menus.run_editor
================

.. py:module:: menus.run_editor


Attributes
----------

.. autoapisummary::

   menus.run_editor.logger


Classes
-------

.. autoapisummary::

   menus.run_editor.RunEditor


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

.. py:data:: logger

.. py:class:: RunEditor(viewer: napari.Viewer)

   Bases: :py:obj:`qtpy.QtWidgets.QGroupBox`


   .. py:attribute:: start_run


   .. py:attribute:: viewer


   .. py:attribute:: solver_params_widget


   .. py:attribute:: run_name
      :type:  qtpy.QtWidgets.QLineEdit


   .. py:attribute:: layer_selection_box
      :type:  qtpy.QtWidgets.QComboBox


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

      Update the layer selection box with the input layers in the viewer



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

      Update the rest of the UI when the selected layer is updated



   .. py:method:: _labels_layer_widget() -> qtpy.QtWidgets.QWidget

      Create the widget to select the input layer. Uses magicgui,
      but explicitly connects to the viewer layers events to keep it synced.

      Returns:
          QWidget: A dropdown select with all the labels layers in layers
              and a refresh button to sync with napari.



   .. py:method:: get_input_layer() -> napari.layers.Layer | None

      Get the input segmentation or points in current selection in the
      layer dropdown.

      Returns:
          napari.layers.Layer | None: The points or labels layer with the name
              that is selected, or None if no layer is selected.



   .. py:method:: _run_widget() -> qtpy.QtWidgets.QWidget

      Construct a widget where you set the run name and start solving.
      Initializes self.run_name and connects the generate tracks button
      to emit the new_run signal.

      Returns:
          QWidget: A row widget with a line edit for run name and a button
              to create a new run and start solving.



   .. py:method:: _has_duplicate_ids(segmentation: numpy.ndarray) -> bool
      :staticmethod:


      Checks if the segmentation has duplicate label ids across time. For efficiency,
      only checks between the first and second time frames.

      Args:
          segmentation (np.ndarray): (t, [z], y, x)

      Returns:
          bool: True if there are duplicate labels between the first two frames, and
              False otherwise.



   .. py:method:: get_run() -> motile_tracker.motile.backend.MotileRun | None

      Construct a motile run from the current state of the run editor
      widget.

      Returns:
          MotileRun: A run with name, parameters, and input segmentation.
              Output segmentation and tracks not yet specified.



   .. py:method:: _convert_da_to_np_array(dask_array: dask.array.core.Array) -> numpy.ndarray

      Convert from dask array to in-memory array.

      Args:
          dask_array (da.core.Array): a dask array

      Returns:
          np.ndarray: data as an in-memory numpy array



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

      Construct a run and start solving by emitting the start run
      signal for the main widget to connect to. If run is invalid, will
      not emit the signal.



   .. py:method:: new_run(run: motile_tracker.motile.backend.MotileRun) -> None

      Configure the run editor to copy the name and params of the given
      run.