npac_detector.machine_learning.load_model
=========================================

.. py:module:: npac_detector.machine_learning.load_model


Attributes
----------

.. autoapisummary::

   npac_detector.machine_learning.load_model.chemin
   npac_detector.machine_learning.load_model.folder_path
   npac_detector.machine_learning.load_model.file_paths
   npac_detector.machine_learning.load_model.layer_nb
   npac_detector.machine_learning.load_model.X_events
   npac_detector.machine_learning.load_model.df_event
   npac_detector.machine_learning.load_model.images
   npac_detector.machine_learning.load_model.images
   npac_detector.machine_learning.load_model.images
   npac_detector.machine_learning.load_model.images
   npac_detector.machine_learning.load_model.input_shape
   npac_detector.machine_learning.load_model.latent_dim
   npac_detector.machine_learning.load_model.image_input
   npac_detector.machine_learning.load_model.image_input
   npac_detector.machine_learning.load_model.model
   npac_detector.machine_learning.load_model.reconstructed
   npac_detector.machine_learning.load_model.reconstructed
   npac_detector.machine_learning.load_model.im1
   npac_detector.machine_learning.load_model.cbar1
   npac_detector.machine_learning.load_model.im2
   npac_detector.machine_learning.load_model.cbar2


Functions
---------

.. autoapisummary::

   npac_detector.machine_learning.load_model.event_hits_to_image


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

.. py:data:: chemin

.. py:data:: folder_path
   :value: '/data/pion_1Gev_noShower/'


.. py:data:: file_paths

.. py:data:: layer_nb
   :value: 0


.. py:function:: event_hits_to_image(df: pandas.DataFrame, n_layers=7, x_size=400, y_size=400) -> numpy.array

   Converts the hits of a dataframe into a 3D numpy array

   :param df: Input dataframe
   :param n_layers: Number of layers in the detector
   :param x_size: Number of pixels along x
   :param y_size: Number of pixel along y
   :type n_layers: int
   :type x_size: int
   :type y_size: int
   :return: Array of dimension (n_layers,x_size,y_size) with take the energy as the value


.. py:data:: X_events
   :value: []


.. py:data:: df_event
   :value: None


.. py:data:: images

.. py:data:: images

.. py:data:: images

.. py:data:: images

.. py:data:: input_shape

.. py:data:: latent_dim
   :value: 32


.. py:data:: image_input

.. py:data:: image_input

.. py:data:: model

.. py:data:: reconstructed

.. py:data:: reconstructed

.. py:data:: im1

.. py:data:: cbar1

.. py:data:: im2

.. py:data:: cbar2