From 6d4cc9f1937ee1134620c8aeb5a55027491706ac Mon Sep 17 00:00:00 2001
From: arokem <arokem@gmail.com>
Date: Fri, 20 Feb 2015 18:28:48 -0800
Subject: [PATCH] Update the notebooks to IPython 3.0 version.

To be merged once IPython 3.0 is out.
---
 pymatbridge/examples/matlab_magic.ipynb | 899 ++++++++++++++++++++----
 pymatbridge/examples/pymatbridge.ipynb  | 607 ++++++++++------
 2 files changed, 1160 insertions(+), 346 deletions(-)

diff --git a/pymatbridge/examples/matlab_magic.ipynb b/pymatbridge/examples/matlab_magic.ipynb
index b2a7172..5f0fbf0 100644
--- a/pymatbridge/examples/matlab_magic.ipynb
+++ b/pymatbridge/examples/matlab_magic.ipynb
@@ -1,145 +1,778 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:48e8fc74a7e448f6413aa94b84b2099f57ab69f6390c266abbd5d1ff0c4a0b14"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pymatbridge as pymat"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ip = get_ipython()\n",
-      "pymat.load_ipython_extension(ip)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting MATLAB on ZMQ socket ipc:///tmp/pymatbridge\n",
-        "Send 'exit' command to kill the server\n",
-        ".MATLAB started and connected!"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%matlab --size 400,350\n",
-      "t = linspace(0,6*pi,100);\n",
-      "plot(sin(t))\n",
-      "grid on\n",
-      "hold on\n",
-      "plot(cos(t), 'r')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ZZbCWQ8k1YzNh2t1qOCgU6nKpHas3NcFuzx0dqZrwqxQtUxTLTDOzHJYmVFQAaBjqVrN2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\nhYVpsByWhYWFabAcloWFhWn4/wFEeUq96GwhzQAAAABJRU5ErkJggg==\n"
-      }
-     ],
-     "prompt_number": 3
-    },
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matlab b = 10*cos(t)+30; plot(b); grid on"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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9FEAujSJMKABMzFyjDgDqOxX7dcepNXOZ3/No7sitmGIjMXA7AUyNlboggRhp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\n0NDtIBFSQQFAPPKNutzUOODv2hOnu+U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-      }
-     ],
-     "prompt_number": 4
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arokem/anaconda3/lib/python3.4/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated.\n",
+      "\n",
+      "- use IPython.nbformat for read/write/validate public API\n",
+      "- use IPython.nbformat.vX directly to composing notebooks of a particular version\n",
+      "\n",
+      "  \"\"\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pymatbridge as pymat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = [1,2,3]\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting MATLAB on ZMQ socket ipc:///tmp/pymatbridge\n",
+      "Send 'exit' command to kill the server\n",
+      "..........MATLAB started and connected!\n"
+     ]
+    }
+   ],
+   "source": [
+    "ip = get_ipython()\n",
+    "pymat.load_ipython_extension(ip)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%matlab -i a -o b\n",
-      "b = a + 3;"
-     ],
-     "language": "python",
+     "data": {
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+       "P129erW2SZJ6xFLw7rvvHjlyZNmyZUkmRs/j4/qIhSvg19vbu3HjRm0BbUVFxSOPPKKNzwqRGR8f\n",
+       "/7d/+7cXXnhhaGiIHcnLy7vvvvvuvffe+FWv6hGLT5bt+PQ8Pn6PWDiDxejp6Tl69Oi8efMWLFjg\n",
+       "9LUojDE8PHz8+PHe3t65c+dWV1d7vd60H1OPWGr0PD4ej1hQg6VQKBSpiOV0VygUiiwog6VQKKRB\n",
+       "GSyFQiENymApFAppUAZLoVBIgzJYCoVCGpTBUigU0qAMlkKhkAZlsBQKhTQog6VQKKRBGSyFQiEN\n",
+       "ymApFAppUAZLoVBIgzJYCoVCGv4/ywisxEuyE80AAAAASUVORK5CYII=\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%matlab --size 400,350\n",
+    "t = linspace(0,6*pi,100);\n",
+    "plot(sin(t))\n",
+    "grid on\n",
+    "hold on\n",
+    "plot(cos(t), 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(b)"
-     ],
-     "language": "python",
+     "data": {
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+       "AFA3NbOmz7cAECR/dphs0xdmvteuXTt16lTHjh379OlDquEtEWayd+7cOXv27KVLlzp37hwXF+e8\n",
+       "M4Qzwsy3JWqmRn/6IggAgiAIogGOm8AIgiCIN6AAIAiCSAoKAIIgiKSgACAIgkgKCgCCIIikoAAg\n",
+       "CIJICgoAgiCIpKAAIAiCSAoKAIIgiKSgACAIgkgKCgCCIIikoAAgCIJICgoAgiCIpKAAIAiCSAoK\n",
+       "AIIgiKSgACAIgkjK/wEH/MzXi5T25AAAAABJRU5ErkJggg==\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[ 4.  5.  6.]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matlab b = 10*cos(t)+30; plot(b); grid on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = [1,2,3]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%matlab -i a -o b\n",
+    "b = a + 3;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 4.  5.  6.]]\n"
+     ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/pymatbridge/examples/pymatbridge.ipynb b/pymatbridge/examples/pymatbridge.ipynb
index d32a2a9..8ae794e 100644
--- a/pymatbridge/examples/pymatbridge.ipynb
+++ b/pymatbridge/examples/pymatbridge.ipynb
@@ -1,239 +1,420 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:97d58e18d73db04b81f296e9b6a194146fc1715e389c5f41eb5d98b818def865"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import os\n",
-      "import numpy as np\n",
-      "import pymatbridge as pymat"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arokem/anaconda3/lib/python3.4/site-packages/IPython/nbformat/current.py:19: UserWarning: IPython.nbformat.current is deprecated.\n",
+      "\n",
+      "- use IPython.nbformat for read/write/validate public API\n",
+      "- use IPython.nbformat.vX directly to composing notebooks of a particular version\n",
+      "\n",
+      "  \"\"\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pymatbridge as pymat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting MATLAB on ZMQ socket ipc:///tmp/pymatbridge\n",
+      "Send 'exit' command to kill the server\n",
+      ".MATLAB started and connected!\n"
+     ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "matlab = pymat.Matlab()\n",
-      "matlab.start()"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 2,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting MATLAB on ZMQ socket ipc:///tmp/pymatbridge\n",
-        "Send 'exit' command to kill the server\n",
-        ".MATLAB started and connected!"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "True"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matlab = pymat.Matlab()\n",
+    "matlab.start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "res = matlab.run_func('%s/matlab/www/demo_func.m'%os.path.dirname(pymat.__file__), {'a': 10})\n",
-      "res['result']"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "11"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = matlab.run_func('%s/matlab/www/demo_func.m'%os.path.dirname(pymat.__file__), {'a': 10})\n",
+    "res['result']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "res_dict = matlab.run_code('a=[1,3,7]')\n",
-      "res_dict = matlab.run_code('plot(a)')\n",
-      "res_dict = matlab.run_code('b=a+1')\n",
-      "matlab.get_variable('b')"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "array([[ 2.,  4.,  8.]])"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "array([[ 2.,  4.,  8.]])"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_dict = matlab.run_code('a=[1,3,7]')\n",
+    "res_dict = matlab.run_code('plot(a)')\n",
+    "res_dict = matlab.run_code('b=a+1')\n",
+    "matlab.get_variable('b')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "res_dict = matlab.run_code('figure; plot(b); b')\n",
-      "\n",
-      "from IPython.core.displaypub import publish_display_data\n",
-      "imgfiles = [res_dict['content']['figures']]\n",
-      "text_output = res_dict['content']['stdout']\n",
-      "\n",
-      "display_data = []\n",
-      "if text_output:\n",
-      "    display_data.append(('MatlabMagic.matlab',\n",
-      "                                 {'text/plain':text_output}))\n",
-      "for imgf in imgfiles:\n",
-      "    try: # python 2\n",
-      "        image = open(imgf[0], 'rb').read().decode('utf-8') \n",
-      "    except: # python 3\n",
-      "        image = open(imgf[0], 'rb').read()\n",
-      "        \n",
-      "    display_data.append(('MatlabMagic.matlab',  {'image/png': image}))\n",
-      "\n",
-      "for tag, disp_d in display_data:\n",
-      "            publish_display_data(tag, disp_d)\n",
-      "\n"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "\n",
+       "b =\n",
+       "\n",
+       "     2     4     8\n",
+       "\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "\n",
-        "b =\n",
-        "\n",
-        "     2     4     8\n",
-        "\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Iq2iI+3Oh0cyF+7tlLIlwc+rFOhHgFQAA9Ly8iobo5Nw6gxnTX1BQAADQswqrGqOTc6sb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\nvvn5QPejC0fKpAL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-      }
-     ],
-     "prompt_number": 5
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "matlab.is_connected()"
-     ],
-     "language": "python",
+     "data": {
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+       "rR04cKDdbjftldrwIl5Ururr67/66qszZ87ceOONUVFRKuwB1hAAAGBS8s9BAABSEAAAYFIEAACY\n",
+       "FAEAACZFAACASREAAGBSBAAAmBQBAAAmRQAAgEkRAABgUgQAAJgUAQAAJkUAJI3JVAAAABdJREFU\n",
+       "AIBJEQAAYFIEAACYFAEAACb1/8CnvhyaPg3gAAAAAElFTkSuQmCC\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "."
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "True"
-       ]
-      }
-     ],
-     "prompt_number": 6
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "res_dict = matlab.run_code('figure; plot(b); b')\n",
+    "\n",
+    "from IPython.core.displaypub import publish_display_data\n",
+    "imgfiles = [res_dict['content']['figures']]\n",
+    "text_output = res_dict['content']['stdout']\n",
+    "\n",
+    "display_data = []\n",
+    "if text_output:\n",
+    "    display_data.append({'text/plain':text_output})\n",
+    "for imgf in imgfiles:\n",
+    "    try: # python 2\n",
+    "        image = open(imgf[0], 'rb').read().decode('utf-8') \n",
+    "    except: # python 3\n",
+    "        image = open(imgf[0], 'rb').read()\n",
+    "        \n",
+    "    display_data.append({'image/png': image})\n",
+    "\n",
+    "for d in display_data:\n",
+    "        publish_display_data(d)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "."
+     ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "matlab.stop()"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "MATLAB closed\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "True"
-       ]
-      }
-     ],
-     "prompt_number": 7
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matlab.is_connected()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MATLAB closed\n"
+     ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "matlab.is_connected()"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "False"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "matlab.stop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
      "metadata": {},
-     "outputs": []
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "matlab.is_connected()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}