Array

analora.array

Contain functions for numpy.ndarrays.

analora.array.check_same_shape

check_same_shape(arrays: Iterable[ndarray]) -> None

Check if arrays have the same shape.

Parameters:

Name	Type	Description	Default
arrays	Iterable[ndarray]	The arrays to check.	required

Raises:

Type	Description
RuntimeError	if the arrays have different shapes.

Example usage:

>>> import numpy as np
>>> from analora.array import check_same_shape
>>> check_same_shape([np.array([1, 0, 0, 1]), np.array([0, 1, 0, 1])])

analora.array.check_square_matrix

check_square_matrix(name: str, array: ndarray) -> None

Check if the input array is a square matrix.

Parameters:

Name	Type	Description	Default
name	str	The name of the variable.	required
array	ndarray	The array to check.	required

Raises:

Type	Description
ValueError	if the array is not a square matrix.

Example usage:

>>> import numpy as np
>>> from analora.array import check_square_matrix
>>> check_square_matrix("var", np.ones((3, 3)))

analora.array.filter_range

filter_range(
    array: ndarray, xmin: float, xmax: float
) -> ndarray

Filter in the values in a given range.

Parameters:

Name	Type	Description	Default
array	ndarray	The input array.	required
xmin	float	The lower bound of the range.	required
xmax	float	The upper bound of the range.	required

Returns:

Type	Description
ndarray	A 1-d array with only the values in the given range.

Example usage:

>>> import numpy as np
>>> from analora.array import filter_range
>>> out = filter_range(np.arange(10), xmin=-1, xmax=5)
>>> out
array([0, 1, 2, 3, 4, 5])

analora.array.find_range

find_range(
    values: ndarray,
    xmin: float | str | None = None,
    xmax: float | str | None = None,
) -> tuple[float, float]

Find a valid range of value.

Parameters:

Name	Type	Description	Default
values	ndarray	The values used to find the quantiles.	required
xmin	float | str | None	The minimum value of the range or its associated quantile. q0.1 means the 10% quantile. 0 is the minimum value and 1 is the maximum value.	None
xmax	float | str | None	The maximum value of the range or its associated quantile. q0.9 means the 90% quantile. 0 is the minimum value and 1 is the maximum value.	None

Returns:

Type	Description
tuple[float, float]	The range of values in the format (min, max). It returns (nan, nan) if the input array is empty.

Example usage:

>>> import numpy as np
>>> from analora.array import find_range
>>> data = np.arange(101)
>>> find_range(data)
(0, 100)
>>> find_range(data, xmin=5, xmax=50)
(5, 50)
>>> find_range(data, xmin="q0.1", xmax="q0.9")
(10.0, 90.0)

analora.array.multi_isnan

multi_isnan(arrays: Sequence[ndarray]) -> ndarray

Test element-wise for NaN for all input arrays and return result as a boolean array.

Parameters:

Name	Type	Description	Default
arrays	Sequence[ndarray]	The input arrays to test. All the arrays must have the same shape.	required

Returns:

Type	Description
ndarray	A boolean array. True where any array is NaN, False otherwise.

Example usage:

>>> import numpy as np
>>> from analora.array import multi_isnan
>>> mask = multi_isnan(
...     [np.array([1, 0, 0, 1, float("nan")]), np.array([1, float("nan"), 0, 1, 1])]
... )
>>> mask
array([False,  True, False, False,  True])

analora.array.nonnan

nonnan(array: ndarray) -> ndarray

Return the non-NaN values of an array.

Parameters:

Name	Type	Description	Default
array	ndarray	The input array.	required

Returns:

Type	Description
ndarray	A 1d array with the non-NaN values of the input array.

Example usage:

>>> import numpy as np
>>> from analora.array import nonnan
>>> nonnan(np.asarray([1, 2, float("nan"), 5, 6]))
array([1., 2., 5., 6.])
>>> nonnan(np.asarray([[1, 2, float("nan")], [4, 5, 6]]))
array([1., 2., 4., 5., 6.])

analora.array.rand_replace

rand_replace(
    arr: ndarray,
    value: Any,
    prob: float = 0.5,
    rng: Generator | None = None,
) -> ndarray

Return an array that contains the same values as the input array, excepts some values are replaced by value.

Parameters:

Name	Type	Description	Default
arr	ndarray	The array with the original values.	required
value	Any	The value used to replace existing values.	required
prob	float	The probability of value replacement. If the value is 0.2, it means each value as 20% chance to be replaced.	0.5
rng	Generator | None	The random number generator used to decide which values are replaced or not. If None, the default random number generator is used.	None

Returns:

Type	Description
ndarray	The generated array.

Example usage:

>>> import numpy as np
>>> from analora.array import rand_replace
>>> rng = np.random.default_rng(42)
>>> out = rand_replace(np.arange(10), value=-1, prob=0.4, rng=rng)