dacapo.experiments.datasplits.datasets.arrays.concat_array
==========================================================

.. py:module:: dacapo.experiments.datasplits.datasets.arrays.concat_array


Attributes
----------

.. autoapisummary::

   dacapo.experiments.datasplits.datasets.arrays.concat_array.logger


Classes
-------

.. autoapisummary::

   dacapo.experiments.datasplits.datasets.arrays.concat_array.ConcatArray


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

.. py:data:: logger

.. py:class:: ConcatArray(array_config)



   This is a wrapper around other `source_arrays` that concatenates
   them along the channel dimension. The `source_arrays` are expected
   to have the same shape and ROI, but can have different data types.

   .. attribute:: name

      The name of the array.

   .. attribute:: channels

      The list of channel names.

   .. attribute:: source_arrays

      A dictionary mapping channel names to source arrays.

   .. attribute:: default_array

      An optional default array to use for channels that are
      not present in `source_arrays`.

   .. method:: from_toml(cls, toml_path

      str) -> ConcatArrayConfig:
      Load the ConcatArrayConfig from a TOML file

   .. method:: to_toml(self, toml_path

      str) -> None:
      Save the ConcatArrayConfig to a TOML file

   .. method:: create_array(self) -> ConcatArray

      
      Create the ConcatArray from the config

   .. note::

      This class is a subclass of Array and inherits all its attributes
      and methods. The only difference is that the array_type is ConcatArray.


   .. py:attribute:: name


   .. py:attribute:: channels


   .. py:property:: source_arrays
      :type: Dict[str, dacapo.experiments.datasplits.datasets.arrays.array.Array]

      Return the source arrays of the ConcatArray.

      :returns: The source arrays of the ConcatArray.
      :rtype: Dict[str, Array]

      :raises AssertionError: If the source arrays are empty.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.source_arrays
      {'A': Array(...), 'B': Array(...)}

      .. note:: The `source_arrays` are expected to have the same shape and ROI.


   .. py:attribute:: default_array


   .. py:property:: attrs
      Return the attributes of the ConcatArray as a dictionary.

      :returns: The attributes of the ConcatArray.
      :rtype: Dict[str, Any]

      :raises AssertionError: If the source arrays have different attributes.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.attrs
      {'axes': 'cxyz', 'roi': Roi(...), 'voxel_size': (1, 1, 1)}

      .. note:: The `source_arrays` are expected to have the same attributes.


   .. py:property:: source_array
      :type: dacapo.experiments.datasplits.datasets.arrays.array.Array

      Return the source array of the ConcatArray.

      :returns: The source array of the ConcatArray.
      :rtype: Array

      :raises AssertionError: If the source array is None.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.source_array
      Array(...)

      .. note:: The `source_array` is expected to have the same shape and ROI.


   .. py:property:: axes
      Return the axes of the ConcatArray.

      :returns: The axes of the ConcatArray.
      :rtype: str

      :raises AssertionError: If the source arrays have different axes.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.axes
      'cxyz'

      .. note:: The `source_arrays` are expected to have the same axes.


   .. py:property:: dims
      Return the dimensions of the ConcatArray.

      :returns: The dimensions of the ConcatArray.
      :rtype: Tuple[int]

      :raises AssertionError: If the source arrays have different dimensions.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.dims
      (2, 100, 100, 100)

      .. note:: The `source_arrays` are expected to have the same dimensions.


   .. py:property:: voxel_size
      Return the voxel size of the ConcatArray.

      :returns: The voxel size of the ConcatArray.
      :rtype: Tuple[float]

      :raises AssertionError: If the source arrays have different voxel sizes.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.voxel_size
      (1, 1, 1)

      .. note:: The `source_arrays` are expected to have the same voxel size.


   .. py:property:: roi
      Return the ROI of the ConcatArray.

      :returns: The ROI of the ConcatArray.
      :rtype: Roi

      :raises AssertionError: If the source arrays have different ROIs.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.roi
      Roi(...)

      .. note:: The `source_arrays` are expected to have the same ROI.


   .. py:property:: writable
      :type: bool

      Return whether the ConcatArray is writable.

      :returns: Whether the ConcatArray is writable.
      :rtype: bool

      :raises AssertionError: If the ConcatArray is writable.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.writable
      False

      .. note:: The ConcatArray is not writable.


   .. py:property:: data
      Return the data of the ConcatArray.

      :returns: The data of the ConcatArray.
      :rtype: np.ndarray

      :raises RuntimeError: If the ConcatArray is not writable.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.data
      np.ndarray(...)

      .. note:: The ConcatArray is not writable.


   .. py:property:: dtype
      Return the data type of the ConcatArray.

      :returns: The data type of the ConcatArray.
      :rtype: np.dtype

      :raises AssertionError: If the source arrays have different data types.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.dtype
      np.float32

      .. note:: The `source_arrays` are expected to have the same data type.


   .. py:property:: num_channels
      Return the number of channels of the ConcatArray.

      :returns: The number of channels of the ConcatArray.
      :rtype: int

      :raises AssertionError: If the source arrays have different numbers of channels.

      .. rubric:: Examples

      >>> config = ConcatArrayConfig(
      ...     name="my_concat_array",
      ...     channels=["A", "B"],
      ...     source_array_configs={
      ...         "A": ArrayConfig(...),
      ...         "B": ArrayConfig(...),
      ...     },
      ...     default_config=ArrayConfig(...),
      ... )
      >>> array = ConcatArray(config)
      >>> array.num_channels
      2

      .. note:: The `source_arrays` are expected to have the same number of channels.