Extending xclim

xclim tries to make it easy for users to add their own indices and indicators. The following goes into details on how to create indices and document them so that xclim can parse most of the metadata directly. We then explain the multiple ways new Indicators can be created and, finally, how we can regroup and structure them in virtual submodules.

Central to xclim are the Indicators, objects computating indices over climate variables, but xclim also provides other modules:

modules

Where subset is a phantom module, kept for legacy code, as it only redirects the calls to clisops.core.subset.

This introduction will focus on the Indicator/Indice part of xclim and how one can extend it by implementing new ones.

Indices vs Indicators

Internally and in the documentation, xclim makes a distinction between “indices” and “indicators”.

indice

A python function accepting DataArrays and other parameters (usually bultin types)
Returns one or several DataArrays.
Handles the units : checks input units and set proper CF-compliant output units. But doesn’t usually prescribe specific units, the output will at minimum have the proper dimensionality.
Performs no other checks or set any (non-unit) metadata.
Accessible through xclim.indices.

indicator

An instance of a subclass of xclim.core.indicator.Indicator that wraps around an indice (stored in its compute property).
Returns one or several DataArrays.
Handles missing values, performs input data and metadata checks (see usage).
Always ouputs data in the same units.
Adds dynamically generated metadata to the output after computation.
Accessible through xclim.indicators

Most metadata stored in the Indicators is parsed from the underlying indice documentation, so defining indices with complete documentation and an appropriate signature helps the process. The two next sections go into details on the definition of both objects.

Call sequence

The following graph shows the steps done when calling an Indicator. Attributes and methods of the Indicator object relating to those steps are listed on the right side.

indicator

Defining new indices

The annotated example below shows the general template to be followed when defining proper indices. In the comments Ind is the indicator instance that would be created from this function.

Note that it is not needed to follow these standards when writing indices that will be wrapped in indicators. Problems in parsing will not raise errors at runtime, but will result in Indicators with poorer metadata than expected by most users, especially those that dynamically use indicators in other applications where the code is inaccessible, like web services.

indice doc

The following code is another example.

[1]:

import xarray as xr
import xclim as xc
from xclim.core.units import declare_units, convert_units_to
from xclim.indices.generic import threshold_count

@declare_units(tasmax="[temperature]", thresh="[temperature]")
def tx_days_compare(tasmax: xr.DataArray, thresh: str = "0 degC", op: str = '>', freq: str = "YS"):
    r"""Number of days where maximum daily temperature. is above or under a threshold.

    The daily maximum temperature is compared to a threshold using a given operator and the number
    of days where the condition is true is returned.

    It assumes a daily input.

    Parameters
    ----------
    tasmax : xarray.DataArray
      Maximum daily temperature.
    thresh : str
      Threshold temperature to compare to.
    op : {'>', '<'}
      The operator to use.
      # A fixed set of choices can be imposed. Only strings, numbers, booleans or None are accepted.
    freq : str
      Resampling frequency.

    Returns
    -------
    xarray.DataArray, [temperature]
      Maximum value of daily maximum temperature.

    Notes
    -----
    Let :math:`TX_{ij}` be the maximum temperature at day :math:`i` of period :math:`j`. Then the maximum
    daily maximum temperature for period :math:`j` is:

    .. math::

        TXx_j = max(TX_{ij})

    References
    ----------
    Smith, John and Tremblay, Robert, An dummy citation for examples in documentation. J. RTD. (2020).
    """
    thresh = convert_units_to(thresh, tasmax)
    out = threshold_count(tasmax, op, thresh, freq)
    out.attrs['units'] = "days"
    return out

Naming and conventions

Variable names should correspond to CMIP6 variables, whenever possible. The file xclim/data/variables.yml lists all variables that xclim can use when generating indicators from yaml files (see below), and new indices should try to reflect these also. For new variables, the xclim.testing.get_all_CMIP6_variables function downloads the official table of CMIP6 variables and puts everything in a dictionary. If possible, use variables names from this list, add them to variables.yml as needed.

Generic functions for common operations

The xclim.indices.generic submodule contains useful functions for common computations (like threshold_count or select_resample_op) and many basic indice functions, as defined by clix-meta. In order to reduce duplicate code, their use is recommended for xclim’s indices. As previously said, the units handling has to be made explicitly when non trivial, xclim.core.units also exposes a few helpers for that (like convert_units_to, to_agg_units or rate2amount).

Extending xclim

Indices vs Indicators

indice

indicator

Call sequence

Defining new indices

Naming and conventions

Generic functions for common operations

Defining new indicators

Metadata parsing vs explicit setting

Internationalization of metadata

Inputs and checks

Indicator creation

Virtual modules

YAML file

Mapping of indicators