rbnvrw/nd2reader

Tutorial
========

Installation
~~~~~~~~~~~~

The package is available on PyPi. Install it using:

::

    pip install nd2reader

If you don't already have the packages ``numpy``, ``pims``, ``six`` and
``xmltodict``, they will be installed automatically if you use the
``setup.py`` script. ``nd2reader`` is an order of magnitude faster in
Python 3. I recommend using it unless you have no other choice. Python
2.7 and Python >= 3.4 are supported.

Installation via Conda Forge
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Installing ``nd2reader`` from the ``conda-forge`` channel can be
achieved by adding ``conda-forge`` to your channels with:

::

    conda config --add channels conda-forge

Once the ``conda-forge`` channel has been enabled, ``nd2reader`` can be
installed with:

::

    conda install nd2reader

It is possible to list all of the versions of ``nd2reader`` available on
your platform with:

::

    conda search nd2reader --channel conda-forge

Opening ND2s
~~~~~~~~~~~~

``nd2reader`` follows the `pims <https://github.com/soft-matter/pims>`__
framework. To open a file and show the first frame:

.. code:: python

    from nd2reader import ND2Reader
    import matplotlib.pyplot as plt

    with ND2Reader('my_directory/example.nd2') as images:
      plt.imshow(images[0])

After opening the file, all ``pims`` features are supported. Please
refer to the `pims
documentation <http://soft-matter.github.io/pims/>`__.

ND2 metadata
~~~~~~~~~~~~

The ND2 file contains various metadata, such as acquisition information,
regions of interest and custom user comments. Most of this metadata is
parsed and available in dictionary form. For example:

.. code:: python

    from nd2reader import ND2Reader

    with ND2Reader('my_directory/example.nd2') as images:
        # width and height of the image
        print('%d x %d px' % (images.metadata['width'], images.metadata['height']))

All metadata properties are:

-  ``width``: the width of the image in pixels
-  ``height``: the height of the image in pixels
-  ``date``: the date the image was taken
-  ``fields_of_view``: the fields of view in the image
-  ``frames``: a list of all frame numbers
-  ``z_levels``: the z levels in the image
-  ``total_images_per_channel``: the number of images per color channel
-  ``channels``: the color channels
-  ``pixel_microns``: the amount of microns per pixel
-  ``rois``: the regions of interest (ROIs) defined by the user
-  ``experiment``: information about the nature and timings of the ND
   experiment

Iterating over fields of view
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Using ``NDExperiments`` in the Nikon software, it is possible to acquire
images on different ``(x, y)`` positions. This is referred to as
different fields of view. Using this reader, the fields of view are on
the ``v`` axis. For example:

.. code:: python

    from nd2reader import ND2Reader

    with ND2Reader('my_directory/example.nd2') as images:
        # width and height of the image
        print(images.metadata)

will output

.. code:: python

    {'channels': ['BF100xoil-1x-R', 'BF+RITC'],
     'date': datetime.datetime(2017, 10, 30, 14, 35, 18),
     'experiment': {'description': 'ND Acquisition',
      'loops': [{'duration': 0,
        'sampling_interval': 0.0,
        'start': 0,
        'stimulation': False}]},
     'fields_of_view': [0, 1],
     'frames': [0],
     'height': 1895,
     'num_frames': 1,
     'pixel_microns': 0.09214285714285715,
     'total_images_per_channel': 6,
     'width': 2368,
     'z_levels': [0, 1, 2]}

for our example file. As you can see from the metadata, it has two
fields of view. We can also look at the sizes of the axes:

.. code:: python

        print(images.sizes)

.. code:: python

    {'c': 2, 't': 1, 'v': 2, 'x': 2368, 'y': 1895, 'z': 3}

As you can see, the fields of view are listed on the ``v`` axis. It is
therefore possible to loop over them like this:

.. code:: python

        images.iter_axes = 'v'
        for fov in images:
            print(fov) # Frame containing one field of view

For more information on axis bundling and iteration, refer to the `pims
documentation <http://soft-matter.github.io/pims/v0.4/multidimensional.html#axes-bundling>`__.