# 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`, `six` and `xmltodict`, 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.
```python
>>> 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:

```python
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:

```python
>>> 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:

```python
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:

```python
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:

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

The `Nd2` object has some programmatically-accessible metadata: 

```python
>>> 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
```

### Contributing

If you'd like to help with the development of nd2reader or just have an idea for improvement, please see the [contributing](https://github.com/rbnvrw/nd2reader/blob/master/CONTRIBUTING.md) page
for more information.

### Bug Reports and Features

If this fails to work exactly as expected, please open an [issue](https://github.com/rbnvrw/nd2reader/issues).
If you get an unhandled exception, please paste the entire stack trace into the issue as well.

### Citation

You can cite nd2reader in your research if you want:

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

### Acknowledgments

Original version by Jim Rybarski. Support for the development of this package was provided by the [Finkelstein Laboratory](http://finkelsteinlab.org/).