doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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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 str... | 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 out... | 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... | 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 fo... | 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 i... | 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 ... | 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 ... | 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 ord... | 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 compl... | 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 t... | 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.c... | 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 o... | 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 ... | 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 str... | 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 locat... | 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. ... | 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... | 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 inv... | 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.... | 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 fla... | 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_fil... | 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 typi... | 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 : ... | 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 ... | 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
A... | 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 n... | 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
A... | 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()]... | 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
corresp... | 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
c... | 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 ... | 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 upd... | 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 ... | 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
... | 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 ... | 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... | 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 sugge... | 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, wh... | 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 ... | 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 i... | 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 ndar... | 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 t... | 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
Retur... | 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 ... | 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
... | 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)... | 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.... | 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, ... | 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 ... | 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... | 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 ... | 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 als... | 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 med... | 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... | 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 ... | 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... | 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(... | 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 ar... | 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
aarra... | 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., nump... | 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 t... | 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. I... | 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 i... | 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 gui... | 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 |
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