package np

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

absolute(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Calculate the absolute value element-wise.

``np.abs`` is a shorthand for this function.

Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- absolute : ndarray An ndarray containing the absolute value of each element in `x`. For complex input, ``a + ib``, the absolute value is :math:`\sqrt a^2 + b^2 `. This is a scalar if `x` is a scalar.

Examples -------- >>> x = np.array(-1.2, 1.2) >>> np.absolute(x) array( 1.2, 1.2) >>> np.absolute(1.2 + 1j) 1.5620499351813308

Plot the function over ``-10, 10``:

>>> import matplotlib.pyplot as plt

>>> x = np.linspace(start=-10, stop=10, num=101) >>> plt.plot(x, np.absolute(x)) >>> plt.show()

Plot the function over the complex plane:

>>> xx = x + 1j * x:, np.newaxis >>> plt.imshow(np.abs(xx), extent=-10, 10, -10, 10, cmap='gray') >>> plt.show()

absolute(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Calculate the absolute value element-wise.

``np.abs`` is a shorthand for this function.

Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- absolute : ndarray An ndarray containing the absolute value of each element in `x`. For complex input, ``a + ib``, the absolute value is :math:`\sqrt a^2 + b^2 `. This is a scalar if `x` is a scalar.

Examples -------- >>> x = np.array(-1.2, 1.2) >>> np.absolute(x) array( 1.2, 1.2) >>> np.absolute(1.2 + 1j) 1.5620499351813308

Plot the function over ``-10, 10``:

>>> import matplotlib.pyplot as plt

>>> x = np.linspace(start=-10, stop=10, num=101) >>> plt.plot(x, np.absolute(x)) >>> plt.show()

Plot the function over the complex plane:

>>> xx = x + 1j * x:, np.newaxis >>> plt.imshow(np.abs(xx), extent=-10, 10, -10, 10, cmap='gray') >>> plt.show()

add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Add arguments element-wise.

Parameters ---------- x1, x2 : array_like The arrays to be added. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- add : ndarray or scalar The sum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars.

Notes ----- Equivalent to `x1` + `x2` in terms of array broadcasting.

Examples -------- >>> np.add(1.0, 4.0) 5.0 >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.add(x1, x2) array([ 0., 2., 4.], [ 3., 5., 7.], [ 6., 8., 10.])

all(self, axis=None, out=None, keepdims=<no value>)

Returns True if all elements evaluate to True.

The output array is masked where all the values along the given axis are masked: if the output would have been a scalar and that all the values are masked, then the output is `masked`.

Refer to `numpy.all` for full documentation.

See Also -------- numpy.ndarray.all : corresponding function for ndarrays numpy.all : equivalent function

Examples -------- >>> np.ma.array(1,2,3).all() True >>> a = np.ma.array(1,2,3, mask=True) >>> (a.all() is np.ma.masked) True

Returns True if two arrays are element-wise equal within a tolerance.

This function is equivalent to `allclose` except that masked values are treated as equal (default) or unequal, depending on the `masked_equal` argument.

Parameters ---------- a, b : array_like Input arrays to compare. masked_equal : bool, optional Whether masked values in `a` and `b` are considered equal (True) or not (False). They are considered equal by default. rtol : float, optional Relative tolerance. The relative difference is equal to ``rtol * b``. Default is 1e-5. atol : float, optional Absolute tolerance. The absolute difference is equal to `atol`. Default is 1e-8.

Returns ------- y : bool Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned.

See Also -------- all, any numpy.allclose : the non-masked `allclose`.

Notes ----- If the following equation is element-wise True, then `allclose` returns True::

absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))

Return True if all elements of `a` and `b` are equal subject to given tolerances.

Examples -------- >>> a = np.ma.array(1e10, 1e-7, 42.0, mask=0, 0, 1) >>> a masked_array(data=10000000000.0, 1e-07, --, mask=False, False, True, fill_value=1e+20) >>> b = np.ma.array(1e10, 1e-8, -42.0, mask=0, 0, 1) >>> np.ma.allclose(a, b) False

>>> a = np.ma.array(1e10, 1e-8, 42.0, mask=0, 0, 1) >>> b = np.ma.array(1.00001e10, 1e-9, -42.0, mask=0, 0, 1) >>> np.ma.allclose(a, b) True >>> np.ma.allclose(a, b, masked_equal=False) False

Masked values are not compared directly.

>>> a = np.ma.array(1e10, 1e-8, 42.0, mask=0, 0, 1) >>> b = np.ma.array(1.00001e10, 1e-9, 42.0, mask=0, 0, 1) >>> np.ma.allclose(a, b) True >>> np.ma.allclose(a, b, masked_equal=False) False

Return True if all entries of a and b are equal, using fill_value as a truth value where either or both are masked.

Parameters ---------- a, b : array_like Input arrays to compare. fill_value : bool, optional Whether masked values in a or b are considered equal (True) or not (False).

Returns ------- y : bool Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned.

See Also -------- all, any numpy.ma.allclose

Examples -------- >>> a = np.ma.array(1e10, 1e-7, 42.0, mask=0, 0, 1) >>> a masked_array(data=10000000000.0, 1e-07, --, mask=False, False, True, fill_value=1e+20)

>>> b = np.array(1e10, 1e-7, -42.0) >>> b array( 1.00000000e+10, 1.00000000e-07, -4.20000000e+01) >>> np.ma.allequal(a, b, fill_value=False) False >>> np.ma.allequal(a, b) True

Reduce `target` along the given `axis`.

Return the maximum of an array or maximum along an axis.

Parameters ---------- a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used.

.. versionadded:: 1.7.0

If this is a tuple of ints, the maximum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See `ufuncs-output-type` for more details.

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.

If the default value is passed, then `keepdims` will not be passed through to the `amax` method of sub-classes of `ndarray`, however any non-default value will be. If the sub-class' method does not implement `keepdims` any exceptions will be raised.

initial : scalar, optional The minimum value of an output element. Must be present to allow computation on empty slice. See `~numpy.ufunc.reduce` for details.

.. versionadded:: 1.15.0

where : array_like of bool, optional Elements to compare for the maximum. See `~numpy.ufunc.reduce` for details.

.. versionadded:: 1.17.0

Returns ------- amax : ndarray or scalar Maximum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is an array of dimension ``a.ndim - 1``.

See Also -------- amin : The minimum value of an array along a given axis, propagating any NaNs. nanmax : The maximum value of an array along a given axis, ignoring any NaNs. maximum : Element-wise maximum of two arrays, propagating any NaNs. fmax : Element-wise maximum of two arrays, ignoring any NaNs. argmax : Return the indices of the maximum values.

nanmin, minimum, fmin

Notes ----- NaN values are propagated, that is if at least one item is NaN, the corresponding max value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmax.

Don't use `amax` for element-wise comparison of 2 arrays; when ``a.shape0`` is 2, ``maximum(a0, a1)`` is faster than ``amax(a, axis=0)``.

Examples -------- >>> a = np.arange(4).reshape((2,2)) >>> a array([0, 1], [2, 3]) >>> np.amax(a) # Maximum of the flattened array 3 >>> np.amax(a, axis=0) # Maxima along the first axis array(2, 3) >>> np.amax(a, axis=1) # Maxima along the second axis array(1, 3) >>> np.amax(a, where=False, True, initial=-1, axis=0) array(-1, 3) >>> b = np.arange(5, dtype=float) >>> b2 = np.NaN >>> np.amax(b) nan >>> np.amax(b, where=~np.isnan(b), initial=-1) 4.0 >>> np.nanmax(b) 4.0

You can use an initial value to compute the maximum of an empty slice, or to initialize it to a different value:

>>> np.max([-50], [10], axis=-1, initial=0) array( 0, 10)

Notice that the initial value is used as one of the elements for which the maximum is determined, unlike for the default argument Python's max function, which is only used for empty iterables.

>>> np.max(5, initial=6) 6 >>> max(5, default=6) 5

Return the minimum of an array or minimum along an axis.

Parameters ---------- a : array_like Input data. axis : None or int or tuple of ints, optional Axis or axes along which to operate. By default, flattened input is used.

.. versionadded:: 1.7.0

If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before. out : ndarray, optional Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See `ufuncs-output-type` for more details.

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.

If the default value is passed, then `keepdims` will not be passed through to the `amin` method of sub-classes of `ndarray`, however any non-default value will be. If the sub-class' method does not implement `keepdims` any exceptions will be raised.

initial : scalar, optional The maximum value of an output element. Must be present to allow computation on empty slice. See `~numpy.ufunc.reduce` for details.

.. versionadded:: 1.15.0

where : array_like of bool, optional Elements to compare for the minimum. See `~numpy.ufunc.reduce` for details.

.. versionadded:: 1.17.0

Returns ------- amin : ndarray or scalar Minimum of `a`. If `axis` is None, the result is a scalar value. If `axis` is given, the result is an array of dimension ``a.ndim - 1``.

See Also -------- amax : The maximum value of an array along a given axis, propagating any NaNs. nanmin : The minimum value of an array along a given axis, ignoring any NaNs. minimum : Element-wise minimum of two arrays, propagating any NaNs. fmin : Element-wise minimum of two arrays, ignoring any NaNs. argmin : Return the indices of the minimum values.

nanmax, maximum, fmax

Notes ----- NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin.

Don't use `amin` for element-wise comparison of 2 arrays; when ``a.shape0`` is 2, ``minimum(a0, a1)`` is faster than ``amin(a, axis=0)``.

Examples -------- >>> a = np.arange(4).reshape((2,2)) >>> a array([0, 1], [2, 3]) >>> np.amin(a) # Minimum of the flattened array 0 >>> np.amin(a, axis=0) # Minima along the first axis array(0, 1) >>> np.amin(a, axis=1) # Minima along the second axis array(0, 2) >>> np.amin(a, where=False, True, initial=10, axis=0) array(10, 1)

>>> b = np.arange(5, dtype=float) >>> b2 = np.NaN >>> np.amin(b) nan >>> np.amin(b, where=~np.isnan(b), initial=10) 0.0 >>> np.nanmin(b) 0.0

>>> np.min([-50], [10], axis=-1, initial=0) array(-50, 0)

Notice that the initial value is used as one of the elements for which the minimum is determined, unlike for the default argument Python's max function, which is only used for empty iterables.

Notice that this isn't the same as Python's ``default`` argument.

>>> np.min(6, initial=5) 5 >>> min(6, default=5) 6

Return the angle of the complex argument.

Parameters ---------- z : array_like A complex number or sequence of complex numbers. deg : bool, optional Return angle in degrees if True, radians if False (default).

Returns ------- angle : ndarray or scalar The counterclockwise angle from the positive real axis on the complex plane in the range ``(-pi, pi]``, with dtype as numpy.float64.

..versionchanged:: 1.16.0 This function works on subclasses of ndarray like `ma.array`.

See Also -------- arctan2 absolute

Examples -------- >>> np.angle(1.0, 1.0j, 1+1j) # in radians array( 0. , 1.57079633, 0.78539816) # may vary >>> np.angle(1+1j, deg=True) # in degrees 45.0

anom(self, axis=None, dtype=None)

Compute the anomalies (deviations from the arithmetic mean) along the given axis.

Returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis.

Parameters ---------- axis : int, optional Axis over which the anomalies are taken. The default is to use the mean of the flattened array as reference. dtype : dtype, optional Type to use in computing the variance. For arrays of integer type the default is float32; for arrays of float types it is the same as the array type.

See Also -------- mean : Compute the mean of the array.

Examples -------- >>> a = np.ma.array(1,2,3) >>> a.anom() masked_array(data=-1., 0., 1., mask=False, fill_value=1e+20)

anom(self, axis=None, dtype=None)

Compute the anomalies (deviations from the arithmetic mean) along the given axis.

Returns an array of anomalies, with the same shape as the input and where the arithmetic mean is computed along the given axis.

Parameters ---------- axis : int, optional Axis over which the anomalies are taken. The default is to use the mean of the flattened array as reference. dtype : dtype, optional Type to use in computing the variance. For arrays of integer type the default is float32; for arrays of float types it is the same as the array type.

See Also -------- mean : Compute the mean of the array.

Examples -------- >>> a = np.ma.array(1,2,3) >>> a.anom() masked_array(data=-1., 0., 1., mask=False, fill_value=1e+20)

any(self, axis=None, out=None, keepdims=<no value>)

Returns True if any of the elements of `a` evaluate to True.

Masked values are considered as False during computation.

Refer to `numpy.any` for full documentation.

See Also -------- numpy.ndarray.any : corresponding function for ndarrays numpy.any : equivalent function

Append values to the end of an array.

.. versionadded:: 1.9.0

Parameters ---------- a : array_like Values are appended to a copy of this array. b : array_like These values are appended to a copy of `a`. It must be of the correct shape (the same shape as `a`, excluding `axis`). If `axis` is not specified, `b` can be any shape and will be flattened before use. axis : int, optional The axis along which `v` are appended. If `axis` is not given, both `a` and `b` are flattened before use.

Returns ------- append : MaskedArray A copy of `a` with `b` appended to `axis`. Note that `append` does not occur in-place: a new array is allocated and filled. If `axis` is None, the result is a flattened array.

See Also -------- numpy.append : Equivalent function in the top-level NumPy module.

Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_values(1, 2, 3, 2) >>> b = ma.masked_values([4, 5, 6], [7, 8, 9], 7) >>> ma.append(a, b) masked_array(data=1, --, 3, 4, 5, 6, --, 8, 9, mask=False, True, False, False, False, False, True, False, False, fill_value=999999)

Apply a function to 1-D slices along the given axis.

Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a` is a 1-D slice of `arr` along `axis`.

This is equivalent to (but faster than) the following use of `ndindex` and `s_`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of indices::

Ni, Nk = a.shape:axis, a.shapeaxis+1: for ii in ndindex(Ni): for kk in ndindex(Nk): f = func1d(arrii + s_[:,] + kk) Nj = f.shape for jj in ndindex(Nj): outii + jj + kk = fjj

Equivalently, eliminating the inner loop, this can be expressed as::

Ni, Nk = a.shape:axis, a.shapeaxis+1: for ii in ndindex(Ni): for kk in ndindex(Nk): outii + s_[...,] + kk = func1d(arrii + s_[:,] + kk)

Parameters ---------- func1d : function (M,) -> (Nj...) This function should accept 1-D arrays. It is applied to 1-D slices of `arr` along the specified axis. axis : integer Axis along which `arr` is sliced. arr : ndarray (Ni..., M, Nk...) Input array. args : any Additional arguments to `func1d`. kwargs : any Additional named arguments to `func1d`.

.. versionadded:: 1.9.0

Returns ------- out : ndarray (Ni..., Nj..., Nk...) The output array. The shape of `out` is identical to the shape of `arr`, except along the `axis` dimension. This axis is removed, and replaced with new dimensions equal to the shape of the return value of `func1d`. So if `func1d` returns a scalar `out` will have one fewer dimensions than `arr`.

See Also -------- apply_over_axes : Apply a function repeatedly over multiple axes.

Examples -------- >>> def my_func(a): ... '''Average first and last element of a 1-D array''' ... return (a0 + a-1) * 0.5 >>> b = np.array([1,2,3], [4,5,6], [7,8,9]) >>> np.apply_along_axis(my_func, 0, b) array(4., 5., 6.) >>> np.apply_along_axis(my_func, 1, b) array(2., 5., 8.)

For a function that returns a 1D array, the number of dimensions in `outarr` is the same as `arr`.

>>> b = np.array([8,1,7], [4,3,9], [5,2,6]) >>> np.apply_along_axis(sorted, 1, b) array([1, 7, 8], [3, 4, 9], [2, 5, 6])

For a function that returns a higher dimensional array, those dimensions are inserted in place of the `axis` dimension.

>>> b = np.array([1,2,3], [4,5,6], [7,8,9]) >>> np.apply_along_axis(np.diag, -1, b) array([[1, 0, 0], [0, 2, 0], [0, 0, 3]], [[4, 0, 0], [0, 5, 0], [0, 0, 6]], [[7, 0, 0], [0, 8, 0], [0, 0, 9]])

Apply a function repeatedly over multiple axes.

`func` is called as `res = func(a, axis)`, where `axis` is the first element of `axes`. The result `res` of the function call must have either the same dimensions as `a` or one less dimension. If `res` has one less dimension than `a`, a dimension is inserted before `axis`. The call to `func` is then repeated for each axis in `axes`, with `res` as the first argument.

Parameters ---------- func : function This function must take two arguments, `func(a, axis)`. a : array_like Input array. axes : array_like Axes over which `func` is applied; the elements must be integers.

Returns ------- apply_over_axis : ndarray The output array. The number of dimensions is the same as `a`, but the shape can be different. This depends on whether `func` changes the shape of its output with respect to its input.

See Also -------- apply_along_axis : Apply a function to 1-D slices of an array along the given axis.

Examples -------- >>> a = np.ma.arange(24).reshape(2,3,4) >>> a:,0,1 = np.ma.masked >>> a:,1,: = np.ma.masked >>> a masked_array( data=[[0, --, 2, 3], [--, --, --, --], [8, 9, 10, 11]], [[12, --, 14, 15], [--, --, --, --], [20, 21, 22, 23]], mask=[[False, True, False, False], [ True, True, True, True], [False, False, False, False]], [[False, True, False, False], [ True, True, True, True], [False, False, False, False]], fill_value=999999) >>> np.ma.apply_over_axes(np.ma.sum, a, 0,2) masked_array( data=[[46], [--], [124]], mask=[[False], [ True], [False]], fill_value=999999)

Tuple axis arguments to ufuncs are equivalent:

>>> np.ma.sum(a, axis=(0,2)).reshape((1,-1,1)) masked_array( data=[[46], [--], [124]], mask=[[False], [ True], [False]], fill_value=999999)

arange(start, stop, step,, dtype=None)

Return evenly spaced values within a given interval.

Values are generated within the half-open interval ``start, stop)`` (in other words, the interval including `start` but excluding `stop`). For integer arguments the function is equivalent to the Python built-in `range` function, but returns an ndarray rather than a list. When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use `numpy.linspace` for these cases. Parameters ---------- start : number, optional Start of interval. The interval includes this value. The default start value is 0. stop : number End of interval. The interval does not include this value, except in some cases where `step` is not an integer and floating point round-off affects the length of `out`. step : number, optional Spacing between values. For any output `out`, this is the distance between two adjacent values, ``out[i+1] - out[i]``. The default step size is 1. If `step` is specified as a position argument, `start` must also be given. dtype : dtype The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. Returns ------- arange : ndarray Array of evenly spaced values. For floating point arguments, the length of the result is ``ceil((stop - start)/step)``. Because of floating point overflow, this rule may result in the last element of `out` being greater than `stop`. See Also -------- numpy.linspace : Evenly spaced numbers with careful handling of endpoints. numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions. numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions. Examples -------- >>> np.arange(3) array([0, 1, 2]) >>> np.arange(3.0) array([ 0., 1., 2.]) >>> np.arange(3,7) array([3, 4, 5, 6]) >>> np.arange(3,7,2) array([3, 5])

arccos(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Trigonometric inverse cosine, element-wise.

The inverse of `cos` so that, if ``y = cos(x)``, then ``x = arccos(y)``.

Parameters ---------- x : array_like `x`-coordinate on the unit circle. For real arguments, the domain is -1, 1. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- angle : ndarray The angle of the ray intersecting the unit circle at the given `x`-coordinate in radians 0, pi. This is a scalar if `x` is a scalar.

See Also -------- cos, arctan, arcsin, emath.arccos

Notes ----- `arccos` is a multivalued function: for each `x` there are infinitely many numbers `z` such that `cos(z) = x`. The convention is to return the angle `z` whose real part lies in `0, pi`.

For real-valued input data types, `arccos` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `arccos` is a complex analytic function that has branch cuts `-inf, -1` and `1, inf` and is continuous from above on the former and from below on the latter.

The inverse `cos` is also known as `acos` or cos^-1.

References ---------- M. Abramowitz and I.A. Stegun, 'Handbook of Mathematical Functions', 10th printing, 1964, pp. 79. http://www.math.sfu.ca/~cbm/aands/

Examples -------- We expect the arccos of 1 to be 0, and of -1 to be pi:

>>> np.arccos(1, -1) array( 0. , 3.14159265)

Plot arccos:

>>> import matplotlib.pyplot as plt >>> x = np.linspace(-1, 1, num=100) >>> plt.plot(x, np.arccos(x)) >>> plt.axis('tight') >>> plt.show()

arccosh(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Inverse hyperbolic cosine, element-wise.

Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- arccosh : ndarray Array of the same shape as `x`. This is a scalar if `x` is a scalar.

See Also --------

cosh, arcsinh, sinh, arctanh, tanh

Notes ----- `arccosh` is a multivalued function: for each `x` there are infinitely many numbers `z` such that `cosh(z) = x`. The convention is to return the `z` whose imaginary part lies in `-pi, pi` and the real part in ``0, inf``.

For real-valued input data types, `arccosh` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `arccosh` is a complex analytical function that has a branch cut `-inf, 1` and is continuous from above on it.

References ---------- .. 1 M. Abramowitz and I.A. Stegun, 'Handbook of Mathematical Functions', 10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/ .. 2 Wikipedia, 'Inverse hyperbolic function', https://en.wikipedia.org/wiki/Arccosh

Examples -------- >>> np.arccosh(np.e, 10.0) array( 1.65745445, 2.99322285) >>> np.arccosh(1) 0.0

arcsin(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Inverse sine, element-wise.

Parameters ---------- x : array_like `y`-coordinate on the unit circle. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- angle : ndarray The inverse sine of each element in `x`, in radians and in the closed interval ``-pi/2, pi/2``. This is a scalar if `x` is a scalar.

See Also -------- sin, cos, arccos, tan, arctan, arctan2, emath.arcsin

Notes ----- `arcsin` is a multivalued function: for each `x` there are infinitely many numbers `z` such that :math:`sin(z) = x`. The convention is to return the angle `z` whose real part lies in -pi/2, pi/2.

For real-valued input data types, *arcsin* always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `arcsin` is a complex analytic function that has, by convention, the branch cuts -inf, -1 and 1, inf and is continuous from above on the former and from below on the latter.

The inverse sine is also known as `asin` or sin^

1

}

.

References ---------- Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 79ff. http://www.math.sfu.ca/~cbm/aands/

Examples -------- >>> np.arcsin(1) # pi/2 1.5707963267948966 >>> np.arcsin(-1) # -pi/2 -1.5707963267948966 >>> np.arcsin(0) 0.0

arcsinh(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Inverse hyperbolic sine element-wise.

Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Array of the same shape as `x`. This is a scalar if `x` is a scalar.

Notes ----- `arcsinh` is a multivalued function: for each `x` there are infinitely many numbers `z` such that `sinh(z) = x`. The convention is to return the `z` whose imaginary part lies in `-pi/2, pi/2`.

For real-valued input data types, `arcsinh` always returns real output. For each value that cannot be expressed as a real number or infinity, it returns ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `arccos` is a complex analytical function that has branch cuts `1j, infj` and `-1j, -infj` and is continuous from the right on the former and from the left on the latter.

The inverse hyperbolic sine is also known as `asinh` or ``sinh^-1``.

References ---------- .. 1 M. Abramowitz and I.A. Stegun, 'Handbook of Mathematical Functions', 10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/ .. 2 Wikipedia, 'Inverse hyperbolic function', https://en.wikipedia.org/wiki/Arcsinh

Examples -------- >>> np.arcsinh(np.array(np.e, 10.0)) array( 1.72538256, 2.99822295)

arctan(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Trigonometric inverse tangent, element-wise.

The inverse of tan, so that if ``y = tan(x)`` then ``x = arctan(y)``.

Parameters ---------- x : array_like out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Out has the same shape as `x`. Its real part is in ``-pi/2, pi/2`` (``arctan(+/-inf)`` returns ``+/-pi/2``). This is a scalar if `x` is a scalar.

See Also -------- arctan2 : The 'four quadrant' arctan of the angle formed by (`x`, `y`) and the positive `x`-axis. angle : Argument of complex values.

Notes ----- `arctan` is a multi-valued function: for each `x` there are infinitely many numbers `z` such that tan(`z`) = `x`. The convention is to return the angle `z` whose real part lies in -pi/2, pi/2.

For real-valued input data types, `arctan` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `arctan` is a complex analytic function that has `1j, infj` and `-1j, -infj` as branch cuts, and is continuous from the left on the former and from the right on the latter.

The inverse tangent is also known as `atan` or tan^

1

}

.

References ---------- Abramowitz, M. and Stegun, I. A., *Handbook of Mathematical Functions*, 10th printing, New York: Dover, 1964, pp. 79. http://www.math.sfu.ca/~cbm/aands/

Examples -------- We expect the arctan of 0 to be 0, and of 1 to be pi/4:

>>> np.arctan(0, 1) array( 0. , 0.78539816)

>>> np.pi/4 0.78539816339744828

Plot arctan:

>>> import matplotlib.pyplot as plt >>> x = np.linspace(-10, 10) >>> plt.plot(x, np.arctan(x)) >>> plt.axis('tight') >>> plt.show()

arctan2(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Element-wise arc tangent of ``x1/x2`` choosing the quadrant correctly.

The quadrant (i.e., branch) is chosen so that ``arctan2(x1, x2)`` is the signed angle in radians between the ray ending at the origin and passing through the point (1,0), and the ray ending at the origin and passing through the point (`x2`, `x1`). (Note the role reversal: the '`y`-coordinate' is the first function parameter, the '`x`-coordinate' is the second.) By IEEE convention, this function is defined for `x2` = +/-0 and for either or both of `x1` and `x2` = +/-inf (see Notes for specific values).

This function is not defined for complex-valued arguments; for the so-called argument of complex values, use `angle`.

Parameters ---------- x1 : array_like, real-valued `y`-coordinates. x2 : array_like, real-valued `x`-coordinates. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- angle : ndarray Array of angles in radians, in the range ``-pi, pi``. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- arctan, tan, angle

Notes ----- *arctan2* is identical to the `atan2` function of the underlying C library. The following special values are defined in the C standard: 1_

====== ====== ================ `x1` `x2` `arctan2(x1,x2)` ====== ====== ================ +/- 0 +0 +/- 0 +/- 0 -0 +/- pi > 0 +/-inf +0 / +pi < 0 +/-inf -0 / -pi +/-inf +inf +/- (pi/4) +/-inf -inf +/- (3*pi/4) ====== ====== ================

Note that +0 and -0 are distinct floating point numbers, as are +inf and -inf.

References ---------- .. 1 ISO/IEC standard 9899:1999, 'Programming language C.'

Examples -------- Consider four points in different quadrants:

>>> x = np.array(-1, +1, +1, -1) >>> y = np.array(-1, -1, +1, +1) >>> np.arctan2(y, x) * 180 / np.pi array(-135., -45., 45., 135.)

Note the order of the parameters. `arctan2` is defined also when `x2` = 0 and at several other special points, obtaining values in the range ``-pi, pi``:

>>> np.arctan2(1., -1., 0., 0.) array( 1.57079633, -1.57079633) >>> np.arctan2(0., 0., np.inf, +0., -0., np.inf) array( 0. , 3.14159265, 0.78539816)

arctanh(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Inverse hyperbolic tangent element-wise.

Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Array of the same shape as `x`. This is a scalar if `x` is a scalar.

See Also -------- emath.arctanh

Notes ----- `arctanh` is a multivalued function: for each `x` there are infinitely many numbers `z` such that `tanh(z) = x`. The convention is to return the `z` whose imaginary part lies in `-pi/2, pi/2`.

For real-valued input data types, `arctanh` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `arctanh` is a complex analytical function that has branch cuts `-1, -inf` and `1, inf` and is continuous from above on the former and from below on the latter.

The inverse hyperbolic tangent is also known as `atanh` or ``tanh^-1``.

References ---------- .. 1 M. Abramowitz and I.A. Stegun, 'Handbook of Mathematical Functions', 10th printing, 1964, pp. 86. http://www.math.sfu.ca/~cbm/aands/ .. 2 Wikipedia, 'Inverse hyperbolic function', https://en.wikipedia.org/wiki/Arctanh

Examples -------- >>> np.arctanh(0, -0.5) array( 0. , -0.54930614)

argmax(self, axis=None, fill_value=None, out=None)

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 :

ar}, 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])

argmin(self, axis=None, fill_value=None, out=None)

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 :

ar}, optional
    Value used to fill in the masked values.  If None, the output of
    minimum_fill_value(self._data) is used instead.
out : {None, array}, optional
    Array into which the result can be placed. Its type is preserved
    and it must be of the right shape to hold the output.

Returns
-------
ndarray or scalar
    If multi-dimension input, returns a new ndarray of indices to the
    minimum values along the given axis.  Otherwise, returns a scalar
    of index to the minimum values along the given axis.

Examples
--------
>>> x = np.ma.array(np.arange(4), mask=[1,1,0,0])
>>> x.shape = (2,2)
>>> x
masked_array(
  data=[[--, --],
        [2, 3]],
  mask=[[ True,  True],
        [False, False]],
  fill_value=999999)
>>> x.argmin(axis=0, fill_value=-1)
array([0, 0])
>>> x.argmin(axis=0, fill_value=9)
array([1, 1])

Return an ndarray of indices that sort the array along the specified axis. Masked values are filled beforehand to `fill_value`.

Parameters ---------- axis : int, optional Axis along which to sort. If None, the default, the flattened array is used.

.. versionchanged:: 1.13.0 Previously, the default was documented to be -1, but that was in error. At some future date, the default will change to -1, as originally intended. Until then, the axis should be given explicitly when ``arr.ndim > 1``, to avoid a FutureWarning. kind : 'quicksort', 'mergesort', 'heapsort', 'stable', optional The sorting algorithm used. order : list, optional When `a` is an array with fields defined, this argument specifies which fields to compare first, second, etc. Not all fields need be specified. endwith : True, False, optional Whether missing values (if any) should be treated as the largest values (True) or the smallest values (False) When the array contains unmasked values at the same extremes of the datatype, the ordering of these values and the masked values is undefined. fill_value :

ar}, 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.
numpy.ndarray.sort : Inplace sort.

Notes
-----
See `sort` for notes on the different sorting algorithms.

Examples
--------
>>> a = np.ma.array([3,2,1], mask=[False, False, True])
>>> a
masked_array(data=[3, 2, --],
             mask=[False, False,  True],
       fill_value=999999)
>>> a.argsort()
array([1, 0, 2])

Round an array to the given number of decimals.

See Also -------- around : equivalent function; see for details.

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.

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'>

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'>

atleast_1d( *args, **kwargs)

Convert inputs to arrays with at least one dimension.

Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved.

Parameters ---------- arys1, arys2, ... : array_like One or more input arrays.

Returns ------- ret : ndarray An array, or list of arrays, each with ``a.ndim >= 1``. Copies are made only if necessary.

See Also -------- atleast_2d, atleast_3d

Examples -------- >>> np.atleast_1d(1.0) array(1.)

>>> x = np.arange(9.0).reshape(3,3) >>> np.atleast_1d(x) array([0., 1., 2.], [3., 4., 5.], [6., 7., 8.]) >>> np.atleast_1d(x) is x True

>>> np.atleast_1d(1, 3, 4) array([1]), array([3, 4])

Notes ----- The function is applied to both the _data and the _mask, if any.

atleast_2d( *args, **kwargs)

View inputs as arrays with at least two dimensions.

Parameters ---------- arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved.

Returns ------- res, res2, ... : ndarray An array, or list of arrays, each with ``a.ndim >= 2``. Copies are avoided where possible, and views with two or more dimensions are returned.

See Also -------- atleast_1d, atleast_3d

Examples -------- >>> np.atleast_2d(3.0) array([3.])

>>> x = np.arange(3.0) >>> np.atleast_2d(x) array([0., 1., 2.]) >>> np.atleast_2d(x).base is x True

>>> np.atleast_2d(1, 1, 2, [1, 2]) array([[1]]), array([[1, 2]]), array([[1, 2]])

Notes ----- The function is applied to both the _data and the _mask, if any.

atleast_3d( *args, **kwargs)

View inputs as arrays with at least three dimensions.

Parameters ---------- arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved.

Returns ------- res1, res2, ... : ndarray An array, or list of arrays, each with ``a.ndim >= 3``. Copies are avoided where possible, and views with three or more dimensions are returned. For example, a 1-D array of shape ``(N,)`` becomes a view of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a view of shape ``(M, N, 1)``.

See Also -------- atleast_1d, atleast_2d

Examples -------- >>> np.atleast_3d(3.0) array([[3.]])

>>> x = np.arange(3.0) >>> np.atleast_3d(x).shape (1, 3, 1)

>>> x = np.arange(12.0).reshape(4,3) >>> np.atleast_3d(x).shape (4, 3, 1) >>> np.atleast_3d(x).base is x.base # x is a reshape, so not base itself True

>>> for arr in np.atleast_3d(1, 2, [1, 2], [[1, 2]]): ... print(arr, arr.shape) # doctest: +SKIP ... [[1] [2]] (1, 2, 1) [[1] [2]] (1, 2, 1) [[1 2]] (1, 1, 2)

Notes ----- The function is applied to both the _data and the _mask, if any.

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. The 1-D calculation is::

avg = sum(a * weights) / sum(weights)

The only constraint on `weights` is that `sum(weights)` must not be 0. 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) >>> x masked_array( data=[0., 1.], [2., 3.], [4., 5.], mask=False, fill_value=1e+20) >>> avg, sumweights = np.ma.average(x, axis=0, weights=1, 2, 3, ... returned=True) >>> avg masked_array(data=2.6666666666666665, 3.6666666666666665, mask=False, False, fill_value=1e+20)

bitwise_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the bit-wise AND of two arrays element-wise.

Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ``&``.

Parameters ---------- x1, x2 : array_like Only integer and boolean types are handled. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Result. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- logical_and bitwise_or bitwise_xor binary_repr : Return the binary representation of the input number as a string.

Examples -------- The number 13 is represented by ``00001101``. Likewise, 17 is represented by ``00010001``. The bit-wise AND of 13 and 17 is therefore ``000000001``, or 1:

>>> np.bitwise_and(13, 17) 1

>>> np.bitwise_and(14, 13) 12 >>> np.binary_repr(12) '1100' >>> np.bitwise_and(14,3, 13) array(12, 1)

>>> np.bitwise_and(11,7, 4,25) array(0, 1) >>> np.bitwise_and(np.array(2,5,255), np.array(3,14,16)) array( 2, 4, 16) >>> np.bitwise_and(True, True, False, True) array(False, True)

bitwise_or(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the bit-wise OR of two arrays element-wise.

Computes the bit-wise OR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ``|``.

Parameters ---------- x1, x2 : array_like Only integer and boolean types are handled. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Result. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- logical_or bitwise_and bitwise_xor binary_repr : Return the binary representation of the input number as a string.

Examples -------- The number 13 has the binaray representation ``00001101``. Likewise, 16 is represented by ``00010000``. The bit-wise OR of 13 and 16 is then ``000111011``, or 29:

>>> np.bitwise_or(13, 16) 29 >>> np.binary_repr(29) '11101'

>>> np.bitwise_or(32, 2) 34 >>> np.bitwise_or(33, 4, 1) array(33, 5) >>> np.bitwise_or(33, 4, 1, 2) array(33, 6)

>>> np.bitwise_or(np.array(2, 5, 255), np.array(4, 4, 4)) array( 6, 5, 255) >>> np.array(2, 5, 255) | np.array(4, 4, 4) array( 6, 5, 255) >>> np.bitwise_or(np.array(2, 5, 255, 2147483647, dtype=np.int32), ... np.array(4, 4, 4, 2147483647, dtype=np.int32)) array( 6, 5, 255, 2147483647) >>> np.bitwise_or(True, True, False, True) array( True, True)

bitwise_xor(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the bit-wise XOR of two arrays element-wise.

Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ``^``.

Parameters ---------- x1, x2 : array_like Only integer and boolean types are handled. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Result. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- logical_xor bitwise_and bitwise_or binary_repr : Return the binary representation of the input number as a string.

Examples -------- The number 13 is represented by ``00001101``. Likewise, 17 is represented by ``00010001``. The bit-wise XOR of 13 and 17 is therefore ``00011100``, or 28:

>>> np.bitwise_xor(13, 17) 28 >>> np.binary_repr(28) '11100'

>>> np.bitwise_xor(31, 5) 26 >>> np.bitwise_xor(31,3, 5) array(26, 6)

>>> np.bitwise_xor(31,3, 5,6) array(26, 5) >>> np.bitwise_xor(True, True, False, True) array( True, False)

ceil(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the ceiling of the input, element-wise.

The ceil of the scalar `x` is the smallest integer `i`, such that `i >= x`. It is often denoted as :math:`\lceil x \rceil`.

Parameters ---------- x : array_like Input data. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or scalar The ceiling of each element in `x`, with `float` dtype. This is a scalar if `x` is a scalar.

See Also -------- floor, trunc, rint

Examples -------- >>> a = np.array(-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0) >>> np.ceil(a) array(-1., -1., -0., 1., 2., 2., 2.)

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 choicesi 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)

Clip (limit) the values in an array.

Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of ``0, 1`` is specified, values smaller than 0 become 0, and values larger than 1 become 1.

Equivalent to but faster than ``np.maximum(a_min, np.minimum(a, a_max))``. No check is performed to ensure ``a_min < a_max``.

Parameters ---------- a : array_like Array containing elements to clip. a_min : scalar or array_like or None Minimum value. If None, clipping is not performed on lower interval edge. Not more than one of `a_min` and `a_max` may be None. a_max : scalar or array_like or None Maximum value. If None, clipping is not performed on upper interval edge. Not more than one of `a_min` and `a_max` may be None. If `a_min` or `a_max` are array_like, then the three arrays will be broadcasted to match their shapes. out : ndarray, optional The results will be placed in this array. It may be the input array for in-place clipping. `out` must be of the right shape to hold the output. Its type is preserved. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

.. versionadded:: 1.17.0

Returns ------- clipped_array : ndarray An array with the elements of `a`, but where values < `a_min` are replaced with `a_min`, and those > `a_max` with `a_max`.

See Also -------- ufuncs-output-type

Examples -------- >>> a = np.arange(10) >>> np.clip(a, 1, 8) array(1, 1, 2, 3, 4, 5, 6, 7, 8, 8) >>> a array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) >>> np.clip(a, 3, 6, out=a) array(3, 3, 3, 3, 4, 5, 6, 6, 6, 6) >>> a = np.arange(10) >>> a array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) >>> np.clip(a, 3, 4, 1, 1, 1, 4, 4, 4, 4, 4, 8) array(3, 4, 2, 3, 4, 5, 6, 7, 8, 8)

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)

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)

column_stack( *args, **kwargs)

Stack 1-D arrays as columns into a 2-D array.

Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with `hstack`. 1-D arrays are turned into 2-D columns first.

Parameters ---------- tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them must have the same first dimension.

Returns ------- stacked : 2-D array The array formed by stacking the given arrays.

See Also -------- stack, hstack, vstack, concatenate

Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.column_stack((a,b)) array([1, 2], [2, 3], [3, 4])

Notes ----- The function is applied to both the _data and the _mask, if any.

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

compress(self, condition, axis=None, out=None)

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) >>> x masked_array( data=[1, --, 3], [--, 5, --], [7, --, 9], mask=[False, True, False], [ True, False, True], [False, True, False], fill_value=999999) >>> x.compress(1, 0, 1) masked_array(data=1, 3, mask=False, False, fill_value=999999)

>>> x.compress(1, 0, 1, axis=1) masked_array( data=[1, 3], [--, --], [7, 9], mask=[False, False], [ True, True], [False, False], fill_value=999999)

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

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.

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])

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

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.

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) >>> a1 = 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)

conjugate(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the complex conjugate, element-wise.

The complex conjugate of a complex number is obtained by changing the sign of its imaginary part.

Parameters ---------- x : array_like Input value. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The complex conjugate of `x`, with same dtype as `y`. This is a scalar if `x` is a scalar.

Notes ----- `conj` is an alias for `conjugate`:

>>> np.conj is np.conjugate True

Examples -------- >>> np.conjugate(1+2j) (1-2j)

>>> x = np.eye(2) + 1j * np.eye(2) >>> np.conjugate(x) array([ 1.-1.j, 0.-0.j], [ 0.-0.j, 1.-1.j])

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.

copy(self, *args, **params) a.copy(order='C')

Return a copy of the array.

Parameters ---------- order : 'C', 'F', 'A', 'K', optional Controls the memory layout of the copy. 'C' means C-order, 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of `a` as closely as possible. (Note that this function and :func:`numpy.copy` are very similar, but have different default values for their order= arguments.)

See also -------- numpy.copy numpy.copyto

Examples -------- >>> x = np.array([1,2,3],[4,5,6], order='F')

>>> y = x.copy()

>>> x.fill(0)

>>> x array([0, 0, 0], [0, 0, 0])

>>> y array([1, 2, 3], [4, 5, 6])

>>> y.flags'C_CONTIGUOUS' True

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.

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.

cos(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Cosine element-wise.

Parameters ---------- x : array_like Input array in radians. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The corresponding cosine values. This is a scalar if `x` is a scalar.

Notes ----- If `out` is provided, the function writes the result into it, and returns a reference to `out`. (See Examples)

References ---------- M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972.

Examples -------- >>> np.cos(np.array(0, np.pi/2, np.pi)) array( 1.00000000e+00, 6.12303177e-17, -1.00000000e+00) >>> >>> # Example of providing the optional output parameter >>> out1 = np.array(0, dtype='d') >>> out2 = np.cos(0.1, out1) >>> out2 is out1 True >>> >>> # Example of ValueError due to provision of shape mis-matched `out` >>> np.cos(np.zeros((3,3)),np.zeros((2,2))) Traceback (most recent call last): File '<stdin>', line 1, in <module> ValueError: operands could not be broadcast together with shapes (3,3) (2,2)

cosh(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Hyperbolic cosine, element-wise.

Equivalent to ``1/2 * (np.exp(x) + np.exp(-x))`` and ``np.cos(1j*x)``.

Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Output array of same shape as `x`. This is a scalar if `x` is a scalar.

Examples -------- >>> np.cosh(0) 1.0

The hyperbolic cosine describes the shape of a hanging cable:

>>> import matplotlib.pyplot as plt >>> x = np.linspace(-4, 4, 1000) >>> plt.plot(x, np.cosh(x)) >>> plt.show()

count(self, axis=None, keepdims=<no value>)

Count the non-masked elements of the array along the given axis.

Parameters ---------- axis : None or int or tuple of ints, optional Axis or axes along which the count is performed. The default, None, performs the count over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis.

.. versionadded:: 1.10.0

If this is a tuple of ints, the count is performed on multiple axes, instead of a single axis or all the axes as before. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the array.

Returns ------- result : ndarray or scalar An array with the same shape as the input array, with the specified axis removed. If the array is a 0-d array, or if `axis` is None, a scalar is returned.

See Also -------- 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)) >>> a1, : = 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)

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) >>> a1, 0 = ma.masked >>> a1, 2 = ma.masked >>> a2, 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)

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 ``xi,j`` is masked, then ``yi,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

cumprod(self, axis=None, dtype=None, out=None)

Return the cumulative product of the array elements over the given axis.

Masked values are set to 1 internally during the computation. However, their position is saved, and the result will be masked at the same locations.

Refer to `numpy.cumprod` for full documentation.

Notes ----- The mask is lost if `out` is not a valid MaskedArray !

Arithmetic is modular when using integer types, and no error is raised on overflow.

See Also -------- numpy.ndarray.cumprod : corresponding function for ndarrays numpy.cumprod : equivalent function

cumsum(self, axis=None, dtype=None, out=None)

Return the cumulative sum of the array elements over the given axis.

Masked values are set to 0 internally during the computation. However, their position is saved, and the result will be masked at the same locations.

Refer to `numpy.cumsum` for full documentation.

Notes ----- The mask is lost if `out` is not a valid :class:`MaskedArray` !

Arithmetic is modular when using integer types, and no error is raised on overflow.

See Also -------- numpy.ndarray.cumsum : corresponding function for ndarrays numpy.cumsum : equivalent function

Examples -------- >>> marr = np.ma.array(np.arange(10), mask=0,0,0,1,1,1,0,0,0,0) >>> marr.cumsum() masked_array(data=0, 1, 3, --, --, --, 9, 16, 24, 33, mask=False, False, False, True, True, True, False, False, False, False, fill_value=999999)

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)

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.

diagflat( *args, **kwargs)

Create a two-dimensional array with the flattened input as a diagonal.

Parameters ---------- v : array_like Input data, which is flattened and set as the `k`-th diagonal of the output. k : int, optional Diagonal to set; 0, the default, corresponds to the 'main' diagonal, a positive (negative) `k` giving the number of the diagonal above (below) the main.

Returns ------- out : ndarray The 2-D output array.

See Also -------- diag : MATLAB work-alike for 1-D and 2-D arrays. diagonal : Return specified diagonals. trace : Sum along diagonals.

Examples -------- >>> np.diagflat([1,2], [3,4]) array([1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4])

>>> np.diagflat(1,2, 1) array([0, 1, 0], [0, 0, 2], [0, 0, 0])

Notes ----- The function is applied to both the _data and the _mask, if any.

diagonal(self, *args, **params) a.diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to :func:`numpy.diagonal` for full documentation.

See Also -------- numpy.diagonal : equivalent function

Calculate the n-th discrete difference along the given axis.

The first difference is given by ``outi = ai+1 - ai`` along the given axis, higher differences are calculated by using `diff` recursively.

Parameters ---------- a : array_like Input array n : int, optional The number of times values are differenced. If zero, the input is returned as-is. axis : int, optional The axis along which the difference is taken, default is the last axis. prepend, append : array_like, optional Values to prepend or append to `a` along axis prior to performing the difference. Scalar values are expanded to arrays with length 1 in the direction of axis and the shape of the input array in along all other axes. Otherwise the dimension and shape must match `a` except along axis.

.. versionadded:: 1.16.0

Returns ------- diff : ndarray The n-th differences. The shape of the output is the same as `a` except along `axis` where the dimension is smaller by `n`. The type of the output is the same as the type of the difference between any two elements of `a`. This is the same as the type of `a` in most cases. A notable exception is `datetime64`, which results in a `timedelta64` output array.

See Also -------- gradient, ediff1d, cumsum

Notes ----- Type is preserved for boolean arrays, so the result will contain `False` when consecutive elements are the same and `True` when they differ.

For unsigned integer arrays, the results will also be unsigned. This should not be surprising, as the result is consistent with calculating the difference directly:

>>> u8_arr = np.array(1, 0, dtype=np.uint8) >>> np.diff(u8_arr) array(255, dtype=uint8) >>> u8_arr1,... - u8_arr0,... 255

If this is not desirable, then the array should be cast to a larger integer type first:

>>> i16_arr = u8_arr.astype(np.int16) >>> np.diff(i16_arr) array(-1, dtype=int16)

Examples -------- >>> x = np.array(1, 2, 4, 7, 0) >>> np.diff(x) array( 1, 2, 3, -7) >>> np.diff(x, n=2) array( 1, 1, -10)

>>> x = np.array([1, 3, 6, 10], [0, 5, 6, 8]) >>> np.diff(x) array([2, 3, 4], [5, 1, 2]) >>> np.diff(x, axis=0) array([-1, 2, 0, -2])

>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64) >>> np.diff(x) array(1, 1, dtype='timedelta64D')

true_divide(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Returns a true division of the inputs, element-wise.

Instead of the Python traditional 'floor division', this returns a true division. True division adjusts the output type to present the best answer, regardless of input types.

Parameters ---------- x1 : array_like Dividend array. x2 : array_like Divisor array. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar This is a scalar if both `x1` and `x2` are scalars.

Notes ----- The floor division operator ``//`` was added in Python 2.2 making ``//`` and ``/`` equivalent operators. The default floor division operation of ``/`` can be replaced by true division with ``from __future__ import division``.

In Python 3.0, ``//`` is the floor division operator and ``/`` the true division operator. The ``true_divide(x1, x2)`` function is equivalent to true division in Python.

Examples -------- >>> x = np.arange(5) >>> np.true_divide(x, 4) array( 0. , 0.25, 0.5 , 0.75, 1. )

>>> x//4 array(0, 0, 0, 0, 1)

>>> from __future__ import division >>> x/4 array( 0. , 0.25, 0.5 , 0.75, 1. ) >>> x//4 array(0, 0, 0, 0, 1)

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 = np.ma.array([1, 2, 3], [4, 5, 6], mask=[1, 0, 0], [0, 0, 0]) >>> b = np.ma.array([1, 2], [3, 4], [5, 6], mask=[1, 0], [0, 0], [0, 0]) >>> np.ma.dot(a, b) masked_array( data=[21, 26], [45, 64], mask=[False, False], [False, False], fill_value=999999) >>> np.ma.dot(a, b, strict=True) masked_array( data=[--, --], [--, 64], mask=[ True, True], [ True, False], fill_value=999999)

dstack( *args, **kwargs)

Stack arrays in sequence depth wise (along third axis).

This is equivalent to concatenation along the third axis after 2-D arrays of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by `dsplit`.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions `concatenate`, `stack` and `block` provide more general stacking and concatenation operations.

Parameters ---------- tup : sequence of arrays The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape.

Returns ------- stacked : ndarray The array formed by stacking the given arrays, will be at least 3-D.

See Also -------- stack : Join a sequence of arrays along a new axis. vstack : Stack along first axis. hstack : Stack along second axis. concatenate : Join a sequence of arrays along an existing axis. dsplit : Split array along third axis.

Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.dstack((a,b)) array([[1, 2], [2, 3], [3, 4]])

>>> a = np.array([1],[2],[3]) >>> b = np.array([2],[3],[4]) >>> np.dstack((a,b)) array([[1, 2]], [[2, 3]], [[3, 4]])

Notes ----- The function is applied to both the _data and the _mask, if any.

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.

empty(shape, dtype=float, order='C')

Return a new array of given shape and type, without initializing entries.

Parameters ---------- shape : int or tuple of int Shape of the empty array, e.g., ``(2, 3)`` or ``2``. dtype : data-type, optional Desired output data-type for the array, e.g, `numpy.int8`. Default is `numpy.float64`. order : 'C', 'F', optional, default: 'C' Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.

Returns ------- out : ndarray Array of uninitialized (arbitrary) data of the given shape, dtype, and order. Object arrays will be initialized to None.

See Also -------- empty_like : Return an empty array with shape and type of input. ones : Return a new array setting values to one. zeros : Return a new array setting values to zero. full : Return a new array of given shape filled with value.

Notes ----- `empty`, unlike `zeros`, does not set the array values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution.

Examples -------- >>> np.empty(2, 2) array([ -9.74499359e+001, 6.69583040e-309], [ 2.13182611e-314, 3.06959433e-309]) #uninitialized

>>> np.empty(2, 2, dtype=int) array([-1073741821, -1067949133], [ 496041986, 19249760]) #uninitialized

empty_like( *args, **kwargs)

empty_like(prototype, dtype=None, order='K', subok=True, shape=None)

Return a new array with the same shape and type as a given array.

Parameters ---------- prototype : array_like The shape and data-type of `prototype` define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result.

.. versionadded:: 1.6.0 order : 'C', 'F', 'A', or 'K', optional Overrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if ``prototype`` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of ``prototype`` as closely as possible.

.. versionadded:: 1.6.0 subok : bool, optional. If True, then the newly created array will use the sub-class type of 'a', otherwise it will be a base-class array. Defaults to True. shape : int or sequence of ints, optional. Overrides the shape of the result. If order='K' and the number of dimensions is unchanged, will try to keep order, otherwise, order='C' is implied.

.. versionadded:: 1.17.0

Returns ------- out : ndarray Array of uninitialized (arbitrary) data with the same shape and type as `prototype`.

See Also -------- ones_like : Return an array of ones with shape and type of input. zeros_like : Return an array of zeros with shape and type of input. full_like : Return a new array with shape of input filled with value. empty : Return a new uninitialized array.

Notes ----- This function does *not* initialize the returned array; to do that use `zeros_like` or `ones_like` instead. It may be marginally faster than the functions that do set the array values.

Examples -------- >>> a = (1,2,3, 4,5,6) # a is array-like >>> np.empty_like(a) array([-1073741821, -1073741821, 3], # uninitialized [ 0, 0, -1073741821]) >>> a = np.array([1., 2., 3.],[4.,5.,6.]) >>> np.empty_like(a) array([ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309])

equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return (x1 == x2) element-wise.

Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- not_equal, greater_equal, less_equal, greater, less

Examples -------- >>> np.equal(0, 1, 3, np.arange(3)) array( True, True, False)

What is compared are values, not types. So an int (1) and an array of length one can evaluate as True:

>>> np.equal(1, np.ones(1)) array( True)

exp(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Calculate the exponential of all elements in the input array.

Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Output array, element-wise exponential of `x`. This is a scalar if `x` is a scalar.

See Also -------- expm1 : Calculate ``exp(x) - 1`` for all elements in the array. exp2 : Calculate ``2**x`` for all elements in the array.

Notes ----- The irrational number ``e`` is also known as Euler's number. It is approximately 2.718281, and is the base of the natural logarithm, ``ln`` (this means that, if :math:`x = \ln y = \log_e y`, then :math:`e^x = y`. For real input, ``exp(x)`` is always positive.

For complex arguments, ``x = a + ib``, we can write :math:`e^x = e^a e^b`. The first term, :math:`e^a`, is already known (it is the real argument, described above). The second term, :math:`e^b`, is :math:`\cos b + i \sin b`, a function with magnitude 1 and a periodic phase.

References ---------- .. 1 Wikipedia, 'Exponential function', https://en.wikipedia.org/wiki/Exponential_function .. 2 M. Abramovitz and I. A. Stegun, 'Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables,' Dover, 1964, p. 69, http://www.math.sfu.ca/~cbm/aands/page_69.htm

Examples -------- Plot the magnitude and phase of ``exp(x)`` in the complex plane:

>>> import matplotlib.pyplot as plt

>>> x = np.linspace(-2*np.pi, 2*np.pi, 100) >>> xx = x + 1j * x:, np.newaxis # a + ib over complex plane >>> out = np.exp(xx)

>>> plt.subplot(121) >>> plt.imshow(np.abs(out), ... extent=-2*np.pi, 2*np.pi, -2*np.pi, 2*np.pi, cmap='gray') >>> plt.title('Magnitude of exp(x)')

>>> plt.subplot(122) >>> plt.imshow(np.angle(out), ... extent=-2*np.pi, 2*np.pi, -2*np.pi, 2*np.pi, cmap='hsv') >>> plt.title('Phase (angle) of exp(x)') >>> plt.show()

Expand the shape of an array.

Insert a new axis that will appear at the `axis` position in the expanded array shape.

Parameters ---------- a : array_like Input array. axis : int or tuple of ints Position in the expanded axes where the new axis (or axes) is placed.

.. deprecated:: 1.13.0 Passing an axis where ``axis > a.ndim`` will be treated as ``axis == a.ndim``, and passing ``axis < -a.ndim - 1`` will be treated as ``axis == 0``. This behavior is deprecated.

.. versionchanged:: 1.18.0 A tuple of axes is now supported. Out of range axes as described above are now forbidden and raise an `AxisError`.

Returns ------- result : ndarray View of `a` with the number of dimensions increased.

See Also -------- squeeze : The inverse operation, removing singleton dimensions reshape : Insert, remove, and combine dimensions, and resize existing ones doc.indexing, atleast_1d, atleast_2d, atleast_3d

Examples -------- >>> x = np.array(1, 2) >>> x.shape (2,)

The following is equivalent to ``xnp.newaxis, :`` or ``xnp.newaxis``:

>>> y = np.expand_dims(x, axis=0) >>> y array([1, 2]) >>> y.shape (1, 2)

The following is equivalent to ``x:, np.newaxis``:

>>> y = np.expand_dims(x, axis=1) >>> y array([1], [2]) >>> y.shape (2, 1)

``axis`` may also be a tuple:

>>> y = np.expand_dims(x, axis=(0, 1)) >>> y array([[1, 2]])

>>> y = np.expand_dims(x, axis=(2, 0)) >>> y array([[1], [2]])

Note that some examples may use ``None`` instead of ``np.newaxis``. These are the same objects:

>>> np.newaxis is None True

fabs(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the absolute values element-wise.

This function returns the absolute values (positive magnitude) of the data in `x`. Complex values are not handled, use `absolute` to find the absolute values of complex data.

Parameters ---------- x : array_like The array of numbers for which the absolute values are required. If `x` is a scalar, the result `y` will also be a scalar. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or scalar The absolute values of `x`, the returned values are always floats. This is a scalar if `x` is a scalar.

See Also -------- absolute : Absolute values including `complex` types.

Examples -------- >>> np.fabs(-1) 1.0 >>> np.fabs(-1.2, 1.2) array( 1.2, 1.2)

Return input as an array with masked data replaced by a fill value.

If `a` is not a `MaskedArray`, `a` itself is returned. If `a` is a `MaskedArray` and `fill_value` is None, `fill_value` is set to ``a.fill_value``.

Parameters ---------- a : MaskedArray or array_like An input object. fill_value : array_like, optional. Can be scalar or non-scalar. If non-scalar, the resulting filled array should be broadcastable over input array. Default is None.

Returns ------- a : ndarray The filled array.

See Also -------- compressed

Examples -------- >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[1, 0, 0], ... [1, 0, 0], ... [0, 0, 0]) >>> x.filled() array([999999, 1, 2], [999999, 4, 5], [ 6, 7, 8]) >>> x.filled(fill_value=333) array([333, 1, 2], [333, 4, 5], [ 6, 7, 8]) >>> x.filled(fill_value=np.arange(3)) array([0, 1, 2], [0, 4, 5], [6, 7, 8])

Return input with invalid data masked and replaced by a fill value.

Invalid data means values of `nan`, `inf`, etc.

Parameters ---------- a : array_like Input array, a (subclass of) ndarray. mask : sequence, optional Mask. Must be convertible to an array of booleans with the same shape as `data`. True indicates a masked (i.e. invalid) data. copy : bool, optional Whether to use a copy of `a` (True) or to fix `a` in place (False). Default is True. fill_value : scalar, optional Value used for fixing invalid data. Default is None, in which case the ``a.fill_value`` is used.

Returns ------- b : MaskedArray The input array with invalid entries fixed.

Notes ----- A copy is performed by default.

Examples -------- >>> x = np.ma.array(1., -1, np.nan, np.inf, mask=1 + 0*3) >>> x masked_array(data=--, -1.0, nan, inf, mask= True, False, False, False, fill_value=1e+20) >>> np.ma.fix_invalid(x) masked_array(data=--, -1.0, --, --, mask= True, False, True, True, fill_value=1e+20)

>>> fixed = np.ma.fix_invalid(x) >>> fixed.data array( 1.e+00, -1.e+00, 1.e+20, 1.e+20) >>> x.data array( 1., -1., nan, inf)

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) >>> amask = 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)

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) >>> np.ma.flatnotmasked_edges(a) array(0, 9)

>>> mask = (a < 3) | (a > 8) | (a == 5) >>> amask = np.ma.masked >>> np.array(a~a.mask) array(3, 4, 6, 7, 8)

>>> np.ma.flatnotmasked_edges(a) array(3, 8)

>>> a: = np.ma.masked >>> print(np.ma.flatnotmasked_edges(a)) None

Returns a completely flattened version of the mask, where nested fields are collapsed.

Parameters ---------- mask : array_like Input array, which will be interpreted as booleans.

Returns ------- flattened_mask : ndarray of bools The flattened input.

Examples -------- >>> mask = np.array(0, 0, 1) >>> np.ma.flatten_mask(mask) array(False, False, True)

>>> mask = np.array((0, 0), (0, 1), dtype=('a', bool), ('b', bool)) >>> np.ma.flatten_mask(mask) array(False, False, False, True)

>>> mdtype = ('a', bool), ('b', [('ba', bool), ('bb', bool)]) >>> mask = np.array((0, (0, 0)), (0, (0, 1)), dtype=mdtype) >>> np.ma.flatten_mask(mask) array(False, False, False, False, False, True)

Flatten a structured array.

The data type of the output is chosen such that it can represent all of the (nested) fields.

Parameters ---------- a : structured array

Returns ------- output : masked array or ndarray A flattened masked array if the input is a masked array, otherwise a standard ndarray.

Examples -------- >>> ndtype = ('a', int), ('b', float) >>> a = np.array((1, 1), (2, 2), dtype=ndtype) >>> np.ma.flatten_structured_array(a) array([1., 1.], [2., 2.])

floor(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the floor of the input, element-wise.

The floor of the scalar `x` is the largest integer `i`, such that `i <= x`. It is often denoted as :math:`\lfloor x \rfloor`.

Parameters ---------- x : array_like Input data. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or scalar The floor of each element in `x`. This is a scalar if `x` is a scalar.

See Also -------- ceil, trunc, rint

Notes ----- Some spreadsheet programs calculate the 'floor-towards-zero', in other words ``floor(-2.5) == -2``. NumPy instead uses the definition of `floor` where `floor(-2.5) == -3`.

Examples -------- >>> a = np.array(-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0) >>> np.floor(a) array(-2., -2., -1., 0., 1., 1., 2.)

floor_divide(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the largest integer smaller or equal to the division of the inputs. It is equivalent to the Python ``//`` operator and pairs with the Python ``%`` (`remainder`), function so that ``a = a % b + b * (a // b)`` up to roundoff.

Parameters ---------- x1 : array_like Numerator. x2 : array_like Denominator. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray y = floor(`x1`/`x2`) This is a scalar if both `x1` and `x2` are scalars.

See Also -------- remainder : Remainder complementary to floor_divide. divmod : Simultaneous floor division and remainder. divide : Standard division. floor : Round a number to the nearest integer toward minus infinity. ceil : Round a number to the nearest integer toward infinity.

Examples -------- >>> np.floor_divide(7,3) 2 >>> np.floor_divide(1., 2., 3., 4., 2.5) array( 0., 0., 1., 1.)

fmod(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the element-wise remainder of division.

This is the NumPy implementation of the C library function fmod, the remainder has the same sign as the dividend `x1`. It is equivalent to the Matlab(TM) ``rem`` function and should not be confused with the Python modulus operator ``x1 % x2``.

Parameters ---------- x1 : array_like Dividend. x2 : array_like Divisor. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : array_like The remainder of the division of `x1` by `x2`. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- remainder : Equivalent to the Python ``%`` operator. divide

Notes ----- The result of the modulo operation for negative dividend and divisors is bound by conventions. For `fmod`, the sign of result is the sign of the dividend, while for `remainder` the sign of the result is the sign of the divisor. The `fmod` function is equivalent to the Matlab(TM) ``rem`` function.

Examples -------- >>> np.fmod(-3, -2, -1, 1, 2, 3, 2) array(-1, 0, -1, 1, 0, 1) >>> np.remainder(-3, -2, -1, 1, 2, 3, 2) array(1, 0, 1, 1, 0, 1)

>>> np.fmod(5, 3, 2, 2.) array( 1., 1.) >>> a = np.arange(-3, 3).reshape(3, 2) >>> a array([-3, -2], [-1, 0], [ 1, 2]) >>> np.fmod(a, 2,2) array([-1, 0], [-1, 0], [ 1, 0])

frombuffer(buffer, dtype=float, count=-1, offset=0)

Interpret a buffer as a 1-dimensional array.

Parameters ---------- buffer : buffer_like An object that exposes the buffer interface. dtype : data-type, optional Data-type of the returned array; default: float. count : int, optional Number of items to read. ``-1`` means all data in the buffer. offset : int, optional Start reading the buffer from this offset (in bytes); default: 0.

Notes ----- If the buffer has data that is not in machine byte-order, this should be specified as part of the data-type, e.g.::

>>> dt = np.dtype(int) >>> dt = dt.newbyteorder('>') >>> np.frombuffer(buf, dtype=dt) # doctest: +SKIP

The data of the resulting array will not be byteswapped, but will be interpreted correctly.

Examples -------- >>> s = b'hello world' >>> np.frombuffer(s, dtype='S1', count=5, offset=6) array(b'w', b'o', b'r', b'l', b'd', dtype='|S1')

>>> np.frombuffer(b'\x01\x02', dtype=np.uint8) array(1, 2, dtype=uint8) >>> np.frombuffer(b'\x01\x02\x03\x04\x05', dtype=np.uint8, count=3) array(1, 2, 3, dtype=uint8)

Build a masked array from a suitable flexible-type array.

The input array has to have a data-type with ``_data`` and ``_mask`` fields. This type of array is output by `MaskedArray.toflex`.

Parameters ---------- fxarray : ndarray The structured input array, containing ``_data`` and ``_mask`` fields. If present, other fields are discarded.

Returns ------- result : MaskedArray The constructed masked array.

See Also -------- MaskedArray.toflex : Build a flexible-type array from a masked array.

Examples -------- >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=0 + 1, 0 * 4) >>> rec = x.toflex() >>> rec array([(0, False), (1, True), (2, False)], [(3, True), (4, False), (5, True)], [(6, False), (7, True), (8, False)], dtype=('_data', '<i8'), ('_mask', '?')) >>> x2 = np.ma.fromflex(rec) >>> x2 masked_array( data=[0, --, 2], [--, 4, --], [6, --, 8], mask=[False, True, False], [ True, False, True], [False, True, False], fill_value=999999)

Extra fields can be present in the structured array but are discarded:

>>> dt = ('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4') >>> rec2 = np.zeros((2, 2), dtype=dt) >>> rec2 array([(0, False, 0.), (0, False, 0.)], [(0, False, 0.), (0, False, 0.)], dtype=('_data', '<i4'), ('_mask', '?'), ('field3', '<f4')) >>> y = np.ma.fromflex(rec2) >>> y masked_array( data=[0, 0], [0, 0], mask=[False, False], [False, False], fill_value=999999, dtype=int32)

fromfunction(function, shape, **kwargs)

Construct an array by executing a function over each coordinate.

The resulting array therefore has a value ``fn(x, y, z)`` at coordinate ``(x, y, z)``.

Parameters ---------- function : callable The function is called with N parameters, where N is the rank of `shape`. Each parameter represents the coordinates of the array varying along a specific axis. For example, if `shape` were ``(2, 2)``, then the parameters would be ``array([0, 0], [1, 1])`` and ``array([0, 1], [0, 1])`` shape : (N,) tuple of ints Shape of the output array, which also determines the shape of the coordinate arrays passed to `function`. dtype : data-type, optional Data-type of the coordinate arrays passed to `function`. By default, `dtype` is float.

Returns ------- fromfunction : any The result of the call to `function` is passed back directly. Therefore the shape of `fromfunction` is completely determined by `function`. If `function` returns a scalar value, the shape of `fromfunction` would not match the `shape` parameter.

See Also -------- indices, meshgrid

Notes ----- Keywords other than `dtype` are passed to `function`.

Examples -------- >>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int) array([ True, False, False], [False, True, False], [False, False, True])

>>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int) array([0, 1, 2], [1, 2, 3], [2, 3, 4])

Return the data of a masked array as an ndarray.

Return the data of `a` (if any) as an ndarray if `a` is a ``MaskedArray``, else return `a` as a ndarray or subclass (depending on `subok`) if not.

Parameters ---------- a : array_like Input ``MaskedArray``, alternatively a ndarray or a subclass thereof. subok : bool Whether to force the output to be a `pure` ndarray (False) or to return a subclass of ndarray if appropriate (True, default).

See Also -------- getmask : Return the mask of a masked array, or nomask. getmaskarray : Return the mask of a masked array, or full array of False.

Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_equal([1,2],[3,4], 2) >>> a masked_array( data=[1, --], [3, 4], mask=[False, True], [False, False], fill_value=2) >>> ma.getdata(a) array([1, 2], [3, 4])

Equivalently use the ``MaskedArray`` `data` attribute.

>>> a.data array([1, 2], [3, 4])

Return the mask of a masked array, or nomask.

Return the mask of `a` as an ndarray if `a` is a `MaskedArray` and the mask is not `nomask`, else return `nomask`. To guarantee a full array of booleans of the same shape as a, use `getmaskarray`.

Parameters ---------- a : array_like Input `MaskedArray` for which the mask is required.

See Also -------- getdata : Return the data of a masked array as an ndarray. getmaskarray : Return the mask of a masked array, or full array of False.

Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_equal([1,2],[3,4], 2) >>> a masked_array( data=[1, --], [3, 4], mask=[False, True], [False, False], fill_value=2) >>> ma.getmask(a) array([False, True], [False, False])

Equivalently use the `MaskedArray` `mask` attribute.

>>> a.mask array([False, True], [False, False])

Result when mask == `nomask`

>>> b = ma.masked_array([1,2],[3,4]) >>> b masked_array( data=[1, 2], [3, 4], mask=False, fill_value=999999) >>> ma.nomask False >>> ma.getmask(b) == ma.nomask True >>> b.mask == ma.nomask True

Return the mask of a masked array, or full boolean array of False.

Return the mask of `arr` as an ndarray if `arr` is a `MaskedArray` and the mask is not `nomask`, else return a full boolean array of False of the same shape as `arr`.

Parameters ---------- arr : array_like Input `MaskedArray` for which the mask is required.

See Also -------- getmask : Return the mask of a masked array, or nomask. getdata : Return the data of a masked array as an ndarray.

Examples -------- >>> import numpy.ma as ma >>> a = ma.masked_equal([1,2],[3,4], 2) >>> a masked_array( data=[1, --], [3, 4], mask=[False, True], [False, False], fill_value=2) >>> ma.getmaskarray(a) array([False, True], [False, False])

Result when mask == ``nomask``

>>> b = ma.masked_array([1,2],[3,4]) >>> b masked_array( data=[1, 2], [3, 4], mask=False, fill_value=999999) >>> ma.getmaskarray(b) array([False, False], [False, False])

greater(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the truth value of (x1 > x2) element-wise.

Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- greater_equal, less, less_equal, equal, not_equal

Examples -------- >>> np.greater(4,2,2,2) array( True, False)

If the inputs are ndarrays, then np.greater is equivalent to '>'.

>>> a = np.array(4,2) >>> b = np.array(2,2) >>> a > b array( True, False)

greater_equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the truth value of (x1 >= x2) element-wise.

Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : bool or ndarray of bool Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- greater, less, less_equal, equal, not_equal

Examples -------- >>> np.greater_equal(4, 2, 1, 2, 2, 2) array( True, True, False)

harden_mask(self)

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

hsplit( *args, **kwargs)

Split an array into multiple sub-arrays horizontally (column-wise).

Please refer to the `split` documentation. `hsplit` is equivalent to `split` with ``axis=1``, the array is always split along the second axis regardless of the array dimension.

See Also -------- split : Split an array into multiple sub-arrays of equal size.

Examples -------- >>> x = np.arange(16.0).reshape(4, 4) >>> x array([ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.], [12., 13., 14., 15.]) >>> np.hsplit(x, 2) array([[ 0., 1.], [ 4., 5.], [ 8., 9.], [12., 13.]]), array([[ 2., 3.], [ 6., 7.], [10., 11.], [14., 15.]]) >>> np.hsplit(x, np.array(3, 6)) array([[ 0., 1., 2.], [ 4., 5., 6.], [ 8., 9., 10.], [12., 13., 14.]]), array([[ 3.], [ 7.], [11.], [15.]]), array([], shape=(4, 0), dtype=float64)

With a higher dimensional array the split is still along the second axis.

>>> x = np.arange(8.0).reshape(2, 2, 2) >>> x array([[0., 1.], [2., 3.]], [[4., 5.], [6., 7.]]) >>> np.hsplit(x, 2) array([[[0., 1.]], [[4., 5.]]]), array([[[2., 3.]], [[6., 7.]]])

Notes ----- The function is applied to both the _data and the _mask, if any.

hstack( *args, **kwargs)

Stack arrays in sequence horizontally (column wise).

This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by `hsplit`.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions `concatenate`, `stack` and `block` provide more general stacking and concatenation operations.

Parameters ---------- tup : sequence of ndarrays The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.

Returns ------- stacked : ndarray The array formed by stacking the given arrays.

See Also -------- stack : Join a sequence of arrays along a new axis. vstack : Stack arrays in sequence vertically (row wise). dstack : Stack arrays in sequence depth wise (along third axis). concatenate : Join a sequence of arrays along an existing axis. hsplit : Split array along second axis. block : Assemble arrays from blocks.

Examples -------- >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.hstack((a,b)) array(1, 2, 3, 2, 3, 4) >>> a = np.array([1],[2],[3]) >>> b = np.array([2],[3],[4]) >>> np.hstack((a,b)) array([1, 2], [2, 3], [3, 4])

Notes ----- The function is applied to both the _data and the _mask, if any.

hypot(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Given the 'legs' of a right triangle, return its hypotenuse.

Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise. If `x1` or `x2` is scalar_like (i.e., unambiguously cast-able to a scalar type), it is broadcast for use with each element of the other argument. (See Examples)

Parameters ---------- x1, x2 : array_like Leg of the triangle(s). If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- z : ndarray The hypotenuse of the triangle(s). This is a scalar if both `x1` and `x2` are scalars.

Examples -------- >>> np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3))) array([ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.])

Example showing broadcast of scalar_like argument:

>>> np.hypot(3*np.ones((3, 3)), 4) array([ 5., 5., 5.], [ 5., 5., 5.], [ 5., 5., 5.])

identity(n, dtype=None)

Return the identity array.

The identity array is a square array with ones on the main diagonal.

Parameters ---------- n : int Number of rows (and columns) in `n` x `n` output. dtype : data-type, optional Data-type of the output. Defaults to ``float``.

Returns ------- out : ndarray `n` x `n` array with its main diagonal set to one, and all other elements 0.

Examples -------- >>> np.identity(3) array([1., 0., 0.], [0., 1., 0.], [0., 0., 1.])

ids(self)

Return the addresses of the data and mask areas.

Parameters ---------- None

Examples -------- >>> x = np.ma.array(1, 2, 3, mask=0, 1, 1) >>> x.ids() (166670640, 166659832) # may vary

If the array has no mask, the address of `nomask` is returned. This address is typically not close to the data in memory:

>>> x = np.ma.array(1, 2, 3) >>> x.ids() (166691080, 3083169284L) # may vary

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

Return an array representing the indices of a grid.

Compute an array where the subarrays contain index values 0, 1, ... varying only along the corresponding axis.

Parameters ---------- dimensions : sequence of ints The shape of the grid. dtype : dtype, optional Data type of the result. sparse : boolean, optional Return a sparse representation of the grid instead of a dense representation. Default is False.

.. versionadded:: 1.17

Returns ------- grid : one ndarray or tuple of ndarrays If sparse is False: Returns one array of grid indices, ``grid.shape = (len(dimensions),) + tuple(dimensions)``. If sparse is True: Returns a tuple of arrays, with ``gridi.shape = (1, ..., 1, dimensionsi, 1, ..., 1)`` with dimensionsi in the ith place

See Also -------- mgrid, ogrid, meshgrid

Notes ----- The output shape in the dense case is obtained by prepending the number of dimensions in front of the tuple of dimensions, i.e. if `dimensions` is a tuple ``(r0, ..., rN-1)`` of length ``N``, the output shape is ``(N, r0, ..., rN-1)``.

The subarrays ``gridk`` contains the N-D array of indices along the ``k-th`` axis. Explicitly::

gridk, i0, i1, ..., iN-1 = ik

Examples -------- >>> grid = np.indices((2, 3)) >>> grid.shape (2, 2, 3) >>> grid0 # row indices array([0, 0, 0], [1, 1, 1]) >>> grid1 # column indices array([0, 1, 2], [0, 1, 2])

The indices can be used as an index into an array.

>>> x = np.arange(20).reshape(5, 4) >>> row, col = np.indices((2, 3)) >>> xrow, col array([0, 1, 2], [4, 5, 6])

Note that it would be more straightforward in the above example to extract the required elements directly with ``x:2, :3``.

If sparse is set to true, the grid will be returned in a sparse representation.

>>> i, j = np.indices((2, 3), sparse=True) >>> i.shape (2, 1) >>> j.shape (1, 3) >>> i # row indices array([0], [1]) >>> j # column indices array([0, 1, 2])

inner(a, b)

Inner product of two arrays.

Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.

Parameters ---------- a, b : array_like If `a` and `b` are nonscalar, their last dimensions must match.

Returns ------- out : ndarray `out.shape = a.shape:-1 + b.shape:-1`

Raises ------ ValueError If the last dimension of `a` and `b` has different size.

See Also -------- tensordot : Sum products over arbitrary axes. dot : Generalised matrix product, using second last dimension of `b`. einsum : Einstein summation convention.

Notes ----- Masked values are replaced by 0.

For vectors (1-D arrays) it computes the ordinary inner-product::

np.inner(a, b) = sum(a:*b:)

More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::

np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))

or explicitly::

np.inner(a, b)i0,...,ir-1,j0,...,js-1 = sum(ai0,...,ir-1,:*bj0,...,js-1,:)

In addition `a` or `b` may be scalars, in which case::

np.inner(a,b) = a*b

Examples -------- Ordinary inner product for vectors:

>>> a = np.array(1,2,3) >>> b = np.array(0,1,0) >>> np.inner(a, b) 2

A multidimensional example:

>>> a = np.arange(24).reshape((2,3,4)) >>> b = np.arange(4) >>> np.inner(a, b) array([ 14, 38, 62], [ 86, 110, 134])

An example where `b` is a scalar:

>>> np.inner(np.eye(2), 7) array([7., 0.], [0., 7.])

inner(a, b)

Inner product of two arrays.

Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.

Parameters ---------- a, b : array_like If `a` and `b` are nonscalar, their last dimensions must match.

Returns ------- out : ndarray `out.shape = a.shape:-1 + b.shape:-1`

Raises ------ ValueError If the last dimension of `a` and `b` has different size.

See Also -------- tensordot : Sum products over arbitrary axes. dot : Generalised matrix product, using second last dimension of `b`. einsum : Einstein summation convention.

Notes ----- Masked values are replaced by 0.

For vectors (1-D arrays) it computes the ordinary inner-product::

np.inner(a, b) = sum(a:*b:)

More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::

np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))

or explicitly::

np.inner(a, b)i0,...,ir-1,j0,...,js-1 = sum(ai0,...,ir-1,:*bj0,...,js-1,:)

In addition `a` or `b` may be scalars, in which case::

np.inner(a,b) = a*b

Examples -------- Ordinary inner product for vectors:

>>> a = np.array(1,2,3) >>> b = np.array(0,1,0) >>> np.inner(a, b) 2

A multidimensional example:

>>> a = np.arange(24).reshape((2,3,4)) >>> b = np.arange(4) >>> np.inner(a, b) array([ 14, 38, 62], [ 86, 110, 134])

An example where `b` is a scalar:

>>> np.inner(np.eye(2), 7) array([7., 0.], [0., 7.])

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 = np.ma.array(1, 3, 3, 3, mask=0, 0, 0, 1) >>> y = np.ma.array(3, 1, 1, 1, mask=0, 0, 0, 1) >>> np.ma.intersect1d(x, y) masked_array(data=1, 3, --, mask=False, False, True, fill_value=999999)

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

Test whether input is an instance of MaskedArray.

This function returns True if `x` is an instance of MaskedArray and returns False otherwise. Any object is accepted as input.

Parameters ---------- x : object Object to test.

Returns ------- result : bool True if `x` is a MaskedArray.

See Also -------- isMA : Alias to isMaskedArray. isarray : Alias to isMaskedArray.

Examples -------- >>> import numpy.ma as ma >>> a = np.eye(3, 3) >>> a array([ 1., 0., 0.], [ 0., 1., 0.], [ 0., 0., 1.]) >>> m = ma.masked_values(a, 0) >>> m masked_array( data=[1.0, --, --], [--, 1.0, --], [--, --, 1.0], mask=[False, True, True], [ True, False, True], [ True, True, False], fill_value=0.0) >>> ma.isMaskedArray(a) False >>> ma.isMaskedArray(m) True >>> ma.isMaskedArray(0, 1, 2) False

Return 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=0) >>> ma.is_mask(m) False >>> ma.is_mask(m.mask) True

Input must be an ndarray (or have similar attributes) for it to be considered a valid mask.

>>> m = False, True, False >>> ma.is_mask(m) False >>> m = np.array(False, True, False) >>> m array(False, True, False) >>> ma.is_mask(m) True

Arrays with complex dtypes don't return True.

>>> dtype = np.dtype('names':['monty', 'pithon'], ... 'formats':[bool, bool]) >>> dtype dtype(('monty', '|b1'), ('pithon', '|b1')) >>> m = np.array((True, False), (False, True), (True, False), ... dtype=dtype) >>> m array(( True, False), (False, True), ( True, False), dtype=('monty', '?'), ('pithon', '?')) >>> ma.is_mask(m) False

Determine whether input has masked values.

Accepts any object as input, but always returns False unless the input is a MaskedArray containing masked values.

Parameters ---------- x : array_like Array to check for masked values.

Returns ------- result : bool True if `x` is a MaskedArray with masked values, False otherwise.

Examples -------- >>> import numpy.ma as ma >>> x = ma.masked_equal(0, 1, 0, 2, 3, 0) >>> x masked_array(data=--, 1, --, 2, 3, mask= True, False, True, False, False, fill_value=0) >>> ma.is_masked(x) True >>> x = ma.masked_equal(0, 1, 0, 2, 3, 42) >>> x masked_array(data=0, 1, 0, 2, 3, mask=False, fill_value=42) >>> ma.is_masked(x) False

Always returns False if `x` isn't a MaskedArray.

>>> x = False, True, False >>> ma.is_masked(x) False >>> x = 'a string' >>> ma.is_masked(x) False

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

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

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

less(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the truth value of (x1 < x2) element-wise.

Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- greater, less_equal, greater_equal, equal, not_equal

Examples -------- >>> np.less(1, 2, 2, 2) array( True, False)

less_equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the truth value of (x1 =< x2) element-wise.

Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- greater, less, greater_equal, equal, not_equal

Examples -------- >>> np.less_equal(4, 2, 1, 2, 2, 2) array(False, True, True)

log(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Natural logarithm, element-wise.

The natural logarithm `log` is the inverse of the exponential function, so that `log(exp(x)) = x`. The natural logarithm is logarithm in base `e`.

Parameters ---------- x : array_like Input value. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The natural logarithm of `x`, element-wise. This is a scalar if `x` is a scalar.

See Also -------- log10, log2, log1p, emath.log

Notes ----- Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `exp(z) = x`. The convention is to return the `z` whose imaginary part lies in `-pi, pi`.

For real-valued input data types, `log` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `log` is a complex analytical function that has a branch cut `-inf, 0` and is continuous from above on it. `log` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

References ---------- .. 1 M. Abramowitz and I.A. Stegun, 'Handbook of Mathematical Functions', 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/ .. 2 Wikipedia, 'Logarithm'. https://en.wikipedia.org/wiki/Logarithm

Examples -------- >>> np.log(1, np.e, np.e**2, 0) array( 0., 1., 2., -Inf)

log10(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return the base 10 logarithm of the input array, element-wise.

Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The logarithm to the base 10 of `x`, element-wise. NaNs are returned where x is negative. This is a scalar if `x` is a scalar.

See Also -------- emath.log10

Notes ----- Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `10**z = x`. The convention is to return the `z` whose imaginary part lies in `-pi, pi`.

For real-valued input data types, `log10` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `log10` is a complex analytical function that has a branch cut `-inf, 0` and is continuous from above on it. `log10` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

References ---------- .. 1 M. Abramowitz and I.A. Stegun, 'Handbook of Mathematical Functions', 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/ .. 2 Wikipedia, 'Logarithm'. https://en.wikipedia.org/wiki/Logarithm

Examples -------- >>> np.log10(1e-15, -3.) array(-15., nan)

log2(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Base-2 logarithm of `x`.

Parameters ---------- x : array_like Input values. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray Base-2 logarithm of `x`. This is a scalar if `x` is a scalar.

See Also -------- log, log10, log1p, emath.log2

Notes ----- .. versionadded:: 1.3.0

Logarithm is a multivalued function: for each `x` there is an infinite number of `z` such that `2**z = x`. The convention is to return the `z` whose imaginary part lies in `-pi, pi`.

For real-valued input data types, `log2` always returns real output. For each value that cannot be expressed as a real number or infinity, it yields ``nan`` and sets the `invalid` floating point error flag.

For complex-valued input, `log2` is a complex analytical function that has a branch cut `-inf, 0` and is continuous from above on it. `log2` handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

Examples -------- >>> x = np.array(0, 1, 2, 2**4) >>> np.log2(x) array(-Inf, 0., 1., 4.)

>>> xi = np.array(0+1.j, 1, 2+0.j, 4.j) >>> np.log2(xi) array( 0.+2.26618007j, 0.+0.j , 1.+0.j , 2.+2.26618007j)

logical_and(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the truth value of x1 AND x2 element-wise.

Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or bool Boolean result of the logical AND operation applied to the elements of `x1` and `x2`; the shape is determined by broadcasting. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- logical_or, logical_not, logical_xor bitwise_and

Examples -------- >>> np.logical_and(True, False) False >>> np.logical_and(True, False, False, False) array(False, False)

>>> x = np.arange(5) >>> np.logical_and(x>1, x<4) array(False, False, True, True, False)

logical_not(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the truth value of NOT x element-wise.

Parameters ---------- x : array_like Logical NOT is applied to the elements of `x`. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : bool or ndarray of bool Boolean result with the same shape as `x` of the NOT operation on elements of `x`. This is a scalar if `x` is a scalar.

See Also -------- logical_and, logical_or, logical_xor

Examples -------- >>> np.logical_not(3) False >>> np.logical_not(True, False, 0, 1) array(False, True, True, False)

>>> x = np.arange(5) >>> np.logical_not(x<3) array(False, False, False, True, True)

logical_or(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the truth value of x1 OR x2 element-wise.

Parameters ---------- x1, x2 : array_like Logical OR is applied to the elements of `x1` and `x2`. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or bool Boolean result of the logical OR operation applied to the elements of `x1` and `x2`; the shape is determined by broadcasting. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- logical_and, logical_not, logical_xor bitwise_or

Examples -------- >>> np.logical_or(True, False) True >>> np.logical_or(True, False, False, False) array( True, False)

>>> x = np.arange(5) >>> np.logical_or(x < 1, x > 3) array( True, False, False, False, True)

logical_xor(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Compute the truth value of x1 XOR x2, element-wise.

Parameters ---------- x1, x2 : array_like Logical XOR is applied to the elements of `x1` and `x2`. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : bool or ndarray of bool Boolean result of the logical XOR operation applied to the elements of `x1` and `x2`; the shape is determined by broadcasting. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- logical_and, logical_or, logical_not, bitwise_xor

Examples -------- >>> np.logical_xor(True, False) True >>> np.logical_xor(True, True, False, False, True, False, True, False) array(False, True, True, False)

>>> x = np.arange(5) >>> np.logical_xor(x < 1, x > 3) array( True, False, False, False, True)

Simple example showing support of broadcasting

>>> np.logical_xor(0, np.eye(2)) array([ True, False], [False, True])

Create a boolean mask from an array.

Return `m` as a boolean mask, creating a copy if necessary or requested. The function can accept any sequence that is convertible to integers, or ``nomask``. Does not require that contents must be 0s and 1s, values of 0 are interpreted as False, everything else as True.

Parameters ---------- m : array_like Potential mask. copy : bool, optional Whether to return a copy of `m` (True) or `m` itself (False). shrink : bool, optional Whether to shrink `m` to ``nomask`` if all its values are False. dtype : dtype, optional Data-type of the output mask. By default, the output mask has a dtype of MaskType (bool). If the dtype is flexible, each field has a boolean dtype. This is ignored when `m` is ``nomask``, in which case ``nomask`` is always returned.

Returns ------- result : ndarray A boolean mask derived from `m`.

Examples -------- >>> import numpy.ma as ma >>> m = True, False, True, True >>> ma.make_mask(m) array( True, False, True, True) >>> m = 1, 0, 1, 1 >>> ma.make_mask(m) array( True, False, True, True) >>> m = 1, 0, 2, -3 >>> ma.make_mask(m) array( True, False, True, True)

Effect of the `shrink` parameter.

>>> m = np.zeros(4) >>> m array(0., 0., 0., 0.) >>> ma.make_mask(m) False >>> ma.make_mask(m, shrink=False) array(False, False, False, False)

Using a flexible `dtype`.

>>> m = 1, 0, 1, 1 >>> n = 0, 1, 0, 0 >>> arr = >>> for man, mouse in zip(m, n): ... arr.append((man, mouse)) >>> arr (1, 0), (0, 1), (1, 0), (1, 0) >>> dtype = np.dtype('names':['man', 'mouse'], ... 'formats':[np.int64, np.int64]) >>> arr = np.array(arr, dtype=dtype) >>> arr array((1, 0), (0, 1), (1, 0), (1, 0), dtype=('man', '<i8'), ('mouse', '<i8')) >>> ma.make_mask(arr, dtype=dtype) array((True, False), (False, True), (True, False), (True, False), dtype=('man', '|b1'), ('mouse', '|b1'))

Construct a dtype description list from a given dtype.

Returns a new dtype object, with the type of all fields in `ndtype` to a boolean type. Field names are not altered.

Parameters ---------- ndtype : dtype The dtype to convert.

Returns ------- result : dtype A dtype that looks like `ndtype`, the type of all fields is boolean.

Examples -------- >>> import numpy.ma as ma >>> dtype = np.dtype('names':['foo', 'bar'], ... 'formats':[np.float32, np.int64]) >>> dtype dtype(('foo', '<f4'), ('bar', '<i8')) >>> ma.make_mask_descr(dtype) dtype(('foo', '|b1'), ('bar', '|b1')) >>> ma.make_mask_descr(np.float32) dtype('bool')

Return a boolean mask of the given shape, filled with False.

This function returns a boolean ndarray with all entries False, that can be used in common mask manipulations. If a complex dtype is specified, the type of each field is converted to a boolean type.

Parameters ---------- newshape : tuple A tuple indicating the shape of the mask. dtype : None, dtype, optional If None, use a MaskType instance. Otherwise, use a new datatype with the same fields as `dtype`, converted to boolean types.

Returns ------- result : ndarray An ndarray of appropriate shape and dtype, filled with False.

See Also -------- make_mask : Create a boolean mask from an array. make_mask_descr : Construct a dtype description list from a given dtype.

Examples -------- >>> import numpy.ma as ma >>> ma.make_mask_none((3,)) array(False, False, False)

Defining a more complex dtype.

>>> dtype = np.dtype('names':['foo', 'bar'], ... 'formats':[np.float32, np.int64]) >>> dtype dtype(('foo', '<f4'), ('bar', '<i8')) >>> ma.make_mask_none((3,), dtype=dtype) array((False, False), (False, False), (False, False), dtype=('foo', '|b1'), ('bar', '|b1'))

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) >>> a1, 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=1) >>> 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=1)

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)

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) >>> a1, 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=1) >>> ma.mask_rowcols(a) masked_array( data=[0, --, 0], [--, --, --], [0, --, 0], mask=[False, True, False], [ True, True, True], [False, True, False], fill_value=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) >>> a1, 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=1)

>>> 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=1)

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, dtype=float64)

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')

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, dtype=float32)

The dtype of the masked array matches the dtype of `arr`.

>>> arr.dtype dtype('float32') >>> ma.masked_all_like(arr).dtype dtype('float32')

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=2)

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)

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)

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)

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) >>> a2 = np.NaN >>> a3 = 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)

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)

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)

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)

Mask the array `x` where the data are exactly equal to value.

This function is similar to `masked_values`, but only suitable for object arrays: for floating point, use `masked_values` instead.

Parameters ---------- x : array_like Array to mask value : object Comparison value copy : True, False, optional Whether to return a copy of `x`. shrink : True, False, optional Whether to collapse a mask full of False to nomask

Returns ------- result : MaskedArray The result of masking `x` where equal to `value`.

See Also -------- masked_where : Mask where a condition is met. masked_equal : Mask where equal to a given value (integers). masked_values : Mask using floating point equality.

Examples -------- >>> import numpy.ma as ma >>> food = np.array('green_eggs', 'ham', dtype=object) >>> # don't eat spoiled food >>> eat = ma.masked_object(food, 'green_eggs') >>> eat masked_array(data=--, 'ham', mask= True, False, fill_value='green_eggs', dtype=object) >>> # plain ol` ham is boring >>> fresh_food = np.array('cheese', 'ham', 'pineapple', dtype=object) >>> eat = ma.masked_object(fresh_food, 'green_eggs') >>> eat masked_array(data='cheese', 'ham', 'pineapple', mask=False, fill_value='green_eggs', dtype=object)

Note that `mask` is set to ``nomask`` if possible.

>>> eat masked_array(data='cheese', 'ham', 'pineapple', mask=False, fill_value='green_eggs', dtype=object)

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)

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=2)

Mask an array where a condition is met.

Return `a` as an array masked where `condition` is True. Any masked values of `a` or `condition` are also masked in the output.

Parameters ---------- condition : array_like Masking condition. When `condition` tests floating point values for equality, consider using ``masked_values`` instead. a : array_like Array to mask. copy : bool If True (default) make a copy of `a` in the result. If False modify `a` in place and return a view.

Returns ------- result : MaskedArray The result of masking `a` where `condition` is True.

See Also -------- masked_values : Mask using floating point equality. masked_equal : Mask where equal to a given value. masked_not_equal : Mask where `not` equal to a given value. masked_less_equal : Mask where less than or equal to a given value. masked_greater_equal : Mask where greater than or equal to a given value. masked_less : Mask where less than a given value. masked_greater : Mask where greater than a given value. masked_inside : Mask inside a given interval. masked_outside : Mask outside a given interval. masked_invalid : Mask invalid values (NaNs or infs).

Examples -------- >>> import numpy.ma as ma >>> a = np.arange(4) >>> a array(0, 1, 2, 3) >>> ma.masked_where(a <= 2, a) masked_array(data=--, --, --, 3, mask= True, True, True, False, fill_value=999999)

Mask array `b` conditional on `a`.

>>> b = 'a', 'b', 'c', 'd' >>> ma.masked_where(a == 2, b) masked_array(data='a', 'b', --, 'd', mask=False, False, True, False, fill_value='N/A', dtype='<U1')

Effect of the `copy` argument.

>>> c = ma.masked_where(a <= 2, a) >>> c masked_array(data=--, --, --, 3, mask= True, True, True, False, fill_value=999999) >>> c0 = 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) >>> c0 = 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)

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 :

ar}, optional
    Value used to fill in the masked values.
    If None, use the output of maximum_fill_value().
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the array.

Returns
-------
amax : array_like
    New array holding the result.
    If ``out`` was specified, ``out`` is returned.

See Also
--------
maximum_fill_value
    Returns the maximum filling value for a given datatype.

maximum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Element-wise maximum of array elements.

Compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated.

Parameters ---------- x1, x2 : array_like The arrays holding the elements to be compared. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or scalar The maximum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- minimum : Element-wise minimum of two arrays, propagates NaNs. fmax : Element-wise maximum of two arrays, ignores NaNs. amax : The maximum value of an array along a given axis, propagates NaNs. nanmax : The maximum value of an array along a given axis, ignores NaNs.

fmin, amin, nanmin

Notes ----- The maximum is equivalent to ``np.where(x1 >= x2, x1, x2)`` when neither x1 nor x2 are nans, but it is faster and does proper broadcasting.

Examples -------- >>> np.maximum(2, 3, 4, 1, 5, 2) array(2, 5, 4)

>>> np.maximum(np.eye(2), 0.5, 2) # broadcasting array([ 1. , 2. ], [ 0.5, 2. ])

>>> np.maximum(np.nan, 0, np.nan, 0, np.nan, np.nan) array(nan, nan, nan) >>> np.maximum(np.Inf, 1) 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

mean(self, axis=None, dtype=None, out=None, keepdims=<no value>)

Returns the average of the array elements along given axis.

Masked entries are ignored, and result elements which are not finite will be masked.

Refer to `numpy.mean` for full documentation.

See Also -------- numpy.ndarray.mean : corresponding function for ndarrays numpy.mean : Equivalent function numpy.ma.average: Weighted average.

Examples -------- >>> a = np.ma.array(1,2,3, mask=False, False, True) >>> a masked_array(data=1, 2, --, mask=False, False, True, fill_value=999999) >>> a.mean() 1.5

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=2.0, 5.0, mask=False, False, fill_value=1e+20)

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 :

ar}, optional
    Value used to fill in the masked values.
    If None, use the output of `minimum_fill_value`.
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the array.

Returns
-------
amin : array_like
    New array holding the result.
    If ``out`` was specified, ``out`` is returned.

See Also
--------
minimum_fill_value
    Returns the minimum filling value for a given datatype.

minimum(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Element-wise minimum of array elements.

Compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated.

Parameters ---------- x1, x2 : array_like The arrays holding the elements to be compared. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or scalar The minimum of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- maximum : Element-wise maximum of two arrays, propagates NaNs. fmin : Element-wise minimum of two arrays, ignores NaNs. amin : The minimum value of an array along a given axis, propagates NaNs. nanmin : The minimum value of an array along a given axis, ignores NaNs.

fmax, amax, nanmax

Notes ----- The minimum is equivalent to ``np.where(x1 <= x2, x1, x2)`` when neither x1 nor x2 are NaNs, but it is faster and does proper broadcasting.

Examples -------- >>> np.minimum(2, 3, 4, 1, 5, 2) array(1, 3, 2)

>>> np.minimum(np.eye(2), 0.5, 2) # broadcasting array([ 0.5, 0. ], [ 0. , 1. ])

>>> np.minimum(np.nan, 0, np.nan,0, np.nan, np.nan) array(nan, nan, nan) >>> np.minimum(-np.Inf, 1) -inf

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

remainder(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return element-wise remainder of division.

Computes the remainder complementary to the `floor_divide` function. It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor `x2`. The MATLAB function equivalent to ``np.remainder`` is ``mod``.

.. warning::

This should not be confused with:

* Python 3.7's `math.remainder` and C's ``remainder``, which computes the IEEE remainder, which are the complement to ``round(x1 / x2)``. * The MATLAB ``rem`` function and or the C ``%`` operator which is the complement to ``int(x1 / x2)``.

Parameters ---------- x1 : array_like Dividend array. x2 : array_like Divisor array. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The element-wise remainder of the quotient ``floor_divide(x1, x2)``. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- floor_divide : Equivalent of Python ``//`` operator. divmod : Simultaneous floor division and remainder. fmod : Equivalent of the MATLAB ``rem`` function. divide, floor

Notes ----- Returns 0 when `x2` is 0 and both `x1` and `x2` are (arrays of) integers. ``mod`` is an alias of ``remainder``.

Examples -------- >>> np.remainder(4, 7, 2, 3) array(0, 1) >>> np.remainder(np.arange(7), 5) array(0, 1, 2, 3, 4, 0, 1)

multiply(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Multiply arguments element-wise.

Parameters ---------- x1, x2 : array_like Input arrays to be multiplied. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The product of `x1` and `x2`, element-wise. This is a scalar if both `x1` and `x2` are scalars.

Notes ----- Equivalent to `x1` * `x2` in terms of array broadcasting.

Examples -------- >>> np.multiply(2.0, 4.0) 8.0

>>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> np.multiply(x1, x2) array([ 0., 1., 4.], [ 0., 4., 10.], [ 0., 7., 16.])

Return the number of dimensions of an array.

Parameters ---------- a : array_like Input array. If it is not already an ndarray, a conversion is attempted.

Returns ------- number_of_dimensions : int The number of dimensions in `a`. Scalars are zero-dimensional.

See Also -------- ndarray.ndim : equivalent method shape : dimensions of array ndarray.shape : dimensions of array

Examples -------- >>> np.ndim([1,2,3],[4,5,6]) 2 >>> np.ndim(np.array([1,2,3],[4,5,6])) 2 >>> np.ndim(1) 0

negative(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Numerical negative, element-wise.

Parameters ---------- x : array_like or scalar Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray or scalar Returned array or scalar: `y = -x`. This is a scalar if `x` is a scalar.

Examples -------- >>> np.negative(1.,-1.) array(-1., 1.)

nonzero(self)

Return the indices of unmasked elements that are not zero.

Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with::

aa.nonzero()

To group the indices by element, rather than dimension, use instead::

np.transpose(a.nonzero())

The result of this is always a 2d array, with a row for each non-zero element.

Parameters ---------- None

Returns ------- tuple_of_arrays : tuple Indices of elements that are non-zero.

See Also -------- numpy.nonzero : Function operating on ndarrays. flatnonzero : Return indices that are non-zero in the flattened version of the input array. numpy.ndarray.nonzero : Equivalent ndarray method. count_nonzero : Counts the number of non-zero elements in the input array.

Examples -------- >>> import numpy.ma as ma >>> x = ma.array(np.eye(3)) >>> x masked_array( data=[1., 0., 0.], [0., 1., 0.], [0., 0., 1.], mask=False, fill_value=1e+20) >>> x.nonzero() (array(0, 1, 2), array(0, 1, 2))

Masked elements are ignored.

>>> x1, 1 = ma.masked >>> x masked_array( data=[1.0, 0.0, 0.0], [0.0, --, 0.0], [0.0, 0.0, 1.0], mask=[False, False, False], [False, True, False], [False, False, False], fill_value=1e+20) >>> x.nonzero() (array(0, 2), array(0, 2))

Indices can also be grouped by element.

>>> np.transpose(x.nonzero()) array([0, 0], [2, 2])

A common use for ``nonzero`` is to find the indices of an array, where a condition is True. Given an array `a`, the condition `a` > 3 is a boolean array and since False is interpreted as 0, ma.nonzero(a > 3) yields the indices of the `a` where the condition is true.

>>> a = ma.array([1,2,3],[4,5,6],[7,8,9]) >>> a > 3 masked_array( data=[False, False, False], [ True, True, True], [ True, True, True], mask=False, fill_value=True) >>> ma.nonzero(a > 3) (array(1, 1, 1, 2, 2, 2), array(0, 1, 2, 0, 1, 2))

The ``nonzero`` method of the condition array can also be called.

>>> (a > 3).nonzero() (array(1, 1, 1, 2, 2, 2), array(0, 1, 2, 0, 1, 2))

not_equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return (x1 != x2) element-wise.

Parameters ---------- x1, x2 : array_like Input arrays. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- out : ndarray or scalar Output array, element-wise comparison of `x1` and `x2`. Typically of type bool, unless ``dtype=object`` is passed. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- equal, greater, greater_equal, less, less_equal

Examples -------- >>> np.not_equal(1.,2., 1., 3.) array(False, True) >>> np.not_equal(1, 2, [1, 3],[1, 4]) array([False, True], [False, True])

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) >>> mask1:, :-1 = 1; mask0, 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)], [], [slice(0, 1, None)], [slice(0, 3, None)]

>>> np.ma.notmasked_contiguous(ma, axis=1) [slice(0, 1, None), slice(2, 4, None)], [slice(3, 4, None)], [slice(0, 1, None), slice(3, 4, 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) >>> m1:, 1: = 1

>>> am = np.ma.array(a, mask=m) >>> np.array(am~am.mask) array(0, 1, 2, 3, 6)

>>> np.ma.notmasked_edges(am) array(0, 6)

ones(shape, dtype=None, order='C')

Return a new array of given shape and type, filled with ones.

Parameters ---------- shape : int or sequence of ints Shape of the new array, e.g., ``(2, 3)`` or ``2``. dtype : data-type, optional The desired data-type for the array, e.g., `numpy.int8`. Default is `numpy.float64`. order : 'C', 'F', optional, default: C Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.

Returns ------- out : ndarray Array of ones with the given shape, dtype, and order.

See Also -------- ones_like : Return an array of ones with shape and type of input. empty : Return a new uninitialized array. zeros : Return a new array setting values to zero. full : Return a new array of given shape filled with value.

Examples -------- >>> np.ones(5) array(1., 1., 1., 1., 1.)

>>> np.ones((5,), dtype=int) array(1, 1, 1, 1, 1)

>>> np.ones((2, 1)) array([1.], [1.])

>>> s = (2,2) >>> np.ones(s) array([1., 1.], [1., 1.])

Compute the outer product of two vectors.

Given two vectors, ``a = a0, a1, ..., aM`` and ``b = b0, b1, ..., bN``, the outer product 1_ is::

[a0*b0 a0*b1 ... a0*bN ] [a1*b0 . [ ... . [aM*b0 aM*bN ]] Parameters ---------- a : (M,) array_like First input vector. Input is flattened if not already 1-dimensional. b : (N,) array_like Second input vector. Input is flattened if not already 1-dimensional. out : (M, N) ndarray, optional A location where the result is stored .. versionadded:: 1.9.0 Returns ------- out : (M, N) ndarray ``out[i, j] = a[i] * b[j]`` See also -------- inner einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent. ufunc.outer : A generalization to N dimensions and other operations. ``np.multiply.outer(a.ravel(), b.ravel())`` is the equivalent. References ---------- .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd ed., Baltimore, MD, Johns Hopkins University Press, 1996, pg. 8. Examples -------- Make a ( *very* coarse) grid for computing a Mandelbrot set: >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5)) >>> rl array([[-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.]]) >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,))) >>> im array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j], [0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j], [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j], [0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]]) >>> grid = rl + im >>> grid array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j], [-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j], [-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j], [-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j], [-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]]) An example using a 'vector' of letters: >>> x = np.array(['a', 'b', 'c'], dtype=object) >>> np.outer(x, [1, 2, 3]) array([['a', 'aa', 'aaa'], ['b', 'bb', 'bbb'], ['c', 'cc', 'ccc']], dtype=object) Notes ----- Masked values are replaced by 0.

Compute the outer product of two vectors.

Given two vectors, ``a = a0, a1, ..., aM`` and ``b = b0, b1, ..., bN``, the outer product 1_ is::

[a0*b0 a0*b1 ... a0*bN ] [a1*b0 . [ ... . [aM*b0 aM*bN ]] Parameters ---------- a : (M,) array_like First input vector. Input is flattened if not already 1-dimensional. b : (N,) array_like Second input vector. Input is flattened if not already 1-dimensional. out : (M, N) ndarray, optional A location where the result is stored .. versionadded:: 1.9.0 Returns ------- out : (M, N) ndarray ``out[i, j] = a[i] * b[j]`` See also -------- inner einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent. ufunc.outer : A generalization to N dimensions and other operations. ``np.multiply.outer(a.ravel(), b.ravel())`` is the equivalent. References ---------- .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd ed., Baltimore, MD, Johns Hopkins University Press, 1996, pg. 8. Examples -------- Make a ( *very* coarse) grid for computing a Mandelbrot set: >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5)) >>> rl array([[-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.]]) >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,))) >>> im array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j], [0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j], [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j], [0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]]) >>> grid = rl + im >>> grid array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j], [-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j], [-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j], [-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j], [-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]]) An example using a 'vector' of letters: >>> x = np.array(['a', 'b', 'c'], dtype=object) >>> np.outer(x, [1, 2, 3]) array([['a', 'aa', 'aaa'], ['b', 'bb', 'bbb'], ['c', 'cc', 'ccc']], dtype=object) Notes ----- Masked values are replaced by 0.

Least squares polynomial fit.

Fit a polynomial ``p(x) = p0 * x**deg + ... + pdeg`` of degree `deg` to points `(x, y)`. Returns a vector of coefficients `p` that minimises the squared error in the order `deg`, `deg-1`, ... `0`.

The `Polynomial.fit <numpy.polynomial.polynomial.Polynomial.fit>` class method is recommended for new code as it is more stable numerically. See the documentation of the method for more information.

Parameters ---------- x : array_like, shape (M,) x-coordinates of the M sample points ``(xi, yi)``. y : array_like, shape (M,) or (M, K) y-coordinates of the sample points. Several data sets of sample points sharing the same x-coordinates can be fitted at once by passing in a 2D-array that contains one dataset per column. deg : int Degree of the fitting polynomial rcond : float, optional Relative condition number of the fit. Singular values smaller than this relative to the largest singular value will be ignored. The default value is len(x)*eps, where eps is the relative precision of the float type, about 2e-16 in most cases. full : bool, optional Switch determining nature of return value. When it is False (the default) just the coefficients are returned, when True diagnostic information from the singular value decomposition is also returned. w : array_like, shape (M,), optional Weights to apply to the y-coordinates of the sample points. For gaussian uncertainties, use 1/sigma (not 1/sigma**2). cov : bool or str, optional If given and not `False`, return not just the estimate but also its covariance matrix. By default, the covariance are scaled by chi2/sqrt(N-dof), i.e., the weights are presumed to be unreliable except in a relative sense and everything is scaled such that the reduced chi2 is unity. This scaling is omitted if ``cov='unscaled'``, as is relevant for the case that the weights are 1/sigma**2, with sigma known to be a reliable estimate of the uncertainty.

Returns ------- p : ndarray, shape (deg + 1,) or (deg + 1, K) Polynomial coefficients, highest power first. If `y` was 2-D, the coefficients for `k`-th data set are in ``p:,k``.

residuals, rank, singular_values, rcond Present only if `full` = True. Residuals is sum of squared residuals of the least-squares fit, the effective rank of the scaled Vandermonde coefficient matrix, its singular values, and the specified value of `rcond`. For more details, see `linalg.lstsq`.

V : ndarray, shape (M,M) or (M,M,K) Present only if `full` = False and `cov`=True. The covariance matrix of the polynomial coefficient estimates. The diagonal of this matrix are the variance estimates for each coefficient. If y is a 2-D array, then the covariance matrix for the `k`-th data set are in ``V:,:,k``

Warns ----- RankWarning The rank of the coefficient matrix in the least-squares fit is deficient. The warning is only raised if `full` = False.

The warnings can be turned off by

>>> import warnings >>> warnings.simplefilter('ignore', np.RankWarning)

See Also -------- polyval : Compute polynomial values. linalg.lstsq : Computes a least-squares fit. scipy.interpolate.UnivariateSpline : Computes spline fits.

Notes -----

Any masked values in x is propagated in y, and vice-versa.

The solution minimizes the squared error

.. math :: E = \sum_j=0^k |p(x_j) - y_j|^2

in the equations::

x0**n * p0 + ... + x0 * pn-1 + pn = y0 x1**n * p0 + ... + x1 * pn-1 + pn = y1 ... xk**n * p0 + ... + xk * pn-1 + pn = yk

The coefficient matrix of the coefficients `p` is a Vandermonde matrix.

`polyfit` issues a `RankWarning` when the least-squares fit is badly conditioned. This implies that the best fit is not well-defined due to numerical error. The results may be improved by lowering the polynomial degree or by replacing `x` by `x` - `x`.mean(). The `rcond` parameter can also be set to a value smaller than its default, but the resulting fit may be spurious: including contributions from the small singular values can add numerical noise to the result.

Note that fitting polynomial coefficients is inherently badly conditioned when the degree of the polynomial is large or the interval of sample points is badly centered. The quality of the fit should always be checked in these cases. When polynomial fits are not satisfactory, splines may be a good alternative.

References ---------- .. 1 Wikipedia, 'Curve fitting', https://en.wikipedia.org/wiki/Curve_fitting .. 2 Wikipedia, 'Polynomial interpolation', https://en.wikipedia.org/wiki/Polynomial_interpolation

Examples -------- >>> import warnings >>> x = np.array(0.0, 1.0, 2.0, 3.0, 4.0, 5.0) >>> y = np.array(0.0, 0.8, 0.9, 0.1, -0.8, -1.0) >>> z = np.polyfit(x, y, 3) >>> z array( 0.08703704, -0.81349206, 1.69312169, -0.03968254) # may vary

It is convenient to use `poly1d` objects for dealing with polynomials:

>>> p = np.poly1d(z) >>> p(0.5) 0.6143849206349179 # may vary >>> p(3.5) -0.34732142857143039 # may vary >>> p(10) 22.579365079365115 # may vary

High-order polynomials may oscillate wildly:

>>> with warnings.catch_warnings(): ... warnings.simplefilter('ignore', np.RankWarning) ... p30 = np.poly1d(np.polyfit(x, y, 30)) ... >>> p30(4) -0.80000000000000204 # may vary >>> p30(5) -0.99999999999999445 # may vary >>> p30(4.5) -0.10547061179440398 # may vary

Illustration:

>>> import matplotlib.pyplot as plt >>> xp = np.linspace(-2, 6, 100) >>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--') >>> plt.ylim(-2,2) (-2, 2) >>> plt.show()

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.

prod(self, axis=None, dtype=None, out=None, keepdims=<no value>)

Return the product of the array elements over the given axis.

Masked elements are set to 1 internally for computation.

Refer to `numpy.prod` for full documentation.

Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow.

See Also -------- numpy.ndarray.prod : corresponding function for ndarrays numpy.prod : equivalent function

prod(self, axis=None, dtype=None, out=None, keepdims=<no value>)

Return the product of the array elements over the given axis.

Masked elements are set to 1 internally for computation.

Refer to `numpy.prod` for full documentation.

Notes ----- Arithmetic is modular when using integer types, and no error is raised on overflow.

See Also -------- numpy.ndarray.prod : corresponding function for ndarrays numpy.prod : equivalent function

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 :

ar}, optional
    Value used to fill in the masked values.
keepdims : bool, optional
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the array.

Returns
-------
ptp : ndarray.
    A new array holding the result, unless ``out`` was
    specified, in which case a reference to ``out`` is returned.

Set storage-indexed locations to corresponding values.

This function is equivalent to `MaskedArray.put`, see that method for details.

See Also -------- MaskedArray.put

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`.

ravel(self, order='C')

Returns a 1D version of self, as a view.

Parameters ---------- order : 'C', 'F', 'A', 'K', optional The elements of `a` are read using this index order. 'C' means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. 'F' means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the 'C' and 'F' options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. 'A' means to read the elements in Fortran-like index order if `m` is Fortran *contiguous* in memory, C-like order otherwise. 'K' means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, 'C' index order is used.

Returns ------- MaskedArray Output view is of shape ``(self.size,)`` (or ``(np.ma.product(self.shape),)``).

Examples -------- >>> x = np.ma.array([1,2,3],[4,5,6],[7,8,9], mask=0 + 1,0*4) >>> x masked_array( data=[1, --, 3], [--, 5, --], [7, --, 9], mask=[False, True, False], [ True, False, True], [False, True, False], fill_value=999999) >>> x.ravel() masked_array(data=1, --, 3, --, 5, --, 7, --, 9, mask=False, True, False, True, False, True, False, True, False, fill_value=999999)

remainder(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Return element-wise remainder of division.

Computes the remainder complementary to the `floor_divide` function. It is equivalent to the Python modulus operator``x1 % x2`` and has the same sign as the divisor `x2`. The MATLAB function equivalent to ``np.remainder`` is ``mod``.

.. warning::

This should not be confused with:

* Python 3.7's `math.remainder` and C's ``remainder``, which computes the IEEE remainder, which are the complement to ``round(x1 / x2)``. * The MATLAB ``rem`` function and or the C ``%`` operator which is the complement to ``int(x1 / x2)``.

Parameters ---------- x1 : array_like Dividend array. x2 : array_like Divisor array. If ``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes the shape of the output). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The element-wise remainder of the quotient ``floor_divide(x1, x2)``. This is a scalar if both `x1` and `x2` are scalars.

See Also -------- floor_divide : Equivalent of Python ``//`` operator. divmod : Simultaneous floor division and remainder. fmod : Equivalent of the MATLAB ``rem`` function. divide, floor

Notes ----- Returns 0 when `x2` is 0 and both `x1` and `x2` are (arrays of) integers. ``mod`` is an alias of ``remainder``.

Examples -------- >>> np.remainder(4, 7, 2, 3) array(0, 1) >>> np.remainder(np.arange(7), 5) array(0, 1, 2, 3, 4, 0, 1)

repeat(self, *args, **params) a.repeat(repeats, axis=None)

Repeat elements of an array.

Refer to `numpy.repeat` for full documentation.

See Also -------- numpy.repeat : equivalent function

Returns an array containing the same data with a new shape.

Refer to `MaskedArray.reshape` for full documentation.

See Also -------- MaskedArray.reshape : equivalent function

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]) >>> a0, 1 = ma.masked >>> a masked_array( data=[1, --], [3, 4], mask=[False, True], [False, False], fill_value=999999) >>> np.resize(a, (3, 3)) masked_array( data=[1, 2, 3], [4, 1, 2], [3, 4, 1], mask=False, fill_value=999999) >>> ma.resize(a, (3, 3)) masked_array( data=[1, --, 3], [4, 1, --], [3, 4, 1], mask=[False, True, False], [False, False, True], [False, False, False], fill_value=999999)

A MaskedArray is always returned, regardless of the input type.

>>> a = np.array([1, 2] ,[3, 4]) >>> ma.resize(a, (3, 3)) masked_array( data=[1, 2, 3], [4, 1, 2], [3, 4, 1], mask=False, fill_value=999999)

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

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!

vstack( *args, **kwargs)

Stack arrays in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by `vsplit`.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions `concatenate`, `stack` and `block` provide more general stacking and concatenation operations.

Parameters ---------- tup : sequence of ndarrays The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.

Returns ------- stacked : ndarray The array formed by stacking the given arrays, will be at least 2-D.

See Also -------- stack : Join a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise). dstack : Stack arrays in sequence depth wise (along third dimension). concatenate : Join a sequence of arrays along an existing axis. vsplit : Split array into a list of multiple sub-arrays vertically. block : Assemble arrays from blocks.

Examples -------- >>> a = np.array(1, 2, 3) >>> b = np.array(2, 3, 4) >>> np.vstack((a,b)) array([1, 2, 3], [2, 3, 4])

>>> a = np.array([1], [2], [3]) >>> b = np.array([2], [3], [4]) >>> np.vstack((a,b)) array([1], [2], [3], [2], [3], [4])

Notes ----- The function is applied to both the _data and the _mask, if any.

Set the filling value of a, if a is a masked array.

This function changes the fill value of the masked array `a` in place. If `a` is not a masked array, the function returns silently, without doing anything.

Parameters ---------- a : array_like Input array. fill_value : dtype Filling value. A consistency test is performed to make sure the value is compatible with the dtype of `a`.

Returns ------- None Nothing returned by this function.

See Also -------- maximum_fill_value : Return the default fill value for a dtype. MaskedArray.fill_value : Return current fill value. MaskedArray.set_fill_value : Equivalent method.

Examples -------- >>> import numpy.ma as ma >>> a = np.arange(5) >>> a array(0, 1, 2, 3, 4) >>> a = ma.masked_where(a < 3, a) >>> a masked_array(data=--, --, --, 3, 4, mask= True, True, True, False, False, fill_value=999999) >>> ma.set_fill_value(a, -999) >>> a masked_array(data=--, --, --, 3, 4, mask= True, True, True, False, False, fill_value=-999)

Nothing happens if `a` is not a masked array.

>>> a = list(range(5)) >>> a 0, 1, 2, 3, 4 >>> ma.set_fill_value(a, 100) >>> a 0, 1, 2, 3, 4 >>> a = np.arange(5) >>> a array(0, 1, 2, 3, 4) >>> ma.set_fill_value(a, 100) >>> a array(0, 1, 2, 3, 4)

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)

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.

Return the shape of an array.

Parameters ---------- a : array_like Input array.

Returns ------- shape : tuple of ints The elements of the shape tuple give the lengths of the corresponding array dimensions.

See Also -------- alen ndarray.shape : Equivalent array method.

Examples -------- >>> np.shape(np.eye(3)) (3, 3) >>> np.shape([1, 2]) (1, 2) >>> np.shape(0) (1,) >>> np.shape(0) ()

>>> a = np.array((1, 2), (3, 4), dtype=('x', 'i4'), ('y', 'i4')) >>> np.shape(a) (2,) >>> a.shape (2,)

sin(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Trigonometric sine, element-wise.

Parameters ---------- x : array_like Angle, in radians (:math:`2 \pi` rad equals 360 degrees). out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : array_like The sine of each element of x. This is a scalar if `x` is a scalar.

See Also -------- arcsin, sinh, cos

Notes ----- The sine is one of the fundamental functions of trigonometry (the mathematical study of triangles). Consider a circle of radius 1 centered on the origin. A ray comes in from the :math:`+x` axis, makes an angle at the origin (measured counter-clockwise from that axis), and departs from the origin. The :math:`y` coordinate of the outgoing ray's intersection with the unit circle is the sine of that angle. It ranges from -1 for :math:`x=3\pi / 2` to +1 for :math:`\pi / 2.` The function has zeroes where the angle is a multiple of :math:`\pi`. Sines of angles between :math:`\pi` and :math:`2\pi` are negative. The numerous properties of the sine and related functions are included in any standard trigonometry text.

Examples -------- Print sine of one angle:

>>> np.sin(np.pi/2.) 1.0

Print sines of an array of angles given in degrees:

>>> np.sin(np.array((0., 30., 45., 60., 90.)) * np.pi / 180. ) array( 0. , 0.5 , 0.70710678, 0.8660254 , 1. )

Plot the sine function:

>>> import matplotlib.pylab as plt >>> x = np.linspace(-np.pi, np.pi, 201) >>> plt.plot(x, np.sin(x)) >>> plt.xlabel('Angle rad') >>> plt.ylabel('sin(x)') >>> plt.axis('tight') >>> plt.show()

sinh(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True, signature, extobj)

Hyperbolic sine, element-wise.

Equivalent to ``1/2 * (np.exp(x) - np.exp(-x))`` or ``-1j * np.sin(1j*x)``.

Parameters ---------- x : array_like Input array. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional This condition is broadcast over the input. At locations where the condition is True, the `out` array will be set to the ufunc result. Elsewhere, the `out` array will retain its original value. Note that if an uninitialized `out` array is created via the default ``out=None``, locations within it where the condition is False will remain uninitialized. **kwargs For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.

Returns ------- y : ndarray The corresponding hyperbolic sine values. This is a scalar if `x` is a scalar.

Notes ----- If `out` is provided, the function writes the result into it, and returns a reference to `out`. (See Examples)

References ---------- M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions. New York, NY: Dover, 1972, pg. 83.

Examples -------- >>> np.sinh(0) 0.0 >>> np.sinh(np.pi*1j/2) 1j >>> np.sinh(np.pi*1j) # (exact value is 0) 1.2246063538223773e-016j >>> # Discrepancy due to vagaries of floating point arithmetic.

>>> # Example of providing the optional output parameter >>> out1 = np.array(0, dtype='d') >>> out2 = np.sinh(0.1, out1) >>> out2 is out1 True

>>> # Example of ValueError due to provision of shape mis-matched `out` >>> np.sinh(np.zeros((3,3)),np.zeros((2,2))) Traceback (most recent call last): File '<stdin>', line 1, in <module> ValueError: operands could not be broadcast together with shapes (3,3) (2,2)

Return the number of elements along a given axis.

Parameters ---------- a : array_like Input data. axis : int, optional Axis along which the elements are counted. By default, give the total number of elements.

Returns ------- element_count : int Number of elements along the specified axis.

See Also -------- shape : dimensions of array ndarray.shape : dimensions of array ndarray.size : number of elements in array

Examples -------- >>> a = np.array([1,2,3],[4,5,6]) >>> np.size(a) 6 >>> np.size(a,1) 3 >>> np.size(a,0) 2

soften_mask(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

Reduce `target` along the given `axis`.