Configuration

Use a custom configuration file

You can overwrite the default PyWPS configuration by providing your own PyWPS configuration file (just modify the options you want to change). Use one of the existing sample-*.cfg files as example and copy them to etc/custom.cfg.

For example change the hostname (demo.org) and logging level:

$ cd finch
$ vim etc/custom.cfg
$ cat etc/custom.cfg
[server]
url = http://demo.org:5000/wps
outputurl = http://demo.org:5000/outputs

[logging]
level = DEBUG

Start the service with your custom configuration:

# start the service with this configuration
$ finch start -c etc/custom.cfg

Content of the configuration file

Configuration sections and values specific to finch are:

finch

datasets_config:

Path to the YAML files defining the available ensemble datasets (see below). The path can be given relative to the “finch/finch/” folder, where default.cfg lives.

default_dataset:

Default dataset to use. Should be a top-level key of the yaml.

subset_threads:

Number of threads to use when performing the subsetting.

xclim_modules:

Comma separated list of virtual xclim modules to include when creating finch indicator processes. Paths can be absolute or relative to the finch directory. Note - In order to include potential custom compute functions or french translations, paths should exclude the .yml file extension (more info on xclim virtual modules)

finch:metadata

All fields here are added as string attributes of computed indices, in addition to xclim’s attributes. Finch always adds the following attributes:

climateindex_package_id:

https://github.com/Ouranosinc/xclim

product:

derived climate index

Structure of the dataset configuration file

The configuration value for finch, datasets_config, points to a YAML file that defines each ensemble datasets that are available for ensemble processes. These processes and their default values are generated according to the “allowed_values” of the datasets. This means that a user sees _all_ available values in the process’s description, but not all are valid depending on the passed dataset.

The YAML file must consist in a mapping of dataset name to dataset configuration, the latter having the same structure as this dataclass:

class finch.processes.utils.DatasetConfiguration(path: str, pattern: str, local: bool, allowed_values: dict, depth: int = 0, suffix: str = '*nc', model_lists: dict = <factory>)

Dataset Configuration class.

path

The path (or url) to the root directory where to search for the data.

Type:

str

pattern

The pattern of the filenames. Must include at least : “variable”, “scenario” and “model”. Patterns must be understandable by parse.parse().

Type:

str

local

Whether the path points to a local directory or a remote THREDDS catalog.

Type:

bool

depth

The depth to which search for files below the directory. < 0 will search recursively.

Type:

int

suffix

When the files are local, this is the suffix of the files.

Type:

str

allowed_values

Mapping from field name to a list of allowed values. Must include “scenario”, “model” and “variable”, the latter defines which variable are available and thus which indicator can be used.

Type:

dict

model_lists

A mapping from list name to a list of model names to provide special sub-lists. The values can also be a tuple of (model name, realization numer), in which case, pattern must include a “realization” field.

Type:

dict