cdochain 0.2b3

Description:

cdochain 0.2b3

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 install


TL;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 cch
For 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: