nd2reader

This library is no longer being developed here

There is an actively-developed fork which may be preferable to this version, and version 3+ installed with pip is based on that codebase. If your code depends on this version of nd2reader, you can specify the final version with pip install "nd2reader==2.1.3". I am no longer supporting this library, as my lab has discovered Micro-Manager and found it to be a far superior application for acquiring microscope data. I would highly recommend switching if it works for what you're doing. I will not be accepting pull requests any longer.

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, six and xmltodict, they will be installed automatically:

pip3 install "nd2reader==2.1.3" for Python 3.x

pip install "nd2reader==2.1.3" 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")
>>> 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)

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

>>> image = nd2[20]
>>> 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)

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

If you only want to view images that meet certain criteria, you can use select(). It's much faster than iterating and checking attributes of images manually. You can specify scalars or lists of values. Criteria that aren't specified default to every possible value. Currently, slicing and selecting can't be done at the same time, but you can set a range with the start and stop arguments:

for image in nd2.select(channels="GFP", fields_of_view=(1, 2, 7)):
    # gets all GFP images in fields of view 1, 2 and 7, regardless of z-level or frame
    do_something(image)

for image in nd2.select(z_levels=(0, 1), start=12, stop=3000):
    # gets images of any channel or field of view, with z-level 0 or 1, between images 12 and 3000
    do_something(image)

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

for image in nd2[50:433]:
    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 image:

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

The Nd2 object has some programmatically-accessible metadata:

>>> nd2.height  # in pixels
1280
>>> nd2.width  # in pixels
800
>>> len(nd2)  # the number of images
30528
>>> nd2.pixel_microns  # the width of a pixel in microns
0.22

Citation

You can cite nd2reader in your research if you want (note that I am not responsible for version 3+):

Rybarski, Jim (2015): nd2reader. figshare.
http://dx.doi.org/10.6084/m9.figshare.1619960

Acknowledgments

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