doc_content
stringlengths 1
386k
| doc_id
stringlengths 5
188
|
|---|---|
numpy.ma.MaskedArray.__rpow__ method ma.MaskedArray.__rpow__(other)[source]
Raise other to the power self, masking the potential NaNs/Infs | numpy.reference.generated.numpy.ma.maskedarray.__rpow__ |
numpy.ma.MaskedArray.__rrshift__ method ma.MaskedArray.__rrshift__(value, /)
Return value>>self. | numpy.reference.generated.numpy.ma.maskedarray.__rrshift__ |
numpy.ma.MaskedArray.__rshift__ method ma.MaskedArray.__rshift__(value, /)
Return self>>value. | numpy.reference.generated.numpy.ma.maskedarray.__rshift__ |
numpy.ma.MaskedArray.__rsub__ method ma.MaskedArray.__rsub__(other)[source]
Subtract self from other, and return a new masked array. | numpy.reference.generated.numpy.ma.maskedarray.__rsub__ |
numpy.ma.MaskedArray.__rtruediv__ method ma.MaskedArray.__rtruediv__(other)[source]
Divide self into other, and return a new masked array. | numpy.reference.generated.numpy.ma.maskedarray.__rtruediv__ |
numpy.ma.MaskedArray.__rxor__ method ma.MaskedArray.__rxor__(value, /)
Return value^self. | numpy.reference.generated.numpy.ma.maskedarray.__rxor__ |
numpy.ma.MaskedArray.__setitem__ method ma.MaskedArray.__setitem__(indx, value)[source]
x.__setitem__(i, y) <==> x[i]=y Set item described by index. If value is masked, masks those locations. | numpy.reference.generated.numpy.ma.maskedarray.__setitem__ |
numpy.ma.MaskedArray.__setmask__ method ma.MaskedArray.__setmask__(mask, copy=False)[source]
Set the mask. | numpy.reference.generated.numpy.ma.maskedarray.__setmask__ |
numpy.ma.MaskedArray.__setstate__ method ma.MaskedArray.__setstate__(state)[source]
Restore the internal state of the masked array, for pickling purposes. state is typically the output of the __getstate__ output, and is a 5-tuple: class name a tuple giving the shape of the data a typecode for the data a binary string for the data a binary string for the mask. | numpy.reference.generated.numpy.ma.maskedarray.__setstate__ |
numpy.ma.MaskedArray.__str__ method ma.MaskedArray.__str__()[source]
Return str(self). | numpy.reference.generated.numpy.ma.maskedarray.__str__ |
numpy.ma.MaskedArray.__sub__ method ma.MaskedArray.__sub__(other)[source]
Subtract other from self, and return a new masked array. | numpy.reference.generated.numpy.ma.maskedarray.__sub__ |
numpy.ma.MaskedArray.__truediv__ method ma.MaskedArray.__truediv__(other)[source]
Divide other into self, and return a new masked array. | numpy.reference.generated.numpy.ma.maskedarray.__truediv__ |
numpy.ma.MaskedArray.__xor__ method ma.MaskedArray.__xor__(value, /)
Return self^value. | numpy.reference.generated.numpy.ma.maskedarray.__xor__ |
numpy.ma.MaskedArray.all method ma.MaskedArray.all(axis=None, out=None, keepdims=<no value>)[source]
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 | numpy.reference.generated.numpy.ma.maskedarray.all |
numpy.ma.MaskedArray.anom method ma.MaskedArray.anom(axis=None, dtype=None)[source]
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
axisint, optional
Axis over which the anomalies are taken. The default is to use the mean of the flattened array as reference.
dtypedtype, 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) | numpy.reference.generated.numpy.ma.maskedarray.anom |
numpy.ma.MaskedArray.any method ma.MaskedArray.any(axis=None, out=None, keepdims=<no value>)[source]
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 | numpy.reference.generated.numpy.ma.maskedarray.any |
numpy.ma.MaskedArray.argmax method ma.MaskedArray.argmax(axis=None, fill_value=None, out=None, *, keepdims=<no value>)[source]
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_valuescalar or None, optional
Value used to fill in the masked values. If None, the output of maximum_fill_value(self._data) is used instead.
out{None, array}, optional
Array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output. Returns
index_array{integer_array}
Examples >>> a = np.arange(6).reshape(2,3)
>>> a.argmax()
5
>>> a.argmax(0)
array([1, 1, 1])
>>> a.argmax(1)
array([2, 2]) | numpy.reference.generated.numpy.ma.maskedarray.argmax |
numpy.ma.MaskedArray.argmin method ma.MaskedArray.argmin(axis=None, fill_value=None, out=None, *, keepdims=<no value>)[source]
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_valuescalar or None, optional
Value used to fill in the masked values. If None, the output of minimum_fill_value(self._data) is used instead.
out{None, array}, optional
Array into which the result can be placed. Its type is preserved and it must be of the right shape to hold the output. Returns
ndarray or scalar
If multi-dimension input, returns a new ndarray of indices to the minimum values along the given axis. Otherwise, returns a scalar of index to the minimum values along the given axis. Examples >>> x = np.ma.array(np.arange(4), mask=[1,1,0,0])
>>> x.shape = (2,2)
>>> x
masked_array(
data=[[--, --],
[2, 3]],
mask=[[ True, True],
[False, False]],
fill_value=999999)
>>> x.argmin(axis=0, fill_value=-1)
array([0, 0])
>>> x.argmin(axis=0, fill_value=9)
array([1, 1]) | numpy.reference.generated.numpy.ma.maskedarray.argmin |
numpy.ma.MaskedArray.argsort method ma.MaskedArray.argsort(axis=<no value>, kind=None, order=None, endwith=True, fill_value=None)[source]
Return an ndarray of indices that sort the array along the specified axis. Masked values are filled beforehand to fill_value. Parameters
axisint, optional
Axis along which to sort. If None, the default, the flattened array is used. Changed in version 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.
orderlist, 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_valuescalar or None, optional
Value used internally for the masked values. If fill_value is not None, it supersedes endwith. Returns
index_arrayndarray, int
Array of indices that sort a along the specified axis. In other words, a[index_array] yields a sorted a. See also ma.MaskedArray.sort
Describes sorting algorithms used. lexsort
Indirect stable sort with multiple keys. numpy.ndarray.sort
Inplace sort. Notes See sort for notes on the different sorting algorithms. Examples >>> a = np.ma.array([3,2,1], mask=[False, False, True])
>>> a
masked_array(data=[3, 2, --],
mask=[False, False, True],
fill_value=999999)
>>> a.argsort()
array([1, 0, 2]) | numpy.reference.generated.numpy.ma.maskedarray.argsort |
numpy.ma.MaskedArray.astype method ma.MaskedArray.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.ma.maskedarray.astype |
numpy.ma.MaskedArray.base attribute ma.MaskedArray.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.ma.maskedarray.base |
numpy.ma.MaskedArray.byteswap method ma.MaskedArray.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.ma.maskedarray.byteswap |
numpy.ma.MaskedArray.choose method ma.MaskedArray.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.ma.maskedarray.choose |
numpy.ma.MaskedArray.clip method ma.MaskedArray.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.ma.maskedarray.clip |
numpy.ma.MaskedArray.compress method ma.MaskedArray.compress(condition, axis=None, out=None)[source]
Return a where condition is True. If condition is a MaskedArray, missing values are considered as False. Parameters
conditionvar
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
resultMaskedArray
A MaskedArray object. Notes Please note the difference with compressed ! The output of compress has a mask, the output of 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) | numpy.reference.generated.numpy.ma.maskedarray.compress |
numpy.ma.MaskedArray.compressed method ma.MaskedArray.compressed()[source]
Return all the non-masked data as a 1-D array. Returns
datandarray
A new ndarray holding the non-masked data is returned. Notes The result is not a MaskedArray! Examples >>> x = np.ma.array(np.arange(5), mask=[0]*2 + [1]*3)
>>> x.compressed()
array([0, 1])
>>> type(x.compressed())
<class 'numpy.ndarray'> | numpy.reference.generated.numpy.ma.maskedarray.compressed |
numpy.ma.MaskedArray.conj method ma.MaskedArray.conj()
Complex-conjugate all elements. Refer to numpy.conjugate for full documentation. See also numpy.conjugate
equivalent function | numpy.reference.generated.numpy.ma.maskedarray.conj |
numpy.ma.MaskedArray.conjugate method ma.MaskedArray.conjugate()
Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation. See also numpy.conjugate
equivalent function | numpy.reference.generated.numpy.ma.maskedarray.conjugate |
numpy.ma.MaskedArray.copy method ma.MaskedArray.copy(order='C')[source]
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.ma.maskedarray.copy |
numpy.ma.MaskedArray.count method ma.MaskedArray.count(axis=None, keepdims=<no value>)[source]
Count the non-masked elements of the array along the given axis. Parameters
axisNone 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. New in version 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.
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
resultndarray or scalar
An array with the same shape as the input array, with the specified axis removed. If the array is a 0-d array, or if axis is None, a scalar is returned. See also ma.count_masked
Count masked elements in array or along a given axis. Examples >>> import numpy.ma as ma
>>> a = ma.arange(6).reshape((2, 3))
>>> a[1, :] = ma.masked
>>> a
masked_array(
data=[[0, 1, 2],
[--, --, --]],
mask=[[False, False, False],
[ True, True, True]],
fill_value=999999)
>>> a.count()
3
When the axis keyword is specified an array of appropriate size is returned. >>> a.count(axis=0)
array([1, 1, 1])
>>> a.count(axis=1)
array([3, 0]) | numpy.reference.generated.numpy.ma.maskedarray.count |
numpy.ma.MaskedArray.ctypes attribute ma.MaskedArray.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.ma.maskedarray.ctypes |
numpy.ma.MaskedArray.cumprod method ma.MaskedArray.cumprod(axis=None, dtype=None, out=None)[source]
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. See also numpy.ndarray.cumprod
corresponding function for ndarrays numpy.cumprod
equivalent function 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. | numpy.reference.generated.numpy.ma.maskedarray.cumprod |
numpy.ma.MaskedArray.cumsum method ma.MaskedArray.cumsum(axis=None, dtype=None, out=None)[source]
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. See also numpy.ndarray.cumsum
corresponding function for ndarrays numpy.cumsum
equivalent function Notes The mask is lost if out is not a valid ma.MaskedArray ! Arithmetic is modular when using integer types, and no error is raised on overflow. 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) | numpy.reference.generated.numpy.ma.maskedarray.cumsum |
numpy.ma.MaskedArray.diagonal method ma.MaskedArray.diagonal(offset=0, axis1=0, axis2=1)[source]
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.ma.maskedarray.diagonal |
numpy.ma.MaskedArray.dump method ma.MaskedArray.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.ma.maskedarray.dump |
numpy.ma.MaskedArray.dumps method ma.MaskedArray.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.ma.maskedarray.dumps |
numpy.ma.MaskedArray.fill method ma.MaskedArray.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.ma.maskedarray.fill |
numpy.ma.MaskedArray.filled method ma.MaskedArray.filled(fill_value=None)[source]
Return a copy of self, with masked values filled with a given value. However, if there are no masked values to fill, self will be returned instead as an ndarray. Parameters
fill_valuearray_like, optional
The value to use for invalid entries. Can be scalar or non-scalar. If non-scalar, the resulting ndarray must be broadcastable over input array. Default is None, in which case, the fill_value attribute of the array is used instead. Returns
filled_arrayndarray
A copy of self with invalid entries replaced by fill_value (be it the function argument or the attribute of self), or self itself as an ndarray if there are no invalid entries to be replaced. Notes The result is not a MaskedArray! Examples >>> x = np.ma.array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999)
>>> x.filled()
array([ 1, 2, -999, 4, -999])
>>> x.filled(fill_value=1000)
array([ 1, 2, 1000, 4, 1000])
>>> type(x.filled())
<class 'numpy.ndarray'>
Subclassing is preserved. This means that if, e.g., the data part of the masked array is a recarray, filled returns a recarray: >>> x = np.array([(-1, 2), (-3, 4)], dtype='i8,i8').view(np.recarray)
>>> m = np.ma.array(x, mask=[(True, False), (False, True)])
>>> m.filled()
rec.array([(999999, 2), ( -3, 999999)],
dtype=[('f0', '<i8'), ('f1', '<i8')]) | numpy.reference.generated.numpy.ma.maskedarray.filled |
numpy.ma.MaskedArray.flags attribute ma.MaskedArray.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.ma.maskedarray.flags |
numpy.ma.MaskedArray.flatten method ma.MaskedArray.flatten(order='C')[source]
Return a copy of the array collapsed into one dimension. 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 a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’. Returns
yndarray
A copy of the input array, flattened to one dimension. See also ravel
Return a flattened array. flat
A 1-D flat iterator over the array. Examples >>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4]) | numpy.reference.generated.numpy.ma.maskedarray.flatten |
numpy.ma.MaskedArray.get_fill_value method ma.MaskedArray.get_fill_value()[source]
The filling value of the masked array is a scalar. When setting, None will set to a default based on the data type. Examples >>> for dt in [np.int32, np.int64, np.float64, np.complex128]:
... np.ma.array([0, 1], dtype=dt).get_fill_value()
...
999999
999999
1e+20
(1e+20+0j)
>>> x = np.ma.array([0, 1.], fill_value=-np.inf)
>>> x.fill_value
-inf
>>> x.fill_value = np.pi
>>> x.fill_value
3.1415926535897931 # may vary
Reset to default: >>> x.fill_value = None
>>> x.fill_value
1e+20 | numpy.reference.generated.numpy.ma.maskedarray.get_fill_value |
numpy.ma.MaskedArray.harden_mask method ma.MaskedArray.harden_mask()[source]
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 ma.MaskedArray.hardmask | numpy.reference.generated.numpy.ma.maskedarray.harden_mask |
numpy.ma.MaskedArray.ids method ma.MaskedArray.ids()[source]
Return the addresses of the data and mask areas. Parameters
None
Examples >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1])
>>> x.ids()
(166670640, 166659832) # may vary
If the array has no mask, the address of nomask is returned. This address is typically not close to the data in memory: >>> x = np.ma.array([1, 2, 3])
>>> x.ids()
(166691080, 3083169284) # may vary | numpy.reference.generated.numpy.ma.maskedarray.ids |
numpy.ma.MaskedArray.iscontiguous method ma.MaskedArray.iscontiguous()[source]
Return a boolean indicating whether the data is contiguous. Parameters
None
Examples >>> x = np.ma.array([1, 2, 3])
>>> x.iscontiguous()
True
iscontiguous returns one of the flags of the masked array: >>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : True
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False | numpy.reference.generated.numpy.ma.maskedarray.iscontiguous |
numpy.ma.MaskedArray.item method ma.MaskedArray.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.ma.maskedarray.item |
numpy.ma.MaskedArray.itemsize attribute ma.MaskedArray.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.ma.maskedarray.itemsize |
numpy.ma.MaskedArray.max method ma.MaskedArray.max(axis=None, out=None, fill_value=None, keepdims=<no value>)[source]
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.
outarray_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_valuescalar or None, optional
Value used to fill in the masked values. If None, use the output of maximum_fill_value().
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
amaxarray_like
New array holding the result. If out was specified, out is returned. See also ma.maximum_fill_value
Returns the maximum filling value for a given datatype. | numpy.reference.generated.numpy.ma.maskedarray.max |
numpy.ma.MaskedArray.mean method ma.MaskedArray.mean(axis=None, dtype=None, out=None, keepdims=<no value>)[source]
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 | numpy.reference.generated.numpy.ma.maskedarray.mean |
numpy.ma.MaskedArray.min method ma.MaskedArray.min(axis=None, out=None, fill_value=None, keepdims=<no value>)[source]
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.
outarray_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_valuescalar or None, optional
Value used to fill in the masked values. If None, use the output of minimum_fill_value.
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
aminarray_like
New array holding the result. If out was specified, out is returned. See also ma.minimum_fill_value
Returns the minimum filling value for a given datatype. | numpy.reference.generated.numpy.ma.maskedarray.min |
numpy.ma.MaskedArray.nbytes attribute ma.MaskedArray.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.ma.maskedarray.nbytes |
numpy.ma.MaskedArray.ndim attribute ma.MaskedArray.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.ma.maskedarray.ndim |
numpy.ma.MaskedArray.nonzero method ma.MaskedArray.nonzero()[source]
Return the indices of unmasked elements that are not zero. Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with: a[a.nonzero()]
To group the indices by element, rather than dimension, use instead: np.transpose(a.nonzero())
The result of this is always a 2d array, with a row for each non-zero element. Parameters
None
Returns
tuple_of_arraystuple
Indices of elements that are non-zero. See also numpy.nonzero
Function operating on ndarrays. flatnonzero
Return indices that are non-zero in the flattened version of the input array. numpy.ndarray.nonzero
Equivalent ndarray method. count_nonzero
Counts the number of non-zero elements in the input array. Examples >>> import numpy.ma as ma
>>> x = ma.array(np.eye(3))
>>> x
masked_array(
data=[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]],
mask=False,
fill_value=1e+20)
>>> x.nonzero()
(array([0, 1, 2]), array([0, 1, 2]))
Masked elements are ignored. >>> x[1, 1] = ma.masked
>>> x
masked_array(
data=[[1.0, 0.0, 0.0],
[0.0, --, 0.0],
[0.0, 0.0, 1.0]],
mask=[[False, False, False],
[False, True, False],
[False, False, False]],
fill_value=1e+20)
>>> x.nonzero()
(array([0, 2]), array([0, 2]))
Indices can also be grouped by element. >>> np.transpose(x.nonzero())
array([[0, 0],
[2, 2]])
A common use for nonzero is to find the indices of an array, where a condition is True. Given an array a, the condition a > 3 is a boolean array and since False is interpreted as 0, ma.nonzero(a > 3) yields the indices of the a where the condition is true. >>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
masked_array(
data=[[False, False, False],
[ True, True, True],
[ True, True, True]],
mask=False,
fill_value=True)
>>> ma.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The nonzero method of the condition array can also be called. >>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) | numpy.reference.generated.numpy.ma.maskedarray.nonzero |
numpy.ma.MaskedArray.prod method ma.MaskedArray.prod(axis=None, dtype=None, out=None, keepdims=<no value>)[source]
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.maskedarray.prod |
numpy.ma.MaskedArray.product method ma.MaskedArray.product(axis=None, dtype=None, out=None, keepdims=<no value>)[source]
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.maskedarray.product |
numpy.ma.MaskedArray.ptp method ma.MaskedArray.ptp(axis=None, out=None, fill_value=None, keepdims=False)[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.maskedarray.ptp |
numpy.ma.MaskedArray.put method ma.MaskedArray.put(indices, values, mode='raise')[source]
Set storage-indexed locations to corresponding values. Sets self._data.flat[n] = values[n] for each n in indices. If values is shorter than indices then it will repeat. If values has some masked values, the initial mask is updated in consequence, else the corresponding values are unmasked. Parameters
indices1-D array_like
Target indices, interpreted as integers.
valuesarray_like
Values to place in self._data copy at target indices.
mode{‘raise’, ‘wrap’, ‘clip’}, optional
Specifies how out-of-bounds indices will behave. ‘raise’ : raise an error. ‘wrap’ : wrap around. ‘clip’ : clip to the range. Notes values can be a scalar or length 1 array. Examples >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
>>> x
masked_array(
data=[[1, --, 3],
[--, 5, --],
[7, --, 9]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=999999)
>>> x.put([0,4,8],[10,20,30])
>>> x
masked_array(
data=[[10, --, 3],
[--, 20, --],
[7, --, 30]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=999999)
>>> x.put(4,999)
>>> x
masked_array(
data=[[10, --, 3],
[--, 999, --],
[7, --, 30]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=999999) | numpy.reference.generated.numpy.ma.maskedarray.put |
numpy.ma.MaskedArray.ravel method ma.MaskedArray.ravel(order='C')[source]
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.maskedarray.ravel |
numpy.ma.MaskedArray.repeat method ma.MaskedArray.repeat(repeats, axis=None)[source]
Repeat elements of an array. Refer to numpy.repeat for full documentation. See also numpy.repeat
equivalent function | numpy.reference.generated.numpy.ma.maskedarray.repeat |
numpy.ma.MaskedArray.reshape method ma.MaskedArray.reshape(*s, **kwargs)[source]
Give a new shape to the array without changing its data. Returns a masked array containing the same data, but with a new shape. The result is a view on the original array; if this is not possible, a ValueError is raised. Parameters
shapeint or tuple of ints
The new shape should be compatible with the original shape. If an integer is supplied, then the result will be a 1-D array of that length.
order{‘C’, ‘F’}, optional
Determines whether the array data should be viewed as in C (row-major) or FORTRAN (column-major) order. Returns
reshaped_arrayarray
A new view on the array. See also reshape
Equivalent function in the masked array module. numpy.ndarray.reshape
Equivalent method on ndarray object. numpy.reshape
Equivalent function in the NumPy module. Notes The reshaping operation cannot guarantee that a copy will not be made, to modify the shape in place, use a.shape = s Examples >>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])
>>> x
masked_array(
data=[[--, 2],
[3, --]],
mask=[[ True, False],
[False, True]],
fill_value=999999)
>>> x = x.reshape((4,1))
>>> x
masked_array(
data=[[--],
[2],
[3],
[--]],
mask=[[ True],
[False],
[False],
[ True]],
fill_value=999999) | numpy.reference.generated.numpy.ma.maskedarray.reshape |
numpy.ma.MaskedArray.resize method ma.MaskedArray.resize(newshape, refcheck=True, order=False)[source]
Warning This method does nothing, except raise a ValueError exception. A masked array does not own its data and therefore cannot safely be resized in place. Use the numpy.ma.resize function instead. This method is difficult to implement safely and may be deprecated in future releases of NumPy. | numpy.reference.generated.numpy.ma.maskedarray.resize |
numpy.ma.MaskedArray.round method ma.MaskedArray.round(decimals=0, out=None)[source]
Return each element rounded to the given number of decimals. Refer to numpy.around for full documentation. See also numpy.ndarray.round
corresponding function for ndarrays numpy.around
equivalent function | numpy.reference.generated.numpy.ma.maskedarray.round |
numpy.ma.MaskedArray.searchsorted method ma.MaskedArray.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.ma.maskedarray.searchsorted |
numpy.ma.MaskedArray.set_fill_value method ma.MaskedArray.set_fill_value(value=None)[source] | numpy.reference.generated.numpy.ma.maskedarray.set_fill_value |
numpy.ma.MaskedArray.shrink_mask method ma.MaskedArray.shrink_mask()[source]
Reduce a mask to nomask when possible. Parameters
None
Returns
None
Examples >>> x = np.ma.array([[1,2 ], [3, 4]], mask=[0]*4)
>>> x.mask
array([[False, False],
[False, False]])
>>> x.shrink_mask()
masked_array(
data=[[1, 2],
[3, 4]],
mask=False,
fill_value=999999)
>>> x.mask
False | numpy.reference.generated.numpy.ma.maskedarray.shrink_mask |
numpy.ma.MaskedArray.size attribute ma.MaskedArray.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.ma.maskedarray.size |
numpy.ma.MaskedArray.soften_mask method ma.MaskedArray.soften_mask()[source]
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.maskedarray.soften_mask |
numpy.ma.MaskedArray.sort method ma.MaskedArray.sort(axis=- 1, kind=None, order=None, endwith=True, fill_value=None)[source]
Sort the array, in-place Parameters
aarray_like
Array to be sorted.
axisint, optional
Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis.
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional
The sorting algorithm used.
orderlist, optional
When a is a structured array, this argument specifies which fields to compare first, second, and so on. This list does not need to include all of the fields.
endwith{True, False}, optional
Whether missing values (if any) should be treated as the largest values (True) or the smallest values (False) When the array contains unmasked values sorting at the same extremes of the datatype, the ordering of these values and the masked values is undefined.
fill_valuescalar or None, optional
Value used internally for the masked values. If fill_value is not None, it supersedes endwith. Returns
sorted_arrayndarray
Array of the same type and shape as a. See also numpy.ndarray.sort
Method to sort an array in-place. argsort
Indirect sort. lexsort
Indirect stable sort on multiple keys. searchsorted
Find elements in a sorted array. Notes See sort for notes on the different sorting algorithms. Examples >>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # Default
>>> a.sort()
>>> a
masked_array(data=[1, 3, 5, --, --],
mask=[False, False, False, True, True],
fill_value=999999)
>>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # Put missing values in the front
>>> a.sort(endwith=False)
>>> a
masked_array(data=[--, --, 1, 3, 5],
mask=[ True, True, False, False, False],
fill_value=999999)
>>> a = np.ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])
>>> # fill_value takes over endwith
>>> a.sort(endwith=False, fill_value=3)
>>> a
masked_array(data=[1, --, --, 3, 5],
mask=[False, True, True, False, False],
fill_value=999999) | numpy.reference.generated.numpy.ma.maskedarray.sort |
numpy.ma.MaskedArray.squeeze method ma.MaskedArray.squeeze(axis=None)[source]
Remove axes of length one from a. Refer to numpy.squeeze for full documentation. See also numpy.squeeze
equivalent function | numpy.reference.generated.numpy.ma.maskedarray.squeeze |
numpy.ma.MaskedArray.std method ma.MaskedArray.std(axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)[source]
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.maskedarray.std |
numpy.ma.MaskedArray.strides attribute ma.MaskedArray.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.ma.maskedarray.strides |
numpy.ma.MaskedArray.sum method ma.MaskedArray.sum(axis=None, dtype=None, out=None, keepdims=<no value>)[source]
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.maskedarray.sum |
numpy.ma.MaskedArray.swapaxes method ma.MaskedArray.swapaxes(axis1, axis2)[source]
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.maskedarray.swapaxes |
numpy.ma.MaskedArray.take method ma.MaskedArray.take(indices, axis=None, out=None, mode='raise')[source] | numpy.reference.generated.numpy.ma.maskedarray.take |
numpy.ma.MaskedArray.tobytes method ma.MaskedArray.tobytes(fill_value=None, order='C')[source]
Return the array data as a string containing the raw bytes in the array. The array is filled with a fill value before the string conversion. New in version 1.9.0. Parameters
fill_valuescalar, optional
Value used to fill in the masked values. Default is None, in which case MaskedArray.fill_value is used.
order{‘C’,’F’,’A’}, optional
Order of the data item in the copy. Default is ‘C’. ‘C’ – C order (row major). ‘F’ – Fortran order (column major). ‘A’ – Any, current order of array. None – Same as ‘A’. See also numpy.ndarray.tobytes
tolist, tofile
Notes As for ndarray.tobytes, information about the shape, dtype, etc., but also about fill_value, will be lost. Examples >>> x = np.ma.array(np.array([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])
>>> x.tobytes()
b'\x01\x00\x00\x00\x00\x00\x00\x00?B\x0f\x00\x00\x00\x00\x00?B\x0f\x00\x00\x00\x00\x00\x04\x00\x00\x00\x00\x00\x00\x00' | numpy.reference.generated.numpy.ma.maskedarray.tobytes |
numpy.ma.MaskedArray.tofile method ma.MaskedArray.tofile(fid, sep='', format='%s')[source]
Save a masked array to a file in binary format. Warning This function is not implemented yet. Raises
NotImplementedError
When tofile is called. | numpy.reference.generated.numpy.ma.maskedarray.tofile |
numpy.ma.MaskedArray.toflex method ma.MaskedArray.toflex()[source]
Transforms a masked array into a flexible-type array. The flexible type array that is returned will have two fields: the _data field stores the _data part of the array. the _mask field stores the _mask part of the array. Parameters
None
Returns
recordndarray
A new flexible-type ndarray with two fields: the first element containing a value, the second element containing the corresponding mask boolean. The returned record shape matches self.shape. Notes A side-effect of transforming a masked array into a flexible ndarray is that meta information (fill_value, …) will be lost. Examples >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
>>> x
masked_array(
data=[[1, --, 3],
[--, 5, --],
[7, --, 9]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=999999)
>>> x.toflex()
array([[(1, False), (2, True), (3, False)],
[(4, True), (5, False), (6, True)],
[(7, False), (8, True), (9, False)]],
dtype=[('_data', '<i8'), ('_mask', '?')]) | numpy.reference.generated.numpy.ma.maskedarray.toflex |
numpy.ma.MaskedArray.tolist method ma.MaskedArray.tolist(fill_value=None)[source]
Return the data portion of the masked array as a hierarchical Python list. Data items are converted to the nearest compatible Python type. Masked values are converted to fill_value. If fill_value is None, the corresponding entries in the output list will be None. Parameters
fill_valuescalar, optional
The value to use for invalid entries. Default is None. Returns
resultlist
The Python list representation of the masked array. Examples >>> x = np.ma.array([[1,2,3], [4,5,6], [7,8,9]], mask=[0] + [1,0]*4)
>>> x.tolist()
[[1, None, 3], [None, 5, None], [7, None, 9]]
>>> x.tolist(-999)
[[1, -999, 3], [-999, 5, -999], [7, -999, 9]] | numpy.reference.generated.numpy.ma.maskedarray.tolist |
numpy.ma.MaskedArray.torecords method ma.MaskedArray.torecords()[source]
Transforms a masked array into a flexible-type array. The flexible type array that is returned will have two fields: the _data field stores the _data part of the array. the _mask field stores the _mask part of the array. Parameters
None
Returns
recordndarray
A new flexible-type ndarray with two fields: the first element containing a value, the second element containing the corresponding mask boolean. The returned record shape matches self.shape. Notes A side-effect of transforming a masked array into a flexible ndarray is that meta information (fill_value, …) will be lost. Examples >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
>>> x
masked_array(
data=[[1, --, 3],
[--, 5, --],
[7, --, 9]],
mask=[[False, True, False],
[ True, False, True],
[False, True, False]],
fill_value=999999)
>>> x.toflex()
array([[(1, False), (2, True), (3, False)],
[(4, True), (5, False), (6, True)],
[(7, False), (8, True), (9, False)]],
dtype=[('_data', '<i8'), ('_mask', '?')]) | numpy.reference.generated.numpy.ma.maskedarray.torecords |
numpy.ma.MaskedArray.tostring method ma.MaskedArray.tostring(fill_value=None, order='C')[source]
A compatibility alias for tobytes, with exactly the same behavior. Despite its name, it returns bytes not strs. Deprecated since version 1.19.0. | numpy.reference.generated.numpy.ma.maskedarray.tostring |
numpy.ma.MaskedArray.trace method ma.MaskedArray.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)[source]
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.maskedarray.trace |
numpy.ma.MaskedArray.transpose method ma.MaskedArray.transpose(*axes)[source]
Returns a view of the array with axes transposed. For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis]. For a 2-D array, this is a standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and a.shape = (i[0], i[1], ... i[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]). Parameters
axesNone, tuple of ints, or n ints
None or no argument: reverses the order of the axes. tuple of ints: i in the j-th place in the tuple means a’s i-th axis becomes a.transpose()’s j-th axis.
n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form) Returns
outndarray
View of a, with axes suitably permuted. See also transpose
Equivalent function ndarray.T
Array property returning the array transposed. ndarray.reshape
Give a new shape to an array without changing its data. Examples >>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]]) | numpy.reference.generated.numpy.ma.maskedarray.transpose |
numpy.ma.MaskedArray.unshare_mask method ma.MaskedArray.unshare_mask()[source]
Copy the mask and set the sharedmask flag to False. Whether the mask is shared between masked arrays can be seen from the sharedmask property. unshare_mask ensures the mask is not shared. A copy of the mask is only made if it was shared. See also sharedmask | numpy.reference.generated.numpy.ma.maskedarray.unshare_mask |
numpy.ma.MaskedArray.var method ma.MaskedArray.var(axis=None, dtype=None, out=None, ddof=0, keepdims=<no value>)[source]
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.maskedarray.var |
numpy.ma.MaskedArray.view method ma.MaskedArray.view(dtype=None, type=None, fill_value=None)[source]
Return a view of the MaskedArray data. Parameters
dtypedata-type or ndarray sub-class, optional
Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. As with ndarray.view, dtype can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).
typePython type, optional
Type of the returned view, either ndarray or a subclass. The default None results in type preservation.
fill_valuescalar, optional
The value to use for invalid entries (None by default). If None, then this argument is inferred from the passed dtype, or in its absence the original array, as discussed in the notes below. See also numpy.ndarray.view
Equivalent method on ndarray object. Notes a.view() is used two different ways: a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory. a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory. If fill_value is not specified, but dtype is specified (and is not an ndarray sub-class), the fill_value of the MaskedArray will be reset. If neither fill_value nor dtype are specified (or if dtype is an ndarray sub-class), then the fill value is preserved. Finally, if fill_value is specified, but dtype is not, the fill value is set to the specified value. For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the behavior of the view cannot be predicted just from the superficial appearance of a (shown by print(a)). It also depends on exactly how a is stored in memory. Therefore if a is C-ordered versus fortran-ordered, versus defined as a slice or transpose, etc., the view may give different results. | numpy.reference.generated.numpy.ma.maskedarray.view |
numpy.ma.max ma.max(obj, axis=None, out=None, fill_value=None, keepdims=<no value>)[source]
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.
outarray_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_valuescalar or None, optional
Value used to fill in the masked values. If None, use the output of maximum_fill_value().
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
amaxarray_like
New array holding the result. If out was specified, out is returned. See also ma.maximum_fill_value
Returns the maximum filling value for a given datatype. | numpy.reference.generated.numpy.ma.max |
numpy.ma.maximum_fill_value ma.maximum_fill_value(obj)[source]
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
objndarray, dtype or scalar
An object that can be queried for it’s numeric type. Returns
valscalar
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 | numpy.reference.generated.numpy.ma.maximum_fill_value |
numpy.ma.mean ma.mean(self, axis=None, dtype=None, out=None, keepdims=<no value>) = <numpy.ma.core._frommethod object>
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 | numpy.reference.generated.numpy.ma.mean |
numpy.ma.median ma.median(a, axis=None, out=None, overwrite_input=False, keepdims=False)[source]
Compute the median along the specified axis. Returns the median of the array elements. Parameters
aarray_like
Input array or object that can be converted to an array.
axisint, optional
Axis along which the medians are computed. The default (None) is to compute the median along a flattened version of the array.
outndarray, 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_inputbool, 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.
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. New in version 1.10.0. Returns
medianndarray
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) | numpy.reference.generated.numpy.ma.median |
numpy.ma.min ma.min(obj, axis=None, out=None, fill_value=None, keepdims=<no value>)[source]
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.
outarray_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_valuescalar or None, optional
Value used to fill in the masked values. If None, use the output of minimum_fill_value.
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
aminarray_like
New array holding the result. If out was specified, out is returned. See also ma.minimum_fill_value
Returns the minimum filling value for a given datatype. | numpy.reference.generated.numpy.ma.min |
numpy.ma.minimum_fill_value ma.minimum_fill_value(obj)[source]
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
objndarray, dtype or scalar
An object that can be queried for it’s numeric type. Returns
valscalar
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 | numpy.reference.generated.numpy.ma.minimum_fill_value |
numpy.ma.mr_ ma.mr_ = <numpy.ma.extras.mr_class object>
Translate slice objects to concatenation along the first axis. This is the masked array version of lib.index_tricks.RClass. See also lib.index_tricks.RClass
Examples >>> np.ma.mr_[np.ma.array([1,2,3]), 0, 0, np.ma.array([4,5,6])]
masked_array(data=[1, 2, 3, ..., 4, 5, 6],
mask=False,
fill_value=999999) | numpy.reference.generated.numpy.ma.mr_ |
numpy.ma.nonzero ma.nonzero(self) = <numpy.ma.core._frommethod object>
Return the indices of unmasked elements that are not zero. Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with: a[a.nonzero()]
To group the indices by element, rather than dimension, use instead: np.transpose(a.nonzero())
The result of this is always a 2d array, with a row for each non-zero element. Parameters
None
Returns
tuple_of_arraystuple
Indices of elements that are non-zero. See also numpy.nonzero
Function operating on ndarrays. flatnonzero
Return indices that are non-zero in the flattened version of the input array. numpy.ndarray.nonzero
Equivalent ndarray method. count_nonzero
Counts the number of non-zero elements in the input array. Examples >>> import numpy.ma as ma
>>> x = ma.array(np.eye(3))
>>> x
masked_array(
data=[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]],
mask=False,
fill_value=1e+20)
>>> x.nonzero()
(array([0, 1, 2]), array([0, 1, 2]))
Masked elements are ignored. >>> x[1, 1] = ma.masked
>>> x
masked_array(
data=[[1.0, 0.0, 0.0],
[0.0, --, 0.0],
[0.0, 0.0, 1.0]],
mask=[[False, False, False],
[False, True, False],
[False, False, False]],
fill_value=1e+20)
>>> x.nonzero()
(array([0, 2]), array([0, 2]))
Indices can also be grouped by element. >>> np.transpose(x.nonzero())
array([[0, 0],
[2, 2]])
A common use for nonzero is to find the indices of an array, where a condition is True. Given an array a, the condition a > 3 is a boolean array and since False is interpreted as 0, ma.nonzero(a > 3) yields the indices of the a where the condition is true. >>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
masked_array(
data=[[False, False, False],
[ True, True, True],
[ True, True, True]],
mask=False,
fill_value=True)
>>> ma.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The nonzero method of the condition array can also be called. >>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) | numpy.reference.generated.numpy.ma.nonzero |
numpy.ma.notmasked_contiguous ma.notmasked_contiguous(a, axis=None)[source]
Find contiguous unmasked data in a masked array along the given axis. Parameters
aarray_like
The input array.
axisint, 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
endpointslist
A list of slices (start and end indexes) of unmasked indexes in the array. If the input is 2d and axis is specified, the result is a list of lists. See also
flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges
clump_masked, clump_unmasked
Notes Only accepts 2-D arrays at most. Examples >>> a = np.arange(12).reshape((3, 4))
>>> mask = np.zeros_like(a)
>>> mask[1:, :-1] = 1; mask[0, 1] = 1; mask[-1, 0] = 0
>>> ma = np.ma.array(a, mask=mask)
>>> ma
masked_array(
data=[[0, --, 2, 3],
[--, --, --, 7],
[8, --, --, 11]],
mask=[[False, True, False, False],
[ True, True, True, False],
[False, True, True, False]],
fill_value=999999)
>>> np.array(ma[~ma.mask])
array([ 0, 2, 3, 7, 8, 11])
>>> np.ma.notmasked_contiguous(ma)
[slice(0, 1, None), slice(2, 4, None), slice(7, 9, None), slice(11, 12, None)]
>>> np.ma.notmasked_contiguous(ma, axis=0)
[[slice(0, 1, None), slice(2, 3, None)], [], [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)]] | numpy.reference.generated.numpy.ma.notmasked_contiguous |
numpy.ma.notmasked_edges ma.notmasked_edges(a, axis=None)[source]
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
aarray_like
The input array.
axisint, optional
Axis along which to perform the operation. If None (default), applies to a flattened version of the array. Returns
edgesndarray or list
An array of start and end indexes if there are any masked data in the array. If there are no masked data in the array, edges is a list of the first and last index. See also
flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous
clump_masked, clump_unmasked
Examples >>> a = np.arange(9).reshape((3, 3))
>>> m = np.zeros_like(a)
>>> m[1:, 1:] = 1
>>> am = np.ma.array(a, mask=m)
>>> np.array(am[~am.mask])
array([0, 1, 2, 3, 6])
>>> np.ma.notmasked_edges(am)
array([0, 6]) | numpy.reference.generated.numpy.ma.notmasked_edges |
numpy.ma.ones ma.ones(shape, dtype=None, order='C') = <numpy.ma.core._convert2ma object>
Return a new array of given shape and type, filled with ones. Parameters
shapeint or sequence 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 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.]]) | numpy.reference.generated.numpy.ma.ones |
numpy.ma.ones_like ma.ones_like(*args, **kwargs) = <numpy.ma.core._convert2ma object>
Return an array of ones 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 ones with the same shape and type as a. See also empty_like
Return an empty array 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. ones
Return a new array setting values to one. Examples >>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.ones_like(x)
array([[1, 1, 1],
[1, 1, 1]])
>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.ones_like(y)
array([1., 1., 1.]) | numpy.reference.generated.numpy.ma.ones_like |
numpy.ma.outer ma.outer(a, b)[source]
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 New in version 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 dimensions other than 1D and other operations. np.multiply.outer(a.ravel(), b.ravel()) is the equivalent. tensordot
np.tensordot(a.ravel(), b.ravel(), axes=((), ())) is the equivalent. Notes Masked values are replaced by 0. 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) | numpy.reference.generated.numpy.ma.outer |
numpy.ma.outerproduct ma.outerproduct(a, b)[source]
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 New in version 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 dimensions other than 1D and other operations. np.multiply.outer(a.ravel(), b.ravel()) is the equivalent. tensordot
np.tensordot(a.ravel(), b.ravel(), axes=((), ())) is the equivalent. Notes Masked values are replaced by 0. 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) | numpy.reference.generated.numpy.ma.outerproduct |
numpy.ma.polyfit ma.polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)[source]
Least squares polynomial fit. Note This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] 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 class method is recommended for new code as it is more stable numerically. See the documentation of the method for more information. Parameters
xarray_like, shape (M,)
x-coordinates of the M sample points (x[i], y[i]).
yarray_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.
degint
Degree of the fitting polynomial
rcondfloat, 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.
fullbool, 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.
warray_like, shape (M,), optional
Weights. If not None, the weight w[i] applies to the unsquared residual y[i] - y_hat[i] at x[i]. Ideally the weights are chosen so that the errors of the products w[i]*y[i] all have the same variance. When using inverse-variance weighting, use w[i] = 1/sigma(y[i]). The default value is None.
covbool 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/dof, where dof = M - (deg + 1), 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 w = 1/sigma, with sigma known to be a reliable estimate of the uncertainty. Returns
pndarray, 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
These values are only returned if full == True residuals – sum of squared residuals of the least squares fit
rank – the effective rank of the scaled Vandermonde
coefficient matrix
singular_values – singular values of the scaled Vandermonde
coefficient matrix rcond – value of rcond. For more details, see numpy.linalg.lstsq.
Vndarray, 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 \[E = \sum_{j=0}^k |p(x_j) - y_j|^2\] in the equations: x[0]**n * p[0] + ... + x[0] * p[n-1] + p[n] = y[0]
x[1]**n * p[0] + ... + x[1] * p[n-1] + p[n] = y[1]
...
x[k]**n * p[0] + ... + x[k] * p[n-1] + p[n] = y[k]
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() | numpy.reference.generated.numpy.ma.polyfit |
numpy.ma.power ma.power(a, b, third=None)[source]
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. | numpy.reference.generated.numpy.ma.power |