Merge pull request #893 from minrk/clearoutput

Adding clear_output to kernel and HTML notebook.

This enables the clearing of output during the execution of a cell. It can be used for simple forms of animation in the notebook.

docs/examples/notebooks/clear_output.ipynb

@@ -0,0 +1,109 b''
+{
+    "worksheets": [
+        {
+            "cells": [
+                {
+                    "source": "A demonstration of the ability to clear the output of a cell during execution.", 
+                    "cell_type": "markdown"
+                }, 
+                {
+                    "cell_type": "code", 
+                    "language": "python", 
+                    "outputs": [], 
+                    "collapsed": true, 
+                    "prompt_number": 8, 
+                    "input": "from IPython.core.display import clear_output, display"
+                }, 
+                {
+                    "cell_type": "code", 
+                    "language": "python", 
+                    "outputs": [], 
+                    "collapsed": true, 
+                    "prompt_number": 4, 
+                    "input": "import time"
+                }, 
+                {
+                    "source": "First we show how this works with ``display``:", 
+                    "cell_type": "markdown"
+                }, 
+                {
+                    "cell_type": "code", 
+                    "language": "python", 
+                    "outputs": [
+                        {
+                            "output_type": "stream", 
+                            "stream": "stdout", 
+                            "text": "\nTime step: 9"
+                        }, 
+                        {
+                            "output_type": "stream", 
+                            "stream": "stdout", 
+                            "text": "\n"
+                        }
+                    ], 
+                    "collapsed": false, 
+                    "prompt_number": 20, 
+                    "input": "for i in range(10):\n    clear_output()\n    print \"Time step: %i\" % i\n    time.sleep(0.5)\n"
+                }, 
+                {
+                    "source": "Next, we show that ``clear_output`` can also be used to create a primitive form of animation using\nmatplotlib:", 
+                    "cell_type": "markdown"
+                }, 
+                {
+                    "cell_type": "code", 
+                    "language": "python", 
+                    "outputs": [
+                        {
+                            "output_type": "display_data", 
+                            "png": 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Dr7Jf8nnyRavpMqUu2vlJKXVR+oWOi7/Yt24lj8Y8bAulra3RlDoldR31/NnP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\n8P8BdhDzr6dIpNsAAAAASUVORK5CYII=\n"
+                        }
+                    ], 
+                    "collapsed": false, 
+                    "prompt_number": 18, 
+                    "input": "for i in range(10):\n    figure()\n    plot(rand(100))\n    clear_output()\n    show()\n    time.sleep(0.25)\n"
+                }, 
+                {
+                    "source": "And we can even selectively clear only a subset of stdout/stderr/other display such as figures\n(all are cleared by default).", 
+                    "cell_type": "markdown"
+                }, 
+                {
+                    "cell_type": "code", 
+                    "language": "python", 
+                    "outputs": [
+                        {
+                            "output_type": "stream", 
+                            "stream": "stdout", 
+                            "text": "Time step: 0\n"
+                        }, 
+                        {
+                            "output_type": "stream", 
+                            "stream": "stdout", 
+                            "text": "Time step: 1\n"
+                        }, 
+                        {
+                            "output_type": "stream", 
+                            "stream": "stdout", 
+                            "text": "Time step: 2\n"
+                        }, 
+                        {
+                            "output_type": "stream", 
+                            "stream": "stdout", 
+                            "text": "Time step: 3\n"
+                        }, 
+                        {
+                            "output_type": "display_data", 
+                            "png": 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VokTqqRQZ/6L2S2kp+dvIiHxP3Quph5H9IkOpp1Jk7IrMCa/2i2xSt73n1dXV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\nZjzrn0OUXq0AAAAASUVORK5CYII=\n"
+                        }
+                    ], 
+                    "collapsed": false, 
+                    "prompt_number": 19, 
+                    "input": "for i in range(4):\n    print \"Time step: %i\" % i\n    figure()\n    plot(rand(100))\n    # clear plots, but not stdout:\n    clear_output(stdout=False)\n    show()\n    time.sleep(0.25)\n"
+                }
+            ]
+        }
+    ], 
+    "metadata": {
+        "name": "clear_output"
+    }, 
+    "nbformat": 2
+}
  No newline at end of file

IPython/core/display.py

@@ -381,3 +381,27 b' class Image(DisplayObject):'
 
     def _find_ext(self, s):
         return unicode(s.split('.')[-1].lower())
+
+
+def clear_output(stdout=True, stderr=True, other=True):
+    """Clear the output of the current cell receiving output.
+    
+    Optionally, each of stdout/stderr or other non-stream data (e.g. anything
+    produced by display()) can be excluded from the clear event.
+    
+    By default, everything is cleared.
+    
+    Parameters
+    ----------
+    stdout : bool [default: True]
+        Whether to clear stdout.
+    stderr : bool [default: True]
+        Whether to clear stderr.
+    other : bool [default: True]
+        Whether to clear everything else that is not stdout/stderr
+        (e.g. figures,images,HTML, any result of display()).
+    """
+    from IPython.core.interactiveshell import InteractiveShell
+    InteractiveShell.instance().display_pub.clear_output(
+        stdout=stdout, stderr=stderr, other=other,
+    )

IPython/core/displaypub.py

@@ -104,6 +104,10 b' class DisplayPublisher(Configurable):'
         if data.has_key('text/plain'):
             print(data['text/plain'], file=io.stdout)
 
+        def clear_output(self, stdout=True, stderr=True, other=True):
+            """Clear the output of the cell receiving output."""
+            pass
+
 
 def publish_display_data(source, data, metadata=None):
     """Publish data and metadata to all frontends.

IPython/frontend/html/notebook/static/js/codecell.js

@@ -364,9 +364,41 b' var IPython = (function (IPython) {'
     }
 
 
-    CodeCell.prototype.clear_output = function () {
-        this.element.find("div.output").html("");
-        this.outputs = [];
+    CodeCell.prototype.clear_output = function (stdout, stderr, other) {
+        var output_div = this.element.find("div.output");
+        if (stdout && stderr && other){
+            // clear all, no need for logic
+            output_div.html("");
+            this.outputs = [];
+            return;
+        }
+        // remove html output
+        // each output_subarea that has an identifying class is in an output_area
+        // which is the element to be removed.
+        if (stdout){
+            output_div.find("div.output_stdout").parent().remove();
+        }
+        if (stderr){
+            output_div.find("div.output_stderr").parent().remove();
+        }
+        if (other){
+            output_div.find("div.output_subarea").not("div.output_stderr").not("div.output_stdout").parent().remove();
+        }
+        
+        // remove cleared outputs from JSON list:
+        for (var i = this.outputs.length - 1; i >= 0; i--){
+            var out = this.outputs[i];
+            var output_type = out.output_type;
+            if (output_type == "display_data" && other){
+                this.outputs.splice(i,1);
+            }else if (output_type == "stream"){
+                if (stdout && out.stream == "stdout"){
+                    this.outputs.splice(i,1);
+                }else if (stderr && out.stream == "stderr"){
+                    this.outputs.splice(i,1);
+                }
+            }
+        }
     };
 
 

IPython/frontend/html/notebook/static/js/notebook.js

@@ -627,7 +627,7 b' var IPython = (function (IPython) {'
         var cells = this.cells();
         for (var i=0; i<ncells; i++) {
             if (cells[i] instanceof IPython.CodeCell) {
-                cells[i].clear_output();
+                cells[i].clear_output(true,true,true);
             }
         };
         this.dirty = true;
@@ -733,7 +733,9 b' var IPython = (function (IPython) {'
             } else if (content.execution_state === 'dead') {
                 this.handle_status_dead();
             };
-        }
+        } else if (msg_type === 'clear_output') {
+            cell.clear_output(content.stdout, content.stderr, content.other);
+        };
     };
 
 
@@ -823,7 +825,7 b' var IPython = (function (IPython) {'
         var cell = that.selected_cell();
         var cell_index = that.find_cell_index(cell);
         if (cell instanceof IPython.CodeCell) {
-            cell.clear_output();
+            cell.clear_output(true, true, true);
             var code = cell.get_code();
             var msg_id = that.kernel.execute(cell.get_code());
             that.msg_cell_map[msg_id] = cell.cell_id;

IPython/zmq/zmqshell.py

@@ -80,6 +80,12 b' class ZMQDisplayPublisher(DisplayPublisher):'
             parent=self.parent_header
         )
 
+    def clear_output(self, stdout=True, stderr=True, other=True):
+        content = dict(stdout=stdout, stderr=stderr, other=other)
+        self.session.send(
+            self.pub_socket, u'clear_output', content,
+            parent=self.parent_header
+        )
 
 class ZMQInteractiveShell(InteractiveShell):
     """A subclass of InteractiveShell for ZMQ."""