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numpy.ma.prod ma.prod(self, axis=None, dtype=None, out=None, keepdims=<no value>) = <numpy.ma.core._frommethod object>
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. See also numpy.ndarray.prod
corresponding function for ndarrays numpy.prod
equivalent function Notes Arithmetic is modular when using integer types, and no error is raised on overflow. | numpy.reference.generated.numpy.ma.prod |
numpy.ma.ptp ma.ptp(obj, axis=None, out=None, fill_value=None, keepdims=<no value>)[source]
Return (maximum - minimum) along the given dimension (i.e. peak-to-peak value). Warning ptp preserves the data type of the array. This means the return value for an input of signed integers with n bits (e.g. np.int8, np.int16, etc) is also a signed integer with n bits. In that case, peak-to-peak values greater than 2**(n-1)-1 will be returned as negative values. An example with a work-around is shown below. Parameters
axis{None, int}, optional
Axis along which to find the peaks. If None (default) the flattened array is used.
out{None, array_like}, optional
Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary.
fill_valuescalar or None, optional
Value used to fill in the masked values.
keepdimsbool, 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
ptpndarray.
A new array holding the result, unless out was specified, in which case a reference to out is returned. Examples >>> x = np.ma.MaskedArray([[4, 9, 2, 10],
... [6, 9, 7, 12]])
>>> x.ptp(axis=1)
masked_array(data=[8, 6],
mask=False,
fill_value=999999)
>>> x.ptp(axis=0)
masked_array(data=[2, 0, 5, 2],
mask=False,
fill_value=999999)
>>> x.ptp()
10
This example shows that a negative value can be returned when the input is an array of signed integers. >>> y = np.ma.MaskedArray([[1, 127],
... [0, 127],
... [-1, 127],
... [-2, 127]], dtype=np.int8)
>>> y.ptp(axis=1)
masked_array(data=[ 126, 127, -128, -127],
mask=False,
fill_value=999999,
dtype=int8)
A work-around is to use the view() method to view the result as unsigned integers with the same bit width: >>> y.ptp(axis=1).view(np.uint8)
masked_array(data=[126, 127, 128, 129],
mask=False,
fill_value=999999,
dtype=uint8) | numpy.reference.generated.numpy.ma.ptp |
numpy.ma.ravel ma.ravel(self, order='C') = <numpy.ma.core._frommethod object>
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) | numpy.reference.generated.numpy.ma.ravel |
numpy.ma.reshape ma.reshape(a, new_shape, order='C')[source]
Returns an array containing the same data with a new shape. Refer to MaskedArray.reshape for full documentation. See also MaskedArray.reshape
equivalent function | numpy.reference.generated.numpy.ma.reshape |
numpy.ma.resize ma.resize(x, new_shape)[source]
Return a new masked array with the specified size and shape. This is the masked equivalent of the numpy.resize function. The new array is filled with repeated copies of x (in the order that the data are stored in memory). If x is masked, the new array will be masked, and the new mask will be a repetition of the old one. See also numpy.resize
Equivalent function in the top level NumPy module. Examples >>> import numpy.ma as ma
>>> a = ma.array([[1, 2] ,[3, 4]])
>>> a[0, 1] = ma.masked
>>> a
masked_array(
data=[[1, --],
[3, 4]],
mask=[[False, True],
[False, False]],
fill_value=999999)
>>> np.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999)
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, --, 3],
[4, 1, --],
[3, 4, 1]],
mask=[[False, True, False],
[False, False, True],
[False, False, False]],
fill_value=999999)
A MaskedArray is always returned, regardless of the input type. >>> a = np.array([[1, 2] ,[3, 4]])
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999) | numpy.reference.generated.numpy.ma.resize |
numpy.ma.round ma.round(a, decimals=0, out=None)[source]
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
decimalsint
Number of decimals to round to. May be negative.
outarray_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! | numpy.reference.generated.numpy.ma.round |
numpy.ma.row_stack ma.row_stack(*args, **kwargs) = <numpy.ma.extras._fromnxfunction_seq object>
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
tupsequence of ndarrays
The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length. Returns
stackedndarray
The array formed by stacking the given arrays, will be at least 2-D. See also concatenate
Join a sequence of arrays along an existing axis. stack
Join a sequence of arrays along a new axis. block
Assemble an nd-array from nested lists of blocks. hstack
Stack arrays in sequence horizontally (column wise). dstack
Stack arrays in sequence depth wise (along third axis). column_stack
Stack 1-D arrays as columns into a 2-D array. vsplit
Split an array into multiple sub-arrays vertically (row-wise). Notes The function is applied to both the _data and the _mask, if any. Examples >>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.vstack((a,b))
array([[1, 2, 3],
[4, 5, 6]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[4], [5], [6]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[4],
[5],
[6]]) | numpy.reference.generated.numpy.ma.row_stack |
numpy.ma.set_fill_value ma.set_fill_value(a, fill_value)[source]
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
aarray_like
Input array.
fill_valuedtype
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]) | numpy.reference.generated.numpy.ma.set_fill_value |
numpy.ma.shape ma.shape(obj)[source]
Return the shape of an array. Parameters
aarray_like
Input array. Returns
shapetuple of ints
The elements of the shape tuple give the lengths of the corresponding array dimensions. See also len
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,) | numpy.reference.generated.numpy.ma.shape |
numpy.ma.size ma.size(obj, axis=None)[source]
Return the number of elements along a given axis. Parameters
aarray_like
Input data.
axisint, optional
Axis along which the elements are counted. By default, give the total number of elements. Returns
element_countint
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 | numpy.reference.generated.numpy.ma.size |
numpy.ma.soften_mask ma.soften_mask(self) = <numpy.ma.core._frommethod object>
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 ma.MaskedArray.hardmask | numpy.reference.generated.numpy.ma.soften_mask |
numpy.ma.sort ma.sort(a, axis=- 1, kind=None, order=None, endwith=True, fill_value=None)[source]
Return a sorted copy of the masked array. Equivalent to creating a copy of the array and applying the MaskedArray sort() method. Refer to MaskedArray.sort for the full documentation See also MaskedArray.sort
equivalent method | numpy.reference.generated.numpy.ma.sort |
numpy.ma.squeeze ma.squeeze(*args, **kwargs) = <numpy.ma.core._convert2ma object>
Remove axes of length one from a. Parameters
aarray_like
Input data.
axisNone or int or tuple of ints, optional
New in version 1.7.0. Selects a subset of the entries of length one in the shape. If an axis is selected with shape entry greater than one, an error is raised. Returns
squeezedMaskedArray
The input array, but with all or a subset of the dimensions of length 1 removed. This is always a itself or a view into a. Note that if all axes are squeezed, the result is a 0d array and not a scalar. Raises
ValueError
If axis is not None, and an axis being squeezed is not of length 1 See also expand_dims
The inverse operation, adding entries of length one reshape
Insert, remove, and combine dimensions, and resize existing ones Examples >>> x = np.array([[[0], [1], [2]]])
>>> x.shape
(1, 3, 1)
>>> np.squeeze(x).shape
(3,)
>>> np.squeeze(x, axis=0).shape
(3, 1)
>>> np.squeeze(x, axis=1).shape
Traceback (most recent call last):
...
ValueError: cannot select an axis to squeeze out which has size not equal to one
>>> np.squeeze(x, axis=2).shape
(1, 3)
>>> x = np.array([[1234]])
>>> x.shape
(1, 1)
>>> np.squeeze(x)
array(1234) # 0d array
>>> np.squeeze(x).shape
()
>>> np.squeeze(x)[()]
1234 | numpy.reference.generated.numpy.ma.squeeze |
numpy.ma.stack ma.stack(*args, **kwargs) = <numpy.ma.extras._fromnxfunction_seq object>
Join a sequence of arrays along a new axis. The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension. New in version 1.10.0. Parameters
arrayssequence of array_like
Each array must have the same shape.
axisint, optional
The axis in the result array along which the input arrays are stacked.
outndarray, optional
If provided, the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified. Returns
stackedndarray
The stacked array has one more dimension than the input arrays. See also concatenate
Join a sequence of arrays along an existing axis. block
Assemble an nd-array from nested lists of blocks. split
Split array into a list of multiple sub-arrays of equal size. Notes The function is applied to both the _data and the _mask, if any. Examples >>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.stack((a, b))
array([[1, 2, 3],
[4, 5, 6]])
>>> np.stack((a, b), axis=-1)
array([[1, 4],
[2, 5],
[3, 6]]) | numpy.reference.generated.numpy.ma.stack |
numpy.ma.std ma.std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>) = <numpy.ma.core._frommethod object>
Returns the standard deviation of the array elements along given axis. Masked entries are ignored. Refer to numpy.std for full documentation. See also numpy.ndarray.std
corresponding function for ndarrays numpy.std
Equivalent function | numpy.reference.generated.numpy.ma.std |
numpy.ma.sum ma.sum(self, axis=None, dtype=None, out=None, keepdims=<no value>) = <numpy.ma.core._frommethod object>
Return the sum of the array elements over the given axis. Masked elements are set to 0 internally. Refer to numpy.sum for full documentation. See also numpy.ndarray.sum
corresponding function for ndarrays numpy.sum
equivalent function Examples >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
>>> x
masked_array(
data=[[1, --, 3],
[--, 5, --],
[7, --, 9]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=999999)
>>> x.sum()
25
>>> x.sum(axis=1)
masked_array(data=[4, 5, 16],
mask=[False, False, False],
fill_value=999999)
>>> x.sum(axis=0)
masked_array(data=[8, 5, 12],
mask=[False, False, False],
fill_value=999999)
>>> print(type(x.sum(axis=0, dtype=np.int64)[0]))
<class 'numpy.int64'> | numpy.reference.generated.numpy.ma.sum |
numpy.ma.swapaxes ma.swapaxes(self, *args, **params) a.swapaxes(axis1, axis2) = <numpy.ma.core._frommethod object>
Return a view of the array with axis1 and axis2 interchanged. Refer to numpy.swapaxes for full documentation. See also numpy.swapaxes
equivalent function | numpy.reference.generated.numpy.ma.swapaxes |
numpy.ma.trace ma.trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None) a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None) = <numpy.ma.core._frommethod object>
Return the sum along diagonals of the array. Refer to numpy.trace for full documentation. See also numpy.trace
equivalent function | numpy.reference.generated.numpy.ma.trace |
numpy.ma.transpose ma.transpose(a, axes=None)[source]
Permute the dimensions of an array. This function is exactly equivalent to numpy.transpose. See also numpy.transpose
Equivalent function in top-level NumPy module. Examples >>> import numpy.ma as ma
>>> x = ma.arange(4).reshape((2,2))
>>> x[1, 1] = ma.masked
>>> x
masked_array(
data=[[0, 1],
[2, --]],
mask=[[False, False],
[False, True]],
fill_value=999999)
>>> ma.transpose(x)
masked_array(
data=[[0, 2],
[1, --]],
mask=[[False, False],
[False, True]],
fill_value=999999) | numpy.reference.generated.numpy.ma.transpose |
numpy.ma.vander ma.vander(x, n=None)[source]
Generate a Vandermonde matrix. The columns of the output matrix are powers of the input vector. The order of the powers is determined by the increasing boolean argument. Specifically, when increasing is False, the i-th output column is the input vector raised element-wise to the power of N - i - 1. Such a matrix with a geometric progression in each row is named for Alexandre- Theophile Vandermonde. Parameters
xarray_like
1-D input array.
Nint, optional
Number of columns in the output. If N is not specified, a square array is returned (N = len(x)).
increasingbool, optional
Order of the powers of the columns. If True, the powers increase from left to right, if False (the default) they are reversed. New in version 1.9.0. Returns
outndarray
Vandermonde matrix. If increasing is False, the first column is x^(N-1), the second x^(N-2) and so forth. If increasing is True, the columns are x^0, x^1, ..., x^(N-1). See also polynomial.polynomial.polyvander
Notes Masked values in the input array result in rows of zeros. Examples >>> x = np.array([1, 2, 3, 5])
>>> N = 3
>>> np.vander(x, N)
array([[ 1, 1, 1],
[ 4, 2, 1],
[ 9, 3, 1],
[25, 5, 1]])
>>> np.column_stack([x**(N-1-i) for i in range(N)])
array([[ 1, 1, 1],
[ 4, 2, 1],
[ 9, 3, 1],
[25, 5, 1]])
>>> x = np.array([1, 2, 3, 5])
>>> np.vander(x)
array([[ 1, 1, 1, 1],
[ 8, 4, 2, 1],
[ 27, 9, 3, 1],
[125, 25, 5, 1]])
>>> np.vander(x, increasing=True)
array([[ 1, 1, 1, 1],
[ 1, 2, 4, 8],
[ 1, 3, 9, 27],
[ 1, 5, 25, 125]])
The determinant of a square Vandermonde matrix is the product of the differences between the values of the input vector: >>> np.linalg.det(np.vander(x))
48.000000000000043 # may vary
>>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)
48 | numpy.reference.generated.numpy.ma.vander |
numpy.ma.var ma.var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>) = <numpy.ma.core._frommethod object>
Compute the variance along the specified axis. Returns the variance of the array elements, a measure of the spread of a distribution. The variance is computed for the flattened array by default, otherwise over the specified axis. Parameters
aarray_like
Array containing numbers whose variance is desired. If a is not an array, a conversion is attempted.
axisNone or int or tuple of ints, optional
Axis or axes along which the variance is computed. The default is to compute the variance of the flattened array. New in version 1.7.0. If this is a tuple of ints, a variance is performed over multiple axes, instead of a single axis or all the axes as before.
dtypedata-type, optional
Type to use in computing the variance. For arrays of integer type the default is float64; for arrays of float types it is the same as the array type.
outndarray, optional
Alternate output array in which to place the result. It must have the same shape as the expected output, but the type is cast if necessary.
ddofint, optional
“Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, where N represents the number of elements. By default ddof is zero.
keepdimsbool, 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 var 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.
wherearray_like of bool, optional
Elements to include in the variance. See reduce for details. New in version 1.20.0. Returns
variancendarray, see dtype parameter above
If out=None, returns a new array containing the variance; otherwise, a reference to the output array is returned. See also
std, mean, nanmean, nanstd, nanvar
Output type determination
Notes The variance is the average of the squared deviations from the mean, i.e., var = mean(x), where x = abs(a - a.mean())**2. The mean is typically calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables. Note that for complex numbers, the absolute value is taken before squaring, so that the result is always real and nonnegative. For floating-point input, the variance is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-accuracy accumulator using the dtype keyword can alleviate this issue. Examples >>> a = np.array([[1, 2], [3, 4]])
>>> np.var(a)
1.25
>>> np.var(a, axis=0)
array([1., 1.])
>>> np.var(a, axis=1)
array([0.25, 0.25])
In single precision, var() can be inaccurate: >>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.var(a)
0.20250003
Computing the variance in float64 is more accurate: >>> np.var(a, dtype=np.float64)
0.20249999932944759 # may vary
>>> ((1-0.55)**2 + (0.1-0.55)**2)/2
0.2025
Specifying a where argument: >>> a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])
>>> np.var(a)
6.833333333333333 # may vary
>>> np.var(a, where=[[True], [True], [False]])
4.0 | numpy.reference.generated.numpy.ma.var |
numpy.ma.vstack ma.vstack(*args, **kwargs) = <numpy.ma.extras._fromnxfunction_seq object>
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
tupsequence of ndarrays
The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length. Returns
stackedndarray
The array formed by stacking the given arrays, will be at least 2-D. See also concatenate
Join a sequence of arrays along an existing axis. stack
Join a sequence of arrays along a new axis. block
Assemble an nd-array from nested lists of blocks. hstack
Stack arrays in sequence horizontally (column wise). dstack
Stack arrays in sequence depth wise (along third axis). column_stack
Stack 1-D arrays as columns into a 2-D array. vsplit
Split an array into multiple sub-arrays vertically (row-wise). Notes The function is applied to both the _data and the _mask, if any. Examples >>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.vstack((a,b))
array([[1, 2, 3],
[4, 5, 6]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[4], [5], [6]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[4],
[5],
[6]]) | numpy.reference.generated.numpy.ma.vstack |
numpy.ma.where ma.where(condition, x=<no value>, y=<no value>)[source]
Return a masked array with elements from x or y, depending on condition. Note When only condition is provided, this function is identical to nonzero. The rest of this documentation covers only the case where all three arguments are provided. Parameters
conditionarray_like, bool
Where True, yield x, otherwise yield y.
x, yarray_like, optional
Values from which to choose. x, y and condition need to be broadcastable to some shape. Returns
outMaskedArray
An masked array with masked elements where the condition is masked, elements from x where condition is True, and elements from y elsewhere. See also numpy.where
Equivalent function in the top-level NumPy module. nonzero
The function that is called when x and y are omitted Examples >>> x = np.ma.array(np.arange(9.).reshape(3, 3), mask=[[0, 1, 0],
... [1, 0, 1],
... [0, 1, 0]])
>>> x
masked_array(
data=[[0.0, --, 2.0],
[--, 4.0, --],
[6.0, --, 8.0]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=1e+20)
>>> np.ma.where(x > 5, x, -3.1416)
masked_array(
data=[[-3.1416, --, -3.1416],
[--, -3.1416, --],
[6.0, --, 8.0]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=1e+20) | numpy.reference.generated.numpy.ma.where |
numpy.ma.zeros ma.zeros(shape, dtype=float, order='C', *, like=None) = <numpy.ma.core._convert2ma object>
Return a new array of given shape and type, filled with zeros. Parameters
shapeint or tuple of ints
Shape of the new array, e.g., (2, 3) or 2.
dtypedata-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.
likearray_like
Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument. New in version 1.20.0. Returns
outMaskedArray
Array of zeros with the given shape, dtype, and order. See also zeros_like
Return an array of zeros with shape and type of input. empty
Return a new uninitialized array. ones
Return a new array setting values to one. full
Return a new array of given shape filled with value. Examples >>> np.zeros(5)
array([ 0., 0., 0., 0., 0.])
>>> np.zeros((5,), dtype=int)
array([0, 0, 0, 0, 0])
>>> np.zeros((2, 1))
array([[ 0.],
[ 0.]])
>>> s = (2,2)
>>> np.zeros(s)
array([[ 0., 0.],
[ 0., 0.]])
>>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
array([(0, 0), (0, 0)],
dtype=[('x', '<i4'), ('y', '<i4')]) | numpy.reference.generated.numpy.ma.zeros |
numpy.ma.zeros_like ma.zeros_like(*args, **kwargs) = <numpy.ma.core._convert2ma object>
Return an array of zeros with the same shape and type as a given array. Parameters
aarray_like
The shape and data-type of a define these same attributes of the returned array.
dtypedata-type, optional
Overrides the data type of the result. New in version 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 a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. New in version 1.6.0.
subokbool, 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.
shapeint 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. New in version 1.17.0. Returns
outMaskedArray
Array of zeros with the same shape and type as a. See also empty_like
Return an empty array with shape and type of input. ones_like
Return an array of ones with shape and type of input. full_like
Return a new array with shape of input filled with value. zeros
Return a new array setting values to zero. Examples >>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.zeros_like(x)
array([[0, 0, 0],
[0, 0, 0]])
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.zeros_like(y)
array([0., 0., 0.]) | numpy.reference.generated.numpy.ma.zeros_like |
make_config_py(name='__config__')[source]
Generate package __config__.py file containing system_info information used during building the package. This file is installed to the package installation directory. | numpy.reference.distutils#numpy.distutils.misc_util.Configuration.make_config_py |
make_svn_version_py(delete=True)[source]
Appends a data function to the data_files list that will generate __svn_version__.py file to the current package directory. Generate package __svn_version__.py file from SVN revision number, it will be removed after python exits but will be available when sdist, etc commands are executed. Notes If __svn_version__.py existed before, nothing is done. This is intended for working with source directories that are in an SVN repository. | numpy.reference.distutils#numpy.distutils.misc_util.Configuration.make_svn_version_py |
Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked
The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0])
>>> x[1] is ma.masked
True
>>> x[-1] = ma.masked
>>> x
masked_array(data=[1, --, --],
mask=[False, True, True],
fill_value=999999)
numpy.ma.nomask
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_.
numpy.ma.masked_print_options
String used in lieu of missing data when a masked array is printed. By default, this string is '--'. | numpy.reference.maskedarray.baseclass#numpy.ma.MaskedArray.baseclass |
Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked
The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0])
>>> x[1] is ma.masked
True
>>> x[-1] = ma.masked
>>> x
masked_array(data=[1, --, --],
mask=[False, True, True],
fill_value=999999)
numpy.ma.nomask
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_.
numpy.ma.masked_print_options
String used in lieu of missing data when a masked array is printed. By default, this string is '--'. | numpy.reference.maskedarray.baseclass#numpy.ma.MaskedArray.fill_value |
Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked
The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0])
>>> x[1] is ma.masked
True
>>> x[-1] = ma.masked
>>> x
masked_array(data=[1, --, --],
mask=[False, True, True],
fill_value=999999)
numpy.ma.nomask
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_.
numpy.ma.masked_print_options
String used in lieu of missing data when a masked array is printed. By default, this string is '--'. | numpy.reference.maskedarray.baseclass#numpy.ma.MaskedArray.hardmask |
Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked
The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0])
>>> x[1] is ma.masked
True
>>> x[-1] = ma.masked
>>> x
masked_array(data=[1, --, --],
mask=[False, True, True],
fill_value=999999)
numpy.ma.nomask
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_.
numpy.ma.masked_print_options
String used in lieu of missing data when a masked array is printed. By default, this string is '--'. | numpy.reference.maskedarray.baseclass#numpy.ma.MaskedArray.mask |
Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked
The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0])
>>> x[1] is ma.masked
True
>>> x[-1] = ma.masked
>>> x
masked_array(data=[1, --, --],
mask=[False, True, True],
fill_value=999999)
numpy.ma.nomask
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_.
numpy.ma.masked_print_options
String used in lieu of missing data when a masked array is printed. By default, this string is '--'. | numpy.reference.maskedarray.baseclass#numpy.ma.MaskedArray.recordmask |
Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants. numpy.ma.masked
The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0])
>>> x[1] is ma.masked
True
>>> x[-1] = ma.masked
>>> x
masked_array(data=[1, --, --],
mask=[False, True, True],
fill_value=999999)
numpy.ma.nomask
Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_.
numpy.ma.masked_print_options
String used in lieu of missing data when a masked array is printed. By default, this string is '--'. | numpy.reference.maskedarray.baseclass#numpy.ma.MaskedArray.sharedmask |
numpy.matlib.empty matlib.empty(shape, dtype=None, order='C')[source]
Return a new matrix of given shape and type, without initializing entries. Parameters
shapeint or tuple of int
Shape of the empty matrix.
dtypedata-type, optional
Desired output data-type.
order{‘C’, ‘F’}, optional
Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. See also
empty_like, zeros
Notes empty, unlike zeros, does not set the matrix 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 >>> import numpy.matlib
>>> np.matlib.empty((2, 2)) # filled with random data
matrix([[ 6.76425276e-320, 9.79033856e-307], # random
[ 7.39337286e-309, 3.22135945e-309]])
>>> np.matlib.empty((2, 2), dtype=int)
matrix([[ 6600475, 0], # random
[ 6586976, 22740995]]) | numpy.reference.generated.numpy.matlib.empty |
numpy.matlib.eye matlib.eye(n, M=None, k=0, dtype=<class 'float'>, order='C')[source]
Return a matrix with ones on the diagonal and zeros elsewhere. Parameters
nint
Number of rows in the output.
Mint, optional
Number of columns in the output, defaults to n.
kint, optional
Index of the diagonal: 0 refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.
dtypedtype, optional
Data-type of the returned matrix.
order{‘C’, ‘F’}, optional
Whether the output should be stored in row-major (C-style) or column-major (Fortran-style) order in memory. New in version 1.14.0. Returns
Imatrix
A n x M matrix where all elements are equal to zero, except for the k-th diagonal, whose values are equal to one. See also numpy.eye
Equivalent array function. identity
Square identity matrix. Examples >>> import numpy.matlib
>>> np.matlib.eye(3, k=1, dtype=float)
matrix([[0., 1., 0.],
[0., 0., 1.],
[0., 0., 0.]]) | numpy.reference.generated.numpy.matlib.eye |
numpy.matlib.identity matlib.identity(n, dtype=None)[source]
Returns the square identity matrix of given size. Parameters
nint
Size of the returned identity matrix.
dtypedata-type, optional
Data-type of the output. Defaults to float. Returns
outmatrix
n x n matrix with its main diagonal set to one, and all other elements zero. See also numpy.identity
Equivalent array function. matlib.eye
More general matrix identity function. Examples >>> import numpy.matlib
>>> np.matlib.identity(3, dtype=int)
matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]) | numpy.reference.generated.numpy.matlib.identity |
numpy.matlib.ones matlib.ones(shape, dtype=None, order='C')[source]
Matrix of ones. Return a matrix of given shape and type, filled with ones. Parameters
shape{sequence of ints, int}
Shape of the matrix
dtypedata-type, optional
The desired data-type for the matrix, default is np.float64.
order{‘C’, ‘F’}, optional
Whether to store matrix in C- or Fortran-contiguous order, default is ‘C’. Returns
outmatrix
Matrix of ones of given shape, dtype, and order. See also ones
Array of ones. matlib.zeros
Zero matrix. Notes If shape has length one i.e. (N,), or is a scalar N, out becomes a single row matrix of shape (1,N). Examples >>> np.matlib.ones((2,3))
matrix([[1., 1., 1.],
[1., 1., 1.]])
>>> np.matlib.ones(2)
matrix([[1., 1.]]) | numpy.reference.generated.numpy.matlib.ones |
numpy.matlib.rand matlib.rand(*args)[source]
Return a matrix of random values with given shape. Create a matrix of the given shape and propagate it with random samples from a uniform distribution over [0, 1). Parameters
*argsArguments
Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape. Returns
outndarray
The matrix of random values with shape given by *args. See also
randn, numpy.random.RandomState.rand
Examples >>> np.random.seed(123)
>>> import numpy.matlib
>>> np.matlib.rand(2, 3)
matrix([[0.69646919, 0.28613933, 0.22685145],
[0.55131477, 0.71946897, 0.42310646]])
>>> np.matlib.rand((2, 3))
matrix([[0.9807642 , 0.68482974, 0.4809319 ],
[0.39211752, 0.34317802, 0.72904971]])
If the first argument is a tuple, other arguments are ignored: >>> np.matlib.rand((2, 3), 4)
matrix([[0.43857224, 0.0596779 , 0.39804426],
[0.73799541, 0.18249173, 0.17545176]]) | numpy.reference.generated.numpy.matlib.rand |
numpy.matlib.randn matlib.randn(*args)[source]
Return a random matrix with data from the “standard normal” distribution. randn generates a matrix filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1. Parameters
*argsArguments
Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape. Returns
Zmatrix of floats
A matrix of floating-point samples drawn from the standard normal distribution. See also
rand, numpy.random.RandomState.randn
Notes For random samples from \(N(\mu, \sigma^2)\), use: sigma * np.matlib.randn(...) + mu Examples >>> np.random.seed(123)
>>> import numpy.matlib
>>> np.matlib.randn(1)
matrix([[-1.0856306]])
>>> np.matlib.randn(1, 2, 3)
matrix([[ 0.99734545, 0.2829785 , -1.50629471],
[-0.57860025, 1.65143654, -2.42667924]])
Two-by-four matrix of samples from \(N(3, 6.25)\): >>> 2.5 * np.matlib.randn((2, 4)) + 3
matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
[2.76322758, 6.72847407, 1.40274501, 1.8900451 ]]) | numpy.reference.generated.numpy.matlib.randn |
numpy.matlib.repmat matlib.repmat(a, m, n)[source]
Repeat a 0-D to 2-D array or matrix MxN times. Parameters
aarray_like
The array or matrix to be repeated.
m, nint
The number of times a is repeated along the first and second axes. Returns
outndarray
The result of repeating a. Examples >>> import numpy.matlib
>>> a0 = np.array(1)
>>> np.matlib.repmat(a0, 2, 3)
array([[1, 1, 1],
[1, 1, 1]])
>>> a1 = np.arange(4)
>>> np.matlib.repmat(a1, 2, 2)
array([[0, 1, 2, 3, 0, 1, 2, 3],
[0, 1, 2, 3, 0, 1, 2, 3]])
>>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
>>> np.matlib.repmat(a2, 2, 3)
matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5],
[0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5]]) | numpy.reference.generated.numpy.matlib.repmat |
numpy.matlib.zeros matlib.zeros(shape, dtype=None, order='C')[source]
Return a matrix of given shape and type, filled with zeros. Parameters
shapeint or sequence of ints
Shape of the matrix
dtypedata-type, optional
The desired data-type for the matrix, default is float.
order{‘C’, ‘F’}, optional
Whether to store the result in C- or Fortran-contiguous order, default is ‘C’. Returns
outmatrix
Zero matrix of given shape, dtype, and order. See also numpy.zeros
Equivalent array function. matlib.ones
Return a matrix of ones. Notes If shape has length one i.e. (N,), or is a scalar N, out becomes a single row matrix of shape (1,N). Examples >>> import numpy.matlib
>>> np.matlib.zeros((2, 3))
matrix([[0., 0., 0.],
[0., 0., 0.]])
>>> np.matlib.zeros(2)
matrix([[0., 0.]]) | numpy.reference.generated.numpy.matlib.zeros |
numpy.matrix.all method matrix.all(axis=None, out=None)[source]
Test whether all matrix elements along a given axis evaluate to True. Parameters
See `numpy.all` for complete descriptions
See also numpy.all
Notes This is the same as ndarray.all, but it returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> y = x[0]; y
matrix([[0, 1, 2, 3]])
>>> (x == y)
matrix([[ True, True, True, True],
[False, False, False, False],
[False, False, False, False]])
>>> (x == y).all()
False
>>> (x == y).all(0)
matrix([[False, False, False, False]])
>>> (x == y).all(1)
matrix([[ True],
[False],
[False]]) | numpy.reference.generated.numpy.matrix.all |
numpy.matrix.any method matrix.any(axis=None, out=None)[source]
Test whether any array element along a given axis evaluates to True. Refer to numpy.any for full documentation. Parameters
axisint, optional
Axis along which logical OR is performed
outndarray, optional
Output to existing array instead of creating new one, must have same shape as expected output Returns
anybool, ndarray
Returns a single bool if axis is None; otherwise, returns ndarray | numpy.reference.generated.numpy.matrix.any |
numpy.matrix.argmax method matrix.argmax(axis=None, out=None)[source]
Indexes of the maximum values along an axis. Return the indexes of the first occurrences of the maximum values along the specified axis. If axis is None, the index is for the flattened matrix. Parameters
See `numpy.argmax` for complete descriptions
See also numpy.argmax
Notes This is the same as ndarray.argmax, but returns a matrix object where ndarray.argmax would return an ndarray. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.argmax()
11
>>> x.argmax(0)
matrix([[2, 2, 2, 2]])
>>> x.argmax(1)
matrix([[3],
[3],
[3]]) | numpy.reference.generated.numpy.matrix.argmax |
numpy.matrix.argmin method matrix.argmin(axis=None, out=None)[source]
Indexes of the minimum values along an axis. Return the indexes of the first occurrences of the minimum values along the specified axis. If axis is None, the index is for the flattened matrix. Parameters
See `numpy.argmin` for complete descriptions.
See also numpy.argmin
Notes This is the same as ndarray.argmin, but returns a matrix object where ndarray.argmin would return an ndarray. Examples >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, -1, -2, -3],
[ -4, -5, -6, -7],
[ -8, -9, -10, -11]])
>>> x.argmin()
11
>>> x.argmin(0)
matrix([[2, 2, 2, 2]])
>>> x.argmin(1)
matrix([[3],
[3],
[3]]) | numpy.reference.generated.numpy.matrix.argmin |
numpy.matrix.argpartition method matrix.argpartition(kth, axis=- 1, kind='introselect', order=None)
Returns the indices that would partition this array. Refer to numpy.argpartition for full documentation. New in version 1.8.0. See also numpy.argpartition
equivalent function | numpy.reference.generated.numpy.matrix.argpartition |
numpy.matrix.argsort method matrix.argsort(axis=- 1, kind=None, order=None)
Returns the indices that would sort this array. Refer to numpy.argsort for full documentation. See also numpy.argsort
equivalent function | numpy.reference.generated.numpy.matrix.argsort |
numpy.matrix.astype method matrix.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type. Parameters
dtypestr or dtype
Typecode or data-type to which the array is cast.
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional
Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility. ‘no’ means the data types should not be cast at all. ‘equiv’ means only byte-order changes are allowed. ‘safe’ means only casts which can preserve values are allowed. ‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed. ‘unsafe’ means any data conversions may be done.
subokbool, optional
If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.
copybool, optional
By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy. Returns
arr_tndarray
Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order. Raises
ComplexWarning
When casting from complex to float or int. To avoid this, one should use a.real.astype(t). Notes Changed in version 1.17.0: Casting between a simple data type and a structured one is possible only for “unsafe” casting. Casting to multiple fields is allowed, but casting from multiple fields is not. Changed in version 1.9.0: Casting from numeric to string types in ‘safe’ casting mode requires that the string dtype length is long enough to store the max integer/float value converted. Examples >>> x = np.array([1, 2, 2.5])
>>> x
array([1. , 2. , 2.5])
>>> x.astype(int)
array([1, 2, 2]) | numpy.reference.generated.numpy.matrix.astype |
numpy.matrix.base attribute matrix.base
Base object if memory is from some other object. Examples The base of an array that owns its memory is None: >>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x: >>> y = x[2:]
>>> y.base is x
True | numpy.reference.generated.numpy.matrix.base |
numpy.matrix.byteswap method matrix.byteswap(inplace=False)
Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually. Parameters
inplacebool, optional
If True, swap bytes in-place, default is False. Returns
outndarray
The byteswapped array. If inplace is True, this is a view to self. Examples >>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([ 256, 1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']
Arrays of byte-strings are not swapped >>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')
A.newbyteorder().byteswap() produces an array with the same values
but different representation in memory >>> A = np.array([1, 2, 3])
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
0, 0], dtype=uint8)
>>> A.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
0, 3], dtype=uint8) | numpy.reference.generated.numpy.matrix.byteswap |
numpy.matrix.choose method matrix.choose(choices, out=None, mode='raise')
Use an index array to construct a new array from a set of choices. Refer to numpy.choose for full documentation. See also numpy.choose
equivalent function | numpy.reference.generated.numpy.matrix.choose |
numpy.matrix.clip method matrix.clip(min=None, max=None, out=None, **kwargs)
Return an array whose values are limited to [min, max]. One of max or min must be given. Refer to numpy.clip for full documentation. See also numpy.clip
equivalent function | numpy.reference.generated.numpy.matrix.clip |
numpy.matrix.compress method matrix.compress(condition, axis=None, out=None)
Return selected slices of this array along given axis. Refer to numpy.compress for full documentation. See also numpy.compress
equivalent function | numpy.reference.generated.numpy.matrix.compress |
numpy.matrix.conj method matrix.conj()
Complex-conjugate all elements. Refer to numpy.conjugate for full documentation. See also numpy.conjugate
equivalent function | numpy.reference.generated.numpy.matrix.conj |
numpy.matrix.conjugate method matrix.conjugate()
Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate
equivalent function | numpy.reference.generated.numpy.matrix.conjugate |
numpy.matrix.copy method matrix.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 numpy.copy are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.) See also numpy.copy
Similar function with different default behavior numpy.copyto
Notes This function is the preferred method for creating an array copy. The function numpy.copy is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default. 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 | numpy.reference.generated.numpy.matrix.copy |
numpy.matrix.ctypes attribute matrix.ctypes
An object to simplify the interaction of the array with the ctypes module. This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library. Parameters
None
Returns
cPython object
Possessing attributes data, shape, strides, etc. See also numpy.ctypeslib
Notes Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes): _ctypes.data
A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code to avoid trouble that can include Python crashing. User Beware! The value of this attribute is exactly the same as self._array_interface_['data'][0]. Note that unlike data_as, a reference will not be kept to the array: code like ctypes.c_void_p((a + b).ctypes.data) will result in a pointer to a deallocated array, and should be spelt (a + b).ctypes.data_as(ctypes.c_void_p)
_ctypes.shape
(c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.
_ctypes.strides
(c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.
_ctypes.data_as(obj)[source]
Return the data pointer cast to a particular c-types object. For example, calling self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)). The returned pointer will keep a reference to the array.
_ctypes.shape_as(obj)[source]
Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).
_ctypes.strides_as(obj)[source]
Return the strides tuple as an array of some other c-types type. For example: self.strides_as(ctypes.c_longlong).
If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as_parameter attribute which will return an integer equal to the data attribute. Examples >>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
[2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary | numpy.reference.generated.numpy.matrix.ctypes |
numpy.matrix.cumprod method matrix.cumprod(axis=None, dtype=None, out=None)
Return the cumulative product of the elements along the given axis. Refer to numpy.cumprod for full documentation. See also numpy.cumprod
equivalent function | numpy.reference.generated.numpy.matrix.cumprod |
numpy.matrix.cumsum method matrix.cumsum(axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along the given axis. Refer to numpy.cumsum for full documentation. See also numpy.cumsum
equivalent function | numpy.reference.generated.numpy.matrix.cumsum |
numpy.matrix.data attribute matrix.data
Python buffer object pointing to the start of the array’s data. | numpy.reference.generated.numpy.matrix.data |
numpy.matrix.diagonal method matrix.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 numpy.diagonal for full documentation. See also numpy.diagonal
equivalent function | numpy.reference.generated.numpy.matrix.diagonal |
numpy.matrix.dump method matrix.dump(file)
Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load. Parameters
filestr or Path
A string naming the dump file. Changed in version 1.17.0: pathlib.Path objects are now accepted. | numpy.reference.generated.numpy.matrix.dump |
numpy.matrix.dumps method matrix.dumps()
Returns the pickle of the array as a string. pickle.loads will convert the string back to an array. Parameters
None | numpy.reference.generated.numpy.matrix.dumps |
numpy.matrix.fill method matrix.fill(value)
Fill the array with a scalar value. Parameters
valuescalar
All elements of a will be assigned this value. Examples >>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1., 1.]) | numpy.reference.generated.numpy.matrix.fill |
numpy.matrix.flags attribute matrix.flags
Information about the memory layout of the array. Notes The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access. Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags. The array flags cannot be set arbitrarily: UPDATEIFCOPY can only be set False. WRITEBACKIFCOPY can only be set False. ALIGNED can only be set True if the data is truly aligned. WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string. Arrays can be both C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional arrays, but can also be true for higher dimensional arrays. Even for contiguous arrays a stride for a given dimension arr.strides[dim] may be arbitrary if arr.shape[dim] == 1 or the array has no elements. It does not generally hold that self.strides[-1] == self.itemsize for C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true. Attributes
C_CONTIGUOUS (C)
The data is in a single, C-style contiguous segment. F_CONTIGUOUS (F)
The data is in a single, Fortran-style contiguous segment. OWNDATA (O)
The array owns the memory it uses or borrows it from another object. WRITEABLE (W)
The data area can be written to. Setting this to False locks the data, making it read-only. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a non-writeable array raises a RuntimeError exception. ALIGNED (A)
The data and all elements are aligned appropriately for the hardware. WRITEBACKIFCOPY (X)
This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array. UPDATEIFCOPY (U)
(Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array. When this array is deallocated, the base array will be updated with the contents of this array. FNC
F_CONTIGUOUS and not C_CONTIGUOUS. FORC
F_CONTIGUOUS or C_CONTIGUOUS (one-segment test). BEHAVED (B)
ALIGNED and WRITEABLE. CARRAY (CA)
BEHAVED and C_CONTIGUOUS. FARRAY (FA)
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS. | numpy.reference.generated.numpy.matrix.flags |
numpy.matrix.flat attribute matrix.flat
A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object. See also flatten
Return a copy of the array collapsed into one dimension. flatiter
Examples >>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
[4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
[2, 5],
[3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<class 'numpy.flatiter'>
An assignment example: >>> x.flat = 3; x
array([[3, 3, 3],
[3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
[3, 1, 3]]) | numpy.reference.generated.numpy.matrix.flat |
numpy.matrix.flatten method matrix.flatten(order='C')[source]
Return a flattened copy of the matrix. All N elements of the matrix are placed into a single row. Parameters
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran-style) order. ‘A’ means to flatten in column-major order if m is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten m in the order the elements occur in memory. The default is ‘C’. Returns
ymatrix
A copy of the matrix, flattened to a (1, N) matrix where N is the number of elements in the original matrix. See also ravel
Return a flattened array. flat
A 1-D flat iterator over the matrix. Examples >>> m = np.matrix([[1,2], [3,4]])
>>> m.flatten()
matrix([[1, 2, 3, 4]])
>>> m.flatten('F')
matrix([[1, 3, 2, 4]]) | numpy.reference.generated.numpy.matrix.flatten |
numpy.matrix.getA method matrix.getA()[source]
Return self as an ndarray object. Equivalent to np.asarray(self). Parameters
None
Returns
retndarray
self as an ndarray Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.getA()
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]]) | numpy.reference.generated.numpy.matrix.geta |
numpy.matrix.getA1 method matrix.getA1()[source]
Return self as a flattened ndarray. Equivalent to np.asarray(x).ravel() Parameters
None
Returns
retndarray
self, 1-D, as an ndarray Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.getA1()
array([ 0, 1, 2, ..., 9, 10, 11]) | numpy.reference.generated.numpy.matrix.geta1 |
numpy.matrix.getfield method matrix.getfield(dtype, offset=0)
Returns a field of the given array as a certain type. A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes. Parameters
dtypestr or dtype
The data type of the view. The dtype size of the view can not be larger than that of the array itself.
offsetint
Number of bytes to skip before beginning the element view. Examples >>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j, 0.+0.j],
[0.+0.j, 2.+4.j]])
>>> x.getfield(np.float64)
array([[1., 0.],
[0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the array for our view: >>> x.getfield(np.float64, offset=8)
array([[1., 0.],
[0., 4.]]) | numpy.reference.generated.numpy.matrix.getfield |
numpy.matrix.getH method matrix.getH()[source]
Returns the (complex) conjugate transpose of self. Equivalent to np.transpose(self) if self is real-valued. Parameters
None
Returns
retmatrix object
complex conjugate transpose of self Examples >>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. -0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]]) | numpy.reference.generated.numpy.matrix.geth |
numpy.matrix.getI method matrix.getI()[source]
Returns the (multiplicative) inverse of invertible self. Parameters
None
Returns
retmatrix object
If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size)) all return True. Raises
numpy.linalg.LinAlgError: Singular matrix
If self is singular. See also linalg.inv
Examples >>> m = np.matrix('[1, 2; 3, 4]'); m
matrix([[1, 2],
[3, 4]])
>>> m.getI()
matrix([[-2. , 1. ],
[ 1.5, -0.5]])
>>> m.getI() * m
matrix([[ 1., 0.], # may vary
[ 0., 1.]]) | numpy.reference.generated.numpy.matrix.geti |
numpy.matrix.getT method matrix.getT()[source]
Returns the transpose of the matrix. Does not conjugate! For the complex conjugate transpose, use .H. Parameters
None
Returns
retmatrix object
The (non-conjugated) transpose of the matrix. See also
transpose, getH
Examples >>> m = np.matrix('[1, 2; 3, 4]')
>>> m
matrix([[1, 2],
[3, 4]])
>>> m.getT()
matrix([[1, 3],
[2, 4]]) | numpy.reference.generated.numpy.matrix.gett |
numpy.matrix.item method matrix.item(*args)
Copy an element of an array to a standard Python scalar and return it. Parameters
*argsArguments (variable number and type)
none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned. int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return. tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array. Returns
zStandard Python scalar object
A copy of the specified element of the array as a suitable Python scalar Notes When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned. item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math. Examples >>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1 | numpy.reference.generated.numpy.matrix.item |
numpy.matrix.itemset method matrix.itemset(*args)
Insert scalar into an array (scalar is cast to array’s dtype, if possible) There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a. Parameters
*argsArguments
If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple. Notes Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute look-up at each loop iteration. Examples >>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
[1, 3, 6],
[1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
[1, 0, 6],
[1, 0, 9]]) | numpy.reference.generated.numpy.matrix.itemset |
numpy.matrix.itemsize attribute matrix.itemsize
Length of one array element in bytes. Examples >>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16 | numpy.reference.generated.numpy.matrix.itemsize |
numpy.matrix.max method matrix.max(axis=None, out=None)[source]
Return the maximum value along an axis. Parameters
See `amax` for complete descriptions
See also
amax, ndarray.max
Notes This is the same as ndarray.max, but returns a matrix object where ndarray.max would return an ndarray. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.max()
11
>>> x.max(0)
matrix([[ 8, 9, 10, 11]])
>>> x.max(1)
matrix([[ 3],
[ 7],
[11]]) | numpy.reference.generated.numpy.matrix.max |
numpy.matrix.mean method matrix.mean(axis=None, dtype=None, out=None)[source]
Returns the average of the matrix elements along the given axis. Refer to numpy.mean for full documentation. See also numpy.mean
Notes Same as ndarray.mean except that, where that returns an ndarray, this returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.mean()
5.5
>>> x.mean(0)
matrix([[4., 5., 6., 7.]])
>>> x.mean(1)
matrix([[ 1.5],
[ 5.5],
[ 9.5]]) | numpy.reference.generated.numpy.matrix.mean |
numpy.matrix.min method matrix.min(axis=None, out=None)[source]
Return the minimum value along an axis. Parameters
See `amin` for complete descriptions.
See also
amin, ndarray.min
Notes This is the same as ndarray.min, but returns a matrix object where ndarray.min would return an ndarray. Examples >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, -1, -2, -3],
[ -4, -5, -6, -7],
[ -8, -9, -10, -11]])
>>> x.min()
-11
>>> x.min(0)
matrix([[ -8, -9, -10, -11]])
>>> x.min(1)
matrix([[ -3],
[ -7],
[-11]]) | numpy.reference.generated.numpy.matrix.min |
numpy.matrix.nbytes attribute matrix.nbytes
Total bytes consumed by the elements of the array. Notes Does not include memory consumed by non-element attributes of the array object. Examples >>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480 | numpy.reference.generated.numpy.matrix.nbytes |
numpy.matrix.ndim attribute matrix.ndim
Number of array dimensions. Examples >>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3 | numpy.reference.generated.numpy.matrix.ndim |
numpy.matrix.newbyteorder method matrix.newbyteorder(new_order='S', /)
Return the array with the same data viewed with a different byte order. Equivalent to: arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and sub-arrays of the array data type. Parameters
new_orderstring, optional
Byte order to force; a value from the byte order specifications below. new_order codes can be any of: ‘S’ - swap dtype from current to opposite endian {‘<’, ‘little’} - little endian {‘>’, ‘big’} - big endian {‘=’, ‘native’} - native order, equivalent to sys.byteorder
{‘|’, ‘I’} - ignore (no change to byte order) The default value (‘S’) results in swapping the current byte order. Returns
new_arrarray
New array object with the dtype reflecting given change to the byte order. | numpy.reference.generated.numpy.matrix.newbyteorder |
numpy.matrix.nonzero method matrix.nonzero()
Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation. See also numpy.nonzero
equivalent function | numpy.reference.generated.numpy.matrix.nonzero |
numpy.matrix.partition method matrix.partition(kth, axis=- 1, kind='introselect', order=None)
Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined. New in version 1.8.0. Parameters
kthint or sequence of ints
Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once. Deprecated since version 1.22.0: Passing booleans as index is deprecated.
axisint, optional
Axis along which to sort. Default is -1, which means sort along the last axis.
kind{‘introselect’}, optional
Selection algorithm. Default is ‘introselect’.
orderstr or list of str, optional
When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties. See also numpy.partition
Return a parititioned copy of an array. argpartition
Indirect partition. sort
Full sort. Notes See np.partition for notes on the different algorithms. Examples >>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4]) | numpy.reference.generated.numpy.matrix.partition |
numpy.matrix.prod method matrix.prod(axis=None, dtype=None, out=None)[source]
Return the product of the array elements over the given axis. Refer to prod for full documentation. See also
prod, ndarray.prod
Notes Same as ndarray.prod, except, where that returns an ndarray, this returns a matrix object instead. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[ 0, 45, 120, 231]])
>>> x.prod(1)
matrix([[ 0],
[ 840],
[7920]]) | numpy.reference.generated.numpy.matrix.prod |
numpy.matrix.ptp method matrix.ptp(axis=None, out=None)[source]
Peak-to-peak (maximum - minimum) value along the given axis. Refer to numpy.ptp for full documentation. See also numpy.ptp
Notes Same as ndarray.ptp, except, where that would return an ndarray object, this returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.ptp()
11
>>> x.ptp(0)
matrix([[8, 8, 8, 8]])
>>> x.ptp(1)
matrix([[3],
[3],
[3]]) | numpy.reference.generated.numpy.matrix.ptp |
numpy.matrix.put method matrix.put(indices, values, mode='raise')
Set a.flat[n] = values[n] for all n in indices. Refer to numpy.put for full documentation. See also numpy.put
equivalent function | numpy.reference.generated.numpy.matrix.put |
numpy.matrix.ravel method matrix.ravel(order='C')[source]
Return a flattened matrix. Refer to numpy.ravel for more documentation. Parameters
order{‘C’, ‘F’, ‘A’, ‘K’}, optional
The elements of m 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
retmatrix
Return the matrix flattened to shape (1, N) where N is the number of elements in the original matrix. A copy is made only if necessary. See also matrix.flatten
returns a similar output matrix but always a copy matrix.flat
a flat iterator on the array. numpy.ravel
related function which returns an ndarray | numpy.reference.generated.numpy.matrix.ravel |
numpy.matrix.repeat method matrix.repeat(repeats, axis=None)
Repeat elements of an array. Refer to numpy.repeat for full documentation. See also numpy.repeat
equivalent function | numpy.reference.generated.numpy.matrix.repeat |
numpy.matrix.reshape method matrix.reshape(shape, order='C')
Returns an array containing the same data with a new shape. Refer to numpy.reshape for full documentation. See also numpy.reshape
equivalent function Notes Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)). | numpy.reference.generated.numpy.matrix.reshape |
numpy.matrix.resize method matrix.resize(new_shape, refcheck=True)
Change shape and size of array in-place. Parameters
new_shapetuple of ints, or n ints
Shape of resized array.
refcheckbool, optional
If False, reference count will not be checked. Default is True. Returns
None
Raises
ValueError
If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist. SystemError
If the order keyword argument is specified. This behaviour is a bug in NumPy. See also resize
Return a new array with the specified shape. Notes This reallocates space for the data area if necessary. Only contiguous arrays (data elements consecutive in memory) can be resized. The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False. Examples Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped: >>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])
Enlarging an array: as above, but missing entries are filled with zeros: >>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])
Referencing an array prevents resizing… >>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...
Unless refcheck is False: >>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]]) | numpy.reference.generated.numpy.matrix.resize |
numpy.matrix.round method matrix.round(decimals=0, out=None)
Return a with each element rounded to the given number of decimals. Refer to numpy.around for full documentation. See also numpy.around
equivalent function | numpy.reference.generated.numpy.matrix.round |
numpy.matrix.searchsorted method matrix.searchsorted(v, side='left', sorter=None)
Find indices where elements of v should be inserted in a to maintain order. For full documentation, see numpy.searchsorted See also numpy.searchsorted
equivalent function | numpy.reference.generated.numpy.matrix.searchsorted |
numpy.matrix.setfield method matrix.setfield(val, dtype, offset=0)
Put a value into a specified place in a field defined by a data-type. Place val into a’s field defined by dtype and beginning offset bytes into the field. Parameters
valobject
Value to be placed in field.
dtypedtype object
Data-type of the field in which to place val.
offsetint, optional
The number of bytes into the field at which to place val. Returns
None
See also getfield
Examples >>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
[1.5e-323, 1.0e+000, 1.5e-323],
[1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]) | numpy.reference.generated.numpy.matrix.setfield |
numpy.matrix.setflags method matrix.setflags(write=None, align=None, uic=None)
Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively. These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.) Parameters
writebool, optional
Describes whether or not a can be written to.
alignbool, optional
Describes whether or not a is aligned properly for its type.
uicbool, optional
Describes whether or not a is a copy of another “base” array. Notes Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only four of which can be changed by the user: WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED. WRITEABLE (W) the data area can be written to; ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler); UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY; WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array. All flags can be accessed using the single (upper case) letter as well as the full name. Examples >>> y = np.array([[3, 1, 7],
... [2, 0, 0],
... [8, 5, 9]])
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True | numpy.reference.generated.numpy.matrix.setflags |
numpy.matrix.size attribute matrix.size
Number of elements in the array. Equal to np.prod(a.shape), i.e., the product of the array’s dimensions. Notes a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type. Examples >>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30 | numpy.reference.generated.numpy.matrix.size |
numpy.matrix.sort method matrix.sort(axis=- 1, kind=None, order=None)
Sort an array in-place. Refer to numpy.sort for full documentation. Parameters
axisint, optional
Axis along which to sort. Default is -1, which means sort along the last axis.
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional
Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility. Changed in version 1.15.0: The ‘stable’ option was added.
orderstr or list of str, optional
When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties. See also numpy.sort
Return a sorted copy of an array. numpy.argsort
Indirect sort. numpy.lexsort
Indirect stable sort on multiple keys. numpy.searchsorted
Find elements in sorted array. numpy.partition
Partial sort. Notes See numpy.sort for notes on the different sorting algorithms. Examples >>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])
Use the order keyword to specify a field to use when sorting a structured array: >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([(b'c', 1), (b'a', 2)],
dtype=[('x', 'S1'), ('y', '<i8')]) | numpy.reference.generated.numpy.matrix.sort |
numpy.matrix.squeeze method matrix.squeeze(axis=None)[source]
Return a possibly reshaped matrix. Refer to numpy.squeeze for more documentation. Parameters
axisNone or int or tuple of ints, optional
Selects a subset of the axes of length one in the shape. If an axis is selected with shape entry greater than one, an error is raised. Returns
squeezedmatrix
The matrix, but as a (1, N) matrix if it had shape (N, 1). See also numpy.squeeze
related function Notes If m has a single column then that column is returned as the single row of a matrix. Otherwise m is returned. The returned matrix is always either m itself or a view into m. Supplying an axis keyword argument will not affect the returned matrix but it may cause an error to be raised. Examples >>> c = np.matrix([[1], [2]])
>>> c
matrix([[1],
[2]])
>>> c.squeeze()
matrix([[1, 2]])
>>> r = c.T
>>> r
matrix([[1, 2]])
>>> r.squeeze()
matrix([[1, 2]])
>>> m = np.matrix([[1, 2], [3, 4]])
>>> m.squeeze()
matrix([[1, 2],
[3, 4]]) | numpy.reference.generated.numpy.matrix.squeeze |
numpy.matrix.std method matrix.std(axis=None, dtype=None, out=None, ddof=0)[source]
Return the standard deviation of the array elements along the given axis. Refer to numpy.std for full documentation. See also numpy.std
Notes This is the same as ndarray.std, except that where an ndarray would be returned, a matrix object is returned instead. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.std()
3.4520525295346629 # may vary
>>> x.std(0)
matrix([[ 3.26598632, 3.26598632, 3.26598632, 3.26598632]]) # may vary
>>> x.std(1)
matrix([[ 1.11803399],
[ 1.11803399],
[ 1.11803399]]) | numpy.reference.generated.numpy.matrix.std |
numpy.matrix.strides attribute matrix.strides
Tuple of bytes to step in each dimension when traversing an array. The byte offset of element (i[0], i[1], ..., i[n]) in an array a is: offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide. See also numpy.lib.stride_tricks.as_strided
Notes Imagine an array of 32-bit integers (each 4 bytes): x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be (20, 4). Examples >>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813 | numpy.reference.generated.numpy.matrix.strides |