Source code for aeolus.subset

"""Subset cubes using iris constraints."""
from datetime import timedelta
from itertools import combinations

import iris

from .coord import get_cube_datetimes
from .model import um


__all__ = (
    "CM_INST_CONSTR",
    "CM_MEAN_CONSTR",
    "DimConstr",
    "extract_last_month",
    "extract_last_n_days",
    "l_range_constr",
    "unique_cubes",
)


def _select_inst(cube):
    """Return True if cube has no cell methods."""
    return len(cube.cell_methods) == 0


def _select_mean(cube):
    """Return True if cube has a "mean" cell method."""
    try:
        return cube.cell_methods[0].method == "mean"
    except IndexError:
        return False


def _dim_constr(*coords, strict=True):
    """Make an `iris.Constraint` from given dimensional coordinates."""
    coord_set = set(coords)

    def __cube_func(cube):  # noqa
        cube_dimcoords = {dc.name() for dc in cube.dim_coords}
        if strict:
            # Cube has exactly the same dim coords
            return cube_dimcoords == coord_set
        else:
            # Cube has these dim coords and possibly other
            return coord_set.issubset(cube_dimcoords)

    return iris.Constraint(cube_func=__cube_func)


[docs]def l_range_constr(h_min, h_max, units="km", coord=um.z): """Make a constraint on length range.""" if units == "km": factor = 1e-3 else: factor = 1 return iris.Constraint(**{coord: lambda x: h_min <= (x.point * factor) <= h_max})
[docs]def extract_last_month(cube, model=um): """Extract time slices within the last months of a cube.""" dt = get_cube_datetimes(cube)[-1] return cube.extract( iris.Constraint( **{model.t: lambda x: (x.point.year == dt.year) and (x.point.month == dt.month)} ) )
[docs]def extract_last_n_days(cube, days=365, model=um): """Extract time slices within the last `n` days of its time dimension.""" dt = get_cube_datetimes(cube)[-1] ndays_before = dt - timedelta(days=days) cube_sub = cube.extract(iris.Constraint(**{model.t: lambda t: t.point > ndays_before})) return cube_sub
CM_INST_CONSTR = iris.Constraint(cube_func=_select_inst) CM_MEAN_CONSTR = iris.Constraint(cube_func=_select_mean) class _ModeDimConstr: """Class for storing constraints in `DimConstr` grouped by their mode.""" def __init__(self, **kwargs): """Assign all keyword arguments to attributes of this class.""" self.__dict__.update(**kwargs)
[docs]class DimConstr: """ Container for strict or relaxed dimensional constraints. Examples -------- Extract cubes that have y and x dimensional coordinates (among others): >>> dc = DimConstr() >>> cubelist.extract(dc.relax.yx) Extract cubes that only have model levels, y and x dimensions: >>> dc = DimConstr() >>> cubelist.extract(dc.strict.myx) """
[docs] def __init__(self, model=um): """ Initialise DimConstr. Parameters ---------- model: aeolus.model.Model, optional Model class with relevant coordinate names. """ abbr_aliases = {"t": "t", "z": "z", "m": "lev", "p": "p", "y": "y", "x": "x"} for mode in ["strict", "relax"]: attrs = {} for key in ("z", "m", "p"): for n in range(1, 5): for seq in combinations(["t", key, "y", "x"], n): model_seq = [abbr_aliases[letter] for letter in seq] attrs["".join(seq)] = _dim_constr( *[getattr(model, i) for i in model_seq], strict=(mode == "strict") ) setattr(self, mode, _ModeDimConstr(**attrs))
[docs]def unique_cubes(cubelist): """Remove duplicate cubes from `iris.cube.CubeList`.""" if len(cubelist) > 1: out = iris.cube.CubeList([cubelist[0]]) for src_cube in cubelist[1:]: exists = False for dest_cube in out: if src_cube == dest_cube: exists = True if not exists: out.append(src_cube) return out else: return cubelist