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https://github.com/csiro-hydroinformatics/hydrodiy

hydrodiy is a set of tools to perform standard data analysis in hydrology. The package is structured around typical tasks: io, data checking, statistical analysis, gis processing and plotting.

https://github.com/csiro-hydroinformatics/hydrodiy

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

hydrodiy is a set of tools to perform standard data analysis in hydrology. The package is structured around typical tasks: io, data checking, statistical analysis, gis processing and plotting.

Basic Info
  • Host: GitHub
  • Owner: csiro-hydroinformatics
  • License: other
  • Language: Python
  • Default Branch: master
  • Size: 20.6 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 3
Created over 2 years ago · Last pushed 7 months ago
Metadata Files
Readme License

README.md

hydrodiy

DOI CI Coverage

Python toolbox for hydrological data processing.

What is hydrodiy?

  • hydrodiy is a set of tools to perform standard data analysis
  • the package is structured around typical tasks: io, data checking, statistical analysis, gis processing and plotting

Installation

  • Create a suitable python environment. We recommend using miniconda combined with the environment specification provided in the env_hydrodiy.yml file in this repository.
  • Git clone this repository and run pip install .

Basic use

```python import numpy as np import matplotlib.pyplot as plt from hydrodiy.plot import violinplot

data = np.random.normal(size=(200, 5)) plt.close('all') fig, ax = plt.subplots(layout='tight')

Draw a nice violin plot

vl = violinplot.Violin(data) vl.draw(ax=ax)

plt.show() ``` A set of examples is provided in the folder examples.

License

The source code and documentation of the hydrodiy package is licensed under the BSD license.

Owner

  • Name: CSIRO Hydroinformatics
  • Login: csiro-hydroinformatics
  • Kind: organization

CSIRO - hydroinformatics repositories

GitHub Events

Total
  • Release event: 2
  • Watch event: 1
  • Delete event: 21
  • Issue comment event: 2
  • Push event: 42
  • Pull request event: 40
  • Create event: 22
Last Year
  • Release event: 2
  • Watch event: 1
  • Delete event: 21
  • Issue comment event: 2
  • Push event: 42
  • Pull request event: 40
  • Create event: 22

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 0
  • Total pull requests: 13
  • Average time to close issues: N/A
  • Average time to close pull requests: about 2 hours
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 11
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 13
  • Average time to close issues: N/A
  • Average time to close pull requests: about 2 hours
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 11
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • jlerat (32)
  • fre171csiro (1)
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Dependencies

requirements.txt pypi
  • Babel ==2.4.0
  • Cython ==0.26
  • Jinja2 ==2.9.6
  • MarkupSafe ==0.23
  • OWSLib ==0.14.0
  • Pillow ==4.2.1
  • PySocks ==1.6.6
  • Pygments ==2.2.0
  • alabaster ==0.7.10
  • asn1crypto ==0.22.0
  • astroid ==1.4.9
  • bleach ==1.5.0
  • certifi ==2016.2.28
  • cffi ==1.10.0
  • chardet ==3.0.4
  • colorama ==0.3.9
  • coverage ==4.4.1
  • cycler ==0.10.0
  • decorator ==4.1.2
  • docutils ==0.14
  • entrypoints ==0.2.3
  • html5lib ==0.9999999
  • idna ==2.6
  • imagesize ==0.7.1
  • inflect ==0.2.5
  • ipykernel ==4.6.1
  • ipython ==6.1.0
  • ipython-genutils ==0.2.0
  • isort ==4.2.15
  • jedi ==0.10.2
  • jsonschema ==2.6.0
  • lazy-object-proxy ==1.3.1
  • lxml ==3.8.0
  • matplotlib ==2.0.2
  • mistune ==0.7.4
  • nbconvert ==5.2.1
  • nbformat ==4.3.0
  • nose ==1.3.7
  • numexpr ==2.6.2
  • numpy ==1.12.1
  • numpydoc ==0.6.0
  • olefile ==0.44
  • packaging ==16.8
  • pandas ==0.21.0
  • pandocfilters ==1.4.1
  • path.py ==10.3.1
  • pickleshare ==0.7.4
  • prompt-toolkit ==1.0.14
  • psutil ==5.2.2
  • psycopg2 ==2.7.1
  • pyOpenSSL ==17.0.0
  • pycodestyle ==2.3.1
  • pycparser ==2.18
  • pyflakes ==1.5.0
  • pylint ==1.6.4
  • pyparsing ==2.2.0
  • pyproj ==1.9.5.1
  • pyshp ==2.1.0
  • python-dateutil ==2.6.1
  • pytz ==2017.2
  • pyzmq ==16.0.2
  • requests ==2.14.2
  • rope-py3k ==0.9.4.post1
  • scipy ==1.0.0
  • simplegeneric ==0.8.1
  • singledispatch ==3.4.0.3
  • six ==1.10.0
  • snowballstemmer ==1.2.1
  • sphinx ==1.6.2
  • sphinxcontrib-websupport ==1.0.1
  • sqlacodegen ==1.1.5
  • testpath ==0.3
  • traitlets ==4.3.2
  • urllib3 ==1.21.1
  • wcwidth ==0.1.7
  • win-inet-pton ==1.0.1
  • wrapt ==1.10.10
  • xlrd ==1.1.0
setup.py pypi