Source code for astropy.utils.masked.function_helpers

# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Helpers for letting numpy functions interact with Masked arrays.

The module supplies helper routines for numpy functions that propagate
masks appropriately., for use in the ``__array_function__``
implementation of `~astropy.utils.masked.MaskedNDArray`.  They are not
very useful on their own, but the ones with docstrings are included in
the documentation so that there is a place to find out how the mask is
interpreted.

"""
import numpy as np

from astropy.units.quantity_helper.function_helpers import (
    FunctionAssigner)
from astropy.utils.compat import NUMPY_LT_1_19, NUMPY_LT_1_20, NUMPY_LT_1_23

# This module should not really be imported, but we define __all__
# such that sphinx can typeset the functions with docstrings.
# The latter are added to __all__ at the end.
__all__ = ['MASKED_SAFE_FUNCTIONS', 'APPLY_TO_BOTH_FUNCTIONS',
           'DISPATCHED_FUNCTIONS', 'UNSUPPORTED_FUNCTIONS']


MASKED_SAFE_FUNCTIONS = set()
"""Set of functions that work fine on Masked classes already.

Most of these internally use `numpy.ufunc` or other functions that
are already covered.
"""

APPLY_TO_BOTH_FUNCTIONS = {}
"""Dict of functions that should apply to both data and mask.

The `dict` is keyed by the numpy function and the values are functions
that take the input arguments of the numpy function and organize these
for passing the data and mask to the numpy function.

Returns
-------
data_args : tuple
    Arguments to pass on to the numpy function for the unmasked data.
mask_args : tuple
    Arguments to pass on to the numpy function for the masked data.
kwargs : dict
    Keyword arguments to pass on for both unmasked data and mask.
out : `~astropy.utils.masked.Masked` instance or None
    Optional instance in which to store the output.

Raises
------
NotImplementedError
   When an arguments is masked when it should not be or vice versa.
"""

DISPATCHED_FUNCTIONS = {}
"""Dict of functions that provide the numpy function's functionality.

These are for more complicated versions where the numpy function itself
cannot easily be used.  It should return either the result of the
function, or a tuple consisting of the unmasked result, the mask for the
result and a possible output instance.

It should raise `NotImplementedError` if one of the arguments is masked
when it should not be or vice versa.
"""

UNSUPPORTED_FUNCTIONS = set()
"""Set of numpy functions that are not supported for masked arrays.

For most, masked input simply makes no sense, but for others it may have
been lack of time.  Issues or PRs for support for functions are welcome.
"""

# Almost all from np.core.fromnumeric defer to methods so are OK.
MASKED_SAFE_FUNCTIONS |= set(
    getattr(np, name) for name in np.core.fromnumeric.__all__
    if name not in ({'choose', 'put', 'resize', 'searchsorted', 'where', 'alen'}))

MASKED_SAFE_FUNCTIONS |= {
    # built-in from multiarray
    np.may_share_memory, np.can_cast, np.min_scalar_type, np.result_type,
    np.shares_memory,
    # np.core.arrayprint
    np.array_repr,
    # np.core.function_base
    np.linspace, np.logspace, np.geomspace,
    # np.core.numeric
    np.isclose, np.allclose, np.flatnonzero, np.argwhere,
    # np.core.shape_base
    np.atleast_1d, np.atleast_2d, np.atleast_3d, np.stack, np.hstack, np.vstack,
    # np.lib.function_base
    np.average, np.diff, np.extract, np.meshgrid, np.trapz, np.gradient,
    # np.lib.index_tricks
    np.diag_indices_from, np.triu_indices_from, np.tril_indices_from,
    np.fill_diagonal,
    # np.lib.shape_base
    np.column_stack, np.row_stack, np.dstack,
    np.array_split, np.split, np.hsplit, np.vsplit, np.dsplit,
    np.expand_dims, np.apply_along_axis, np.kron, np.tile,
    np.take_along_axis, np.put_along_axis,
    # np.lib.type_check (all but asfarray, nan_to_num)
    np.iscomplexobj, np.isrealobj, np.imag, np.isreal,
    np.real, np.real_if_close, np.common_type,
    # np.lib.ufunclike
    np.fix, np.isneginf, np.isposinf,
    # np.lib.function_base
    np.angle, np.i0,
}

IGNORED_FUNCTIONS = {
    # I/O - useless for Masked, since no way to store the mask.
    np.save, np.savez, np.savetxt, np.savez_compressed,
    # Polynomials
    np.poly, np.polyadd, np.polyder, np.polydiv, np.polyfit, np.polyint,
    np.polymul, np.polysub, np.polyval, np.roots, np.vander}
if NUMPY_LT_1_20:
    # financial
    IGNORED_FUNCTIONS |= {np.fv, np.ipmt, np.irr, np.mirr, np.nper,
                          np.npv, np.pmt, np.ppmt, np.pv, np.rate}

# TODO: some of the following could in principle be supported.
IGNORED_FUNCTIONS |= {
    np.pad,
    np.searchsorted, np.digitize,
    np.is_busday, np.busday_count, np.busday_offset,
    # numpy.lib.function_base
    np.cov, np.corrcoef, np.trim_zeros,
    # numpy.core.numeric
    np.correlate, np.convolve,
    # numpy.lib.histograms
    np.histogram, np.histogram2d, np.histogramdd, np.histogram_bin_edges,
    # TODO!!
    np.dot, np.vdot, np.inner, np.tensordot, np.cross,
    np.einsum, np.einsum_path,
}

# Really should do these...
IGNORED_FUNCTIONS |= set(getattr(np, setopsname)
                         for setopsname in np.lib.arraysetops.__all__)


if NUMPY_LT_1_23:
    IGNORED_FUNCTIONS |= {
        # Deprecated, removed in numpy 1.23
        np.asscalar, np.alen,
    }

# Explicitly unsupported functions
UNSUPPORTED_FUNCTIONS |= {
    np.unravel_index, np.ravel_multi_index, np.ix_,
}

# No support for the functions also not supported by Quantity
# (io, polynomial, etc.).
UNSUPPORTED_FUNCTIONS |= IGNORED_FUNCTIONS


apply_to_both = FunctionAssigner(APPLY_TO_BOTH_FUNCTIONS)
dispatched_function = FunctionAssigner(DISPATCHED_FUNCTIONS)


def _get_data_and_masks(*args):
    """Separate out arguments into tuples of data and masks.

    An all-False mask is created if an argument does not have a mask.
    """
    from .core import Masked

    data, masks = Masked._get_data_and_masks(*args)
    masks = tuple(m if m is not None else np.zeros(np.shape(d), bool)
                  for d, m in zip(data, masks))
    return data, masks


# Following are simple ufunc-like functions which should just copy the mask.
@dispatched_function
def datetime_as_string(arr, *args, **kwargs):
    return (np.datetime_as_string(arr.unmasked, *args, **kwargs),
            arr.mask.copy(), None)


@dispatched_function
def sinc(x):
    return np.sinc(x.unmasked), x.mask.copy(), None


@dispatched_function
def iscomplex(x):
    return np.iscomplex(x.unmasked), x.mask.copy(), None


@dispatched_function
def unwrap(p, *args, **kwargs):
    return np.unwrap(p.unmasked, *args, **kwargs), p.mask.copy(), None


@dispatched_function
def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None):
    data = np.nan_to_num(x.unmasked, copy=copy,
                         nan=nan, posinf=posinf, neginf=neginf)
    return (data, x.mask.copy(), None) if copy else x


# Following are simple functions related to shapes, where the same function
# should be applied to the data and the mask.  They cannot all share the
# same helper, because the first arguments have different names.
@apply_to_both(helps={
    np.copy, np.asfarray, np.resize, np.moveaxis, np.rollaxis, np.roll})
def masked_a_helper(a, *args, **kwargs):
    data, mask = _get_data_and_masks(a)
    return data + args, mask + args, kwargs, None


@apply_to_both(helps={np.flip, np.flipud, np.fliplr, np.rot90, np.triu, np.tril})
def masked_m_helper(m, *args, **kwargs):
    data, mask = _get_data_and_masks(m)
    return data + args, mask + args, kwargs, None


@apply_to_both(helps={np.diag, np.diagflat})
def masked_v_helper(v, *args, **kwargs):
    data, mask = _get_data_and_masks(v)
    return data + args, mask + args, kwargs, None


@apply_to_both(helps={np.delete})
def masked_arr_helper(array, *args, **kwargs):
    data, mask = _get_data_and_masks(array)
    return data + args, mask + args, kwargs, None


[docs]@apply_to_both def broadcast_to(array, shape, subok=False): """Broadcast array to the given shape. Like `numpy.broadcast_to`, and applied to both unmasked data and mask. Note that ``subok`` is taken to mean whether or not subclasses of the unmasked data and mask are allowed, i.e., for ``subok=False``, a `~astropy.utils.masked.MaskedNDArray` will be returned. """ data, mask = _get_data_and_masks(array) return data, mask, dict(shape=shape, subok=subok), None
@dispatched_function def outer(a, b, out=None): return np.multiply.outer(np.ravel(a), np.ravel(b), out=out)
[docs]@dispatched_function def empty_like(prototype, dtype=None, order='K', subok=True, shape=None): """Return a new array with the same shape and type as a given array. Like `numpy.empty_like`, but will add an empty mask. """ unmasked = np.empty_like(prototype.unmasked, dtype=dtype, order=order, subok=subok, shape=shape) if dtype is not None: dtype = (np.ma.make_mask_descr(unmasked.dtype) if unmasked.dtype.names else np.dtype('?')) mask = np.empty_like(prototype.mask, dtype=dtype, order=order, subok=subok, shape=shape) return unmasked, mask, None
[docs]@dispatched_function def zeros_like(a, dtype=None, order='K', subok=True, shape=None): """Return an array of zeros with the same shape and type as a given array. Like `numpy.zeros_like`, but will add an all-false mask. """ unmasked = np.zeros_like(a.unmasked, dtype=dtype, order=order, subok=subok, shape=shape) return unmasked, False, None
[docs]@dispatched_function def ones_like(a, dtype=None, order='K', subok=True, shape=None): """Return an array of ones with the same shape and type as a given array. Like `numpy.ones_like`, but will add an all-false mask. """ unmasked = np.ones_like(a.unmasked, dtype=dtype, order=order, subok=subok, shape=shape) return unmasked, False, None
[docs]@dispatched_function def full_like(a, fill_value, dtype=None, order='K', subok=True, shape=None): """Return a full array with the same shape and type as a given array. Like `numpy.full_like`, but with a mask that is also set. If ``fill_value`` is `numpy.ma.masked`, the data will be left unset (i.e., as created by `numpy.empty_like`). """ result = np.empty_like(a, dtype=dtype, order=order, subok=subok, shape=shape) result[...] = fill_value return result
[docs]@dispatched_function def put(a, ind, v, mode='raise'): """Replaces specified elements of an array with given values. Like `numpy.put`, but for masked array ``a`` and possibly masked value ``v``. Masked indices ``ind`` are not supported. """ from astropy.utils.masked import Masked if isinstance(ind, Masked) or not isinstance(a, Masked): raise NotImplementedError v_data, v_mask = a._get_data_and_mask(v) if v_data is not None: np.put(a.unmasked, ind, v_data, mode=mode) # v_mask of None will be correctly interpreted as False. np.put(a.mask, ind, v_mask, mode=mode) return None
[docs]@dispatched_function def putmask(a, mask, values): """Changes elements of an array based on conditional and input values. Like `numpy.putmask`, but for masked array ``a`` and possibly masked ``values``. Masked ``mask`` is not supported. """ from astropy.utils.masked import Masked if isinstance(mask, Masked) or not isinstance(a, Masked): raise NotImplementedError values_data, values_mask = a._get_data_and_mask(values) if values_data is not None: np.putmask(a.unmasked, mask, values_data) np.putmask(a.mask, mask, values_mask) return None
[docs]@dispatched_function def place(arr, mask, vals): """Change elements of an array based on conditional and input values. Like `numpy.place`, but for masked array ``a`` and possibly masked ``values``. Masked ``mask`` is not supported. """ from astropy.utils.masked import Masked if isinstance(mask, Masked) or not isinstance(arr, Masked): raise NotImplementedError vals_data, vals_mask = arr._get_data_and_mask(vals) if vals_data is not None: np.place(arr.unmasked, mask, vals_data) np.place(arr.mask, mask, vals_mask) return None
[docs]@dispatched_function def copyto(dst, src, casting='same_kind', where=True): """Copies values from one array to another, broadcasting as necessary. Like `numpy.copyto`, but for masked destination ``dst`` and possibly masked source ``src``. """ from astropy.utils.masked import Masked if not isinstance(dst, Masked) or isinstance(where, Masked): raise NotImplementedError src_data, src_mask = dst._get_data_and_mask(src) if src_data is not None: np.copyto(dst.unmasked, src_data, casting=casting, where=where) if src_mask is not None: np.copyto(dst.mask, src_mask, where=where) return None
@dispatched_function def packbits(a, *args, **kwargs): result = np.packbits(a.unmasked, *args, **kwargs) mask = np.packbits(a.mask, *args, **kwargs).astype(bool) return result, mask, None @dispatched_function def unpackbits(a, *args, **kwargs): result = np.unpackbits(a.unmasked, *args, **kwargs) mask = np.zeros(a.shape, dtype='u1') mask[a.mask] = 255 mask = np.unpackbits(mask, *args, **kwargs).astype(bool) return result, mask, None
[docs]@dispatched_function def bincount(x, weights=None, minlength=0): """Count number of occurrences of each value in array of non-negative ints. Like `numpy.bincount`, but masked entries in ``x`` will be skipped. Any masked entries in ``weights`` will lead the corresponding bin to be masked. """ from astropy.utils.masked import Masked if weights is not None: weights = np.asanyarray(weights) if isinstance(x, Masked) and x.ndim <= 1: # let other dimensions lead to errors. if weights is not None and weights.ndim == x.ndim: weights = weights[~x.mask] x = x.unmasked[~x.mask] mask = None if weights is not None: weights, w_mask = Masked._get_data_and_mask(weights) if w_mask is not None: mask = np.bincount(x, w_mask.astype(int), minlength=minlength).astype(bool) result = np.bincount(x, weights, minlength=0) return result, mask, None
@dispatched_function def msort(a): result = a.copy() result.sort(axis=0) return result @dispatched_function def sort_complex(a): # Just a copy of function_base.sort_complex, to avoid the asarray. b = a.copy() b.sort() if not issubclass(b.dtype.type, np.complexfloating): # pragma: no cover if b.dtype.char in 'bhBH': return b.astype('F') elif b.dtype.char == 'g': return b.astype('G') else: return b.astype('D') else: return b @apply_to_both def concatenate(arrays, axis=0, out=None): data, masks = _get_data_and_masks(*arrays) return (data,), (masks,), dict(axis=axis), out @apply_to_both def append(arr, values, axis=None): data, masks = _get_data_and_masks(arr, values) return data, masks, dict(axis=axis), None @dispatched_function def block(arrays): # We need to override block since the numpy implementation can take two # different paths, one for concatenation, one for creating a large empty # result array in which parts are set. Each assumes array input and # cannot be used directly. Since it would be very costly to inspect all # arrays and then turn them back into a nested list, we just copy here the # second implementation, np.core.shape_base._block_slicing, since it is # shortest and easiest. from astropy.utils.masked import Masked (arrays, list_ndim, result_ndim, final_size) = np.core.shape_base._block_setup(arrays) shape, slices, arrays = np.core.shape_base._block_info_recursion( arrays, list_ndim, result_ndim) dtype = np.result_type(*[arr.dtype for arr in arrays]) F_order = all(arr.flags['F_CONTIGUOUS'] for arr in arrays) C_order = all(arr.flags['C_CONTIGUOUS'] for arr in arrays) order = 'F' if F_order and not C_order else 'C' result = Masked(np.empty(shape=shape, dtype=dtype, order=order)) for the_slice, arr in zip(slices, arrays): result[(Ellipsis,) + the_slice] = arr return result
[docs]@dispatched_function def broadcast_arrays(*args, subok=True): """Broadcast arrays to a common shape. Like `numpy.broadcast_arrays`, applied to both unmasked data and masks. Note that ``subok`` is taken to mean whether or not subclasses of the unmasked data and masks are allowed, i.e., for ``subok=False``, `~astropy.utils.masked.MaskedNDArray` instances will be returned. """ from .core import Masked are_masked = [isinstance(arg, Masked) for arg in args] data = [(arg.unmasked if is_masked else arg) for arg, is_masked in zip(args, are_masked)] results = np.broadcast_arrays(*data, subok=subok) shape = results[0].shape if isinstance(results, list) else results.shape masks = [(np.broadcast_to(arg.mask, shape, subok=subok) if is_masked else None) for arg, is_masked in zip(args, are_masked)] results = [(Masked(result, mask) if mask is not None else result) for (result, mask) in zip(results, masks)] return results if len(results) > 1 else results[0]
[docs]@apply_to_both def insert(arr, obj, values, axis=None): """Insert values along the given axis before the given indices. Like `numpy.insert` but for possibly masked ``arr`` and ``values``. Masked ``obj`` is not supported. """ from astropy.utils.masked import Masked if isinstance(obj, Masked) or not isinstance(arr, Masked): raise NotImplementedError (arr_data, val_data), (arr_mask, val_mask) = _get_data_and_masks(arr, values) return ((arr_data, obj, val_data, axis), (arr_mask, obj, val_mask, axis), {}, None)
if NUMPY_LT_1_19: @dispatched_function def count_nonzero(a, axis=None): """Counts the number of non-zero values in the array ``a``. Like `numpy.count_nonzero`, with masked values counted as 0 or `False`. """ filled = a.filled(np.zeros((), a.dtype)) return np.count_nonzero(filled, axis) else:
[docs] @dispatched_function def count_nonzero(a, axis=None, *, keepdims=False): """Counts the number of non-zero values in the array ``a``. Like `numpy.count_nonzero`, with masked values counted as 0 or `False`. """ filled = a.filled(np.zeros((), a.dtype)) return np.count_nonzero(filled, axis, keepdims=keepdims)
if NUMPY_LT_1_19: def _zeros_like(a, dtype=None, order='K', subok=True, shape=None): if shape != (): return np.zeros_like(a, dtype=dtype, order=order, subok=subok, shape=shape) else: return np.zeros_like(a, dtype=dtype, order=order, subok=subok, shape=(1,))[0] else: _zeros_like = np.zeros_like def _masked_median_1d(a, overwrite_input): # TODO: need an in-place mask-sorting option. unmasked = a.unmasked[~a.mask] if unmasked.size: return a.from_unmasked( np.median(unmasked, overwrite_input=overwrite_input)) else: return a.from_unmasked(_zeros_like(a.unmasked, shape=(1,))[0], mask=True) def _masked_median(a, axis=None, out=None, overwrite_input=False): # As for np.nanmedian, but without a fast option as yet. if axis is None or a.ndim == 1: part = a.ravel() result = _masked_median_1d(part, overwrite_input) else: result = np.apply_along_axis(_masked_median_1d, axis, a, overwrite_input) if out is not None: out[...] = result return result @dispatched_function def median(a, axis=None, out=None, overwrite_input=False, keepdims=False): from astropy.utils.masked import Masked if out is not None and not isinstance(out, Masked): raise NotImplementedError a = Masked(a) r, k = np.lib.function_base._ureduce( a, func=_masked_median, axis=axis, out=out, overwrite_input=overwrite_input) return (r.reshape(k) if keepdims else r) if out is None else out def _masked_quantile_1d(a, q, **kwargs): """ Private function for rank 1 arrays. Compute quantile ignoring NaNs. See nanpercentile for parameter usage """ unmasked = a.unmasked[~a.mask] if unmasked.size: result = np.lib.function_base._quantile_unchecked(unmasked, q, **kwargs) return a.from_unmasked(result) else: return a.from_unmasked(_zeros_like(a.unmasked, shape=q.shape), True) def _masked_quantile(a, q, axis=None, out=None, **kwargs): # As for np.nanmedian, but without a fast option as yet. if axis is None or a.ndim == 1: part = a.ravel() result = _masked_quantile_1d(part, q, **kwargs) else: result = np.apply_along_axis(_masked_quantile_1d, axis, a, q, **kwargs) # apply_along_axis fills in collapsed axis with results. # Move that axis to the beginning to match percentile's # convention. if q.ndim != 0: result = np.moveaxis(result, axis, 0) if out is not None: out[...] = result return result @dispatched_function def quantile(a, q, axis=None, out=None, **kwargs): from astropy.utils.masked import Masked if isinstance(q, Masked) or out is not None and not isinstance(out, Masked): raise NotImplementedError a = Masked(a) q = np.asanyarray(q) if not np.lib.function_base._quantile_is_valid(q): raise ValueError("Quantiles must be in the range [0, 1]") keepdims = kwargs.pop('keepdims', False) r, k = np.lib.function_base._ureduce( a, func=_masked_quantile, q=q, axis=axis, out=out, **kwargs) return (r.reshape(k) if keepdims else r) if out is None else out @dispatched_function def percentile(a, q, *args, **kwargs): q = np.true_divide(q, 100) return quantile(a, q, *args, **kwargs) @dispatched_function def array_equal(a1, a2, equal_nan=False): (a1d, a2d), (a1m, a2m) = _get_data_and_masks(a1, a2) if a1d.shape != a2d.shape: return False equal = (a1d == a2d) if equal_nan: equal |= np.isnan(a1d) & np.isnan(a2d) return bool((equal | a1m | a2m).all()) @dispatched_function def array_equiv(a1, a2): return bool((a1 == a2).all()) @dispatched_function def where(condition, *args): from astropy.utils.masked import Masked if not args: return condition.nonzero(), None, None condition, c_mask = Masked._get_data_and_mask(condition) data, masks = _get_data_and_masks(*args) unmasked = np.where(condition, *data) mask = np.where(condition, *masks) if c_mask is not None: mask |= c_mask return Masked(unmasked, mask=mask)
[docs]@dispatched_function def choose(a, choices, out=None, mode='raise'): """Construct an array from an index array and a set of arrays to choose from. Like `numpy.choose`. Masked indices in ``a`` will lead to masked output values and underlying data values are ignored if out of bounds (for ``mode='raise'``). Any values masked in ``choices`` will be propagated if chosen. """ from astropy.utils.masked import Masked a_data, a_mask = Masked._get_data_and_mask(a) if a_mask is not None and mode == 'raise': # Avoid raising on masked indices. a_data = a.filled(fill_value=0) kwargs = {'mode': mode} if out is not None: if not isinstance(out, Masked): raise NotImplementedError kwargs['out'] = out.unmasked data, masks = _get_data_and_masks(*choices) data_chosen = np.choose(a_data, data, **kwargs) if out is not None: kwargs['out'] = out.mask mask_chosen = np.choose(a_data, masks, **kwargs) if a_mask is not None: mask_chosen |= a_mask return Masked(data_chosen, mask_chosen) if out is None else out
[docs]@apply_to_both def select(condlist, choicelist, default=0): """Return an array drawn from elements in choicelist, depending on conditions. Like `numpy.select`, with masks in ``choicelist`` are propagated. Any masks in ``condlist`` are ignored. """ from astropy.utils.masked import Masked condlist = [c.unmasked if isinstance(c, Masked) else c for c in condlist] data_list, mask_list = _get_data_and_masks(*choicelist) default = Masked(default) if default is not np.ma.masked else Masked(0, mask=True) return ((condlist, data_list, default.unmasked), (condlist, mask_list, default.mask), {}, None)
[docs]@dispatched_function def piecewise(x, condlist, funclist, *args, **kw): """Evaluate a piecewise-defined function. Like `numpy.piecewise` but for masked input array ``x``. Any masks in ``condlist`` are ignored. """ # Copied implementation from numpy.lib.function_base.piecewise, # just to ensure output is Masked. n2 = len(funclist) # undocumented: single condition is promoted to a list of one condition if np.isscalar(condlist) or ( not isinstance(condlist[0], (list, np.ndarray)) and x.ndim != 0): # pragma: no cover condlist = [condlist] condlist = np.array(condlist, dtype=bool) n = len(condlist) if n == n2 - 1: # compute the "otherwise" condition. condelse = ~np.any(condlist, axis=0, keepdims=True) condlist = np.concatenate([condlist, condelse], axis=0) n += 1 elif n != n2: raise ValueError( f"with {n} condition(s), either {n} or {n + 1} functions are expected" ) # The one real change... y = np.zeros_like(x) where = [] what = [] for k in range(n): item = funclist[k] if not callable(item): where.append(condlist[k]) what.append(item) else: vals = x[condlist[k]] if vals.size > 0: where.append(condlist[k]) what.append(item(vals, *args, **kw)) for item, value in zip(where, what): y[item] = value return y
[docs]@dispatched_function def interp(x, xp, fp, *args, **kwargs): """One-dimensional linear interpolation. Like `numpy.interp`, but any masked points in ``xp`` and ``fp`` are ignored. Any masked values in ``x`` will still be evaluated, but masked on output. """ from astropy.utils.masked import Masked xd, xm = Masked._get_data_and_mask(x) if isinstance(xp, Masked) or isinstance(fp, Masked): (xp, fp), (xpm, fpm) = _get_data_and_masks(xp, fp) if xp.ndim == fp.ndim == 1: # Avoid making arrays 1-D; will just raise below. m = xpm | fpm xp = xp[m] fp = fp[m] result = np.interp(xd, xp, fp, *args, **kwargs) return result if xm is None else Masked(result, xm.copy())
[docs]@dispatched_function def lexsort(keys, axis=-1): """Perform an indirect stable sort using a sequence of keys. Like `numpy.lexsort` but for possibly masked ``keys``. Masked values are sorted towards the end for each key. """ # Sort masks to the end. from .core import Masked new_keys = [] for key in keys: if isinstance(key, Masked): # If there are other keys below, want to be sure that # for masked values, those other keys set the order. new_key = key.unmasked if new_keys and key.mask.any(): new_key = new_key.copy() new_key[key.mask] = new_key.flat[0] new_keys.extend([new_key, key.mask]) else: new_keys.append(key) return np.lexsort(new_keys, axis=axis)
@dispatched_function def apply_over_axes(func, a, axes): # Copied straight from numpy/lib/shape_base, just to omit its # val = asarray(a); if only it had been asanyarray, or just not there # since a is assumed to an an array in the next line... # Which is what we do here - we can only get here if it is Masked. val = a N = a.ndim if np.array(axes).ndim == 0: axes = (axes,) for axis in axes: if axis < 0: axis = N + axis args = (val, axis) res = func(*args) if res.ndim == val.ndim: val = res else: res = np.expand_dims(res, axis) if res.ndim == val.ndim: val = res else: raise ValueError("function is not returning " "an array of the correct shape") return val class MaskedFormat: """Formatter for masked array scalars. For use in `numpy.array2string`, wrapping the regular formatters such that if a value is masked, its formatted string is replaced. Typically initialized using the ``from_data`` class method. """ def __init__(self, format_function): self.format_function = format_function # Special case for structured void: we need to make all the # format functions for the items masked as well. # TODO: maybe is a separate class is more logical? ffs = getattr(format_function, 'format_functions', None) if ffs: self.format_function.format_functions = [MaskedFormat(ff) for ff in ffs] def __call__(self, x): if x.dtype.names: # The replacement of x with a list is needed because the function # inside StructuredVoidFormat iterates over x, which works for an # np.void but not an array scalar. return self.format_function([x[field] for field in x.dtype.names]) string = self.format_function(x.unmasked[()]) if x.mask: # Strikethrough would be neat, but terminal needs a different # formatting than, say, jupyter notebook. # return "\x1B[9m"+string+"\x1B[29m" # return ''.join(s+'\u0336' for s in string) n = min(3, max(1, len(string))) return ' ' * (len(string)-n) + '\u2014' * n else: return string @classmethod def from_data(cls, data, **options): from numpy.core.arrayprint import _get_format_function return cls(_get_format_function(data, **options)) def _array2string(a, options, separator=' ', prefix=""): # Mostly copied from numpy.core.arrayprint, except: # - The format function is wrapped in a mask-aware class; # - Arrays scalars are not cast as arrays. from numpy.core.arrayprint import _leading_trailing, _formatArray data = np.asarray(a) if a.size > options['threshold']: summary_insert = "..." data = _leading_trailing(data, options['edgeitems']) else: summary_insert = "" # find the right formatting function for the array format_function = MaskedFormat.from_data(data, **options) # skip over "[" next_line_prefix = " " # skip over array( next_line_prefix += " "*len(prefix) lst = _formatArray(a, format_function, options['linewidth'], next_line_prefix, separator, options['edgeitems'], summary_insert, options['legacy']) return lst @dispatched_function def array2string(a, max_line_width=None, precision=None, suppress_small=None, separator=' ', prefix="", style=np._NoValue, formatter=None, threshold=None, edgeitems=None, sign=None, floatmode=None, suffix=""): # Copied from numpy.core.arrayprint, but using _array2string above. from numpy.core.arrayprint import _make_options_dict, _format_options overrides = _make_options_dict(precision, threshold, edgeitems, max_line_width, suppress_small, None, None, sign, formatter, floatmode) options = _format_options.copy() options.update(overrides) options['linewidth'] -= len(suffix) # treat as a null array if any of shape elements == 0 if a.size == 0: return "[]" return _array2string(a, options, separator, prefix) @dispatched_function def array_str(a, max_line_width=None, precision=None, suppress_small=None): # Override to avoid special treatment of array scalars. return array2string(a, max_line_width, precision, suppress_small, ' ', "") # For the nanfunctions, we just treat any nan as an additional mask. _nanfunc_fill_values = {'nansum': 0, 'nancumsum': 0, 'nanprod': 1, 'nancumprod': 1} def masked_nanfunc(nanfuncname): np_func = getattr(np, nanfuncname[3:]) fill_value = _nanfunc_fill_values.get(nanfuncname, None) def nanfunc(a, *args, **kwargs): from astropy.utils.masked import Masked a, mask = Masked._get_data_and_mask(a) if issubclass(a.dtype.type, np.inexact): nans = np.isnan(a) mask = nans if mask is None else (nans | mask) if mask is not None: a = Masked(a, mask) if fill_value is not None: a = a.filled(fill_value) return np_func(a, *args, **kwargs) doc = f"Like `numpy.{nanfuncname}`, skipping masked values as well.\n\n" if fill_value is not None: # sum, cumsum, prod, cumprod doc += (f"Masked/NaN values are replaced with {fill_value}. " "The output is not masked.") elif "arg" in nanfuncname: doc += ("No exceptions are raised for fully masked/NaN slices.\n" "Instead, these give index 0.") else: doc += ("No warnings are given for fully masked/NaN slices.\n" "Instead, they are masked in the output.") nanfunc.__doc__ = doc nanfunc.__name__ = nanfuncname return nanfunc for nanfuncname in np.lib.nanfunctions.__all__: globals()[nanfuncname] = dispatched_function(masked_nanfunc(nanfuncname), helps=getattr(np, nanfuncname)) # Add any dispatched or helper function that has a docstring to # __all__, so they will be typeset by sphinx. The logic is that for # those presumably the use of the mask is not entirely obvious. __all__ += sorted(helper.__name__ for helper in ( set(APPLY_TO_BOTH_FUNCTIONS.values()) | set(DISPATCHED_FUNCTIONS.values())) if helper.__doc__)