Skip to main content

Python package to perform timeseries analysis of multiplehydrological time series using a dynamic factor model.

Project description

metran Documentation Status Codacy Badge Codacy Badge PyPI

Metran

Metran is a package for performing multivariate timeseries analysis using a technique called dynamic factor modelling. It can be used to describe the variation among many variables in terms of a few underlying but unobserved variables called factors.

Installation

To install Metran, a working version of Python 3.7 or 3.8 has to be installed on your computer. We recommend using the Anaconda Distribution with Python 3.8 as it includes most of the python package dependencies and the Jupyter Notebook software to run the notebooks. However, you are free to install any Python distribution you want.

To install metran, type the following command

pip install metran

To install in development mode, clone the repository and type the following from the module root directory:

pip install -e .

Documentation

The docs can be viewed here.

Examples

For a brief introduction of the theory behind Metran on multivariate timeseries analysis with dynamic factor modeling see the notebook:

A practical real-world example, as published in Stromingen (Van Geer, 2015), is given in the following notebook:

A notebook on how to use Pastas models output with Metran:

Dependencies

Metran has the following dependencies which are automatically installed if not already available:

  • numpy>=1.16.5
  • pandas>=1.0
  • scipy>=1.1
  • matplotlib>=3.0
  • pastas>=0.16.0
  • numba

References

  • Berendrecht, W.L. (2004). State space modeling of groundwater fluctuations.
  • Berendrecht, W.L., F.C. van Geer (2016). A dynamic factor modeling framework for analyzing multiple groundwater head series simultaneously, Journal of Hydrology, 536, pp. 50-60, doi:http://dx.doi.org/10.1016/j.jhydrol.2016.02.028.
  • Van Geer, F.C. en W.L. Berendrecht (2015) Meervoudige tijdreeksmodellen en de samenhang in stijghoogtereeksen. Stromingen 23 nummer 3, pp. 25-36.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

metran-0.2.0.tar.gz (27.1 kB view details)

Uploaded Source

Built Distribution

metran-0.2.0-py3-none-any.whl (28.2 kB view details)

Uploaded Python 3

File details

Details for the file metran-0.2.0.tar.gz.

File metadata

  • Download URL: metran-0.2.0.tar.gz
  • Upload date:
  • Size: 27.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for metran-0.2.0.tar.gz
Algorithm Hash digest
SHA256 efc73785e7c2371868acd67007e8e97c79aa6e2f4a38b3c2014467c91682297f
MD5 1f1bf865b81a7609ed41eb56533e8920
BLAKE2b-256 14cf1d6e8eb82997a2029178cf0256d4c26c729d19578da1a8ce22279287327e

See more details on using hashes here.

File details

Details for the file metran-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: metran-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 28.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for metran-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 bbceb57a2c5cff24c84ea05e3ad281caae74af73b5349c441eb8de2f29a2d0f3
MD5 f204f02a3effba8c5d4ccc7def0b512d
BLAKE2b-256 8cd51c049d8a962d6c873fbe9e8adaaadc9a9e55bcf8b2eda9dbfcb7fc623aeb

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page