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""" numpy.ma : a package to handle missing or invalid values. This package was initially written for numarray by Paul F. Dubois at Lawrence Livermore National Laboratory. In 2006, the package was completely rewritten by Pierre Gerard-Marchant (University of Georgia) to make the MaskedArray class a subclass of ndarray, and to improve support of structured arrays. Copyright 1999, 2000, 2001 Regents of the University of California. Released for unlimited redistribution. * Adapted for numpy_core 2005 by Travis Oliphant and (mainly) Paul Dubois. * Subclassing of the base `ndarray` 2006 by Pierre Gerard-Marchant (pgmdevlist_AT_gmail_DOT_com) * Improvements suggested by Reggie Dugard (reggie_AT_merfinllc_DOT_com) .. moduleauthor:: Pierre Gerard-Marchant """ # pylint: disable-msg=E1002 import builtins import inspect import operator import warnings import textwrap import re from functools import reduce import numpy as np import numpy.core.umath as umath import numpy.core.numerictypes as ntypes from numpy.core import multiarray as mu from numpy import ndarray, amax, amin, iscomplexobj, bool_, _NoValue from numpy import array as narray from numpy.lib.function_base import angle from numpy.compat import ( getargspec, formatargspec, long, unicode, bytes ) from numpy import expand_dims from numpy.core.numeric import normalize_axis_tuple __all__ = [ 'MAError', 'MaskError', 'MaskType', 'MaskedArray', 'abs', 'absolute', 'add', 'all', 'allclose', 'allequal', 'alltrue', 'amax', 'amin', 'angle', 'anom', 'anomalies', 'any', 'append', 'arange', 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh', 'argmax', 'argmin', 'argsort', 'around', 'array', 'asanyarray', 'asarray', 'bitwise_and', 'bitwise_or', 'bitwise_xor', 'bool_', 'ceil', 'choose', 'clip', 'common_fill_value', 'compress', 'compressed', 'concatenate', 'conjugate', 'convolve', 'copy', 'correlate', 'cos', 'cosh', 'count', 'cumprod', 'cumsum', 'default_fill_value', 'diag', 'diagonal', 'diff', 'divide', 'empty', 'empty_like', 'equal', 'exp', 'expand_dims', 'fabs', 'filled', 'fix_invalid', 'flatten_mask', 'flatten_structured_array', 'floor', 'floor_divide', 'fmod', 'frombuffer', 'fromflex', 'fromfunction', 'getdata', 'getmask', 'getmaskarray', 'greater', 'greater_equal', 'harden_mask', 'hypot', 'identity', 'ids', 'indices', 'inner', 'innerproduct', 'isMA', 'isMaskedArray', 'is_mask', 'is_masked', 'isarray', 'left_shift', 'less', 'less_equal', 'log', 'log10', 'log2', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'make_mask', 'make_mask_descr', 'make_mask_none', 'mask_or', 'masked', 'masked_array', 'masked_equal', 'masked_greater', 'masked_greater_equal', 'masked_inside', 'masked_invalid', 'masked_less', 'masked_less_equal', 'masked_not_equal', 'masked_object', 'masked_outside', 'masked_print_option', 'masked_singleton', 'masked_values', 'masked_where', 'max', 'maximum', 'maximum_fill_value', 'mean', 'min', 'minimum', 'minimum_fill_value', 'mod', 'multiply', 'mvoid', 'ndim', 'negative', 'nomask', 'nonzero', 'not_equal', 'ones', 'ones_like', 'outer', 'outerproduct', 'power', 'prod', 'product', 'ptp', 'put', 'putmask', 'ravel', 'remainder', 'repeat', 'reshape', 'resize', 'right_shift', 'round', 'round_', 'set_fill_value', 'shape', 'sin', 'sinh', 'size', 'soften_mask', 'sometrue', 'sort', 'sqrt', 'squeeze', 'std', 'subtract', 'sum', 'swapaxes', 'take', 'tan', 'tanh', 'trace', 'transpose', 'true_divide', 'var', 'where', 'zeros', 'zeros_like', ] MaskType = np.bool_ nomask = MaskType(0) class MaskedArrayFutureWarning(FutureWarning): pass def _deprecate_argsort_axis(arr): """ Adjust the axis passed to argsort, warning if necessary Parameters ---------- arr The array which argsort was called on np.ma.argsort has a long-term bug where the default of the axis argument is wrong (gh-8701), which now must be kept for backwards compatibility. Thankfully, this only makes a difference when arrays are 2- or more- dimensional, so we only need a warning then. """ if arr.ndim <= 1: # no warning needed - but switch to -1 anyway, to avoid surprising # subclasses, which are more likely to implement scalar axes. return -1 else: # 2017-04-11, Numpy 1.13.0, gh-8701: warn on axis default warnings.warn( "In the future the default for argsort will be axis=-1, not the " "current None, to match its documentation and np.argsort. " "Explicitly pass -1 or None to silence this warning.", MaskedArrayFutureWarning, stacklevel=3) return None def doc_note(initialdoc, note): """ Adds a Notes section to an existing docstring. """ if initialdoc is None: return if note is None: return initialdoc notesplit = re.split(r'\n\s*?Notes\n\s*?-----', inspect.cleandoc(initialdoc)) notedoc = "\n\nNotes\n-----\n%s\n" % inspect.cleandoc(note) return ''.join(notesplit[:1] + [notedoc] + notesplit[1:]) def get_object_signature(obj): """ Get the signature from obj """ try: sig = formatargspec(*getargspec(obj)) except TypeError: sig = '' return sig ############################################################################### # Exceptions # ############################################################################### class MAError(Exception): """ Class for masked array related errors. """ pass class MaskError(MAError): """ Class for mask related errors. """ pass ############################################################################### # Filling options # ############################################################################### # b: boolean - c: complex - f: floats - i: integer - O: object - S: string default_filler = {'b': True, 'c': 1.e20 + 0.0j, 'f': 1.e20, 'i': 999999, 'O': '?', 'S': b'N/A', 'u': 999999, 'V': b'???', 'U': 'N/A' } # Add datetime64 and timedelta64 types for v in ["Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as"]: default_filler["M8[" + v + "]"] = np.datetime64("NaT", v) default_filler["m8[" + v + "]"] = np.timedelta64("NaT", v) float_types_list = [np.half, np.single, np.double, np.longdouble, np.csingle, np.cdouble, np.clongdouble] max_filler = ntypes._minvals max_filler.update([(k, -np.inf) for k in float_types_list[:4]]) max_filler.update([(k, complex(-np.inf, -np.inf)) for k in float_types_list[-3:]]) min_filler = ntypes._maxvals min_filler.update([(k, +np.inf) for k in float_types_list[:4]]) min_filler.update([(k, complex(+np.inf, +np.inf)) for k in float_types_list[-3:]]) del float_types_list def _recursive_fill_value(dtype, f): """ Recursively produce a fill value for `dtype`, calling f on scalar dtypes """ if dtype.names is not None: # We wrap into `array` here, which ensures we use NumPy cast rules # for integer casts, this allows the use of 99999 as a fill value # for int8. # TODO: This is probably a mess, but should best preserve behavior? vals = tuple( np.array(_recursive_fill_value(dtype[name], f)) for name in dtype.names) return np.array(vals, dtype=dtype)[()] # decay to void scalar from 0d elif dtype.subdtype: subtype, shape = dtype.subdtype subval = _recursive_fill_value(subtype, f) return np.full(shape, subval) else: return f(dtype) def _get_dtype_of(obj): """ Convert the argument for *_fill_value into a dtype """ if isinstance(obj, np.dtype): return obj elif hasattr(obj, 'dtype'): return obj.dtype else: return np.asanyarray(obj).dtype def default_fill_value(obj): """ Return the default fill value for the argument object. The default filling value depends on the datatype of the input array or the type of the input scalar: ======== ======== datatype default ======== ======== bool True int 999999 float 1.e20 complex 1.e20+0j object '?' string 'N/A' ======== ======== For structured types, a structured scalar is returned, with each field the default fill value for its type. For subarray types, the fill value is an array of the same size containing the default scalar fill value. Parameters ---------- obj : ndarray, dtype or scalar The array data-type or scalar for which the default fill value is returned. Returns ------- fill_value : scalar The default fill value. Examples -------- >>> np.ma.default_fill_value(1) 999999 >>> np.ma.default_fill_value(np.array([1.1, 2., np.pi])) 1e+20 >>> np.ma.default_fill_value(np.dtype(complex)) (1e+20+0j) """ def _scalar_fill_value(dtype): if dtype.kind in 'Mm': return default_filler.get(dtype.str[1:], '?') else: return default_filler.get(dtype.kind, '?') dtype = _get_dtype_of(obj) return _recursive_fill_value(dtype, _scalar_fill_value) def _extremum_fill_value(obj, extremum, extremum_name): def _scalar_fill_value(dtype): try: return extremum[dtype] except KeyError as e: raise TypeError( f"Unsuitable type {dtype} for calculating {extremum_name}." ) from None dtype = _get_dtype_of(obj) return _recursive_fill_value(dtype, _scalar_fill_value) def minimum_fill_value(obj): """ Return the maximum value that can be represented by the dtype of an object. This function is useful for calculating a fill value suitable for taking the minimum of an array with a given dtype. Parameters ---------- obj : ndarray, dtype or scalar An object that can be queried for it's numeric type. Returns ------- val : scalar The maximum representable value. Raises ------ TypeError If `obj` isn't a suitable numeric type. See Also -------- maximum_fill_value : The inverse function. set_fill_value : Set the filling value of a masked array. MaskedArray.fill_value : Return current fill value. Examples -------- >>> import numpy.ma as ma >>> a = np.int8() >>> ma.minimum_fill_value(a) 127 >>> a = np.int32() >>> ma.minimum_fill_value(a) 2147483647 An array of numeric data can also be passed. >>> a = np.array([1, 2, 3], dtype=np.int8) >>> ma.minimum_fill_value(a) 127 >>> a = np.array([1, 2, 3], dtype=np.float32) >>> ma.minimum_fill_value(a) inf """ return _extremum_fill_value(obj, min_filler, "minimum") def maximum_fill_value(obj): """ Return the minimum value that can be represented by the dtype of an object. This function is useful for calculating a fill value suitable for taking the maximum of an array with a given dtype. Parameters ---------- obj : ndarray, dtype or scalar An object that can be queried for it's numeric type. Returns ------- val : scalar The minimum representable value. Raises ------ TypeError If `obj` isn't a suitable numeric type. See Also -------- minimum_fill_value : The inverse function. set_fill_value : Set the filling value of a masked array. MaskedArray.fill_value : Return current fill value. Examples -------- >>> import numpy.ma as ma >>> a = np.int8() >>> ma.maximum_fill_value(a) -128 >>> a = np.int32() >>> ma.maximum_fill_value(a) -2147483648 An array of numeric data can also be passed. >>> a = np.array([1, 2, 3], dtype=np.int8) >>> ma.maximum_fill_value(a) -128 >>> a = np.array([1, 2, 3], dtype=np.float32) >>> ma.maximum_fill_value(a) -inf """ return _extremum_fill_value(obj, max_filler, "maximum") def _recursive_set_fill_value(fillvalue, dt): """ Create a fill value for a structured dtype. Parameters ---------- fillvalue : scalar or array_like Scalar or array representing the fill value. If it is of shorter length than the number of fields in dt, it will be resized. dt : dtype The structured dtype for which to create the fill value. Returns ------- val : tuple A tuple of values corresponding to the structured fill value. """ fillvalue = np.resize(fillvalue, len(dt.names)) output_value = [] for (fval, name) in zip(fillvalue, dt.names): cdtype = dt[name] if cdtype.subdtype: cdtype = cdtype.subdtype[0] if cdtype.names is not None: output_value.append(tuple(_recursive_set_fill_value(fval, cdtype))) else: output_value.append(np.array(fval, dtype=cdtype).item()) return tuple(output_value) def _check_fill_value(fill_value, ndtype): """ Private function validating the given `fill_value` for the given dtype. If fill_value is None, it is set to the default corresponding to the dtype. If fill_value is not None, its value is forced to the given dtype. The result is always a 0d array. """ ndtype = np.dtype(ndtype) if fill_value is None: fill_value = default_fill_value(ndtype) elif ndtype.names is not None: if isinstance(fill_value, (ndarray, np.void)): try: fill_value = np.array(fill_value, copy=False, dtype=ndtype) except ValueError as e: err_msg = "Unable to transform %s to dtype %s" raise ValueError(err_msg % (fill_value, ndtype)) from e else: fill_value = np.asarray(fill_value, dtype=object) fill_value = np.array(_recursive_set_fill_value(fill_value, ndtype), dtype=ndtype) else: if isinstance(fill_value, str) and (ndtype.char not in 'OSVU'): # Note this check doesn't work if fill_value is not a scalar err_msg = "Cannot set fill value of string with array of dtype %s" raise TypeError(err_msg % ndtype) else: # In case we want to convert 1e20 to int. # Also in case of converting string arrays. try: fill_value = np.array(fill_value, copy=False, dtype=ndtype) except (OverflowError, ValueError) as e: # Raise TypeError instead of OverflowError or ValueError. # OverflowError is seldom used, and the real problem here is # that the passed fill_value is not compatible with the ndtype. err_msg = "Cannot convert fill_value %s to dtype %s" raise TypeError(err_msg % (fill_value, ndtype)) from e return np.array(fill_value) def set_fill_value(a, fill_value): """ Set the filling value of a, if a is a masked array. This function changes the fill value of the masked array `a` in place. If `a` is not a masked array, the function returns silently, without doing anything. Parameters ---------- a : array_like Input array. fill_value : dtype Filling value. A consistency test is performed to make sure the value is compatible with the dtype of `a`. Returns ------- None Nothing returned by this function. See Also -------- maximum_fill_value : Return the default fill value for a dtype. MaskedArray.fill_value : Return current fill value. MaskedArray.set_fill_value : Equivalent method. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a = ma.masked_where(a < 3, a) >>> a masked_array(data=[--, --, --, 3, 4], mask=[ True, True, True, False, False], fill_value=999999) >>> ma.set_fill_value(a, -999) >>> a masked_array(data=[--, --, --, 3, 4], mask=[ True, True, True, False, False], fill_value=-999) Nothing happens if `a` is not a masked array. >>> a = list(range(5)) >>> a [0, 1, 2, 3, 4] >>> ma.set_fill_value(a, 100) >>> a [0, 1, 2, 3, 4] >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> ma.set_fill_value(a, 100) >>> a array([0, 1, 2, 3, 4]) """ if isinstance(a, MaskedArray): a.set_fill_value(fill_value) return def get_fill_value(a): """ Return the filling value of a, if any. Otherwise, returns the default filling value for that type. """ if isinstance(a, MaskedArray): result = a.fill_value else: result = default_fill_value(a) return result def common_fill_value(a, b): """ Return the common filling value of two masked arrays, if any. If ``a.fill_value == b.fill_value``, return the fill value, otherwise return None. Parameters ---------- a, b : MaskedArray The masked arrays for which to compare fill values. Returns ------- fill_value : scalar or None The common fill value, or None. Examples -------- >>> x = np.ma.array([0, 1.], fill_value=3) >>> y = np.ma.array([0, 1.], fill_value=3) >>> np.ma.common_fill_value(x, y) 3.0 """ t1 = get_fill_value(a) t2 = get_fill_value(b) if t1 == t2: return t1 return None def filled(a, fill_value=None): """ Return input as an array with masked data replaced by a fill value. If `a` is not a `MaskedArray`, `a` itself is returned. If `a` is a `MaskedArray` and `fill_value` is None, `fill_value` is set to ``a.fill_value``. Parameters ---------- a : MaskedArray or array_like An input object. fill_value : array_like, optional. Can be scalar or non-scalar. If non-scalar, the resulting filled array should be broadcastable over input array. Default is None. Returns ------- a : ndarray The filled array. See Also -------- compressed Examples -------- >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0], ... [1, 0, 0], ... [0, 0, 0]]) >>> x.filled() array([[999999, 1, 2], [999999, 4, 5], [ 6, 7, 8]]) >>> x.filled(fill_value=333) array([[333, 1, 2], [333, 4, 5], [ 6, 7, 8]]) >>> x.filled(fill_value=np.arange(3)) array([[0, 1, 2], [0, 4, 5], [6, 7, 8]]) """ if hasattr(a, 'filled'): return a.filled(fill_value) elif isinstance(a, ndarray): # Should we check for contiguity ? and a.flags['CONTIGUOUS']: return a elif isinstance(a, dict): return np.array(a, 'O') else: return np.array(a) def get_masked_subclass(*arrays): """ Return the youngest subclass of MaskedArray from a list of (masked) arrays. In case of siblings, the first listed takes over. """ if len(arrays) == 1: arr = arrays[0] if isinstance(arr, MaskedArray): rcls = type(arr) else: rcls = MaskedArray else: arrcls = [type(a) for a in arrays] rcls = arrcls[0] if not issubclass(rcls, MaskedArray): rcls = MaskedArray for cls in arrcls[1:]: if issubclass(cls, rcls): rcls = cls # Don't return MaskedConstant as result: revert to MaskedArray if rcls.__name__ == 'MaskedConstant': return MaskedArray return rcls def getdata(a, subok=True): """ Return the data of a masked array as an ndarray. Return the data of `a` (if any) as an ndarray if `a` is a ``MaskedArray``, else return `a` as a ndarray or subclass (depending on `subok`) if not. Parameters ---------- a : array_like Input ``MaskedArray``, alternatively a ndarray or a subclass thereof. subok : bool Whether to force the output to be a `pure` ndarray (False) or to return a subclass of ndarray if appropriate (True, default). See Also -------- getmask : Return the mask of a masked array, or nomask. getmaskarray : Return the mask of a masked array, or full array of False. Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_equal([[1,2],[3,4]], 2) >>> a masked_array( data=[[1, --], [3, 4]], mask=[[False, True], [False, False]], fill_value=2) >>> ma.getdata(a) array([[1, 2], [3, 4]]) Equivalently use the ``MaskedArray`` `data` attribute. >>> a.data array([[1, 2], [3, 4]]) """ try: data = a._data except AttributeError: data = np.array(a, copy=False, subok=subok) if not subok: return data.view(ndarray) return data get_data = getdata def fix_invalid(a, mask=nomask, copy=True, fill_value=None): """ Return input with invalid data masked and replaced by a fill value. Invalid data means values of `nan`, `inf`, etc. Parameters ---------- a : array_like Input array, a (subclass of) ndarray. mask : sequence, optional Mask. Must be convertible to an array of booleans with the same shape as `data`. True indicates a masked (i.e. invalid) data. copy : bool, optional Whether to use a copy of `a` (True) or to fix `a` in place (False). Default is True. fill_value : scalar, optional Value used for fixing invalid data. Default is None, in which case the ``a.fill_value`` is used. Returns ------- b : MaskedArray The input array with invalid entries fixed. Notes ----- A copy is performed by default. Examples -------- >>> x = np.ma.array([1., -1, np.nan, np.inf], mask=[1] + [0]*3) >>> x masked_array(data=[--, -1.0, nan, inf], mask=[ True, False, False, False], fill_value=1e+20) >>> np.ma.fix_invalid(x) masked_array(data=[--, -1.0, --, --], mask=[ True, False, True, True], fill_value=1e+20) >>> fixed = np.ma.fix_invalid(x) >>> fixed.data array([ 1.e+00, -1.e+00, 1.e+20, 1.e+20]) >>> x.data array([ 1., -1., nan, inf]) """ a = masked_array(a, copy=copy, mask=mask, subok=True) invalid = np.logical_not(np.isfinite(a._data)) if not invalid.any(): return a a._mask |= invalid if fill_value is None: fill_value = a.fill_value a._data[invalid] = fill_value return a def is_string_or_list_of_strings(val): return (isinstance(val, str) or (isinstance(val, list) and val and builtins.all(isinstance(s, str) for s in val))) ############################################################################### # Ufuncs # ############################################################################### ufunc_domain = {} ufunc_fills = {} class _DomainCheckInterval: """ Define a valid interval, so that : ``domain_check_interval(a,b)(x) == True`` where ``x < a`` or ``x > b``. """ def __init__(self, a, b): "domain_check_interval(a,b)(x) = true where x < a or y > b" if a > b: (a, b) = (b, a) self.a = a self.b = b def __call__(self, x): "Execute the call behavior." # nans at masked positions cause RuntimeWarnings, even though # they are masked. To avoid this we suppress warnings. with np.errstate(invalid='ignore'): return umath.logical_or(umath.greater(x, self.b), umath.less(x, self.a)) class _DomainTan: """ Define a valid interval for the `tan` function, so that: ``domain_tan(eps) = True`` where ``abs(cos(x)) < eps`` """ def __init__(self, eps): "domain_tan(eps) = true where abs(cos(x)) < eps)" self.eps = eps def __call__(self, x): "Executes the call behavior." with np.errstate(invalid='ignore'): return umath.less(umath.absolute(umath.cos(x)), self.eps) class _DomainSafeDivide: """ Define a domain for safe division. """ def __init__(self, tolerance=None): self.tolerance = tolerance def __call__(self, a, b): # Delay the selection of the tolerance to here in order to reduce numpy # import times. The calculation of these parameters is a substantial # component of numpy's import time. if self.tolerance is None: self.tolerance = np.finfo(float).tiny # don't call ma ufuncs from __array_wrap__ which would fail for scalars a, b = np.asarray(a), np.asarray(b) with np.errstate(invalid='ignore'): return umath.absolute(a) * self.tolerance >= umath.absolute(b) class _DomainGreater: """ DomainGreater(v)(x) is True where x <= v. """ def __init__(self, critical_value): "DomainGreater(v)(x) = true where x <= v" self.critical_value = critical_value def __call__(self, x): "Executes the call behavior." with np.errstate(invalid='ignore'): return umath.less_equal(x, self.critical_value) class _DomainGreaterEqual: """ DomainGreaterEqual(v)(x) is True where x < v. """ def __init__(self, critical_value): "DomainGreaterEqual(v)(x) = true where x < v" self.critical_value = critical_value def __call__(self, x): "Executes the call behavior." with np.errstate(invalid='ignore'): return umath.less(x, self.critical_value) class _MaskedUFunc: def __init__(self, ufunc): self.f = ufunc self.__doc__ = ufunc.__doc__ self.__name__ = ufunc.__name__ def __str__(self): return f"Masked version of {self.f}" class _MaskedUnaryOperation(_MaskedUFunc): """ Defines masked version of unary operations, where invalid values are pre-masked. Parameters ---------- mufunc : callable The function for which to define a masked version. Made available as ``_MaskedUnaryOperation.f``. fill : scalar, optional Filling value, default is 0. domain : class instance Domain for the function. Should be one of the ``_Domain*`` classes. Default is None. """ def __init__(self, mufunc, fill=0, domain=None): super().__init__(mufunc) self.fill = fill self.domain = domain ufunc_domain[mufunc] = domain ufunc_fills[mufunc] = fill def __call__(self, a, *args, **kwargs): """ Execute the call behavior. """ d = getdata(a) # Deal with domain if self.domain is not None: # Case 1.1. : Domained function # nans at masked positions cause RuntimeWarnings, even though # they are masked. To avoid this we suppress warnings. with np.errstate(divide='ignore', invalid='ignore'): result = self.f(d, *args, **kwargs) # Make a mask m = ~umath.isfinite(result) m |= self.domain(d) m |= getmask(a) else: # Case 1.2. : Function without a domain # Get the result and the mask with np.errstate(divide='ignore', invalid='ignore'): result = self.f(d, *args, **kwargs) m = getmask(a) if not result.ndim: # Case 2.1. : The result is scalarscalar if m: return masked return result if m is not nomask: # Case 2.2. The result is an array # We need to fill the invalid data back w/ the input Now, # that's plain silly: in C, we would just skip the element and # keep the original, but we do have to do it that way in Python # In case result has a lower dtype than the inputs (as in # equal) try: np.copyto(result, d, where=m) except TypeError: pass # Transform to masked_result = result.view(get_masked_subclass(a)) masked_result._mask = m masked_result._update_from(a) return masked_result class _MaskedBinaryOperation(_MaskedUFunc): """ Define masked version of binary operations, where invalid values are pre-masked. Parameters ---------- mbfunc : function The function for which to define a masked version. Made available as ``_MaskedBinaryOperation.f``. domain : class instance Default domain for the function. Should be one of the ``_Domain*`` classes. Default is None. fillx : scalar, optional Filling value for the first argument, default is 0. filly : scalar, optional Filling value for the second argument, default is 0. """ def __init__(self, mbfunc, fillx=0, filly=0): """ abfunc(fillx, filly) must be defined. abfunc(x, filly) = x for all x to enable reduce. """ super().__init__(mbfunc) self.fillx = fillx self.filly = filly ufunc_domain[mbfunc] = None ufunc_fills[mbfunc] = (fillx, filly) def __call__(self, a, b, *args, **kwargs): """ Execute the call behavior. """ # Get the data, as ndarray (da, db) = (getdata(a), getdata(b)) # Get the result with np.errstate(): np.seterr(divide='ignore', invalid='ignore') result = self.f(da, db, *args, **kwargs) # Get the mask for the result (ma, mb) = (getmask(a), getmask(b)) if ma is nomask: if mb is nomask: m = nomask else: m = umath.logical_or(getmaskarray(a), mb) elif mb is nomask: m = umath.logical_or(ma, getmaskarray(b)) else: m = umath.logical_or(ma, mb) # Case 1. : scalar if not result.ndim: if m: return masked return result # Case 2. : array # Revert result to da where masked if m is not nomask and m.any(): # any errors, just abort; impossible to guarantee masked values try: np.copyto(result, da, casting='unsafe', where=m) except Exception: pass # Transforms to a (subclass of) MaskedArray masked_result = result.view(get_masked_subclass(a, b)) masked_result._mask = m if isinstance(a, MaskedArray): masked_result._update_from(a) elif isinstance(b, MaskedArray): masked_result._update_from(b) return masked_result def reduce(self, target, axis=0, dtype=None): """ Reduce `target` along the given `axis`. """ tclass = get_masked_subclass(target) m = getmask(target) t = filled(target, self.filly) if t.shape == (): t = t.reshape(1) if m is not nomask: m = make_mask(m, copy=True) m.shape = (1,) if m is nomask: tr = self.f.reduce(t, axis) mr = nomask else: tr = self.f.reduce(t, axis, dtype=dtype) mr = umath.logical_and.reduce(m, axis) if not tr.shape: if mr: return masked else: return tr masked_tr = tr.view(tclass) masked_tr._mask = mr return masked_tr def outer(self, a, b): """ Return the function applied to the outer product of a and b. """ (da, db) = (getdata(a), getdata(b)) d = self.f.outer(da, db) ma = getmask(a) mb = getmask(b) if ma is nomask and mb is nomask: m = nomask else: ma = getmaskarray(a) mb = getmaskarray(b) m = umath.logical_or.outer(ma, mb) if (not m.ndim) and m: return masked if m is not nomask: np.copyto(d, da, where=m) if not d.shape: return d masked_d = d.view(get_masked_subclass(a, b)) masked_d._mask = m return masked_d def accumulate(self, target, axis=0): """Accumulate `target` along `axis` after filling with y fill value. """ tclass = get_masked_subclass(target) t = filled(target, self.filly) result = self.f.accumulate(t, axis) masked_result = result.view(tclass) return masked_result class _DomainedBinaryOperation(_MaskedUFunc): """ Define binary operations that have a domain, like divide. They have no reduce, outer or accumulate. Parameters ---------- mbfunc : function The function for which to define a masked version. Made available as ``_DomainedBinaryOperation.f``. domain : class instance Default domain for the function. Should be one of the ``_Domain*`` classes. fillx : scalar, optional Filling value for the first argument, default is 0. filly : scalar, optional Filling value for the second argument, default is 0. """ def __init__(self, dbfunc, domain, fillx=0, filly=0): """abfunc(fillx, filly) must be defined. abfunc(x, filly) = x for all x to enable reduce. """ super().__init__(dbfunc) self.domain = domain self.fillx = fillx self.filly = filly ufunc_domain[dbfunc] = domain ufunc_fills[dbfunc] = (fillx, filly) def __call__(self, a, b, *args, **kwargs): "Execute the call behavior." # Get the data (da, db) = (getdata(a), getdata(b)) # Get the result with np.errstate(divide='ignore', invalid='ignore'): result = self.f(da, db, *args, **kwargs) # Get the mask as a combination of the source masks and invalid m = ~umath.isfinite(result) m |= getmask(a) m |= getmask(b) # Apply the domain domain = ufunc_domain.get(self.f, None) if domain is not None: m |= domain(da, db) # Take care of the scalar case first if not m.ndim: if m: return masked else: return result # When the mask is True, put back da if possible # any errors, just abort; impossible to guarantee masked values try: np.copyto(result, 0, casting='unsafe', where=m) # avoid using "*" since this may be overlaid masked_da = umath.multiply(m, da) # only add back if it can be cast safely if np.can_cast(masked_da.dtype, result.dtype, casting='safe'): result += masked_da except Exception: pass # Transforms to a (subclass of) MaskedArray masked_result = result.view(get_masked_subclass(a, b)) masked_result._mask = m if isinstance(a, MaskedArray): masked_result._update_from(a) elif isinstance(b, MaskedArray): masked_result._update_from(b) return masked_result # Unary ufuncs exp = _MaskedUnaryOperation(umath.exp) conjugate = _MaskedUnaryOperation(umath.conjugate) sin = _MaskedUnaryOperation(umath.sin) cos = _MaskedUnaryOperation(umath.cos) arctan = _MaskedUnaryOperation(umath.arctan) arcsinh = _MaskedUnaryOperation(umath.arcsinh) sinh = _MaskedUnaryOperation(umath.sinh) cosh = _MaskedUnaryOperation(umath.cosh) tanh = _MaskedUnaryOperation(umath.tanh) abs = absolute = _MaskedUnaryOperation(umath.absolute) angle = _MaskedUnaryOperation(angle) # from numpy.lib.function_base fabs = _MaskedUnaryOperation(umath.fabs) negative = _MaskedUnaryOperation(umath.negative) floor = _MaskedUnaryOperation(umath.floor) ceil = _MaskedUnaryOperation(umath.ceil) around = _MaskedUnaryOperation(np.round_) logical_not = _MaskedUnaryOperation(umath.logical_not) # Domained unary ufuncs sqrt = _MaskedUnaryOperation(umath.sqrt, 0.0, _DomainGreaterEqual(0.0)) log = _MaskedUnaryOperation(umath.log, 1.0, _DomainGreater(0.0)) log2 = _MaskedUnaryOperation(umath.log2, 1.0, _DomainGreater(0.0)) log10 = _MaskedUnaryOperation(umath.log10, 1.0, _DomainGreater(0.0)) tan = _MaskedUnaryOperation(umath.tan, 0.0, _DomainTan(1e-35)) arcsin = _MaskedUnaryOperation(umath.arcsin, 0.0, _DomainCheckInterval(-1.0, 1.0)) arccos = _MaskedUnaryOperation(umath.arccos, 0.0, _DomainCheckInterval(-1.0, 1.0)) arccosh = _MaskedUnaryOperation(umath.arccosh, 1.0, _DomainGreaterEqual(1.0)) arctanh = _MaskedUnaryOperation(umath.arctanh, 0.0, _DomainCheckInterval(-1.0 + 1e-15, 1.0 - 1e-15)) # Binary ufuncs add = _MaskedBinaryOperation(umath.add) subtract = _MaskedBinaryOperation(umath.subtract) multiply = _MaskedBinaryOperation(umath.multiply, 1, 1) arctan2 = _MaskedBinaryOperation(umath.arctan2, 0.0, 1.0) equal = _MaskedBinaryOperation(umath.equal) equal.reduce = None not_equal = _MaskedBinaryOperation(umath.not_equal) not_equal.reduce = None less_equal = _MaskedBinaryOperation(umath.less_equal) less_equal.reduce = None greater_equal = _MaskedBinaryOperation(umath.greater_equal) greater_equal.reduce = None less = _MaskedBinaryOperation(umath.less) less.reduce = None greater = _MaskedBinaryOperation(umath.greater) greater.reduce = None logical_and = _MaskedBinaryOperation(umath.logical_and) alltrue = _MaskedBinaryOperation(umath.logical_and, 1, 1).reduce logical_or = _MaskedBinaryOperation(umath.logical_or) sometrue = logical_or.reduce logical_xor = _MaskedBinaryOperation(umath.logical_xor) bitwise_and = _MaskedBinaryOperation(umath.bitwise_and) bitwise_or = _MaskedBinaryOperation(umath.bitwise_or) bitwise_xor = _MaskedBinaryOperation(umath.bitwise_xor) hypot = _MaskedBinaryOperation(umath.hypot) # Domained binary ufuncs divide = _DomainedBinaryOperation(umath.divide, _DomainSafeDivide(), 0, 1) true_divide = _DomainedBinaryOperation(umath.true_divide, _DomainSafeDivide(), 0, 1) floor_divide = _DomainedBinaryOperation(umath.floor_divide, _DomainSafeDivide(), 0, 1) remainder = _DomainedBinaryOperation(umath.remainder, _DomainSafeDivide(), 0, 1) fmod = _DomainedBinaryOperation(umath.fmod, _DomainSafeDivide(), 0, 1) mod = _DomainedBinaryOperation(umath.mod, _DomainSafeDivide(), 0, 1) ############################################################################### # Mask creation functions # ############################################################################### def _replace_dtype_fields_recursive(dtype, primitive_dtype): "Private function allowing recursion in _replace_dtype_fields." _recurse = _replace_dtype_fields_recursive # Do we have some name fields ? if dtype.names is not None: descr = [] for name in dtype.names: field = dtype.fields[name] if len(field) == 3: # Prepend the title to the name name = (field[-1], name) descr.append((name, _recurse(field[0], primitive_dtype))) new_dtype = np.dtype(descr) # Is this some kind of composite a la (float,2) elif dtype.subdtype: descr = list(dtype.subdtype) descr[0] = _recurse(dtype.subdtype[0], primitive_dtype) new_dtype = np.dtype(tuple(descr)) # this is a primitive type, so do a direct replacement else: new_dtype = primitive_dtype # preserve identity of dtypes if new_dtype == dtype: new_dtype = dtype return new_dtype def _replace_dtype_fields(dtype, primitive_dtype): """ Construct a dtype description list from a given dtype. Returns a new dtype object, with all fields and subtypes in the given type recursively replaced with `primitive_dtype`. Arguments are coerced to dtypes first. """ dtype = np.dtype(dtype) primitive_dtype = np.dtype(primitive_dtype) return _replace_dtype_fields_recursive(dtype, primitive_dtype) def make_mask_descr(ndtype): """ Construct a dtype description list from a given dtype. Returns a new dtype object, with the type of all fields in `ndtype` to a boolean type. Field names are not altered. Parameters ---------- ndtype : dtype The dtype to convert. Returns ------- result : dtype A dtype that looks like `ndtype`, the type of all fields is boolean. Examples -------- >>> import numpy.ma as ma >>> dtype = np.dtype({'names':['foo', 'bar'], ... 'formats':[np.float32, np.int64]}) >>> dtype dtype([('foo', '<f4'), ('bar', '<i8')]) >>> ma.make_mask_descr(dtype) dtype([('foo', '|b1'), ('bar', '|b1')]) >>> ma.make_mask_descr(np.float32) dtype('bool') """ return _replace_dtype_fields(ndtype, MaskType) def getmask(a): """ Return the mask of a masked array, or nomask. Return the mask of `a` as an ndarray if `a` is a `MaskedArray` and the mask is not `nomask`, else return `nomask`. To guarantee a full array of booleans of the same shape as a, use `getmaskarray`. Parameters ---------- a : array_like Input `MaskedArray` for which the mask is required. See Also -------- getdata : Return the data of a masked array as an ndarray. getmaskarray : Return the mask of a masked array, or full array of False. Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_equal([[1,2],[3,4]], 2) >>> a masked_array( data=[[1, --], [3, 4]], mask=[[False, True], [False, False]], fill_value=2) >>> ma.getmask(a) array([[False, True], [False, False]]) Equivalently use the `MaskedArray` `mask` attribute. >>> a.mask array([[False, True], [False, False]]) Result when mask == `nomask` >>> b = ma.masked_array([[1,2],[3,4]]) >>> b masked_array( data=[[1, 2], [3, 4]], mask=False, fill_value=999999) >>> ma.nomask False >>> ma.getmask(b) == ma.nomask True >>> b.mask == ma.nomask True """ return getattr(a, '_mask', nomask) get_mask = getmask def getmaskarray(arr): """ Return the mask of a masked array, or full boolean array of False. Return the mask of `arr` as an ndarray if `arr` is a `MaskedArray` and the mask is not `nomask`, else return a full boolean array of False of the same shape as `arr`. Parameters ---------- arr : array_like Input `MaskedArray` for which the mask is required. See Also -------- getmask : Return the mask of a masked array, or nomask. getdata : Return the data of a masked array as an ndarray. Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_equal([[1,2],[3,4]], 2) >>> a masked_array( data=[[1, --], [3, 4]], mask=[[False, True], [False, False]], fill_value=2) >>> ma.getmaskarray(a) array([[False, True], [False, False]]) Result when mask == ``nomask`` >>> b = ma.masked_array([[1,2],[3,4]]) >>> b masked_array( data=[[1, 2], [3, 4]], mask=False, fill_value=999999) >>> ma.getmaskarray(b) array([[False, False], [False, False]]) """ mask = getmask(arr) if mask is nomask: mask = make_mask_none(np.shape(arr), getattr(arr, 'dtype', None)) return mask def is_mask(m): """ Return True if m is a valid, standard mask. This function does not check the contents of the input, only that the type is MaskType. In particular, this function returns False if the mask has a flexible dtype. Parameters ---------- m : array_like Array to test. Returns ------- result : bool True if `m.dtype.type` is MaskType, False otherwise. See Also -------- ma.isMaskedArray : Test whether input is an instance of MaskedArray. Examples -------- >>> import numpy.ma as ma >>> m = ma.masked_equal([0, 1, 0, 2, 3], 0) >>> m masked_array(data=[--, 1, --, 2, 3], mask=[ True, False, True, False, False], fill_value=0) >>> ma.is_mask(m) False >>> ma.is_mask(m.mask) True Input must be an ndarray (or have similar attributes) for it to be considered a valid mask. >>> m = [False, True, False] >>> ma.is_mask(m) False >>> m = np.array([False, True, False]) >>> m array([False, True, False]) >>> ma.is_mask(m) True Arrays with complex dtypes don't return True. >>> dtype = np.dtype({'names':['monty', 'pithon'], ... 'formats':[bool, bool]}) >>> dtype dtype([('monty', '|b1'), ('pithon', '|b1')]) >>> m = np.array([(True, False), (False, True), (True, False)], ... dtype=dtype) >>> m array([( True, False), (False, True), ( True, False)], dtype=[('monty', '?'), ('pithon', '?')]) >>> ma.is_mask(m) False """ try: return m.dtype.type is MaskType except AttributeError: return False def _shrink_mask(m): """ Shrink a mask to nomask if possible """ if m.dtype.names is None and not m.any(): return nomask else: return m def make_mask(m, copy=False, shrink=True, dtype=MaskType): """ Create a boolean mask from an array. Return `m` as a boolean mask, creating a copy if necessary or requested. The function can accept any sequence that is convertible to integers, or ``nomask``. Does not require that contents must be 0s and 1s, values of 0 are interpreted as False, everything else as True. Parameters ---------- m : array_like Potential mask. copy : bool, optional Whether to return a copy of `m` (True) or `m` itself (False). shrink : bool, optional Whether to shrink `m` to ``nomask`` if all its values are False. dtype : dtype, optional Data-type of the output mask. By default, the output mask has a dtype of MaskType (bool). If the dtype is flexible, each field has a boolean dtype. This is ignored when `m` is ``nomask``, in which case ``nomask`` is always returned. Returns ------- result : ndarray A boolean mask derived from `m`. Examples -------- >>> import numpy.ma as ma >>> m = [True, False, True, True] >>> ma.make_mask(m) array([ True, False, True, True]) >>> m = [1, 0, 1, 1] >>> ma.make_mask(m) array([ True, False, True, True]) >>> m = [1, 0, 2, -3] >>> ma.make_mask(m) array([ True, False, True, True]) Effect of the `shrink` parameter. >>> m = np.zeros(4) >>> m array([0., 0., 0., 0.]) >>> ma.make_mask(m) False >>> ma.make_mask(m, shrink=False) array([False, False, False, False]) Using a flexible `dtype`. >>> m = [1, 0, 1, 1] >>> n = [0, 1, 0, 0] >>> arr = [] >>> for man, mouse in zip(m, n): ... arr.append((man, mouse)) >>> arr [(1, 0), (0, 1), (1, 0), (1, 0)] >>> dtype = np.dtype({'names':['man', 'mouse'], ... 'formats':[np.int64, np.int64]}) >>> arr = np.array(arr, dtype=dtype) >>> arr array([(1, 0), (0, 1), (1, 0), (1, 0)], dtype=[('man', '<i8'), ('mouse', '<i8')]) >>> ma.make_mask(arr, dtype=dtype) array([(True, False), (False, True), (True, False), (True, False)], dtype=[('man', '|b1'), ('mouse', '|b1')]) """ if m is nomask: return nomask # Make sure the input dtype is valid. dtype = make_mask_descr(dtype) # legacy boolean special case: "existence of fields implies true" if isinstance(m, ndarray) and m.dtype.fields and dtype == np.bool_: return np.ones(m.shape, dtype=dtype) # Fill the mask in case there are missing data; turn it into an ndarray. result = np.array(filled(m, True), copy=copy, dtype=dtype, subok=True) # Bas les masques ! if shrink: result = _shrink_mask(result) return result def make_mask_none(newshape, dtype=None): """ Return a boolean mask of the given shape, filled with False. This function returns a boolean ndarray with all entries False, that can be used in common mask manipulations. If a complex dtype is specified, the type of each field is converted to a boolean type. Parameters ---------- newshape : tuple A tuple indicating the shape of the mask. dtype : {None, dtype}, optional If None, use a MaskType instance. Otherwise, use a new datatype with the same fields as `dtype`, converted to boolean types. Returns ------- result : ndarray An ndarray of appropriate shape and dtype, filled with False. See Also -------- make_mask : Create a boolean mask from an array. make_mask_descr : Construct a dtype description list from a given dtype. Examples -------- >>> import numpy.ma as ma >>> ma.make_mask_none((3,)) array([False, False, False]) Defining a more complex dtype. >>> dtype = np.dtype({'names':['foo', 'bar'], ... 'formats':[np.float32, np.int64]}) >>> dtype dtype([('foo', '<f4'), ('bar', '<i8')]) >>> ma.make_mask_none((3,), dtype=dtype) array([(False, False), (False, False), (False, False)], dtype=[('foo', '|b1'), ('bar', '|b1')]) """ if dtype is None: result = np.zeros(newshape, dtype=MaskType) else: result = np.zeros(newshape, dtype=make_mask_descr(dtype)) return result def _recursive_mask_or(m1, m2, newmask): names = m1.dtype.names for name in names: current1 = m1[name] if current1.dtype.names is not None: _recursive_mask_or(current1, m2[name], newmask[name]) else: umath.logical_or(current1, m2[name], newmask[name]) def mask_or(m1, m2, copy=False, shrink=True): """ Combine two masks with the ``logical_or`` operator. The result may be a view on `m1` or `m2` if the other is `nomask` (i.e. False). Parameters ---------- m1, m2 : array_like Input masks. copy : bool, optional If copy is False and one of the inputs is `nomask`, return a view of the other input mask. Defaults to False. shrink : bool, optional Whether to shrink the output to `nomask` if all its values are False. Defaults to True. Returns ------- mask : output mask The result masks values that are masked in either `m1` or `m2`. Raises ------ ValueError If `m1` and `m2` have different flexible dtypes. Examples -------- >>> m1 = np.ma.make_mask([0, 1, 1, 0]) >>> m2 = np.ma.make_mask([1, 0, 0, 0]) >>> np.ma.mask_or(m1, m2) array([ True, True, True, False]) """ if (m1 is nomask) or (m1 is False): dtype = getattr(m2, 'dtype', MaskType) return make_mask(m2, copy=copy, shrink=shrink, dtype=dtype) if (m2 is nomask) or (m2 is False): dtype = getattr(m1, 'dtype', MaskType) return make_mask(m1, copy=copy, shrink=shrink, dtype=dtype) if m1 is m2 and is_mask(m1): return m1 (dtype1, dtype2) = (getattr(m1, 'dtype', None), getattr(m2, 'dtype', None)) if dtype1 != dtype2: raise ValueError("Incompatible dtypes '%s'<>'%s'" % (dtype1, dtype2)) if dtype1.names is not None: # Allocate an output mask array with the properly broadcast shape. newmask = np.empty(np.broadcast(m1, m2).shape, dtype1) _recursive_mask_or(m1, m2, newmask) return newmask return make_mask(umath.logical_or(m1, m2), copy=copy, shrink=shrink) def flatten_mask(mask): """ Returns a completely flattened version of the mask, where nested fields are collapsed. Parameters ---------- mask : array_like Input array, which will be interpreted as booleans. Returns ------- flattened_mask : ndarray of bools The flattened input. Examples -------- >>> mask = np.array([0, 0, 1]) >>> np.ma.flatten_mask(mask) array([False, False, True]) >>> mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)]) >>> np.ma.flatten_mask(mask) array([False, False, False, True]) >>> mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])] >>> mask = np.array([(0, (0, 0)), (0, (0, 1))], dtype=mdtype) >>> np.ma.flatten_mask(mask) array([False, False, False, False, False, True]) """ def _flatmask(mask): "Flatten the mask and returns a (maybe nested) sequence of booleans." mnames = mask.dtype.names if mnames is not None: return [flatten_mask(mask[name]) for name in mnames] else: return mask def _flatsequence(sequence): "Generates a flattened version of the sequence." try: for element in sequence: if hasattr(element, '__iter__'): yield from _flatsequence(element) else: yield element except TypeError: yield sequence mask = np.asarray(mask) flattened = _flatsequence(_flatmask(mask)) return np.array([_ for _ in flattened], dtype=bool) def _check_mask_axis(mask, axis, keepdims=np._NoValue): "Check whether there are masked values along the given axis" kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} if mask is not nomask: return mask.all(axis=axis, **kwargs) return nomask ############################################################################### # Masking functions # ############################################################################### def masked_where(condition, a, copy=True): """ Mask an array where a condition is met. Return `a` as an array masked where `condition` is True. Any masked values of `a` or `condition` are also masked in the output. Parameters ---------- condition : array_like Masking condition. When `condition` tests floating point values for equality, consider using ``masked_values`` instead. a : array_like Array to mask. copy : bool If True (default) make a copy of `a` in the result. If False modify `a` in place and return a view. Returns ------- result : MaskedArray The result of masking `a` where `condition` is True. See Also -------- masked_values : Mask using floating point equality. masked_equal : Mask where equal to a given value. masked_not_equal : Mask where `not` equal to a given value. masked_less_equal : Mask where less than or equal to a given value. masked_greater_equal : Mask where greater than or equal to a given value. masked_less : Mask where less than a given value. masked_greater : Mask where greater than a given value. masked_inside : Mask inside a given interval. masked_outside : Mask outside a given interval. masked_invalid : Mask invalid values (NaNs or infs). Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_where(a <= 2, a) masked_array(data=[--, --, --, 3], mask=[ True, True, True, False], fill_value=999999) Mask array `b` conditional on `a`. >>> b = ['a', 'b', 'c', 'd'] >>> ma.masked_where(a == 2, b) masked_array(data=['a', 'b', --, 'd'], mask=[False, False, True, False], fill_value='N/A', dtype='<U1') Effect of the `copy` argument. >>> c = ma.masked_where(a <= 2, a) >>> c masked_array(data=[--, --, --, 3], mask=[ True, True, True, False], fill_value=999999) >>> c[0] = 99 >>> c masked_array(data=[99, --, --, 3], mask=[False, True, True, False], fill_value=999999) >>> a array([0, 1, 2, 3]) >>> c = ma.masked_where(a <= 2, a, copy=False) >>> c[0] = 99 >>> c masked_array(data=[99, --, --, 3], mask=[False, True, True, False], fill_value=999999) >>> a array([99, 1, 2, 3]) When `condition` or `a` contain masked values. >>> a = np.arange(4) >>> a = ma.masked_where(a == 2, a) >>> a masked_array(data=[0, 1, --, 3], mask=[False, False, True, False], fill_value=999999) >>> b = np.arange(4) >>> b = ma.masked_where(b == 0, b) >>> b masked_array(data=[--, 1, 2, 3], mask=[ True, False, False, False], fill_value=999999) >>> ma.masked_where(a == 3, b) masked_array(data=[--, 1, --, --], mask=[ True, False, True, True], fill_value=999999) """ # Make sure that condition is a valid standard-type mask. cond = make_mask(condition, shrink=False) a = np.array(a, copy=copy, subok=True) (cshape, ashape) = (cond.shape, a.shape) if cshape and cshape != ashape: raise IndexError("Inconsistent shape between the condition and the input" " (got %s and %s)" % (cshape, ashape)) if hasattr(a, '_mask'): cond = mask_or(cond, a._mask) cls = type(a) else: cls = MaskedArray result = a.view(cls) # Assign to *.mask so that structured masks are handled correctly. result.mask = _shrink_mask(cond) # There is no view of a boolean so when 'a' is a MaskedArray with nomask # the update to the result's mask has no effect. if not copy and hasattr(a, '_mask') and getmask(a) is nomask: a._mask = result._mask.view() return result def masked_greater(x, value, copy=True): """ Mask an array where greater than a given value. This function is a shortcut to ``masked_where``, with `condition` = (x > value). See Also -------- masked_where : Mask where a condition is met. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_greater(a, 2) masked_array(data=[0, 1, 2, --], mask=[False, False, False, True], fill_value=999999) """ return masked_where(greater(x, value), x, copy=copy) def masked_greater_equal(x, value, copy=True): """ Mask an array where greater than or equal to a given value. This function is a shortcut to ``masked_where``, with `condition` = (x >= value). See Also -------- masked_where : Mask where a condition is met. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_greater_equal(a, 2) masked_array(data=[0, 1, --, --], mask=[False, False, True, True], fill_value=999999) """ return masked_where(greater_equal(x, value), x, copy=copy) def masked_less(x, value, copy=True): """ Mask an array where less than a given value. This function is a shortcut to ``masked_where``, with `condition` = (x < value). See Also -------- masked_where : Mask where a condition is met. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_less(a, 2) masked_array(data=[--, --, 2, 3], mask=[ True, True, False, False], fill_value=999999) """ return masked_where(less(x, value), x, copy=copy) def masked_less_equal(x, value, copy=True): """ Mask an array where less than or equal to a given value. This function is a shortcut to ``masked_where``, with `condition` = (x <= value). See Also -------- masked_where : Mask where a condition is met. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_less_equal(a, 2) masked_array(data=[--, --, --, 3], mask=[ True, True, True, False], fill_value=999999) """ return masked_where(less_equal(x, value), x, copy=copy) def masked_not_equal(x, value, copy=True): """ Mask an array where `not` equal to a given value. This function is a shortcut to ``masked_where``, with `condition` = (x != value). See Also -------- masked_where : Mask where a condition is met. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_not_equal(a, 2) masked_array(data=[--, --, 2, --], mask=[ True, True, False, True], fill_value=999999) """ return masked_where(not_equal(x, value), x, copy=copy) def masked_equal(x, value, copy=True): """ Mask an array where equal to a given value. Return a MaskedArray, masked where the data in array `x` are equal to `value`. The fill_value of the returned MaskedArray is set to `value`. For floating point arrays, consider using ``masked_values(x, value)``. See Also -------- masked_where : Mask where a condition is met. masked_values : Mask using floating point equality. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array([0, 1, 2, 3]) >>> ma.masked_equal(a, 2) masked_array(data=[0, 1, --, 3], mask=[False, False, True, False], fill_value=2) """ output = masked_where(equal(x, value), x, copy=copy) output.fill_value = value return output def masked_inside(x, v1, v2, copy=True): """ Mask an array inside a given interval. Shortcut to ``masked_where``, where `condition` is True for `x` inside the interval [v1,v2] (v1 <= x <= v2). The boundaries `v1` and `v2` can be given in either order. See Also -------- masked_where : Mask where a condition is met. Notes ----- The array `x` is prefilled with its filling value. Examples -------- >>> import numpy.ma as ma >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1] >>> ma.masked_inside(x, -0.3, 0.3) masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1], mask=[False, False, True, True, False, False], fill_value=1e+20) The order of `v1` and `v2` doesn't matter. >>> ma.masked_inside(x, 0.3, -0.3) masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1], mask=[False, False, True, True, False, False], fill_value=1e+20) """ if v2 < v1: (v1, v2) = (v2, v1) xf = filled(x) condition = (xf >= v1) & (xf <= v2) return masked_where(condition, x, copy=copy) def masked_outside(x, v1, v2, copy=True): """ Mask an array outside a given interval. Shortcut to ``masked_where``, where `condition` is True for `x` outside the interval [v1,v2] (x < v1)|(x > v2). The boundaries `v1` and `v2` can be given in either order. See Also -------- masked_where : Mask where a condition is met. Notes ----- The array `x` is prefilled with its filling value. Examples -------- >>> import numpy.ma as ma >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1] >>> ma.masked_outside(x, -0.3, 0.3) masked_array(data=[--, --, 0.01, 0.2, --, --], mask=[ True, True, False, False, True, True], fill_value=1e+20) The order of `v1` and `v2` doesn't matter. >>> ma.masked_outside(x, 0.3, -0.3) masked_array(data=[--, --, 0.01, 0.2, --, --], mask=[ True, True, False, False, True, True], fill_value=1e+20) """ if v2 < v1: (v1, v2) = (v2, v1) xf = filled(x) condition = (xf < v1) | (xf > v2) return masked_where(condition, x, copy=copy) def masked_object(x, value, copy=True, shrink=True): """ Mask the array `x` where the data are exactly equal to value. This function is similar to `masked_values`, but only suitable for object arrays: for floating point, use `masked_values` instead. Parameters ---------- x : array_like Array to mask value : object Comparison value copy : {True, False}, optional Whether to return a copy of `x`. shrink : {True, False}, optional Whether to collapse a mask full of False to nomask Returns ------- result : MaskedArray The result of masking `x` where equal to `value`. See Also -------- masked_where : Mask where a condition is met. masked_equal : Mask where equal to a given value (integers). masked_values : Mask using floating point equality. Examples -------- >>> import numpy.ma as ma >>> food = np.array(['green_eggs', 'ham'], dtype=object) >>> # don't eat spoiled food >>> eat = ma.masked_object(food, 'green_eggs') >>> eat masked_array(data=[--, 'ham'], mask=[ True, False], fill_value='green_eggs', dtype=object) >>> # plain ol` ham is boring >>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object) >>> eat = ma.masked_object(fresh_food, 'green_eggs') >>> eat masked_array(data=['cheese', 'ham', 'pineapple'], mask=False, fill_value='green_eggs', dtype=object) Note that `mask` is set to ``nomask`` if possible. >>> eat masked_array(data=['cheese', 'ham', 'pineapple'], mask=False, fill_value='green_eggs', dtype=object) """ if isMaskedArray(x): condition = umath.equal(x._data, value) mask = x._mask else: condition = umath.equal(np.asarray(x), value) mask = nomask mask = mask_or(mask, make_mask(condition, shrink=shrink)) return masked_array(x, mask=mask, copy=copy, fill_value=value) def masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True): """ Mask using floating point equality. Return a MaskedArray, masked where the data in array `x` are approximately equal to `value`, determined using `isclose`. The default tolerances for `masked_values` are the same as those for `isclose`. For integer types, exact equality is used, in the same way as `masked_equal`. The fill_value is set to `value` and the mask is set to ``nomask`` if possible. Parameters ---------- x : array_like Array to mask. value : float Masking value. rtol, atol : float, optional Tolerance parameters passed on to `isclose` copy : bool, optional Whether to return a copy of `x`. shrink : bool, optional Whether to collapse a mask full of False to ``nomask``. Returns ------- result : MaskedArray The result of masking `x` where approximately equal to `value`. See Also -------- masked_where : Mask where a condition is met. masked_equal : Mask where equal to a given value (integers). Examples -------- >>> import numpy.ma as ma >>> x = np.array([1, 1.1, 2, 1.1, 3]) >>> ma.masked_values(x, 1.1) masked_array(data=[1.0, --, 2.0, --, 3.0], mask=[False, True, False, True, False], fill_value=1.1) Note that `mask` is set to ``nomask`` if possible. >>> ma.masked_values(x, 2.1) masked_array(data=[1. , 1.1, 2. , 1.1, 3. ], mask=False, fill_value=2.1) Unlike `masked_equal`, `masked_values` can perform approximate equalities. >>> ma.masked_values(x, 2.1, atol=1e-1) masked_array(data=[1.0, 1.1, --, 1.1, 3.0], mask=[False, False, True, False, False], fill_value=2.1) """ xnew = filled(x, value) if np.issubdtype(xnew.dtype, np.floating): mask = np.isclose(xnew, value, atol=atol, rtol=rtol) else: mask = umath.equal(xnew, value) ret = masked_array(xnew, mask=mask, copy=copy, fill_value=value) if shrink: ret.shrink_mask() return ret def masked_invalid(a, copy=True): """ Mask an array where invalid values occur (NaNs or infs). This function is a shortcut to ``masked_where``, with `condition` = ~(np.isfinite(a)). Any pre-existing mask is conserved. Only applies to arrays with a dtype where NaNs or infs make sense (i.e. floating point types), but accepts any array_like object. See Also -------- masked_where : Mask where a condition is met. Examples -------- >>> import numpy.ma as ma >>> a = np.arange(5, dtype=float) >>> a[2] = np.NaN >>> a[3] = np.PINF >>> a array([ 0., 1., nan, inf, 4.]) >>> ma.masked_invalid(a) masked_array(data=[0.0, 1.0, --, --, 4.0], mask=[False, False, True, True, False], fill_value=1e+20) """ a = np.array(a, copy=False, subok=True) res = masked_where(~(np.isfinite(a)), a, copy=copy) # masked_invalid previously never returned nomask as a mask and doing so # threw off matplotlib (gh-22842). So use shrink=False: if res._mask is nomask: res._mask = make_mask_none(res.shape, res.dtype) return res ############################################################################### # Printing options # ############################################################################### class _MaskedPrintOption: """ Handle the string used to represent missing data in a masked array. """ def __init__(self, display): """ Create the masked_print_option object. """ self._display = display self._enabled = True def display(self): """ Display the string to print for masked values. """ return self._display def set_display(self, s): """ Set the string to print for masked values. """ self._display = s def enabled(self): """ Is the use of the display value enabled? """ return self._enabled def enable(self, shrink=1): """ Set the enabling shrink to `shrink`. """ self._enabled = shrink def __str__(self): return str(self._display) __repr__ = __str__ # if you single index into a masked location you get this object. masked_print_option = _MaskedPrintOption('--') def _recursive_printoption(result, mask, printopt): """ Puts printoptions in result where mask is True. Private function allowing for recursion """ names = result.dtype.names if names is not None: for name in names: curdata = result[name] curmask = mask[name] _recursive_printoption(curdata, curmask, printopt) else: np.copyto(result, printopt, where=mask) return # For better or worse, these end in a newline _legacy_print_templates = dict( long_std=textwrap.dedent("""\ masked_%(name)s(data = %(data)s, %(nlen)s mask = %(mask)s, %(nlen)s fill_value = %(fill)s) """), long_flx=textwrap.dedent("""\ masked_%(name)s(data = %(data)s, %(nlen)s mask = %(mask)s, %(nlen)s fill_value = %(fill)s, %(nlen)s dtype = %(dtype)s) """), short_std=textwrap.dedent("""\ masked_%(name)s(data = %(data)s, %(nlen)s mask = %(mask)s, %(nlen)s fill_value = %(fill)s) """), short_flx=textwrap.dedent("""\ masked_%(name)s(data = %(data)s, %(nlen)s mask = %(mask)s, %(nlen)s fill_value = %(fill)s, %(nlen)s dtype = %(dtype)s) """) ) ############################################################################### # MaskedArray class # ############################################################################### def _recursive_filled(a, mask, fill_value): """ Recursively fill `a` with `fill_value`. """ names = a.dtype.names for name in names: current = a[name] if current.dtype.names is not None: _recursive_filled(current, mask[name], fill_value[name]) else: np.copyto(current, fill_value[name], where=mask[name]) def flatten_structured_array(a): """ Flatten a structured array. The data type of the output is chosen such that it can represent all of the (nested) fields. Parameters ---------- a : structured array Returns ------- output : masked array or ndarray A flattened masked array if the input is a masked array, otherwise a standard ndarray. Examples -------- >>> ndtype = [('a', int), ('b', float)] >>> a = np.array([(1, 1), (2, 2)], dtype=ndtype) >>> np.ma.flatten_structured_array(a) array([[1., 1.], [2., 2.]]) """ def flatten_sequence(iterable): """ Flattens a compound of nested iterables. """ for elm in iter(iterable): if hasattr(elm, '__iter__'): yield from flatten_sequence(elm) else: yield elm a = np.asanyarray(a) inishape = a.shape a = a.ravel() if isinstance(a, MaskedArray): out = np.array([tuple(flatten_sequence(d.item())) for d in a._data]) out = out.view(MaskedArray) out._mask = np.array([tuple(flatten_sequence(d.item())) for d in getmaskarray(a)]) else: out = np.array([tuple(flatten_sequence(d.item())) for d in a]) if len(inishape) > 1: newshape = list(out.shape) newshape[0] = inishape out.shape = tuple(flatten_sequence(newshape)) return out def _arraymethod(funcname, onmask=True): """ Return a class method wrapper around a basic array method. Creates a class method which returns a masked array, where the new ``_data`` array is the output of the corresponding basic method called on the original ``_data``. If `onmask` is True, the new mask is the output of the method called on the initial mask. Otherwise, the new mask is just a reference to the initial mask. Parameters ---------- funcname : str Name of the function to apply on data. onmask : bool Whether the mask must be processed also (True) or left alone (False). Default is True. Make available as `_onmask` attribute. Returns ------- method : instancemethod Class method wrapper of the specified basic array method. """ def wrapped_method(self, *args, **params): result = getattr(self._data, funcname)(*args, **params) result = result.view(type(self)) result._update_from(self) mask = self._mask if not onmask: result.__setmask__(mask) elif mask is not nomask: # __setmask__ makes a copy, which we don't want result._mask = getattr(mask, funcname)(*args, **params) return result methdoc = getattr(ndarray, funcname, None) or getattr(np, funcname, None) if methdoc is not None: wrapped_method.__doc__ = methdoc.__doc__ wrapped_method.__name__ = funcname return wrapped_method class MaskedIterator: """ Flat iterator object to iterate over masked arrays. A `MaskedIterator` iterator is returned by ``x.flat`` for any masked array `x`. It allows iterating over the array as if it were a 1-D array, either in a for-loop or by calling its `next` method. Iteration is done in C-contiguous style, with the last index varying the fastest. The iterator can also be indexed using basic slicing or advanced indexing. See Also -------- MaskedArray.flat : Return a flat iterator over an array. MaskedArray.flatten : Returns a flattened copy of an array. Notes ----- `MaskedIterator` is not exported by the `ma` module. Instead of instantiating a `MaskedIterator` directly, use `MaskedArray.flat`. Examples -------- >>> x = np.ma.array(arange(6).reshape(2, 3)) >>> fl = x.flat >>> type(fl) <class 'numpy.ma.core.MaskedIterator'> >>> for item in fl: ... print(item) ... 0 1 2 3 4 5 Extracting more than a single element b indexing the `MaskedIterator` returns a masked array: >>> fl[2:4] masked_array(data = [2 3], mask = False, fill_value = 999999) """ def __init__(self, ma): self.ma = ma self.dataiter = ma._data.flat if ma._mask is nomask: self.maskiter = None else: self.maskiter = ma._mask.flat def __iter__(self): return self def __getitem__(self, indx): result = self.dataiter.__getitem__(indx).view(type(self.ma)) if self.maskiter is not None: _mask = self.maskiter.__getitem__(indx) if isinstance(_mask, ndarray): # set shape to match that of data; this is needed for matrices _mask.shape = result.shape result._mask = _mask elif isinstance(_mask, np.void): return mvoid(result, mask=_mask, hardmask=self.ma._hardmask) elif _mask: # Just a scalar, masked return masked return result # This won't work if ravel makes a copy def __setitem__(self, index, value): self.dataiter[index] = getdata(value) if self.maskiter is not None: self.maskiter[index] = getmaskarray(value) def __next__(self): """ Return the next value, or raise StopIteration. Examples -------- >>> x = np.ma.array([3, 2], mask=[0, 1]) >>> fl = x.flat >>> next(fl) 3 >>> next(fl) masked >>> next(fl) Traceback (most recent call last): ... StopIteration """ d = next(self.dataiter) if self.maskiter is not None: m = next(self.maskiter) if isinstance(m, np.void): return mvoid(d, mask=m, hardmask=self.ma._hardmask) elif m: # Just a scalar, masked return masked return d class MaskedArray(ndarray): """ An array class with possibly masked values. Masked values of True exclude the corresponding element from any computation. Construction:: x = MaskedArray(data, mask=nomask, dtype=None, copy=False, subok=True, ndmin=0, fill_value=None, keep_mask=True, hard_mask=None, shrink=True, order=None) Parameters ---------- data : array_like Input data. mask : sequence, optional Mask. Must be convertible to an array of booleans with the same shape as `data`. True indicates a masked (i.e. invalid) data. dtype : dtype, optional Data type of the output. If `dtype` is None, the type of the data argument (``data.dtype``) is used. If `dtype` is not None and different from ``data.dtype``, a copy is performed. copy : bool, optional Whether to copy the input data (True), or to use a reference instead. Default is False. subok : bool, optional Whether to return a subclass of `MaskedArray` if possible (True) or a plain `MaskedArray`. Default is True. ndmin : int, optional Minimum number of dimensions. Default is 0. fill_value : scalar, optional Value used to fill in the masked values when necessary. If None, a default based on the data-type is used. keep_mask : bool, optional Whether to combine `mask` with the mask of the input data, if any (True), or to use only `mask` for the output (False). Default is True. hard_mask : bool, optional Whether to use a hard mask or not. With a hard mask, masked values cannot be unmasked. Default is False. shrink : bool, optional Whether to force compression of an empty mask. Default is True. order : {'C', 'F', 'A'}, optional Specify the order of the array. If order is 'C', then the array will be in C-contiguous order (last-index varies the fastest). If order is 'F', then the returned array will be in Fortran-contiguous order (first-index varies the fastest). If order is 'A' (default), then the returned array may be in any order (either C-, Fortran-contiguous, or even discontiguous), unless a copy is required, in which case it will be C-contiguous. Examples -------- The ``mask`` can be initialized with an array of boolean values with the same shape as ``data``. >>> data = np.arange(6).reshape((2, 3)) >>> np.ma.MaskedArray(data, mask=[[False, True, False], ... [False, False, True]]) masked_array( data=[[0, --, 2], [3, 4, --]], mask=[[False, True, False], [False, False, True]], fill_value=999999) Alternatively, the ``mask`` can be initialized to homogeneous boolean array with the same shape as ``data`` by passing in a scalar boolean value: >>> np.ma.MaskedArray(data, mask=False) masked_array( data=[[0, 1, 2], [3, 4, 5]], mask=[[False, False, False], [False, False, False]], fill_value=999999) >>> np.ma.MaskedArray(data, mask=True) masked_array( data=[[--, --, --], [--, --, --]], mask=[[ True, True, True], [ True, True, True]], fill_value=999999, dtype=int64) .. note:: The recommended practice for initializing ``mask`` with a scalar boolean value is to use ``True``/``False`` rather than ``np.True_``/``np.False_``. The reason is :attr:`nomask` is represented internally as ``np.False_``. >>> np.False_ is np.ma.nomask True """ __array_priority__ = 15 _defaultmask = nomask _defaulthardmask = False _baseclass = ndarray # Maximum number of elements per axis used when printing an array. The # 1d case is handled separately because we need more values in this case. _print_width = 100 _print_width_1d = 1500 def __new__(cls, data=None, mask=nomask, dtype=None, copy=False, subok=True, ndmin=0, fill_value=None, keep_mask=True, hard_mask=None, shrink=True, order=None): """ Create a new masked array from scratch. Notes ----- A masked array can also be created by taking a .view(MaskedArray). """ # Process data. _data = np.array(data, dtype=dtype, copy=copy, order=order, subok=True, ndmin=ndmin) _baseclass = getattr(data, '_baseclass', type(_data)) # Check that we're not erasing the mask. if isinstance(data, MaskedArray) and (data.shape != _data.shape): copy = True # Here, we copy the _view_, so that we can attach new properties to it # we must never do .view(MaskedConstant), as that would create a new # instance of np.ma.masked, which make identity comparison fail if isinstance(data, cls) and subok and not isinstance(data, MaskedConstant): _data = ndarray.view(_data, type(data)) else: _data = ndarray.view(_data, cls) # Handle the case where data is not a subclass of ndarray, but # still has the _mask attribute like MaskedArrays if hasattr(data, '_mask') and not isinstance(data, ndarray): _data._mask = data._mask # FIXME: should we set `_data._sharedmask = True`? # Process mask. # Type of the mask mdtype = make_mask_descr(_data.dtype) if mask is nomask: # Case 1. : no mask in input. # Erase the current mask ? if not keep_mask: # With a reduced version if shrink: _data._mask = nomask # With full version else: _data._mask = np.zeros(_data.shape, dtype=mdtype) # Check whether we missed something elif isinstance(data, (tuple, list)): try: # If data is a sequence of masked array mask = np.array( [getmaskarray(np.asanyarray(m, dtype=_data.dtype)) for m in data], dtype=mdtype) except (ValueError, TypeError): # If data is nested mask = nomask # Force shrinking of the mask if needed (and possible) if (mdtype == MaskType) and mask.any(): _data._mask = mask _data._sharedmask = False else: _data._sharedmask = not copy if copy: _data._mask = _data._mask.copy() # Reset the shape of the original mask if getmask(data) is not nomask: # gh-21022 encounters an issue here # because data._mask.shape is not writeable, but # the op was also pointless in that case, because # the shapes were the same, so we can at least # avoid that path if data._mask.shape != data.shape: data._mask.shape = data.shape else: # Case 2. : With a mask in input. # If mask is boolean, create an array of True or False # if users pass `mask=None` be forgiving here and cast it False # for speed; although the default is `mask=nomask` and can differ. if mask is None: mask = False if mask is True and mdtype == MaskType: mask = np.ones(_data.shape, dtype=mdtype) elif mask is False and mdtype == MaskType: mask = np.zeros(_data.shape, dtype=mdtype) else: # Read the mask with the current mdtype try: mask = np.array(mask, copy=copy, dtype=mdtype) # Or assume it's a sequence of bool/int except TypeError: mask = np.array([tuple([m] * len(mdtype)) for m in mask], dtype=mdtype) # Make sure the mask and the data have the same shape if mask.shape != _data.shape: (nd, nm) = (_data.size, mask.size) if nm == 1: mask = np.resize(mask, _data.shape) elif nm == nd: mask = np.reshape(mask, _data.shape) else: msg = "Mask and data not compatible: data size is %i, " + \ "mask size is %i." raise MaskError(msg % (nd, nm)) copy = True # Set the mask to the new value if _data._mask is nomask: _data._mask = mask _data._sharedmask = not copy else: if not keep_mask: _data._mask = mask _data._sharedmask = not copy else: if _data.dtype.names is not None: def _recursive_or(a, b): "do a|=b on each field of a, recursively" for name in a.dtype.names: (af, bf) = (a[name], b[name]) if af.dtype.names is not None: _recursive_or(af, bf) else: af |= bf _recursive_or(_data._mask, mask) else: _data._mask = np.logical_or(mask, _data._mask) _data._sharedmask = False # Update fill_value. if fill_value is None: fill_value = getattr(data, '_fill_value', None) # But don't run the check unless we have something to check. if fill_value is not None: _data._fill_value = _check_fill_value(fill_value, _data.dtype) # Process extra options .. if hard_mask is None: _data._hardmask = getattr(data, '_hardmask', False) else: _data._hardmask = hard_mask _data._baseclass = _baseclass return _data def _update_from(self, obj): """ Copies some attributes of obj to self. """ if isinstance(obj, ndarray): _baseclass = type(obj) else: _baseclass = ndarray # We need to copy the _basedict to avoid backward propagation _optinfo = {} _optinfo.update(getattr(obj, '_optinfo', {})) _optinfo.update(getattr(obj, '_basedict', {})) if not isinstance(obj, MaskedArray): _optinfo.update(getattr(obj, '__dict__', {})) _dict = dict(_fill_value=getattr(obj, '_fill_value', None), _hardmask=getattr(obj, '_hardmask', False), _sharedmask=getattr(obj, '_sharedmask', False), _isfield=getattr(obj, '_isfield', False), _baseclass=getattr(obj, '_baseclass', _baseclass), _optinfo=_optinfo, _basedict=_optinfo) self.__dict__.update(_dict) self.__dict__.update(_optinfo) return def __array_finalize__(self, obj): """ Finalizes the masked array. """ # Get main attributes. self._update_from(obj) # We have to decide how to initialize self.mask, based on # obj.mask. This is very difficult. There might be some # correspondence between the elements in the array we are being # created from (= obj) and us. Or there might not. This method can # be called in all kinds of places for all kinds of reasons -- could # be empty_like, could be slicing, could be a ufunc, could be a view. # The numpy subclassing interface simply doesn't give us any way # to know, which means that at best this method will be based on # guesswork and heuristics. To make things worse, there isn't even any # clear consensus about what the desired behavior is. For instance, # most users think that np.empty_like(marr) -- which goes via this # method -- should return a masked array with an empty mask (see # gh-3404 and linked discussions), but others disagree, and they have # existing code which depends on empty_like returning an array that # matches the input mask. # # Historically our algorithm was: if the template object mask had the # same *number of elements* as us, then we used *it's mask object # itself* as our mask, so that writes to us would also write to the # original array. This is horribly broken in multiple ways. # # Now what we do instead is, if the template object mask has the same # number of elements as us, and we do not have the same base pointer # as the template object (b/c views like arr[...] should keep the same # mask), then we make a copy of the template object mask and use # that. This is also horribly broken but somewhat less so. Maybe. if isinstance(obj, ndarray): # XX: This looks like a bug -- shouldn't it check self.dtype # instead? if obj.dtype.names is not None: _mask = getmaskarray(obj) else: _mask = getmask(obj) # If self and obj point to exactly the same data, then probably # self is a simple view of obj (e.g., self = obj[...]), so they # should share the same mask. (This isn't 100% reliable, e.g. self # could be the first row of obj, or have strange strides, but as a # heuristic it's not bad.) In all other cases, we make a copy of # the mask, so that future modifications to 'self' do not end up # side-effecting 'obj' as well. if (_mask is not nomask and obj.__array_interface__["data"][0] != self.__array_interface__["data"][0]): # We should make a copy. But we could get here via astype, # in which case the mask might need a new dtype as well # (e.g., changing to or from a structured dtype), and the # order could have changed. So, change the mask type if # needed and use astype instead of copy. if self.dtype == obj.dtype: _mask_dtype = _mask.dtype else: _mask_dtype = make_mask_descr(self.dtype) if self.flags.c_contiguous: order = "C" elif self.flags.f_contiguous: order = "F" else: order = "K" _mask = _mask.astype(_mask_dtype, order) else: # Take a view so shape changes, etc., do not propagate back. _mask = _mask.view() else: _mask = nomask self._mask = _mask # Finalize the mask if self._mask is not nomask: try: self._mask.shape = self.shape except ValueError: self._mask = nomask except (TypeError, AttributeError): # When _mask.shape is not writable (because it's a void) pass # Finalize the fill_value if self._fill_value is not None: self._fill_value = _check_fill_value(self._fill_value, self.dtype) elif self.dtype.names is not None: # Finalize the default fill_value for structured arrays self._fill_value = _check_fill_value(None, self.dtype) def __array_wrap__(self, obj, context=None): """ Special hook for ufuncs. Wraps the numpy array and sets the mask according to context. """ if obj is self: # for in-place operations result = obj else: result = obj.view(type(self)) result._update_from(self) if context is not None: result._mask = result._mask.copy() func, args, out_i = context # args sometimes contains outputs (gh-10459), which we don't want input_args = args[:func.nin] m = reduce(mask_or, [getmaskarray(arg) for arg in input_args]) # Get the domain mask domain = ufunc_domain.get(func, None) if domain is not None: # Take the domain, and make sure it's a ndarray with np.errstate(divide='ignore', invalid='ignore'): d = filled(domain(*input_args), True) if d.any(): # Fill the result where the domain is wrong try: # Binary domain: take the last value fill_value = ufunc_fills[func][-1] except TypeError: # Unary domain: just use this one fill_value = ufunc_fills[func] except KeyError: # Domain not recognized, use fill_value instead fill_value = self.fill_value np.copyto(result, fill_value, where=d) # Update the mask if m is nomask: m = d else: # Don't modify inplace, we risk back-propagation m = (m | d) # Make sure the mask has the proper size if result is not self and result.shape == () and m: return masked else: result._mask = m result._sharedmask = False return result def view(self, dtype=None, type=None, fill_value=None): """ Return a view of the MaskedArray data. Parameters ---------- dtype : data-type or ndarray sub-class, optional Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as `a`. As with ``ndarray.view``, dtype can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the ``type`` parameter). type : Python type, optional Type of the returned view, either ndarray or a subclass. The default None results in type preservation. fill_value : scalar, optional The value to use for invalid entries (None by default). If None, then this argument is inferred from the passed `dtype`, or in its absence the original array, as discussed in the notes below. See Also -------- numpy.ndarray.view : Equivalent method on ndarray object. Notes ----- ``a.view()`` is used two different ways: ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view of the array's memory with a different data-type. This can cause a reinterpretation of the bytes of memory. ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just returns an instance of `ndarray_subclass` that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory. If `fill_value` is not specified, but `dtype` is specified (and is not an ndarray sub-class), the `fill_value` of the MaskedArray will be reset. If neither `fill_value` nor `dtype` are specified (or if `dtype` is an ndarray sub-class), then the fill value is preserved. Finally, if `fill_value` is specified, but `dtype` is not, the fill value is set to the specified value. For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the behavior of the view cannot be predicted just from the superficial appearance of ``a`` (shown by ``print(a)``). It also depends on exactly how ``a`` is stored in memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus defined as a slice or transpose, etc., the view may give different results. """ if dtype is None: if type is None: output = ndarray.view(self) else: output = ndarray.view(self, type) elif type is None: try: if issubclass(dtype, ndarray): output = ndarray.view(self, dtype) dtype = None else: output = ndarray.view(self, dtype) except TypeError: output = ndarray.view(self, dtype) else: output = ndarray.view(self, dtype, type) # also make the mask be a view (so attr changes to the view's # mask do no affect original object's mask) # (especially important to avoid affecting np.masked singleton) if getmask(output) is not nomask: output._mask = output._mask.view() # Make sure to reset the _fill_value if needed if getattr(output, '_fill_value', None) is not None: if fill_value is None: if dtype is None: pass # leave _fill_value as is else: output._fill_value = None else: output.fill_value = fill_value return output def __getitem__(self, indx): """ x.__getitem__(y) <==> x[y] Return the item described by i, as a masked array. """ # We could directly use ndarray.__getitem__ on self. # But then we would have to modify __array_finalize__ to prevent the # mask of being reshaped if it hasn't been set up properly yet # So it's easier to stick to the current version dout = self.data[indx] _mask = self._mask def _is_scalar(m): return not isinstance(m, np.ndarray) def _scalar_heuristic(arr, elem): """ Return whether `elem` is a scalar result of indexing `arr`, or None if undecidable without promoting nomask to a full mask """ # obviously a scalar if not isinstance(elem, np.ndarray): return True # object array scalar indexing can return anything elif arr.dtype.type is np.object_: if arr.dtype is not elem.dtype: # elem is an array, but dtypes do not match, so must be # an element return True # well-behaved subclass that only returns 0d arrays when # expected - this is not a scalar elif type(arr).__getitem__ == ndarray.__getitem__: return False return None if _mask is not nomask: # _mask cannot be a subclass, so it tells us whether we should # expect a scalar. It also cannot be of dtype object. mout = _mask[indx] scalar_expected = _is_scalar(mout) else: # attempt to apply the heuristic to avoid constructing a full mask mout = nomask scalar_expected = _scalar_heuristic(self.data, dout) if scalar_expected is None: # heuristics have failed # construct a full array, so we can be certain. This is costly. # we could also fall back on ndarray.__getitem__(self.data, indx) scalar_expected = _is_scalar(getmaskarray(self)[indx]) # Did we extract a single item? if scalar_expected: # A record if isinstance(dout, np.void): # We should always re-cast to mvoid, otherwise users can # change masks on rows that already have masked values, but not # on rows that have no masked values, which is inconsistent. return mvoid(dout, mask=mout, hardmask=self._hardmask) # special case introduced in gh-5962 elif (self.dtype.type is np.object_ and isinstance(dout, np.ndarray) and dout is not masked): # If masked, turn into a MaskedArray, with everything masked. if mout: return MaskedArray(dout, mask=True) else: return dout # Just a scalar else: if mout: return masked else: return dout else: # Force dout to MA dout = dout.view(type(self)) # Inherit attributes from self dout._update_from(self) # Check the fill_value if is_string_or_list_of_strings(indx): if self._fill_value is not None: dout._fill_value = self._fill_value[indx] # Something like gh-15895 has happened if this check fails. # _fill_value should always be an ndarray. if not isinstance(dout._fill_value, np.ndarray): raise RuntimeError('Internal NumPy error.') # If we're indexing a multidimensional field in a # structured array (such as dtype("(2,)i2,(2,)i1")), # dimensionality goes up (M[field].ndim == M.ndim + # M.dtype[field].ndim). That's fine for # M[field] but problematic for M[field].fill_value # which should have shape () to avoid breaking several # methods. There is no great way out, so set to # first element. See issue #6723. if dout._fill_value.ndim > 0: if not (dout._fill_value == dout._fill_value.flat[0]).all(): warnings.warn( "Upon accessing multidimensional field " f"{indx!s}, need to keep dimensionality " "of fill_value at 0. Discarding " "heterogeneous fill_value and setting " f"all to {dout._fill_value[0]!s}.", stacklevel=2) # Need to use `.flat[0:1].squeeze(...)` instead of just # `.flat[0]` to ensure the result is a 0d array and not # a scalar. dout._fill_value = dout._fill_value.flat[0:1].squeeze(axis=0) dout._isfield = True # Update the mask if needed if mout is not nomask: # set shape to match that of data; this is needed for matrices dout._mask = reshape(mout, dout.shape) dout._sharedmask = True # Note: Don't try to check for m.any(), that'll take too long return dout # setitem may put NaNs into integer arrays or occasionally overflow a # float. But this may happen in masked values, so avoid otherwise # correct warnings (as is typical also in masked calculations). @np.errstate(over='ignore', invalid='ignore') def __setitem__(self, indx, value): """ x.__setitem__(i, y) <==> x[i]=y Set item described by index. If value is masked, masks those locations. """ if self is masked: raise MaskError('Cannot alter the masked element.') _data = self._data _mask = self._mask if isinstance(indx, str): _data[indx] = value if _mask is nomask: self._mask = _mask = make_mask_none(self.shape, self.dtype) _mask[indx] = getmask(value) return _dtype = _data.dtype if value is masked: # The mask wasn't set: create a full version. if _mask is nomask: _mask = self._mask = make_mask_none(self.shape, _dtype) # Now, set the mask to its value. if _dtype.names is not None: _mask[indx] = tuple([True] * len(_dtype.names)) else: _mask[indx] = True return # Get the _data part of the new value dval = getattr(value, '_data', value) # Get the _mask part of the new value mval = getmask(value) if _dtype.names is not None and mval is nomask: mval = tuple([False] * len(_dtype.names)) if _mask is nomask: # Set the data, then the mask _data[indx] = dval if mval is not nomask: _mask = self._mask = make_mask_none(self.shape, _dtype) _mask[indx] = mval elif not self._hardmask: # Set the data, then the mask if (isinstance(indx, masked_array) and not isinstance(value, masked_array)): _data[indx.data] = dval else: _data[indx] = dval _mask[indx] = mval elif hasattr(indx, 'dtype') and (indx.dtype == MaskType): indx = indx * umath.logical_not(_mask) _data[indx] = dval else: if _dtype.names is not None: err_msg = "Flexible 'hard' masks are not yet supported." raise NotImplementedError(err_msg) mindx = mask_or(_mask[indx], mval, copy=True) dindx = self._data[indx] if dindx.size > 1: np.copyto(dindx, dval, where=~mindx) elif mindx is nomask: dindx = dval _data[indx] = dindx _mask[indx] = mindx return # Define so that we can overwrite the setter. @property def dtype(self): return super().dtype @dtype.setter def dtype(self, dtype): super(MaskedArray, type(self)).dtype.__set__(self, dtype) if self._mask is not nomask: self._mask = self._mask.view(make_mask_descr(dtype), ndarray) # Try to reset the shape of the mask (if we don't have a void). # This raises a ValueError if the dtype change won't work. try: self._mask.shape = self.shape except (AttributeError, TypeError): pass @property def shape(self): return super().shape @shape.setter def shape(self, shape): super(MaskedArray, type(self)).shape.__set__(self, shape) # Cannot use self._mask, since it may not (yet) exist when a # masked matrix sets the shape. if getmask(self) is not nomask: self._mask.shape = self.shape def __setmask__(self, mask, copy=False): """ Set the mask. """ idtype = self.dtype current_mask = self._mask if mask is masked: mask = True if current_mask is nomask: # Make sure the mask is set # Just don't do anything if there's nothing to do. if mask is nomask: return current_mask = self._mask = make_mask_none(self.shape, idtype) if idtype.names is None: # No named fields. # Hardmask: don't unmask the data if self._hardmask: current_mask |= mask # Softmask: set everything to False # If it's obviously a compatible scalar, use a quick update # method. elif isinstance(mask, (int, float, np.bool_, np.number)): current_mask[...] = mask # Otherwise fall back to the slower, general purpose way. else: current_mask.flat = mask else: # Named fields w/ mdtype = current_mask.dtype mask = np.array(mask, copy=False) # Mask is a singleton if not mask.ndim: # It's a boolean : make a record if mask.dtype.kind == 'b': mask = np.array(tuple([mask.item()] * len(mdtype)), dtype=mdtype) # It's a record: make sure the dtype is correct else: mask = mask.astype(mdtype) # Mask is a sequence else: # Make sure the new mask is a ndarray with the proper dtype try: mask = np.array(mask, copy=copy, dtype=mdtype) # Or assume it's a sequence of bool/int except TypeError: mask = np.array([tuple([m] * len(mdtype)) for m in mask], dtype=mdtype) # Hardmask: don't unmask the data if self._hardmask: for n in idtype.names: current_mask[n] |= mask[n] # Softmask: set everything to False # If it's obviously a compatible scalar, use a quick update # method. elif isinstance(mask, (int, float, np.bool_, np.number)): current_mask[...] = mask # Otherwise fall back to the slower, general purpose way. else: current_mask.flat = mask # Reshape if needed if current_mask.shape: current_mask.shape = self.shape return _set_mask = __setmask__ @property def mask(self): """ Current mask. """ # We could try to force a reshape, but that wouldn't work in some # cases. # Return a view so that the dtype and shape cannot be changed in place # This still preserves nomask by identity return self._mask.view() @mask.setter def mask(self, value): self.__setmask__(value) @property def recordmask(self): """ Get or set the mask of the array if it has no named fields. For structured arrays, returns a ndarray of booleans where entries are ``True`` if **all** the fields are masked, ``False`` otherwise: >>> x = np.ma.array([(1, 1), (2, 2), (3, 3), (4, 4), (5, 5)], ... mask=[(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)], ... dtype=[('a', int), ('b', int)]) >>> x.recordmask array([False, False, True, False, False]) """ _mask = self._mask.view(ndarray) if _mask.dtype.names is None: return _mask return np.all(flatten_structured_array(_mask), axis=-1) @recordmask.setter def recordmask(self, mask): raise NotImplementedError("Coming soon: setting the mask per records!") def harden_mask(self): """ Force the mask to hard, preventing unmasking by assignment. Whether the mask of a masked array is hard or soft is determined by its `~ma.MaskedArray.hardmask` property. `harden_mask` sets `~ma.MaskedArray.hardmask` to ``True`` (and returns the modified self). See Also -------- ma.MaskedArray.hardmask ma.MaskedArray.soften_mask """ self._hardmask = True return self def soften_mask(self): """ Force the mask to soft (default), allowing unmasking by assignment. Whether the mask of a masked array is hard or soft is determined by its `~ma.MaskedArray.hardmask` property. `soften_mask` sets `~ma.MaskedArray.hardmask` to ``False`` (and returns the modified self). See Also -------- ma.MaskedArray.hardmask ma.MaskedArray.harden_mask """ self._hardmask = False return self @property def hardmask(self): """ Specifies whether values can be unmasked through assignments. By default, assigning definite values to masked array entries will unmask them. When `hardmask` is ``True``, the mask will not change through assignments. See Also -------- ma.MaskedArray.harden_mask ma.MaskedArray.soften_mask Examples -------- >>> x = np.arange(10) >>> m = np.ma.masked_array(x, x>5) >>> assert not m.hardmask Since `m` has a soft mask, assigning an element value unmasks that element: >>> m[8] = 42 >>> m masked_array(data=[0, 1, 2, 3, 4, 5, --, --, 42, --], mask=[False, False, False, False, False, False, True, True, False, True], fill_value=999999) After hardening, the mask is not affected by assignments: >>> hardened = np.ma.harden_mask(m) >>> assert m.hardmask and hardened is m >>> m[:] = 23 >>> m masked_array(data=[23, 23, 23, 23, 23, 23, --, --, 23, --], mask=[False, False, False, False, False, False, True, True, False, True], fill_value=999999) """ return self._hardmask def unshare_mask(self): """ Copy the mask and set the `sharedmask` flag to ``False``. Whether the mask is shared between masked arrays can be seen from the `sharedmask` property. `unshare_mask` ensures the mask is not shared. A copy of the mask is only made if it was shared. See Also -------- sharedmask """ if self._sharedmask: self._mask = self._mask.copy() self._sharedmask = False return self @property def sharedmask(self): """ Share status of the mask (read-only). """ return self._sharedmask def shrink_mask(self): """ Reduce a mask to nomask when possible. Parameters ---------- None Returns ------- None Examples -------- >>> x = np.ma.array([[1,2 ], [3, 4]], mask=[0]*4) >>> x.mask array([[False, False], [False, False]]) >>> x.shrink_mask() masked_array( data=[[1, 2], [3, 4]], mask=False, fill_value=999999) >>> x.mask False """ self._mask = _shrink_mask(self._mask) return self @property def baseclass(self): """ Class of the underlying data (read-only). """ return self._baseclass def _get_data(self): """ Returns the underlying data, as a view of the masked array. If the underlying data is a subclass of :class:`numpy.ndarray`, it is returned as such. >>> x = np.ma.array(np.matrix([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]]) >>> x.data matrix([[1, 2], [3, 4]]) The type of the data can be accessed through the :attr:`baseclass` attribute. """ return ndarray.view(self, self._baseclass) _data = property(fget=_get_data) data = property(fget=_get_data) @property def flat(self): """ Return a flat iterator, or set a flattened version of self to value. """ return MaskedIterator(self) @flat.setter def flat(self, value): y = self.ravel() y[:] = value @property def fill_value(self): """ The filling value of the masked array is a scalar. When setting, None will set to a default based on the data type. Examples -------- >>> for dt in [np.int32, np.int64, np.float64, np.complex128]: ... np.ma.array([0, 1], dtype=dt).get_fill_value() ... 999999 999999 1e+20 (1e+20+0j) >>> x = np.ma.array([0, 1.], fill_value=-np.inf) >>> x.fill_value -inf >>> x.fill_value = np.pi >>> x.fill_value 3.1415926535897931 # may vary Reset to default: >>> x.fill_value = None >>> x.fill_value 1e+20 """ if self._fill_value is None: self._fill_value = _check_fill_value(None, self.dtype) # Temporary workaround to account for the fact that str and bytes # scalars cannot be indexed with (), whereas all other numpy # scalars can. See issues #7259 and #7267. # The if-block can be removed after #7267 has been fixed. if isinstance(self._fill_value, ndarray): return self._fill_value[()] return self._fill_value @fill_value.setter def fill_value(self, value=None): target = _check_fill_value(value, self.dtype) if not target.ndim == 0: # 2019-11-12, 1.18.0 warnings.warn( "Non-scalar arrays for the fill value are deprecated. Use " "arrays with scalar values instead. The filled function " "still supports any array as `fill_value`.", DeprecationWarning, stacklevel=2) _fill_value = self._fill_value if _fill_value is None: # Create the attribute if it was undefined self._fill_value = target else: # Don't overwrite the attribute, just fill it (for propagation) _fill_value[()] = target # kept for compatibility get_fill_value = fill_value.fget set_fill_value = fill_value.fset def filled(self, fill_value=None): """ Return a copy of self, with masked values filled with a given value. **However**, if there are no masked values to fill, self will be returned instead as an ndarray. Parameters ---------- fill_value : array_like, optional The value to use for invalid entries. Can be scalar or non-scalar. If non-scalar, the resulting ndarray must be broadcastable over input array. Default is None, in which case, the `fill_value` attribute of the array is used instead. Returns ------- filled_array : ndarray A copy of ``self`` with invalid entries replaced by *fill_value* (be it the function argument or the attribute of ``self``), or ``self`` itself as an ndarray if there are no invalid entries to be replaced. Notes ----- The result is **not** a MaskedArray! Examples -------- >>> x = np.ma.array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999) >>> x.filled() array([ 1, 2, -999, 4, -999]) >>> x.filled(fill_value=1000) array([ 1, 2, 1000, 4, 1000]) >>> type(x.filled()) <class 'numpy.ndarray'> Subclassing is preserved. This means that if, e.g., the data part of the masked array is a recarray, `filled` returns a recarray: >>> x = np.array([(-1, 2), (-3, 4)], dtype='i8,i8').view(np.recarray) >>> m = np.ma.array(x, mask=[(True, False), (False, True)]) >>> m.filled() rec.array([(999999, 2), ( -3, 999999)], dtype=[('f0', '<i8'), ('f1', '<i8')]) """ m = self._mask if m is nomask: return self._data if fill_value is None: fill_value = self.fill_value else: fill_value = _check_fill_value(fill_value, self.dtype) if self is masked_singleton: return np.asanyarray(fill_value) if m.dtype.names is not None: result = self._data.copy('K') _recursive_filled(result, self._mask, fill_value) elif not m.any(): return self._data else: result = self._data.copy('K') try: np.copyto(result, fill_value, where=m) except (TypeError, AttributeError): fill_value = narray(fill_value, dtype=object) d = result.astype(object) result = np.choose(m, (d, fill_value)) except IndexError: # ok, if scalar if self._data.shape: raise elif m: result = np.array(fill_value, dtype=self.dtype) else: result = self._data return result def compressed(self): """ Return all the non-masked data as a 1-D array. Returns ------- data : ndarray A new `ndarray` holding the non-masked data is returned. Notes ----- The result is **not** a MaskedArray! Examples -------- >>> x = np.ma.array(np.arange(5), mask=[0]*2 + [1]*3) >>> x.compressed() array([0, 1]) >>> type(x.compressed()) <class 'numpy.ndarray'> """ data = ndarray.ravel(self._data) if self._mask is not nomask: data = data.compress(np.logical_not(ndarray.ravel(self._mask))) return data def compress(self, condition, axis=None, out=None): """ Return `a` where condition is ``True``. If condition is a `~ma.MaskedArray`, missing values are considered as ``False``. Parameters ---------- condition : var Boolean 1-d array selecting which entries to return. If len(condition) is less than the size of a along the axis, then output is truncated to length of condition array. axis : {None, int}, optional Axis along which the operation must be performed. out : {None, ndarray}, optional Alternative output array in which to place the result. It must have the same shape as the expected output but the type will be cast if necessary. Returns ------- result : MaskedArray A :class:`~ma.MaskedArray` object. Notes ----- Please note the difference with :meth:`compressed` ! The output of :meth:`compress` has a mask, the output of :meth:`compressed` does not. Examples -------- >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4) >>> x masked_array( data=[[1, --, 3], [--, 5, --], [7, --, 9]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) >>> x.compress([1, 0, 1]) masked_array(data=[1, 3], mask=[False, False], fill_value=999999) >>> x.compress([1, 0, 1], axis=1) masked_array( data=[[1, 3], [--, --], [7, 9]], mask=[[False, False], [ True, True], [False, False]], fill_value=999999) """ # Get the basic components (_data, _mask) = (self._data, self._mask) # Force the condition to a regular ndarray and forget the missing # values. condition = np.asarray(condition) _new = _data.compress(condition, axis=axis, out=out).view(type(self)) _new._update_from(self) if _mask is not nomask: _new._mask = _mask.compress(condition, axis=axis) return _new def _insert_masked_print(self): """ Replace masked values with masked_print_option, casting all innermost dtypes to object. """ if masked_print_option.enabled(): mask = self._mask if mask is nomask: res = self._data else: # convert to object array to make filled work data = self._data # For big arrays, to avoid a costly conversion to the # object dtype, extract the corners before the conversion. print_width = (self._print_width if self.ndim > 1 else self._print_width_1d) for axis in range(self.ndim): if data.shape[axis] > print_width: ind = print_width // 2 arr = np.split(data, (ind, -ind), axis=axis) data = np.concatenate((arr[0], arr[2]), axis=axis) arr = np.split(mask, (ind, -ind), axis=axis) mask = np.concatenate((arr[0], arr[2]), axis=axis) rdtype = _replace_dtype_fields(self.dtype, "O") res = data.astype(rdtype) _recursive_printoption(res, mask, masked_print_option) else: res = self.filled(self.fill_value) return res def __str__(self): return str(self._insert_masked_print()) def __repr__(self): """ Literal string representation. """ if self._baseclass is np.ndarray: name = 'array' else: name = self._baseclass.__name__ # 2016-11-19: Demoted to legacy format if np.core.arrayprint._get_legacy_print_mode() <= 113: is_long = self.ndim > 1 parameters = dict( name=name, nlen=" " * len(name), data=str(self), mask=str(self._mask), fill=str(self.fill_value), dtype=str(self.dtype) ) is_structured = bool(self.dtype.names) key = '{}_{}'.format( 'long' if is_long else 'short', 'flx' if is_structured else 'std' ) return _legacy_print_templates[key] % parameters prefix = f"masked_{name}(" dtype_needed = ( not np.core.arrayprint.dtype_is_implied(self.dtype) or np.all(self.mask) or self.size == 0 ) # determine which keyword args need to be shown keys = ['data', 'mask', 'fill_value'] if dtype_needed: keys.append('dtype') # array has only one row (non-column) is_one_row = builtins.all(dim == 1 for dim in self.shape[:-1]) # choose what to indent each keyword with min_indent = 2 if is_one_row: # first key on the same line as the type, remaining keys # aligned by equals indents = {} indents[keys[0]] = prefix for k in keys[1:]: n = builtins.max(min_indent, len(prefix + keys[0]) - len(k)) indents[k] = ' ' * n prefix = '' # absorbed into the first indent else: # each key on its own line, indented by two spaces indents = {k: ' ' * min_indent for k in keys} prefix = prefix + '\n' # first key on the next line # format the field values reprs = {} reprs['data'] = np.array2string( self._insert_masked_print(), separator=", ", prefix=indents['data'] + 'data=', suffix=',') reprs['mask'] = np.array2string( self._mask, separator=", ", prefix=indents['mask'] + 'mask=', suffix=',') reprs['fill_value'] = repr(self.fill_value) if dtype_needed: reprs['dtype'] = np.core.arrayprint.dtype_short_repr(self.dtype) # join keys with values and indentations result = ',\n'.join( '{}{}={}'.format(indents[k], k, reprs[k]) for k in keys ) return prefix + result + ')' def _delegate_binop(self, other): # This emulates the logic in # private/binop_override.h:forward_binop_should_defer if isinstance(other, type(self)): return False array_ufunc = getattr(other, "__array_ufunc__", False) if array_ufunc is False: other_priority = getattr(other, "__array_priority__", -1000000) return self.__array_priority__ < other_priority else: # If array_ufunc is not None, it will be called inside the ufunc; # None explicitly tells us to not call the ufunc, i.e., defer. return array_ufunc is None def _comparison(self, other, compare): """Compare self with other using operator.eq or operator.ne. When either of the elements is masked, the result is masked as well, but the underlying boolean data are still set, with self and other considered equal if both are masked, and unequal otherwise. For structured arrays, all fields are combined, with masked values ignored. The result is masked if all fields were masked, with self and other considered equal only if both were fully masked. """ omask = getmask(other) smask = self.mask mask = mask_or(smask, omask, copy=True) odata = getdata(other) if mask.dtype.names is not None: # only == and != are reasonably defined for structured dtypes, # so give up early for all other comparisons: if compare not in (operator.eq, operator.ne): return NotImplemented # For possibly masked structured arrays we need to be careful, # since the standard structured array comparison will use all # fields, masked or not. To avoid masked fields influencing the # outcome, we set all masked fields in self to other, so they'll # count as equal. To prepare, we ensure we have the right shape. broadcast_shape = np.broadcast(self, odata).shape sbroadcast = np.broadcast_to(self, broadcast_shape, subok=True) sbroadcast._mask = mask sdata = sbroadcast.filled(odata) # Now take care of the mask; the merged mask should have an item # masked if all fields were masked (in one and/or other). mask = (mask == np.ones((), mask.dtype)) else: # For regular arrays, just use the data as they come. sdata = self.data check = compare(sdata, odata) if isinstance(check, (np.bool_, bool)): return masked if mask else check if mask is not nomask and compare in (operator.eq, operator.ne): # Adjust elements that were masked, which should be treated # as equal if masked in both, unequal if masked in one. # Note that this works automatically for structured arrays too. # Ignore this for operations other than `==` and `!=` check = np.where(mask, compare(smask, omask), check) if mask.shape != check.shape: # Guarantee consistency of the shape, making a copy since the # the mask may need to get written to later. mask = np.broadcast_to(mask, check.shape).copy() check = check.view(type(self)) check._update_from(self) check._mask = mask # Cast fill value to bool_ if needed. If it cannot be cast, the # default boolean fill value is used. if check._fill_value is not None: try: fill = _check_fill_value(check._fill_value, np.bool_) except (TypeError, ValueError): fill = _check_fill_value(None, np.bool_) check._fill_value = fill return check def __eq__(self, other): """Check whether other equals self elementwise. When either of the elements is masked, the result is masked as well, but the underlying boolean data are still set, with self and other considered equal if both are masked, and unequal otherwise. For structured arrays, all fields are combined, with masked values ignored. The result is masked if all fields were masked, with self and other considered equal only if both were fully masked. """ return self._comparison(other, operator.eq) def __ne__(self, other): """Check whether other does not equal self elementwise. When either of the elements is masked, the result is masked as well, but the underlying boolean data are still set, with self and other considered equal if both are masked, and unequal otherwise. For structured arrays, all fields are combined, with masked values ignored. The result is masked if all fields were masked, with self and other considered equal only if both were fully masked. """ return self._comparison(other, operator.ne) # All other comparisons: def __le__(self, other): return self._comparison(other, operator.le) def __lt__(self, other): return self._comparison(other, operator.lt) def __ge__(self, other): return self._comparison(other, operator.ge) def __gt__(self, other): return self._comparison(other, operator.gt) def __add__(self, other): """ Add self to other, and return a new masked array. """ if self._delegate_binop(other): return NotImplemented return add(self, other) def __radd__(self, other): """ Add other to self, and return a new masked array. """ # In analogy with __rsub__ and __rdiv__, use original order: # we get here from `other + self`. return add(other, self) def __sub__(self, other): """ Subtract other from self, and return a new masked array. """ if self._delegate_binop(other): return NotImplemented return subtract(self, other) def __rsub__(self, other): """ Subtract self from other, and return a new masked array. """ return subtract(other, self) def __mul__(self, other): "Multiply self by other, and return a new masked array." if self._delegate_binop(other): return NotImplemented return multiply(self, other) def __rmul__(self, other): """ Multiply other by self, and return a new masked array. """ # In analogy with __rsub__ and __rdiv__, use original order: # we get here from `other * self`. return multiply(other, self) def __div__(self, other): """ Divide other into self, and return a new masked array. """ if self._delegate_binop(other): return NotImplemented return divide(self, other) def __truediv__(self, other): """ Divide other into self, and return a new masked array. """ if self._delegate_binop(other): return NotImplemented return true_divide(self, other) def __rtruediv__(self, other): """ Divide self into other, and return a new masked array. """ return true_divide(other, self) def __floordiv__(self, other): """ Divide other into self, and return a new masked array. """ if self._delegate_binop(other): return NotImplemented return floor_divide(self, other) def __rfloordiv__(self, other): """ Divide self into other, and return a new masked array. """ return floor_divide(other, self) def __pow__(self, other): """ Raise self to the power other, masking the potential NaNs/Infs """ if self._delegate_binop(other): return NotImplemented return power(self, other) def __rpow__(self, other): """ Raise other to the power self, masking the potential NaNs/Infs """ return power(other, self) def __iadd__(self, other): """ Add other to self in-place. """ m = getmask(other) if self._mask is nomask: if m is not nomask and m.any(): self._mask = make_mask_none(self.shape, self.dtype) self._mask += m else: if m is not nomask: self._mask += m other_data = getdata(other) other_data = np.where(self._mask, other_data.dtype.type(0), other_data) self._data.__iadd__(other_data) return self def __isub__(self, other): """ Subtract other from self in-place. """ m = getmask(other) if self._mask is nomask: if m is not nomask and m.any(): self._mask = make_mask_none(self.shape, self.dtype) self._mask += m elif m is not nomask: self._mask += m other_data = getdata(other) other_data = np.where(self._mask, other_data.dtype.type(0), other_data) self._data.__isub__(other_data) return self def __imul__(self, other): """ Multiply self by other in-place. """ m = getmask(other) if self._mask is nomask: if m is not nomask and m.any(): self._mask = make_mask_none(self.shape, self.dtype) self._mask += m elif m is not nomask: self._mask += m other_data = getdata(other) other_data = np.where(self._mask, other_data.dtype.type(1), other_data) self._data.__imul__(other_data) return self def __idiv__(self, other): """ Divide self by other in-place. """ other_data = getdata(other) dom_mask = _DomainSafeDivide().__call__(self._data, other_data) other_mask = getmask(other) new_mask = mask_or(other_mask, dom_mask) # The following 4 lines control the domain filling if dom_mask.any(): (_, fval) = ufunc_fills[np.divide] other_data = np.where( dom_mask, other_data.dtype.type(fval), other_data) self._mask |= new_mask other_data = np.where(self._mask, other_data.dtype.type(1), other_data) self._data.__idiv__(other_data) return self def __ifloordiv__(self, other): """ Floor divide self by other in-place. """ other_data = getdata(other) dom_mask = _DomainSafeDivide().__call__(self._data, other_data) other_mask = getmask(other) new_mask = mask_or(other_mask, dom_mask) # The following 3 lines control the domain filling if dom_mask.any(): (_, fval) = ufunc_fills[np.floor_divide] other_data = np.where( dom_mask, other_data.dtype.type(fval), other_data) self._mask |= new_mask other_data = np.where(self._mask, other_data.dtype.type(1), other_data) self._data.__ifloordiv__(other_data) return self def __itruediv__(self, other): """ True divide self by other in-place. """ other_data = getdata(other) dom_mask = _DomainSafeDivide().__call__(self._data, other_data) other_mask = getmask(other) new_mask = mask_or(other_mask, dom_mask) # The following 3 lines control the domain filling if dom_mask.any(): (_, fval) = ufunc_fills[np.true_divide] other_data = np.where( dom_mask, other_data.dtype.type(fval), other_data) self._mask |= new_mask other_data = np.where(self._mask, other_data.dtype.type(1), other_data) self._data.__itruediv__(other_data) return self def __ipow__(self, other): """ Raise self to the power other, in place. """ other_data = getdata(other) other_data = np.where(self._mask, other_data.dtype.type(1), other_data) other_mask = getmask(other) with np.errstate(divide='ignore', invalid='ignore'): self._data.__ipow__(other_data) invalid = np.logical_not(np.isfinite(self._data)) if invalid.any(): if self._mask is not nomask: self._mask |= invalid else: self._mask = invalid np.copyto(self._data, self.fill_value, where=invalid) new_mask = mask_or(other_mask, invalid) self._mask = mask_or(self._mask, new_mask) return self def __float__(self): """ Convert to float. """ if self.size > 1: raise TypeError("Only length-1 arrays can be converted " "to Python scalars") elif self._mask: warnings.warn("Warning: converting a masked element to nan.", stacklevel=2) return np.nan return float(self.item()) def __int__(self): """ Convert to int. """ if self.size > 1: raise TypeError("Only length-1 arrays can be converted " "to Python scalars") elif self._mask: raise MaskError('Cannot convert masked element to a Python int.') return int(self.item()) @property def imag(self): """ The imaginary part of the masked array. This property is a view on the imaginary part of this `MaskedArray`. See Also -------- real Examples -------- >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False]) >>> x.imag masked_array(data=[1.0, --, 1.6], mask=[False, True, False], fill_value=1e+20) """ result = self._data.imag.view(type(self)) result.__setmask__(self._mask) return result # kept for compatibility get_imag = imag.fget @property def real(self): """ The real part of the masked array. This property is a view on the real part of this `MaskedArray`. See Also -------- imag Examples -------- >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False]) >>> x.real masked_array(data=[1.0, --, 3.45], mask=[False, True, False], fill_value=1e+20) """ result = self._data.real.view(type(self)) result.__setmask__(self._mask) return result # kept for compatibility get_real = real.fget def count(self, axis=None, keepdims=np._NoValue): """ Count the non-masked elements of the array along the given axis. Parameters ---------- axis : None or int or tuple of ints, optional Axis or axes along which the count is performed. The default, None, performs the count over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis. .. versionadded:: 1.10.0 If this is a tuple of ints, the count is performed on multiple axes, instead of a single axis or all the axes as before. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array. Returns ------- result : ndarray or scalar An array with the same shape as the input array, with the specified axis removed. If the array is a 0-d array, or if `axis` is None, a scalar is returned. See Also -------- ma.count_masked : Count masked elements in array or along a given axis. Examples -------- >>> import numpy.ma as ma >>> a = ma.arange(6).reshape((2, 3)) >>> a[1, :] = ma.masked >>> a masked_array( data=[[0, 1, 2], [--, --, --]], mask=[[False, False, False], [ True, True, True]], fill_value=999999) >>> a.count() 3 When the `axis` keyword is specified an array of appropriate size is returned. >>> a.count(axis=0) array([1, 1, 1]) >>> a.count(axis=1) array([3, 0]) """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} m = self._mask # special case for matrices (we assume no other subclasses modify # their dimensions) if isinstance(self.data, np.matrix): if m is nomask: m = np.zeros(self.shape, dtype=np.bool_) m = m.view(type(self.data)) if m is nomask: # compare to _count_reduce_items in _methods.py if self.shape == (): if axis not in (None, 0): raise np.AxisError(axis=axis, ndim=self.ndim) return 1 elif axis is None: if kwargs.get('keepdims', False): return np.array(self.size, dtype=np.intp, ndmin=self.ndim) return self.size axes = normalize_axis_tuple(axis, self.ndim) items = 1 for ax in axes: items *= self.shape[ax] if kwargs.get('keepdims', False): out_dims = list(self.shape) for a in axes: out_dims[a] = 1 else: out_dims = [d for n, d in enumerate(self.shape) if n not in axes] # make sure to return a 0-d array if axis is supplied return np.full(out_dims, items, dtype=np.intp) # take care of the masked singleton if self is masked: return 0 return (~m).sum(axis=axis, dtype=np.intp, **kwargs) def ravel(self, order='C'): """ Returns a 1D version of self, as a view. Parameters ---------- order : {'C', 'F', 'A', 'K'}, optional The elements of `a` are read using this index order. 'C' means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. 'F' means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the 'C' and 'F' options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. 'A' means to read the elements in Fortran-like index order if `m` is Fortran *contiguous* in memory, C-like order otherwise. 'K' means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, 'C' index order is used. (Masked arrays currently use 'A' on the data when 'K' is passed.) Returns ------- MaskedArray Output view is of shape ``(self.size,)`` (or ``(np.ma.product(self.shape),)``). Examples -------- >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4) >>> x masked_array( data=[[1, --, 3], [--, 5, --], [7, --, 9]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) >>> x.ravel() masked_array(data=[1, --, 3, --, 5, --, 7, --, 9], mask=[False, True, False, True, False, True, False, True, False], fill_value=999999) """ # The order of _data and _mask could be different (it shouldn't be # normally). Passing order `K` or `A` would be incorrect. # So we ignore the mask memory order. # TODO: We don't actually support K, so use A instead. We could # try to guess this correct by sorting strides or deprecate. if order in "kKaA": order = "F" if self._data.flags.fnc else "C" r = ndarray.ravel(self._data, order=order).view(type(self)) r._update_from(self) if self._mask is not nomask: r._mask = ndarray.ravel(self._mask, order=order).reshape(r.shape) else: r._mask = nomask return r def reshape(self, *s, **kwargs): """ Give a new shape to the array without changing its data. Returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised. Parameters ---------- shape : int or tuple of ints The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length. order : {'C', 'F'}, optional Determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order. Returns ------- reshaped_array : array A new view on the array. See Also -------- reshape : Equivalent function in the masked array module. numpy.ndarray.reshape : Equivalent method on ndarray object. numpy.reshape : Equivalent function in the NumPy module. Notes ----- The reshaping operation cannot guarantee that a copy will not be made, to modify the shape in place, use ``a.shape = s`` Examples -------- >>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1]) >>> x masked_array( data=[[--, 2], [3, --]], mask=[[ True, False], [False, True]], fill_value=999999) >>> x = x.reshape((4,1)) >>> x masked_array( data=[[--], [2], [3], [--]], mask=[[ True], [False], [False], [ True]], fill_value=999999) """ kwargs.update(order=kwargs.get('order', 'C')) result = self._data.reshape(*s, **kwargs).view(type(self)) result._update_from(self) mask = self._mask if mask is not nomask: result._mask = mask.reshape(*s, **kwargs) return result def resize(self, newshape, refcheck=True, order=False): """ .. warning:: This method does nothing, except raise a ValueError exception. A masked array does not own its data and therefore cannot safely be resized in place. Use the `numpy.ma.resize` function instead. This method is difficult to implement safely and may be deprecated in future releases of NumPy. """ # Note : the 'order' keyword looks broken, let's just drop it errmsg = "A masked array does not own its data "\ "and therefore cannot be resized.\n" \ "Use the numpy.ma.resize function instead." raise ValueError(errmsg) def put(self, indices, values, mode='raise'): """ Set storage-indexed locations to corresponding values. Sets self._data.flat[n] = values[n] for each n in indices. If `values` is shorter than `indices` then it will repeat. If `values` has some masked values, the initial mask is updated in consequence, else the corresponding values are unmasked. Parameters ---------- indices : 1-D array_like Target indices, interpreted as integers. values : array_like Values to place in self._data copy at target indices. mode : {'raise', 'wrap', 'clip'}, optional Specifies how out-of-bounds indices will behave. 'raise' : raise an error. 'wrap' : wrap around. 'clip' : clip to the range. Notes ----- `values` can be a scalar or length 1 array. Examples -------- >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4) >>> x masked_array( data=[[1, --, 3], [--, 5, --], [7, --, 9]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) >>> x.put([0,4,8],[10,20,30]) >>> x masked_array( data=[[10, --, 3], [--, 20, --], [7, --, 30]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) >>> x.put(4,999) >>> x masked_array( data=[[10, --, 3], [--, 999, --], [7, --, 30]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) """ # Hard mask: Get rid of the values/indices that fall on masked data if self._hardmask and self._mask is not nomask: mask = self._mask[indices] indices = narray(indices, copy=False) values = narray(values, copy=False, subok=True) values.resize(indices.shape) indices = indices[~mask] values = values[~mask] self._data.put(indices, values, mode=mode) # short circuit if neither self nor values are masked if self._mask is nomask and getmask(values) is nomask: return m = getmaskarray(self) if getmask(values) is nomask: m.put(indices, False, mode=mode) else: m.put(indices, values._mask, mode=mode) m = make_mask(m, copy=False, shrink=True) self._mask = m return def ids(self): """ Return the addresses of the data and mask areas. Parameters ---------- None Examples -------- >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1]) >>> x.ids() (166670640, 166659832) # may vary If the array has no mask, the address of `nomask` is returned. This address is typically not close to the data in memory: >>> x = np.ma.array([1, 2, 3]) >>> x.ids() (166691080, 3083169284) # may vary """ if self._mask is nomask: return (self.ctypes.data, id(nomask)) return (self.ctypes.data, self._mask.ctypes.data) def iscontiguous(self): """ Return a boolean indicating whether the data is contiguous. Parameters ---------- None Examples -------- >>> x = np.ma.array([1, 2, 3]) >>> x.iscontiguous() True `iscontiguous` returns one of the flags of the masked array: >>> x.flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False """ return self.flags['CONTIGUOUS'] def all(self, axis=None, out=None, keepdims=np._NoValue): """ Returns True if all elements evaluate to True. The output array is masked where all the values along the given axis are masked: if the output would have been a scalar and that all the values are masked, then the output is `masked`. Refer to `numpy.all` for full documentation. See Also -------- numpy.ndarray.all : corresponding function for ndarrays numpy.all : equivalent function Examples -------- >>> np.ma.array([1,2,3]).all() True >>> a = np.ma.array([1,2,3], mask=True) >>> (a.all() is np.ma.masked) True """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} mask = _check_mask_axis(self._mask, axis, **kwargs) if out is None: d = self.filled(True).all(axis=axis, **kwargs).view(type(self)) if d.ndim: d.__setmask__(mask) elif mask: return masked return d self.filled(True).all(axis=axis, out=out, **kwargs) if isinstance(out, MaskedArray): if out.ndim or mask: out.__setmask__(mask) return out def any(self, axis=None, out=None, keepdims=np._NoValue): """ Returns True if any of the elements of `a` evaluate to True. Masked values are considered as False during computation. Refer to `numpy.any` for full documentation. See Also -------- numpy.ndarray.any : corresponding function for ndarrays numpy.any : equivalent function """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} mask = _check_mask_axis(self._mask, axis, **kwargs) if out is None: d = self.filled(False).any(axis=axis, **kwargs).view(type(self)) if d.ndim: d.__setmask__(mask) elif mask: d = masked return d self.filled(False).any(axis=axis, out=out, **kwargs) if isinstance(out, MaskedArray): if out.ndim or mask: out.__setmask__(mask) return out def nonzero(self): """ Return the indices of unmasked elements that are not zero. Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with:: a[a.nonzero()] To group the indices by element, rather than dimension, use instead:: np.transpose(a.nonzero()) The result of this is always a 2d array, with a row for each non-zero element. Parameters ---------- None Returns ------- tuple_of_arrays : tuple Indices of elements that are non-zero. See Also -------- numpy.nonzero : Function operating on ndarrays. flatnonzero : Return indices that are non-zero in the flattened version of the input array. numpy.ndarray.nonzero : Equivalent ndarray method. count_nonzero : Counts the number of non-zero elements in the input array. Examples -------- >>> import numpy.ma as ma >>> x = ma.array(np.eye(3)) >>> x masked_array( data=[[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]], mask=False, fill_value=1e+20) >>> x.nonzero() (array([0, 1, 2]), array([0, 1, 2])) Masked elements are ignored. >>> x[1, 1] = ma.masked >>> x masked_array( data=[[1.0, 0.0, 0.0], [0.0, --, 0.0], [0.0, 0.0, 1.0]], mask=[[False, False, False], [False, True, False], [False, False, False]], fill_value=1e+20) >>> x.nonzero() (array([0, 2]), array([0, 2])) Indices can also be grouped by element. >>> np.transpose(x.nonzero()) array([[0, 0], [2, 2]]) A common use for ``nonzero`` is to find the indices of an array, where a condition is True. Given an array `a`, the condition `a` > 3 is a boolean array and since False is interpreted as 0, ma.nonzero(a > 3) yields the indices of the `a` where the condition is true. >>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]]) >>> a > 3 masked_array( data=[[False, False, False], [ True, True, True], [ True, True, True]], mask=False, fill_value=True) >>> ma.nonzero(a > 3) (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) The ``nonzero`` method of the condition array can also be called. >>> (a > 3).nonzero() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) """ return narray(self.filled(0), copy=False).nonzero() def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None): """ (this docstring should be overwritten) """ #!!!: implement out + test! m = self._mask if m is nomask: result = super().trace(offset=offset, axis1=axis1, axis2=axis2, out=out) return result.astype(dtype) else: D = self.diagonal(offset=offset, axis1=axis1, axis2=axis2) return D.astype(dtype).filled(0).sum(axis=-1, out=out) trace.__doc__ = ndarray.trace.__doc__ def dot(self, b, out=None, strict=False): """ a.dot(b, out=None) Masked dot product of two arrays. Note that `out` and `strict` are located in different positions than in `ma.dot`. In order to maintain compatibility with the functional version, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory. .. versionadded:: 1.10.0 Parameters ---------- b : masked_array_like Inputs array. out : masked_array, optional Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for `ma.dot(a,b)`. This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible. strict : bool, optional Whether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that if a masked value appears in a row or column, the whole row or column is considered masked. .. versionadded:: 1.10.2 See Also -------- numpy.ma.dot : equivalent function """ return dot(self, b, out=out, strict=strict) def sum(self, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ Return the sum of the array elements over the given axis. Masked elements are set to 0 internally. Refer to `numpy.sum` for full documentation. See Also -------- numpy.ndarray.sum : corresponding function for ndarrays numpy.sum : equivalent function Examples -------- >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4) >>> x masked_array( data=[[1, --, 3], [--, 5, --], [7, --, 9]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) >>> x.sum() 25 >>> x.sum(axis=1) masked_array(data=[4, 5, 16], mask=[False, False, False], fill_value=999999) >>> x.sum(axis=0) masked_array(data=[8, 5, 12], mask=[False, False, False], fill_value=999999) >>> print(type(x.sum(axis=0, dtype=np.int64)[0])) <class 'numpy.int64'> """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} _mask = self._mask newmask = _check_mask_axis(_mask, axis, **kwargs) # No explicit output if out is None: result = self.filled(0).sum(axis, dtype=dtype, **kwargs) rndim = getattr(result, 'ndim', 0) if rndim: result = result.view(type(self)) result.__setmask__(newmask) elif newmask: result = masked return result # Explicit output result = self.filled(0).sum(axis, dtype=dtype, out=out, **kwargs) if isinstance(out, MaskedArray): outmask = getmask(out) if outmask is nomask: outmask = out._mask = make_mask_none(out.shape) outmask.flat = newmask return out def cumsum(self, axis=None, dtype=None, out=None): """ Return the cumulative sum of the array elements over the given axis. Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Refer to `numpy.cumsum` for full documentation. Notes ----- The mask is lost if `out` is not a valid :class:`ma.MaskedArray` ! Arithmetic is modular when using integer types, and no error is raised on overflow. See Also -------- numpy.ndarray.cumsum : corresponding function for ndarrays numpy.cumsum : equivalent function Examples -------- >>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0]) >>> marr.cumsum() masked_array(data=[0, 1, 3, --, --, --, 9, 16, 24, 33], mask=[False, False, False, True, True, True, False, False, False, False], fill_value=999999) """ result = self.filled(0).cumsum(axis=axis, dtype=dtype, out=out) if out is not None: if isinstance(out, MaskedArray): out.__setmask__(self.mask) return out result = result.view(type(self)) result.__setmask__(self._mask) return result def prod(self, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ Return the product of the array elements over the given axis. Masked elements are set to 1 internally for computation. Refer to `numpy.prod` for full documentation. Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow. See Also -------- numpy.ndarray.prod : corresponding function for ndarrays numpy.prod : equivalent function """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} _mask = self._mask newmask = _check_mask_axis(_mask, axis, **kwargs) # No explicit output if out is None: result = self.filled(1).prod(axis, dtype=dtype, **kwargs) rndim = getattr(result, 'ndim', 0) if rndim: result = result.view(type(self)) result.__setmask__(newmask) elif newmask: result = masked return result # Explicit output result = self.filled(1).prod(axis, dtype=dtype, out=out, **kwargs) if isinstance(out, MaskedArray): outmask = getmask(out) if outmask is nomask: outmask = out._mask = make_mask_none(out.shape) outmask.flat = newmask return out product = prod def cumprod(self, axis=None, dtype=None, out=None): """ Return the cumulative product of the array elements over the given axis. Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations. Refer to `numpy.cumprod` for full documentation. Notes ----- The mask is lost if `out` is not a valid MaskedArray ! Arithmetic is modular when using integer types, and no error is raised on overflow. See Also -------- numpy.ndarray.cumprod : corresponding function for ndarrays numpy.cumprod : equivalent function """ result = self.filled(1).cumprod(axis=axis, dtype=dtype, out=out) if out is not None: if isinstance(out, MaskedArray): out.__setmask__(self._mask) return out result = result.view(type(self)) result.__setmask__(self._mask) return result def mean(self, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ Returns the average of the array elements along given axis. Masked entries are ignored, and result elements which are not finite will be masked. Refer to `numpy.mean` for full documentation. See Also -------- numpy.ndarray.mean : corresponding function for ndarrays numpy.mean : Equivalent function numpy.ma.average : Weighted average. Examples -------- >>> a = np.ma.array([1,2,3], mask=[False, False, True]) >>> a masked_array(data=[1, 2, --], mask=[False, False, True], fill_value=999999) >>> a.mean() 1.5 """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} if self._mask is nomask: result = super().mean(axis=axis, dtype=dtype, **kwargs)[()] else: is_float16_result = False if dtype is None: if issubclass(self.dtype.type, (ntypes.integer, ntypes.bool_)): dtype = mu.dtype('f8') elif issubclass(self.dtype.type, ntypes.float16): dtype = mu.dtype('f4') is_float16_result = True dsum = self.sum(axis=axis, dtype=dtype, **kwargs) cnt = self.count(axis=axis, **kwargs) if cnt.shape == () and (cnt == 0): result = masked elif is_float16_result: result = self.dtype.type(dsum * 1. / cnt) else: result = dsum * 1. / cnt if out is not None: out.flat = result if isinstance(out, MaskedArray): outmask = getmask(out) if outmask is nomask: outmask = out._mask = make_mask_none(out.shape) outmask.flat = getmask(result) return out return result def anom(self, axis=None, dtype=None): """ Compute the anomalies (deviations from the arithmetic mean) along the given axis. Returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis. Parameters ---------- axis : int, optional Axis over which the anomalies are taken. The default is to use the mean of the flattened array as reference. dtype : dtype, optional Type to use in computing the variance. For arrays of integer type the default is float32; for arrays of float types it is the same as the array type. See Also -------- mean : Compute the mean of the array. Examples -------- >>> a = np.ma.array([1,2,3]) >>> a.anom() masked_array(data=[-1., 0., 1.], mask=False, fill_value=1e+20) """ m = self.mean(axis, dtype) if not axis: return self - m else: return self - expand_dims(m, axis) def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): """ Returns the variance of the array elements along given axis. Masked entries are ignored, and result elements which are not finite will be masked. Refer to `numpy.var` for full documentation. See Also -------- numpy.ndarray.var : corresponding function for ndarrays numpy.var : Equivalent function """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} # Easy case: nomask, business as usual if self._mask is nomask: ret = super().var(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)[()] if out is not None: if isinstance(out, MaskedArray): out.__setmask__(nomask) return out return ret # Some data are masked, yay! cnt = self.count(axis=axis, **kwargs) - ddof danom = self - self.mean(axis, dtype, keepdims=True) if iscomplexobj(self): danom = umath.absolute(danom) ** 2 else: danom *= danom dvar = divide(danom.sum(axis, **kwargs), cnt).view(type(self)) # Apply the mask if it's not a scalar if dvar.ndim: dvar._mask = mask_or(self._mask.all(axis, **kwargs), (cnt <= 0)) dvar._update_from(self) elif getmask(dvar): # Make sure that masked is returned when the scalar is masked. dvar = masked if out is not None: if isinstance(out, MaskedArray): out.flat = 0 out.__setmask__(True) elif out.dtype.kind in 'biu': errmsg = "Masked data information would be lost in one or "\ "more location." raise MaskError(errmsg) else: out.flat = np.nan return out # In case with have an explicit output if out is not None: # Set the data out.flat = dvar # Set the mask if needed if isinstance(out, MaskedArray): out.__setmask__(dvar.mask) return out return dvar var.__doc__ = np.var.__doc__ def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): """ Returns the standard deviation of the array elements along given axis. Masked entries are ignored. Refer to `numpy.std` for full documentation. See Also -------- numpy.ndarray.std : corresponding function for ndarrays numpy.std : Equivalent function """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} dvar = self.var(axis, dtype, out, ddof, **kwargs) if dvar is not masked: if out is not None: np.power(out, 0.5, out=out, casting='unsafe') return out dvar = sqrt(dvar) return dvar def round(self, decimals=0, out=None): """ Return each element rounded to the given number of decimals. Refer to `numpy.around` for full documentation. See Also -------- numpy.ndarray.round : corresponding function for ndarrays numpy.around : equivalent function """ result = self._data.round(decimals=decimals, out=out).view(type(self)) if result.ndim > 0: result._mask = self._mask result._update_from(self) elif self._mask: # Return masked when the scalar is masked result = masked # No explicit output: we're done if out is None: return result if isinstance(out, MaskedArray): out.__setmask__(self._mask) return out def argsort(self, axis=np._NoValue, kind=None, order=None, endwith=True, fill_value=None): """ Return an ndarray of indices that sort the array along the specified axis. Masked values are filled beforehand to `fill_value`. Parameters ---------- axis : int, optional Axis along which to sort. If None, the default, the flattened array is used. .. versionchanged:: 1.13.0 Previously, the default was documented to be -1, but that was in error. At some future date, the default will change to -1, as originally intended. Until then, the axis should be given explicitly when ``arr.ndim > 1``, to avoid a FutureWarning. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional The sorting algorithm used. order : list, optional When `a` is an array with fields defined, this argument specifies which fields to compare first, second, etc. Not all fields need be specified. endwith : {True, False}, optional Whether missing values (if any) should be treated as the largest values (True) or the smallest values (False) When the array contains unmasked values at the same extremes of the datatype, the ordering of these values and the masked values is undefined. fill_value : scalar or None, optional Value used internally for the masked values. If ``fill_value`` is not None, it supersedes ``endwith``. Returns ------- index_array : ndarray, int Array of indices that sort `a` along the specified axis. In other words, ``a[index_array]`` yields a sorted `a`. See Also -------- ma.MaskedArray.sort : Describes sorting algorithms used. lexsort : Indirect stable sort with multiple keys. numpy.ndarray.sort : Inplace sort. Notes ----- See `sort` for notes on the different sorting algorithms. Examples -------- >>> a = np.ma.array([3,2,1], mask=[False, False, True]) >>> a masked_array(data=[3, 2, --], mask=[False, False, True], fill_value=999999) >>> a.argsort() array([1, 0, 2]) """ # 2017-04-11, Numpy 1.13.0, gh-8701: warn on axis default if axis is np._NoValue: axis = _deprecate_argsort_axis(self) if fill_value is None: if endwith: # nan > inf if np.issubdtype(self.dtype, np.floating): fill_value = np.nan else: fill_value = minimum_fill_value(self) else: fill_value = maximum_fill_value(self) filled = self.filled(fill_value) return filled.argsort(axis=axis, kind=kind, order=order) def argmin(self, axis=None, fill_value=None, out=None, *, keepdims=np._NoValue): """ Return array of indices to the minimum values along the given axis. Parameters ---------- axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axis fill_value : scalar or None, optional Value used to fill in the masked values. If None, the output of minimum_fill_value(self._data) is used instead. out : {None, array}, optional Array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output. Returns ------- ndarray or scalar If multi-dimension input, returns a new ndarray of indices to the minimum values along the given axis. Otherwise, returns a scalar of index to the minimum values along the given axis. Examples -------- >>> x = np.ma.array(np.arange(4), mask=[1,1,0,0]) >>> x.shape = (2,2) >>> x masked_array( data=[[--, --], [2, 3]], mask=[[ True, True], [False, False]], fill_value=999999) >>> x.argmin(axis=0, fill_value=-1) array([0, 0]) >>> x.argmin(axis=0, fill_value=9) array([1, 1]) """ if fill_value is None: fill_value = minimum_fill_value(self) d = self.filled(fill_value).view(ndarray) keepdims = False if keepdims is np._NoValue else bool(keepdims) return d.argmin(axis, out=out, keepdims=keepdims) def argmax(self, axis=None, fill_value=None, out=None, *, keepdims=np._NoValue): """ Returns array of indices of the maximum values along the given axis. Masked values are treated as if they had the value fill_value. Parameters ---------- axis : {None, integer} If None, the index is into the flattened array, otherwise along the specified axis fill_value : scalar or None, optional Value used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead. out : {None, array}, optional Array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output. Returns ------- index_array : {integer_array} Examples -------- >>> a = np.arange(6).reshape(2,3) >>> a.argmax() 5 >>> a.argmax(0) array([1, 1, 1]) >>> a.argmax(1) array([2, 2]) """ if fill_value is None: fill_value = maximum_fill_value(self._data) d = self.filled(fill_value).view(ndarray) keepdims = False if keepdims is np._NoValue else bool(keepdims) return d.argmax(axis, out=out, keepdims=keepdims) def sort(self, axis=-1, kind=None, order=None, endwith=True, fill_value=None): """ Sort the array, in-place Parameters ---------- a : array_like Array to be sorted. axis : int, optional Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional The sorting algorithm used. order : list, optional When `a` is a structured array, this argument specifies which fields to compare first, second, and so on. This list does not need to include all of the fields. endwith : {True, False}, optional Whether missing values (if any) should be treated as the largest values (True) or the smallest values (False) When the array contains unmasked values sorting at the same extremes of the datatype, the ordering of these values and the masked values is undefined. fill_value : scalar or None, optional Value used internally for the masked values. If ``fill_value`` is not None, it supersedes ``endwith``. Returns ------- sorted_array : ndarray Array of the same type and shape as `a`. See Also -------- numpy.ndarray.sort : Method to sort an array in-place. argsort : Indirect sort. lexsort : Indirect stable sort on multiple keys. searchsorted : Find elements in a sorted array. Notes ----- See ``sort`` for notes on the different sorting algorithms. Examples -------- >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) >>> # Default >>> a.sort() >>> a masked_array(data=[1, 3, 5, --, --], mask=[False, False, False, True, True], fill_value=999999) >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) >>> # Put missing values in the front >>> a.sort(endwith=False) >>> a masked_array(data=[--, --, 1, 3, 5], mask=[ True, True, False, False, False], fill_value=999999) >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) >>> # fill_value takes over endwith >>> a.sort(endwith=False, fill_value=3) >>> a masked_array(data=[1, --, --, 3, 5], mask=[False, True, True, False, False], fill_value=999999) """ if self._mask is nomask: ndarray.sort(self, axis=axis, kind=kind, order=order) return if self is masked: return sidx = self.argsort(axis=axis, kind=kind, order=order, fill_value=fill_value, endwith=endwith) self[...] = np.take_along_axis(self, sidx, axis=axis) def min(self, axis=None, out=None, fill_value=None, keepdims=np._NoValue): """ Return the minimum along a given axis. Parameters ---------- axis : None or int or tuple of ints, optional Axis along which to operate. By default, ``axis`` is None and the flattened input is used. .. versionadded:: 1.7.0 If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before. out : array_like, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. fill_value : scalar or None, optional Value used to fill in the masked values. If None, use the output of `minimum_fill_value`. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array. Returns ------- amin : array_like New array holding the result. If ``out`` was specified, ``out`` is returned. See Also -------- ma.minimum_fill_value Returns the minimum filling value for a given datatype. Examples -------- >>> import numpy.ma as ma >>> x = [[1., -2., 3.], [0.2, -0.7, 0.1]] >>> mask = [[1, 1, 0], [0, 0, 1]] >>> masked_x = ma.masked_array(x, mask) >>> masked_x masked_array( data=[[--, --, 3.0], [0.2, -0.7, --]], mask=[[ True, True, False], [False, False, True]], fill_value=1e+20) >>> ma.min(masked_x) -0.7 >>> ma.min(masked_x, axis=-1) masked_array(data=[3.0, -0.7], mask=[False, False], fill_value=1e+20) >>> ma.min(masked_x, axis=0, keepdims=True) masked_array(data=[[0.2, -0.7, 3.0]], mask=[[False, False, False]], fill_value=1e+20) >>> mask = [[1, 1, 1,], [1, 1, 1]] >>> masked_x = ma.masked_array(x, mask) >>> ma.min(masked_x, axis=0) masked_array(data=[--, --, --], mask=[ True, True, True], fill_value=1e+20, dtype=float64) """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} _mask = self._mask newmask = _check_mask_axis(_mask, axis, **kwargs) if fill_value is None: fill_value = minimum_fill_value(self) # No explicit output if out is None: result = self.filled(fill_value).min( axis=axis, out=out, **kwargs).view(type(self)) if result.ndim: # Set the mask result.__setmask__(newmask) # Get rid of Infs if newmask.ndim: np.copyto(result, result.fill_value, where=newmask) elif newmask: result = masked return result # Explicit output result = self.filled(fill_value).min(axis=axis, out=out, **kwargs) if isinstance(out, MaskedArray): outmask = getmask(out) if outmask is nomask: outmask = out._mask = make_mask_none(out.shape) outmask.flat = newmask else: if out.dtype.kind in 'biu': errmsg = "Masked data information would be lost in one or more"\ " location." raise MaskError(errmsg) np.copyto(out, np.nan, where=newmask) return out def max(self, axis=None, out=None, fill_value=None, keepdims=np._NoValue): """ Return the maximum along a given axis. Parameters ---------- axis : None or int or tuple of ints, optional Axis along which to operate. By default, ``axis`` is None and the flattened input is used. .. versionadded:: 1.7.0 If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. out : array_like, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. fill_value : scalar or None, optional Value used to fill in the masked values. If None, use the output of maximum_fill_value(). keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array. Returns ------- amax : array_like New array holding the result. If ``out`` was specified, ``out`` is returned. See Also -------- ma.maximum_fill_value Returns the maximum filling value for a given datatype. Examples -------- >>> import numpy.ma as ma >>> x = [[-1., 2.5], [4., -2.], [3., 0.]] >>> mask = [[0, 0], [1, 0], [1, 0]] >>> masked_x = ma.masked_array(x, mask) >>> masked_x masked_array( data=[[-1.0, 2.5], [--, -2.0], [--, 0.0]], mask=[[False, False], [ True, False], [ True, False]], fill_value=1e+20) >>> ma.max(masked_x) 2.5 >>> ma.max(masked_x, axis=0) masked_array(data=[-1.0, 2.5], mask=[False, False], fill_value=1e+20) >>> ma.max(masked_x, axis=1, keepdims=True) masked_array( data=[[2.5], [-2.0], [0.0]], mask=[[False], [False], [False]], fill_value=1e+20) >>> mask = [[1, 1], [1, 1], [1, 1]] >>> masked_x = ma.masked_array(x, mask) >>> ma.max(masked_x, axis=1) masked_array(data=[--, --, --], mask=[ True, True, True], fill_value=1e+20, dtype=float64) """ kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} _mask = self._mask newmask = _check_mask_axis(_mask, axis, **kwargs) if fill_value is None: fill_value = maximum_fill_value(self) # No explicit output if out is None: result = self.filled(fill_value).max( axis=axis, out=out, **kwargs).view(type(self)) if result.ndim: # Set the mask result.__setmask__(newmask) # Get rid of Infs if newmask.ndim: np.copyto(result, result.fill_value, where=newmask) elif newmask: result = masked return result # Explicit output result = self.filled(fill_value).max(axis=axis, out=out, **kwargs) if isinstance(out, MaskedArray): outmask = getmask(out) if outmask is nomask: outmask = out._mask = make_mask_none(out.shape) outmask.flat = newmask else: if out.dtype.kind in 'biu': errmsg = "Masked data information would be lost in one or more"\ " location." raise MaskError(errmsg) np.copyto(out, np.nan, where=newmask) return out def ptp(self, axis=None, out=None, fill_value=None, keepdims=False): """ Return (maximum - minimum) along the given dimension (i.e. peak-to-peak value). .. warning:: `ptp` preserves the data type of the array. This means the return value for an input of signed integers with n bits (e.g. `np.int8`, `np.int16`, etc) is also a signed integer with n bits. In that case, peak-to-peak values greater than ``2**(n-1)-1`` will be returned as negative values. An example with a work-around is shown below. Parameters ---------- axis : {None, int}, optional Axis along which to find the peaks. If None (default) the flattened array is used. out : {None, array_like}, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary. fill_value : scalar or None, optional Value used to fill in the masked values. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array. Returns ------- ptp : ndarray. A new array holding the result, unless ``out`` was specified, in which case a reference to ``out`` is returned. Examples -------- >>> x = np.ma.MaskedArray([[4, 9, 2, 10], ... [6, 9, 7, 12]]) >>> x.ptp(axis=1) masked_array(data=[8, 6], mask=False, fill_value=999999) >>> x.ptp(axis=0) masked_array(data=[2, 0, 5, 2], mask=False, fill_value=999999) >>> x.ptp() 10 This example shows that a negative value can be returned when the input is an array of signed integers. >>> y = np.ma.MaskedArray([[1, 127], ... [0, 127], ... [-1, 127], ... [-2, 127]], dtype=np.int8) >>> y.ptp(axis=1) masked_array(data=[ 126, 127, -128, -127], mask=False, fill_value=999999, dtype=int8) A work-around is to use the `view()` method to view the result as unsigned integers with the same bit width: >>> y.ptp(axis=1).view(np.uint8) masked_array(data=[126, 127, 128, 129], mask=False, fill_value=999999, dtype=uint8) """ if out is None: result = self.max(axis=axis, fill_value=fill_value, keepdims=keepdims) result -= self.min(axis=axis, fill_value=fill_value, keepdims=keepdims) return result out.flat = self.max(axis=axis, out=out, fill_value=fill_value, keepdims=keepdims) min_value = self.min(axis=axis, fill_value=fill_value, keepdims=keepdims) np.subtract(out, min_value, out=out, casting='unsafe') return out def partition(self, *args, **kwargs): warnings.warn("Warning: 'partition' will ignore the 'mask' " f"of the {self.__class__.__name__}.", stacklevel=2) return super().partition(*args, **kwargs) def argpartition(self, *args, **kwargs): warnings.warn("Warning: 'argpartition' will ignore the 'mask' " f"of the {self.__class__.__name__}.", stacklevel=2) return super().argpartition(*args, **kwargs) def take(self, indices, axis=None, out=None, mode='raise'): """ """ (_data, _mask) = (self._data, self._mask) cls = type(self) # Make sure the indices are not masked maskindices = getmask(indices) if maskindices is not nomask: indices = indices.filled(0) # Get the data, promoting scalars to 0d arrays with [...] so that # .view works correctly if out is None: out = _data.take(indices, axis=axis, mode=mode)[...].view(cls) else: np.take(_data, indices, axis=axis, mode=mode, out=out) # Get the mask if isinstance(out, MaskedArray): if _mask is nomask: outmask = maskindices else: outmask = _mask.take(indices, axis=axis, mode=mode) outmask |= maskindices out.__setmask__(outmask) # demote 0d arrays back to scalars, for consistency with ndarray.take return out[()] # Array methods copy = _arraymethod('copy') diagonal = _arraymethod('diagonal') flatten = _arraymethod('flatten') repeat = _arraymethod('repeat') squeeze = _arraymethod('squeeze') swapaxes = _arraymethod('swapaxes') T = property(fget=lambda self: self.transpose()) transpose = _arraymethod('transpose') def tolist(self, fill_value=None): """ Return the data portion of the masked array as a hierarchical Python list. Data items are converted to the nearest compatible Python type. Masked values are converted to `fill_value`. If `fill_value` is None, the corresponding entries in the output list will be ``None``. Parameters ---------- fill_value : scalar, optional The value to use for invalid entries. Default is None. Returns ------- result : list The Python list representation of the masked array. Examples -------- >>> x = np.ma.array([[1,2,3], [4,5,6], [7,8,9]], mask=[0] + [1,0]*4) >>> x.tolist() [[1, None, 3], [None, 5, None], [7, None, 9]] >>> x.tolist(-999) [[1, -999, 3], [-999, 5, -999], [7, -999, 9]] """ _mask = self._mask # No mask ? Just return .data.tolist ? if _mask is nomask: return self._data.tolist() # Explicit fill_value: fill the array and get the list if fill_value is not None: return self.filled(fill_value).tolist() # Structured array. names = self.dtype.names if names: result = self._data.astype([(_, object) for _ in names]) for n in names: result[n][_mask[n]] = None return result.tolist() # Standard arrays. if _mask is nomask: return [None] # Set temps to save time when dealing w/ marrays. inishape = self.shape result = np.array(self._data.ravel(), dtype=object) result[_mask.ravel()] = None result.shape = inishape return result.tolist() def tostring(self, fill_value=None, order='C'): r""" A compatibility alias for `tobytes`, with exactly the same behavior. Despite its name, it returns `bytes` not `str`\ s. .. deprecated:: 1.19.0 """ # 2020-03-30, Numpy 1.19.0 warnings.warn( "tostring() is deprecated. Use tobytes() instead.", DeprecationWarning, stacklevel=2) return self.tobytes(fill_value, order=order) def tobytes(self, fill_value=None, order='C'): """ Return the array data as a string containing the raw bytes in the array. The array is filled with a fill value before the string conversion. .. versionadded:: 1.9.0 Parameters ---------- fill_value : scalar, optional Value used to fill in the masked values. Default is None, in which case `MaskedArray.fill_value` is used. order : {'C','F','A'}, optional Order of the data item in the copy. Default is 'C'. - 'C' -- C order (row major). - 'F' -- Fortran order (column major). - 'A' -- Any, current order of array. - None -- Same as 'A'. See Also -------- numpy.ndarray.tobytes tolist, tofile Notes ----- As for `ndarray.tobytes`, information about the shape, dtype, etc., but also about `fill_value`, will be lost. Examples -------- >>> x = np.ma.array(np.array([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]]) >>> x.tobytes() b'\\x01\\x00\\x00\\x00\\x00\\x00\\x00\\x00?B\\x0f\\x00\\x00\\x00\\x00\\x00?B\\x0f\\x00\\x00\\x00\\x00\\x00\\x04\\x00\\x00\\x00\\x00\\x00\\x00\\x00' """ return self.filled(fill_value).tobytes(order=order) def tofile(self, fid, sep="", format="%s"): """ Save a masked array to a file in binary format. .. warning:: This function is not implemented yet. Raises ------ NotImplementedError When `tofile` is called. """ raise NotImplementedError("MaskedArray.tofile() not implemented yet.") def toflex(self): """ Transforms a masked array into a flexible-type array. The flexible type array that is returned will have two fields: * the ``_data`` field stores the ``_data`` part of the array. * the ``_mask`` field stores the ``_mask`` part of the array. Parameters ---------- None Returns ------- record : ndarray A new flexible-type `ndarray` with two fields: the first element containing a value, the second element containing the corresponding mask boolean. The returned record shape matches self.shape. Notes ----- A side-effect of transforming a masked array into a flexible `ndarray` is that meta information (``fill_value``, ...) will be lost. Examples -------- >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4) >>> x masked_array( data=[[1, --, 3], [--, 5, --], [7, --, 9]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) >>> x.toflex() array([[(1, False), (2, True), (3, False)], [(4, True), (5, False), (6, True)], [(7, False), (8, True), (9, False)]], dtype=[('_data', '<i8'), ('_mask', '?')]) """ # Get the basic dtype. ddtype = self.dtype # Make sure we have a mask _mask = self._mask if _mask is None: _mask = make_mask_none(self.shape, ddtype) # And get its dtype mdtype = self._mask.dtype record = np.ndarray(shape=self.shape, dtype=[('_data', ddtype), ('_mask', mdtype)]) record['_data'] = self._data record['_mask'] = self._mask return record torecords = toflex # Pickling def __getstate__(self): """Return the internal state of the masked array, for pickling purposes. """ cf = 'CF'[self.flags.fnc] data_state = super().__reduce__()[2] return data_state + (getmaskarray(self).tobytes(cf), self._fill_value) def __setstate__(self, state): """Restore the internal state of the masked array, for pickling purposes. ``state`` is typically the output of the ``__getstate__`` output, and is a 5-tuple: - class name - a tuple giving the shape of the data - a typecode for the data - a binary string for the data - a binary string for the mask. """ (_, shp, typ, isf, raw, msk, flv) = state super().__setstate__((shp, typ, isf, raw)) self._mask.__setstate__((shp, make_mask_descr(typ), isf, msk)) self.fill_value = flv def __reduce__(self): """Return a 3-tuple for pickling a MaskedArray. """ return (_mareconstruct, (self.__class__, self._baseclass, (0,), 'b',), self.__getstate__()) def __deepcopy__(self, memo=None): from copy import deepcopy copied = MaskedArray.__new__(type(self), self, copy=True) if memo is None: memo = {} memo[id(self)] = copied for (k, v) in self.__dict__.items(): copied.__dict__[k] = deepcopy(v, memo) # as clearly documented for np.copy(), you need to use # deepcopy() directly for arrays of object type that may # contain compound types--you cannot depend on normal # copy semantics to do the right thing here if self.dtype.hasobject: copied._data[...] = deepcopy(copied._data) return copied def _mareconstruct(subtype, baseclass, baseshape, basetype,): """Internal function that builds a new MaskedArray from the information stored in a pickle. """ _data = ndarray.__new__(baseclass, baseshape, basetype) _mask = ndarray.__new__(ndarray, baseshape, make_mask_descr(basetype)) return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,) class mvoid(MaskedArray): """ Fake a 'void' object to use for masked array with structured dtypes. """ def __new__(self, data, mask=nomask, dtype=None, fill_value=None, hardmask=False, copy=False, subok=True): _data = np.array(data, copy=copy, subok=subok, dtype=dtype) _data = _data.view(self) _data._hardmask = hardmask if mask is not nomask: if isinstance(mask, np.void): _data._mask = mask else: try: # Mask is already a 0D array _data._mask = np.void(mask) except TypeError: # Transform the mask to a void mdtype = make_mask_descr(dtype) _data._mask = np.array(mask, dtype=mdtype)[()] if fill_value is not None: _data.fill_value = fill_value return _data @property def _data(self): # Make sure that the _data part is a np.void return super()._data[()] def __getitem__(self, indx): """ Get the index. """ m = self._mask if isinstance(m[indx], ndarray): # Can happen when indx is a multi-dimensional field: # A = ma.masked_array(data=[([0,1],)], mask=[([True, # False],)], dtype=[("A", ">i2", (2,))]) # x = A[0]; y = x["A"]; then y.mask["A"].size==2 # and we can not say masked/unmasked. # The result is no longer mvoid! # See also issue #6724. return masked_array( data=self._data[indx], mask=m[indx], fill_value=self._fill_value[indx], hard_mask=self._hardmask) if m is not nomask and m[indx]: return masked return self._data[indx] def __setitem__(self, indx, value): self._data[indx] = value if self._hardmask: self._mask[indx] |= getattr(value, "_mask", False) else: self._mask[indx] = getattr(value, "_mask", False) def __str__(self): m = self._mask if m is nomask: return str(self._data) rdtype = _replace_dtype_fields(self._data.dtype, "O") data_arr = super()._data res = data_arr.astype(rdtype) _recursive_printoption(res, self._mask, masked_print_option) return str(res) __repr__ = __str__ def __iter__(self): "Defines an iterator for mvoid" (_data, _mask) = (self._data, self._mask) if _mask is nomask: yield from _data else: for (d, m) in zip(_data, _mask): if m: yield masked else: yield d def __len__(self): return self._data.__len__() def filled(self, fill_value=None): """ Return a copy with masked fields filled with a given value. Parameters ---------- fill_value : array_like, optional The value to use for invalid entries. Can be scalar or non-scalar. If latter is the case, the filled array should be broadcastable over input array. Default is None, in which case the `fill_value` attribute is used instead. Returns ------- filled_void A `np.void` object See Also -------- MaskedArray.filled """ return asarray(self).filled(fill_value)[()] def tolist(self): """ Transforms the mvoid object into a tuple. Masked fields are replaced by None. Returns ------- returned_tuple Tuple of fields """ _mask = self._mask if _mask is nomask: return self._data.tolist() result = [] for (d, m) in zip(self._data, self._mask): if m: result.append(None) else: # .item() makes sure we return a standard Python object result.append(d.item()) return tuple(result) ############################################################################## # Shortcuts # ############################################################################## def isMaskedArray(x): """ Test whether input is an instance of MaskedArray. This function returns True if `x` is an instance of MaskedArray and returns False otherwise. Any object is accepted as input. Parameters ---------- x : object Object to test. Returns ------- result : bool True if `x` is a MaskedArray. See Also -------- isMA : Alias to isMaskedArray. isarray : Alias to isMaskedArray. Examples -------- >>> import numpy.ma as ma >>> a = np.eye(3, 3) >>> a array([[ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]]) >>> m = ma.masked_values(a, 0) >>> m masked_array( data=[[1.0, --, --], [--, 1.0, --], [--, --, 1.0]], mask=[[False, True, True], [ True, False, True], [ True, True, False]], fill_value=0.0) >>> ma.isMaskedArray(a) False >>> ma.isMaskedArray(m) True >>> ma.isMaskedArray([0, 1, 2]) False """ return isinstance(x, MaskedArray) isarray = isMaskedArray isMA = isMaskedArray # backward compatibility class MaskedConstant(MaskedArray): # the lone np.ma.masked instance __singleton = None @classmethod def __has_singleton(cls): # second case ensures `cls.__singleton` is not just a view on the # superclass singleton return cls.__singleton is not None and type(cls.__singleton) is cls def __new__(cls): if not cls.__has_singleton(): # We define the masked singleton as a float for higher precedence. # Note that it can be tricky sometimes w/ type comparison data = np.array(0.) mask = np.array(True) # prevent any modifications data.flags.writeable = False mask.flags.writeable = False # don't fall back on MaskedArray.__new__(MaskedConstant), since # that might confuse it - this way, the construction is entirely # within our control cls.__singleton = MaskedArray(data, mask=mask).view(cls) return cls.__singleton def __array_finalize__(self, obj): if not self.__has_singleton(): # this handles the `.view` in __new__, which we want to copy across # properties normally return super().__array_finalize__(obj) elif self is self.__singleton: # not clear how this can happen, play it safe pass else: # everywhere else, we want to downcast to MaskedArray, to prevent a # duplicate maskedconstant. self.__class__ = MaskedArray MaskedArray.__array_finalize__(self, obj) def __array_prepare__(self, obj, context=None): return self.view(MaskedArray).__array_prepare__(obj, context) def __array_wrap__(self, obj, context=None): return self.view(MaskedArray).__array_wrap__(obj, context) def __str__(self): return str(masked_print_option._display) def __repr__(self): if self is MaskedConstant.__singleton: return 'masked' else: # it's a subclass, or something is wrong, make it obvious return object.__repr__(self) def __format__(self, format_spec): # Replace ndarray.__format__ with the default, which supports no format characters. # Supporting format characters is unwise here, because we do not know what type # the user was expecting - better to not guess. try: return object.__format__(self, format_spec) except TypeError: # 2020-03-23, NumPy 1.19.0 warnings.warn( "Format strings passed to MaskedConstant are ignored, but in future may " "error or produce different behavior", FutureWarning, stacklevel=2 ) return object.__format__(self, "") def __reduce__(self): """Override of MaskedArray's __reduce__. """ return (self.__class__, ()) # inplace operations have no effect. We have to override them to avoid # trying to modify the readonly data and mask arrays def __iop__(self, other): return self __iadd__ = \ __isub__ = \ __imul__ = \ __ifloordiv__ = \ __itruediv__ = \ __ipow__ = \ __iop__ del __iop__ # don't leave this around def copy(self, *args, **kwargs): """ Copy is a no-op on the maskedconstant, as it is a scalar """ # maskedconstant is a scalar, so copy doesn't need to copy. There's # precedent for this with `np.bool_` scalars. return self def __copy__(self): return self def __deepcopy__(self, memo): return self def __setattr__(self, attr, value): if not self.__has_singleton(): # allow the singleton to be initialized return super().__setattr__(attr, value) elif self is self.__singleton: raise AttributeError( f"attributes of {self!r} are not writeable") else: # duplicate instance - we can end up here from __array_finalize__, # where we set the __class__ attribute return super().__setattr__(attr, value) masked = masked_singleton = MaskedConstant() masked_array = MaskedArray def array(data, dtype=None, copy=False, order=None, mask=nomask, fill_value=None, keep_mask=True, hard_mask=False, shrink=True, subok=True, ndmin=0): """ Shortcut to MaskedArray. The options are in a different order for convenience and backwards compatibility. """ return MaskedArray(data, mask=mask, dtype=dtype, copy=copy, subok=subok, keep_mask=keep_mask, hard_mask=hard_mask, fill_value=fill_value, ndmin=ndmin, shrink=shrink, order=order) array.__doc__ = masked_array.__doc__ def is_masked(x): """ Determine whether input has masked values. Accepts any object as input, but always returns False unless the input is a MaskedArray containing masked values. Parameters ---------- x : array_like Array to check for masked values. Returns ------- result : bool True if `x` is a MaskedArray with masked values, False otherwise. Examples -------- >>> import numpy.ma as ma >>> x = ma.masked_equal([0, 1, 0, 2, 3], 0) >>> x masked_array(data=[--, 1, --, 2, 3], mask=[ True, False, True, False, False], fill_value=0) >>> ma.is_masked(x) True >>> x = ma.masked_equal([0, 1, 0, 2, 3], 42) >>> x masked_array(data=[0, 1, 0, 2, 3], mask=False, fill_value=42) >>> ma.is_masked(x) False Always returns False if `x` isn't a MaskedArray. >>> x = [False, True, False] >>> ma.is_masked(x) False >>> x = 'a string' >>> ma.is_masked(x) False """ m = getmask(x) if m is nomask: return False elif m.any(): return True return False ############################################################################## # Extrema functions # ############################################################################## class _extrema_operation(_MaskedUFunc): """ Generic class for maximum/minimum functions. .. note:: This is the base class for `_maximum_operation` and `_minimum_operation`. """ def __init__(self, ufunc, compare, fill_value): super().__init__(ufunc) self.compare = compare self.fill_value_func = fill_value def __call__(self, a, b): "Executes the call behavior." return where(self.compare(a, b), a, b) def reduce(self, target, axis=np._NoValue): "Reduce target along the given axis." target = narray(target, copy=False, subok=True) m = getmask(target) if axis is np._NoValue and target.ndim > 1: # 2017-05-06, Numpy 1.13.0: warn on axis default warnings.warn( f"In the future the default for ma.{self.__name__}.reduce will be axis=0, " f"not the current None, to match np.{self.__name__}.reduce. " "Explicitly pass 0 or None to silence this warning.", MaskedArrayFutureWarning, stacklevel=2) axis = None if axis is not np._NoValue: kwargs = dict(axis=axis) else: kwargs = dict() if m is nomask: t = self.f.reduce(target, **kwargs) else: target = target.filled( self.fill_value_func(target)).view(type(target)) t = self.f.reduce(target, **kwargs) m = umath.logical_and.reduce(m, **kwargs) if hasattr(t, '_mask'): t._mask = m elif m: t = masked return t def outer(self, a, b): "Return the function applied to the outer product of a and b." ma = getmask(a) mb = getmask(b) if ma is nomask and mb is nomask: m = nomask else: ma = getmaskarray(a) mb = getmaskarray(b) m = logical_or.outer(ma, mb) result = self.f.outer(filled(a), filled(b)) if not isinstance(result, MaskedArray): result = result.view(MaskedArray) result._mask = m return result def min(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue): kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} try: return obj.min(axis=axis, fill_value=fill_value, out=out, **kwargs) except (AttributeError, TypeError): # If obj doesn't have a min method, or if the method doesn't accept a # fill_value argument return asanyarray(obj).min(axis=axis, fill_value=fill_value, out=out, **kwargs) min.__doc__ = MaskedArray.min.__doc__ def max(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue): kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} try: return obj.max(axis=axis, fill_value=fill_value, out=out, **kwargs) except (AttributeError, TypeError): # If obj doesn't have a max method, or if the method doesn't accept a # fill_value argument return asanyarray(obj).max(axis=axis, fill_value=fill_value, out=out, **kwargs) max.__doc__ = MaskedArray.max.__doc__ def ptp(obj, axis=None, out=None, fill_value=None, keepdims=np._NoValue): kwargs = {} if keepdims is np._NoValue else {'keepdims': keepdims} try: return obj.ptp(axis, out=out, fill_value=fill_value, **kwargs) except (AttributeError, TypeError): # If obj doesn't have a ptp method or if the method doesn't accept # a fill_value argument return asanyarray(obj).ptp(axis=axis, fill_value=fill_value, out=out, **kwargs) ptp.__doc__ = MaskedArray.ptp.__doc__ ############################################################################## # Definition of functions from the corresponding methods # ############################################################################## class _frommethod: """ Define functions from existing MaskedArray methods. Parameters ---------- methodname : str Name of the method to transform. """ def __init__(self, methodname, reversed=False): self.__name__ = methodname self.__doc__ = self.getdoc() self.reversed = reversed def getdoc(self): "Return the doc of the function (from the doc of the method)." meth = getattr(MaskedArray, self.__name__, None) or\ getattr(np, self.__name__, None) signature = self.__name__ + get_object_signature(meth) if meth is not None: doc = """ %s\n%s""" % ( signature, getattr(meth, '__doc__', None)) return doc def __call__(self, a, *args, **params): if self.reversed: args = list(args) a, args[0] = args[0], a marr = asanyarray(a) method_name = self.__name__ method = getattr(type(marr), method_name, None) if method is None: # use the corresponding np function method = getattr(np, method_name) return method(marr, *args, **params) all = _frommethod('all') anomalies = anom = _frommethod('anom') any = _frommethod('any') compress = _frommethod('compress', reversed=True) cumprod = _frommethod('cumprod') cumsum = _frommethod('cumsum') copy = _frommethod('copy') diagonal = _frommethod('diagonal') harden_mask = _frommethod('harden_mask') ids = _frommethod('ids') maximum = _extrema_operation(umath.maximum, greater, maximum_fill_value) mean = _frommethod('mean') minimum = _extrema_operation(umath.minimum, less, minimum_fill_value) nonzero = _frommethod('nonzero') prod = _frommethod('prod') product = _frommethod('prod') ravel = _frommethod('ravel') repeat = _frommethod('repeat') shrink_mask = _frommethod('shrink_mask') soften_mask = _frommethod('soften_mask') std = _frommethod('std') sum = _frommethod('sum') swapaxes = _frommethod('swapaxes') #take = _frommethod('take') trace = _frommethod('trace') var = _frommethod('var') count = _frommethod('count') def take(a, indices, axis=None, out=None, mode='raise'): """ """ a = masked_array(a) return a.take(indices, axis=axis, out=out, mode=mode) def power(a, b, third=None): """ Returns element-wise base array raised to power from second array. This is the masked array version of `numpy.power`. For details see `numpy.power`. See Also -------- numpy.power Notes ----- The *out* argument to `numpy.power` is not supported, `third` has to be None. Examples -------- >>> import numpy.ma as ma >>> x = [11.2, -3.973, 0.801, -1.41] >>> mask = [0, 0, 0, 1] >>> masked_x = ma.masked_array(x, mask) >>> masked_x masked_array(data=[11.2, -3.973, 0.801, --], mask=[False, False, False, True], fill_value=1e+20) >>> ma.power(masked_x, 2) masked_array(data=[125.43999999999998, 15.784728999999999, 0.6416010000000001, --], mask=[False, False, False, True], fill_value=1e+20) >>> y = [-0.5, 2, 0, 17] >>> masked_y = ma.masked_array(y, mask) >>> masked_y masked_array(data=[-0.5, 2.0, 0.0, --], mask=[False, False, False, True], fill_value=1e+20) >>> ma.power(masked_x, masked_y) masked_array(data=[0.29880715233359845, 15.784728999999999, 1.0, --], mask=[False, False, False, True], fill_value=1e+20) """ if third is not None: raise MaskError("3-argument power not supported.") # Get the masks ma = getmask(a) mb = getmask(b) m = mask_or(ma, mb) # Get the rawdata fa = getdata(a) fb = getdata(b) # Get the type of the result (so that we preserve subclasses) if isinstance(a, MaskedArray): basetype = type(a) else: basetype = MaskedArray # Get the result and view it as a (subclass of) MaskedArray with np.errstate(divide='ignore', invalid='ignore'): result = np.where(m, fa, umath.power(fa, fb)).view(basetype) result._update_from(a) # Find where we're in trouble w/ NaNs and Infs invalid = np.logical_not(np.isfinite(result.view(ndarray))) # Add the initial mask if m is not nomask: if not result.ndim: return masked result._mask = np.logical_or(m, invalid) # Fix the invalid parts if invalid.any(): if not result.ndim: return masked elif result._mask is nomask: result._mask = invalid result._data[invalid] = result.fill_value return result argmin = _frommethod('argmin') argmax = _frommethod('argmax') def argsort(a, axis=np._NoValue, kind=None, order=None, endwith=True, fill_value=None): "Function version of the eponymous method." a = np.asanyarray(a) # 2017-04-11, Numpy 1.13.0, gh-8701: warn on axis default if axis is np._NoValue: axis = _deprecate_argsort_axis(a) if isinstance(a, MaskedArray): return a.argsort(axis=axis, kind=kind, order=order, endwith=endwith, fill_value=fill_value) else: return a.argsort(axis=axis, kind=kind, order=order) argsort.__doc__ = MaskedArray.argsort.__doc__ def sort(a, axis=-1, kind=None, order=None, endwith=True, fill_value=None): """ Return a sorted copy of the masked array. Equivalent to creating a copy of the array and applying the MaskedArray ``sort()`` method. Refer to ``MaskedArray.sort`` for the full documentation See Also -------- MaskedArray.sort : equivalent method Examples -------- >>> import numpy.ma as ma >>> x = [11.2, -3.973, 0.801, -1.41] >>> mask = [0, 0, 0, 1] >>> masked_x = ma.masked_array(x, mask) >>> masked_x masked_array(data=[11.2, -3.973, 0.801, --], mask=[False, False, False, True], fill_value=1e+20) >>> ma.sort(masked_x) masked_array(data=[-3.973, 0.801, 11.2, --], mask=[False, False, False, True], fill_value=1e+20) """ a = np.array(a, copy=True, subok=True) if axis is None: a = a.flatten() axis = 0 if isinstance(a, MaskedArray): a.sort(axis=axis, kind=kind, order=order, endwith=endwith, fill_value=fill_value) else: a.sort(axis=axis, kind=kind, order=order) return a def compressed(x): """ Return all the non-masked data as a 1-D array. This function is equivalent to calling the "compressed" method of a `ma.MaskedArray`, see `ma.MaskedArray.compressed` for details. See Also -------- ma.MaskedArray.compressed : Equivalent method. Examples -------- Create an array with negative values masked: >>> import numpy as np >>> x = np.array([[1, -1, 0], [2, -1, 3], [7, 4, -1]]) >>> masked_x = np.ma.masked_array(x, mask=x < 0) >>> masked_x masked_array( data=[[1, --, 0], [2, --, 3], [7, 4, --]], mask=[[False, True, False], [False, True, False], [False, False, True]], fill_value=999999) Compress the masked array into a 1-D array of non-masked values: >>> np.ma.compressed(masked_x) array([1, 0, 2, 3, 7, 4]) """ return asanyarray(x).compressed() def concatenate(arrays, axis=0): """ Concatenate a sequence of arrays along the given axis. Parameters ---------- arrays : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to `axis` (the first, by default). axis : int, optional The axis along which the arrays will be joined. Default is 0. Returns ------- result : MaskedArray The concatenated array with any masked entries preserved. See Also -------- numpy.concatenate : Equivalent function in the top-level NumPy module. Examples -------- >>> import numpy.ma as ma >>> a = ma.arange(3) >>> a[1] = ma.masked >>> b = ma.arange(2, 5) >>> a masked_array(data=[0, --, 2], mask=[False, True, False], fill_value=999999) >>> b masked_array(data=[2, 3, 4], mask=False, fill_value=999999) >>> ma.concatenate([a, b]) masked_array(data=[0, --, 2, 2, 3, 4], mask=[False, True, False, False, False, False], fill_value=999999) """ d = np.concatenate([getdata(a) for a in arrays], axis) rcls = get_masked_subclass(*arrays) data = d.view(rcls) # Check whether one of the arrays has a non-empty mask. for x in arrays: if getmask(x) is not nomask: break else: return data # OK, so we have to concatenate the masks dm = np.concatenate([getmaskarray(a) for a in arrays], axis) dm = dm.reshape(d.shape) # If we decide to keep a '_shrinkmask' option, we want to check that # all of them are True, and then check for dm.any() data._mask = _shrink_mask(dm) return data def diag(v, k=0): """ Extract a diagonal or construct a diagonal array. This function is the equivalent of `numpy.diag` that takes masked values into account, see `numpy.diag` for details. See Also -------- numpy.diag : Equivalent function for ndarrays. Examples -------- Create an array with negative values masked: >>> import numpy as np >>> x = np.array([[11.2, -3.973, 18], [0.801, -1.41, 12], [7, 33, -12]]) >>> masked_x = np.ma.masked_array(x, mask=x < 0) >>> masked_x masked_array( data=[[11.2, --, 18.0], [0.801, --, 12.0], [7.0, 33.0, --]], mask=[[False, True, False], [False, True, False], [False, False, True]], fill_value=1e+20) Isolate the main diagonal from the masked array: >>> np.ma.diag(masked_x) masked_array(data=[11.2, --, --], mask=[False, True, True], fill_value=1e+20) Isolate the first diagonal below the main diagonal: >>> np.ma.diag(masked_x, -1) masked_array(data=[0.801, 33.0], mask=[False, False], fill_value=1e+20) """ output = np.diag(v, k).view(MaskedArray) if getmask(v) is not nomask: output._mask = np.diag(v._mask, k) return output def left_shift(a, n): """ Shift the bits of an integer to the left. This is the masked array version of `numpy.left_shift`, for details see that function. See Also -------- numpy.left_shift """ m = getmask(a) if m is nomask: d = umath.left_shift(filled(a), n) return masked_array(d) else: d = umath.left_shift(filled(a, 0), n) return masked_array(d, mask=m) def right_shift(a, n): """ Shift the bits of an integer to the right. This is the masked array version of `numpy.right_shift`, for details see that function. See Also -------- numpy.right_shift Examples -------- >>> import numpy.ma as ma >>> x = [11, 3, 8, 1] >>> mask = [0, 0, 0, 1] >>> masked_x = ma.masked_array(x, mask) >>> masked_x masked_array(data=[11, 3, 8, --], mask=[False, False, False, True], fill_value=999999) >>> ma.right_shift(masked_x,1) masked_array(data=[5, 1, 4, --], mask=[False, False, False, True], fill_value=999999) """ m = getmask(a) if m is nomask: d = umath.right_shift(filled(a), n) return masked_array(d) else: d = umath.right_shift(filled(a, 0), n) return masked_array(d, mask=m) def put(a, indices, values, mode='raise'): """ Set storage-indexed locations to corresponding values. This function is equivalent to `MaskedArray.put`, see that method for details. See Also -------- MaskedArray.put """ # We can't use 'frommethod', the order of arguments is different try: return a.put(indices, values, mode=mode) except AttributeError: return narray(a, copy=False).put(indices, values, mode=mode) def putmask(a, mask, values): # , mode='raise'): """ Changes elements of an array based on conditional and input values. This is the masked array version of `numpy.putmask`, for details see `numpy.putmask`. See Also -------- numpy.putmask Notes ----- Using a masked array as `values` will **not** transform a `ndarray` into a `MaskedArray`. """ # We can't use 'frommethod', the order of arguments is different if not isinstance(a, MaskedArray): a = a.view(MaskedArray) (valdata, valmask) = (getdata(values), getmask(values)) if getmask(a) is nomask: if valmask is not nomask: a._sharedmask = True a._mask = make_mask_none(a.shape, a.dtype) np.copyto(a._mask, valmask, where=mask) elif a._hardmask: if valmask is not nomask: m = a._mask.copy() np.copyto(m, valmask, where=mask) a.mask |= m else: if valmask is nomask: valmask = getmaskarray(values) np.copyto(a._mask, valmask, where=mask) np.copyto(a._data, valdata, where=mask) return def transpose(a, axes=None): """ Permute the dimensions of an array. This function is exactly equivalent to `numpy.transpose`. See Also -------- numpy.transpose : Equivalent function in top-level NumPy module. Examples -------- >>> import numpy.ma as ma >>> x = ma.arange(4).reshape((2,2)) >>> x[1, 1] = ma.masked >>> x masked_array( data=[[0, 1], [2, --]], mask=[[False, False], [False, True]], fill_value=999999) >>> ma.transpose(x) masked_array( data=[[0, 2], [1, --]], mask=[[False, False], [False, True]], fill_value=999999) """ # We can't use 'frommethod', as 'transpose' doesn't take keywords try: return a.transpose(axes) except AttributeError: return narray(a, copy=False).transpose(axes).view(MaskedArray) def reshape(a, new_shape, order='C'): """ Returns an array containing the same data with a new shape. Refer to `MaskedArray.reshape` for full documentation. See Also -------- MaskedArray.reshape : equivalent function """ # We can't use 'frommethod', it whine about some parameters. Dmmit. try: return a.reshape(new_shape, order=order) except AttributeError: _tmp = narray(a, copy=False).reshape(new_shape, order=order) return _tmp.view(MaskedArray) def resize(x, new_shape): """ Return a new masked array with the specified size and shape. This is the masked equivalent of the `numpy.resize` function. The new array is filled with repeated copies of `x` (in the order that the data are stored in memory). If `x` is masked, the new array will be masked, and the new mask will be a repetition of the old one. See Also -------- numpy.resize : Equivalent function in the top level NumPy module. Examples -------- >>> import numpy.ma as ma >>> a = ma.array([[1, 2] ,[3, 4]]) >>> a[0, 1] = ma.masked >>> a masked_array( data=[[1, --], [3, 4]], mask=[[False, True], [False, False]], fill_value=999999) >>> np.resize(a, (3, 3)) masked_array( data=[[1, 2, 3], [4, 1, 2], [3, 4, 1]], mask=False, fill_value=999999) >>> ma.resize(a, (3, 3)) masked_array( data=[[1, --, 3], [4, 1, --], [3, 4, 1]], mask=[[False, True, False], [False, False, True], [False, False, False]], fill_value=999999) A MaskedArray is always returned, regardless of the input type. >>> a = np.array([[1, 2] ,[3, 4]]) >>> ma.resize(a, (3, 3)) masked_array( data=[[1, 2, 3], [4, 1, 2], [3, 4, 1]], mask=False, fill_value=999999) """ # We can't use _frommethods here, as N.resize is notoriously whiny. m = getmask(x) if m is not nomask: m = np.resize(m, new_shape) result = np.resize(x, new_shape).view(get_masked_subclass(x)) if result.ndim: result._mask = m return result def ndim(obj): """ maskedarray version of the numpy function. """ return np.ndim(getdata(obj)) ndim.__doc__ = np.ndim.__doc__ def shape(obj): "maskedarray version of the numpy function." return np.shape(getdata(obj)) shape.__doc__ = np.shape.__doc__ def size(obj, axis=None): "maskedarray version of the numpy function." return np.size(getdata(obj), axis) size.__doc__ = np.size.__doc__ def diff(a, /, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue): """ Calculate the n-th discrete difference along the given axis. The first difference is given by ``out[i] = a[i+1] - a[i]`` along the given axis, higher differences are calculated by using `diff` recursively. Preserves the input mask. Parameters ---------- a : array_like Input array n : int, optional The number of times values are differenced. If zero, the input is returned as-is. axis : int, optional The axis along which the difference is taken, default is the last axis. prepend, append : array_like, optional Values to prepend or append to `a` along axis prior to performing the difference. Scalar values are expanded to arrays with length 1 in the direction of axis and the shape of the input array in along all other axes. Otherwise the dimension and shape must match `a` except along axis. Returns ------- diff : MaskedArray The n-th differences. The shape of the output is the same as `a` except along `axis` where the dimension is smaller by `n`. The type of the output is the same as the type of the difference between any two elements of `a`. This is the same as the type of `a` in most cases. A notable exception is `datetime64`, which results in a `timedelta64` output array. See Also -------- numpy.diff : Equivalent function in the top-level NumPy module. Notes ----- Type is preserved for boolean arrays, so the result will contain `False` when consecutive elements are the same and `True` when they differ. For unsigned integer arrays, the results will also be unsigned. This should not be surprising, as the result is consistent with calculating the difference directly: >>> u8_arr = np.array([1, 0], dtype=np.uint8) >>> np.ma.diff(u8_arr) masked_array(data=[255], mask=False, fill_value=999999, dtype=uint8) >>> u8_arr[1,...] - u8_arr[0,...] 255 If this is not desirable, then the array should be cast to a larger integer type first: >>> i16_arr = u8_arr.astype(np.int16) >>> np.ma.diff(i16_arr) masked_array(data=[-1], mask=False, fill_value=999999, dtype=int16) Examples -------- >>> a = np.array([1, 2, 3, 4, 7, 0, 2, 3]) >>> x = np.ma.masked_where(a < 2, a) >>> np.ma.diff(x) masked_array(data=[--, 1, 1, 3, --, --, 1], mask=[ True, False, False, False, True, True, False], fill_value=999999) >>> np.ma.diff(x, n=2) masked_array(data=[--, 0, 2, --, --, --], mask=[ True, False, False, True, True, True], fill_value=999999) >>> a = np.array([[1, 3, 1, 5, 10], [0, 1, 5, 6, 8]]) >>> x = np.ma.masked_equal(a, value=1) >>> np.ma.diff(x) masked_array( data=[[--, --, --, 5], [--, --, 1, 2]], mask=[[ True, True, True, False], [ True, True, False, False]], fill_value=1) >>> np.ma.diff(x, axis=0) masked_array(data=[[--, --, --, 1, -2]], mask=[[ True, True, True, False, False]], fill_value=1) """ if n == 0: return a if n < 0: raise ValueError("order must be non-negative but got " + repr(n)) a = np.ma.asanyarray(a) if a.ndim == 0: raise ValueError( "diff requires input that is at least one dimensional" ) combined = [] if prepend is not np._NoValue: prepend = np.ma.asanyarray(prepend) if prepend.ndim == 0: shape = list(a.shape) shape[axis] = 1 prepend = np.broadcast_to(prepend, tuple(shape)) combined.append(prepend) combined.append(a) if append is not np._NoValue: append = np.ma.asanyarray(append) if append.ndim == 0: shape = list(a.shape) shape[axis] = 1 append = np.broadcast_to(append, tuple(shape)) combined.append(append) if len(combined) > 1: a = np.ma.concatenate(combined, axis) # GH 22465 np.diff without prepend/append preserves the mask return np.diff(a, n, axis) ############################################################################## # Extra functions # ############################################################################## def where(condition, x=_NoValue, y=_NoValue): """ Return a masked array with elements from `x` or `y`, depending on condition. .. note:: When only `condition` is provided, this function is identical to `nonzero`. The rest of this documentation covers only the case where all three arguments are provided. Parameters ---------- condition : array_like, bool Where True, yield `x`, otherwise yield `y`. x, y : array_like, optional Values from which to choose. `x`, `y` and `condition` need to be broadcastable to some shape. Returns ------- out : MaskedArray An masked array with `masked` elements where the condition is masked, elements from `x` where `condition` is True, and elements from `y` elsewhere. See Also -------- numpy.where : Equivalent function in the top-level NumPy module. nonzero : The function that is called when x and y are omitted Examples -------- >>> x = np.ma.array(np.arange(9.).reshape(3, 3), mask=[[0, 1, 0], ... [1, 0, 1], ... [0, 1, 0]]) >>> x masked_array( data=[[0.0, --, 2.0], [--, 4.0, --], [6.0, --, 8.0]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=1e+20) >>> np.ma.where(x > 5, x, -3.1416) masked_array( data=[[-3.1416, --, -3.1416], [--, -3.1416, --], [6.0, --, 8.0]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=1e+20) """ # handle the single-argument case missing = (x is _NoValue, y is _NoValue).count(True) if missing == 1: raise ValueError("Must provide both 'x' and 'y' or neither.") if missing == 2: return nonzero(condition) # we only care if the condition is true - false or masked pick y cf = filled(condition, False) xd = getdata(x) yd = getdata(y) # we need the full arrays here for correct final dimensions cm = getmaskarray(condition) xm = getmaskarray(x) ym = getmaskarray(y) # deal with the fact that masked.dtype == float64, but we don't actually # want to treat it as that. if x is masked and y is not masked: xd = np.zeros((), dtype=yd.dtype) xm = np.ones((), dtype=ym.dtype) elif y is masked and x is not masked: yd = np.zeros((), dtype=xd.dtype) ym = np.ones((), dtype=xm.dtype) data = np.where(cf, xd, yd) mask = np.where(cf, xm, ym) mask = np.where(cm, np.ones((), dtype=mask.dtype), mask) # collapse the mask, for backwards compatibility mask = _shrink_mask(mask) return masked_array(data, mask=mask) def choose(indices, choices, out=None, mode='raise'): """ Use an index array to construct a new array from a list of choices. Given an array of integers and a list of n choice arrays, this method will create a new array that merges each of the choice arrays. Where a value in `index` is i, the new array will have the value that choices[i] contains in the same place. Parameters ---------- indices : ndarray of ints This array must contain integers in ``[0, n-1]``, where n is the number of choices. choices : sequence of arrays Choice arrays. The index array and all of the choices should be broadcastable to the same shape. out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and `dtype`. mode : {'raise', 'wrap', 'clip'}, optional Specifies how out-of-bounds indices will behave. * 'raise' : raise an error * 'wrap' : wrap around * 'clip' : clip to the range Returns ------- merged_array : array See Also -------- choose : equivalent function Examples -------- >>> choice = np.array([[1,1,1], [2,2,2], [3,3,3]]) >>> a = np.array([2, 1, 0]) >>> np.ma.choose(a, choice) masked_array(data=[3, 2, 1], mask=False, fill_value=999999) """ def fmask(x): "Returns the filled array, or True if masked." if x is masked: return True return filled(x) def nmask(x): "Returns the mask, True if ``masked``, False if ``nomask``." if x is masked: return True return getmask(x) # Get the indices. c = filled(indices, 0) # Get the masks. masks = [nmask(x) for x in choices] data = [fmask(x) for x in choices] # Construct the mask outputmask = np.choose(c, masks, mode=mode) outputmask = make_mask(mask_or(outputmask, getmask(indices)), copy=False, shrink=True) # Get the choices. d = np.choose(c, data, mode=mode, out=out).view(MaskedArray) if out is not None: if isinstance(out, MaskedArray): out.__setmask__(outputmask) return out d.__setmask__(outputmask) return d def round_(a, decimals=0, out=None): """ Return a copy of a, rounded to 'decimals' places. When 'decimals' is negative, it specifies the number of positions to the left of the decimal point. The real and imaginary parts of complex numbers are rounded separately. Nothing is done if the array is not of float type and 'decimals' is greater than or equal to 0. Parameters ---------- decimals : int Number of decimals to round to. May be negative. out : array_like Existing array to use for output. If not given, returns a default copy of a. Notes ----- If out is given and does not have a mask attribute, the mask of a is lost! Examples -------- >>> import numpy.ma as ma >>> x = [11.2, -3.973, 0.801, -1.41] >>> mask = [0, 0, 0, 1] >>> masked_x = ma.masked_array(x, mask) >>> masked_x masked_array(data=[11.2, -3.973, 0.801, --], mask=[False, False, False, True], fill_value=1e+20) >>> ma.round_(masked_x) masked_array(data=[11.0, -4.0, 1.0, --], mask=[False, False, False, True], fill_value=1e+20) >>> ma.round(masked_x, decimals=1) masked_array(data=[11.2, -4.0, 0.8, --], mask=[False, False, False, True], fill_value=1e+20) >>> ma.round_(masked_x, decimals=-1) masked_array(data=[10.0, -0.0, 0.0, --], mask=[False, False, False, True], fill_value=1e+20) """ if out is None: return np.round_(a, decimals, out) else: np.round_(getdata(a), decimals, out) if hasattr(out, '_mask'): out._mask = getmask(a) return out round = round_ def _mask_propagate(a, axis): """ Mask whole 1-d vectors of an array that contain masked values. """ a = array(a, subok=False) m = getmask(a) if m is nomask or not m.any() or axis is None: return a a._mask = a._mask.copy() axes = normalize_axis_tuple(axis, a.ndim) for ax in axes: a._mask |= m.any(axis=ax, keepdims=True) return a # Include masked dot here to avoid import problems in getting it from # extras.py. Note that it is not included in __all__, but rather exported # from extras in order to avoid backward compatibility problems. def dot(a, b, strict=False, out=None): """ Return the dot product of two arrays. This function is the equivalent of `numpy.dot` that takes masked values into account. Note that `strict` and `out` are in different position than in the method version. In order to maintain compatibility with the corresponding method, it is recommended that the optional arguments be treated as keyword only. At some point that may be mandatory. Parameters ---------- a, b : masked_array_like Inputs arrays. strict : bool, optional Whether masked data are propagated (True) or set to 0 (False) for the computation. Default is False. Propagating the mask means that if a masked value appears in a row or column, the whole row or column is considered masked. out : masked_array, optional Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be C-contiguous, and its dtype must be the dtype that would be returned for `dot(a,b)`. This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible. .. versionadded:: 1.10.2 See Also -------- numpy.dot : Equivalent function for ndarrays. Examples -------- >>> a = np.ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]]) >>> b = np.ma.array([[1, 2], [3, 4], [5, 6]], mask=[[1, 0], [0, 0], [0, 0]]) >>> np.ma.dot(a, b) masked_array( data=[[21, 26], [45, 64]], mask=[[False, False], [False, False]], fill_value=999999) >>> np.ma.dot(a, b, strict=True) masked_array( data=[[--, --], [--, 64]], mask=[[ True, True], [ True, False]], fill_value=999999) """ if strict is True: if np.ndim(a) == 0 or np.ndim(b) == 0: pass elif b.ndim == 1: a = _mask_propagate(a, a.ndim - 1) b = _mask_propagate(b, b.ndim - 1) else: a = _mask_propagate(a, a.ndim - 1) b = _mask_propagate(b, b.ndim - 2) am = ~getmaskarray(a) bm = ~getmaskarray(b) if out is None: d = np.dot(filled(a, 0), filled(b, 0)) m = ~np.dot(am, bm) if np.ndim(d) == 0: d = np.asarray(d) r = d.view(get_masked_subclass(a, b)) r.__setmask__(m) return r else: d = np.dot(filled(a, 0), filled(b, 0), out._data) if out.mask.shape != d.shape: out._mask = np.empty(d.shape, MaskType) np.dot(am, bm, out._mask) np.logical_not(out._mask, out._mask) return out def inner(a, b): """ Returns the inner product of a and b for arrays of floating point types. Like the generic NumPy equivalent the product sum is over the last dimension of a and b. The first argument is not conjugated. """ fa = filled(a, 0) fb = filled(b, 0) if fa.ndim == 0: fa.shape = (1,) if fb.ndim == 0: fb.shape = (1,) return np.inner(fa, fb).view(MaskedArray) inner.__doc__ = doc_note(np.inner.__doc__, "Masked values are replaced by 0.") innerproduct = inner def outer(a, b): "maskedarray version of the numpy function." fa = filled(a, 0).ravel() fb = filled(b, 0).ravel() d = np.outer(fa, fb) ma = getmask(a) mb = getmask(b) if ma is nomask and mb is nomask: return masked_array(d) ma = getmaskarray(a) mb = getmaskarray(b) m = make_mask(1 - np.outer(1 - ma, 1 - mb), copy=False) return masked_array(d, mask=m) outer.__doc__ = doc_note(np.outer.__doc__, "Masked values are replaced by 0.") outerproduct = outer def _convolve_or_correlate(f, a, v, mode, propagate_mask): """ Helper function for ma.correlate and ma.convolve """ if propagate_mask: # results which are contributed to by either item in any pair being invalid mask = ( f(getmaskarray(a), np.ones(np.shape(v), dtype=bool), mode=mode) | f(np.ones(np.shape(a), dtype=bool), getmaskarray(v), mode=mode) ) data = f(getdata(a), getdata(v), mode=mode) else: # results which are not contributed to by any pair of valid elements mask = ~f(~getmaskarray(a), ~getmaskarray(v)) data = f(filled(a, 0), filled(v, 0), mode=mode) return masked_array(data, mask=mask) def correlate(a, v, mode='valid', propagate_mask=True): """ Cross-correlation of two 1-dimensional sequences. Parameters ---------- a, v : array_like Input sequences. mode : {'valid', 'same', 'full'}, optional Refer to the `np.convolve` docstring. Note that the default is 'valid', unlike `convolve`, which uses 'full'. propagate_mask : bool If True, then a result element is masked if any masked element contributes towards it. If False, then a result element is only masked if no non-masked element contribute towards it Returns ------- out : MaskedArray Discrete cross-correlation of `a` and `v`. See Also -------- numpy.correlate : Equivalent function in the top-level NumPy module. """ return _convolve_or_correlate(np.correlate, a, v, mode, propagate_mask) def convolve(a, v, mode='full', propagate_mask=True): """ Returns the discrete, linear convolution of two one-dimensional sequences. Parameters ---------- a, v : array_like Input sequences. mode : {'valid', 'same', 'full'}, optional Refer to the `np.convolve` docstring. propagate_mask : bool If True, then if any masked element is included in the sum for a result element, then the result is masked. If False, then the result element is only masked if no non-masked cells contribute towards it Returns ------- out : MaskedArray Discrete, linear convolution of `a` and `v`. See Also -------- numpy.convolve : Equivalent function in the top-level NumPy module. """ return _convolve_or_correlate(np.convolve, a, v, mode, propagate_mask) def allequal(a, b, fill_value=True): """ Return True if all entries of a and b are equal, using fill_value as a truth value where either or both are masked. Parameters ---------- a, b : array_like Input arrays to compare. fill_value : bool, optional Whether masked values in a or b are considered equal (True) or not (False). Returns ------- y : bool Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned. See Also -------- all, any numpy.ma.allclose Examples -------- >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1]) >>> a masked_array(data=[10000000000.0, 1e-07, --], mask=[False, False, True], fill_value=1e+20) >>> b = np.array([1e10, 1e-7, -42.0]) >>> b array([ 1.00000000e+10, 1.00000000e-07, -4.20000000e+01]) >>> np.ma.allequal(a, b, fill_value=False) False >>> np.ma.allequal(a, b) True """ m = mask_or(getmask(a), getmask(b)) if m is nomask: x = getdata(a) y = getdata(b) d = umath.equal(x, y) return d.all() elif fill_value: x = getdata(a) y = getdata(b) d = umath.equal(x, y) dm = array(d, mask=m, copy=False) return dm.filled(True).all(None) else: return False def allclose(a, b, masked_equal=True, rtol=1e-5, atol=1e-8): """ Returns True if two arrays are element-wise equal within a tolerance. This function is equivalent to `allclose` except that masked values are treated as equal (default) or unequal, depending on the `masked_equal` argument. Parameters ---------- a, b : array_like Input arrays to compare. masked_equal : bool, optional Whether masked values in `a` and `b` are considered equal (True) or not (False). They are considered equal by default. rtol : float, optional Relative tolerance. The relative difference is equal to ``rtol * b``. Default is 1e-5. atol : float, optional Absolute tolerance. The absolute difference is equal to `atol`. Default is 1e-8. Returns ------- y : bool Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned. See Also -------- all, any numpy.allclose : the non-masked `allclose`. Notes ----- If the following equation is element-wise True, then `allclose` returns True:: absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`)) Return True if all elements of `a` and `b` are equal subject to given tolerances. Examples -------- >>> a = np.ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1]) >>> a masked_array(data=[10000000000.0, 1e-07, --], mask=[False, False, True], fill_value=1e+20) >>> b = np.ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1]) >>> np.ma.allclose(a, b) False >>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1]) >>> b = np.ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1]) >>> np.ma.allclose(a, b) True >>> np.ma.allclose(a, b, masked_equal=False) False Masked values are not compared directly. >>> a = np.ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1]) >>> b = np.ma.array([1.00001e10, 1e-9, 42.0], mask=[0, 0, 1]) >>> np.ma.allclose(a, b) True >>> np.ma.allclose(a, b, masked_equal=False) False """ x = masked_array(a, copy=False) y = masked_array(b, copy=False) # make sure y is an inexact type to avoid abs(MIN_INT); will cause # casting of x later. # NOTE: We explicitly allow timedelta, which used to work. This could # possibly be deprecated. See also gh-18286. # timedelta works if `atol` is an integer or also a timedelta. # Although, the default tolerances are unlikely to be useful if y.dtype.kind != "m": dtype = np.result_type(y, 1.) if y.dtype != dtype: y = masked_array(y, dtype=dtype, copy=False) m = mask_or(getmask(x), getmask(y)) xinf = np.isinf(masked_array(x, copy=False, mask=m)).filled(False) # If we have some infs, they should fall at the same place. if not np.all(xinf == filled(np.isinf(y), False)): return False # No infs at all if not np.any(xinf): d = filled(less_equal(absolute(x - y), atol + rtol * absolute(y)), masked_equal) return np.all(d) if not np.all(filled(x[xinf] == y[xinf], masked_equal)): return False x = x[~xinf] y = y[~xinf] d = filled(less_equal(absolute(x - y), atol + rtol * absolute(y)), masked_equal) return np.all(d) def asarray(a, dtype=None, order=None): """ Convert the input to a masked array of the given data-type. No copy is performed if the input is already an `ndarray`. If `a` is a subclass of `MaskedArray`, a base class `MaskedArray` is returned. Parameters ---------- a : array_like Input data, in any form that can be converted to a masked array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists, ndarrays and masked arrays. dtype : dtype, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional Whether to use row-major ('C') or column-major ('FORTRAN') memory representation. Default is 'C'. Returns ------- out : MaskedArray Masked array interpretation of `a`. See Also -------- asanyarray : Similar to `asarray`, but conserves subclasses. Examples -------- >>> x = np.arange(10.).reshape(2, 5) >>> x array([[0., 1., 2., 3., 4.], [5., 6., 7., 8., 9.]]) >>> np.ma.asarray(x) masked_array( data=[[0., 1., 2., 3., 4.], [5., 6., 7., 8., 9.]], mask=False, fill_value=1e+20) >>> type(np.ma.asarray(x)) <class 'numpy.ma.core.MaskedArray'> """ order = order or 'C' return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=False, order=order) def asanyarray(a, dtype=None): """ Convert the input to a masked array, conserving subclasses. If `a` is a subclass of `MaskedArray`, its class is conserved. No copy is performed if the input is already an `ndarray`. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. dtype : dtype, optional By default, the data-type is inferred from the input data. order : {'C', 'F'}, optional Whether to use row-major ('C') or column-major ('FORTRAN') memory representation. Default is 'C'. Returns ------- out : MaskedArray MaskedArray interpretation of `a`. See Also -------- asarray : Similar to `asanyarray`, but does not conserve subclass. Examples -------- >>> x = np.arange(10.).reshape(2, 5) >>> x array([[0., 1., 2., 3., 4.], [5., 6., 7., 8., 9.]]) >>> np.ma.asanyarray(x) masked_array( data=[[0., 1., 2., 3., 4.], [5., 6., 7., 8., 9.]], mask=False, fill_value=1e+20) >>> type(np.ma.asanyarray(x)) <class 'numpy.ma.core.MaskedArray'> """ # workaround for #8666, to preserve identity. Ideally the bottom line # would handle this for us. if isinstance(a, MaskedArray) and (dtype is None or dtype == a.dtype): return a return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True) ############################################################################## # Pickling # ############################################################################## def fromfile(file, dtype=float, count=-1, sep=''): raise NotImplementedError( "fromfile() not yet implemented for a MaskedArray.") def fromflex(fxarray): """ Build a masked array from a suitable flexible-type array. The input array has to have a data-type with ``_data`` and ``_mask`` fields. This type of array is output by `MaskedArray.toflex`. Parameters ---------- fxarray : ndarray The structured input array, containing ``_data`` and ``_mask`` fields. If present, other fields are discarded. Returns ------- result : MaskedArray The constructed masked array. See Also -------- MaskedArray.toflex : Build a flexible-type array from a masked array. Examples -------- >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[0] + [1, 0] * 4) >>> rec = x.toflex() >>> rec array([[(0, False), (1, True), (2, False)], [(3, True), (4, False), (5, True)], [(6, False), (7, True), (8, False)]], dtype=[('_data', '<i8'), ('_mask', '?')]) >>> x2 = np.ma.fromflex(rec) >>> x2 masked_array( data=[[0, --, 2], [--, 4, --], [6, --, 8]], mask=[[False, True, False], [ True, False, True], [False, True, False]], fill_value=999999) Extra fields can be present in the structured array but are discarded: >>> dt = [('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')] >>> rec2 = np.zeros((2, 2), dtype=dt) >>> rec2 array([[(0, False, 0.), (0, False, 0.)], [(0, False, 0.), (0, False, 0.)]], dtype=[('_data', '<i4'), ('_mask', '?'), ('field3', '<f4')]) >>> y = np.ma.fromflex(rec2) >>> y masked_array( data=[[0, 0], [0, 0]], mask=[[False, False], [False, False]], fill_value=999999, dtype=int32) """ return masked_array(fxarray['_data'], mask=fxarray['_mask']) class _convert2ma: """ Convert functions from numpy to numpy.ma. Parameters ---------- _methodname : string Name of the method to transform. """ __doc__ = None def __init__(self, funcname, np_ret, np_ma_ret, params=None): self._func = getattr(np, funcname) self.__doc__ = self.getdoc(np_ret, np_ma_ret) self._extras = params or {} def getdoc(self, np_ret, np_ma_ret): "Return the doc of the function (from the doc of the method)." doc = getattr(self._func, '__doc__', None) sig = get_object_signature(self._func) if doc: doc = self._replace_return_type(doc, np_ret, np_ma_ret) # Add the signature of the function at the beginning of the doc if sig: sig = "%s%s\n" % (self._func.__name__, sig) doc = sig + doc return doc def _replace_return_type(self, doc, np_ret, np_ma_ret): """ Replace documentation of ``np`` function's return type. Replaces it with the proper type for the ``np.ma`` function. Parameters ---------- doc : str The documentation of the ``np`` method. np_ret : str The return type string of the ``np`` method that we want to replace. (e.g. "out : ndarray") np_ma_ret : str The return type string of the ``np.ma`` method. (e.g. "out : MaskedArray") """ if np_ret not in doc: raise RuntimeError( f"Failed to replace `{np_ret}` with `{np_ma_ret}`. " f"The documentation string for return type, {np_ret}, is not " f"found in the docstring for `np.{self._func.__name__}`. " f"Fix the docstring for `np.{self._func.__name__}` or " "update the expected string for return type." ) return doc.replace(np_ret, np_ma_ret) def __call__(self, *args, **params): # Find the common parameters to the call and the definition _extras = self._extras common_params = set(params).intersection(_extras) # Drop the common parameters from the call for p in common_params: _extras[p] = params.pop(p) # Get the result result = self._func.__call__(*args, **params).view(MaskedArray) if "fill_value" in common_params: result.fill_value = _extras.get("fill_value", None) if "hardmask" in common_params: result._hardmask = bool(_extras.get("hard_mask", False)) return result arange = _convert2ma( 'arange', params=dict(fill_value=None, hardmask=False), np_ret='arange : ndarray', np_ma_ret='arange : MaskedArray', ) clip = _convert2ma( 'clip', params=dict(fill_value=None, hardmask=False), np_ret='clipped_array : ndarray', np_ma_ret='clipped_array : MaskedArray', ) empty = _convert2ma( 'empty', params=dict(fill_value=None, hardmask=False), np_ret='out : ndarray', np_ma_ret='out : MaskedArray', ) empty_like = _convert2ma( 'empty_like', np_ret='out : ndarray', np_ma_ret='out : MaskedArray', ) frombuffer = _convert2ma( 'frombuffer', np_ret='out : ndarray', np_ma_ret='out: MaskedArray', ) fromfunction = _convert2ma( 'fromfunction', np_ret='fromfunction : any', np_ma_ret='fromfunction: MaskedArray', ) identity = _convert2ma( 'identity', params=dict(fill_value=None, hardmask=False), np_ret='out : ndarray', np_ma_ret='out : MaskedArray', ) indices = _convert2ma( 'indices', params=dict(fill_value=None, hardmask=False), np_ret='grid : one ndarray or tuple of ndarrays', np_ma_ret='grid : one MaskedArray or tuple of MaskedArrays', ) ones = _convert2ma( 'ones', params=dict(fill_value=None, hardmask=False), np_ret='out : ndarray', np_ma_ret='out : MaskedArray', ) ones_like = _convert2ma( 'ones_like', np_ret='out : ndarray', np_ma_ret='out : MaskedArray', ) squeeze = _convert2ma( 'squeeze', params=dict(fill_value=None, hardmask=False), np_ret='squeezed : ndarray', np_ma_ret='squeezed : MaskedArray', ) zeros = _convert2ma( 'zeros', params=dict(fill_value=None, hardmask=False), np_ret='out : ndarray', np_ma_ret='out : MaskedArray', ) zeros_like = _convert2ma( 'zeros_like', np_ret='out : ndarray', np_ma_ret='out : MaskedArray', ) def append(a, b, axis=None): """Append values to the end of an array. .. versionadded:: 1.9.0 Parameters ---------- a : array_like Values are appended to a copy of this array. b : array_like These values are appended to a copy of `a`. It must be of the correct shape (the same shape as `a`, excluding `axis`). If `axis` is not specified, `b` can be any shape and will be flattened before use. axis : int, optional The axis along which `v` are appended. If `axis` is not given, both `a` and `b` are flattened before use. Returns ------- append : MaskedArray A copy of `a` with `b` appended to `axis`. Note that `append` does not occur in-place: a new array is allocated and filled. If `axis` is None, the result is a flattened array. See Also -------- numpy.append : Equivalent function in the top-level NumPy module. Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_values([1, 2, 3], 2) >>> b = ma.masked_values([[4, 5, 6], [7, 8, 9]], 7) >>> ma.append(a, b) masked_array(data=[1, --, 3, 4, 5, 6, --, 8, 9], mask=[False, True, False, False, False, False, True, False, False], fill_value=999999) """ return concatenate([a, b], axis)