Calculating a Custom Statistic#

This example shows how to define and use a custom iris.analysis.Aggregator, that provides a new statistical operator for use with cube aggregation functions such as collapsed(), aggregated_by() or rolling_window().

In this case, we have a 240-year sequence of yearly average surface temperature over North America, and we want to calculate in how many years these exceed a certain temperature over a spell of 5 years or more.

Number of 5-year warm spells in 240 years
import matplotlib.pyplot as plt
import numpy as np

import iris
from iris.analysis import Aggregator
import iris.plot as iplt
import iris.quickplot as qplt
from iris.util import rolling_window

# Define a function to perform the custom statistical operation.
# Note: in order to meet the requirements of iris.analysis.Aggregator, it must
# do the calculation over an arbitrary (given) data axis.
def count_spells(data, threshold, axis, spell_length):
    """Calculate the number of points in a sequence.

    Function to calculate the number of points in a sequence where the value
    has exceeded a threshold value for at least a certain number of timepoints.

    Generalised to operate on multiple time sequences arranged on a specific
    axis of a multidimensional array.

    data : array
        Raw data to be compared with value threshold.
    threshold : float
        Threshold point for 'significant' datapoints.
    axis : int
        Number of the array dimension mapping the time sequences.
        (Can also be negative, e.g. '-1' means last dimension).
    spell_length : int
        Number of consecutive times at which value > threshold to "count".

    if axis < 0:
        # just cope with negative axis numbers
        axis += data.ndim
    # Threshold the data to find the 'significant' points.
    data_hits = data > threshold
    # Make an array with data values "windowed" along the time axis.
    hit_windows = rolling_window(data_hits, window=spell_length, axis=axis)
    # Find the windows "full of True-s" (along the added 'window axis').
    full_windows = np.all(hit_windows, axis=axis + 1)
    # Count points fulfilling the condition (along the time axis).
    spell_point_counts = np.sum(full_windows, axis=axis, dtype=int)
    return spell_point_counts

def main():
    # Load the whole time-sequence as a single cube.
    file_path = iris.sample_data_path("")
    cube = iris.load_cube(file_path)

    # Make an aggregator from the user function.
    SPELL_COUNT = Aggregator(
        "spell_count", count_spells, units_func=lambda units, **kwargs: 1

    # Define the parameters of the test.
    threshold_temperature = 280.0
    spell_years = 5

    # Calculate the statistic.
    warm_periods = cube.collapsed(
    warm_periods.rename("Number of 5-year warm spells in 240 years")

    # Plot the results.
    qplt.contourf(warm_periods, cmap="RdYlBu_r")

if __name__ == "__main__":

Total running time of the script: (0 minutes 0.843 seconds)

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