test clear_output callback

examples/tests/Test Output Callbacks.ipynb

@@ -23,8 +23,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -34,16 +33,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hi\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -53,16 +43,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "'hi'"
-       ]
-      }
-     ],
-     "prompt_number": 13
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -72,17 +53,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "1"
-       ]
-      }
-     ],
-     "prompt_number": 14
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -94,25 +65,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x10a1da150>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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AqbiPFuDB7Iahl7Yd1WXRgZqQHmV2HLiJqECbnhibrK+KavXa9iL1jwrQfcPiFRnIRtXw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\nKFoAAAAO8v8BvGxDv1+F5xsAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10a2b5790>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -123,19 +76,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "console.log(\"I ran!\");"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x10aae8310>"
-       ]
-      }
-     ],
-     "prompt_number": 20
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -146,19 +87,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<b>bold</b>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML at 0x10aae80d0>"
-       ]
-      }
-     ],
-     "prompt_number": 21
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -171,21 +100,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\n",
-        "a = 5\n",
-        "$$"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Latex at 0x10aae8310>"
-       ]
-      }
-     ],
-     "prompt_number": 22
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -203,25 +118,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prompt > 1\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "'1'"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -241,16 +138,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Overwriting tst.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -260,8 +148,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -279,8 +166,7 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
+     "outputs": []
     },
     {
      "cell_type": "heading",
@@ -319,6 +205,62 b''
      "language": "python",
      "metadata": {},
      "outputs": []
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "clear_output"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "from IPython.display import clear_output"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(10):\n",
+      "    clear_output()\n",
+      "    time.sleep(0.25)\n",
+      "    print i\n",
+      "    sys.stdout.flush()\n",
+      "    time.sleep(0.25)\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(10):\n",
+      "    clear_output(wait=True)\n",
+      "    time.sleep(0.25)\n",
+      "    print i\n",
+      "    sys.stdout.flush()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}