Timelapse Processor
meta plugin to ease processing timelapse image data
meta plugin to ease processing timelapse image data
API
This plugin exposes two principal funcionalities:
TimelapseConverter
The TimelapseConverter
class allows you to stack or unstack any of the supported napari layers from 4D data into a list of 3D layers or vice versa. Currently supported layers are:
napari.layers.Image
napari.layers.Labels
napari.layers.Points
napari.layers.Vectors
napari.layers.Surface
napari.layers.Tracks
are intrinsically 4D and thus not supported.
Unstacking example usage:
from napari_timelapse_processor import TimelapseConverter
import numpy as np
image_4d = np.random.rand(10, 32, 32, 32) # 10 timepoints of 32x32x32 data
converter = TimelapseConverter()
list_of_images = converter.unstack(image_4d, layertype='napari.types.ImageData')
Stacking example usage:
from napari_timelapse_processor import TimelapseConverter
import numpy as np
random_points = [np.random.rand(10, 3) for _ in range(10)] # 10 timepoints of 10 random 3D points
converter = TimelapseConverter()
# stack the points into a single 4D layer
stacked_points = converter.stack(random_points, layertype='napari.types.PointsData')
The TimeLapseConverter
class also supports (un)stacking the napari.layers.Layer
type (and its above-listed subclasses). Importantly, features
that are associated with the respective layer are also (un)stacked.
Layer example usage
from napari_timelapse_processor import TimelapseConverter
import numpy as np
from napari.layers import Points
import pandas as pd
random_points = [np.random.rand(10, 3) for _ in range(10)] # 10 timepoints of 10 random 3D points
random_features = [pd.DataFrame(np.random.rand(10)) for _ in range(10)] # 10 timepoints of 10 random feature values
# create a list of 10 Points layers
points = [Points(random_points[i], properties=random_features[i]) for i in range(10)]
converter = TimelapseConverter()
stacked_points = converter.stack(points, layertype='napari.layers.Points')
frame_by_frame
The frame-by-frame functionality provides a decorator that will inspect the decorated function for TimelapseConverter
-compatible arguments and, if a 4D value is passed as argument, will automatically (un)stack the data before and after the function call. This allows for a more intuitive API when working with timelapse data. Currently supported type annotations are:
napari.types.ImageData
napari.types.LabelsData
napari.types.PointsData
napari.types.VectorsData
napari.types.SurfaceData
napari.layers.Layer
napari.layers.Image
napari.layers.Labels
napari.layers.Points
napari.layers.Vectors
napari.layers.Surface
Additionally, the frame_by_frame
supports parallelization with dask.distributed. To use it, simply pass the use_dask=True
argument to the decorated function, even if the function itself does not require this argument. The decorater will then automatically parallelize the function call over the time-axis and remove the use_dask
argument when calling the function.
Example interactive code usage: If you want to use the frame_by_frame
functionality in, say, a Jupyter notebook, use it like this:
from napari_timelapse_processor import frame_by_frame
import numpy as np
def my_function(image: 'napari.types.ImageData') -> 'napari.types.ImageData':
return 2 * image
image_4d = np.random.rand(10, 32, 32, 32) # 10 timepoints of 32x32x32 data
image_4d_processed = frame_by_frame(my_function)(image_4d) # without dask
image_4d_processed = frame_by_frame(my_function)(image_4d, use_dask=True) # with dask
Example napari code If you want to use the frame_by_frame
functionality in a napari plugin, use it like this:
from napari_timelapse_processor import frame_by_frame
@frame_by_frame
def my_function(image: 'napari.types.ImageData') -> 'napari.types.ImageData':
return 2 * image
Hint: The frame_by_frame
functionality runs under the assumption that input napari-data (e.g., an Image, a Surface, Points, etc) are always arguments and any other parameters are always keyword arguments. If this is not the case, the decorator will not work as intended.
# This works
frame_by_frame(my_function)(image_4d, some_parameter=2, use_dask=True)
# This does not work
frame_by_frame(my_function)(image=image_4d, some_parameter=2, use_dask=True)
This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-timelapse-processor
via pip:
pip install napari-timelapse-processor
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the BSD-3 license, "napari-timelapse-processor" is free and open source software
Issues
If you encounter any problems, please [file an issue] along with a detailed description.
Version:
- 0.1.1
Last updated:
- 2025-06-16
First released:
- 2024-07-15
License:
- Copyright (c) 2024, Johannes S...
Supported data:
- Information not submitted
Plugin type:
Open extension:
Save extension: