Finite-Observation-Dynamic-Range

Simulation and analysis code in order to assess the impact of finite observation times on the concept of dynamics range.

Installation (conda)

For reproduction it should be sufficient to install the working environment via

conda env create -f environment.yml
conda activate finite-observation

Alternatively, a step-by-step installation using only conda was done as follows

conda create -n finite-observation python=3.10
conda activate finite-observation
conda install pytorch torchvision torchaudio -c pytorch
conda install ipykernel matplotlib pandas tqdm h5py scipy 
conda install scikit-learn=1.3.2 dask distributed

If pure conda does not work, there is an option to combine with pip

pip install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu

#conda env export --no-builds | grep -v "prefix" > environment.yml

if database result is to be put into repository do this by dumping the sqlite file with

sqlite3 file.db .dump > db_file.txt