.. _dataviewer:

===========
Data viewer
===========

The :ref:`JavaScope` is used to observe log data during operation of the UltraZohm.
In addition, log files can be created, which can be further analyzed.

..  _dataviewer_log:

..  figure:: log.png
    :align: center
    :width: 500px

    Javascope logging the lifecheck signal.

:numref:`dataviewer_log` shows logging the lifecheck signal of the UltraZohm.
Two options are available to further analyze this log file:

   1. `Matlab Data Inspector`_
   2. `Python Data Viewer`_


Matlab Data Inspector
=====================

To open the log file, open Matlab and navigate to ``ultrazohm_sw/javascope``.
Make sure that this is the current working directory for Matlab.
Call the script ``JS_plot_data.m``.

..  _dataviewer_call_matlab:

..  figure:: matlab_js_plot_data.png
    :align: center
    :width: 500px

    Call ``JS_plot_data.m``.

The script opens the latest log that is present in ``ultrazohm_sw/javascope`` in the `Matlab Data Inspector <https://de.mathworks.com/help/simulink/slref/simulationdatainspector.html>`_.
The read data are also available in the Matlab workspace for further processing.

..  _dataviewer_matlab_dataviewer:

..  figure:: dataviewer_matlab.png
    :align: center
    :width: 500px

    Use the Matlab Data Inspector to analyze the data.

To load a specific log file, set the file name accordingly, like so:

.. code-block::

    file_name = Logfile_list(logfile_list_index).name
    paste file name here if you want to open a specific file
    file_name = 'Log_2024-03-11_99-99-99.csv';

Additional settings, such as converting the log file to ``.mat`` for further usage in Matlab, can be set:

.. code-block::

   %% settings
   delete_empty_log_files = 0;
   save_all_logged_data = 0; 
   plot_all_channels = 0;
   rename_channels_manually = 0;
   import_data_to_simulink_datainspector = 1;
   overwrite_data_in_simulink_datainspector = 1;

Python Data Viewer
==================

.. note:: The Python Data Viewer works out of the box inside the :ref:`vscode_remote_container`. If the remote container is not used, the dependencies of ``ultrazohm_sw/docs`` have to be installed on the system.

The following steps start the Python-based data viewer:

1. Open a terminal and navigate to ``ultrazohm_sw/javascope``
2. Call the dataviewer like so

.. code-block:: python

    python dataviewer.py

..  _dataviewer_python_start:

..  figure:: dataviewer_python.png
    :align: center
    :width: 500px

    Start the Python data viewer by calling ``dataviewer.py``. A dash server is started, which can be open by typing the IP into the browser or clicking the pop-up (right corner).

3. Open the web page of the data viewer
4. Upload the log file that you want to inspect. Note that the data are only *uploaded* to your local machine since the dash app runs on a local server. I.e., all data are only present on your hard drive.

..  _dataviewer_python_open:

..  figure:: dataviewer_python_open.png
    :align: center
    :width: 500px

    Data viewer after uploading log data. The figure dynamically rescales using `plotly resampler <https://github.com/predict-idlab/plotly-resampler>`_. Note that when viewing large timescales, aliasing might be present, which is reduced if the x-axis is zoomed in.

5. Inspect the log file

Data format
-----------

The Python-based data viewer can read ``.csv`` and ``.parquet`` (which stores the logs more efficiently, i.e., with less disk space).
Call ``convert_csv_to_parquet.py`` to convert all logs present in the directory to parquet data format, like so

.. code-block:: python

    python convert_csv_to_parquet.py