transforms.aggregate.climatology

Attributes

anomalazy

Functions

reduce(dataarray[, time_dim, how, groupby_kwargs])	Group data annually over a given frequency and reduce using the specified how method.
mean(dataarray, *args, **kwargs)	Calculate the climatological mean.
median(dataarray, **kwargs)	Calculate the climatological median.
min(dataarray, *args, **kwargs)	Calculate the climatological minimum.
max(dataarray, *args, **kwargs)	Calculate the climatological maximum.
std(dataarray, *args, **kwargs)	Calculate the climatological standard deviation.
daily_reduce(dataarray, *args, **kwargs)	Reduce the data to the daily climatology of the provided "how" method.
daily_mean(dataarray, *args, **kwargs)	Calculate the daily climatological mean.
daily_median(dataarray, *args, **kwargs)	Calculate the daily climatological median.
daily_min(dataarray, *args, **kwargs)	Calculate the daily climatological min.
daily_max(dataarray, *args, **kwargs)	Calculate the daily climatological max.
daily_std(dataarray, *args, **kwargs)	Calculate the daily climatological standard deviation.
monthly_reduce(dataarray, *args, **kwargs)	Reduce the data to the monthly climatology of the provided "how" method.
monthly_mean(dataarray, *args, **kwargs)	Calculate the monthly climatological mean.
monthly_median(dataarray, *args, **kwargs)	Calculate the monthly climatological median.
monthly_min(dataarray, *args, **kwargs)	Calculate the monthly climatological min.
monthly_max(dataarray, *args, **kwargs)	Calculate the monthly climatological max.
monthly_std(dataarray, *args, **kwargs)	Calculate the monthly climatological standard deviation.
quantiles(dataarray, q[, time_dim, groupby_kwargs])	Calculate a set of climatological quantiles.
percentiles(dataarray, p, **kwargs)	Calculate a set of climatological percentiles.
anomaly(dataarray, climatology, **kwargs)	Calculate the anomaly from a reference climatology.
update_anomaly_array(anomaly_array, original_array, ...)
relative_anomaly(dataarray, *args, **kwargs)	Calculate the relative anomaly from a reference climatology, i.e. percentage change.
auto_anomaly(dataarray, *args[, climatology_range, ...])	Calculate the anomaly from a reference climatology.

Module Contents

transforms.aggregate.climatology.reduce(dataarray, time_dim=None, how='mean', groupby_kwargs={}, **reduce_kwargs)

Group data annually over a given frequency and reduce using the specified how method.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• how (str or callable) – Method used to reduce data. Default=’mean’, which will implement the xarray in-built mean. If string, it must be an in-built xarray reduce method, an earthkit how method or any numpy method. In the case of duplicate names, method selection is first in the order: xarray, earthkit, numpy. Otherwise it can be any function which can be called in the form f(x, axis=axis, **kwargs) to return the result of reducing an np.ndarray over an integer valued axis

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.mean(dataarray, *args, **kwargs)

Calculate the climatological mean.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.median(dataarray, **kwargs)

Calculate the climatological median.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological median. Must contain a time dimension.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.min(dataarray, *args, **kwargs)

Calculate the climatological minimum.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.max(dataarray, *args, **kwargs)

Calculate the climatological maximum.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.std(dataarray, *args, **kwargs)

Calculate the climatological standard deviation.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological standard deviation. Must contain a time dimension.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.daily_reduce(dataarray, *args, **kwargs)

Reduce the data to the daily climatology of the provided “how” method.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• how (str or callable) – Method used to reduce data. Default=’mean’, which will implement the xarray in-built mean. If string, it must be an in-built xarray reduce method, an earthkit how method or any numpy method. In the case of duplicate names, method selection is first in the order: xarray, earthkit, numpy. Otherwise it can be any function which can be called in the form f(x, axis=axis, **kwargs) to return the result of reducing an np.ndarray over an integer valued axis

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.daily_mean(dataarray, *args, **kwargs)

Calculate the daily climatological mean.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.daily_median(dataarray, *args, **kwargs)

Calculate the daily climatological median.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological median. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.daily_min(dataarray, *args, **kwargs)

Calculate the daily climatological min.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological min. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.daily_max(dataarray, *args, **kwargs)

Calculate the daily climatological max.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological max. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.daily_std(dataarray, *args, **kwargs)

Calculate the daily climatological standard deviation.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological standard deviation. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.monthly_reduce(dataarray, *args, **kwargs)

Reduce the data to the monthly climatology of the provided “how” method.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• how (str or callable) – Method used to reduce data. Default=’mean’, which will implement the xarray in-built mean. If string, it must be an in-built xarray reduce method, an earthkit how method or any numpy method. In the case of duplicate names, method selection is first in the order: xarray, earthkit, numpy. Otherwise it can be any function which can be called in the form f(x, axis=axis, **kwargs) to return the result of reducing an np.ndarray over an integer valued axis

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.monthly_mean(dataarray, *args, **kwargs)

Calculate the monthly climatological mean.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological mean. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.monthly_median(dataarray, *args, **kwargs)

Calculate the monthly climatological median.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological median. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.monthly_min(dataarray, *args, **kwargs)

Calculate the monthly climatological min.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological min. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.monthly_max(dataarray, *args, **kwargs)

Calculate the monthly climatological max.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological max. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.monthly_std(dataarray, *args, **kwargs)

Calculate the monthly climatological standard deviation.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological standard deviation. Must contain a time dimension.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.quantiles(dataarray, q, time_dim=None, groupby_kwargs={}, **reduce_kwargs)

Calculate a set of climatological quantiles.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological quantiles. Must contain a time dimension.

• q (float | list) – The quantile, or list of quantiles, to calculate the climatology.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.percentiles(dataarray, p, **kwargs)

Calculate a set of climatological percentiles.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the climatological percentiles. Must contain a time dimension.

• percentiles (float | list) – The pecentile, or list of percentiles, to calculate the climatology.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.reduce (except how)

Return type:

xr.DataArray

transforms.aggregate.climatology.anomaly(dataarray, climatology, **kwargs)

Calculate the anomaly from a reference climatology.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the anomaly from the reference climatology. Must contain a time dimension indicated by time_dim.

• climatology ((xr.DataArray, optional)) – Reference climatology data against which the anomaly is to be calculated. If not provided then the climatological mean is calculated from dataarray.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• relative (bool (optional)) – Return the relative anomaly, i.e. the percentage change w.r.t the climatological period

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.climatology.mean

Return type:

xr.DataArray

transforms.aggregate.climatology.update_anomaly_array(anomaly_array, original_array, var_name, name_tag, update_attrs)

transforms.aggregate.climatology.relative_anomaly(dataarray, *args, **kwargs)

Calculate the relative anomaly from a reference climatology, i.e. percentage change.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the anomaly from the reference climatology. Must contain a time dimension.

• climatology ((xr.DataArray, optional)) – Reference climatology data against which the anomaly is to be calculated. If not provided then the climatological mean is calculated from dataarray.

• climatology_range ((list or tuple, optional)) – Start and end year of the period to be used for the reference climatology. Default is to use the entire time-series.

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.climatology.mean

Return type:

xr.DataArray

transforms.aggregate.climatology.auto_anomaly(dataarray, *args, climatology_range=None, climatology_how='mean', relative=False, **kwargs)

Calculate the anomaly from a reference climatology.

Parameters:

• dataarray (xr.DataArray) – The DataArray over which to calculate the anomaly from the reference climatology. Must contain a time dimension.

• climatology ((xr.DataArray, optional)) – Reference climatology data against which the anomaly is to be calculated. If not provided then the climatological mean is calculated from dataarray.

• climatology_range ((list or tuple, optional)) – Start and end year of the period to be used for the reference climatology. Default is to use the entire time-series.

• climatology_how (string) – Method used to calculate climatology, default is “mean”. Accepted values are “median”, “min”, “max”

• frequency (str (optional)) – Valid options are day, week and month.

• bin_widths (int or list (optional)) – If bin_widths is an int, it defines the width of each group bin on the frequency provided by frequency. If bin_widths is a sequence it defines the edges of each bin, allowing for non-uniform bin widths.

• time_dim (str (optional)) – Name of the time dimension in the data object, default behviour is to detect the time dimension from the input object

• relative (bool (optional)) – Return the relative anomaly, i.e. the percentage change w.r.t the climatological period

• **reduce_kwargs – Any other kwargs that are accepted by earthkit.transforms.aggregate.climatology.mean

Return type:

xr.DataArray

transforms.aggregate.climatology.anomalazy