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""" Masked arrays add-ons.
A collection of utilities for `numpy.ma`.
:author: Pierre Gerard-Marchant :contact: pierregm_at_uga_dot_edu :version: $Id: extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $
"""
'apply_along_axis', 'apply_over_axes', 'atleast_1d', 'atleast_2d', 'atleast_3d', 'average', 'clump_masked', 'clump_unmasked', 'column_stack', 'compress_cols', 'compress_nd', 'compress_rowcols', 'compress_rows', 'count_masked', 'corrcoef', 'cov', 'diagflat', 'dot', 'dstack', 'ediff1d', 'flatnotmasked_contiguous', 'flatnotmasked_edges', 'hsplit', 'hstack', 'isin', 'in1d', 'intersect1d', 'mask_cols', 'mask_rowcols', 'mask_rows', 'masked_all', 'masked_all_like', 'median', 'mr_', 'notmasked_contiguous', 'notmasked_edges', 'polyfit', 'row_stack', 'setdiff1d', 'setxor1d', 'stack', 'unique', 'union1d', 'vander', 'vstack', ]
MaskedArray, MAError, add, array, asarray, concatenate, filled, count, getmask, getmaskarray, make_mask_descr, masked, masked_array, mask_or, nomask, ones, sort, zeros, getdata, get_masked_subclass, dot, mask_rowcols )
""" Is seq a sequence (ndarray, list or tuple)?
""" return isinstance(seq, (ndarray, tuple, list))
""" Count the number of masked elements along the given axis.
Parameters ---------- arr : array_like An array with (possibly) masked elements. axis : int, optional Axis along which to count. If None (default), a flattened version of the array is used.
Returns ------- count : int, ndarray The total number of masked elements (axis=None) or the number of masked elements along each slice of the given axis.
See Also -------- MaskedArray.count : Count non-masked elements.
Examples -------- >>> import numpy.ma as ma >>> a = np.arange(9).reshape((3,3)) >>> a = ma.array(a) >>> a[1, 0] = ma.masked >>> a[1, 2] = ma.masked >>> a[2, 1] = ma.masked >>> a masked_array(data = [[0 1 2] [-- 4 --] [6 -- 8]], mask = [[False False False] [ True False True] [False True False]], fill_value=999999) >>> ma.count_masked(a) 3
When the `axis` keyword is used an array is returned.
>>> ma.count_masked(a, axis=0) array([1, 1, 1]) >>> ma.count_masked(a, axis=1) array([0, 2, 1])
""" m = getmaskarray(arr) return m.sum(axis)
""" Empty masked array with all elements masked.
Return an empty masked array of the given shape and dtype, where all the data are masked.
Parameters ---------- shape : tuple Shape of the required MaskedArray. dtype : dtype, optional Data type of the output.
Returns ------- a : MaskedArray A masked array with all data masked.
See Also -------- masked_all_like : Empty masked array modelled on an existing array.
Examples -------- >>> import numpy.ma as ma >>> ma.masked_all((3, 3)) masked_array(data = [[-- -- --] [-- -- --] [-- -- --]], mask = [[ True True True] [ True True True] [ True True True]], fill_value=1e+20)
The `dtype` parameter defines the underlying data type.
>>> a = ma.masked_all((3, 3)) >>> a.dtype dtype('float64') >>> a = ma.masked_all((3, 3), dtype=np.int32) >>> a.dtype dtype('int32')
""" a = masked_array(np.empty(shape, dtype), mask=np.ones(shape, make_mask_descr(dtype))) return a
""" Empty masked array with the properties of an existing array.
Return an empty masked array of the same shape and dtype as the array `arr`, where all the data are masked.
Parameters ---------- arr : ndarray An array describing the shape and dtype of the required MaskedArray.
Returns ------- a : MaskedArray A masked array with all data masked.
Raises ------ AttributeError If `arr` doesn't have a shape attribute (i.e. not an ndarray)
See Also -------- masked_all : Empty masked array with all elements masked.
Examples -------- >>> import numpy.ma as ma >>> arr = np.zeros((2, 3), dtype=np.float32) >>> arr array([[ 0., 0., 0.], [ 0., 0., 0.]], dtype=float32) >>> ma.masked_all_like(arr) masked_array(data = [[-- -- --] [-- -- --]], mask = [[ True True True] [ True True True]], fill_value=1e+20)
The dtype of the masked array matches the dtype of `arr`.
>>> arr.dtype dtype('float32') >>> ma.masked_all_like(arr).dtype dtype('float32')
""" a = np.empty_like(arr).view(MaskedArray) a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype)) return a
#####-------------------------------------------------------------------------- #---- --- Standard functions --- #####-------------------------------------------------------------------------- """ Defines a wrapper to adapt NumPy functions to masked arrays.
An instance of `_fromnxfunction` can be called with the same parameters as the wrapped NumPy function. The docstring of `newfunc` is adapted from the wrapped function as well, see `getdoc`.
This class should not be used directly. Instead, one of its extensions that provides support for a specific type of input should be used.
Parameters ---------- funcname : str The name of the function to be adapted. The function should be in the NumPy namespace (i.e. ``np.funcname``).
"""
""" Retrieve the docstring and signature from the function.
The ``__doc__`` attribute of the function is used as the docstring for the new masked array version of the function. A note on application of the function to the mask is appended.
.. warning:: If the function docstring already contained a Notes section, the new docstring will have two Notes sections instead of appending a note to the existing section.
Parameters ---------- None
""" " and the _mask, if any." return
pass
""" A version of `_fromnxfunction` that is called with a single array argument followed by auxiliary args that are passed verbatim for both the data and mask calls. """ func = getattr(np, self.__name__) if isinstance(x, ndarray): _d = func(x.__array__(), *args, **params) _m = func(getmaskarray(x), *args, **params) return masked_array(_d, mask=_m) else: _d = func(np.asarray(x), *args, **params) _m = func(getmaskarray(x), *args, **params) return masked_array(_d, mask=_m)
""" A version of `_fromnxfunction` that is called with a single sequence of arrays followed by auxiliary args that are passed verbatim for both the data and mask calls. """ func = getattr(np, self.__name__) _d = func(tuple([np.asarray(a) for a in x]), *args, **params) _m = func(tuple([getmaskarray(a) for a in x]), *args, **params) return masked_array(_d, mask=_m)
""" A version of `_fromnxfunction` that is called with multiple array arguments. The first non-array-like input marks the beginning of the arguments that are passed verbatim for both the data and mask calls. Array arguments are processed independently and the results are returned in a list. If only one array is found, the return value is just the processed array instead of a list. """ func = getattr(np, self.__name__) arrays = [] args = list(args) while len(args) > 0 and issequence(args[0]): arrays.append(args.pop(0)) res = [] for x in arrays: _d = func(np.asarray(x), *args, **params) _m = func(getmaskarray(x), *args, **params) res.append(masked_array(_d, mask=_m)) if len(arrays) == 1: return res[0] return res
""" A version of `_fromnxfunction` that is called with multiple array arguments. Similar to `_fromnxfunction_args` except that all args are converted to arrays even if they are not so already. This makes it possible to process scalars as 1-D arrays. Only keyword arguments are passed through verbatim for the data and mask calls. Arrays arguments are processed independently and the results are returned in a list. If only one arg is present, the return value is just the processed array instead of a list. """ func = getattr(np, self.__name__) res = [] for x in args: _d = func(np.asarray(x), **params) _m = func(getmaskarray(x), **params) res.append(masked_array(_d, mask=_m)) if len(args) == 1: return res[0] return res
#####-------------------------------------------------------------------------- #---- #####-------------------------------------------------------------------------- """Flatten a sequence in place.""" k = 0 while (k != len(seq)): while hasattr(seq[k], '__iter__'): seq[k:(k + 1)] = seq[k] k += 1 return seq
""" (This docstring should be overwritten) """ arr = array(arr, copy=False, subok=True) nd = arr.ndim axis = normalize_axis_index(axis, nd) ind = [0] * (nd - 1) i = np.zeros(nd, 'O') indlist = list(range(nd)) indlist.remove(axis) i[axis] = slice(None, None) outshape = np.asarray(arr.shape).take(indlist) i.put(indlist, ind) j = i.copy() res = func1d(arr[tuple(i.tolist())], *args, **kwargs) # if res is a number, then we have a smaller output array asscalar = np.isscalar(res) if not asscalar: try: len(res) except TypeError: asscalar = True # Note: we shouldn't set the dtype of the output from the first result # so we force the type to object, and build a list of dtypes. We'll # just take the largest, to avoid some downcasting dtypes = [] if asscalar: dtypes.append(np.asarray(res).dtype) outarr = zeros(outshape, object) outarr[tuple(ind)] = res Ntot = np.product(outshape) k = 1 while k < Ntot: # increment the index ind[-1] += 1 n = -1 while (ind[n] >= outshape[n]) and (n > (1 - nd)): ind[n - 1] += 1 ind[n] = 0 n -= 1 i.put(indlist, ind) res = func1d(arr[tuple(i.tolist())], *args, **kwargs) outarr[tuple(ind)] = res dtypes.append(asarray(res).dtype) k += 1 else: res = array(res, copy=False, subok=True) j = i.copy() j[axis] = ([slice(None, None)] * res.ndim) j.put(indlist, ind) Ntot = np.product(outshape) holdshape = outshape outshape = list(arr.shape) outshape[axis] = res.shape dtypes.append(asarray(res).dtype) outshape = flatten_inplace(outshape) outarr = zeros(outshape, object) outarr[tuple(flatten_inplace(j.tolist()))] = res k = 1 while k < Ntot: # increment the index ind[-1] += 1 n = -1 while (ind[n] >= holdshape[n]) and (n > (1 - nd)): ind[n - 1] += 1 ind[n] = 0 n -= 1 i.put(indlist, ind) j.put(indlist, ind) res = func1d(arr[tuple(i.tolist())], *args, **kwargs) outarr[tuple(flatten_inplace(j.tolist()))] = res dtypes.append(asarray(res).dtype) k += 1 max_dtypes = np.dtype(np.asarray(dtypes).max()) if not hasattr(arr, '_mask'): result = np.asarray(outarr, dtype=max_dtypes) else: result = asarray(outarr, dtype=max_dtypes) result.fill_value = ma.default_fill_value(result) return result
""" (This docstring will be overwritten) """ val = asarray(a) N = a.ndim if array(axes).ndim == 0: axes = (axes,) for axis in axes: if axis < 0: axis = N + axis args = (val, axis) res = func(*args) if res.ndim == val.ndim: val = res else: res = ma.expand_dims(res, axis) if res.ndim == val.ndim: val = res else: raise ValueError("function is not returning " "an array of the correct shape") return val
:np.apply_over_axes.__doc__.find('Notes')].rstrip() + \ """
Examples -------- >>> a = ma.arange(24).reshape(2,3,4) >>> a[:,0,1] = ma.masked >>> a[:,1,:] = ma.masked >>> print(a) [[[0 -- 2 3] [-- -- -- --] [8 9 10 11]]
[[12 -- 14 15] [-- -- -- --] [20 21 22 23]]] >>> print(ma.apply_over_axes(ma.sum, a, [0,2])) [[[46] [--] [124]]]
Tuple axis arguments to ufuncs are equivalent:
>>> print(ma.sum(a, axis=(0,2)).reshape((1,-1,1))) [[[46] [--] [124]]] """
""" Return the weighted average of array over the given axis.
Parameters ---------- a : array_like Data to be averaged. Masked entries are not taken into account in the computation. axis : int, optional Axis along which to average `a`. If `None`, averaging is done over the flattened array. weights : array_like, optional The importance that each element has in the computation of the average. The weights array can either be 1-D (in which case its length must be the size of `a` along the given axis) or of the same shape as `a`. If ``weights=None``, then all data in `a` are assumed to have a weight equal to one. If `weights` is complex, the imaginary parts are ignored. returned : bool, optional Flag indicating whether a tuple ``(result, sum of weights)`` should be returned as output (True), or just the result (False). Default is False.
Returns ------- average, [sum_of_weights] : (tuple of) scalar or MaskedArray The average along the specified axis. When returned is `True`, return a tuple with the average as the first element and the sum of the weights as the second element. The return type is `np.float64` if `a` is of integer type and floats smaller than `float64`, or the input data-type, otherwise. If returned, `sum_of_weights` is always `float64`.
Examples -------- >>> a = np.ma.array([1., 2., 3., 4.], mask=[False, False, True, True]) >>> np.ma.average(a, weights=[3, 1, 0, 0]) 1.25
>>> x = np.ma.arange(6.).reshape(3, 2) >>> print(x) [[ 0. 1.] [ 2. 3.] [ 4. 5.]] >>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3], ... returned=True) >>> print(avg) [2.66666666667 3.66666666667]
""" a = asarray(a) m = getmask(a)
# inspired by 'average' in numpy/lib/function_base.py
if weights is None: avg = a.mean(axis) scl = avg.dtype.type(a.count(axis)) else: wgt = np.asanyarray(weights)
if issubclass(a.dtype.type, (np.integer, np.bool_)): result_dtype = np.result_type(a.dtype, wgt.dtype, 'f8') else: result_dtype = np.result_type(a.dtype, wgt.dtype)
# Sanity checks if a.shape != wgt.shape: if axis is None: raise TypeError( "Axis must be specified when shapes of a and weights " "differ.") if wgt.ndim != 1: raise TypeError( "1D weights expected when shapes of a and weights differ.") if wgt.shape[0] != a.shape[axis]: raise ValueError( "Length of weights not compatible with specified axis.")
# setup wgt to broadcast along axis wgt = np.broadcast_to(wgt, (a.ndim-1)*(1,) + wgt.shape) wgt = wgt.swapaxes(-1, axis)
if m is not nomask: wgt = wgt*(~a.mask)
scl = wgt.sum(axis=axis, dtype=result_dtype) avg = np.multiply(a, wgt, dtype=result_dtype).sum(axis)/scl
if returned: if scl.shape != avg.shape: scl = np.broadcast_to(scl, avg.shape).copy() return avg, scl else: return avg
""" Compute the median along the specified axis.
Returns the median of the array elements.
Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : int, optional Axis along which the medians are computed. The default (None) is to compute the median along a flattened version of the array. out : ndarray, 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. overwrite_input : bool, optional If True, then allow use of memory of input array (a) for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if `overwrite_input` is True, and the input is not already an `ndarray`, an error will be raised. 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 input array.
.. versionadded:: 1.10.0
Returns ------- median : ndarray A new array holding the result is returned unless out is specified, in which case a reference to out is returned. Return data-type is `float64` for integers and floats smaller than `float64`, or the input data-type, otherwise.
See Also -------- mean
Notes ----- Given a vector ``V`` with ``N`` non masked values, the median of ``V`` is the middle value of a sorted copy of ``V`` (``Vs``) - i.e. ``Vs[(N-1)/2]``, when ``N`` is odd, or ``{Vs[N/2 - 1] + Vs[N/2]}/2`` when ``N`` is even.
Examples -------- >>> x = np.ma.array(np.arange(8), mask=[0]*4 + [1]*4) >>> np.ma.median(x) 1.5
>>> x = np.ma.array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4) >>> np.ma.median(x) 2.5 >>> np.ma.median(x, axis=-1, overwrite_input=True) masked_array(data = [ 2. 5.], mask = False, fill_value = 1e+20)
""" m = np.median(getdata(a, subok=True), axis=axis, out=out, overwrite_input=overwrite_input, keepdims=keepdims) if isinstance(m, np.ndarray) and 1 <= m.ndim: return masked_array(m, copy=False) else: return m
overwrite_input=overwrite_input) return r.reshape(k) else:
# when an unmasked NaN is present return it, so we need to sort the NaN # values behind the mask else: fill_value = None if axis is None: asorted = a.ravel() asorted.sort(fill_value=fill_value) else: a.sort(axis=axis, fill_value=fill_value) asorted = a else:
axis = 0 else:
# for empty axis integer indices fail so use slicing to get same result # as median (which is mean of empty slice = nan) indexer = [slice(None)] * asorted.ndim indexer[axis] = slice(0, 0) indexer = tuple(indexer) return np.ma.mean(asorted[indexer], axis=axis, out=out)
counts = count(asorted) idx, odd = divmod(count(asorted), 2) mid = asorted[idx + odd - 1:idx + 1] if np.issubdtype(asorted.dtype, np.inexact) and asorted.size > 0: # avoid inf / x = masked s = mid.sum(out=out) if not odd: s = np.true_divide(s, 2., casting='safe', out=out) s = np.lib.utils._median_nancheck(asorted, s, axis, out) else: s = mid.mean(out=out)
# if result is masked either the input contained enough # minimum_fill_value so that it would be the median or all values # masked if np.ma.is_masked(s) and not np.all(asorted.mask): return np.ma.minimum_fill_value(asorted) return s
# duplicate high if odd number of elements so mean does nothing
# get low and high median
# Replace masked entries with minimum_full_value unless it all values # are masked. This is required as the sort order of values equal or # larger than the fill value is undefined and a valid value placed # elsewhere, e.g. [4, --, inf]. rep = (~np.all(asorted.mask, axis=axis, keepdims=True)) & s.mask s.data[rep] = np.ma.minimum_fill_value(asorted) s.mask[rep] = False
# avoid inf / x = masked
else: s = np.ma.mean(low_high, axis=axis, out=out)
"""Suppress slices from multiple dimensions which contain masked values.
Parameters ---------- x : array_like, MaskedArray The array to operate on. If not a MaskedArray instance (or if no array elements are masked, `x` is interpreted as a MaskedArray with `mask` set to `nomask`. axis : tuple of ints or int, optional Which dimensions to suppress slices from can be configured with this parameter. - If axis is a tuple of ints, those are the axes to suppress slices from. - If axis is an int, then that is the only axis to suppress slices from. - If axis is None, all axis are selected.
Returns ------- compress_array : ndarray The compressed array. """ x = asarray(x) m = getmask(x) # Set axis to tuple of ints if axis is None: axis = tuple(range(x.ndim)) else: axis = normalize_axis_tuple(axis, x.ndim)
# Nothing is masked: return x if m is nomask or not m.any(): return x._data # All is masked: return empty if m.all(): return nxarray([]) # Filter elements through boolean indexing data = x._data for ax in axis: axes = tuple(list(range(ax)) + list(range(ax + 1, x.ndim))) data = data[(slice(None),)*ax + (~m.any(axis=axes),)] return data
""" Suppress the rows and/or columns of a 2-D array that contain masked values.
The suppression behavior is selected with the `axis` parameter.
- If axis is None, both rows and columns are suppressed. - If axis is 0, only rows are suppressed. - If axis is 1 or -1, only columns are suppressed.
Parameters ---------- x : array_like, MaskedArray The array to operate on. If not a MaskedArray instance (or if no array elements are masked), `x` is interpreted as a MaskedArray with `mask` set to `nomask`. Must be a 2D array. axis : int, optional Axis along which to perform the operation. Default is None.
Returns ------- compressed_array : ndarray The compressed array.
Examples -------- >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0], ... [1, 0, 0], ... [0, 0, 0]]) >>> x masked_array(data = [[-- 1 2] [-- 4 5] [6 7 8]], mask = [[ True False False] [ True False False] [False False False]], fill_value = 999999)
>>> np.ma.compress_rowcols(x) array([[7, 8]]) >>> np.ma.compress_rowcols(x, 0) array([[6, 7, 8]]) >>> np.ma.compress_rowcols(x, 1) array([[1, 2], [4, 5], [7, 8]])
""" if asarray(x).ndim != 2: raise NotImplementedError("compress_rowcols works for 2D arrays only.") return compress_nd(x, axis=axis)
""" Suppress whole rows of a 2-D array that contain masked values.
This is equivalent to ``np.ma.compress_rowcols(a, 0)``, see `extras.compress_rowcols` for details.
See Also -------- extras.compress_rowcols
""" a = asarray(a) if a.ndim != 2: raise NotImplementedError("compress_rows works for 2D arrays only.") return compress_rowcols(a, 0)
""" Suppress whole columns of a 2-D array that contain masked values.
This is equivalent to ``np.ma.compress_rowcols(a, 1)``, see `extras.compress_rowcols` for details.
See Also -------- extras.compress_rowcols
""" a = asarray(a) if a.ndim != 2: raise NotImplementedError("compress_cols works for 2D arrays only.") return compress_rowcols(a, 1)
""" Mask rows of a 2D array that contain masked values.
This function is a shortcut to ``mask_rowcols`` with `axis` equal to 0.
See Also -------- mask_rowcols : Mask rows and/or columns of a 2D array. masked_where : Mask where a condition is met.
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_rows(a) masked_array(data = [[0 0 0] [-- -- --] [0 0 0]], mask = [[False False False] [ True True True] [False False False]], fill_value=999999)
""" return mask_rowcols(a, 0)
""" Mask columns of a 2D array that contain masked values.
This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1.
See Also -------- mask_rowcols : Mask rows and/or columns of a 2D array. masked_where : Mask where a condition is met.
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_cols(a) masked_array(data = [[0 -- 0] [0 -- 0] [0 -- 0]], mask = [[False True False] [False True False] [False True False]], fill_value=999999)
""" return mask_rowcols(a, 1)
#####-------------------------------------------------------------------------- #---- --- arraysetops --- #####--------------------------------------------------------------------------
""" Compute the differences between consecutive elements of an array.
This function is the equivalent of `numpy.ediff1d` that takes masked values into account, see `numpy.ediff1d` for details.
See Also -------- numpy.ediff1d : Equivalent function for ndarrays.
""" arr = ma.asanyarray(arr).flat ed = arr[1:] - arr[:-1] arrays = [ed] # if to_begin is not None: arrays.insert(0, to_begin) if to_end is not None: arrays.append(to_end) # if len(arrays) != 1: # We'll save ourselves a copy of a potentially large array in the common # case where neither to_begin or to_end was given. ed = hstack(arrays) # return ed
""" Finds the unique elements of an array.
Masked values are considered the same element (masked). The output array is always a masked array. See `numpy.unique` for more details.
See Also -------- numpy.unique : Equivalent function for ndarrays.
""" output = np.unique(ar1, return_index=return_index, return_inverse=return_inverse) if isinstance(output, tuple): output = list(output) output[0] = output[0].view(MaskedArray) output = tuple(output) else: output = output.view(MaskedArray) return output
""" Returns the unique elements common to both arrays.
Masked values are considered equal one to the other. The output is always a masked array.
See `numpy.intersect1d` for more details.
See Also -------- numpy.intersect1d : Equivalent function for ndarrays.
Examples -------- >>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1]) >>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1]) >>> intersect1d(x, y) masked_array(data = [1 3 --], mask = [False False True], fill_value = 999999)
""" if assume_unique: aux = ma.concatenate((ar1, ar2)) else: # Might be faster than unique( intersect1d( ar1, ar2 ) )? aux = ma.concatenate((unique(ar1), unique(ar2))) aux.sort() return aux[:-1][aux[1:] == aux[:-1]]
""" Set exclusive-or of 1-D arrays with unique elements.
The output is always a masked array. See `numpy.setxor1d` for more details.
See Also -------- numpy.setxor1d : Equivalent function for ndarrays.
""" if not assume_unique: ar1 = unique(ar1) ar2 = unique(ar2)
aux = ma.concatenate((ar1, ar2)) if aux.size == 0: return aux aux.sort() auxf = aux.filled() # flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0 flag = ma.concatenate(([True], (auxf[1:] != auxf[:-1]), [True])) # flag2 = ediff1d( flag ) == 0 flag2 = (flag[1:] == flag[:-1]) return aux[flag2]
""" Test whether each element of an array is also present in a second array.
The output is always a masked array. See `numpy.in1d` for more details.
We recommend using :func:`isin` instead of `in1d` for new code.
See Also -------- isin : Version of this function that preserves the shape of ar1. numpy.in1d : Equivalent function for ndarrays.
Notes ----- .. versionadded:: 1.4.0
""" if not assume_unique: ar1, rev_idx = unique(ar1, return_inverse=True) ar2 = unique(ar2)
ar = ma.concatenate((ar1, ar2)) # We need this to be a stable sort, so always use 'mergesort' # here. The values from the first array should always come before # the values from the second array. order = ar.argsort(kind='mergesort') sar = ar[order] if invert: bool_ar = (sar[1:] != sar[:-1]) else: bool_ar = (sar[1:] == sar[:-1]) flag = ma.concatenate((bool_ar, [invert])) indx = order.argsort(kind='mergesort')[:len(ar1)]
if assume_unique: return flag[indx] else: return flag[indx][rev_idx]
""" Calculates `element in test_elements`, broadcasting over `element` only.
The output is always a masked array of the same shape as `element`. See `numpy.isin` for more details.
See Also -------- in1d : Flattened version of this function. numpy.isin : Equivalent function for ndarrays.
Notes ----- .. versionadded:: 1.13.0
""" element = ma.asarray(element) return in1d(element, test_elements, assume_unique=assume_unique, invert=invert).reshape(element.shape)
""" Union of two arrays.
The output is always a masked array. See `numpy.union1d` for more details.
See also -------- numpy.union1d : Equivalent function for ndarrays.
""" return unique(ma.concatenate((ar1, ar2), axis=None))
""" Set difference of 1D arrays with unique elements.
The output is always a masked array. See `numpy.setdiff1d` for more details.
See Also -------- numpy.setdiff1d : Equivalent function for ndarrays.
Examples -------- >>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1]) >>> np.ma.setdiff1d(x, [1, 2]) masked_array(data = [3 --], mask = [False True], fill_value = 999999)
""" if assume_unique: ar1 = ma.asarray(ar1).ravel() else: ar1 = unique(ar1) ar2 = unique(ar2) return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)]
############################################################################### # Covariance # ###############################################################################
""" Private function for the computation of covariance and correlation coefficients.
""" x = ma.array(x, ndmin=2, copy=True, dtype=float) xmask = ma.getmaskarray(x) # Quick exit if we can't process masked data if not allow_masked and xmask.any(): raise ValueError("Cannot process masked data.") # if x.shape[0] == 1: rowvar = True # Make sure that rowvar is either 0 or 1 rowvar = int(bool(rowvar)) axis = 1 - rowvar if rowvar: tup = (slice(None), None) else: tup = (None, slice(None)) # if y is None: xnotmask = np.logical_not(xmask).astype(int) else: y = array(y, copy=False, ndmin=2, dtype=float) ymask = ma.getmaskarray(y) if not allow_masked and ymask.any(): raise ValueError("Cannot process masked data.") if xmask.any() or ymask.any(): if y.shape == x.shape: # Define some common mask common_mask = np.logical_or(xmask, ymask) if common_mask is not nomask: xmask = x._mask = y._mask = ymask = common_mask x._sharedmask = False y._sharedmask = False x = ma.concatenate((x, y), axis) xnotmask = np.logical_not(np.concatenate((xmask, ymask), axis)).astype(int) x -= x.mean(axis=rowvar)[tup] return (x, xnotmask, rowvar)
""" Estimate the covariance matrix.
Except for the handling of missing data this function does the same as `numpy.cov`. For more details and examples, see `numpy.cov`.
By default, masked values are recognized as such. If `x` and `y` have the same shape, a common mask is allocated: if ``x[i,j]`` is masked, then ``y[i,j]`` will also be masked. Setting `allow_masked` to False will raise an exception if values are missing in either of the input arrays.
Parameters ---------- x : array_like A 1-D or 2-D array containing multiple variables and observations. Each row of `x` represents a variable, and each column a single observation of all those variables. Also see `rowvar` below. y : array_like, optional An additional set of variables and observations. `y` has the same form as `x`. rowvar : bool, optional If `rowvar` is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations. bias : bool, optional Default normalization (False) is by ``(N-1)``, where ``N`` is the number of observations given (unbiased estimate). If `bias` is True, then normalization is by ``N``. This keyword can be overridden by the keyword ``ddof`` in numpy versions >= 1.5. allow_masked : bool, optional If True, masked values are propagated pair-wise: if a value is masked in `x`, the corresponding value is masked in `y`. If False, raises a `ValueError` exception when some values are missing. ddof : {None, int}, optional If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is the number of observations; this overrides the value implied by ``bias``. The default value is ``None``.
.. versionadded:: 1.5
Raises ------ ValueError Raised if some values are missing and `allow_masked` is False.
See Also -------- numpy.cov
""" # Check inputs if ddof is not None and ddof != int(ddof): raise ValueError("ddof must be an integer") # Set up ddof if ddof is None: if bias: ddof = 0 else: ddof = 1
(x, xnotmask, rowvar) = _covhelper(x, y, rowvar, allow_masked) if not rowvar: fact = np.dot(xnotmask.T, xnotmask) * 1. - ddof result = (dot(x.T, x.conj(), strict=False) / fact).squeeze() else: fact = np.dot(xnotmask, xnotmask.T) * 1. - ddof result = (dot(x, x.T.conj(), strict=False) / fact).squeeze() return result
ddof=np._NoValue): """ Return Pearson product-moment correlation coefficients.
Except for the handling of missing data this function does the same as `numpy.corrcoef`. For more details and examples, see `numpy.corrcoef`.
Parameters ---------- x : array_like A 1-D or 2-D array containing multiple variables and observations. Each row of `x` represents a variable, and each column a single observation of all those variables. Also see `rowvar` below. y : array_like, optional An additional set of variables and observations. `y` has the same shape as `x`. rowvar : bool, optional If `rowvar` is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations. bias : _NoValue, optional Has no effect, do not use.
.. deprecated:: 1.10.0 allow_masked : bool, optional If True, masked values are propagated pair-wise: if a value is masked in `x`, the corresponding value is masked in `y`. If False, raises an exception. Because `bias` is deprecated, this argument needs to be treated as keyword only to avoid a warning. ddof : _NoValue, optional Has no effect, do not use.
.. deprecated:: 1.10.0
See Also -------- numpy.corrcoef : Equivalent function in top-level NumPy module. cov : Estimate the covariance matrix.
Notes ----- This function accepts but discards arguments `bias` and `ddof`. This is for backwards compatibility with previous versions of this function. These arguments had no effect on the return values of the function and can be safely ignored in this and previous versions of numpy. """ msg = 'bias and ddof have no effect and are deprecated' if bias is not np._NoValue or ddof is not np._NoValue: # 2015-03-15, 1.10 warnings.warn(msg, DeprecationWarning, stacklevel=2) # Get the data (x, xnotmask, rowvar) = _covhelper(x, y, rowvar, allow_masked) # Compute the covariance matrix if not rowvar: fact = np.dot(xnotmask.T, xnotmask) * 1. c = (dot(x.T, x.conj(), strict=False) / fact).squeeze() else: fact = np.dot(xnotmask, xnotmask.T) * 1. c = (dot(x, x.T.conj(), strict=False) / fact).squeeze() # Check whether we have a scalar try: diag = ma.diagonal(c) except ValueError: return 1 # if xnotmask.all(): _denom = ma.sqrt(ma.multiply.outer(diag, diag)) else: _denom = diagflat(diag) _denom._sharedmask = False # We know return is always a copy n = x.shape[1 - rowvar] if rowvar: for i in range(n - 1): for j in range(i + 1, n): _x = mask_cols(vstack((x[i], x[j]))).var(axis=1) _denom[i, j] = _denom[j, i] = ma.sqrt(ma.multiply.reduce(_x)) else: for i in range(n - 1): for j in range(i + 1, n): _x = mask_cols( vstack((x[:, i], x[:, j]))).var(axis=1) _denom[i, j] = _denom[j, i] = ma.sqrt(ma.multiply.reduce(_x)) return c / _denom
#####-------------------------------------------------------------------------- #---- --- Concatenation helpers --- #####--------------------------------------------------------------------------
""" Translate slice objects to concatenation along an axis.
For documentation on usage, see `mr_class`.
See Also -------- mr_class
"""
def makemat(cls, arr): # There used to be a view as np.matrix here, but we may eventually # deprecate that class. In preparation, we use the unmasked version # to construct the matrix (with copy=False for backwards compatibility # with the .view) data = super(MAxisConcatenator, cls).makemat(arr.data, copy=False) return array(data, mask=arr.mask)
# matrix builder syntax, like 'a, b; c, d' if isinstance(key, str): raise MAError("Unavailable for masked array.")
return super(MAxisConcatenator, self).__getitem__(key)
""" Translate slice objects to concatenation along the first axis.
This is the masked array version of `lib.index_tricks.RClass`.
See Also -------- lib.index_tricks.RClass
Examples -------- >>> np.ma.mr_[np.ma.array([1,2,3]), 0, 0, np.ma.array([4,5,6])] array([1, 2, 3, 0, 0, 4, 5, 6])
"""
#####-------------------------------------------------------------------------- #---- Find unmasked data --- #####--------------------------------------------------------------------------
""" Find the indices of the first and last unmasked values.
Expects a 1-D `MaskedArray`, returns None if all values are masked.
Parameters ---------- a : array_like Input 1-D `MaskedArray`
Returns ------- edges : ndarray or None The indices of first and last non-masked value in the array. Returns None if all values are masked.
See Also -------- flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked
Notes ----- Only accepts 1-D arrays.
Examples -------- >>> a = np.ma.arange(10) >>> flatnotmasked_edges(a) [0,-1]
>>> mask = (a < 3) | (a > 8) | (a == 5) >>> a[mask] = np.ma.masked >>> np.array(a[~a.mask]) array([3, 4, 6, 7, 8])
>>> flatnotmasked_edges(a) array([3, 8])
>>> a[:] = np.ma.masked >>> print(flatnotmasked_edges(ma)) None
""" m = getmask(a) if m is nomask or not np.any(m): return np.array([0, a.size - 1]) unmasked = np.flatnonzero(~m) if len(unmasked) > 0: return unmasked[[0, -1]] else: return None
""" Find the indices of the first and last unmasked values along an axis.
If all values are masked, return None. Otherwise, return a list of two tuples, corresponding to the indices of the first and last unmasked values respectively.
Parameters ---------- a : array_like The input array. axis : int, optional Axis along which to perform the operation. If None (default), applies to a flattened version of the array.
Returns ------- edges : ndarray or list An array of start and end indexes if there are any masked data in the array. If there are no masked data in the array, `edges` is a list of the first and last index.
See Also -------- flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous, clump_masked, clump_unmasked
Examples -------- >>> a = np.arange(9).reshape((3, 3)) >>> m = np.zeros_like(a) >>> m[1:, 1:] = 1
>>> am = np.ma.array(a, mask=m) >>> np.array(am[~am.mask]) array([0, 1, 2, 3, 6])
>>> np.ma.notmasked_edges(ma) array([0, 6])
""" a = asarray(a) if axis is None or a.ndim == 1: return flatnotmasked_edges(a) m = getmaskarray(a) idx = array(np.indices(a.shape), mask=np.asarray([m] * a.ndim)) return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]), tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]), ]
""" Find contiguous unmasked data in a masked array along the given axis.
Parameters ---------- a : narray The input array.
Returns ------- slice_list : list A sorted sequence of `slice` objects (start index, end index).
..versionchanged:: 1.15.0 Now returns an empty list instead of None for a fully masked array
See Also -------- flatnotmasked_edges, notmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked
Notes ----- Only accepts 2-D arrays at most.
Examples -------- >>> a = np.ma.arange(10) >>> np.ma.flatnotmasked_contiguous(a) [slice(0, 10, None)]
>>> mask = (a < 3) | (a > 8) | (a == 5) >>> a[mask] = np.ma.masked >>> np.array(a[~a.mask]) array([3, 4, 6, 7, 8])
>>> np.ma.flatnotmasked_contiguous(a) [slice(3, 5, None), slice(6, 9, None)] >>> a[:] = np.ma.masked >>> np.ma.flatnotmasked_contiguous(a) []
""" m = getmask(a) if m is nomask: return [slice(0, a.size)] i = 0 result = [] for (k, g) in itertools.groupby(m.ravel()): n = len(list(g)) if not k: result.append(slice(i, i + n)) i += n return result
""" Find contiguous unmasked data in a masked array along the given axis.
Parameters ---------- a : array_like The input array. axis : int, optional Axis along which to perform the operation. If None (default), applies to a flattened version of the array, and this is the same as `flatnotmasked_contiguous`.
Returns ------- endpoints : list A list of slices (start and end indexes) of unmasked indexes in the array.
If the input is 2d and axis is specified, the result is a list of lists.
See Also -------- flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, clump_masked, clump_unmasked
Notes ----- Only accepts 2-D arrays at most.
Examples -------- >>> a = np.arange(12).reshape((3, 4)) >>> mask = np.zeros_like(a) >>> mask[1:, :-1] = 1; mask[0, 1] = 1; mask[-1, 0] = 0 >>> ma = np.ma.array(a, mask=mask) >>> ma masked_array( data=[[0, --, 2, 3], [--, --, --, 7], [8, --, --, 11]], mask=[[False, True, False, False], [ True, True, True, False], [False, True, True, False]], fill_value=999999) >>> np.array(ma[~ma.mask]) array([ 0, 2, 3, 7, 8, 11])
>>> np.ma.notmasked_contiguous(ma) [slice(0, 1, None), slice(2, 4, None), slice(7, 9, None), slice(11, 12, None)]
>>> np.ma.notmasked_contiguous(ma, axis=0) [[slice(0, 1, None), slice(2, 3, None)], # column broken into two segments [], # fully masked column [slice(0, 1, None)], [slice(0, 3, None)]]
>>> np.ma.notmasked_contiguous(ma, axis=1) [[slice(0, 1, None), slice(2, 4, None)], # row broken into two segments [slice(3, 4, None)], [slice(0, 1, None), slice(3, 4, None)]] """ a = asarray(a) nd = a.ndim if nd > 2: raise NotImplementedError("Currently limited to atmost 2D array.") if axis is None or nd == 1: return flatnotmasked_contiguous(a) # result = [] # other = (axis + 1) % 2 idx = [0, 0] idx[axis] = slice(None, None) # for i in range(a.shape[other]): idx[other] = i result.append(flatnotmasked_contiguous(a[tuple(idx)])) return result
""" Finds the clumps (groups of data with the same values) for a 1D bool array.
Returns a series of slices. """ if mask.ndim > 1: mask = mask.ravel() idx = (mask[1:] ^ mask[:-1]).nonzero() idx = idx[0] + 1
if mask[0]: if len(idx) == 0: return [slice(0, mask.size)]
r = [slice(0, idx[0])] r.extend((slice(left, right) for left, right in zip(idx[1:-1:2], idx[2::2]))) else: if len(idx) == 0: return []
r = [slice(left, right) for left, right in zip(idx[:-1:2], idx[1::2])]
if mask[-1]: r.append(slice(idx[-1], mask.size)) return r
""" Return list of slices corresponding to the unmasked clumps of a 1-D array. (A "clump" is defined as a contiguous region of the array).
Parameters ---------- a : ndarray A one-dimensional masked array.
Returns ------- slices : list of slice The list of slices, one for each continuous region of unmasked elements in `a`.
Notes ----- .. versionadded:: 1.4.0
See Also -------- flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, notmasked_contiguous, clump_masked
Examples -------- >>> a = np.ma.masked_array(np.arange(10)) >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked >>> np.ma.clump_unmasked(a) [slice(3, 6, None), slice(7, 8, None)]
""" mask = getattr(a, '_mask', nomask) if mask is nomask: return [slice(0, a.size)] return _ezclump(~mask)
""" Returns a list of slices corresponding to the masked clumps of a 1-D array. (A "clump" is defined as a contiguous region of the array).
Parameters ---------- a : ndarray A one-dimensional masked array.
Returns ------- slices : list of slice The list of slices, one for each continuous region of masked elements in `a`.
Notes ----- .. versionadded:: 1.4.0
See Also -------- flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges, notmasked_contiguous, clump_unmasked
Examples -------- >>> a = np.ma.masked_array(np.arange(10)) >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked >>> np.ma.clump_masked(a) [slice(0, 3, None), slice(6, 7, None), slice(8, 10, None)]
""" mask = ma.getmask(a) if mask is nomask: return [] return _ezclump(mask)
############################################################################### # Polynomial fit # ###############################################################################
""" Masked values in the input array result in rows of zeros.
""" _vander = np.vander(x, n) m = getmask(x) if m is not nomask: _vander[m] = 0 return _vander
""" Any masked values in x is propagated in y, and vice-versa.
""" x = asarray(x) y = asarray(y)
m = getmask(x) if y.ndim == 1: m = mask_or(m, getmask(y)) elif y.ndim == 2: my = getmask(mask_rows(y)) if my is not nomask: m = mask_or(m, my[:, 0]) else: raise TypeError("Expected a 1D or 2D array for y!")
if w is not None: w = asarray(w) if w.ndim != 1: raise TypeError("expected a 1-d array for weights") if w.shape[0] != y.shape[0]: raise TypeError("expected w and y to have the same length") m = mask_or(m, getmask(w))
if m is not nomask: not_m = ~m if w is not None: w = w[not_m] return np.polyfit(x[not_m], y[not_m], deg, rcond, full, w, cov) else: return np.polyfit(x, y, deg, rcond, full, w, cov)
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