AnDiChallenge's repositories
andi_datasets
andi_datasets provides functions to generate, save, load and manipulate datasets of diffusion anomalous trajectories. It is part of the Anomalous Diffusion (ANDI) Challenge.
AnDi2020_TeamK_TSA
Here are 3 python codes, used to generate the "training" data and the real results for the ANDI challenge. In addition, we upload a *.txt file containing an example of a ready-to-use "training" data set. For more detail, see the ANDI challenge webpage.
AnDi2020_TeamI_QuBI
Extreme Learning Machine used for the AnDi Challenge 2020
AnDi2020_TeamO_WustMLB1
The code for the anomalous exponent estimation in ANDI challenge
AnDi2020_TeamL_UCL
Code for characterising individual anomalous diffusion trajectories (CONDOR). The code was developed in response of the AnDi Challenge for the inference of the anomalous diffusion exponent and for the prediction of the diffusion model in single trajectories.
AnDi2020_TeamG_HNU
Methods of HNU for Task 1 in AnDi Challenge
AnDi2020_TeamC_DecBayComp
Trajectory analysis tool using graph neural networks
AnDi2020_TeamM_UPV-MAT
UPV-MAT code for ANDI Challenge
AnDi2020_TeamD_DeepSPT
2020 AnDi challenge
AnDi2020_TeamH_NOA
PyTorch code for convolutional LSTM used in AnDi Challenge
AnDi2020_TeamE_eduN
notebooks and nets for andi challenge
AnDi2020_TeamF_ErasmusMC
FEST method for tasks 1 & 2 of the Anomalous Diffusion challenge