Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README -
✓Academic publication links
Links to: joss.theoj.org -
✓Committers with academic emails
5 of 14 committers (35.7%) from academic institutions -
✓Institutional organization owner
Organization ncas-cms has institutional domain (cms.ncas.ac.uk) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (9.2%) to scientific vocabulary
Keywords
cf
cfdm
cfunits
data-analysis
earth-science
metadata
netcdf
pp
python
um
Keywords from Contributors
climate
atmospheric-science
ocean
hydrology
conda
earth-system-model
transformation
Last synced: 6 months ago
·
JSON representation
Repository
A CF-compliant Earth Science data analysis library
Basic Info
- Host: GitHub
- Owner: NCAS-CMS
- License: mit
- Language: Python
- Default Branch: main
- Homepage: http://ncas-cms.github.io/cf-python
- Size: 139 MB
Statistics
- Stars: 142
- Watchers: 3
- Forks: 23
- Open Issues: 116
- Releases: 20
Topics
cf
cfdm
cfunits
data-analysis
earth-science
metadata
netcdf
pp
python
um
Created over 6 years ago
· Last pushed 6 months ago
Metadata Files
Readme
Changelog
Contributing
License
Code of conduct
README.md
cf-python
The Python cf package is an Earth Science data analysis library that
is built on a complete implementation of the CF data model.
References
Dask
From version 3.14.0 the cf package uses
Dask for all of its data manipulations.
Documentation
http://ncas-cms.github.io/cf-python
Installation
http://ncas-cms.github.io/cf-python/installation.html
Cheat Sheet
https://ncas-cms.github.io/cf-python/cheat_sheet.html
Recipes
https://ncas-cms.github.io/cf-python/recipes
Tutorial
https://ncas-cms.github.io/cf-python/tutorial.html
Functionality
The cf package implements the CF data
model
for its internal data structures and so is able to process any
CF-compliant dataset. It is not strict about CF-compliance, however,
so that partially conformant datasets may be ingested from existing
datasets and written to new datasets. This is so that datasets which
are partially conformant may nonetheless be modified in memory.
A simple example of reading a field construct from a file and inspecting it:
>>> import cf
>>> f = cf.read('file.nc')
>>> print(f[0])
Field: air_temperature (ncvar%tas)
----------------------------------
Data : air_temperature(time(12), latitude(64), longitude(128)) K
Cell methods : time(12): mean (interval: 1.0 month)
Dimension coords: time(12) = [1991-11-16 00:00:00, ..., 1991-10-16 12:00:00] noleap
: latitude(64) = [-87.8638, ..., 87.8638] degrees_north
: longitude(128) = [0.0, ..., 357.1875] degrees_east
: height(1) = [2.0] m
The cf package uses
Dask for all
of its array manipulation and can:
- read field constructs from netCDF, CDL, Zarr, PP and UM datasets with a choice of netCDF backends,and in local, http, and s3 locations,
- create new field constructs in memory,
- write and append field and domain constructs to netCDF datasets on disk,
- read, create, and manipulate UGRID mesh topologies,
- read, write, and create coordinates defined by geometry cells,
- read netCDF and CDL datasets containing hierarchical groups,
- inspect field constructs,
- test whether two field constructs are the same,
- modify field construct metadata and data,
- create subspaces of field constructs,
- write field constructs to netCDF datasets on disk,
- incorporate, and create, metadata stored in external files,
- read, write, and create data that have been compressed by convention (i.e. ragged or gathered arrays, or coordinate arrays compressed by subsampling), whilst presenting a view of the data in its uncompressed form,
- combine field constructs arithmetically,
- manipulate field construct data by arithmetical and trigonometrical operations,
- perform weighted statistical collapses on field constructs, including those with geometry cells and UGRID mesh topologies,
- perform histogram, percentile and binning operations on field constructs,
- regrid structured grid, mesh and DSG field constructs with (multi-)linear, nearest neighbour, first- and second-order conservative and higher order patch recovery methods, including 3-d regridding,
- apply convolution filters to field constructs,
- create running means from field constructs,
- apply differential operators to field constructs,
- create derived quantities (such as relative vorticity).
- read and write that data that are quantized to eliminate false precision.
Visualization
Powerful and flexible visualizations of cf field constructs,
designed to be produced and configured in as few lines of code as
possible, are available with the cf-plot
package, which
needs to be installed separately to the cf package.
See the cf-plot gallery for a range of plotting possibilities with example code.
Command line utilities
During installation the cfa command line utility is also
installed, which
generates text descriptions of field constructs contained in files, and
creates new datasets aggregated from existing files.
Tests
Tests are run from within the cf/test directory:
python run_tests.py
Owner
- Name: NCAS CMS
- Login: NCAS-CMS
- Kind: organization
- Location: UK
- Website: http://cms.ncas.ac.uk/
- Repositories: 10
- Profile: https://github.com/NCAS-CMS
Useful tools to support NERC weather and climate research
GitHub Events
Total
- Create event: 3
- Release event: 2
- Issues event: 42
- Watch event: 15
- Issue comment event: 83
- Push event: 43
- Pull request event: 45
- Pull request review comment event: 87
- Pull request review event: 101
- Fork event: 2
Last Year
- Create event: 3
- Release event: 2
- Issues event: 42
- Watch event: 15
- Issue comment event: 83
- Push event: 43
- Pull request event: 45
- Pull request review comment event: 87
- Pull request review event: 101
- Fork event: 2
Committers
Last synced: 6 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| David Hassell | d****l@n****k | 2,893 |
| Sadie Louise Bartholomew | s****w@n****k | 1,087 |
| Ankit Bhandekar | 8****t | 219 |
| Thibault Hallouin | t****n@u****e | 13 |
| Bruno P. Kinoshita | k****w | 11 |
| Charles Roberts | c****s@r****k | 10 |
| Matt | m****3@g****m | 9 |
| Ollie | 5****t | 6 |
| Natalia Hunt | 5 | |
| Tyge Løvset | t****o@n****o | 2 |
| Alan Iwi | a****i@s****k | 1 |
| Alexander James Phillips | a****l@g****m | 1 |
| Sharar-A | s****i@n****k | 1 |
| William McGinty | 3****S | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 367
- Total pull requests: 555
- Average time to close issues: 3 months
- Average time to close pull requests: 11 days
- Total issue authors: 42
- Total pull request authors: 11
- Average comments per issue: 1.87
- Average comments per pull request: 1.35
- Merged pull requests: 504
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 39
- Pull requests: 59
- Average time to close issues: 10 days
- Average time to close pull requests: 15 days
- Issue authors: 9
- Pull request authors: 4
- Average comments per issue: 1.31
- Average comments per pull request: 0.76
- Merged pull requests: 46
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- davidhassell (137)
- sadielbartholomew (126)
- bnlawrence (22)
- JonathanGregory (13)
- dlrhodson (7)
- ThibHlln (6)
- bewithankit (6)
- ThatDesert (3)
- AJamesPhillips (3)
- lguo-uk (3)
- m-couldrey (2)
- kinow (2)
- ellgil82 (2)
- matthew-shin (2)
- dwest77a (2)
Pull Request Authors
- davidhassell (397)
- sadielbartholomew (105)
- bewithankit (36)
- kinow (6)
- mattjbr123 (5)
- alaniwi (3)
- ThibHlln (2)
- tylov (2)
- ThatDesert (1)
- Sharar-A (1)
- AJamesPhillips (1)
Top Labels
Issue Labels
enhancement (136)
bug (115)
question (36)
documentation (28)
aggregation (23)
dask (22)
testing (16)
performance (15)
um/pp (13)
netCDF read (11)
API review (4.0.0) (9)
CFA (9)
netCDF write (9)
regridding (8)
code tidy (8)
installation (7)
GitHub Actions (6)
release (4)
dataset write (3)
dataset read (3)
UGRID (3)
CI (3)
low priority (2)
active storage (2)
high priority (2)
good first issue (1)
compression (1)
bug? (1)
Pull Request Labels
dask (196)
enhancement (61)
documentation (51)
bug (46)
low priority (28)
code tidy (23)
aggregation (17)
performance (13)
regridding (12)
CFA (9)
high priority (9)
testing (9)
netCDF read (8)
netCDF write (7)
CI (6)
installation (6)
dataset write (5)
dataset read (4)
release (3)
GitHub Actions (3)
question (3)
UGRID (3)
um/pp (3)
active storage (3)
linting (2)
top priority (1)
compression (1)
Packages
- Total packages: 1
-
Total downloads:
- pypi 1,753 last-month
- Total dependent packages: 1
- Total dependent repositories: 9
- Total versions: 114
- Total maintainers: 2
pypi.org: cf-python
A CF-compliant earth science data analysis library
- Homepage: https://ncas-cms.github.io/cf-python
- Documentation: https://cf-python.readthedocs.io/
- License: MIT
-
Latest release: 3.18.1
published 6 months ago
Rankings
Dependent packages count: 4.8%
Dependent repos count: 4.8%
Average: 7.0%
Stargazers count: 7.8%
Forks count: 8.5%
Downloads: 9.3%
Maintainers (2)
Last synced:
6 months ago
Dependencies
requirements.txt
pypi
- cfdm >=1.9.0.2,<1.9.1.0
- cftime >=1.5.0
- cfunits >=3.3.4
- netCDF4 >=1.5.4
- numpy >=1.15
- packaging >=20.0
- psutil >=0.6.0
setup.py
pypi
- ESMF >=8.0
- cfdm >=1.8.5,
- cftime >=1.1.3
- cfunits >=3.2.7
- matplotlib >=3.0.0
- mpi4py >=3.0.0
- netCDF4 >=1.5.3
- numpy >=1.15
- psutil >=0.6.0
- scipy >=1.1.0
- udunits2 ==2.2.25
.github/workflows/linting.yml
actions
- actions/checkout v3 composite
- actions/setup-python v4 composite
- pre-commit/action v3.0.0 composite
.github/workflows/run-test-suite.yml
actions
- actions/checkout v3 composite
- actions/setup-python v4 composite
- codecov/codecov-action v3 composite
- conda-incubator/setup-miniconda v2 composite
.github/workflows/test-source-dist.yml
actions
- actions/checkout v3 composite
- actions/setup-python v4 composite
- conda-incubator/setup-miniconda v2 composite
docker/Dockerfile
docker
- continuumio/miniconda3 latest build
pyproject.toml
pypi