pandora2d.profiling
===================

.. py:module:: pandora2d.profiling

.. autoapi-nested-parse::

   Contains functions for profiling pandora2d



Attributes
----------

.. autoapisummary::

   pandora2d.profiling.THREAD_TIMEOUT
   pandora2d.profiling.expert_mode_config
   pandora2d.profiling.data


Classes
-------

.. autoapisummary::

   pandora2d.profiling.ExpertModeConfig
   pandora2d.profiling.Data
   pandora2d.profiling.MemProf
   pandora2d.profiling.PerformanceSummaryItem
   pandora2d.profiling.PerformanceSummary


Functions
---------

.. autoapisummary::

   pandora2d.profiling.get_current_memory
   pandora2d.profiling.mem_time_profile
   pandora2d.profiling.generate_barh_figure
   pandora2d.profiling.generate_box_figure
   pandora2d.profiling.generate_summary


Module Contents
---------------

.. py:data:: THREAD_TIMEOUT
   :value: 2


.. py:class:: ExpertModeConfig

   Expert mode config class


   .. py:attribute:: enable
      :type:  bool
      :value: False



.. py:class:: Data

   Data class


   .. py:attribute:: _data
      :type:  list[Any]
      :value: []



   .. py:property:: timestamp
      :type: str



   .. py:method:: append(line)


   .. py:method:: reset() -> None


.. py:data:: expert_mode_config

.. py:data:: data

.. py:function:: get_current_memory() -> float

   Get current memory of process

   :return: memory



.. py:class:: MemProf(pid, pipe, interval=0.1)

   Bases: :py:obj:`threading.Thread`


   MemProf

   Profiling thread with time and memory performances in seconds and  MiB


   .. py:attribute:: pipe


   .. py:attribute:: interval
      :value: 0.1



   .. py:attribute:: cpu_interval
      :value: 0.1



   .. py:attribute:: process


   .. py:method:: run() -> None

      Run



.. py:function:: mem_time_profile(name=None, interval=0.1)

   Pandora2d profiling decorator

   :param: func: function to monitor



.. py:function:: generate_barh_figure(series: pandas.Series, values: Any, title: str = '') -> matplotlib.figure.Figure

   Barh figure.

   :param series: Series containing the data
   :param values: Values for bar chart
   :param title: Title of the chart
   :return: Performance graph


.. py:function:: generate_box_figure(dataframe: pandas.DataFrame, title: str = '', xlabel: str = '', ylabel: str = '') -> matplotlib.figure.Figure

   Box figure.

   :param dataframe: DataFrame containing the data
   :param title: Title of the chart
   :param xlabel: Label for x-axis
   :param ylabel: Label for y-axis
   :return: Performance graph


.. py:class:: PerformanceSummaryItem

   Bases: :py:obj:`TypedDict`


   Item of a Performance Summary.


   .. py:attribute:: df
      :type:  pandas.DataFrame


   .. py:attribute:: unit
      :type:  str


.. py:class:: PerformanceSummary

   Bases: :py:obj:`TypedDict`


   Performance Summary.


   .. py:attribute:: Time
      :type:  PerformanceSummaryItem


   .. py:attribute:: Process_time
      :type:  PerformanceSummaryItem


   .. py:attribute:: Maximum_memory
      :type:  PerformanceSummaryItem


   .. py:attribute:: Start_RAM
      :type:  PerformanceSummaryItem


   .. py:attribute:: End_RAM
      :type:  PerformanceSummaryItem


   .. py:attribute:: MAX_CPU
      :type:  PerformanceSummaryItem


.. py:function:: generate_summary(path_output: os.PathLike, expert_mode_cfg: dict) -> None

   Generate graphs referencing memory management and time for each step.

   :param path_output: output directory
   :param expert_mode_cfg: Dictionary containing expert_mode parameters