qusi

Installation

To install qusi, use

pip install qusi

Note

Although not required, as is generally good practice for any development project, we highly recommend creating a separate virtual environment for each distinct project. For example, via Conda, creating a virtual environment for a project using qusi might look like

conda create -n project_using_qusi_env python=3.11

Then before working, be sure to activate your environment with

conda activate project_using_qusi_env

Then install qusi within this environment.

Contents:

• Basic transit identification with prebuilt components
  • Getting the example code
  • Downloading the dataset
  • Preparing for training
  • Train the network
  • Test the fitted model
• Basic transit identification dataset construction
  • User defined components of a qusi dataset
  • Creating a function to find the data
  • Creating a function to load the data
  • Creating a function to provide a label for the data
  • Creating a light curve collection
  • Creating a dataset
  • Adjusting this for your own binary classification task
• Crafting standard datasets
  • Light curve lengths
  • Modifying the preprocessing
• Crafting injected datasets
• Basic multi classification
• Advanced crafting datasets
• Reference
  • LightCurve
  • LightCurveCollection
  • LightCurveDataset
  • LightCurveObservationCollection
  • FiniteStandardLightCurveDataset
  • FiniteStandardLightCurveObservationDataset
  • Hadryss
  • get_device()
  • infer_session()
  • train_session()
  • TrainHyperparameterConfiguration

Indices and tables

• Index

• Module Index

• Search Page