CHwfeBYo\nbOgmd59K3HMREalL3cEvIiLLU0vP3DWzETM7bGYvmNmWCm3uDF7fb2YDrexfKy21L8zsE8E+eNbM\nnjCz97Sjn60Q5/ciaPdeMztpZhta2b9Wivk3kjGzp4OTInMt7mLLxPgb6TezKTN7JtgXf9OGbjad\nmX3DzI6a2YEqbWrLTXdvyQ1YARwBzgZOBZ4B3hVpcwnwSHD/fcAPW9W/Vt5i7ov3A38U3B9J874I\ntftP8hMFNra73238vTgdOAicGTzub3e/27gvxoDbCvsBOAac0u6+N2Ff/Bn5qfAHKrxec2628oj/\nQuCIu7/o7ieAncClkTYLJ4W5+1PA6WZW9fyAZWrJfeHuT7r7b4OHTwFntriPrRLn9wLgOvJTgn/V\nys61WJx98XHgIXd/GcDdZ1vcx1aJsy9+AZwW3D8NOObuJ1vYx5Zw9x8Av67SpObcbGXwnwG8FHr8\ncvDcUm26MfDi7IuwTwGPNLVH7bPkvjCzM8j/0d8dPNWtA1Nxfi9WAyuDa2DtNbNPtqx3rRVnX+wA\n1pjZq8B+4LMt6lunqTk3k0znrFXcP9bo1M5u/COP/TOZ2YeAq4CLmtedtoqzL7YBN7q7B1OEu3X6\nb5x9cSpwPvBh4HXAk2b2Q3d/oak9a704++JLwDPunjGztwEzZnaeu/+uyX3rRDXlZiuD/xXgLaHH\nbyH/zlStzZnBc90mzr4gGNDdAYy4e7WPestZnH1xAbAzn/n0Ax8xsxPuPtmaLrZMnH3xEjDr7nPA\nnJl9HzgP6Lbgj7MvPgDcCuDu/2NmPwfeCextSQ87R8252cpSz15gtZmdbWa9wGXkTwILmwT+GsDM\n1gK/8eL1gLrJkvvCzM4CHgaucPcjbehjqyy5L9z9re5+jrufQ77O/5kuDH2I9zfyHeCDZrbCzF5H\nfjDvuRb3sxXi7IvDwMUAQU37ncDPWtrLzlBzbrbsiN/dT5rZtcA0+RH7+9z9kJldE7x+r7s/YmaX\nmNkR4PfAla3qXyvF2RfAzcDrgbuDI90T7n5hu/rcLDH3RSrE/Bs5bGZT5E+KnCd/smTXBX/M34t/\nAO43s/3kD2JvcPfX2tbpJjGzbwGDQL+ZvQTcQr7kV3du6gQuEZGU0VcvioikjIJfRCRlFPwiIimj\n4BcRSRkFv4hIyij4RURSRsEvIpIyCn4RkZT5fz1XL4PwuEedAAAAAElFTkSuQmCC\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10ad75c18>"
+       "<matplotlib.figure.Figure at 0x11c183c50>"
       ]
      },
      "metadata": {},
@@ -561,15 +531,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 500/500 [00:37<00:00,  6.89it/s]\n"
+      "500it [00:06, 71.59it/s]  \n"
      ]
     },
     {
@@ -584,9 +552,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nu82Vedy9LgAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a8b6f60>"
+       "<matplotlib.figure.Figure at 0x11cbe6630>"
       ]
      },
      "metadata": {},
@@ -594,7 +562,8 @@
     }
    ],
    "source": [
-    "param_run = BatchRunner(ForestFire, param_set, iterations=5, model_reporters=model_reporter)\n",
+    "param_run = BatchRunner(ForestFire, variable_params, fixed_params, \n",
+    "                        iterations=5, model_reporters=model_reporter)\n",
     "param_run.run_all()\n",
     "df = param_run.get_model_vars_dataframe()\n",
     "plt.scatter(df.density, df.BurnedOut)\n",
@@ -605,9 +574,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [mesa_dev]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [mesa_dev]"
   },
   "language_info": {
    "codemirror_mode": {
@@ -619,7 +588,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.2"
+   "version": "3.5.3"
   },
   "widgets": {
    "state": {},
@@ -627,5 +596,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }
diff --git a/mesa/batchrunner.py b/mesa/batchrunner.py
index 897255c6..e5f23652 100644
--- a/mesa/batchrunner.py
+++ b/mesa/batchrunner.py
@@ -6,11 +6,33 @@ Batchrunner
 A single class to manage a batch run or parameter sweep of a given model.
 
 """
-from itertools import product
+import collections
+import copy
+from itertools import product, count
 import pandas as pd
 from tqdm import tqdm
 
 
+def combinations(*items):
+    """
+    A small fix to handle dictionary type parameters in cartesian product.
+    """
+    prepared = [(item,) if isinstance(item, collections.Mapping) else item
+                for item in items]
+    yield from (param for param in product(*prepared))
+
+
+class VariableParameterError(TypeError):
+    MESSAGE = ('variable_parameters must map a name to a sequence of values. '
+            'These parameters were given with non-sequence values: {}')
+
+    def __init__(self, bad_names):
+        self.bad_names = bad_names
+
+    def __str__(self):
+        return self.MESSAGE.format(self.bad_names)
+
+
 class BatchRunner:
     """ This class is instantiated with a model class, and model parameters
     associated with one or more values. It is also instantiated with model and
@@ -23,19 +45,26 @@ class BatchRunner:
     entire DataCollector object.
 
     """
-    def __init__(self, model_cls, parameter_values, iterations=1,
-                 max_steps=1000, model_reporters=None, agent_reporters=None,
-                 display_progress=True):
+    def __init__(self, model_cls, variable_parameters=None,
+            fixed_parameters=None, iterations=1, max_steps=1000,
+            model_reporters=None, agent_reporters=None, display_progress=True):
         """ Create a new BatchRunner for a given model with the given
         parameters.
 
         Args:
             model_cls: The class of model to batch-run.
-            parameter_values: Dictionary of parameters to their values or
-                ranges of values. For example:
+            variable_parameters: Dictionary of parameters to lists of values.
+                The model will be run with every combination of these paramters.
+                For example, given variable_parameters of
                     {"param_1": range(5),
-                     "param_2": [1, 5, 10],
-                      "const_param": 100}
+                     "param_2": [1, 5, 10]}
+                models will be run with {param_1=1, param_2=1},
+                    {param_1=2, param_2=1}, ..., {param_1=4, param_2=10}.
+            fixed_parameters: Dictionary of parameters that stay same through
+                all batch runs. For example, given fixed_parameters of
+                    {"constant_parameter": 3},
+                every instantiated model will be passed constant_parameter=3
+                as a kwarg.
             iterations: The total number of times to run the model for each
                 combination of parameters.
             max_steps: The upper limit of steps above which each run will be halted
@@ -51,8 +80,8 @@ class BatchRunner:
 
         """
         self.model_cls = model_cls
-        self.parameter_values = {param: self.make_iterable(vals)
-                                 for param, vals in parameter_values.items()}
+        self.variable_parameters = self._process_parameters(variable_parameters)
+        self.fixed_parameters = fixed_parameters or {}
         self.iterations = iterations
         self.max_steps = max_steps
 
@@ -67,36 +96,42 @@ class BatchRunner:
 
         self.display_progress = display_progress
 
+    def _process_parameters(self, params):
+        params = copy.deepcopy(params)
+        bad_names = []
+        for name, values in params.items():
+            if (isinstance(values, str) or
+                    not hasattr(values, "__iter__")):
+                bad_names.append(name)
+        if bad_names:
+            raise VariableParameterError(bad_names)
+        return params
+
     def run_all(self):
         """ Run the model at all parameter combinations and store results. """
-        params = self.parameter_values.keys()
-        param_ranges = self.parameter_values.values()
-        run_count = 0
-
-        if self.display_progress:
-            pbar = tqdm(total=len(list(product(*param_ranges))) * self.iterations)
-
-        for param_values in list(product(*param_ranges)):
-            kwargs = dict(zip(params, param_values))
-            for _ in range(self.iterations):
+        param_names, param_ranges = zip(*self.variable_parameters.items())
+        run_count = count()
+        total_iterations = self.iterations
+        for param_range in param_ranges:
+            total_iterations *= len(param_range)
+        with tqdm(total_iterations, disable=not self.display_progress) as pbar:
+            for param_values in product(*param_ranges):
+                kwargs = dict(zip(param_names, param_values))
+                kwargs.update(self.fixed_parameters)
                 model = self.model_cls(**kwargs)
-                self.run_model(model)
-                # Collect and store results:
-                if self.model_reporters:
-                    key = tuple(list(param_values) + [run_count])
-                    self.model_vars[key] = self.collect_model_vars(model)
-                if self.agent_reporters:
-                    agent_vars = self.collect_agent_vars(model)
-                    for agent_id, reports in agent_vars.items():
-                        key = tuple(list(param_values) + [run_count, agent_id])
-                        self.agent_vars[key] = reports
-                if self.display_progress:
-                    pbar.update()
-
-                run_count += 1
 
-        if self.display_progress:
-            pbar.close()
+                for _ in range(self.iterations):
+                    self.run_model(model)
+                    # Collect and store results:
+                    model_key = param_values + (next(run_count),)
+                    if self.model_reporters:
+                        self.model_vars[model_key] = self.collect_model_vars(model)
+                    if self.agent_reporters:
+                        agent_vars = self.collect_agent_vars(model)
+                        for agent_id, reports in agent_vars.items():
+                            agent_key = model_key + (agent_id,)
+                            self.agent_vars[agent_key] = reports
+                    pbar.update()
 
     def run_model(self, model):
         """ Run a model object to completion, or until reaching max steps.
@@ -126,38 +161,36 @@ class BatchRunner:
         return agent_vars
 
     def get_model_vars_dataframe(self):
-        """ Generate a pandas DataFrame from the model-level variables collected.
+        """ Generate a pandas DataFrame from the model-level variables
+        collected.
 
         """
-        index_col_names = list(self.parameter_values.keys())
-        index_col_names.append("Run")
-        records = []
-        for key, val in self.model_vars.items():
-            record = dict(zip(index_col_names, key))
-            for k, v in val.items():
-                record[k] = v
-            records.append(record)
-        return pd.DataFrame(records)
+        return self._prepare_report_table(self.model_vars)
 
     def get_agent_vars_dataframe(self):
         """ Generate a pandas DataFrame from the agent-level variables
         collected.
 
         """
-        index_col_names = list(self.parameter_values.keys())
-        index_col_names += ["Run", "AgentID"]
+        return self._prepare_report_table(self.agent_vars,
+                extra_cols=['AgentId'])
+
+    def _prepare_report_table(self, vars_dict, extra_cols=None):
+        """
+        Creates a dataframe from collected records and sorts it using 'Run'
+        column as a key.
+        """
+        extra_cols = ['Run'] + (extra_cols or [])
+        index_cols = list(self.variable_parameters.keys()) + extra_cols
+
         records = []
-        for key, val in self.agent_vars.items():
-            record = dict(zip(index_col_names, key))
-            for k, v in val.items():
-                record[k] = v
+        for param_key, values in vars_dict.items():
+            record = dict(zip(index_cols, param_key))
+            record.update(values)
             records.append(record)
-        return pd.DataFrame(records)
-
-    @staticmethod
-    def make_iterable(val):
-        """ Helper method to ensure a value is a non-string iterable. """
-        if hasattr(val, "__iter__") and not isinstance(val, str):
-            return val
-        else:
-            return [val]
+
+        df = pd.DataFrame(records)
+        rest_cols = set(df.columns) - set(index_cols)
+        ordered = df[index_cols + list(sorted(rest_cols))]
+        ordered.sort_values(by='Run', inplace=True)
+        return ordered
diff --git a/mesa/time.py b/mesa/time.py
index 75bac083..ad5af978 100644
--- a/mesa/time.py
+++ b/mesa/time.py
@@ -139,7 +139,7 @@ class StagedActivation(BaseScheduler):
     shuffle_between_stages = False
     stage_time = 1
 
-    def __init__(self, model, stage_list=["step"], shuffle=False,
+    def __init__(self, model, stage_list=None, shuffle=False,
                  shuffle_between_stages=False):
         """ Create an empty Staged Activation schedule.
 
@@ -154,7 +154,7 @@ class StagedActivation(BaseScheduler):
 
         """
         super().__init__(model)
-        self.stage_list = stage_list
+        self.stage_list = stage_list or ["step"]
         self.shuffle = shuffle
         self.shuffle_between_stages = shuffle_between_stages
         self.stage_time = 1 / len(self.stage_list)


Test patch:
diff --git a/tests/test_batchrunner.py b/tests/test_batchrunner.py
index 2e9242c4..e9b9904b 100644
--- a/tests/test_batchrunner.py
+++ b/tests/test_batchrunner.py
@@ -1,27 +1,28 @@
 """
 Test the BatchRunner
 """
+from functools import reduce
+from operator import mul
 import unittest
 
 from mesa import Agent, Model
-from mesa.batchrunner import BatchRunner
 from mesa.time import BaseScheduler
+from mesa.batchrunner import BatchRunner
+
 
 NUM_AGENTS = 7
 
 
 class MockAgent(Agent):
     """
-    Minimalistic model for testing purposes
+    Minimalistic agent implementation for testing purposes
     """
-    def __init__(self, unique_id, val):
+    def __init__(self, unique_id, model, val):
+        super().__init__(unique_id, model)
         self.unique_id = unique_id
         self.val = val
 
     def step(self):
-        """
-        increment val by 1
-        """
         self.val += 1
 
 
@@ -29,18 +30,34 @@ class MockModel(Model):
     """
     Minimalistic model for testing purposes
     """
-    def __init__(self, model_param, agent_param):
-        """
-        Args:
-            model_param (any): parameter specific to the model
-            agent_param (int): parameter specific to the agent
-        """
-        self.schedule = BaseScheduler(None)
-        self.model_param = model_param
+    def __init__(self, variable_model_param, variable_agent_param,
+                 fixed_model_param=None, schedule=None, **kwargs):
+        super().__init__()
+        self.schedule = BaseScheduler(None) if schedule is None else schedule
+        self.variable_model_param = variable_model_param
+        self.variable_agent_param = variable_agent_param
+        self.fixed_model_param = fixed_model_param
+        self.n_agents = kwargs.get('n_agents', NUM_AGENTS)
+        self.running = True
+        self.init_agents()
+
+    def init_agents(self):
+        for i in range(self.n_agents):
+            self.schedule.add(MockAgent(i, self, self.variable_agent_param))
+
+    def step(self):
+        self.schedule.step()
+
+
+class MockMixedModel(Model):
+
+    def __init__(self, **other_params):
+        super().__init__()
+        self.variable_name = other_params.get('variable_name', 42)
+        self.fixed_name = other_params.get('fixed_name')
         self.running = True
-        for i in range(NUM_AGENTS):
-            a = MockAgent(i, agent_param)
-            self.schedule.add(a)
+        self.schedule = BaseScheduler(None)
+        self.schedule.add(MockAgent(1, self, 0))
 
     def step(self):
         self.schedule.step()
@@ -51,44 +68,95 @@ class TestBatchRunner(unittest.TestCase):
     Test that BatchRunner is running batches
     """
     def setUp(self):
-        """
-        Create the model and run it for some steps
-        """
-        self.model_reporter = {"model": lambda m: m.model_param}
-        self.agent_reporter = {
+        self.mock_model = MockModel
+        self.model_reporters = {
+            "reported_variable_value": lambda m: m.variable_model_param,
+            "reported_fixed_value": lambda m: m.fixed_model_param
+        }
+        self.agent_reporters = {
             "agent_id": lambda a: a.unique_id,
-            "agent_val": lambda a: a.val}
-        self.params = {
-            'model_param': range(3),
-            'agent_param': [1, 8],
+            "agent_val": lambda a: a.val
+        }
+        self.variable_params = {
+            "variable_model_param": range(3),
+            "variable_agent_param": [1, 8]
         }
+        self.fixed_params = None
         self.iterations = 17
-        self.batch = BatchRunner(
-            MockModel,
-            self.params,
+        self.max_steps = 3
+
+    def launch_batch_processing(self):
+        batch = BatchRunner(
+            self.mock_model,
+            variable_parameters=self.variable_params,
+            fixed_parameters=self.fixed_params,
             iterations=self.iterations,
-            max_steps=3,
-            model_reporters=self.model_reporter,
-            agent_reporters=self.agent_reporter)
-        self.batch.run_all()
+            max_steps=self.max_steps,
+            model_reporters=self.model_reporters,
+            agent_reporters=self.agent_reporters)
+        batch.run_all()
+        return batch
+
+    @property
+    def model_runs(self):
+        """
+        Returns total number of batch runner's iterations.
+        """
+        return (reduce(mul, map(len, self.variable_params.values())) *
+                self.iterations)
 
     def test_model_level_vars(self):
         """
         Test that model-level variable collection is of the correct size
         """
-        model_vars = self.batch.get_model_vars_dataframe()
-        rows = len(self.params['model_param']) * \
-            len(self.params['agent_param']) * \
-            self.iterations
-        assert model_vars.shape == (rows, 4)
+        batch = self.launch_batch_processing()
+        model_vars = batch.get_model_vars_dataframe()
+        expected_cols = (len(self.variable_params) +
+                         len(self.model_reporters) +
+                         1)  # extra column with run index
+
+        self.assertEqual(model_vars.shape, (self.model_runs, expected_cols))
 
     def test_agent_level_vars(self):
         """
         Test that agent-level variable collection is of the correct size
         """
-        agent_vars = self.batch.get_agent_vars_dataframe()
-        rows = NUM_AGENTS * \
-            len(self.params['agent_param']) * \
-            len(self.params['model_param']) * \
-            self.iterations
-        assert agent_vars.shape == (rows, 6)
+        batch = self.launch_batch_processing()
+        agent_vars = batch.get_agent_vars_dataframe()
+        expected_cols = (len(self.variable_params) +
+                         len(self.agent_reporters) +
+                         2)  # extra columns with run index and agentId
+
+        self.assertEqual(agent_vars.shape,
+             (self.model_runs * NUM_AGENTS, expected_cols))
+
+    def test_model_with_fixed_parameters_as_kwargs(self):
+        """
+        Test that model with fixed parameters passed like kwargs is
+        properly handled
+        """
+        self.fixed_params = {'fixed_model_param': 'Fixed', 'n_agents': 1}
+        batch = self.launch_batch_processing()
+        model_vars = batch.get_model_vars_dataframe()
+        agent_vars = batch.get_agent_vars_dataframe()
+
+        self.assertEqual(len(model_vars), len(agent_vars))
+        self.assertEqual(len(model_vars), self.model_runs)
+        self.assertEqual(model_vars['reported_fixed_value'].unique(), ['Fixed'])
+
+    def test_model_with_variable_and_fixed_kwargs(self):
+        self.mock_model = MockMixedModel
+        self.model_reporters = {
+            'reported_fixed_param': lambda m: m.fixed_name,
+            'reported_variable_param': lambda m: m.variable_name
+        }
+        self.fixed_params = {'fixed_name': 'Fixed'}
+        self.variable_params = {'variable_name': [1, 2, 3]}
+        batch = self.launch_batch_processing()
+        model_vars = batch.get_model_vars_dataframe()
+        expected_cols = (len(self.variable_params) +
+                         len(self.model_reporters) +
+                         1)
+        self.assertEqual(model_vars.shape, (self.model_runs, expected_cols))
+        self.assertEqual(model_vars['reported_fixed_param'].iloc[0],
+                self.fixed_params['fixed_name'])
diff --git a/tests/test_visualization.py b/tests/test_visualization.py
index 553b68e2..e46f78dc 100644
--- a/tests/test_visualization.py
+++ b/tests/test_visualization.py
@@ -24,7 +24,7 @@ class MockModel(Model):
         self.grid = Grid(width, height, torus=True)
 
         for (c, x, y) in self.grid.coord_iter():
-            a = MockAgent(x + y * 100, x * y * 3)
+            a = MockAgent(x + y * 100, self, x * y * 3)
             self.grid.place_agent(a, (x, y))
             self.schedule.add(a)