An interactive visualizer to help explore high-dimensional likelihoods and their observables.
Project description
InViz
InViz (Interactive Visualizer) is a tool to aid likelihood analysis of model parameters where samples from a distribution in the parameter space are used as inputs to calculate a given observable. For example, selecting a range of samples will allow you to easily see how the observables change as you traverse the sample distribution. At the core of InViz is the Observable
object, which contains the data for a given observable and instructions for plotting it. It is modular, so you can write your own function that takes the parameter values as inputs, and InViz will use it to compute observables on the fly. It also accepts tabular data, so if you have pre-computed observables, simply import them alongside the dataset containing the sample distribution to start visualizing!
Installation
Dependencies
- Python versions $\geq$ 3.8 and $<$ 3.11 are supported.
- Holoviews $\geq$ 1.15.4 (this package and its dependencies will be installed automatically)
- Bokeh 2.4.3
InViz can be installed with pip:
python -m pip install inviz
Or, if you want to test the latest changes, you can clone the repository with
git clone https://github.com/wen-jams/inviz
cd inviz
python setup.py install
Getting Started
Test Installation
To verify that inviz and all the dependencies have been installed correctly, try running:
import inviz as iv
If no errors appear, all the dependencies were installed correctly and we're ready to start visualizing!
Example
Download and run the live_data_example
notebook in the tutorials folder to see an example of how inviz can be used.
Here's InViz being used in an astrophysics context! The parameters come from a cosmological model of dark matter, and the observables are the matter and CMB power spectra.
Contributing
Make feature requests and bug reports using the issue tracker: https://github.com/wen-jams/inviz/issues
License
MIT License
Contact
Project details
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