=========================================================
Tutorial of *PageTransition*
=========================================================

| *PageTransition* class has the aggregated log as the member variable and has the method functions to get the information.

.. image:: page_transition.png
 :scale: 70%
 :align: center

.. code-block:: python

    import openLA as la

    operations = ["NEXT", "PREV", "ADD MARKER"]

    course_info, event_stream = la.start_analysis(files_dir="dataset_sample", course_id="A")

    operation_count = la.convert_into_page_transition(event_stream,
                                                      invalid_seconds=10,
                                                      timeout_seconds=30*60,
                                                      user_id=event_stream.user_id()[:10],
                                                      contents_id=event_stream.contents_id()[0],
                                                      operation_name=operations,
                                                      count_operation=True
                                                      )


| The method functions in below table are available now.
| You can get the information of the log like ``page_transition.num_users()``.
| The documentation is in :doc:`../scripts/pagewise_aggregation`

+-----------------------------+--------------------------------------------------------------+
| function                    | description                                                  |
+=============================+==============================================================+
| num_users                   | Get the number of users in the log                           |
+-----------------------------+--------------------------------------------------------------+
| user_id                     | Get the unique user ids in the log                           |
+-----------------------------+--------------------------------------------------------------+
| contents_id                 | Get the unique contents ids in the log                       |
+-----------------------------+--------------------------------------------------------------+
| operation_name              | Get the unique operation name in the log                     |
+-----------------------------+--------------------------------------------------------------+
| operation_count             | Get the count of each (or specified) operation in the log    |
+-----------------------------+--------------------------------------------------------------+
| reading_seconds             | Get the reading seconds in the log                           |
+-----------------------------+--------------------------------------------------------------+
| reading_time                | Get the reading time (seconds, minutes, or hours) in the log |
+-----------------------------+--------------------------------------------------------------+
| num_unique_pages            | Get the number of unique pages in the log                    |
+-----------------------------+--------------------------------------------------------------+
| unique_pages                | Get the unique page numbers in the log                       |
+-----------------------------+--------------------------------------------------------------+
| num_transition              | Get the number of transition in the log                      |
+-----------------------------+--------------------------------------------------------------+

If you want to process other than the above functions, you can get DataFrame type event stream by ``page_transition.df`` and process with Pandas library.

.. code-block:: python

    import OpenLA as la
    import pandas as pd

    operations = ["NEXT", "PREV", "ADD MARKER"]

    course_info, event_stream = la.start_analysis(files_dir="dataset_sample", course_id="A")

    page_transition = la.convert_into_page_transition(event_stream,
                                                      invalid_seconds=10,
                                                      timeout_seconds=30*60,
                                                      user_id=event_stream.user_id()[:10],
                                                      contents_id=event_stream.contents_id()[0],
                                                      operation_name=operations,
                                                      count_operation=True
                                                      )

    page_transition_df = page_transition.df
    print(page_transition_df)
    """
         userid contentsid  pageno  reading_seconds      time_of_entry        time_of_exit       NEXT  PREV  ADD MARKER
    0       U1         C1       1               14   2018-04-08 17:53:47   2018-04-08 17:54:01     1     0           0
    1       U1         C1       2               20   2018-04-08 17:54:01   2018-04-08 17:54:21     1     0           0
    2       U1         C1       1               22   2018-04-10 15:00:06   2018-04-10 15:00:28     1     0           0
    3       U1         C1       2               11   2018-04-10 15:00:28   2018-04-10 15:00:39     1     0           0
    ...     ...       ...       ...     ...         ...
    """


Example
=============

.. toctree::
   :maxdepth: 1

   ../jupyter_examples/example_transition