jlstevens/gist:3994938

## gistfile1.txt
{
 "metadata": {
  "name": "Displaying Topographica sheets"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import topo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run -i ./examples/gcal.ty"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def imact(objs, size=1.0, show_axes=False, label_name=True, norm_type=0, fig={},\n",
      "              show={'interpolation':'nearest', 'cmap':plt.cm.gray}):\n",
      "    \"\"\"\n",
      "    Displays the activity array of the given list of objects in row format.\n",
      "\n",
      "    size:       An overall size factor.\n",
      "    show_axes:  Whether or not to show axes for each subplot.\n",
      "    label_name: Show the object name above the subplot\n",
      "    norm_type:  0:No normalization, 1:Individually, 2: Together.\n",
      "\n",
      "    fig:  Extra kwargs for the pyplot figure.\n",
      "    show: Extra kwargs for imshow. Note: norm will override norm_type if set.\n",
      "    \"\"\"\n",
      "\n",
      "    fig = plt.figure(*fig)\n",
      "    (width, height) = fig.get_figwidth(), fig.get_figheight()\n",
      "    fig.set_figwidth(width*size)\n",
      "    fig.set_figheight(height*size)\n",
      "    \n",
      "    if norm==1:\n",
      "        show= dict({'norm':matplotlib.colors.Normalize()}, **show)\n",
      "    if norm==2:\n",
      "        activities = [obj.activity for obj in objs]\n",
      "        vmax = min(act.max() for act in activities)\n",
      "        vmin = min(act.min() for act in activities)\n",
      "        show= dict({'norm':matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)}, **show)\n",
      "\n",
      "    plot_num = len(objs)\n",
      "    for (i, obj) in enumerate(objs):\n",
      "        ax = plt.subplot(1,plot_num,i)\n",
      "        if label_name: plt.title(obj.name)\n",
      "        if not show_axes:\n",
      "            ax.yaxis.set_visible(False)\n",
      "            ax.xaxis.set_visible(False)\n",
      "        imshow(obj.activity, **show)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "topo.sim.run(1)\n",
      "imact([topo.sim.Retina, topo.sim.LGNOn, topo.sim.LGNOff, topo.sim.V1], size=3, norm_type=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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NnWhiRR8AAAAAgAhhog8AAAAAQISkXeg+AADoe7pSxK0ni+9ZNhVQxA2lFRGZ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\nAAAAABHCRB8AAAAAgAhhog8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      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": "Displaying Topographica sheets"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%pylab inline"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\n",
	"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
	"For more information, type 'help(pylab)'.\n"
	]
	}
	],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import topo"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"%run -i ./examples/gcal.ty"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 3
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"def imact(objs, size=1.0, show_axes=False, label_name=True, norm_type=0, fig={},\n",
	" show={'interpolation':'nearest', 'cmap':plt.cm.gray}):\n",
	" \"\"\"\n",
	" Displays the activity array of the given list of objects in row format.\n",
	"\n",
	" size: An overall size factor.\n",
	" show_axes: Whether or not to show axes for each subplot.\n",
	" label_name: Show the object name above the subplot\n",
	" norm_type: 0:No normalization, 1:Individually, 2: Together.\n",
	"\n",
	" fig: Extra kwargs for the pyplot figure.\n",
	" show: Extra kwargs for imshow. Note: norm will override norm_type if set.\n",
	" \"\"\"\n",
	"\n",
	" fig = plt.figure(*fig)\n",
	" (width, height) = fig.get_figwidth(), fig.get_figheight()\n",
	" fig.set_figwidth(width*size)\n",
	" fig.set_figheight(height*size)\n",
	" \n",
	" if norm==1:\n",
	" show= dict({'norm':matplotlib.colors.Normalize()}, **show)\n",
	" if norm==2:\n",
	" activities = [obj.activity for obj in objs]\n",
	" vmax = min(act.max() for act in activities)\n",
	" vmin = min(act.min() for act in activities)\n",
	" show= dict({'norm':matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)}, **show)\n",
	"\n",
	" plot_num = len(objs)\n",
	" for (i, obj) in enumerate(objs):\n",
	" ax = plt.subplot(1,plot_num,i)\n",
	" if label_name: plt.title(obj.name)\n",
	" if not show_axes:\n",
	" ax.yaxis.set_visible(False)\n",
	" ax.xaxis.set_visible(False)\n",
	" imshow(obj.activity, **show)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 4
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"topo.sim.run(1)\n",
	"imact([topo.sim.Retina, topo.sim.LGNOn, topo.sim.LGNOff, topo.sim.V1], size=3, norm_type=1)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "display_data",
	"png": 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	}
	],
	"prompt_number": 5
	}
	],
	"metadata": {}
	}
	]
	}