Jupyter: automatic rendering of objects as HTML

Rendering of last-returning objects wasn't implemented using a custom display
hook, like in IPyKernel, because it is not thread-safe. Instead user's code is
modified to call an external method, Base_renderAsHtml, with the object as
argument to render it. This external method can be called manually as the user
wishes.

To render a object of a given class a processor needs to be created and
associated with that class. When the class has no processor, the default one is
used to render the object's string representation. Many processors were created
for some special classes: Image class from the Image Module, Figure class from
matplotlib and all the IPython's display objects classes (Audio, IFrame,
YouTubeVideo, VimeoVideo, ScribdDocument, FileLink, and FileLinks).

The user can manually add processors as he likes using the `_processor_list`
object that is automatically injected into the code context.

bt5/erp5_data_notebook/ExtensionTemplateItem/portal_components/extension.erp5.JupyterCompile.py

@@ -2,17 +2,20 @@
 
 from cStringIO import StringIO
 from Products.ERP5Type.Globals import  PersistentMapping
-from OFS.Image import Image as OFSImage
+from erp5.portal_type import Image
+
 
 import sys
+import traceback
 import ast
 import types
-import inspect
+import base64
+import transaction
+
+from matplotlib.figure import Figure
+from IPython.core.display import DisplayObject
+from IPython.lib.display import IFrame
 
-mime_type = 'text/plain'
-# IPython expects 2 status message - 'ok', 'error'
-status = u'ok'
-ename, evalue, tb_list = None, None, None
 
 def Base_executeJupyter(self, python_expression=None, reference=None, title=None, request_reference=False, **kw):
   context = self
@@ -117,7 +120,6 @@ def Base_executeJupyter(self, python_expression=None, reference=None, title=None
   
   return serialized_result
 
-
 def Base_compileJupyterCode(self, jupyter_code, old_local_variable_dict):
   """
     Function to execute jupyter code and update the local_varibale dictionary.
@@ -152,26 +154,21 @@ def Base_compileJupyterCode(self, jupyter_code, old_local_variable_dict):
       out2 = '12'
 
   """
-  # Updating global variable mime_type to its original value
-  # Required when call to Base_displayImage is made which is changing
-  # the value of gloabl mime_type
-  # Same for status, ename, evalue, tb_list
-  global mime_type, status, ename, evalue, tb_list
   mime_type = 'text/plain'
   status = u'ok'
   ename, evalue, tb_list = None, None, None
   
   # Other way would be to use all the globals variables instead of just an empty
   # dictionary, but that might hamper the speed of exec or eval.
-  # Something like -- g = globals(); g['context'] = self;
-  g = {}
+  # Something like -- user_context = globals(); user_context['context'] = self;
+  user_context = {}
 
   # Saving the initial globals dict so as to compare it after code execution
   globals_dict = globals()
-  g['context'] = self
+  user_context['context'] = self
   result_string = ''
   # Update globals dict and use it while running exec command
-  g.update(old_local_variable_dict['variables'])
+  user_context.update(old_local_variable_dict['variables'])
 
   # XXX: The focus is on 'ok' status only, we're letting errors to be raised on
   # erp5 for now, so as not to hinder the transactions while catching them.
@@ -184,6 +181,7 @@ def Base_compileJupyterCode(self, jupyter_code, old_local_variable_dict):
   
     # Create ast parse tree
     ast_node = ast.parse(jupyter_code)
+    
     # Get the node list from the parsed tree
     nodelist = ast_node.body
 
@@ -196,7 +194,7 @@ def Base_compileJupyterCode(self, jupyter_code, old_local_variable_dict):
       # So, here we would try to get the name 'posixpath' and import it as 'path'
       for k, v in old_local_variable_dict['imports'].iteritems():
         import_statement_code = 'import %s as %s'%(v, k)
-        exec(import_statement_code, g, g)
+        exec(import_statement_code, user_context, user_context)
       
       # If the last node is instance of ast.Expr, set its interactivity as 'last'
       # This would be the case if the last node is expression
@@ -216,34 +214,65 @@ def Base_compileJupyterCode(self, jupyter_code, old_local_variable_dict):
       result = StringIO()
       sys.stdout = result
       
+      # Variables used at the display hook to get the proper form to display
+      # the last returning variable of any code cell.
+      #
+      display_data = {'result': '', 'mime_type': None}
+      
+      # This is where one part of the  display magic happens. We create an 
+      # instance of ProcessorList and add each of the built-in processors.
+      # The classes which each of them are responsiblefor rendering are defined
+      # in the classes themselves.
+      #
+      # The customized display hook will automatically use the processor
+      # of the matching class to decide how the object should be displayed.
+      #        
+      processor_list = ProcessorList()
+      processor_list.addProcessor(IPythonDisplayObjectProcessor)
+      processor_list.addProcessor(MatplotlibFigureProcessor)
+      processor_list.addProcessor(ERP5ImageProcessor)
+      processor_list.addProcessor(IPythonDisplayObjectProcessor)
+      
+      # Putting necessary variables in the `exec` calls context.
+      # 
+      # - result: is required to store the order of manual calls to the rendering
+      #   function;
+      #
+      # - display_data: is required to support mime type changes;
+      #
+      # - processor_list: is required for the proper rendering of the objects
+      #
+      user_context['_display_data'] = display_data
+      user_context['_processor_list'] = processor_list
+
       # Execute the nodes with 'exec' mode
       for node in to_run_exec:
         mod = ast.Module([node])
         code = compile(mod, '<string>', "exec")
-        exec(code, g, g)
+        exec(code, user_context, user_context)
       
       # Execute the interactive nodes with 'single' mode
       for node in to_run_interactive:
         mod = ast.Interactive([node])
         code = compile(mod, '<string>', "single")
-        context = self
-        exec(code, g, g)
-
         # Letting the code fail in case of error while executing the python script/code
         # XXX: Need to be refactored so to acclimitize transactions failure as well as
         # normal python code failure and show it to user on jupyter frontend.
         # Decided to let this fail silently in backend without letting the frontend
         # user know the error so as to let tranasction or its error be handled by ZODB
         # in uniform way instead of just using half transactions.
+        exec(code, user_context, user_context)
 
       sys.stdout = old_stdout
-      result_string = result.getvalue()
+      mime_type = display_data['mime_type'] or mime_type
+      result_string = result.getvalue() + display_data['result']
 
     # Difference between the globals variable before and after exec/eval so that
     # we don't have to save unnecessary variables in database which might or might
     # not be picklabale
-    local_variable_dict_new = {key: val for key, val in g.items() if key not in globals_dict.keys()}
-    local_variable_dict['variables'].update(local_variable_dict_new)
+    for key, val in user_context.items():
+      if key not in globals_dict.keys():
+        local_variable_dict['variables'][key] = val
 
     # Differentiate 'module' objects from local_variable_dict and save them as
     # string in the dict as {'imports': {'numpy': 'np', 'matplotlib': 'mp']}
@@ -273,6 +302,33 @@ def Base_compileJupyterCode(self, jupyter_code, old_local_variable_dict):
 
   return result
   
+def renderAsHtml(self, renderable_object):
+  '''
+    renderAsHtml will render its parameter as HTML by using the matching 
+    display processor for that class. Some processors can be found in this
+    file. 
+  '''
+  # Ugly frame hack to access the processor list defined in the body of the
+  # kernel's code, where `exec` is called.
+  #
+  # At this point the stack should be, from top to the bottom:
+  #
+  #   5. ExternalMethod Patch call
+  #   4. Base_compileJupyterCode frame (where we want to change variable)
+  #   3. exec call to run the user's code
+  #   2. ExternalMethod Patch call through `context.Base_renderAsHtml` in the notebook
+  #   1. renderAsHtml frame (where the function is)
+  # 
+  # So sys._getframe(3) is enough to get us up into the frame we want.
+  #
+  compile_jupyter_frame = sys._getframe(3)
+  compile_jupyter_locals = compile_jupyter_frame.f_locals
+  processor = compile_jupyter_locals['processor_list'].getProcessorFor(renderable_object)
+  result, mime_type = processor(renderable_object).process()
+  compile_jupyter_locals['result'].write(result)
+  compile_jupyter_locals['display_data']['mime_type'] = 'text/html'
+  return ''
+
 def AddNewLocalVariableDict(self):
   """
   Function to add a new Local Variable for a Data Notebook
@@ -295,156 +351,101 @@ def UpdateLocalVariableDict(self, existing_dict):
     new_dict['imports'][key] = val
   return new_dict
 
-def Base_displayHTML(self, node):
-  """
-  External function to identify Jupyter display classes and render them as
-  HTML. There are many classes from IPython.core.display or IPython.lib.display 
-  that we can use to display media, like audios, videos, images and generic
-  HTML/CSS/Javascript. All of them hold their HTML representation in the
-  `_repr_html_` method.
-  """
-  if getattr(node, '_repr_html_'):
-    global mime_type
-    mime_type = 'text/html'
-    html = node._repr_html_()
-    print html
-    return
-    
-
-def Base_displayImage(self, image_object=None):
-  """
-  External function to display Image objects to jupyter frontend.
+class ObjectProcessor(object):
+  '''
+    Basic object processor that stores the first parameters of the constructor
+    in the `subject` attribute and store the target classes for that processor.
+  '''
+  TARGET_CLASSES=None
   
-  XXX:  This function is intented to be called from Base_executeJupyter 
-        or Jupyter frontend.That's why printing string and returning None.
-        Also, it clears the plot for Matplotlib object after every call, so
-        in case of saving the plot, its essential to call Base_saveImage before
-        calling Base_displayImage.
+  @classmethod
+  def getTargetClasses(cls):
+    return cls.TARGET_CLASSES
     
-  Parameters
-  ----------
+  def __init__(self, something):
+    self.subject = something
 
-  image_object :Any image object from ERP5 
-                Any matplotlib object from which we can create a plot.
-                Can be <matplotlib.lines.Line2D>, <matplotlib.text.Text>, etc.
+class MatplotlibFigureProcessor(ObjectProcessor):
+  '''
+    MatplotlibFigureProcessor handles the rich display of 
+    matplotlib.figure.Figure objects. It displays them using an img tag with
+    the inline png image encoded as base64.
+  '''
+  TARGET_CLASSES=[Figure,]
 
-  Output
-  -----
+  def process(self):
+    image_io = StringIO()
+    self.subject.savefig(image_io, format='png')
+    image_io.seek(0)
+    return self._getImageHtml(image_io), 'text/html'
   
-  Prints base64 encoded string of the plot on which it has been called.
+  def _getImageHtml(self, image_io):
+    return '<img src="data:image/png;base64,%s" /><br />' % base64.b64encode(image_io.getvalue())
     
-  """
-  if image_object:
-
-    import base64
-    # Chanage global variable 'mime_type' to 'image/png'
-    global mime_type
-
-    # Image object in ERP5 is instance of OFS.Image object
-    if isinstance(image_object, OFSImage):
-      figdata = base64.b64encode(image_object.getData())
-      mime_type = image_object.getContentType()
-
-    # Ensure that the object we are taking as `image_object` is basically a
-    # Matplotlib.pyplot module object from which we are seekign the data of the
-    # plot .
-    elif inspect.ismodule(image_object) and image_object.__name__=="matplotlib.pyplot":
-
-      # Create a ByteFile on the server which would be used to save the plot
-      figfile = StringIO()
-      # Save plot as 'png' format in the ByteFile
-      image_object.savefig(figfile, format='png')
-      figfile.seek(0)
-      # Encode the value in figfile to base64 string so as to serve it jupyter frontend
-      figdata = base64.b64encode(figfile.getvalue())
-      mime_type = 'image/png'
-      # Clear the plot figures after every execution
-      image_object.close()
-
-    # XXX: We are not returning anything because we want this function to be called
-    # by Base_executeJupyter , inside exec(), and its better to get the printed string
-    # instead of returned string from this function as after exec, we are getting
-    # value from stdout and using return we would get that value as string inside
-    # an string which is unfavourable.
-    print figdata
-    return None
-
-def Base_saveImage(self, plot=None, reference=None, **kw):
-  """
-  Saves generated plots from matplotlib in ERP5 Image module
+class ERP5ImageProcessor(ObjectProcessor):
+  '''
+   ERP5ImageProcessor handles the rich display of ERP5's image_module object.
+   It gets the image data and content type and use them to create a proper img
+   tag.
+  '''
+  TARGET_CLASSES=[Image,]
   
-  XXX:  Use only if bt5 'erp5_wendelin' installed
-        This function is intented to be called from Base_executeJupyter 
-        or Jupyter frontend.
+  def process(self):
+    from base64 import b64encode
+    figure_data = b64encode(self.subject.getData())
+    mime_type = self.subject.getContentType()
+    return '<img src="data:%s;base64,%s" /><br />' % (mime_type, figure_data), 'text/html'
 
-  Parameters
-  ----------
-  plot : Matplotlib plot object
+class IPythonDisplayObjectProcessor(ObjectProcessor):
+  '''
+    IPythonDisplayObjectProcessor handles the display of all objects from the
+    IPython.display module, including: Audio, IFrame, YouTubeVideo, VimeoVideo, 
+    ScribdDocument, FileLink, and FileLinks. 
     
-  reference: Reference of Image object which would be generated
-             Id and reference should be always unique
+    All these objects have the `_repr_html_` method, which is used by the class
+    to render them.
+  '''
+  TARGET_CLASSES=[DisplayObject, IFrame]
   
-  Output
-  ------
-  Returns None, but saves the plot object as ERP5 image in Image Module with
-  reference same as that of data_array_object.
+  def process(self):
+    html_repr = self.subject._repr_html_()
+    return html_repr + '<br />', 'text/html' 
 
-  """
+class GenericProcessor(ObjectProcessor):
+  '''
+    Generic processor to render objects as string.
+  '''
   
-  # As already specified in docstring, this function should be called from
-  # Base_executeJupyter or Jupyter Frontend which means that it would pass
-  # through exec and hence the printed result would be caught in a string and
-  # that's why we are using print and returning None.
-  if not reference:
-    print 'No reference specified for Image object'
-    return None
-  if not plot:
-    print 'No matplotlib plot object specified'
-    return None
-
-  filename = '%s.png'%reference
-  # Save plot data in buffer
-  buff = StringIO()
-  plot.savefig(buff, format='png')
-  buff.seek(0)
-  data = buff.getvalue()
-
-  import time
-  image_id = reference+str(time.time())
-  # Add new Image object in erp5 with id and reference
-  image_module = self.getDefaultModule(portal_type='Image')
-  image_module.newContent(
-    portal_type='Image',
-    id=image_id,
-    reference=reference,
-    data=data,
-    filename=filename)
+  def process(self):
+    return str(self.subject), 'text/plain'
     
-  return None
+class ProcessorList(object):
+  '''
+    ProcessorList is responsible to store all the processors in a dict using
+    the classes they handle as the key. Subclasses of these classes will have
+    the same processor of the eigen class. This means that the order of adding
+    processors is important, as some classes' processors may be overwritten in
+    some situations.
+    
+    The `getProcessorFor` method uses `something.__class__' and not 
+    `type(something)` because using the later onobjects returned by portal 
+    catalog queries will return an AcquisitionWrapper type instead of the 
+    object's real class.
+  '''
   
-def getError(self, previous=1):
-  """
-  Show error to the frontend and change status of code as 'error' from 'ok'
+  def __init__(self, default=GenericProcessor):
+    self.processors = {}
+    self.default_processor = GenericProcessor
   
-  Parameters
-  ----------
-  previous: Type - int. The number of the error you want to see.
-  Ex: 1 for last error
-      2 for 2nd last error and so on..
+  def addProcessor(self, processor):
+    classes = processor.getTargetClasses()
+    for klass in classes:
+      self.processors[klass] = processor
+      for subclass in klass.__subclasses__():
+        self.processors[subclass] = processor
       
-  """
-  error_log_list = self.error_log._getLog()
-  if error_log_list:
-    if isinstance(previous, int):
-      # We need to get the object for last index of list
-      error = error_log_list[-previous]
-  global status, ename, evalue, tb_list
-  status = u'error'
-  ename = unicode(error['type'])
-  evalue = unicode(error['value'])
-  tb_list = [l+'\n' for l in error['tb_text'].split('\n')]
-
-  return None
+  def getProcessorFor(self, something):
+    return self.processors.get(something.__class__, self.default_processor)
 
 def storeIFrame(self, html, key):
   memcached_tool = self.getPortalObject().portal_memcached
@@ -452,6 +453,11 @@ def storeIFrame(self, html, key):
   memcached_dict[key] = html
   return True
 
+# WARNING! 
+# 
+# This is a highly experimental PivotTableJs integration which does not follow
+# ERP5 Javascrpt standards and it will be refactored to use JIO and RenderJS.
+#
 def erp5PivotTableUI(self, df, erp5_url):
   from IPython.display import IFrame
   template = """
@@ -522,5 +528,4 @@ def erp5PivotTableUI(self, df, erp5_url):
   key = sha512(html_string).hexdigest()
   storeIFrame(self, html_string, key)
   url = "%s/Base_displayPivotTableFrame?key=%s" % (erp5_url, key)
-  iframe = IFrame(src=url, width='100%', height='500')
-  return Base_displayHTML(self, iframe)
+  return IFrame(src=url, width='100%', height='500')

bt5/erp5_data_notebook/ExtensionTemplateItem/portal_components/extension.erp5.JupyterCompile.xml

@@ -46,12 +46,14 @@
             <key> <string>text_content_warning_message</string> </key>
             <value>
               <tuple>
-                <string>W: 55,  2: Using the global statement (global-statement)</string>
-                <string>W: 95,  8: Use of exec (exec-used)</string>
-                <string>W:119,  8: Use of exec (exec-used)</string>
-                <string>W:125,  8: Use of exec (exec-used)</string>
-                <string>W:220,  4: Using the global statement (global-statement)</string>
-                <string>W:320,  2: Using the global statement (global-statement)</string>
+                <string>W: 85,  2: Redefining name \'traceback\' from outer scope (line 9) (redefined-outer-name)</string>
+                <string>W:197,  8: Use of exec (exec-used)</string>
+                <string>W:252,  8: Use of exec (exec-used)</string>
+                <string>W:264,  8: Use of exec (exec-used)</string>
+                <string>W:327, 10: Unused variable \'mime_type\' (unused-variable)</string>
+                <string>W:462,  2: Redefining name \'IFrame\' from outer scope (line 17) (redefined-outer-name)</string>
+                <string>W:  9,  0: Unused import traceback (unused-import)</string>
+                <string>W: 13,  0: Unused import transaction (unused-import)</string>
               </tuple>
             </value>
         </item>

bt5/erp5_data_notebook/PathTemplateItem/web_page_module/PivotTableJs_getMovementHistoryList.html

+<html>
+  <head>
+    <script type="text/javascript" src="http://localhost:2200/erp5/jquery/core/jquery.min.js"></script>
+    <script type="text/javascript" src="http://localhost:2200/erp5/jquery/ui/js/jquery-ui.min.js"></script>
+    <script type="text/javascript" src="http://evanplaice.github.io/jquery-csv/src/jquery.csv.js"></script>
+    
+    <link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.css">
+    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script>
+    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.js"></script>
+    
+    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/pivot.min.js"></script>
+    <link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/pivot.min.css">
+    
+    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/d3_renderers.min.js"></script>
+    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/c3_renderers.min.js"></script>
+    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/export_renderers.min.js"></script>
+    
+    <script type="text/javascript">
+      $(document).ready(function () {
+        $('#filter_button').on('click', function (){
+          var data = $('form').serializeArray();
+          $('#pivottablejs').html('Loading...');
+
+          $.ajax({
+            type: "POST",
+            url: "http://localhost:2200/erp5/portal_skins/erp5_inventory_pandas/filterDataFrame?as_csv=True",
+            data: data,
+            success: function (response) {
+              var input = $.csv.toArrays(response);
+              $('#pivottablejs').pivotUI(input, {
+                renderers: $.extend(
+                $.pivotUtilities.renderers, 
+                $.pivotUtilities.c3_renderers, 
+                $.pivotUtilities.d3_renderers,
+                $.pivotUtilities.export_renderers
+                ),
+                hiddenAttributes: [""],
+                rows: 'Sequence',
+                cols: 'Data'
+              });
+            },
+            error: function (response) {
+             $('#pivottablejs').html('Error while requesting data from server.');
+            }
+          })
+        });
+      });
+    </script>
+  </head>
+  <body>
+    <h1>Integration between Pandas-based Inventory API and PivotTableJS</h1>
+    <p><b>NOTE: for this protoype the code will use the Big Array object with title "Wendelin + Jupyter"</b></p>
+    
+    <form>
+      <fieldset>
+        <legend>Is accountable?</legend>
+        <input type="radio" name="is_accountable" value="1" checked> Yes
+        <input type="radio" name="is_accountable" value="0"> No
+      </fieldset>
+      
+      <fieldset>
+        <legend>Omit</legend>
+        <input type="checkbox" name="omit_input"> Omit Input
+        <input type="checkbox" name="omit_output"> Omit Output
+        <input type="checkbox" name="omit_asset_increase"> Omit Asset Increase
+        <input type="checkbox" name="omit_asset_decrease"> Omit Asset Decrease
+      </fieldset>
+      
+      <fieldset>
+        <legend>Simulation State</legend>
+        <p>Simulation State: <input name="simulation_state"></p>
+        <p>Input Simulation State: <input name="input_simulation_state"></p>
+        <p>Output Simulation State: <input name="output_simulation_state"></p>
+      </fieldset>
+      
+      <fieldset>
+        <legend>Dates</legend>
+        <p>From date (yyyy-mm-dd): <input name="from_date"></p>
+        <p>To date (yyyy-mm-dd): <input name="to_date"></p>
+      </fieldset>
+      
+      <button type="button" id="filter_button">Filter!</button>
+    </form>
+    
+    <div id="pivottablejs">
+    </div>
+  </body>
+</html>
\ No newline at end of file

bt5/erp5_data_notebook/PathTemplateItem/web_page_module/PivotTableJs_getMovementHistoryList.xml

@@ -105,101 +105,6 @@
             <key> <string>short_title</string> </key>
             <value> <string>getMovementHistoryList</string> </value>
         </item>
-        <item>
-            <key> <string>text_content</string> </key>
-            <value> <string encoding="cdata"><![CDATA[
-
-<html>\n
-  <head>\n
-    <script type="text/javascript" src="http://localhost:2200/erp5/jquery/core/jquery.min.js"></script>\n
-    <script type="text/javascript" src="http://localhost:2200/erp5/jquery/ui/js/jquery-ui.min.js"></script>\n
-    <script type="text/javascript" src="http://evanplaice.github.io/jquery-csv/src/jquery.csv.js"></script>\n
-    \n
-    <link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.css">\n
-    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js"></script>\n
-    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.js"></script>\n
-    \n
-    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/pivot.min.js"></script>\n
-    <link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/pivot.min.css">\n
-    \n
-    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/d3_renderers.min.js"></script>\n
-    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/c3_renderers.min.js"></script>\n
-    <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/pivottable/2.0.2/export_renderers.min.js"></script>\n
-    \n
-    <script type="text/javascript">\n
-      $(document).ready(function () {\n
-        $(\'#filter_button\').on(\'click\', function (){\n
-          var data = $(\'form\').serializeArray();\n
-          $(\'#pivottablejs\').html(\'Loading...\');\n
-\n
-          $.ajax({\n
-            type: "POST",\n
-            url: "http://localhost:2200/erp5/portal_skins/erp5_inventory_pandas/filterDataFrame?as_csv=True",\n
-            data: data,\n
-            success: function (response) {\n
-              var input = $.csv.toArrays(response);\n
-              $(\'#pivottablejs\').pivotUI(input, {\n
-                renderers: $.extend(\n
-                $.pivotUtilities.renderers, \n
-                $.pivotUtilities.c3_renderers, \n
-                $.pivotUtilities.d3_renderers,\n
-                $.pivotUtilities.export_renderers\n
-                ),\n
-                hiddenAttributes: [""],\n
-                rows: \'Sequence\',\n
-                cols: \'Data\'\n
-              });\n
-            },\n
-            error: function (response) {\n
-             $(\'#pivottablejs\').html(\'Error while requesting data from server.\');\n
-            }\n
-          })\n
-        });\n
-      });\n
-    </script>\n
-  </head>\n
-  <body>\n
-    <h1>Integration between Pandas-based Inventory API and PivotTableJS</h1>\n
-    <p><b>NOTE: for this protoype the code will use the Big Array object with title "Wendelin + Jupyter"</b></p>\n
-    \n
-    <form>\n
-      <fieldset>\n
-        <legend>Is accountable?</legend>\n
-        <input type="radio" name="is_accountable" value="1" checked> Yes\n
-        <input type="radio" name="is_accountable" value="0"> No\n
-      </fieldset>\n
-      \n
-      <fieldset>\n
-        <legend>Omit</legend>\n
-        <input type="checkbox" name="omit_input"> Omit Input\n
-        <input type="checkbox" name="omit_output"> Omit Output\n
-        <input type="checkbox" name="omit_asset_increase"> Omit Asset Increase\n
-        <input type="checkbox" name="omit_asset_decrease"> Omit Asset Decrease\n
-      </fieldset>\n
-      \n
-      <fieldset>\n
-        <legend>Simulation State</legend>\n
-        <p>Simulation State: <input name="simulation_state"></p>\n
-        <p>Input Simulation State: <input name="input_simulation_state"></p>\n
-        <p>Output Simulation State: <input name="output_simulation_state"></p>\n
-      </fieldset>\n
-      \n
-      <fieldset>\n
-        <legend>Dates</legend>\n
-        <p>From date (yyyy-mm-dd): <input name="from_date"></p>\n
-        <p>To date (yyyy-mm-dd): <input name="to_date"></p>\n
-      </fieldset>\n
-      \n
-      <button type="button" id="filter_button">Filter!</button>\n
-    </form>\n
-    \n
-    <div id="pivottablejs">\n
-    </div>\n
-  </body>\n
-</html>
-
-]]></string> </value>
-        </item>
         <item>
             <key> <string>title</string> </key>
             <value> <string>Web Interface for getMovementHistoryList</string> </value>

bt5/erp5_data_notebook/SkinTemplateItem/portal_skins/erp5_data_notebook/Base_displayPivotTableFrame.py

+memcached_tool = context.getPortalObject().portal_memcached
+memcached_dict = memcached_tool.getMemcachedDict(key_prefix='pivottablejs', plugin_path='portal_memcached/default_memcached_plugin')
+return memcached_dict[key]

bt5/erp5_data_notebook/SkinTemplateItem/portal_skins/erp5_data_notebook/Base_displayPivotTableFrame.xml

@@ -48,13 +48,6 @@
               </object>
             </value>
         </item>
-        <item>
-            <key> <string>_body</string> </key>
-            <value> <string>memcached_tool = context.getPortalObject().portal_memcached\n
-memcached_dict = memcached_tool.getMemcachedDict(key_prefix=\'pivottablejs\', plugin_path=\'portal_memcached/default_memcached_plugin\')\n
-return memcached_dict[key]\n
-</string> </value>
-        </item>
         <item>
             <key> <string>_params</string> </key>
             <value> <string>key</string> </value>

bt5/erp5_data_notebook/SkinTemplateItem/portal_skins/erp5_data_notebook/Base_renderAsHtml.xml

+<?xml version="1.0"?>
+<ZopeData>
+  <record id="1" aka="AAAAAAAAAAE=">
+    <pickle>
+      <global name="ExternalMethod" module="Products.ExternalMethod.ExternalMethod"/>
+    </pickle>
+    <pickle>
+      <dictionary>
+        <item>
+            <key> <string>_function</string> </key>
+            <value> <string>renderAsHtml</string> </value>
+        </item>
+        <item>
+            <key> <string>_module</string> </key>
+            <value> <string>JupyterCompile</string> </value>
+        </item>
+        <item>
+            <key> <string>id</string> </key>
+            <value> <string>Base_renderAsHtml</string> </value>
+        </item>
+        <item>
+            <key> <string>title</string> </key>
+            <value> <string></string> </value>
+        </item>
+      </dictionary>
+    </pickle>
+  </record>
+</ZopeData>

bt5/erp5_data_notebook/TestTemplateItem/portal_components/test.erp5.testExecuteJupyter.py

@@ -32,8 +32,10 @@ from Products.ERP5Type.tests.utils import createZODBPythonScript, removeZODBPyth
 import time
 import json
 import base64
+
 import transaction
 
+
 class TestExecuteJupyter(ERP5TypeTestCase):
   
   def afterSetUp(self):
@@ -73,7 +75,9 @@ class TestExecuteJupyter(ERP5TypeTestCase):
 
   def testJupyterCompileErrorRaise(self):
     """
-    Test if JupyterCompile portal_component raises error on the server side.
+    Test if JupyterCompile portal_component correctly catches exceptions as 
+    expected by the Jupyter frontend as also automatically abort the current
+    transaction.
     Take the case in which one line in a statement is valid and another is not.
     """
     portal = self.getPortalObject()
@@ -114,6 +118,7 @@ portal.%s()
     transaction.abort()
     # Clear the portal cache from previous transaction
     self.portal.portal_caches.clearAllCache()
+
     # Remove the ZODB python script created above
     removeZODBPythonScript(script_container, script_id)
 
@@ -360,19 +365,20 @@ import sys
     self.assertEquals(json.loads(result)['code_result'].rstrip(), 'imghdr')
     self.assertEquals(json.loads(result)['mime_type'].rstrip(), 'text/plain')
 
-  def testBaseDisplayImageERP5Image(self):
+  def testERP5ImageProcessor(self):
     """
-    Test the fucntioning of Base_displayImage external method of erp5_data_notebook
-    BT5 for ERP5 image object as parameter and change
+    Test the fucntioning of the ERP5ImageProcessor and the custom system 
+    display hook too. 
     """
     self.image_module = self.portal.getDefaultModule('Image')
     self.assertTrue(self.image_module is not None)
     # Create a new ERP5 image object
-    reference = 'testBase_displayImageReference'
+    reference = 'testBase_displayImageReference5'
+    data_template = '<img src="data:application/unknown;base64,%s" />'
     data = 'qwertyuiopasdfghjklzxcvbnm<somerandomcharacterstosaveasimagedata>'
     self.image_module.newContent(
       portal_type='Image',
-      id='testBase_displayImageID',
+      id='testBase_displayImageID5',
       reference=reference,
       data=data,
       filename='test.png'
@@ -382,7 +388,7 @@ import sys
     # Call Base_displayImage from inside of Base_runJupyter
     jupyter_code = """
 image = context.portal_catalog.getResultValue(portal_type='Image',reference='%s')
-context.Base_displayImage(image_object=image)
+image
 """%reference
 
     local_variable_dict = {'imports' : {}, 'variables' : {}}
@@ -391,7 +397,7 @@ context.Base_displayImage(image_object=image)
       old_local_variable_dict=local_variable_dict
       )
 
-    self.assertEquals(result['result_string'].rstrip(), base64.b64encode(data))
+    self.assertEquals(result['result_string'].rstrip(), data_template % base64.b64encode(data))
     # Mime_type shouldn't be  image/png just because of filename, instead it is
     # dependent on file and file data
     self.assertNotEqual(result['mime_type'], 'image/png')
@@ -430,3 +436,33 @@ context.Base_displayImage(image_object=image)
       python_expression=jupyter_code2
       )
     self.assertEquals(json.loads(result)['code_result'].rstrip(), 'sys')
+    
+  def testPivotTableJsIntegration(self):
+    '''
+      This test ensures the PivotTableJs user interface is correctly integrated
+      into our Jupyter kernel.
+    '''
+    portal = self.portal
+    self.login('dev_user')
+    jupyter_code = '''
+class DataFrameMock(object):
+    def to_csv(self):
+        return "column1, column2; 1, 2;" 
+
+my_df = DataFrameMock()
+iframe = context.Base_erp5PivotTableUI(my_df, 'https://localhost:2202/erp5')
+context.Base_renderAsHtml(iframe)
+'''
+    reference = 'Test.Notebook.PivotTableJsIntegration %s' % time.time()
+    notebook = self._newNotebook(reference=reference)
+    result = portal.Base_executeJupyter(
+      reference=reference,
+      python_expression=jupyter_code
+    )
+    json_result = json.loads(result)
+    
+    # The big hash in this string was previous calculated using the expect hash
+    # of the pivot table page's html.
+    pivottable_frame_display_path = 'Base_displayPivotTableFrame?key=853524757258b19805d13beb8c6bd284a7af4a974a96a3e5a4847885df069a74d3c8c1843f2bcc4d4bb3c7089194b57c90c14fe8dd0c776d84ce0868e19ac411'
+    self.assertTrue(pivottable_frame_display_path in json_result['code_result'])
+

bt5/erp5_data_notebook/TestTemplateItem/portal_components/test.erp5.testExecuteJupyter.xml

@@ -43,7 +43,9 @@
         <item>
             <key> <string>text_content_warning_message</string> </key>
             <value>
-              <tuple/>
+              <tuple>
+                <string>W:457,  4: Unused variable \'notebook\' (unused-variable)</string>
+              </tuple>
             </value>
         </item>
         <item>

bt5/erp5_data_notebook/bt/description

@@ -2,4 +2,5 @@ Interaction between Jupyter(IPython Notebook) and ERP5.
 
 !WARNING!
 This business template is unsafe to install on a public server as one of the extensions uses eval and allows remote code execution. Proper security should be taken into account. 
+This template includes a highly exprimental integration with PivotTableJs which doesn't follow ERP5 Javascript standards and will be refactored to use JIO and RenderJS.
 !WARNING!
\ No newline at end of file