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

Just use pip:

pip install nd2reader

If you want to install via git, clone the repo and run:

python setup.py install

Simple Iteration

For most cases, you'll just want to iterate over each image:

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

    # 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)

Direct Image Access

There is a method, get_image, which allows random access to images. This might not always return an image, however, if you acquired different numbers of images in each cycle of a program. For example, if you acquire GFP images every other minute, but acquire bright field images every minute, get_image will return None at certain time indexes.

Images

Image objects provide several pieces of useful data.

>>> import nd2reader
>>> nd2 = nd2reader.Nd2("/path/to/my_images.nd2")
>>> image = nd2.get_image(14, 2, "GFP", 1)
>>> image.data
array([[1809, 1783, 1830, ..., 1923, 1920, 1914],
       [1687, 1855, 1792, ..., 1986, 1903, 1889],
       [1758, 1901, 1849, ..., 1911, 2010, 1954],
       ...,
       [3363, 3370, 3570, ..., 3565, 3601, 3459],
       [3480, 3428, 3328, ..., 3542, 3461, 3575],
       [3497, 3666, 3635, ..., 3817, 3867, 3779]])
>>> image.channel
'GFP'
>>> image.timestamp
1699.7947813408175
>>> image.field_of_view
2
>>> image.z_level
1

# You can also get a quick summary of image data:

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