pandora.profiler
================

.. py:module:: pandora.profiler

.. autoapi-nested-parse::

   Contains functions for wrapper logs



Attributes
----------

.. autoapisummary::

   pandora.profiler._HAS_PLOTLY_PSUTIL


Classes
-------

.. autoapisummary::

   pandora.profiler.Profiler
   pandora.profiler.MemProf


Functions
---------

.. autoapisummary::

   pandora.profiler.profile


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

.. py:data:: _HAS_PLOTLY_PSUTIL
   :value: True


.. py:class:: Profiler

   Main profiler class for Pandora


   .. py:attribute:: enabled
      :value: False



   .. py:attribute:: save_graphs
      :value: False



   .. py:attribute:: save_raw_data
      :value: False



   .. py:attribute:: _profiling_info


   .. py:attribute:: running_processes
      :value: []



   .. py:method:: enable_from_config(conf: dict)
      :staticmethod:


      Enables the profiler if specified in the config file

      :param conf: The configuration dict
      :type conf: dict



   .. py:method:: add_profiling_info(info: dict)
      :staticmethod:


      Add profiling info to the profiling DataFrame.

      :param info: dictionary with profiling data keys
      :type info: dict



   .. py:method:: generate_summary(base_output: str)
      :staticmethod:


      Generate Profiling summary

      :param base_output: Pandora's output directory
      :type base_output: str



   .. py:method:: plot_trace_for_call(call_uuid, data_name)
      :staticmethod:


      Plot memory (or any resource tracked) usage over time for a function call, with markers for its subcalls.

      :param call_uuid: UUID of the parent function call
      :type call_uuid: str
      :param data_name: The name of the data to plot (if cpu consumption were to be added for example)
      :type data_name: str

      :return: The generated plotly figure
      :rtype: plotly.graph_objs._figure.Figure



.. py:function:: profile(name, interval=0.05, memprof=False)

   Pandora profiling decorator

   :param name: name of the function in the report
   :type name: str
   :param interval: memory sampling interval (seconds)
   :type interval: int or float
   :param memprof: whether to profile the memory consumption
   :type memprof: bool


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

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


   MemProf

   Profiling thread


   .. py:attribute:: pipe


   .. py:attribute:: interval


   .. py:attribute:: process


   .. py:attribute:: mem_data
      :value: []



   .. py:method:: run()

      Run the memory profiling thread