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148 lines
4.1 KiB
148 lines
4.1 KiB
Tutorial
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========
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Installation
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~~~~~~~~~~~~
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The package is available on PyPi. Install it using:
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::
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pip install nd2reader
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If you don't already have the packages ``numpy``, ``pims``, ``six`` and
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``xmltodict``, they will be installed automatically if you use the
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``setup.py`` script. ``nd2reader`` is an order of magnitude faster in
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Python 3. I recommend using it unless you have no other choice. Python
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2.7 and Python >= 3.4 are supported.
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Installation via Conda Forge
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Installing ``nd2reader`` from the ``conda-forge`` channel can be
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achieved by adding ``conda-forge`` to your channels with:
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::
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conda config --add channels conda-forge
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Once the ``conda-forge`` channel has been enabled, ``nd2reader`` can be
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installed with:
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::
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conda install nd2reader
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It is possible to list all of the versions of ``nd2reader`` available on
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your platform with:
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::
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conda search nd2reader --channel conda-forge
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Opening ND2s
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~~~~~~~~~~~~
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``nd2reader`` follows the `pims <https://github.com/soft-matter/pims>`__
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framework. To open a file and show the first frame:
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.. code:: python
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from nd2reader import ND2Reader
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import matplotlib.pyplot as plt
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with ND2Reader('my_directory/example.nd2') as images:
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plt.imshow(images[0])
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After opening the file, all ``pims`` features are supported. Please
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refer to the `pims
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documentation <http://soft-matter.github.io/pims/>`__.
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ND2 metadata
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~~~~~~~~~~~~
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The ND2 file contains various metadata, such as acquisition information,
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regions of interest and custom user comments. Most of this metadata is
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parsed and available in dictionary form. For example:
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.. code:: python
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from nd2reader import ND2Reader
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with ND2Reader('my_directory/example.nd2') as images:
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# width and height of the image
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print('%d x %d px' % (images.metadata['width'], images.metadata['height']))
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All metadata properties are:
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- ``width``: the width of the image in pixels
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- ``height``: the height of the image in pixels
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- ``date``: the date the image was taken
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- ``fields_of_view``: the fields of view in the image
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- ``frames``: a list of all frame numbers
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- ``z_levels``: the z levels in the image
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- ``total_images_per_channel``: the number of images per color channel
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- ``channels``: the color channels
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- ``pixel_microns``: the amount of microns per pixel
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- ``rois``: the regions of interest (ROIs) defined by the user
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- ``experiment``: information about the nature and timings of the ND
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experiment
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Iterating over fields of view
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Using ``NDExperiments`` in the Nikon software, it is possible to acquire
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images on different ``(x, y)`` positions. This is referred to as
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different fields of view. Using this reader, the fields of view are on
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the ``v`` axis. For example:
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.. code:: python
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from nd2reader import ND2Reader
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with ND2Reader('my_directory/example.nd2') as images:
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# width and height of the image
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print(images.metadata)
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will output
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.. code:: python
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{'channels': ['BF100xoil-1x-R', 'BF+RITC'],
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'date': datetime.datetime(2017, 10, 30, 14, 35, 18),
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'experiment': {'description': 'ND Acquisition',
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'loops': [{'duration': 0,
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'sampling_interval': 0.0,
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'start': 0,
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'stimulation': False}]},
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'fields_of_view': [0, 1],
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'frames': [0],
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'height': 1895,
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'num_frames': 1,
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'pixel_microns': 0.09214285714285715,
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'total_images_per_channel': 6,
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'width': 2368,
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'z_levels': [0, 1, 2]}
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for our example file. As you can see from the metadata, it has two
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fields of view. We can also look at the sizes of the axes:
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.. code:: python
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print(images.sizes)
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.. code:: python
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{'c': 2, 't': 1, 'v': 2, 'x': 2368, 'y': 1895, 'z': 3}
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As you can see, the fields of view are listed on the ``v`` axis. It is
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therefore possible to loop over them like this:
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.. code:: python
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images.iter_axes = 'v'
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for fov in images:
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print(fov) # Frame containing one field of view
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For more information on axis bundling and iteration, refer to the `pims
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documentation <http://soft-matter.github.io/pims/v0.4/multidimensional.html#axes-bundling>`__.
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