Getting started

Gordias is a core package for Climix and MIdAS (MultI-scale bias AdjuStment). It contains utility tools to load, save and process data. The package is built on Dask and Iris.

Load files

To load netCDF datafiles with gordias the gordias.datahandling.prepare_input_data() function can be used:

import gordias.datahandling
filenames = ['/path/to/files/*.nc']
cubes = gordias.datahandling.prepare_input_data(filenames)

The function tries to merge cubes of the same variables. It returns a cube list containing a single cube for each variable.

Note

If the input files for a variable could not be merged into a single cube an exception will be raised and a description of the difference between the files will be printed.

Save files

A cube can be saved to a netCDF file using the gordias.datahandling.save() function:

import gordias.datahandling

gordias.datahandling.save(cube, "/path/to/file/my-result-file.nc")

The output can be split into multiple files using the split_output option:

import gordias.datahandling

gordias.datahandling.save(cube, "/path/to/file/my-result-file.nc", split_output="year[10]")

Read more about available options here: gordias.datahandling.save().

Configuration of global attributes

A configuration file can be used to specify how the global attributes from the input files should be transferred to the output cube and what global attributes that should be created for the cube.

There is a Default configuration-file that can be used with the gordias.config.get_configuration() function:

import gordias.datahandling
import gordias.config

configuration = gordias.config.get_configuration()
filenames = ['/path/to/files/*.nc']
cubes = gordias.datahandling.prepare_input_data(filenames, configuration)

By giving the configuration as a argument to gordias.datahandling.prepare_input_data() the input configuration will be applied when loading the data.

It is possible to use your own configuration file. First you need to create a configuration yml-file, it should follow the rules of the Configuration template. Then the file can be loaded with the gordias.metadata.load_configuration_metadata() function and the configuration can be generated with the gordias.config.get_configuration() function:

import gordias.config
import gordias.datahandling
import gordias.metadata

path = "/path/to/my-config.yml"
metadata = gordias.metadata.load_configuration_metadata(path)
configuration = gordias.config.get_configuration(metadata)
filenames = ["/path/to/files/*.nc"]
cubes = gordias.datahandling.prepare_input_data(filenames, configuration)

Note

If no configuration is used when loading multiple files all global attributes that are not equal among the input files will be removed.

To save a cube and apply the output configuration, the configuration needs to be given as an argument to the gordias.datahandling.save() function

import gordias.config
import gordias.datahandling
import gordias.metadata

path = "/path/to/my-config.yml"
metadata = gordias.metadata.load_configuration_metadata(path)
configuration = gordias.config.get_configuration(metadata)
filenames = ["/path/to/files/*.nc"]
cubes = gordias.datahandling.prepare_input_data(filenames, configuration)
gordias.datahandling.save(cubes[0], "/path/to/file/my-result-file.nc", configuration=configuration)

Note

The input and output configurations can be applied at any time with the gordias.config.configure_global_attributes_input() and gordias.config.configure_global_attributes_output() functions.

Setup Dask Scheduler

Gordias supports setting up a dask scheduler that can be used for computations in a parallel environment. To setup a schedeuler:

import gordias.datahandling
import gordias.dask_setup

def main():
    scheduler = gordias.dask_setup.DistributedLocalClusterScheduler()
    with scheduler:
        filenames = ["/path/to/files/my-input-file.nc"]
        cubes = gordias.datahandling.prepare_input_data(filenames)
        ### do calculations ###
        gordias.datahandling.save(cube, "/path/to/file/my-result-file.nc", client=scheduler.client)

if __name__ in "__main__":
    main()

The schedulers in Schedulers are context managers, following PEP 343, using the with statement makes it easier to shutdown the schedulers.