nd2reader

About

nd2reader is a pure-Python package that reads images produced by NIS Elements 4.0+. It has only been definitively tested on NIS Elements 4.30.02 Build 1053. Support for older versions is being actively worked on.

.nd2 files contain images and metadata, which can be split along multiple dimensions: time, fields of view (xy-plane), focus (z-plane), and filter channel.

nd2reader loads images as Numpy arrays, which makes it trivial to use with the image analysis packages such as scikit-image and OpenCV.

Installation

If you don't already have the packages numpy and six, they will be installed automatically:

pip3 install nd2reader for Python 3.x

pip install nd2reader for Python 2.x

nd2reader is an order of magnitude faster in Python 3. I recommend using it unless you have no other choice.

ND2s

A quick summary of ND2 metadata can be obtained as shown below.

>>> import nd2reader
>>> nd2 = nd2reader.Nd2("/path/to/my_images.nd2")
>>> print(nd2)
<ND2 /path/to/my_images.nd2>
Created: 2014-11-11 15:59:19
Image size: 1280x800 (HxW)
Image cycles: 636
Channels: 'brightfield', 'GFP'
Fields of View: 8
Z-Levels: 3

You can iterate over each image in the order they were acquired:

import nd2reader
nd2 = nd2reader.Nd2("/path/to/my_images.nd2")
for image in nd2:
    do_something(image)

Slicing is also supported and is extremely memory efficient, as images are only read when directly accessed:

my_subset = nd2[50:433]
for image in my_subset:
    do_something(image)

# get every other image in the first 100 images
for image in nd2[:100:2]:
    do_something(image)

# iterate backwards over every image
for image in nd2[::-1]:
    do_something(image)

You can also just index a single images:

# gets the 18th image
my_important_image = nd2[17] 

The Nd2 object has some useful metadata:

>>> nd2.height
1280
>>> nd2.width
800
>>> len(nd2)
30528

It can also be used as a context manager:

import nd2reader
with nd2reader.Nd2("/path/to/my_images.nd2") as nd2:
    for image in nd2:
        do_something(image)

Images

Image objects are just Numpy arrays with some extra metadata bolted on:

>>> image = nd2[20]
>>> print(image)
array([[1894, 1949, 1941, ..., 2104, 2135, 2114],
       [1825, 1846, 1848, ..., 1994, 2149, 2064],
       [1909, 1820, 1821, ..., 1995, 1952, 2062],
       ...,
       [3487, 3512, 3594, ..., 3603, 3643, 3492],
       [3642, 3475, 3525, ..., 3712, 3682, 3609],
       [3687, 3777, 3738, ..., 3784, 3870, 4008]], dtype=uint16)

>>> print(image.timestamp)
10.1241241248
>>> print(image.frame_number)
11
>>> print(image.field_of_view)
6
>>> print(image.channel)
'GFP'
>>> print(image.z_level)
0

Bug Reports and Features

If this fails to work exactly as expected, please open a Github issue. If you get an unhandled exception, please paste the entire stack trace into the issue as well.

Contributing

Please feel free to submit a pull request with any new features you think would be useful. You can also create an issue if you'd just like to propose or discuss a potential idea.

Acknowledgments

Support for the development of this package was provided by the Finkelstein Laboratory.