SolDet

SolDet is an object-oriented package for solitonic fearture detection in absorption images of Bose-Einstein condensates, with wider use for cold atom image analysis. It is featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in https://arxiv.org/abs/2111.04881.

The dataset used to prepare SolDet is currently being prepared for public release. Until then, it can be requested from Justyna Zwolak. Note that SolDet assumes the following folder structure for the data files:

SolDet |- ... |- data |- data_files |- class-0 |- class-1 |- ... |- class-9 |- data_info |- data_roster.csv |- data_roster.npy |- ...

Installation

(1) (Recommended) Create an Anaconda environment with python 3.7, then activate it conda create -n SDenv python=3.7 conda activate SDenv

(2) Clone this repository and change directory to it cd your_directory/SolDet Replace your_directory with your root directory.

(3) You can install SolDet by running: python setup.py build python setup.py install

Getting started

All features of SolDet are integrated in the SolitonDetector object. To start using SolDet: from soldet import SolitonDetector sd = SolitonDetector()

Import data

To import labeled dataset, use load_data method: sd.load_data(directory='directory_str', data_key={'train':0.9, 'test':0.1}, data_type='labeled')

To import unlabeled dataset, use sd.load_data(directory='directory_str', data_key='new', data_type='unlabeled')

Train models

To train an ML model, use use train_ML method: sd.train_ML(model_key='object_detector', data_key='train', save=save_str) where model_key can be 'object_detector' or 'classifier'.

To train the physics-based model, use define_PIE_classifier method: sd.define_PIE_classifier(data_key='train', save=save_str) to define a phycics-informed exictiation (PIE) classifier. Use train_quality_estimator method: sd.train_quality_estimator(data_key='train', save=save_str) to train a quality estimator.

Predict and label with pre-trained models

To predict new or test data, use predict method: pred = sd.predict(data_key='test', model_key=model_key) where model_key can be 'classifier', 'object_detector', 'PIE_classifier', or 'quality_estimator'.

To filter predicted data with certain criteria, use pipeline method: checks ={'classifier_labels':[1,2], 'object_detector_positions_length':[1,2,3], 'PIE_types':[0], 'quality_estimates_bounds':[0.3,1]} refined_data = sd.pipeline(data_key='new', new_data_key='refined', checks=checks)