Science Score: 46.0%
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Repository
Kriging Toolkit for Python
Basic Info
- Host: GitHub
- Owner: GeoStat-Framework
- License: bsd-3-clause
- Language: Python
- Default Branch: main
- Homepage: https://pykrige.readthedocs.io
- Size: 1.3 MB
Statistics
- Stars: 828
- Watchers: 35
- Forks: 193
- Open Issues: 42
- Releases: 11
Topics
Metadata Files
README.md
PyKrige
Kriging Toolkit for Python.
Purpose
The code supports 2D and 3D ordinary and universal kriging. Standard
variogram models (linear, power, spherical, gaussian, exponential) are
built in, but custom variogram models can also be used. The 2D universal
kriging code currently supports regional-linear, point-logarithmic, and
external drift terms, while the 3D universal kriging code supports a
regional-linear drift term in all three spatial dimensions. Both
universal kriging classes also support generic 'specified' and
'functional' drift capabilities. With the 'specified' drift capability,
the user may manually specify the values of the drift(s) at each data
point and all grid points. With the 'functional' drift capability, the
user may provide callable function(s) of the spatial coordinates that
define the drift(s). The package includes a module that contains
functions that should be useful in working with ASCII grid files (\*.asc).
See the documentation at http://pykrige.readthedocs.io/ for more details and examples.
Installation
PyKrige requires Python 3.5+ as well as numpy, scipy. It can be installed from PyPi with,
bash
pip install pykrige
scikit-learn is an optional dependency needed for parameter tuning and regression kriging. matplotlib is an optional dependency needed for plotting.
If you use conda, PyKrige can be installed from the conda-forge channel with,
bash
conda install -c conda-forge pykrige
Features
Kriging algorithms
-
OrdinaryKriging: 2D ordinary kriging with estimated mean -
UniversalKriging: 2D universal kriging providing drift terms -
OrdinaryKriging3D: 3D ordinary kriging -
UniversalKriging3D: 3D universal kriging -
RegressionKriging: An implementation of Regression-Kriging -
ClassificationKriging: An implementation of Simplicial Indicator Kriging
Wrappers
-
rk.Krige: A scikit-learn wrapper class for Ordinary and Universal Kriging
Tools
-
kriging_tools.write_asc_grid: Writes gridded data to ASCII grid file (\*.asc) -
kriging_tools.read_asc_grid: Reads ASCII grid file (\*.asc) -
kriging_tools.write_zmap_grid: Writes gridded data to zmap file (\*.zmap) -
kriging_tools.read_zmap_grid: Reads zmap file (\*.zmap)
Kriging Parameters Tuning
A scikit-learn compatible API for parameter tuning by cross-validation is exposed in sklearn.model_selection.GridSearchCV. See the Krige CV example for a more practical illustration.
Regression Kriging
Regression kriging
can be performed with
pykrige.rk.RegressionKriging.
This class takes as parameters a scikit-learn regression model, and
details of either the OrdinaryKriging or the UniversalKriging
class, and performs a correction step on the ML regression prediction.
A demonstration of the regression kriging is provided in the corresponding example.
Classification Kriging
Simplifical Indicator
kriging
can be performed with
pykrige.ck.ClassificationKriging.
This class takes as parameters a scikit-learn classification model, and
details of either the OrdinaryKriging or the UniversalKriging class,
and performs a correction step on the ML classification prediction.
A demonstration of the classification kriging is provided in the corresponding example.
License
PyKrige uses the BSD 3-Clause License.
Owner
- Name: GeoStat Framework
- Login: GeoStat-Framework
- Kind: organization
- Email: info@geostat-framework.org
- Location: UFZ Leipzig
- Website: https://geostat-framework.org
- Twitter: GSFramework
- Repositories: 7
- Profile: https://github.com/GeoStat-Framework
Python framework for geostatistical simulations
GitHub Events
Total
- Issues event: 4
- Watch event: 67
- Issue comment event: 6
- Pull request event: 4
- Fork event: 4
Last Year
- Issues event: 4
- Watch event: 67
- Issue comment event: 6
- Pull request event: 4
- Fork event: 4
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Sebastian Müller | m****b@p****e | 103 |
| Roman Yurchak | r****h@c****g | 37 |
| Roman Yurchak | r****k@g****m | 35 |
| Benjamin Murphy | b****y@g****m | 27 |
| bsmurphy | m****e@g****u | 19 |
| Nic Annau | n****u@g****m | 16 |
| Malte Ziebarth | m****h@f****e | 8 |
| Sudipta Basak | b****s@g****m | 6 |
| Mark Vrijlandt | m****t@t****l | 3 |
| mralbu | m****e@g****m | 3 |
| Matthew Peveler | m****r@g****m | 2 |
| Roman Yurchak | r****k@p****e | 1 |
| Jordan Porter | t****m@g****m | 1 |
| root | r****t@h****n | 1 |
| Will Chang | w****g@g****m | 1 |
| JuHolland | j****d@w****g | 1 |
| kvanlombeek | k****k@g****m | 1 |
| Scott Staniewicz | s****e@g****m | 1 |
| Daniel Mejía Raigosa | d****5@g****m | 1 |
| Rhilip | R****p | 1 |
| Harry Matchette-Downes | 3****d | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 93
- Total pull requests: 35
- Average time to close issues: 8 months
- Average time to close pull requests: 15 days
- Total issue authors: 76
- Total pull request authors: 15
- Average comments per issue: 2.69
- Average comments per pull request: 1.51
- Merged pull requests: 26
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 7
- Pull requests: 7
- Average time to close issues: N/A
- Average time to close pull requests: 1 minute
- Issue authors: 7
- Pull request authors: 5
- Average comments per issue: 0.43
- Average comments per pull request: 0.43
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- MuellerSeb (9)
- rth (3)
- sachin9390 (2)
- NikhilMunna (2)
- flydream0428 (2)
- austindowney (2)
- codename5281 (2)
- ghost (2)
- JuHolland (2)
- MDTocean (1)
- shihabkhan1 (1)
- oguzhannysr (1)
- ivan-marroquin (1)
- xudongf (1)
- CorLeonis-FFXV (1)
Pull Request Authors
- MuellerSeb (19)
- mralbu (2)
- cvelascof (2)
- mwtoews (2)
- dependabot[bot] (2)
- nannau (1)
- Rhilip (1)
- SimonKraatz-USDA (1)
- tunasplam (1)
- scottstanie (1)
- MarkTNO (1)
- AlexeyOlkhovikov (1)
- rth (1)
- JuHolland (1)
- yoel2000 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 4
-
Total downloads:
- pypi 50,079 last-month
-
Total dependent packages: 14
(may contain duplicates) -
Total dependent repositories: 37
(may contain duplicates) - Total versions: 58
- Total maintainers: 3
pypi.org: pykrige
Kriging Toolkit for Python.
- Homepage: https://github.com/GeoStat-Framework/PyKrige
- Documentation: https://pykrige.readthedocs.io
- License: BSD-3-Clause
-
Latest release: 1.7.2
published over 1 year ago
Rankings
Maintainers (3)
proxy.golang.org: github.com/GeoStat-Framework/PyKrige
- Documentation: https://pkg.go.dev/github.com/GeoStat-Framework/PyKrige#section-documentation
- License: bsd-3-clause
-
Latest release: v1.7.2
published over 1 year ago
Rankings
proxy.golang.org: github.com/geostat-framework/pykrige
- Documentation: https://pkg.go.dev/github.com/geostat-framework/pykrige#section-documentation
- License: bsd-3-clause
-
Latest release: v1.7.2
published over 1 year ago
Rankings
conda-forge.org: pykrige
The code supports two- and three- dimensional ordinary and universal kriging. Standard variogram models (linear, power, spherical, gaussian, exponential) are built in, but custom variogram models can also be used with the code.
- Homepage: https://github.com/GeoStat-Framework/PyKrige
- License: BSD-3-Clause
-
Latest release: 1.7.0
published over 3 years ago
Rankings
Dependencies
- actions/checkout v2 composite
- actions/download-artifact v2 composite
- actions/setup-python v2 composite
- actions/upload-artifact v2 composite
- pypa/cibuildwheel v2.8.1 composite
- pypa/gh-action-pypi-publish release/v1 composite
- numpy >=1.14.5,<2
- scipy >=1.1.0,<2