This codec enables one to equalize noise across imaging levels for shot-noise limited imaging. This allows for some lossy compression based on fundamental statistics coming from each pixel. Precision can be tuned to adjust the compression trade-off.
Zarr.
Install via pip:
pip install poisson-numcodecs
conda create -n poisson_numcodecs python=3.xx
conda activate poisson_numcodecs
git clone https://github.com/AllenNeuralDynamics/poisson-numcodecs.git
cd poisson-numcodecs
pip install -r requirements.txt
pip install -e .
Make sure everything works:
pip install pytest
pytest tests/
This is a simple example on how to use the Poisson codec with zarr:
from poisson_numcodecs import Poisson
data = ... # any 2D dumpy array
# here we assume that the data has a shape of (num_frames, x, y)
# dark_signal and signal_to_photon_gain must be calculated from the data or
# measured directly on the instrument.
# dark_signal is the signal level when no photons are recorded.
# signal_to_photon_gain is the conversion scalor to convert the measure signal
# into absolute photon numbers.
# instantiate Poisson object
poisson_filter = Poisson(dark_signal, signal_to_photon_gain, encoded_dtype, decoded_dtype, integer_per_photon)
# using default Zarr compressor
photon_data = zarr.array(data, filters=[poisson_filter])
data_read = photon_data[:]
Available **kwargs can be browsed with: Poisson?
NOTE:
In order to reload in zarr an array saved with the Poisson, you just need to have the poisson_numcodecs package
installed.