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Version 3.3.0

upstream/backport/20220103
Ruben Verweij 3 years ago
committed by Lorenzo ZOLFANELLI
parent
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[submodule "docs"]
path = docs
url = https://github.com/rbnvrw/nd2reader.git
url = git@github.com:rbnvrw/nd2reader.git
branch = gh-pages

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docs

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Subproject commit f700c239f8f9d7d1f99a3c10d9f67e2b3b8ef307
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setup.py View File

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from setuptools import setup
VERSION = '3.2.3'
VERSION = '3.3.0'
if __name__ == '__main__':
setup(


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sphinx/tutorial.md View File

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# Tutorial
### Installation
The package is available on PyPi. Install it using:
```
pip install nd2reader
```
If you don't already have the packages `numpy`, `pims`, `six` and `xmltodict`, they will be installed automatically if you use the `setup.py` script.
`nd2reader` is an order of magnitude faster in Python 3. I recommend using it unless you have no other choice. Python 2.7 and Python >= 3.4 are supported.
#### Installation via Conda Forge
Installing `nd2reader` from the `conda-forge` channel can be achieved by adding `conda-forge` to your channels with:
```
conda config --add channels conda-forge
```
Once the `conda-forge` channel has been enabled, `nd2reader` can be installed with:
```
conda install nd2reader
```
It is possible to list all of the versions of `nd2reader` available on your platform with:
```
conda search nd2reader --channel conda-forge
```
### Opening ND2s
`nd2reader` follows the [pims](https://github.com/soft-matter/pims) framework. To open a file and show the first frame:
```python
from nd2reader import ND2Reader
import matplotlib.pyplot as plt
with ND2Reader('my_directory/example.nd2') as images:
plt.imshow(images[0])
```
After opening the file, all `pims` features are supported. Please refer to the [pims documentation](http://soft-matter.github.io/pims/).
### ND2 metadata
The ND2 file contains various metadata, such as acquisition information,
regions of interest and custom user comments. Most of this metadata is parsed
and available in dictionary form. For example:
```python
from nd2reader import ND2Reader
with ND2Reader('my_directory/example.nd2') as images:
# width and height of the image
print('%d x %d px' % (images.metadata['width'], images.metadata['height']))
```
All metadata properties are:
* `width`: the width of the image in pixels
* `height`: the height of the image in pixels
* `date`: the date the image was taken
* `fields_of_view`: the fields of view in the image
* `frames`: a list of all frame numbers
* `z_levels`: the z levels in the image
* `total_images_per_channel`: the number of images per color channel
* `channels`: the color channels
* `pixel_microns`: the amount of microns per pixel
* `rois`: the regions of interest (ROIs) defined by the user
* `experiment`: information about the nature and timings of the ND experiment
### Iterating over fields of view
Using `NDExperiments` in the Nikon software, it is possible to acquire images on different `(x, y)` positions.
This is referred to as different fields of view. Using this reader, the fields of view are on the `v` axis.
For example:
```python
from nd2reader import ND2Reader
with ND2Reader('my_directory/example.nd2') as images:
# width and height of the image
print(images.metadata)
```
will output
```python
{'channels': ['BF100xoil-1x-R', 'BF+RITC'],
'date': datetime.datetime(2017, 10, 30, 14, 35, 18),
'experiment': {'description': 'ND Acquisition',
'loops': [{'duration': 0,
'sampling_interval': 0.0,
'start': 0,
'stimulation': False}]},
'fields_of_view': [0, 1],
'frames': [0],
'height': 1895,
'num_frames': 1,
'pixel_microns': 0.09214285714285715,
'total_images_per_channel': 6,
'width': 2368,
'z_levels': [0, 1, 2]}
```
for our example file. As you can see from the metadata, it has two fields of view. We can also look at the sizes of the axes:
```python
print(images.sizes)
```
```python
{'c': 2, 't': 1, 'v': 2, 'x': 2368, 'y': 1895, 'z': 3}
```
As you can see, the fields of view are listed on the `v` axis. It is therefore possible to loop over them like this:
```python
images.iter_axes = 'v'
for fov in images:
print(fov) # Frame containing one field of view
```
For more information on axis bundling and iteration, refer to the [pims documentation](http://soft-matter.github.io/pims/v0.4/multidimensional.html#axes-bundling).

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Tutorial
========
Installation
~~~~~~~~~~~~
The package is available on PyPi. Install it using:
::
pip install nd2reader
If you don't already have the packages ``numpy``, ``pims``, ``six`` and
``xmltodict``, they will be installed automatically if you use the
``setup.py`` script. ``nd2reader`` is an order of magnitude faster in
Python 3. I recommend using it unless you have no other choice. Python
2.7 and Python >= 3.4 are supported.
Installation via Conda Forge
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Installing ``nd2reader`` from the ``conda-forge`` channel can be
achieved by adding ``conda-forge`` to your channels with:
::
conda config --add channels conda-forge
Once the ``conda-forge`` channel has been enabled, ``nd2reader`` can be
installed with:
::
conda install nd2reader
It is possible to list all of the versions of ``nd2reader`` available on
your platform with:
::
conda search nd2reader --channel conda-forge
Opening ND2s
~~~~~~~~~~~~
``nd2reader`` follows the `pims <https://github.com/soft-matter/pims>`__
framework. To open a file and show the first frame:
.. code:: python
from nd2reader import ND2Reader
import matplotlib.pyplot as plt
with ND2Reader('my_directory/example.nd2') as images:
plt.imshow(images[0])
After opening the file, all ``pims`` features are supported. Please
refer to the `pims
documentation <http://soft-matter.github.io/pims/>`__.
ND2 metadata
~~~~~~~~~~~~
The ND2 file contains various metadata, such as acquisition information,
regions of interest and custom user comments. Most of this metadata is
parsed and available in dictionary form. For example:
.. code:: python
from nd2reader import ND2Reader
with ND2Reader('my_directory/example.nd2') as images:
# width and height of the image
print('%d x %d px' % (images.metadata['width'], images.metadata['height']))
All metadata properties are:
- ``width``: the width of the image in pixels
- ``height``: the height of the image in pixels
- ``date``: the date the image was taken
- ``fields_of_view``: the fields of view in the image
- ``frames``: a list of all frame numbers
- ``z_levels``: the z levels in the image
- ``total_images_per_channel``: the number of images per color channel
- ``channels``: the color channels
- ``pixel_microns``: the amount of microns per pixel
- ``rois``: the regions of interest (ROIs) defined by the user
- ``experiment``: information about the nature and timings of the ND
experiment
Iterating over fields of view
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Using ``NDExperiments`` in the Nikon software, it is possible to acquire
images on different ``(x, y)`` positions. This is referred to as
different fields of view. Using this reader, the fields of view are on
the ``v`` axis. For example:
.. code:: python
from nd2reader import ND2Reader
with ND2Reader('my_directory/example.nd2') as images:
# width and height of the image
print(images.metadata)
will output
.. code:: python
{'channels': ['BF100xoil-1x-R', 'BF+RITC'],
'date': datetime.datetime(2017, 10, 30, 14, 35, 18),
'experiment': {'description': 'ND Acquisition',
'loops': [{'duration': 0,
'sampling_interval': 0.0,
'start': 0,
'stimulation': False}]},
'fields_of_view': [0, 1],
'frames': [0],
'height': 1895,
'num_frames': 1,
'pixel_microns': 0.09214285714285715,
'total_images_per_channel': 6,
'width': 2368,
'z_levels': [0, 1, 2]}
for our example file. As you can see from the metadata, it has two
fields of view. We can also look at the sizes of the axes:
.. code:: python
print(images.sizes)
.. code:: python
{'c': 2, 't': 1, 'v': 2, 'x': 2368, 'y': 1895, 'z': 3}
As you can see, the fields of view are listed on the ``v`` axis. It is
therefore possible to loop over them like this:
.. code:: python
images.iter_axes = 'v'
for fov in images:
print(fov) # Frame containing one field of view
For more information on axis bundling and iteration, refer to the `pims
documentation <http://soft-matter.github.io/pims/v0.4/multidimensional.html#axes-bundling>`__.

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