NeuroCarto package
==================

.. toctree::
    :maxdepth: 1
    :caption: Modules:

    main_app
    main_index
    config
    files

.. toctree::
    :maxdepth: 1
    :caption: Probe Abstract:

    probe

.. toctree::
    :maxdepth: 1
    :caption: Probe Implementation:

    probe_npx

.. toctree::
    :maxdepth: 2
    :caption: UI components:

    views

.. toctree::
    :maxdepth: 2
    :caption: Utilities:

    util

Symbols
-------

The symbols and expressions used the document.

Numpy Array
~~~~~~~~~~~

``Array[Dtype, *Shapes]``

**Examples**

* ``Array[int, 3, 2]`` is a ``3x2`` int number array.
* ``Array[index:int, N]`` is a 1-d (`N`-length) int index array.
* ``Array[int, N, (shank, col, row, state, category)]`` is a ``Nx5`` int array with 5 columns (fields).
* ``Array[float, [S,], R, C]`` is either a 2-d (shape ``(R, C)``) or a 3-d (shape ``(S, R, C)``) float array.
* ``Array[V, ..., N, ...]`` is a multi-dimension V-domain array, which has one N-length axis.
* ``Array[bool, N]`` is a ``bool`` N-length array, often named as a mark.

**Use examples**

* An array is applied with a mask::

    a = np.arange(10)
    a = a[a > 5]

  We can document the `__getitem__` of a numpy array as::

    def __getitem__(self: Array[V, N], mark: Array[bool, N]) -> Array[V, M]:
        """
        V : any value type
        N : N-length array
        M = np.count_nonzero(mask)
        """

**Some transform**

* ``a: Array[int, *A]``: ``a.shape: A``, ``a.shape[i] == A[i]``
* ``a: Array[int, A, B]``: ``a.T: Array[int, B, A]``
* ``a: Array[int, N, (A, B, C)]``: ``a[:, 1]: Array[B:int, N]``, ``a[:, [1]]: Array[B:int, N, 1]``
* ``a: Array[int, A]`` and ``b: Array[A, *B]``: ``a[b]: Array[int, *B]``
* ``a: Array[int, A]`` and ``b: Array[bool, A]``: ``a[b]: Array[int, A']``

  where ``A' = np.count_nonzero(b)``

* ``a: Array[int, A, B]``: ``np.sum(a, axis=0) : Array[int, B]``