A test-driven framework for formally validating scientific models against data.
Project description
SciUnit: A Test-Driven Framework for Formally Validating Scientific Models Against Data
Concept
Documentation
Jupyter Tutorials
API Documentation
Installation
pip install sciunit
or
conda install -c conda-forge sciunit
Basic Usage
my_model = MyModel(**my_args) # Instantiate a class that wraps your model of interest.
my_test = MyTest(**my_params) # Instantiate a test that you write.
score = my_test.judge() # Runs the test and return a rich score containing test results and more.
Domain-specific libraries and information
NeuronUnit for neuron and ion channel physiology
See others here
Mailing List
There is a mailing list for announcements and discussion. Please join it if you are at all interested!
Contributors
- Rick Gerkin, Arizona State University (School of Life Science)
- Cyrus Omar, Carnegie Mellon University (Dept. of Computer Science)
Reproducible Research ID
RRID:SCR_014528
License
SciUnit is released under the permissive MIT license, requiring only attribution in derivative works. See the LICENSE file for terms.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sciunit-0.2.5.1.tar.gz
(65.4 kB
view details)
File details
Details for the file sciunit-0.2.5.1.tar.gz
.
File metadata
- Download URL: sciunit-0.2.5.1.tar.gz
- Upload date:
- Size: 65.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.3.0 pkginfo/1.7.0 requests/2.25.1 setuptools/57.4.0 requests-toolbelt/0.9.1 tqdm/4.61.0 CPython/3.9.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6148704f92a29c9d6de65ca9455b03ebe1f05101dae5e706aee2186e5a09fab3 |
|
MD5 | 0707fc6728dedfaddc94f9a201b43535 |
|
BLAKE2b-256 | ab07e42459112a658557739783e8b8bba747ded91c336f4ce4a5ec99b843635f |