Source code for esmvalcore.preprocessor._multimodel

"""Functions to compute multi-cube statistics."""

import logging
import re
import warnings
from datetime import datetime
from functools import reduce

import cf_units
import iris
import numpy as np
from iris.util import equalise_attributes

from esmvalcore.iris_helpers import date2num
from esmvalcore.preprocessor import remove_fx_variables

logger = logging.getLogger(__name__)

    'gmean': iris.analysis.GMEAN,  # not lazy in iris
    'hmean': iris.analysis.HMEAN,  # not lazy in iris
    'max': iris.analysis.MAX,
    'median': iris.analysis.MEDIAN,  # not lazy in iris
    'min': iris.analysis.MIN,
    'rms': iris.analysis.RMS,
    'sum': iris.analysis.SUM,
    'mean': iris.analysis.MEAN,
    'std_dev': iris.analysis.STD_DEV,
    'variance': iris.analysis.VARIANCE,
    # The following require extra kwargs,
    # atm this is only supported for percentiles via e.g. `pXX`
    'count': iris.analysis.COUNT,
    'peak': iris.analysis.PEAK,
    'percentile': iris.analysis.PERCENTILE,  # not lazy in iris
    'proportion': iris.analysis.PROPORTION,  # not lazy in iris
    'wpercentile': iris.analysis.WPERCENTILE,  # not lazy in iris

CONCAT_DIM = 'multi-model'

def _resolve_operator(statistic: str):
    """Find the operator corresponding to the statistic."""
    statistic = statistic.lower()
    kwargs = {}

    # special cases
    if statistic == 'std':
            "Changing statistics from specified `std` to `std_dev`, "
            "since multimodel statistics is now using the iris.analysis module"
            ", which also uses `std_dev`. Please consider replacing 'std' "
            " with 'std_dev' in your recipe or code.")
        statistic = 'std_dev'

    elif re.match(r"^(p\d{1,2})(\.\d*)?$", statistic):
        # percentiles between p0 and p99.99999...
        percentile = float(statistic[1:])
        kwargs['percent'] = percentile
        statistic = 'percentile'

        operator = STATISTIC_MAPPING[statistic]
    except KeyError as err:
        raise ValueError(
            f'Statistic `{statistic}` not supported by multicube statistics. '
            f'Must be one of {tuple(STATISTIC_MAPPING.keys())}.') from err

    return operator, kwargs

def _get_consistent_time_unit(cubes):
    """Return cubes' time unit if consistent, standard calendar otherwise."""
    t_units = [cube.coord('time').units for cube in cubes]
    if len(set(t_units)) == 1:
        return t_units[0]
    return cf_units.Unit("days since 1850-01-01", calendar="standard")

def _unify_time_coordinates(cubes):
    """Make sure all cubes' share the same time coordinate.

    This function extracts the date information from the cube and
    reconstructs the time coordinate, resetting the actual dates to the
    15th of the month or 1st of july for yearly data (consistent with
    `regrid_time`), so that there are no mismatches in the time arrays.

    If cubes have different time units, it will use reset the calendar to
    a default gregorian calendar with unit "days since 1850-01-01".

    Might not work for (sub)daily data, because different calendars may have
    different number of days in the year.
    t_unit = _get_consistent_time_unit(cubes)

    for cube in cubes:
        # Extract date info from cube
        coord = cube.coord('time')
        years = [p.year for p in coord.units.num2date(coord.points)]
        months = [p.month for p in coord.units.num2date(coord.points)]
        days = [ for p in coord.units.num2date(coord.points)]

        # Reconstruct default calendar
        if 0 not in np.diff(years):
            # yearly data
            dates = [datetime(year, 7, 1, 0, 0, 0) for year in years]
        elif 0 not in np.diff(months):
            # monthly data
            dates = [
                datetime(year, month, 15, 0, 0, 0)
                for year, month in zip(years, months)
        elif 0 not in np.diff(days):
            # daily data
            dates = [
                datetime(year, month, day, 0, 0, 0)
                for year, month, day in zip(years, months, days)
            if coord.units != t_unit:
                    "Multimodel encountered (sub)daily data and inconsistent "
                    "time units or calendars. Attempting to continue, but "
                    "might produce unexpected results.")
            raise ValueError(
                "Multimodel statistics preprocessor currently does not "
                "support sub-daily data.")

        # Update the cubes' time coordinate (both point values and the units!)
        cube.coord('time').points = date2num(dates, t_unit, coord.dtype)
        cube.coord('time').units = t_unit
        cube.coord('time').bounds = None

def _time_coords_are_aligned(cubes):
    """Return `True` if time coords are aligned."""
    first_time_array = cubes[0].coord('time').points

    for cube in cubes[1:]:
        other_time_array = cube.coord('time').points
        if not np.array_equal(first_time_array, other_time_array):
            return False

    return True

def _map_to_new_time(cube, time_points):
    """Map cube onto new cube with specified time points.

    Missing data inside original bounds is filled with nearest neighbour
    Missing data outside original bounds is masked.
    time_points = cube.coord('time').units.num2date(time_points)
    sample_points = [('time', time_points)]
    scheme = iris.analysis.Nearest(extrapolation_mode='mask')

        new_cube = cube.interpolate(sample_points, scheme)
    except Exception as excinfo:
        raise ValueError(
            "Tried to align cubes in multi-model statistics, but failed for"
            f" cube {cube} and time points {time_points}. Encountered the "
            f"following exception: {excinfo}")

    return new_cube

def _align(cubes, span):
    """Expand or subset cubes so they share a common time span."""

    if _time_coords_are_aligned(cubes):
        return cubes

    all_time_arrays = [cube.coord('time').points for cube in cubes]

    if span == 'overlap':
        new_time_points = reduce(np.intersect1d, all_time_arrays)
    elif span == 'full':
        new_time_points = reduce(np.union1d, all_time_arrays)
        raise ValueError(f"Invalid argument for span: {span!r}"
                         "Must be one of 'overlap', 'full'.")

    new_cubes = [_map_to_new_time(cube, new_time_points) for cube in cubes]

    for cube in new_cubes:
        # Make sure bounds exist and are consistent
        cube.coord('time').bounds = None

    return new_cubes

def _combine(cubes):
    """Merge iris cubes into a single big cube with new dimension.

    This assumes that all input cubes have the same shape.
    equalise_attributes(cubes)  # in-place

    for i, cube in enumerate(cubes):
        concat_dim = iris.coords.AuxCoord(i, var_name=CONCAT_DIM)


        # Clear some metadata that can cause merge to fail
        #    merge_and_concat.html#common-issues-with-merge-and-concatenate

        cube.cell_methods = None

        for coord in cube.coords():
            coord.long_name = None
            coord.attributes = None

    cubes = iris.cube.CubeList(cubes)

    merged_cube = cubes.merge_cube()

    return merged_cube

def _compute_slices(cubes):
    """Create cube slices resulting in a combined cube of about 1 GB."""
    gigabyte = 2**30
    total_bytes = cubes[0].data.nbytes * len(cubes)
    n_slices = int(np.ceil(total_bytes / gigabyte))

    n_timesteps = cubes[0].shape[0]
    slice_len = int(np.ceil(n_timesteps / n_slices))

    for i in range(n_slices):
        start = i * slice_len
        end = (i + 1) * slice_len
        if end > n_timesteps:
            end = n_timesteps
        yield slice(start, end)

def _compute_eager(cubes: list, *, operator: iris.analysis.Aggregator,
    """Compute statistics one slice at a time."""
    _ = [ for cube in cubes]  # make sure the cubes' data are realized

    result_slices = []
    for chunk in _compute_slices(cubes):
        single_model_slices = [cube[chunk] for cube in cubes]
        combined_slice = _combine(single_model_slices)
        with warnings.catch_warnings():
                    "Collapsing a non-contiguous coordinate. "
                    f"Metadata may not be fully descriptive for '{CONCAT_DIM}."
            collapsed_slice = combined_slice.collapsed(CONCAT_DIM, operator,

        # some iris aggregators modify dtype, see e.g.
        # =


        result_cube = iris.cube.CubeList(result_slices).concatenate_cube()
    except Exception as excinfo:
        raise ValueError(
            "Multi-model statistics failed to concatenate results into a"
            f" single array. This happened for operator {operator}"
            f" with computed statistics {result_slices}."
            "This can happen e.g. if the calculation results in inconsistent"
            f" dtypes. Encountered the following exception: {excinfo}") =
    return result_cube

def _multicube_statistics(cubes, statistics, span):
    """Compute statistics over multiple cubes.

    Can be used e.g. for ensemble or multi-model statistics.

    Cubes are merged and subsequently collapsed along a new auxiliary
    coordinate. Inconsistent attributes will be removed.
    if len(cubes) == 1:
        raise ValueError('Cannot perform multicube statistics '
                         'for a single cube.')

    copied_cubes = [cube.copy() for cube in cubes]  # avoid modifying inputs
    aligned_cubes = _align(copied_cubes, span=span)

    statistics_cubes = {}
    for statistic in statistics:
        logger.debug('Multicube statistics: computing: %s', statistic)
        operator, kwargs = _resolve_operator(statistic)

        result_cube = _compute_eager(aligned_cubes,

        statistics_cubes[statistic] = result_cube

    return statistics_cubes

def _multiproduct_statistics(products,
    """Compute multi-cube statistics on ESMValCore products.

    Extract cubes from products, calculate multicube statistics and
    assign the resulting output cubes to the output_products.
    cubes = [cube for product in products for cube in product.cubes]
    statistics_cubes = _multicube_statistics(cubes=cubes,
    statistics_products = set()
    for statistic, cube in statistics_cubes.items():
        statistics_product = output_products[statistic]
        statistics_product.cubes = [cube]

        for product in products:
            statistics_product.wasderivedfrom(product)"Generated %s", statistics_product)

    if not keep_input_datasets:
        return statistics_products

    return products | statistics_products

[docs]def multi_model_statistics(products, span, statistics, output_products=None, keep_input_datasets=True): """Compute multi-model statistics. This function computes multi-model statistics on a list of ``products``, which can be instances of :py:class:`~iris.cube.Cube` or :py:class:`~esmvalcore.preprocessor.PreprocessorFile`. The latter is used internally by ESMValCore to store workflow and provenance information, and this option should typically be ignored. Apart from the time coordinate, cubes must have consistent shapes. There are two options to combine time coordinates of different lengths, see the ``span`` argument. Uses the statistical operators in :py:mod:`iris.analysis`, including ``mean``, ``median``, ``min``, ``max``, and ``std``. Percentiles are also supported and can be specified like ``pXX.YY`` (for percentile ``XX.YY``; decimal part optional). Notes ----- Some of the operators in :py:mod:`iris.analysis` require additional arguments. Except for percentiles, these operators are currently not supported. Parameters ---------- products: list Cubes (or products) over which the statistics will be computed. statistics: list Statistical metrics to be computed, e.g. [``mean``, ``max``]. Choose from the operators listed in the iris.analysis package. Percentiles can be specified like ``pXX.YY``. span: str Overlap or full; if overlap, statitstics are computed on common time- span; if full, statistics are computed on full time spans, ignoring missing data. output_products: dict For internal use only. A dict with statistics names as keys and preprocessorfiles as values. If products are passed as input, the statistics cubes will be assigned to these output products. keep_input_datasets: bool If True, the output will include the input datasets. If False, only the computed statistics will be returned. Returns ------- dict A dictionary of statistics cubes with statistics' names as keys. (If input type is products, then it will return a set of output_products.) Raises ------ ValueError If span is neither overlap nor full, or if input type is neither cubes nor products. """ if all(isinstance(p, iris.cube.Cube) for p in products): return _multicube_statistics( cubes=products, statistics=statistics, span=span, ) if all(type(p).__name__ == 'PreprocessorFile' for p in products): # Avoid circular input: return _multiproduct_statistics( products=products, statistics=statistics, output_products=output_products, span=span, keep_input_datasets=keep_input_datasets, ) raise ValueError( "Input type for multi_model_statistics not understood. Expected " "iris.cube.Cube or esmvalcore.preprocessor.PreprocessorFile, " "got {}".format(products))