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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
else: import __builtin__ as builtins
getargspec, formatargspec, long, basestring, unicode, bytes )
'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', 'dump', 'dumps', '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', 'load', 'loads', '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', 'outer', 'outerproduct', 'power', 'prod', 'product', 'ptp', 'put', 'putmask', 'rank', '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', ]
""" 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 compatibiity. 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
""" Adds a Notes section to an existing docstring.
""" return return initialdoc
Notes ----- %s""" % note
""" Get the signature from obj
"""
############################################################################### # Exceptions # ###############################################################################
""" Class for masked array related errors.
"""
""" Class for mask related errors.
"""
############################################################################### # Filling options # ###############################################################################
# b: boolean - c: complex - f: floats - i: integer - O: object - S: string 'c': 1.e20 + 0.0j, 'f': 1.e20, 'i': 999999, 'O': '?', 'S': b'N/A', 'u': 999999, 'V': b'???', 'U': u'N/A' }
# Add datetime64 and timedelta64 types "fs", "as"]:
""" Recursively produce a fill value for `dtype`, calling f on scalar dtypes """ vals = tuple(_recursive_fill_value(dtype[name], f) for name in dtype.names) return np.array(vals, dtype=dtype)[()] # decay to void scalar from 0d subtype, shape = dtype.subdtype subval = _recursive_fill_value(subtype, f) return np.full(shape, subval) else:
""" Convert the argument for *_fill_value into a dtype """ else: return np.asanyarray(obj).dtype
""" 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)
""" return default_filler.get(dtype.str[1:], '?') else:
except KeyError: raise TypeError( "Unsuitable type {} for calculating {}." .format(dtype, extremum_name) )
""" 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 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
"""
""" 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)
""" 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.
""" if isinstance(fill_value, (ndarray, np.void)): try: fill_value = np.array(fill_value, copy=False, dtype=ndtype) except ValueError: err_msg = "Unable to transform %s to dtype %s" raise ValueError(err_msg % (fill_value, ndtype)) else: fill_value = np.asarray(fill_value, dtype=object) fill_value = np.array(_recursive_set_fill_value(fill_value, ndtype), dtype=ndtype) else: # 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. except (OverflowError, ValueError): # 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))
""" 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 = 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
""" 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
""" 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
""" 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 : scalar, optional Filling value. 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]])
""" # Should we check for contiguity ? and a.flags['CONTIGUOUS']: elif isinstance(a, dict): return np.array(a, 'O') else: return np.array(a)
""" Return the youngest subclass of MaskedArray from a list of (masked) arrays.
In case of siblings, the first listed takes over.
""" arr = arrays[0] if isinstance(arr, MaskedArray): rcls = type(arr) else: rcls = MaskedArray else: rcls = MaskedArray # Don't return MaskedConstant as result: revert to MaskedArray return MaskedArray
""" 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=999999) >>> ma.getdata(a) array([[1, 2], [3, 4]])
Equivalently use the ``MaskedArray`` `data` attribute.
>>> a.data array([[1, 2], [3, 4]])
""" return data.view(ndarray)
""" 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([ 1.00000000e+00, -1.00000000e+00, 1.00000000e+20, 1.00000000e+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
(isinstance(val, list) and val and builtins.all(isinstance(s, basestring) for s in val)))
############################################################################### # Ufuncs # ###############################################################################
""" Define a valid interval, so that :
``domain_check_interval(a,b)(x) == True`` where ``x < a`` or ``x > b``.
"""
"domain_check_interval(a,b)(x) = true where x < a or y > b" (a, b) = (b, a)
"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))
""" Define a valid interval for the `tan` function, so that:
``domain_tan(eps) = True`` where ``abs(cos(x)) < eps``
"""
"domain_tan(eps) = true where abs(cos(x)) < eps)"
"Executes the call behavior." with np.errstate(invalid='ignore'): return umath.less(umath.absolute(umath.cos(x)), self.eps)
""" Define a domain for safe division.
"""
# 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)
""" DomainGreater(v)(x) is True where x <= v.
"""
"DomainGreater(v)(x) = true where x <= v"
"Executes the call behavior." with np.errstate(invalid='ignore'): return umath.less_equal(x, self.critical_value)
""" DomainGreaterEqual(v)(x) is True where x < v.
"""
"DomainGreaterEqual(v)(x) = true where x < v"
"Executes the call behavior."
def __str__(self): return "Masked version of {}".format(self.f)
""" 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.
"""
""" 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
""" 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.
"""
""" abfunc(fillx, filly) must be defined.
abfunc(x, filly) = x for all x to enable reduce.
"""
""" Execute the call behavior.
""" # Get the data, as ndarray # Get the result # Get the mask for the result else: m = umath.logical_or(getmaskarray(a), mb) m = umath.logical_or(ma, getmaskarray(b)) else:
# Case 1. : scalar if m: return masked return result
# Case 2. : array # Revert result to da where masked # any errors, just abort; impossible to guarantee masked values except Exception: pass
# Transforms to a (subclass of) MaskedArray elif isinstance(b, MaskedArray): masked_result._update_from(b)
""" 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=1) m.shape = (1,)
if m is nomask: tr = self.f.reduce(t, axis) mr = nomask else: tr = self.f.reduce(t, axis, dtype=dtype or t.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
""" 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
"""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
""" 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.
"""
"""abfunc(fillx, filly) must be defined. abfunc(x, filly) = x for all x to enable reduce. """
"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
# Domained unary ufuncs _DomainGreaterEqual(0.0)) _DomainGreater(0.0)) _DomainGreater(0.0)) _DomainGreater(0.0)) _DomainTan(1e-35)) _DomainCheckInterval(-1.0, 1.0)) _DomainCheckInterval(-1.0, 1.0)) _DomainGreaterEqual(1.0)) _DomainCheckInterval(-1.0 + 1e-15, 1.0 - 1e-15))
# Binary ufuncs
# Domained binary ufuncs _DomainSafeDivide(), 0, 1) _DomainSafeDivide(), 0, 1) _DomainSafeDivide(), 0, 1)
############################################################################### # Mask creation functions # ###############################################################################
"Private function allowing recursion in _replace_dtype_fields."
# Do we have some name fields ? 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) 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:
# preserve identity of dtypes
""" 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. """
""" 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, int]}) >>> dtype dtype([('foo', '<f4'), ('bar', '<i4')]) >>> ma.make_mask_descr(dtype) dtype([('foo', '|b1'), ('bar', '|b1')]) >>> ma.make_mask_descr(np.float32) dtype('bool')
"""
""" 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=999999) >>> 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 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=999999) >>> 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]])
"""
""" 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 -------- 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=999999) >>> 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', '|b1'), ('pithon', '|b1')]) >>> ma.is_mask(m) False
""" try: return m.dtype.type is MaskType except AttributeError: return False
""" Shrink a mask to nomask if possible """ else:
""" 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 interepreted 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':[int, int]}) >>> arr = np.array(arr, dtype=dtype) >>> arr array([(1, 0), (0, 1), (1, 0), (1, 0)], dtype=[('man', '<i4'), ('mouse', '<i4')]) >>> ma.make_mask(arr, dtype=dtype) array([(True, False), (False, True), (True, False), (True, False)], dtype=[('man', '|b1'), ('mouse', '|b1')])
""" return nomask
# Make sure the input dtype is valid.
# legacy boolean special case: "existence of fields implies true" return np.ones(m.shape, dtype=dtype)
# Fill the mask in case there are missing data; turn it into an ndarray. # Bas les masques !
""" 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, int]}) >>> dtype dtype([('foo', '<f4'), ('bar', '<i4')]) >>> ma.make_mask_none((3,), dtype=dtype) array([(False, False), (False, False), (False, False)], dtype=[('foo', '|b1'), ('bar', '|b1')])
""" else:
""" 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])
"""
def _recursive_mask_or(self, m1, m2, newmask): names = m1.dtype.names for name in names: current1 = m1[name] if current1.dtype.names is not None: self(current1, m2[name], newmask[name]) else: umath.logical_or(current1, m2[name], newmask[name]) return
dtype = getattr(m2, 'dtype', MaskType) return make_mask(m2, copy=copy, shrink=shrink, dtype=dtype) return m1 raise ValueError("Incompatible dtypes '%s'<>'%s'" % (dtype1, dtype2)) # 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
""" 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]) >>> flatten_mask(mask) array([False, False, True])
>>> mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)]) >>> 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) >>> 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__'): for f in _flatsequence(element): yield f else: yield element except TypeError: yield sequence
mask = np.asarray(mask) flattened = _flatsequence(_flatmask(mask)) return np.array([_ for _ in flattened], dtype=bool)
"Check whether there are masked values along the given axis"
############################################################################### # Masking functions # ###############################################################################
""" 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)
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) return result
""" 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)
""" 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)
""" 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)
""" 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)
""" 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)
""" Mask an array where equal to a given value.
This function is a shortcut to ``masked_where``, with `condition` = (x == 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=999999)
""" output = masked_where(equal(x, value), x, copy=copy) output.fill_value = value return output
""" 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)
""" 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)
""" 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') >>> print(eat) [-- ham] >>> # plain ol` ham is boring >>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object) >>> eat = ma.masked_object(fresh_food, 'green_eggs') >>> print(eat) [cheese ham pineapple]
Note that `mask` is set to ``nomask`` if possible.
>>> eat masked_array(data = [cheese ham pineapple], mask = False, fill_value=?)
""" 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)
""" 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, 1.5) masked_array(data = [ 1. 1.1 2. 1.1 3. ], mask = False, fill_value=1.5)
For integers, the fill value will be different in general to the result of ``masked_equal``.
>>> x = np.arange(5) >>> x array([0, 1, 2, 3, 4]) >>> ma.masked_values(x, 2) masked_array(data = [0 1 -- 3 4], mask = [False False True False False], fill_value=2) >>> ma.masked_equal(x, 2) masked_array(data = [0 1 -- 3 4], mask = [False False True False False], fill_value=999999)
""" 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
""" 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)
""" condition |= mask else:
############################################################################### # Printing options # ###############################################################################
""" Handle the string used to represent missing data in a masked array.
"""
""" Create the masked_print_option object.
"""
""" Display the string to print for masked values.
""" return self._display
""" Set the string to print for masked values.
""" self._display = s
""" Is the use of the display value enabled?
""" return self._enabled
""" Set the enabling shrink to `shrink`.
""" self._enabled = shrink
def __str__(self): return str(self._display)
# if you single index into a masked location you get this object.
""" 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 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 # ###############################################################################
""" 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])
""" 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) >>> 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__'): for f in flatten_sequence(elm): yield f 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
""" 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.
""" result.__setmask__(mask) # __setmask__ makes a copy, which we don't want
""" 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)
"""
self.ma = ma self.dataiter = ma._data.flat
if ma._mask is nomask: self.maskiter = None else: self.maskiter = ma._mask.flat
return self
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 self.dataiter[index] = getdata(value) if self.maskiter is not None: self.maskiter[index] = getmaskarray(value)
""" Return the next value, or raise StopIteration.
Examples -------- >>> x = np.ma.array([3, 2], mask=[0, 1]) >>> fl = x.flat >>> fl.next() 3 >>> fl.next() masked_array(data = --, mask = True, fill_value = 1e+20) >>> fl.next() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/ralf/python/numpy/numpy/ma/core.py", line 2243, in next d = self.dataiter.next() 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
""" 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.
"""
# 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.
subok=True, ndmin=0, fill_value=None, keep_mask=True, hard_mask=None, shrink=True, order=None, **options): """ Create a new masked array from scratch.
Notes ----- A masked array can also be created by taking a .view(MaskedArray).
""" # Process data. order=order, subok=True, ndmin=ndmin) # Check that we're not erasing the mask. 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 else: # Backwards compatibility w/ numpy.core.ma. _data._mask = data._mask # FIXME _sharedmask is never used. _sharedmask = True # Process mask. # Type of the mask
# Case 1. : no mask in input. # Erase the current 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 # If data is a sequence of masked array dtype=mdtype) except ValueError: # If data is nested mask = nomask # Force shrinking of the mask if needed (and possible) _data._mask = mask _data._sharedmask = False else: # Reset the shape of the original mask else: # Case 2. : With a mask in input. # If mask is boolean, create an array of True or False mask = np.ones(_data.shape, dtype=mdtype) mask = np.zeros(_data.shape, dtype=mdtype) else: # Read the mask with the current 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 (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 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. # But don't run the check unless we have something to check. # Process extra options .. else:
""" Copies some attributes of obj to self.
""" else: # We need to copy the _basedict to avoid backward propagation _hardmask=getattr(obj, '_hardmask', False), _sharedmask=getattr(obj, '_sharedmask', False), _isfield=getattr(obj, '_isfield', False), _baseclass=getattr(obj, '_baseclass', _baseclass), _optinfo=_optinfo, _basedict=_optinfo)
""" Finalizes the masked array.
""" # Get main attributes.
# 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. # XX: This looks like a bug -- shouldn't it check self.dtype # instead? _mask = getmaskarray(obj) else:
# 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. != 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. else:
elif self.flags.f_contiguous: order = "F" else: order = "K"
else: # Take a view so shape changes, etc., do not propagate back. else:
# Finalize the mask except ValueError: self._mask = nomask except (TypeError, AttributeError): # When _mask.shape is not writable (because it's a void) pass
# Finalize the fill_value # Finalize the default fill_value for structured arrays self._fill_value = _check_fill_value(None, self.dtype)
""" Special hook for ufuncs.
Wraps the numpy array and sets the mask according to context.
""" result = obj else:
# args sometimes contains outputs (gh-10459), which we don't want # Get the domain mask # Take the domain, and make sure it's a ndarray
# Fill the result where the domain is wrong # Binary domain: take the last value # Unary domain: just use this one except KeyError: # Domain not recognized, use fill_value instead fill_value = self.fill_value
# Update the mask m = d else: # Don't modify inplace, we risk back-propagation
# Make sure the mask has the proper size return masked else:
""" 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.
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 type is None: output = ndarray.view(self) else: output = ndarray.view(self, type) 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)
# Make sure to reset the _fill_value if needed else: output._fill_value = None else: output.fill_value = fill_value
""" 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
""" Return whether `elem` is a scalar result of indexing `arr`, or None if undecidable without promoting nomask to a full mask """ # obviously a scalar
# object array scalar indexing can return anything 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
return None
# _mask cannot be a subclass, so it tells us whether we should # expect a scalar. It also cannot be of dtype object.
else: # attempt to apply the heuristic to avoid constructing a full mask # 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? # A record # 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 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: return masked else: else: # Force dout to MA # Inherit attributes from self # Check the fill_value if self._fill_value is not None: dout._fill_value = self._fill_value[indx]
# 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 " "{indx:s}, need to keep dimensionality " "of fill_value at 0. Discarding " "heterogeneous fill_value and setting " "all to {fv!s}.".format(indx=indx, fv=dout._fill_value[0]), stacklevel=2) dout._fill_value = dout._fill_value.flat[0] dout._isfield = True # Update the mask if needed # set shape to match that of data; this is needed for matrices # Note: Don't try to check for m.any(), that'll take too long
""" x.__setitem__(i, y) <==> x[i]=y
Set item described by index. If value is masked, masks those locations.
""" raise MaskError('Cannot alter the masked element.') _data[indx] = value if _mask is nomask: self._mask = _mask = make_mask_none(self.shape, self.dtype) _mask[indx] = getmask(value) return
# 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 # Get the _mask part of the new value mval = tuple([False] * len(_dtype.names)) # 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 # Set the data, then the mask 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
# Define so that we can overwrite the setter. def dtype(self):
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
def shape(self):
def shape(self, shape): # Cannot use self._mask, since it may not (yet) exist when a # masked matrix sets the shape. self._mask.shape = self.shape
""" Set the mask.
""" mask = True
# Make sure the mask is set # Just don't do anything if there's nothing to do.
# No named fields. # Hardmask: don't unmask the data current_mask |= mask # Softmask: set everything to False # If it's obviously a compatible scalar, use a quick update # method. current_mask[...] = mask # Otherwise fall back to the slower, general purpose way. else: 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
"""Return the current mask.
""" # We could try to force a reshape, but that wouldn't work in some # cases.
""" Return the mask of the records.
A record is masked when all the fields are masked.
""" _mask = self._mask.view(ndarray) if _mask.dtype.names is None: return _mask return np.all(flatten_structured_array(_mask), axis=-1)
""" Return the mask of the records.
A record is masked when all the fields are masked.
""" raise NotImplementedError("Coming soon: setting the mask per records!")
""" Force the mask to hard.
Whether the mask of a masked array is hard or soft is determined by its `hardmask` property. `harden_mask` sets `hardmask` to True.
See Also -------- hardmask
""" self._hardmask = True return self
""" Force the mask to soft.
Whether the mask of a masked array is hard or soft is determined by its `hardmask` property. `soften_mask` sets `hardmask` to False.
See Also -------- hardmask
""" self._hardmask = False return self
doc="Hardness of the mask")
""" 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
doc="Share status of the mask (read-only).")
""" 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() >>> x.mask False
"""
doc="Class of the underlying data (read-only).")
"""Return the current data, as a view of the original underlying data.
"""
"Return a flat iterator." return MaskedIterator(self)
"Set a flattened version of self to value." y = self.ravel() y[:] = value
doc="Flat version of the array.")
""" Return the filling value of the masked array.
Returns ------- fill_value : scalar The filling value.
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.get_fill_value() -inf
"""
# 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. return self._fill_value
""" Set the filling value of the masked array.
Parameters ---------- value : scalar, optional The new filling value. Default is None, in which case a default based on the data type is used.
See Also -------- ma.set_fill_value : Equivalent function.
Examples -------- >>> x = np.ma.array([0, 1.], fill_value=-np.inf) >>> x.fill_value -inf >>> x.set_fill_value(np.pi) >>> x.fill_value 3.1415926535897931
Reset to default:
>>> x.set_fill_value() >>> x.fill_value 1e+20
""" target = _check_fill_value(value, self.dtype) _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
doc="Filling value.")
""" 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 : scalar, optional The value to use for invalid entries (None by default). If None, 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]) >>> type(x.filled()) <type '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')]) """
else:
return np.asanyarray(fill_value)
result = self._data.copy('K') _recursive_filled(result, self._mask, fill_value) else: 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 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()) <type 'numpy.ndarray'>
"""
""" Return `a` where condition is ``True``.
If condition is a `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:`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) >>> print(x) [[1 -- 3] [-- 5 --] [7 -- 9]] >>> 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.array(condition, copy=False, subok=False)
_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
""" 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 __unicode__(self): return unicode(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.get_printoptions()['legacy'] == '1.13': 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 = 'masked_{}('.format(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 + ')'
# This emulates the logic in # private/binop_override.h:forward_binop_should_defer 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
"""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: # 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: # 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. 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
"""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)
"""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)
""" Add self to other, and return a new masked array.
""" return NotImplemented
""" 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)
""" Subtract other from self, and return a new masked array.
""" return NotImplemented
""" Subtract self from other, and return a new masked array.
""" return subtract(other, self)
"Multiply self by other, and return a new masked array." return NotImplemented
""" 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)
""" Divide other into self, and return a new masked array.
""" if self._delegate_binop(other): return NotImplemented return divide(self, other)
""" Divide other into self, and return a new masked array.
""" if self._delegate_binop(other): return NotImplemented return true_divide(self, other)
""" Divide self into other, and return a new masked array.
""" return true_divide(other, self)
""" Divide other into self, and return a new masked array.
""" if self._delegate_binop(other): return NotImplemented return floor_divide(self, other)
""" Divide self into other, and return a new masked array.
""" return floor_divide(other, self)
""" Raise self to the power other, masking the potential NaNs/Infs
""" return NotImplemented
""" Raise other to the power self, masking the potential NaNs/Infs
""" return power(other, self)
""" 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 self._data.__iadd__(np.where(self._mask, self.dtype.type(0), getdata(other))) return self
""" 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 self._data.__isub__(np.where(self._mask, self.dtype.type(0), getdata(other))) return self
""" 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 self._data.__imul__(np.where(self._mask, self.dtype.type(1), getdata(other))) return self
""" 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.divide] other_data = np.where(dom_mask, fval, other_data) self._mask |= new_mask self._data.__idiv__(np.where(self._mask, self.dtype.type(1), other_data)) return self
""" 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, fval, other_data) self._mask |= new_mask self._data.__ifloordiv__(np.where(self._mask, self.dtype.type(1), other_data)) return self
""" 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, fval, other_data) self._mask |= new_mask self._data.__itruediv__(np.where(self._mask, self.dtype.type(1), other_data)) return self
""" Raise self to the power other, in place.
""" other_data = getdata(other) other_mask = getmask(other) with np.errstate(divide='ignore', invalid='ignore'): self._data.__ipow__(np.where(self._mask, self.dtype.type(1), 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
""" 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())
""" Convert to int.
""" raise TypeError("Only length-1 arrays can be converted " "to Python scalars") raise MaskError('Cannot convert masked element to a Python int.')
""" Convert to long. """ 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 long.') return long(self.item())
""" Return the imaginary part of the masked array.
The returned array is a view on the imaginary part of the `MaskedArray` whose `get_imag` method is called.
Parameters ---------- None
Returns ------- result : MaskedArray The imaginary part of the masked array.
See Also -------- get_real, real, imag
Examples -------- >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False]) >>> x.get_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
""" Return the real part of the masked array.
The returned array is a view on the real part of the `MaskedArray` whose `get_real` method is called.
Parameters ---------- None
Returns ------- result : MaskedArray The real part of the masked array.
See Also -------- get_imag, real, imag
Examples -------- >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False]) >>> x.get_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
""" 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 (`axis` = `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 -------- 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])
"""
# special case for matrices (we assume no other subclasses modify # their dimensions) if m is nomask: m = np.zeros(self.shape, dtype=np.bool_) m = m.view(type(self.data))
# compare to _count_reduce_items in _methods.py
if self.shape is (): 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 return 0
""" 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.
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) >>> print(x) [[1 -- 3] [-- 5 --] [7 -- 9]] >>> print(x.ravel()) [1 -- 3 -- 5 -- 7 -- 9]
""" else:
""" 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]) >>> print(x) [[-- 2] [3 --]] >>> x = x.reshape((4,1)) >>> print(x) [[--] [2] [3] [--]]
"""
""" .. 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) >>> print(x) [[1 -- 3] [-- 5 --] [7 -- 9]] >>> x.put([0,4,8],[10,20,30]) >>> print(x) [[10 -- 3] [-- 20 --] [7 -- 30]]
>>> x.put(4,999) >>> print(x) [[10 -- 3] [-- 999 --] [7 -- 30]]
""" # 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
""" 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)
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, 3083169284L)
""" if self._mask is nomask: return (self.ctypes.data, id(nomask)) return (self.ctypes.data, self._mask.ctypes.data)
""" 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 UPDATEIFCOPY : False
""" return self.flags['CONTIGUOUS']
""" 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 -------- 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
""" 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 -------- 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
""" 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. 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=999999) >>> 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()
""" (this docstring should be overwritten) """ #!!!: implement out + test! m = self._mask if m is nomask: result = super(MaskedArray, self).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)
""" 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)
""" 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 -------- 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) >>> print(x) [[1 -- 3] [-- 5 --] [7 -- 9]] >>> print(x.sum()) 25 >>> print(x.sum(axis=1)) [4 5 16] >>> print(x.sum(axis=0)) [8 5 12] >>> print(type(x.sum(axis=0, dtype=np.int64)[0])) <type 'numpy.int64'>
"""
# No explicit output elif newmask: result = masked # 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
""" 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:`MaskedArray` !
Arithmetic is modular when using integer types, and no error is raised on overflow.
See Also -------- 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]) >>> print(marr.cumsum()) [0 1 3 -- -- -- 9 16 24 33]
""" 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
""" 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 -------- 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
""" 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 -------- 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
""" 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 -------- 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(MaskedArray, self).mean(axis=axis, dtype=dtype, **kwargs)[()] else: dsum = self.sum(axis=axis, dtype=dtype, **kwargs) cnt = self.count(axis=axis, **kwargs) if cnt.shape == () and (cnt == 0): result = masked 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
""" 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 m is masked: return m
if not axis: return (self - m) else: return (self - expand_dims(m, axis))
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 -------- 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(MaskedArray, self).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
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 -------- 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
""" Return each element rounded to the given number of decimals.
Refer to `numpy.around` for full documentation.
See Also -------- ndarray.around : 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
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 Sorting algorithm. 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 : {var}, 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 -------- MaskedArray.sort : Describes sorting algorithms used. lexsort : Indirect stable sort with multiple keys. 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 axis = _deprecate_argsort_axis(self)
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)
""" 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 : {var}, 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(arange(4), mask=[1,1,0,0]) >>> x.shape = (2,2) >>> print(x) [[-- --] [2 3]] >>> print(x.argmin(axis=0, fill_value=-1)) [0 0] >>> print(x.argmin(axis=0, fill_value=9)) [1 1]
""" if fill_value is None: fill_value = minimum_fill_value(self) d = self.filled(fill_value).view(ndarray) return d.argmin(axis, out=out)
""" 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 : {var}, 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) return d.argmax(axis, out=out)
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 Sorting algorithm. Default is 'quicksort'. 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 at the same extremes of the datatype, the ordering of these values and the masked values is undefined. fill_value : {var}, 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 -------- 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 = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) >>> # Default >>> a.sort() >>> print(a) [1 3 5 -- --]
>>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0]) >>> # Put missing values in the front >>> a.sort(endwith=False) >>> print(a) [-- -- 1 3 5]
>>> a = 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) >>> print(a) [1 -- -- 3 5]
""" ndarray.sort(self, axis=axis, kind=kind, order=order) return
return
fill_value=fill_value, endwith=endwith)
""" Return the minimum along a given axis.
Parameters ---------- axis : {None, int}, optional Axis along which to operate. By default, ``axis`` is None and the flattened input is used. 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 : {var}, optional Value used to fill in the masked values. If None, use the output of `minimum_fill_value`.
Returns ------- amin : array_like New array holding the result. If ``out`` was specified, ``out`` is returned.
See Also -------- minimum_fill_value Returns the minimum filling value for a given datatype.
"""
# No explicit output axis=axis, out=out, **kwargs).view(type(self)) # Set the mask result.__setmask__(newmask) # Get rid of Infs if newmask.ndim: np.copyto(result, result.fill_value, where=newmask) result = masked # 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
# unique to masked arrays """ Return the array minimum along the specified axis.
.. deprecated:: 1.13.0 This function is identical to both:
* ``self.min(keepdims=True, axis=axis).squeeze(axis=axis)`` * ``np.ma.minimum.reduce(self, axis=axis)``
Typically though, ``self.min(axis=axis)`` is sufficient.
Parameters ---------- axis : int, optional The axis along which to find the minima. Default is None, in which case the minimum value in the whole array is returned.
Returns ------- min : scalar or MaskedArray If `axis` is None, the result is a scalar. Otherwise, if `axis` is given and the array is at least 2-D, the result is a masked array with dimension one smaller than the array on which `mini` is called.
Examples -------- >>> x = np.ma.array(np.arange(6), mask=[0 ,1, 0, 0, 0 ,1]).reshape(3, 2) >>> print(x) [[0 --] [2 3] [4 --]] >>> x.mini() 0 >>> x.mini(axis=0) masked_array(data = [0 3], mask = [False False], fill_value = 999999) >>> print(x.mini(axis=1)) [0 2 4]
There is a small difference between `mini` and `min`:
>>> x[:,1].mini(axis=0) masked_array(data = --, mask = True, fill_value = 999999) >>> x[:,1].min(axis=0) masked """
# 2016-04-13, 1.13.0, gh-8764 warnings.warn( "`mini` is deprecated; use the `min` method or " "`np.ma.minimum.reduce instead.", DeprecationWarning, stacklevel=2) return minimum.reduce(self, axis)
""" Return the maximum along a given axis.
Parameters ---------- axis : {None, int}, optional Axis along which to operate. By default, ``axis`` is None and the flattened input is used. 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 : {var}, optional Value used to fill in the masked values. If None, use the output of maximum_fill_value().
Returns ------- amax : array_like New array holding the result. If ``out`` was specified, ``out`` is returned.
See Also -------- maximum_fill_value Returns the maximum filling value for a given datatype.
"""
# No explicit output axis=axis, out=out, **kwargs).view(type(self)) # Set the mask result.__setmask__(newmask) # Get rid of Infs if newmask.ndim: np.copyto(result, result.fill_value, where=newmask) result = masked # 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
""" Return (maximum - minimum) along the given dimension (i.e. peak-to-peak value).
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 : {var}, optional Value used to fill in the masked values.
Returns ------- ptp : ndarray. A new array holding the result, unless ``out`` was specified, in which case a reference to ``out`` is returned.
""" 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
warnings.warn("Warning: 'partition' will ignore the 'mask' " "of the {}.".format(self.__class__.__name__), stacklevel=2) return super(MaskedArray, self).partition(*args, **kwargs)
warnings.warn("Warning: 'argpartition' will ignore the 'mask' " "of the {}.".format(self.__class__.__name__), stacklevel=2) return super(MaskedArray, self).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
""" 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()
""" This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings. """
return self.tobytes(fill_value, 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 -------- 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() '\\x01\\x00\\x00\\x00?B\\x0f\\x00?B\\x0f\\x00\\x04\\x00\\x00\\x00'
""" return self.filled(fill_value).tobytes(order=order)
""" 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.")
""" 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) >>> print(x) [[1 -- 3] [-- 5 --] [7 -- 9]] >>> print(x.toflex()) [[(1, False) (2, True) (3, False)] [(4, True) (5, False) (6, True)] [(7, False) (8, True) (9, False)]]
""" # 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
# Pickling """Return the internal state of the masked array, for pickling purposes.
""" cf = 'CF'[self.flags.fnc] data_state = super(MaskedArray, self).__reduce__()[2] return data_state + (getmaskarray(self).tobytes(cf), self._fill_value)
"""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(MaskedArray, self).__setstate__((shp, typ, isf, raw)) self._mask.__setstate__((shp, make_mask_descr(typ), isf, msk)) self.fill_value = flv
"""Return a 3-tuple for pickling a MaskedArray.
""" return (_mareconstruct, (self.__class__, self._baseclass, (0,), 'b',), self.__getstate__())
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) return copied
"""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,)
""" Fake a 'void' object to use for masked array with structured dtypes. """
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
# Make sure that the _data part is a np.void return super(mvoid, self)._data[()]
""" 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]
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(mvoid, self)._data res = data_arr.astype(rdtype) _recursive_printoption(res, self._mask, masked_print_option) return str(res)
"Defines an iterator for mvoid" (_data, _mask) = (self._data, self._mask) if _mask is nomask: for d in _data: yield d else: for (d, m) in zip(_data, _mask): if m: yield masked else: yield d
return self._data.__len__()
""" Return a copy with masked fields filled with a given value.
Parameters ---------- fill_value : scalar, optional The value to use for invalid entries (None by default). If None, the `fill_value` attribute is used instead.
Returns ------- filled_void A `np.void` object
See Also -------- MaskedArray.filled
""" return asarray(self).filled(fill_value)[()]
""" 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 # ##############################################################################
""" 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
"""
# the lone np.ma.masked instance
def __has_singleton(cls): # second case ensures `cls.__singleton` is not just a view on the # superclass singleton
# We define the masked singleton as a float for higher precedence. # Note that it can be tricky sometimes w/ type comparison
# prevent any modifications
# don't fall back on MaskedArray.__new__(MaskedConstant), since # that might confuse it - this way, the construction is entirely # within our control
# this handles the `.view` in __new__, which we want to copy across # properties normally 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)
return self.view(MaskedArray).__array_prepare__(obj, context)
return self.view(MaskedArray).__array_wrap__(obj, context)
def __str__(self): return str(masked_print_option._display)
def __unicode__(self): return unicode(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)
"""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 return self __isub__ = \ __imul__ = \ __ifloordiv__ = \ __itruediv__ = \ __ipow__ = \ __iop__
""" 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
return self
return self
# allow the singleton to be initialized elif self is self.__singleton: raise AttributeError( "attributes of {!r} are not writeable".format(self)) else: # duplicate instance - we can end up here from __array_finalize__, # where we set the __class__ attribute return super(MaskedConstant, self).__setattr__(attr, value)
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.
""" subok=subok, keep_mask=keep_mask, hard_mask=hard_mask, fill_value=fill_value, ndmin=ndmin, shrink=shrink, order=order)
""" 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=999999) >>> 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=999999) >>> 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
""" return True
############################################################################## # Extrema functions # ##############################################################################
""" Generic class for maximum/minimum functions.
.. note:: This is the base class for `_maximum_operation` and `_minimum_operation`.
"""
"Executes the call behavior." if b is None: # 2016-04-13, 1.13.0 warnings.warn( "Single-argument form of np.ma.{0} is deprecated. Use " "np.ma.{0}.reduce instead.".format(self.__name__), DeprecationWarning, stacklevel=2) return self.reduce(a) return where(self.compare(a, b), a, b)
"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( "In the future the default for ma.{0}.reduce will be axis=0, " "not the current None, to match np.{0}.reduce. " "Explicitly pass 0 or None to silence this warning.".format( self.__name__ ), 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
"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
# If obj doesn't have a min method, or if the method doesn't accept a # fill_value argument out=out, **kwargs)
# If obj doesn't have a max method, or if the method doesn't accept a # fill_value argument out=out, **kwargs)
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)
############################################################################## # Definition of functions from the corresponding methods # ##############################################################################
""" Define functions from existing MaskedArray methods.
Parameters ---------- methodname : str Name of the method to transform.
"""
"Return the doc of the function (from the doc of the method)." getattr(np, self.__name__, None) signature, getattr(meth, '__doc__', None))
args = list(args) a, args[0] = args[0], a
# use the corresponding np function method = getattr(np, method_name)
#take = _frommethod('take')
def take(a, indices, axis=None, out=None, mode='raise'): """ """ a = masked_array(a) return a.take(indices, axis=axis, out=out, mode=mode)
""" 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.
""" raise MaskError("3-argument power not supported.") # Get the masks # Get the rawdata # Get the type of the result (so that we preserve subclasses) else: basetype = MaskedArray # Get the result and view it as a (subclass of) MaskedArray # Find where we're in trouble w/ NaNs and Infs # Add the initial mask return masked # Fix the invalid parts if not result.ndim: return masked elif result._mask is nomask: result._mask = invalid result._data[invalid] = result.fill_value
"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)
"Function version of the eponymous method." a = a.flatten() axis = 0
endwith=endwith, fill_value=fill_value) else: a.sort(axis=axis, kind=kind, order=order)
""" Return all the non-masked data as a 1-D array.
This function is equivalent to calling the "compressed" method of a `MaskedArray`, see `MaskedArray.compressed` for details.
See Also -------- MaskedArray.compressed Equivalent method.
""" return asanyarray(x).compressed()
""" 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
""" 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.
""" output = np.diag(v, k).view(MaskedArray) if getmask(v) is not nomask: output._mask = np.diag(v._mask, k) return output
""" 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)
""" 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
""" 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
""" 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)
""" 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. except AttributeError: _tmp = narray(a, copy=False).reshape(new_shape, order=order) return _tmp.view(MaskedArray)
""" 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)) array([[1, 2, 3], [4, 1, 2], [3, 4, 1]]) >>> 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
""" maskedarray version of the numpy function.
.. note:: Deprecated since 1.10.0
""" # 2015-04-12, 1.10.0 warnings.warn( "`rank` is deprecated; use the `ndim` function instead. ", np.VisibleDeprecationWarning, stacklevel=2) return np.ndim(getdata(obj))
""" maskedarray version of the numpy function.
""" return np.ndim(getdata(obj))
"maskedarray version of the numpy function." return np.shape(getdata(obj))
"maskedarray version of the numpy function." return np.size(getdata(obj), axis)
############################################################################## # Extra functions # ##############################################################################
""" 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]]) >>> print(x) [[0.0 -- 2.0] [-- 4.0 --] [6.0 -- 8.0]] >>> print(np.ma.where(x > 5, x, -3.1416)) [[-3.1416 -- -3.1416] [-- -3.1416 --] [6.0 -- 8.0]]
"""
# 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 set of choices.
Given an array of integers and a set of n choice arrays, this method will create a new array that merges each of the choice arrays. Where a value in `a` is i, the new array will have the value that choices[i] contains in the same place.
Parameters ---------- a : 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=0, 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
""" 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!
""" 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
# Needed by dot, so move here from extras.py. It will still be exported # from extras.py for compatibility. """ Mask rows and/or columns of a 2D array that contain masked values.
Mask whole rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the `axis` parameter.
- If `axis` is None, rows *and* columns are masked. - If `axis` is 0, only rows are masked. - If `axis` is 1 or -1, only columns are masked.
Parameters ---------- a : array_like, MaskedArray The array to mask. If not a MaskedArray instance (or if no array elements are masked). The result is a MaskedArray with `mask` set to `nomask` (False). Must be a 2D array. axis : int, optional Axis along which to perform the operation. If None, applies to a flattened version of the array.
Returns ------- a : MaskedArray A modified version of the input array, masked depending on the value of the `axis` parameter.
Raises ------ NotImplementedError If input array `a` is not 2D.
See Also -------- mask_rows : Mask rows of a 2D array that contain masked values. mask_cols : Mask cols of a 2D array that contain masked values. masked_where : Mask where a condition is met.
Notes ----- The input array's mask is modified by this function.
Examples -------- >>> import numpy.ma as ma >>> a = np.zeros((3, 3), dtype=int) >>> a[1, 1] = 1 >>> a array([[0, 0, 0], [0, 1, 0], [0, 0, 0]]) >>> a = ma.masked_equal(a, 1) >>> a masked_array(data = [[0 0 0] [0 -- 0] [0 0 0]], mask = [[False False False] [False True False] [False False False]], fill_value=999999) >>> ma.mask_rowcols(a) masked_array(data = [[0 -- 0] [-- -- --] [0 -- 0]], mask = [[False True False] [ True True True] [False True False]], fill_value=999999)
""" a = array(a, subok=False) if a.ndim != 2: raise NotImplementedError("mask_rowcols works for 2D arrays only.") m = getmask(a) # Nothing is masked: return a if m is nomask or not m.any(): return a maskedval = m.nonzero() a._mask = a._mask.copy() if not axis: a[np.unique(maskedval[0])] = masked if axis in [None, 1, -1]: a[:, np.unique(maskedval[1])] = masked 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. """ 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.
.. note:: Works only with 2-D arrays at the moment.
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 = ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]]) >>> b = 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)
""" # !!!: Works only with 2D arrays. There should be a way to get it to run # with higher dimension if strict and (a.ndim == 2) and (b.ndim == 2): a = mask_rowcols(a, 0) b = mask_rowcols(b, 1) 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 d.ndim == 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
""" 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) "Masked values are replaced by 0.")
"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=0) return masked_array(d, mask=m) "Masked values are replaced by 0.")
""" 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)
""" 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)
""" 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)
""" 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 = 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 = array([1e10, 1e-7, -42.0]) >>> b array([ 1.00000000e+10, 1.00000000e-07, -4.20000000e+01]) >>> ma.allequal(a, b, fill_value=False) False >>> 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
""" 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 = 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 = ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1]) >>> ma.allclose(a, b) False
>>> a = ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1]) >>> b = ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1]) >>> ma.allclose(a, b) True >>> ma.allclose(a, b, masked_equal=False) False
Masked values are not compared directly.
>>> a = ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1]) >>> b = ma.array([1.00001e10, 1e-9, 42.0], mask=[0, 0, 1]) >>> ma.allclose(a, b) True >>> 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. 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)
""" 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'>
""" subok=False, order=order)
""" 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.
############################################################################## # Pickling # ##############################################################################
# NumPy 1.15.0, 2017-12-10 warnings.warn( "np.ma.{method} is deprecated, use pickle.{method} instead" .format(method=method), DeprecationWarning, stacklevel=3)
""" Pickle a masked array to a file.
This is a wrapper around ``cPickle.dump``.
Parameters ---------- a : MaskedArray The array to be pickled. F : str or file-like object The file to pickle `a` to. If a string, the full path to the file.
""" _pickle_warn('dump') if not hasattr(F, 'readline'): with open(F, 'w') as F: pickle.dump(a, F) else: pickle.dump(a, F)
""" Return a string corresponding to the pickling of a masked array.
This is a wrapper around ``cPickle.dumps``.
Parameters ---------- a : MaskedArray The array for which the string representation of the pickle is returned.
""" _pickle_warn('dumps') return pickle.dumps(a)
""" Wrapper around ``cPickle.load`` which accepts either a file-like object or a filename.
Parameters ---------- F : str or file The file or file name to load.
See Also -------- dump : Pickle an array
Notes ----- This is different from `numpy.load`, which does not use cPickle but loads the NumPy binary .npy format.
""" _pickle_warn('load') if not hasattr(F, 'readline'): with open(F, 'r') as F: return pickle.load(F) else: return pickle.load(F)
""" Load a pickle from the current string.
The result of ``cPickle.loads(strg)`` is returned.
Parameters ---------- strg : str The string to load.
See Also -------- dumps : Return a string corresponding to the pickling of a masked array.
""" _pickle_warn('loads') return pickle.loads(strg)
raise NotImplementedError( "fromfile() not yet implemented for a MaskedArray.")
""" 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', '<i4'), ('_mask', '|b1')]) >>> 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), (0, False, 0.0)], [(0, False, 0.0), (0, False, 0.0)]], dtype=[('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')]) >>> y = np.ma.fromflex(rec2) >>> y masked_array(data = [[0 0] [0 0]], mask = [[False False] [False False]], fill_value = 999999)
""" return masked_array(fxarray['_data'], mask=fxarray['_mask'])
""" Convert functions from numpy to numpy.ma.
Parameters ---------- _methodname : string Name of the method to transform.
"""
"Return the doc of the function (from the doc of the method)." # Add the signature of the function at the beginning of the doc
# 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
'identity', params=dict(fill_value=None, hardmask=False))
"""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) >>> print(ma.append(a, b)) [1 -- 3 4 5 6 -- 8 9] """ return concatenate([a, b], axis) |