This release is a pre-release and may not be stable for production use.
This module helps create chains of cdo commands for easy manipulation of climate data.
Features
Method execution is lazy and gets processed only on function call Chain.execute().
Input supports Unix style pathname pattern search.
The Input will be first run with glob and checked if several files match. :exclamation: If that is the case a temporary file will be created.
Output can be a file on disc, an netCDF4.Dataset or (not) masked numpy.ndarray.
Installation
python3.5 -m pip install cdochain --pre— or —
git clone https://github.com/OnionNinja/cdochain.git
cd cdochain
python3.5 setup.py installTL;DR
from cdochain import chaining as cch
input = './tests/testdata/sresa1b_ncar_ccsm3-example.nc'
output = './enso34-mm.nc'
data = cch.Chain(ifile=input, ofile=output)
enso34 = data.sellonlatbox(190,240,-5,5).monmean()
out = enso34.execute()Usage
This module implements method chaining for the Climate Data Operators (CDO) tool from the Max Planck Institute for Meteorology. Let us start:
from cdochain import chaining as cchFor initialisation one has to define input, output, and may define several options.
Input
Now we have to define the files we want to work on:
To use one file
input = './tests/testdata/sresa1b_ncar_ccsm3-example.nc'To use several files you can give a Unix style pattern
input = './tests/testdata/*.nc'This creates a temporary file :exclamation:
Output
For defining the output we have several options.
To output a file on disc:
data = cch.Chain(ifile=input, ofile='/path/to/output.nc')To output an netcdf4.Dataset object:
data = cch.Chain(ifile=input, ofile='netCDF4')To output an numpy.ndarray object:
data = cch.Chain(ifile=input, ofile='array:<var>') # numpy.ndarray
# or
data = cch.Chain(ifile=input, ofile='maarray:<var>') # masked numpy.ndarray<var> defines the variable to be extracted and saved to numpy.ndarray.
Options
As for options one can use the same as described on the CDO website. The default is options='-O -f nc'.
Operations
The operations defined in CDO can now be used on the data element.
analysis = data.sellonlatbox(190,240,-5,5).sellevidx(1).mermean()
fn = analysis.execute()Have fun :neckbeard:
1 maintainer
Release files for cdochain 0.2b3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| cdochain-0.2b3.tar.gz | 5.7 kB | Details |
Release files / cdochain-0.2b3.tar.gz
| Download URL | cdochain-0.2b3.tar.gz |
|---|---|
| Size | 5.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
452a6400d4dd3936854947aa02aa535f271e73ac169d20ec61453db4ca645036
|
|
BLAKE2b-256 checksum How to use checksums |
bbf6e27b219b14b840fb2d49e2b00790f38e90aee23dfab3079e42a5269053f2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
