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 framework. To open a file and show the first frame:

from nd2reader import ND2Reader
import matplotlib.pyplot as plt

with ND2Reader('my_directory/example.nd2') as images:

After opening the file, all pims features are supported. Please refer to the pims documentation.

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:

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:

from nd2reader import ND2Reader

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

will output

{'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:

{'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:

    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.