update parallel example notebooks to remove `%pylab`

examples/parallel/InteractiveMPI-publish-data.ipynb

@@ -1,6 +1,6 b''
 {
  "metadata": {
-  "name": "InteractiveMPI-publish-data"
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -66,21 +66,13 b''
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline"
+      "%matplotlib inline\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
-        "For more information, type 'help(pylab)'.\n"
-       ]
-      }
-     ],
+     "outputs": [],
      "prompt_number": 2
     },
     {
@@ -118,9 +110,9 b''
        "stream": "stdout",
        "text": [
         "[stdout:0] MPI rank: 2/4\n",
-        "[stdout:1] MPI rank: 1/4\n",
-        "[stdout:2] MPI rank: 0/4\n",
-        "[stdout:3] MPI rank: 3/4\n"
+        "[stdout:1] MPI rank: 0/4\n",
+        "[stdout:2] MPI rank: 3/4\n",
+        "[stdout:3] MPI rank: 1/4\n"
        ]
       }
      ],
@@ -221,7 +213,7 b''
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",
@@ -242,7 +234,7 b''
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.display import clear_output\n",
+      "from IPython.display import display, clear_output\n",
       "\n",
       "def plot_current_results(ar, in_place=True):\n",
       "    \"\"\"Makes a blocking call to retrieve remote data and displays the solution mesh\n",
@@ -275,7 +267,7 b''
       "        fig, ax = plt.subplots()\n",
       "        ax.contourf(Z)\n",
       "        ax.set_title('Mesh: %i x %i, step %i/%i' % (nx, nyt, j+1, nsteps))\n",
-      "        axis('off')\n",
+      "        plt.axis('off')\n",
       "        # We clear the notebook output before plotting this if in-place \n",
       "        # plot updating is requested\n",
       "        if in_place:\n",
@@ -287,7 +279,7 b''
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -350,7 +342,7 b''
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 34
+     "prompt_number": 7
     },
     {
      "cell_type": "heading",
@@ -378,14 +370,12 b''
       "view = cluster[:]\n",
       "# And now we call on all available nodes our simulation routine,\n",
       "# as an asynchronous task\n",
-      "nsteps = 10\n",
-      "delay = 0.1\n",
-      "ar = view.apply_async(lambda : simulation(nsteps, delay))"
+      "ar = view.apply_async(lambda : simulation(nsteps=10, delay=0.1))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 38
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
@@ -399,9 +389,9 b''
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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YdJ6pJtc3mCciciGTKMHBIRiYBgJ2/wIAmn+JTSWehWp+uJnkgcaiBzJkD+QSfkfqyd9k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\njyB0yc4JcHx5KHSZpSgyT6V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AAGzPU3fkO97I2zu3nnACADot76Apd8IJAKpQrQVNVxFOANCFBE1lE04AEIKGNMIJgLIn\naigXwgmAThM01BrhBFCFBA0Uh3AC6EKCBiqbcAIIQQOkEU5AWRM0QDkRTkCnCBqgFgknqEKiBqA4\nhBN0IUEDUNmEE4SgASCNcKKsCRoAyolwolMEDQC1SDhVGUEDAMUjnLqQqAGAyiacikAgAUB1Ek5F\nMHJ8qWdQPUQoAOVEOFHWRGj5ErVALRJOQKeI2vIlaqF4hBNAlRG15UvUVj7hBABdRNRWvrpSTwAA\noFLkd8TpotxG6rhrSrhtAKBmVMepulJGGx0ndAGoUNURTlQWoVt5xC5ARAgnIIXYrSxCF4pGOAFU\nG6FbWYRuRSlkWZblMlJTIZdhAACKbnjn8sfjCAAAEuV2qu7+4UclLXdi04K8NgkA0KW6/Bqn1MCi\n/IlgAGqNi8PpNBFcPUQwQBrhBIjgKiKCobiEE0AVEcHVQQCXL+EEAGVGABffiZ1cz+MIAAASCScA\ngES5naqbEefmNRRd5Ly4sdRTAICK4hqnGiZ2a4NABsiPcIIqJ5Brg0CGriGcAKqAQK4dIrm0hBMA\nVBCRnA+PIwAAKDLhBACQKLdTdQ8/OTavodiOY0fdU+opAEBNco1TBRKpRAhogFIQTlChBDQfEtHQ\ndYQTQIUT0UQI6K5SyLIsy2WgJ/MYBQCg+LJRnVvPXXUAAImEEwBAovyucWrMbaTq0ljqCQAAeXFx\neLE1lnoCVJTGUk8AgG0RTlBOGks9ASpKY6knALVHOAFUqsZST4CK0ljqCVSH/B5HMDqPUQAAii/7\nfefWc1cdAEAi4QQAkCi/a5wW/Sm3obrcFw8t9QwAgArg4vCIyo4+ypcgB6g6wgmKRZBTLKIcSkY4\nAVQaUU6xiPLtyu9xBAX/RwYAKkOWdS4S3VUHAJBIOAEAJBJOAACJhBMAQCJ31QEApVchd/QJJ4Ad\nUSG/7IF8CKda5Zc9AHRYfuFkRwwAVDkXhwMAJBJOAACJhBMAQCLhBACQyF11ALWosdQTgMqUXzg1\n5jYSAEBZcqoOACCRcAIASCScAAASCScAgETCCQAgkXACAEiU2+MIjh11T15DAQAU2dhOreWIEwBA\nIuEEAJBIOAEAJBJOAACJhBMAQKLc7qo7L27Mayiq0Iw4t9RTAIAdlls4wbYIawDKi8cRAAAUlXAC\nAEgknAAAEgknAIBEwgkAIFFud9Wd2LQgr6GgJtw//KhSTwGADvI4AigR/9iA6uEfQrWjkGVZlstI\nTYVchgEAKLrhncsf1zgBACQSTgAAiYQTAECi/C4Ovya3karHRaWeAACQJ3fVFZOYpBQEO0DRCCeo\nNoKdUhDs1AjhBMCOE+yUQgmCPb/nOJ3uOU4AQIW43XOcAACKKrdweuqOD/4AAFSr3K9xEk/VaeT4\nUs8AAErPxeEkEcTVRwwDdJxwgholhquPGIbiE04AVUIMVx8xXH5yexzBUwWPIwAAKsPITuaPI04A\nQElU4hE14QRARajEnSzVRzgBVclOFigG4QRhJwtAGuHUCXayAFCbcgsnMQEAVDsf8gsAkMipOgCg\nPF1U6glsTTgBUDpluGOEbRFOQPHYKQJVRjixY+wYAaghuX1WXTT5rDoAoEIM71z+uKsOACCRcAIA\nSOQaJwCg4t0//KgOLX9iJ7cjnADotI7urKDSCSeoMHZUAKUjnBLZWQEAuYWTsAAAqp276gAAEgkn\nAIBEwgkAIJGLwwGq3Iw4t9RTgLLjOU7UFDsCAEoht3CyIwMAqp1rnAAAEgknAIBEwgkAIJFwAgBI\n5K46gBw8/OTYUk8B6IhRnVtNOLXBL0AAoC2FLMuyXAZ6Mo9RAACKL+vkESfXOAEAJBJOAACJhBMA\nQCLhBACQyF11QNsaSz0BgCL6fedWyy+cGnMbCQCgLDlVBwCQSDgBACQSTgAAiYQTAEAi4QQAkKgy\nH0ew6E+lngEAUNEO7dRa+X3Ib0HMAACVIcs6F05O1QEAJBJOAACJhBMAQCLhBACQSDgBACQSTgAA\niYQTAEAi4QQAkEg4AQAkEk4AAImEEwBAIuEEAJBo51JPAIAy8sXOffAp1ArhRPnyCxyAMpNfONnJ\nAQBVzjVOAACJhBMAQCLhBACQSDgBACQSTgAAiTyOAACoHI2l3bxwAoAPNZZ6ApQ74QTQVRpLPQFg\nRwknqCaNpZ4AQHUTTqRrLPUEAKC0yiucGks9AQCA9uUXTo25jQQAUJY8xwkAIJFwAgBIVF7XOAEA\nVevYUfeUegr/Y2yn1hJOAFAk5RUK5EE4AZALkUAtEE4AnSQUoPYIJyCJSAAQTtAuoQDAloQTrYQC\nAGxbzYaTSAAAOiq3cBIiAEC1q9kjTgBAvs6LG0s9hQ7wAEwAqDiVFRsIJwAqjtigVIQTQI0QG7Dj\nhBPAdggO4EPCCSgKsQFUI+EEZURsAJQ34UTFExsAdBXhVKPEBgDV6sSmBdtfaHjnxhZOHSQ4AKgV\nSQFSY7oknMQGALVKfFSXQpZlWR4D3R9H5zEMALRJgJCr4Z3LH6fqAGqI+IAdI5wAOkGAQG0STkDJ\niA+g0ggnqGHCBaBjhBOUESEDUN6EE2yHmAHgQ8KJiiNkACgV4UQuxAwAtUA4VSkhAwD/zzVtvHZ7\n54YSTl1EyABQ89oKmApT0+EkZgCoOVUQL6VUVuEkZACoGQKmIuX2Ib/RVMhlGADoUgKmNt3uQ34B\nqFTihQohnAD4/wQMbJNwAihHAgbKknAC2BYBA/wP4QSUP/EClAnhBKQTMECNE05QiQQMQEkIJ+gs\n8QJQc4QTlU/AANBFhBP5ETAAVDnhVI0EDAAUhXAqFvECAFWn+sNJwAAAOem6cBIwAECFyy+chBEA\nUOXqSj0BAIBKIZwAABJV/8XhAEDVeeqOHVt/5O2dW084AUCN2dHoqGXCCQASCQ6EEwBFJzioFsIJ\noIwJDigvwgmoSoIDKAbhBGxGcAC0TzhBTgQHQPUTTpQF0QFAJRBOFU5wAEDXqdlwEhwAQEcVsizL\n8hjoqUIhj2EAAIpuZCfzx4f8AgAkEk4AAImEEwBAIuEEAJBIOAEAJBJOAACJhBMAQCLhBACQSDgB\nACQSTgAAiYQTAEAi4QQAkEg4AQAkEk4AAImEEwBAIuEEAJBIOAEAJBJOAACJhBMAQCLhBACQSDgB\nACQSTgAAiYQTAEAi4QQAkEg4AQAkEk4AAImEEwBAIuEEAJBIOAEAJBJOAACJhBMAQCLhBACQSDgB\nACQSTgAAiYQTAEAi4QQAkEg4AQAkEk4AAImEEwBAIuEEAJBIOAEAJBJOAACJhBMAQCLhBACQSDgB\nACQSTgAAiYQTAEAi4QQAkEg4AQAkEk4AAImEEwBAIuEEAJBIOAEAJBJOAACJhBMAQCLhBACQSDgB\nACQSTgAAiYQTAEAi4QQAkKiQZVlW6kkAAFQCR5wAABIJJwCARMIJACCRcAIASCScAAASCScAgETC\nCQAgkXACAEgknAAAEgknAIBEwgkAIJFwAgBIJJwAABIJJwCARMIJACCRcAIASCScAAASCScAgETC\nCQAgkXACAEgknAAAEgknAIBEwgkAINH/Acd8GCUQEYlkAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x57a9ad0>"
+        "<matplotlib.figure.Figure at 0x10b00b350>"
        ]
       },
       {
@@ -409,19 +399,11 b''
        "stream": "stdout",
        "text": [
         "Simulation completed!\n",
-        "Monitored for: 0:00:39.732973.\n"
+        "Monitored for: 0:00:01.229672.\n"
        ]
       }
      ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 9
     }
    ],
    "metadata": {}

examples/parallel/InteractiveMPI.ipynb

@@ -1,6 +1,6 b''
 {
  "metadata": {
-  "name": "InteractiveMPI"
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -23,8 +23,13 b''
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline\n",
+      "%matplotlib inline\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "from IPython.display import display\n",
       "from IPython.parallel import Client, error\n",
+      "\n",
       "cluster = Client(profile=\"mpi\")\n",
       "view = cluster[:]\n",
       "view.block = True"
@@ -35,18 +40,8 b''
        "slide_start": false
       }
      },
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
-        "For more information, type 'help(pylab)'.\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
+     "outputs": [],
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -60,13 +55,13 b''
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 2,
        "text": [
         "[0, 1, 2, 3]"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -115,7 +110,7 b''
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -148,7 +143,7 b''
       }
      },
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 4
     },
     {
      "cell_type": "heading",
@@ -225,7 +220,7 b''
       }
      },
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 5
     },
     {
      "cell_type": "heading",
@@ -283,7 +278,7 b''
       "    fig, ax = plt.subplots()\n",
       "    ax.contourf(Z)\n",
       "    ax.set_title('Mesh: %i x %i, step %i/%i' % (nx, nyt, j+1, nsteps))\n",
-      "    axis('off')\n",
+      "    plt.axis('off')\n",
       "    # We clear the notebook output before plotting this if in-place plot updating is requested\n",
       "    if in_place:\n",
       "        clear_output()\n",
@@ -297,7 +292,7 b''
       }
      },
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -326,7 +321,7 b''
       }
      },
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -395,7 +390,7 b''
       }
      },
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 8
     },
     {
      "cell_type": "heading",
@@ -431,7 +426,7 b''
       }
      },
      "outputs": [],
-     "prompt_number": 24
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -449,20 +444,21 b''
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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ZVNESJliUKUJCkFLiTJhufYqYDwGrLF6ehYuS1ZlK4uUgXC6yVUS0nCTLFG8K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\nSAUoU4QQQgghFfj/AcxNvvk8Uc7VAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x5fd82d0>"
+        "<matplotlib.figure.Figure at 0x1141a1450>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Simulation has already finished, no monitoring to do.\n"
+        "Simulation completed!\n",
+        "Monitored for: 0:00:50.653178.\n"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -488,7 +484,7 b''
       }
      },
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -506,27 +502,27 b''
        "stream": "stdout",
        "text": [
         "{\n",
-        "  \"stdin_port\": 62126, \n",
+        "  \"stdin_port\": 65310, \n",
         "  \"ip\": \"127.0.0.1\", \n",
-        "  \"control_port\": 41092, \n",
-        "  \"hb_port\": 42611, \n",
-        "  \"key\": \"00d832a7-200f-41e2-9d74-aac119f126a3\", \n",
-        "  \"shell_port\": 60748, \n",
+        "  \"control_port\": 58188, \n",
+        "  \"hb_port\": 58187, \n",
+        "  \"key\": \"e4f5cda8-faa8-48d3-a62c-dbde67db9827\", \n",
+        "  \"shell_port\": 65083, \n",
         "  \"transport\": \"tcp\", \n",
-        "  \"iopub_port\": 55151\n",
+        "  \"iopub_port\": 54934\n",
         "}\n",
         "\n",
         "Paste the above JSON into a file, and connect with:\n",
         "    $> ipython <app> --existing <file>\n",
         "or, if you are local, you can connect with just:\n",
-        "    $> ipython <app> --existing kernel-32429.json \n",
+        "    $> ipython <app> --existing kernel-64604.json \n",
         "or even just:\n",
         "    $> ipython <app> --existing \n",
         "if this is the most recent IPython session you have started.\n"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
@@ -538,15 +534,7 b''
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 13
     }
    ],
    "metadata": {}

examples/parallel/Parallel Magics.ipynb

@@ -1,6 +1,6 b''
 {
  "metadata": {
-  "name": "Parallel Magics"
+  "name": ""
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -163,7 +163,19 b''
      "collapsed": false,
      "input": [
       "%pxconfig --block\n",
-      "%px %pylab inline"
+      "%px %matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%px\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt"
      ],
      "language": "python",
      "metadata": {},
@@ -194,10 +206,10 b''
      "collapsed": false,
      "input": [
       "%%px --noblock\n",
-      "x = linspace(0,pi,1000)\n",
+      "x = np.linspace(0,np.pi,1000)\n",
       "for n in range(id,12, stride):\n",
       "    print n\n",
-      "    plt.plot(x,sin(n*x))\n",
+      "    plt.plot(x,np.sin(n*x))\n",
       "plt.title(\"Plot %i\" % id)"
      ],
      "language": "python",
@@ -234,11 +246,11 b''
      "collapsed": false,
      "input": [
       "%%px --group-outputs=engine\n",
-      "x = linspace(0,pi,1000)\n",
+      "x = np.linspace(0,np.pi,1000)\n",
       "for n in range(id+1,12, stride):\n",
       "    print n\n",
       "    plt.figure()\n",
-      "    plt.plot(x,sin(n*x))\n",
+      "    plt.plot(x,np.sin(n*x))\n",
       "    plt.title(\"Plot %i\" % n)"
      ],
      "language": "python",
@@ -291,7 +303,7 b''
       "    \"\"\"\n",
       "    \n",
       "    import sys,os\n",
-      "    from IPython.core.display import display, HTML, Math\n",
+      "    from IPython.display import display, HTML, Math\n",
       "    \n",
       "    print \"stdout\"\n",
       "    print >> sys.stderr, \"stderr\"\n",
@@ -431,7 +443,7 b''
       "from numpy.random import random\n",
       "from numpy.linalg import norm\n",
       "A = random((100,100))\n",
-      "norm(A, 2)    "
+      "norm(A, 2)"
      ],
      "language": "python",
      "metadata": {},