cellmap_flow.utils.data
=======================

.. py:module:: cellmap_flow.utils.data


Attributes
----------

.. autoapisummary::

   cellmap_flow.utils.data.logger


Classes
-------

.. autoapisummary::

   cellmap_flow.utils.data.ModelConfig
   cellmap_flow.utils.data.ScriptModelConfig
   cellmap_flow.utils.data.DaCapoModelConfig
   cellmap_flow.utils.data.FlyModelConfig
   cellmap_flow.utils.data.BioModelConfig
   cellmap_flow.utils.data.Config
   cellmap_flow.utils.data.CellMapModelConfig


Functions
---------

.. autoapisummary::

   cellmap_flow.utils.data.load_eval_model
   cellmap_flow.utils.data.check_config
   cellmap_flow.utils.data.get_dacapo_channels
   cellmap_flow.utils.data.get_dacapo_run_model
   cellmap_flow.utils.data.concat_along_c
   cellmap_flow.utils.data.reorder_axes
   cellmap_flow.utils.data.process_chunk_bioimage
   cellmap_flow.utils.data.process_chunk_bioimage_test
   cellmap_flow.utils.data.format_output_bioimage
   cellmap_flow.utils.data.parse_model_configs


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

.. py:data:: logger

.. py:class:: ModelConfig

   .. py:property:: config


   .. py:property:: output_dtype

      Returns the output dtype of the model.
      If not defined, defaults to np.float32.


.. py:class:: ScriptModelConfig(script_path, name=None, scale=None)



   .. py:attribute:: script_path


   .. py:attribute:: name
      :value: None



   .. py:attribute:: scale
      :value: None



   .. py:property:: command


.. py:class:: DaCapoModelConfig(run_name: str, iteration: int, name=None)



   .. py:attribute:: run_name


   .. py:attribute:: iteration


   .. py:attribute:: name
      :value: None



   .. py:property:: command


.. py:function:: load_eval_model(num_channels, checkpoint_path)

.. py:class:: FlyModelConfig(checkpoint_path: str, channels: [str], input_voxel_size: tuple, output_voxel_size: tuple, name: str = None, input_size=(178, 178, 178), output_size=(56, 56, 56))



   .. py:attribute:: name
      :value: None



   .. py:attribute:: checkpoint_path


   .. py:attribute:: channels


   .. py:attribute:: input_voxel_size


   .. py:attribute:: output_voxel_size


   .. py:attribute:: input_size
      :value: (178, 178, 178)



   .. py:attribute:: output_size
      :value: (56, 56, 56)



   .. py:property:: command


   .. py:property:: model


.. py:class:: BioModelConfig(model_name: str, voxel_size, edge_length_to_process=None, name=None)



   .. py:attribute:: model_name


   .. py:attribute:: voxel_size


   .. py:attribute:: name
      :value: None



   .. py:attribute:: voxels_to_process
      :value: None



   .. py:property:: command


   .. py:method:: load_input_information(model)


   .. py:method:: load_output_information(model)


   .. py:method:: get_axes_and_dims(sample)


   .. py:method:: get_spatial_dims(axes, dims)


   .. py:method:: get_input_slicer(input_axes)


.. py:function:: check_config(config)

.. py:class:: Config

.. py:function:: get_dacapo_channels(task)

.. py:function:: get_dacapo_run_model(run_name, iteration)

.. py:function:: concat_along_c(arrs, axes_list, channel_axis_name='c')

   Concatenate a list of arrays along the axis named `channel_axis_name`.

   :param arrs: The list of arrays to concatenate.
   :type arrs: list of np.ndarray
   :param axes_list: The list of list-of-axis-names. axes_list[i] is the axis names
                     corresponding to arrs[i].
   :type axes_list: list of list of str
   :param channel_axis_name: The name of the "channel" axis. Default is "c".
   :type channel_axis_name: str

   :returns: * **out** (*np.ndarray*) -- The concatenated array.
             * **out_axes** (*list of str*) -- The list of axis names for the output array.


.. py:function:: reorder_axes(arr: numpy.ndarray, axes: list[str], desired_order: list[str] = ['z', 'y', 'x', 'c']) -> tuple[numpy.ndarray, list[str]]

   Reorder/remove axes so that the final array has axes in the desired order.

   - Any axis not in desired_order is removed IF its size == 1,
   otherwise a ValueError is raised.
   - If an axis from desired_order is missing, we insert a size-1 dimension
   in the correct position so the final shape has exactly 4 dimensions.

   :param arr: Input array.
   :type arr: np.ndarray
   :param axes: Axis labels corresponding to arr.shape in order.
   :type axes: list of str

   :returns: * **arr** (*np.ndarray*) -- The reshaped/reordered array with axes in desired_order.
             * **out_axes** (*list of str*) -- The final axis labels, which should be exactly desired_order.


.. py:function:: process_chunk_bioimage(self, idi: cellmap_flow.image_data_interface.ImageDataInterface, input_roi: funlib.geometry.Roi)

.. py:function:: process_chunk_bioimage_test(self, idi: cellmap_flow.image_data_interface.ImageDataInterface, input_roi: funlib.geometry.Roi)

.. py:function:: format_output_bioimage(self, output_sample, output_names=None, output_axes=None)

.. py:function:: parse_model_configs(yaml_file_path: str) -> List[ModelConfig]

   Reads a YAML file that defines a list of model configs.
   Validates them manually, then returns a list of constructed ModelConfig objects.


.. py:class:: CellMapModelConfig(folder_path, name, scale=None)



   Configuration class for a CellmapModel.
   Similar to DaCapoModelConfig, but uses a CellmapModel object
   to populate the necessary metadata and define a prediction function.


   .. py:attribute:: cellmap_model


   .. py:attribute:: name


   .. py:attribute:: scale
      :value: None



   .. py:property:: command
      :type: str


      You can either return a placeholder command or remove this property if not needed.
      For consistency with your DaCapoModelConfig, we return something minimal here.