Pharmpy is a library and toolkit for pharmacometrics. It can be used as a regular Python package, in R via the pharmr package or via its built in command line interface.
Current features:
- A model abstraction as a foundation for higher level operations on models
- Functions for manipulation of models, e.g. changing model components like elimination or absorption
- Reading NONMEM models and results
- Running models and complex workflows (with NONMEM or to some extent nlmixr)
For installation in R see pharmr.
Install the latest stable version from PyPI:
pip install pharmpy-core # or 'pip3 install' if that is your default python3 pipTo be able to use components using machine learning the tflite package is needed. It can be installed using:
pip install --index-url https://google-coral.github.io/py-repo/ tflite_runtime>>> from pharmpy.modeling import read_model, read_modelfit_results
>>> model = read_model("run1.mod")
>>> res = read_modelfit_results("run1.mod")
>>> res.parameter_estimates
THETA(1) 0.004696
THETA(2) 0.984258
THETA(3) 0.158920
OMEGA(1,1) 0.029351
OMEGA(2,2) 0.027906
SIGMA(1,1) 0.013241
Name: 2, dtype: float64
>>> model.parameters
name value lower upper fix
THETA(1) 0.004693 0.00 1000000 False
THETA(2) 1.009160 0.00 1000000 False
THETA(3) 0.100000 -0.99 1000000 False
OMEGA(1,1) 0.030963 0.00 oo False
OMEGA(2,2) 0.031128 0.00 oo False
SIGMA(1,1) 0.013086 0.00 oo False
>>># Get help
pharmpy -h
# Remove first ID from dataset and save new model using new dataset
pharmpy data filter run1.mod 'ID!=1'
# Extract and print ofvs from multiple model runs
pharmpy results ofv run*.mod
This is the team behind Pharmpy
Please ask a question in an issue or contact one of the maintainers if you have any questions.
If you interested in contributing to Pharmpy, you can find more information under Contribute.