Module unitexpr.qarray
Numpy array with the additional attribute unit.
None
View Source
"""
Numpy array with the additional attribute `unit`.
"""
from __future__ import annotations
from typing import Callable, Union
import numpy as np
from numpy.core._exceptions import UFuncTypeError
from .errors import OperationNotSupported
from .unit import UnitBase, UnitExprBase
class qarray(np.ndarray):
"""
An array with elements representing the magnitudes of quantity that can be
described by a numerical value and a unit.
`qarray` is a sub-class of ndarray with the additional instance
attributes `unit` and `info`.
Implementation closely follows:
https://numpy.org/devdocs/user/basics.subclassing.html#basics-subclassing
"""
__unit_types = (UnitBase, UnitExprBase)
__slots__ = ("__unit", "__info")
def __new__(
subtype,
shape,
dtype=float,
buffer=None,
offset=0,
strides=None,
order=None,
unit=1.0,
info="",
):
# The call in the next line triggers a call to
# qarray.__array_finalize__
obj = super().__new__(
subtype, shape, dtype, buffer, offset, strides, order
)
obj.unit = unit
obj.__info = info
return obj
def __array_finalize__(self, obj):
# ``self`` is a new object resulting from
# ndarray.__new__(qarray, ...), therefore it only has
# attributes that the ndarray.__new__ constructor gave it -
# i.e. those of a standard ndarray.
#
# We could have got to the ndarray.__new__ call in 3 ways:
# From an explicit constructor - e.g. qarray():
# obj is None
# (we're in the middle of the qarray.__new__
# constructor, and self.unit will be set when we return to
# qarray.__new__)
if obj is None:
return
# From view casting - e.g arr.view(qarray):
# obj is arr
# (type(obj) can be qarray)
# From new-from-template - e.g infoarr[:3]
# type(obj) is qarray
#
# Note that it is here, rather than in the __new__ method,
# that we set the default value for 'unit', because this
# method sees all creation of default objects - with the
# qarray.__new__ constructor, but also with
# arr.view(qarray).
self.__unit = getattr(obj, "unit", 1.0)
@classmethod
def from_input(cls, input, unit=1.0, info="") -> qarray:
"""Constructs a `qarray` from an existing ndarray
or from a (nested) sequence of entries.
"""
obj = np.asarray(input).view(cls)
obj.unit = unit
obj.info = info
return obj
@property
def unit(self):
"""Returns the unit of the object."""
return self.__unit
@unit.setter
def unit(self, value) -> None:
factor = (
value.factor
if isinstance(value, self.__unit_types)
else float(value)
)
if factor == 1.0:
self.__unit = value
return None
if factor == 0.0:
raise ValueError(
f"Could not set unit with zero magnitude: {value}."
)
try:
self *= factor
self.__unit = value / factor
except UFuncTypeError:
cfactor = self.dtype.type(factor)
if cfactor == factor:
self *= cfactor
self.__unit = value / factor
else:
# If factor can not be safely converted to dtype do not
# normalize unit:
self.__unit = value
@property
def info(self):
try:
return self.__info
except AttributeError:
return ""
@info.setter
def info(self, value: str) -> None:
self.__info = value
@property
def base(self):
"""
Returns the quantity in terms of base units.
Note: Returns `self` if the quantity has `unit == 1.0`.
"""
if self.unit == 1.0:
return self
other = self.copy()
other.unit = self.unit.base_expr
return other
def __format__(self, __format_spec: str) -> str:
if self.ndim == 0:
return self.__str__()
return super().__format__(__format_spec)
def __str__(self) -> str:
if self.ndim == 0:
unit = f" {self.unit}" if (self.unit != 1.0) else ""
return super().__str__() + unit
else:
unit = f" unit: {self.unit}" if (self.unit != 1.0) else ""
return super().__str__() + unit
def __repr__(self) -> str:
unit = f", unit={self.unit}" if self.unit != 1.0 else ""
info = f", info={self.info.__repr__()}" if self.info != "" else ""
return super().__repr__()[:-1] + unit + info + ")"
def __add__(self, other) -> qarray:
if isinstance(other, self.__unit_types):
beta = other.scaling_factor(self.unit)
if beta is None:
raise OperationNotSupported(self, other, "+")
return super().__add__(1.0 / beta)
other_unit = getattr(other, "unit", 1.0)
# If units match simply add arrays.
if self.unit == other_unit:
return super().__add__(other)
alpha = other_unit / self.unit
if isinstance(alpha, float):
return super().__add__(other * alpha)
if (
isinstance(alpha, UnitExprBase)
and alpha.base_exponents == alpha.base_exponents_zero
):
return super().__add__(other * alpha.base_factor)
raise OperationNotSupported(self, other, "+")
def __radd__(self, other) -> qarray:
return self.__add__(other)
def __sub__(self, other) -> qarray:
if isinstance(other, self.__unit_types):
beta = other.scaling_factor(self.unit)
if beta is None:
raise OperationNotSupported(self, other, "+")
return super().__sub__(1.0 / beta)
other_unit = getattr(other, "unit", 1.0)
# If units match simply subtract arrays.
if self.unit == other_unit:
return super().__sub__(other)
alpha = other_unit / self.unit
if isinstance(alpha, float):
return super().__sub__(other * alpha)
if (
isinstance(alpha, UnitExprBase)
and alpha.base_exponents == alpha.base_exponents_zero
):
return super().__sub__(other * alpha.base_factor)
raise OperationNotSupported(self, other, "-")
def __mul__(self, other) -> qarray:
"""
Returns the result of multiplying the united ndarray `self`
with `other`.
"""
if isinstance(other, self.__unit_types):
obj = self.copy()
obj.unit = self.unit * other
return obj
obj = super().__mul__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = self.unit * other_unit
return obj
def __rmul__(self, other) -> qarray:
"""
Returns the result of multiplying `other` with the united array
`self`.
"""
if isinstance(other, self.__unit_types):
obj = self.copy()
obj.unit = other * self.unit
return obj
obj = super().__rmul__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = other_unit * self.unit
return obj
def __truediv__(self, other) -> qarray:
"""
Returns the result of dividing the united ndarray `self`
with `other`.
"""
if isinstance(other, self.__unit_types):
obj = self.copy()
obj.unit = self.unit / other
return obj
obj = super().__truediv__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = self.unit / other_unit
return obj
def __rtruediv__(self, other) -> qarray:
"""
Returns the result of dividing `other` by the united ndarray `self`.
"""
if isinstance(other, self.__unit_types):
obj = super().__rtruediv__(other.factor)
obj.unit = other / self.unit
return obj
obj = super().__rtruediv__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = other_unit / self.unit
return obj
def __pow__(self, other: Union[float, int]) -> qarray:
obj = super().__pow__(other)
obj.unit = self.unit.__pow__(other)
return obj
def __abs__(self) -> qarray:
obj = super().__abs__()
obj.unit = self.unit
return obj
def __neg__(self) -> qarray:
obj = super().__neg__()
obj.unit = self.unit
return obj
def __pos__(self) -> qarray:
obj = super().__pos__()
obj.unit = self.unit.__pos__()
return obj
def __eq__(self, other) -> qarray:
result = self.compare(other, super().__eq__)
if result is None:
result = super().__eq__(other)
result.unit = 1.0
result.fill(False)
return result
def __le__(self, other) -> qarray:
result = self.compare(other, super().__le__)
if result is None:
raise OperationNotSupported(self, other, "<=")
return result
def __ge__(self, other) -> qarray:
result = self.compare(other, super().__ge__)
if result is None:
raise OperationNotSupported(self, other, ">=")
return result
def __gt__(self, other) -> qarray:
result = self.compare(other, super().__gt__)
if result is None:
raise OperationNotSupported(self, other, ">")
return result
def __lt__(self, other) -> qarray:
result = self.compare(other, super().__lt__)
if result is None:
raise OperationNotSupported(self, other, "<")
return result
def compare(
self, other, comparison_operator: Callable
) -> Union[qarray, None]:
"""
Generic comparison function that handles input of type `Number`,
`ndarray` and `qarray` using `comparison_operator`.
Returns `None` if the comparison failed due to incompatible units.
"""
if isinstance(other, self.__unit_types):
beta = other.scaling_factor(self.unit)
if beta is None:
return None
obj = comparison_operator(1.0 / beta)
obj.unit = 1.0
return obj
other_unit = getattr(other, "unit", 1.0)
if self.unit == other_unit:
obj = comparison_operator(other)
obj.unit = 1.0
return obj
alpha = other_unit / self.unit
if isinstance(alpha, float):
obj = comparison_operator(other * alpha)
obj.unit = 1.0
return obj
if (
isinstance(alpha, UnitExprBase)
and alpha.base_exponents == alpha.base_exponents_zero
):
obj = comparison_operator(other * alpha.base_factor)
obj.unit = 1.0
return obj
return None
Classes
qarray
class qarray(
/,
*args,
**kwargs
)
View Source
class qarray(np.ndarray):
"""
An array with elements representing the magnitudes of quantity that can be
described by a numerical value and a unit.
`qarray` is a sub-class of ndarray with the additional instance
attributes `unit` and `info`.
Implementation closely follows:
https://numpy.org/devdocs/user/basics.subclassing.html#basics-subclassing
"""
__unit_types = (UnitBase, UnitExprBase)
__slots__ = ("__unit", "__info")
def __new__(
subtype,
shape,
dtype=float,
buffer=None,
offset=0,
strides=None,
order=None,
unit=1.0,
info="",
):
# The call in the next line triggers a call to
# qarray.__array_finalize__
obj = super().__new__(
subtype, shape, dtype, buffer, offset, strides, order
)
obj.unit = unit
obj.__info = info
return obj
def __array_finalize__(self, obj):
# ``self`` is a new object resulting from
# ndarray.__new__(qarray, ...), therefore it only has
# attributes that the ndarray.__new__ constructor gave it -
# i.e. those of a standard ndarray.
#
# We could have got to the ndarray.__new__ call in 3 ways:
# From an explicit constructor - e.g. qarray():
# obj is None
# (we're in the middle of the qarray.__new__
# constructor, and self.unit will be set when we return to
# qarray.__new__)
if obj is None:
return
# From view casting - e.g arr.view(qarray):
# obj is arr
# (type(obj) can be qarray)
# From new-from-template - e.g infoarr[:3]
# type(obj) is qarray
#
# Note that it is here, rather than in the __new__ method,
# that we set the default value for 'unit', because this
# method sees all creation of default objects - with the
# qarray.__new__ constructor, but also with
# arr.view(qarray).
self.__unit = getattr(obj, "unit", 1.0)
@classmethod
def from_input(cls, input, unit=1.0, info="") -> qarray:
"""Constructs a `qarray` from an existing ndarray
or from a (nested) sequence of entries.
"""
obj = np.asarray(input).view(cls)
obj.unit = unit
obj.info = info
return obj
@property
def unit(self):
"""Returns the unit of the object."""
return self.__unit
@unit.setter
def unit(self, value) -> None:
factor = (
value.factor
if isinstance(value, self.__unit_types)
else float(value)
)
if factor == 1.0:
self.__unit = value
return None
if factor == 0.0:
raise ValueError(
f"Could not set unit with zero magnitude: {value}."
)
try:
self *= factor
self.__unit = value / factor
except UFuncTypeError:
cfactor = self.dtype.type(factor)
if cfactor == factor:
self *= cfactor
self.__unit = value / factor
else:
# If factor can not be safely converted to dtype do not
# normalize unit:
self.__unit = value
@property
def info(self):
try:
return self.__info
except AttributeError:
return ""
@info.setter
def info(self, value: str) -> None:
self.__info = value
@property
def base(self):
"""
Returns the quantity in terms of base units.
Note: Returns `self` if the quantity has `unit == 1.0`.
"""
if self.unit == 1.0:
return self
other = self.copy()
other.unit = self.unit.base_expr
return other
def __format__(self, __format_spec: str) -> str:
if self.ndim == 0:
return self.__str__()
return super().__format__(__format_spec)
def __str__(self) -> str:
if self.ndim == 0:
unit = f" {self.unit}" if (self.unit != 1.0) else ""
return super().__str__() + unit
else:
unit = f" unit: {self.unit}" if (self.unit != 1.0) else ""
return super().__str__() + unit
def __repr__(self) -> str:
unit = f", unit={self.unit}" if self.unit != 1.0 else ""
info = f", info={self.info.__repr__()}" if self.info != "" else ""
return super().__repr__()[:-1] + unit + info + ")"
def __add__(self, other) -> qarray:
if isinstance(other, self.__unit_types):
beta = other.scaling_factor(self.unit)
if beta is None:
raise OperationNotSupported(self, other, "+")
return super().__add__(1.0 / beta)
other_unit = getattr(other, "unit", 1.0)
# If units match simply add arrays.
if self.unit == other_unit:
return super().__add__(other)
alpha = other_unit / self.unit
if isinstance(alpha, float):
return super().__add__(other * alpha)
if (
isinstance(alpha, UnitExprBase)
and alpha.base_exponents == alpha.base_exponents_zero
):
return super().__add__(other * alpha.base_factor)
raise OperationNotSupported(self, other, "+")
def __radd__(self, other) -> qarray:
return self.__add__(other)
def __sub__(self, other) -> qarray:
if isinstance(other, self.__unit_types):
beta = other.scaling_factor(self.unit)
if beta is None:
raise OperationNotSupported(self, other, "+")
return super().__sub__(1.0 / beta)
other_unit = getattr(other, "unit", 1.0)
# If units match simply subtract arrays.
if self.unit == other_unit:
return super().__sub__(other)
alpha = other_unit / self.unit
if isinstance(alpha, float):
return super().__sub__(other * alpha)
if (
isinstance(alpha, UnitExprBase)
and alpha.base_exponents == alpha.base_exponents_zero
):
return super().__sub__(other * alpha.base_factor)
raise OperationNotSupported(self, other, "-")
def __mul__(self, other) -> qarray:
"""
Returns the result of multiplying the united ndarray `self`
with `other`.
"""
if isinstance(other, self.__unit_types):
obj = self.copy()
obj.unit = self.unit * other
return obj
obj = super().__mul__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = self.unit * other_unit
return obj
def __rmul__(self, other) -> qarray:
"""
Returns the result of multiplying `other` with the united array
`self`.
"""
if isinstance(other, self.__unit_types):
obj = self.copy()
obj.unit = other * self.unit
return obj
obj = super().__rmul__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = other_unit * self.unit
return obj
def __truediv__(self, other) -> qarray:
"""
Returns the result of dividing the united ndarray `self`
with `other`.
"""
if isinstance(other, self.__unit_types):
obj = self.copy()
obj.unit = self.unit / other
return obj
obj = super().__truediv__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = self.unit / other_unit
return obj
def __rtruediv__(self, other) -> qarray:
"""
Returns the result of dividing `other` by the united ndarray `self`.
"""
if isinstance(other, self.__unit_types):
obj = super().__rtruediv__(other.factor)
obj.unit = other / self.unit
return obj
obj = super().__rtruediv__(other)
other_unit = getattr(other, "unit", 1.0)
obj.unit = other_unit / self.unit
return obj
def __pow__(self, other: Union[float, int]) -> qarray:
obj = super().__pow__(other)
obj.unit = self.unit.__pow__(other)
return obj
def __abs__(self) -> qarray:
obj = super().__abs__()
obj.unit = self.unit
return obj
def __neg__(self) -> qarray:
obj = super().__neg__()
obj.unit = self.unit
return obj
def __pos__(self) -> qarray:
obj = super().__pos__()
obj.unit = self.unit.__pos__()
return obj
def __eq__(self, other) -> qarray:
result = self.compare(other, super().__eq__)
if result is None:
result = super().__eq__(other)
result.unit = 1.0
result.fill(False)
return result
def __le__(self, other) -> qarray:
result = self.compare(other, super().__le__)
if result is None:
raise OperationNotSupported(self, other, "<=")
return result
def __ge__(self, other) -> qarray:
result = self.compare(other, super().__ge__)
if result is None:
raise OperationNotSupported(self, other, ">=")
return result
def __gt__(self, other) -> qarray:
result = self.compare(other, super().__gt__)
if result is None:
raise OperationNotSupported(self, other, ">")
return result
def __lt__(self, other) -> qarray:
result = self.compare(other, super().__lt__)
if result is None:
raise OperationNotSupported(self, other, "<")
return result
def compare(
self, other, comparison_operator: Callable
) -> Union[qarray, None]:
"""
Generic comparison function that handles input of type `Number`,
`ndarray` and `qarray` using `comparison_operator`.
Returns `None` if the comparison failed due to incompatible units.
"""
if isinstance(other, self.__unit_types):
beta = other.scaling_factor(self.unit)
if beta is None:
return None
obj = comparison_operator(1.0 / beta)
obj.unit = 1.0
return obj
other_unit = getattr(other, "unit", 1.0)
if self.unit == other_unit:
obj = comparison_operator(other)
obj.unit = 1.0
return obj
alpha = other_unit / self.unit
if isinstance(alpha, float):
obj = comparison_operator(other * alpha)
obj.unit = 1.0
return obj
if (
isinstance(alpha, UnitExprBase)
and alpha.base_exponents == alpha.base_exponents_zero
):
obj = comparison_operator(other * alpha.base_factor)
obj.unit = 1.0
return obj
return None
Ancestors (in MRO)
- numpy.ndarray
Class variables
T
ctypes
data
dtype
flags
flat
imag
itemsize
nbytes
ndim
real
shape
size
strides
Static methods
from_input
def from_input(
input,
unit=1.0,
info=''
) -> 'qarray'
Constructs a qarray from an existing ndarray
or from a (nested) sequence of entries.
View Source
@classmethod
def from_input(cls, input, unit=1.0, info="") -> qarray:
"""Constructs a `qarray` from an existing ndarray
or from a (nested) sequence of entries.
"""
obj = np.asarray(input).view(cls)
obj.unit = unit
obj.info = info
return obj
Instance variables
base
Returns the quantity in terms of base units.
Note: Returns self if the quantity has unit == 1.0.
info
unit
Returns the unit of the object.
Methods
all
def all(
...
)
a.all(axis=None, out=None, keepdims=False, *, where=True)
Returns True if all elements evaluate to True.
Refer to numpy.all for full documentation.
any
def any(
...
)
a.any(axis=None, out=None, keepdims=False, *, where=True)
Returns True if any of the elements of a evaluate to True.
Refer to numpy.any for full documentation.
argmax
def argmax(
...
)
a.argmax(axis=None, out=None)
Return indices of the maximum values along the given axis.
Refer to numpy.argmax for full documentation.
argmin
def argmin(
...
)
a.argmin(axis=None, out=None)
Return indices of the minimum values along the given axis.
Refer to numpy.argmin for detailed documentation.
argpartition
def argpartition(
...
)
a.argpartition(kth, axis=-1, kind='introselect', order=None)
Returns the indices that would partition this array.
Refer to numpy.argpartition for full documentation.
.. versionadded:: 1.8.0
argsort
def argsort(
...
)
a.argsort(axis=-1, kind=None, order=None)
Returns the indices that would sort this array.
Refer to numpy.argsort for full documentation.
astype
def astype(
...
)
a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| dtype | str or dtype | Typecode or data-type to which the array is cast. | None |
| order | {'C', 'F', 'A', 'K'} | 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'. | is | ||
| casting | {'no', 'equiv', 'safe', 'same_kind', 'unsafe'} | 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. | s |
| subok | bool | If True, then sub-classes will be passed-through (default), otherwise
the returned array will be forced to be a base-class array. | None |
| copy | bool | By default, astype always returns a newly allocated array. If this
is set to false, and the
dtype,order, andsubokrequirements are satisfied, the input array is returned instead of a copy. | None |
Returns:
| Type | Description |
|---|---|
| ndarray | 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:
| Type | Description |
|---|---|
| ComplexWarning | When casting from complex to float or int. To avoid this, |
one should use a.real.astype(t). |
byteswap
def byteswap(
...
)
a.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:
| Name | Type | Description | Default |
|---|---|---|---|
| inplace | bool | If True, swap bytes in-place, default is False. |
is |
Returns:
| Type | Description |
|---|---|
| ndarray | The byteswapped array. If inplace is True, this is |
| a view to self. |
choose
def choose(
...
)
a.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.
clip
def clip(
...
)
a.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.
compare
def compare(
self,
other,
comparison_operator: 'Callable'
) -> 'Union[qarray, None]'
Generic comparison function that handles input of type Number,
ndarray and qarray using comparison_operator.
Returns None if the comparison failed due to incompatible units.
View Source
def compare(
self, other, comparison_operator: Callable
) -> Union[qarray, None]:
"""
Generic comparison function that handles input of type `Number`,
`ndarray` and `qarray` using `comparison_operator`.
Returns `None` if the comparison failed due to incompatible units.
"""
if isinstance(other, self.__unit_types):
beta = other.scaling_factor(self.unit)
if beta is None:
return None
obj = comparison_operator(1.0 / beta)
obj.unit = 1.0
return obj
other_unit = getattr(other, "unit", 1.0)
if self.unit == other_unit:
obj = comparison_operator(other)
obj.unit = 1.0
return obj
alpha = other_unit / self.unit
if isinstance(alpha, float):
obj = comparison_operator(other * alpha)
obj.unit = 1.0
return obj
if (
isinstance(alpha, UnitExprBase)
and alpha.base_exponents == alpha.base_exponents_zero
):
obj = comparison_operator(other * alpha.base_factor)
obj.unit = 1.0
return obj
return None
compress
def compress(
...
)
a.compress(condition, axis=None, out=None)
Return selected slices of this array along given axis.
Refer to numpy.compress for full documentation.
conj
def conj(
...
)
a.conj()
Complex-conjugate all elements.
Refer to numpy.conjugate for full documentation.
conjugate
def conjugate(
...
)
a.conjugate()
Return the complex conjugate, element-wise.
Refer to numpy.conjugate for full documentation.
copy
def copy(
...
)
a.copy(order='C')
Return a copy of the array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| order | {'C', 'F', 'A', 'K'} | Controls the memory layout of the copy. 'C' means C-order, | |
'F' means F-order, 'A' means 'F' if a is Fortran contiguous, |
|||
'C' otherwise. 'K' means match the layout of a as closely |
|||
as possible. (Note that this function and :func:numpy.copy are very |
|||
| similar but have different default values for their order= | |||
| arguments, and this function always passes sub-classes through.) | values | ||
| See also | None | None | None |
| -------- | None | None | None |
| numpy.copy | Similar function with different default behavior | None | None |
| numpy.copyto | None | None | None |
cumprod
def cumprod(
...
)
a.cumprod(axis=None, dtype=None, out=None)
Return the cumulative product of the elements along the given axis.
Refer to numpy.cumprod for full documentation.
cumsum
def cumsum(
...
)
a.cumsum(axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along the given axis.
Refer to numpy.cumsum for full documentation.
diagonal
def diagonal(
...
)
a.diagonal(offset=0, axis1=0, axis2=1)
Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.
Refer to :func:numpy.diagonal for full documentation.
dot
def dot(
...
)
a.dot(b, out=None)
Dot product of two arrays.
Refer to numpy.dot for full documentation.
dump
def dump(
...
)
a.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:
| Name | Type | Description | Default |
|---|---|---|---|
| file | str or Path | A string naming the dump file. |
.. versionchanged:: 1.17.0
pathlib.Path objects are now accepted. | None |
dumps
def dumps(
...
)
a.dumps()
Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| None | None | None | None |
fill
def fill(
...
)
a.fill(value)
Fill the array with a scalar value.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| value | scalar | All elements of a will be assigned this value. |
None |
flatten
def flatten(
...
)
a.flatten(order='C')
Return a copy of the array collapsed into one dimension.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| order | {'C', 'F', 'A', 'K'} | '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'. | is |
Returns:
| Type | Description |
|---|---|
| ndarray | A copy of the input array, flattened to one dimension. |
getfield
def getfield(
...
)
a.getfield(dtype, offset=0)
Returns a field of the given array as a certain type.
A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| dtype | str or dtype | The data type of the view. The dtype size of the view can not be larger | |
| than that of the array itself. | None | ||
| offset | int | Number of bytes to skip before beginning the element view. | None |
item
def item(
...
)
a.item(*args)
Copy an element of an array to a standard Python scalar and return it.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| *args | Arguments (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. | None |
Returns:
| Type | Description |
|---|---|
| Standard Python scalar object | A copy of the specified element of the array as a suitable |
| Python scalar |
itemset
def itemset(
...
)
a.itemset(*args)
Insert scalar into an array (scalar is cast to array's dtype, if possible)
There must be at least 1 argument, and define the last argument
as item. Then, a.itemset(*args) is equivalent to but faster
than a[args] = item. The item should be a scalar value and args
must select a single item in the array a.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| *args | Arguments | If one argument: a scalar, only used in case a is of size 1. |
|
| If two arguments: the last argument is the value to be set | |||
| and must be a scalar, the first argument specifies a single array | |||
| element location. It is either an int or a tuple. | None |
max
def max(
...
)
a.max(axis=None, out=None, keepdims=False, initial=
Return the maximum along a given axis.
Refer to numpy.amax for full documentation.
mean
def mean(
...
)
a.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)
Returns the average of the array elements along given axis.
Refer to numpy.mean for full documentation.
min
def min(
...
)
a.min(axis=None, out=None, keepdims=False, initial=
Return the minimum along a given axis.
Refer to numpy.amin for full documentation.
newbyteorder
def newbyteorder(
...
)
arr.newbyteorder(new_order='S', /)
Return the array with the same data viewed with a different byte order.
Equivalent to::
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and sub-arrays of the array data type.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| new_order | string | Byte order to force; a value from the byte order specifications | |
below. new_order codes can be any of: |
- 'S' - swap dtype from current to opposite endian
- {'<', 'little'} - little endian
- {'>', 'big'} - big endian
- '=' - native order, equivalent to
sys.byteorder - {'|', 'I'} - ignore (no change to byte order)
The default value ('S') results in swapping the current byte order. | value |
Returns:
| Type | Description |
|---|---|
| array | New array object with the dtype reflecting given change to the |
| byte order. |
nonzero
def nonzero(
...
)
a.nonzero()
Return the indices of the elements that are non-zero.
Refer to numpy.nonzero for full documentation.
partition
def partition(
...
)
a.partition(kth, axis=-1, kind='introselect', order=None)
Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.
.. versionadded:: 1.8.0
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| kth | int or sequence of ints | Element index to partition by. The kth element value will be in its | |
| final sorted position and all smaller elements will be moved before it | |||
| and all equal or greater elements behind it. | |||
| The order of all elements in the partitions is undefined. | |||
| If provided with a sequence of kth it will partition all elements | |||
| indexed by kth of them into their sorted position at once. | None | ||
| axis | int | Axis along which to sort. Default is -1, which means sort along the | |
| last axis. | -1 | ||
| kind | {'introselect'} | Selection algorithm. Default is 'introselect'. | is |
| order | str or list of str | When a is an array with fields defined, this argument specifies |
|
| which fields to compare first, second, etc. A single field can | |||
| be specified as a string, and not all fields need to be specified, | |||
| but unspecified fields will still be used, in the order in which | |||
| they come up in the dtype, to break ties. | None |
prod
def prod(
...
)
a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)
Return the product of the array elements over the given axis
Refer to numpy.prod for full documentation.
ptp
def ptp(
...
)
a.ptp(axis=None, out=None, keepdims=False)
Peak to peak (maximum - minimum) value along a given axis.
Refer to numpy.ptp for full documentation.
put
def put(
...
)
a.put(indices, values, mode='raise')
Set a.flat[n] = values[n] for all n in indices.
Refer to numpy.put for full documentation.
ravel
def ravel(
...
)
a.ravel([order])
Return a flattened array.
Refer to numpy.ravel for full documentation.
repeat
def repeat(
...
)
a.repeat(repeats, axis=None)
Repeat elements of an array.
Refer to numpy.repeat for full documentation.
reshape
def reshape(
...
)
a.reshape(shape, order='C')
Returns an array containing the same data with a new shape.
Refer to numpy.reshape for full documentation.
resize
def resize(
...
)
a.resize(new_shape, refcheck=True)
Change shape and size of array in-place.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| new_shape | tuple of ints, or n ints |
Shape of resized array. | None |
| refcheck | bool | If False, reference count will not be checked. Default is True. | True. |
Returns:
| Type | Description |
|---|---|
| None | None |
Raises:
| Type | Description |
|---|---|
| ValueError | If a does not own its own data or references or views to it exist, |
| and the data memory must be changed. | |
| PyPy only: will always raise if the data memory must be changed, since | |
| there is no reliable way to determine if references or views to it | |
| exist. | |
| SystemError | If the order keyword argument is specified. This behaviour is a |
| bug in NumPy. |
round
def round(
...
)
a.round(decimals=0, out=None)
Return a with each element rounded to the given number of decimals.
Refer to numpy.around for full documentation.
searchsorted
def searchsorted(
...
)
a.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
setfield
def setfield(
...
)
a.setfield(val, dtype, offset=0)
Put a value into a specified place in a field defined by a data-type.
Place val into a's field defined by dtype and beginning offset
bytes into the field.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| val | object | Value to be placed in field. | None |
| dtype | dtype object | Data-type of the field in which to place val. |
None |
| offset | int | The number of bytes into the field at which to place val. |
None |
Returns:
| Type | Description |
|---|---|
| None | None |
setflags
def setflags(
...
)
a.setflags(write=None, align=None, uic=None)
Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively.
These Boolean-valued flags affect how numpy interprets the memory
area used by a (see Notes below). The ALIGNED flag can only
be set to True if the data is actually aligned according to the type.
The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set
to True. The flag WRITEABLE can only be set to True if the array owns its
own memory, or the ultimate owner of the memory exposes a writeable buffer
interface, or is a string. (The exception for string is made so that
unpickling can be done without copying memory.)
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| write | bool | Describes whether or not a can be written to. |
None |
| align | bool | Describes whether or not a is aligned properly for its type. |
None |
| uic | bool | Describes whether or not a is a copy of another "base" array. |
None |
sort
def sort(
...
)
a.sort(axis=-1, kind=None, order=None)
Sort an array in-place. Refer to numpy.sort for full documentation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| axis | int | Axis along which to sort. Default is -1, which means sort along the | |
| last axis. | -1 | ||
| kind | {'quicksort', 'mergesort', 'heapsort', 'stable'} | Sorting algorithm. The default is 'quicksort'. Note that both 'stable' | |
| and 'mergesort' use timsort under the covers and, in general, the | |||
| actual implementation will vary with datatype. The 'mergesort' option | |||
| is retained for backwards compatibility. |
.. versionchanged:: 1.15.0
The 'stable' option was added. | is |
| order | str or list of str | When a is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties. | None |
squeeze
def squeeze(
...
)
a.squeeze(axis=None)
Remove axes of length one from a.
Refer to numpy.squeeze for full documentation.
std
def std(
...
)
a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
Returns the standard deviation of the array elements along given axis.
Refer to numpy.std for full documentation.
sum
def sum(
...
)
a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)
Return the sum of the array elements over the given axis.
Refer to numpy.sum for full documentation.
swapaxes
def swapaxes(
...
)
a.swapaxes(axis1, axis2)
Return a view of the array with axis1 and axis2 interchanged.
Refer to numpy.swapaxes for full documentation.
take
def take(
...
)
a.take(indices, axis=None, out=None, mode='raise')
Return an array formed from the elements of a at the given indices.
Refer to numpy.take for full documentation.
tobytes
def tobytes(
...
)
a.tobytes(order='C')
Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of
data memory. The bytes object is produced in C-order by default.
This behavior is controlled by the order parameter.
.. versionadded:: 1.9.0
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| order | {'C', 'F', 'A'} | Controls the memory layout of the bytes object. 'C' means C-order, | |
'F' means F-order, 'A' (short for Any) means 'F' if a is |
|||
| Fortran contiguous, 'C' otherwise. Default is 'C'. | is |
Returns:
| Type | Description |
|---|---|
| bytes | Python bytes exhibiting a copy of a's raw data. |
tofile
def tofile(
...
)
a.tofile(fid, sep="", format="%s")
Write array to a file as text or binary (default).
Data is always written in 'C' order, independent of the order of a.
The data produced by this method can be recovered using the function
fromfile().
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| fid | file or str or Path | An open file object, or a string containing a filename. |
.. versionchanged:: 1.17.0
pathlib.Path objects are now accepted. | None |
| sep | str | Separator between array items for text output.
If "" (empty), a binary file is written, equivalent to
file.write(a.tobytes()). | None |
| format | str | Format string for text file output.
Each entry in the array is formatted to text by first converting
it to the closest Python type, and then using "format" % item. | None |
tolist
def tolist(
...
)
a.tolist()
Return the array as an a.ndim-levels deep nested list of Python scalars.
Return a copy of the array data as a (nested) Python list.
Data items are converted to the nearest compatible builtin Python type, via
the ~numpy.ndarray.item function.
If a.ndim is 0, then since the depth of the nested list is 0, it will
not be a list at all, but a simple Python scalar.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| none | None | None | None |
Returns:
| Type | Description |
|---|---|
| object, or list of object, or list of list of object, or ... | The possibly nested list of array elements. |
tostring
def tostring(
...
)
a.tostring(order='C')
A compatibility alias for tobytes, with exactly the same behavior.
Despite its name, it returns bytes not str\ s.
trace
def trace(
...
)
a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
Return the sum along diagonals of the array.
Refer to numpy.trace for full documentation.
transpose
def transpose(
...
)
a.transpose(*axes)
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:
| Name | Type | Description | Default |
|---|---|---|---|
| axes | None, tuple of ints, or n ints |
* None or no argument: reverses the order of the axes. |
-
tuple of ints:
iin thej-th place in the tuple meansa'si-th axis becomesa.transpose()'sj-th axis. -
nints: same as an n-tuple of the same ints (this form is intended simply as a "convenience" alternative to the tuple form) | None |
Returns:
| Type | Description |
|---|---|
| ndarray | View of a, with axes suitably permuted. |
var
def var(
...
)
a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)
Returns the variance of the array elements, along given axis.
Refer to numpy.var for full documentation.
view
def view(
...
)
a.view([dtype][, type])
New view of array with the same data.
.. note::
Passing None for dtype is different from omitting the parameter,
since the former invokes dtype(None) which is an alias for
dtype('float_').
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
| dtype | data-type or ndarray sub-class | Data-type descriptor of the returned view, e.g., float32 or int16. | |
Omitting it results in the view having the same data-type as a. |
|||
| This argument 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). |
None | ||
| type | Python type | Type of the returned view, e.g., ndarray or matrix. Again, omission | |
| of the parameter results in type preservation. | None |