<commit_before># spotpy
A Statistical Parameter Optimization Framework for Python

---

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[![PyPI Version][pypi-v-image]][pypi-v-link]
[![Python Versions][pypi-v-image]][pypi-v-link]
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[pypi-dl-image]: https://img.shields.io/pypi/dm/spotpy.png
[pypi-dl-link]: https://pypi.python.org/pypi/spotpy
[pypi-v-image]: https://img.shields.io/pypi/v/spotpy.png
[pypi-v-link]: https://pypi.python.org/pypi/spotpy
[pypi-pyv-image]: https://img.shields.io/pypi/pyversions/spotpy.png
[pypi-pyv-link]: https://img.shields.io/pypi/pyversions/spotpy
[travis-image]: https://img.shields.io/travis/thouska/spotpy/master.png
[travis-link]: https://travis-ci.org/thouska/spotpy
[license-image]: https://img.shields.io/badge/license-MIT-blue.png
[license-link]: http://opensource.org/licenses/MIT


=================
Purpose
=================

SPOTPY is a Python tool that enables the use of Computational optimization techniques for calibration, uncertainty 
and sensitivity analysis techniques of almost every (environmental-) model.
 
The simplicity and flexibility enables the use and test of different 
algorithms without the need of complex codes::

	sampler = spotpy.algorithms.sceua(model_setup())     # Initialize your model with a setup file
	sampler.sample(10000)                                # Run the model
	results = sampler.getdata()                          # Load the results
	spotpy.analyser.plot_parametertrace(results)         # Show the results
<commit_msg>Added Python Version shield icon<commit_after># spotpy
A Statistical Parameter Optimization Framework for Python

---

[![PyPI Downloads][pypi-dl-image]][pypi-dl-link]
[![PyPI Version][pypi-v-image]][pypi-v-link]
[![Python Versions][pypi-pyv-image]][pypi-pyv-link]
[![Build Status][travis-image]][travis-link]
[![License][license-image]][license-link]

[pypi-dl-image]: https://img.shields.io/pypi/dm/spotpy.png
[pypi-dl-link]: https://pypi.python.org/pypi/spotpy
[pypi-v-image]: https://img.shields.io/pypi/v/spotpy.png
[pypi-v-link]: https://pypi.python.org/pypi/spotpy
[pypi-pyv-image]: https://img.shields.io/pypi/pyversions/spotpy.png
[pypi-pyv-link]: https://img.shields.io/pypi/pyversions/spotpy
[travis-image]: https://img.shields.io/travis/thouska/spotpy/master.png
[travis-link]: https://travis-ci.org/thouska/spotpy
[license-image]: https://img.shields.io/badge/license-MIT-blue.png
[license-link]: http://opensource.org/licenses/MIT


=================
Purpose
=================

SPOTPY is a Python tool that enables the use of Computational optimization techniques for calibration, uncertainty 
and sensitivity analysis techniques of almost every (environmental-) model.
 
The simplicity and flexibility enables the use and test of different 
algorithms without the need of complex codes::

	sampler = spotpy.algorithms.sceua(model_setup())     # Initialize your model with a setup file
	sampler.sample(10000)                                # Run the model
	results = sampler.getdata()                          # Load the results
	spotpy.analyser.plot_parametertrace(results)         # Show the results

{'fraction_non_alphanumeric': 0.16142100342785914, 'fraction_numerical': 0.0031162355874104083, 'mean_word_length': 4.119617224880383, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 18, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 4, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '6552250', 'n_tokens_mistral': 1131, 'n_tokens_neox': 1064, 'n_words': 212}