Metadata-Version: 2.3
Name: pydashi
Version: 2.0.0
Summary: TODO
Project-URL: Homepage, https://github.com/emiddell/dashi
Author-email: The dashi developers <eike@middell.net>
License-File: LICENSE
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.10
Requires-Dist: docutils>=0.12
Requires-Dist: jinja2>=2.7.3
Requires-Dist: markupsafe>=0.23
Requires-Dist: matplotlib>=3.0.0
Requires-Dist: mock>=1.0.1
Requires-Dist: nose>=1.3.4
Requires-Dist: numpy>=2.0.0
Requires-Dist: pygments>=2.0.2
Requires-Dist: pyparsing>=2.0.3
Requires-Dist: python-dateutil>=2.4.0
Requires-Dist: pytz>=2014.10
Requires-Dist: scipy>=0.15.1
Requires-Dist: six>=1.9.0
Requires-Dist: sphinx>=1.2.3
Description-Content-Type: text/markdown

# dashi 


Elaborate data analyses are possible with the functionality offered by the great
`numpy <http://numpy.scipy.org/>`_, `matplotlib
<http://matplotlib.sourceforge.net/>`_, and `pytables
<http://www.pytables.org/moin>`_ libraries. However their support for
HEP-typical problems (histograms, fitting routines,visualization of those,..) is
limited. Dashi is intended to be a thin wrapper around these libraries to
provide some useful tools for these problems without obstructing the user the access
to the underlying libraries and without being a dependency sink.

# Installation

The easiest way to install dashi is with `pip`::

	pip install pydashi

# Documentation

