nd2reader

About

nd2reader is a pure-Python package that reads images produced by NIS Elements.

.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 produces data in numpy arrays, which makes it trivial to use with the image analysis packages such as scikit-image and OpenCV.

Installation

Dependencies will automatically be installed if you don't have them. That said, for optimal performance, you should install the following packages before installing nd2reader:

Ubuntu

apt-get install python-numpy python-six (Python 2.x) apt-get install python3-numpy python3-six (Python 3.x)

Other operating systems

These have not been tested yet.

nd2reader is compatible with both Python 2.x and 3.x. I recommend installing using pip:

pip install nd2reader (Python 2.x) or pip3 install nd2reader (Python 3.x)

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: '', 'GFP'
Fields of View: 8
Z-Levels: 3

You can also get some metadata about the nd2 programatically:

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

Images

Every method returns an Image object, which contains some metadata about the image as well as the raw pixel data itself. Images are always a 16-bit grayscale image. The data attribute holds the numpy array with the image data:

>>> image = nd2[20]
>>> print(image.data)
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)

You can get a quick summary of image data by examining the Image object:

>>> image
<ND2 Image>
1280x800 (HxW)
Timestamp: 1699.79478134
Field of View: 2
Channel: GFP
Z-Level: 1

Or you can access it programmatically:

image = nd2[0]
print(image.timestamp)
print(image.field_of_view)
print(image.channel)
print(image.z_level)

Often, you may want to just iterate over each image:

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

You can also get an image directly by indexing. Here, we look at the 38th image:

>>> nd2[37]
<ND2 Image>
1280x800 (HxW)
Timestamp: 1699.79478134
Field of View: 2
Channel: GFP
Z-Level: 1

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.data)

Step sizes are also accepted:

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

for image in nd2[::-1]:
    # iterate backwards over every image, if you're into that kind of thing
    do_something_image.data)

Image Sets

If you have complicated hierarchical data, it may be easier to use image sets, which groups images together if they share the same time index (not timestamp!) and field of view:

import nd2reader
nd2 = nd2reader.Nd2("/path/to/my_complicated_images.nd2")
for image_set in nd2.image_sets:
    # you can select images by channel
    gfp_image = image_set.get("GFP")
    do_something_gfp_related(gfp_image.data)

    # you can also specify the z-level. this defaults to 0 if not given
    out_of_focus_image = image_set.get("Bright Field", z_level=1)
    do_something_out_of_focus_related(out_of_focus_image.data)

To get an image from an image set, you must specify a channel. It defaults to the 0th z-level, so if you have more than one z-level you will need to specify it when using get:

image = image_set.get("YFP")
image = image_set.get("YFP", z_level=2)

You can also see how many images are in your image set:

>>> len(image_set)
7

Protips

nd2reader is about 14 times faster under Python 3.4 compared to Python 2.7. If you know why, please get in touch!

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.