The numpy.acosh() function is same as that of numpy.arccosh() function.
NumPy acosh()
The numpy.acosh() function computes the inverse hyperbolic cosine (acosh) of each element in an input array.
The function requires input values to be greater than or equal to 1, as acosh(x) is only defined for x ≥ 1.
Syntax
numpy.acosh(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)Parameters
| Parameter | Type | Description |
|---|---|---|
x | array_like | Input values. Must be greater than or equal to 1. |
out | ndarray, None, or tuple of ndarray and None, optional | Optional output array to store the result. If None, a new array is created. |
where | array_like, optional | Boolean mask specifying which elements to compute. Elements where where=False retain their original value. |
casting | str, optional | Defines the casting behavior when computing the function. |
order | str, optional | Memory layout order of the output array. |
dtype | data-type, optional | Defines the data type of the output array. |
subok | bool, optional | Determines if subclasses of ndarray are preserved in the output. |
Return Value
Returns an array with the inverse hyperbolic cosine values of the input elements. If the input is a scalar, a scalar is returned.
Examples
1. Computing acosh of a Single Value
Here, we compute the inverse hyperbolic cosine of a single value.
import numpy as np
# Define a value greater than or equal to 1
x = 2.0
# Compute the inverse hyperbolic cosine
result = np.acosh(x)
# Print the result
print("acosh(2.0):", result)Output:
acosh(2.0): 1.3169578969248166
2. Computing acosh for an Array of Values
We compute the inverse hyperbolic cosine for multiple values provided in an array.
import numpy as np
# Define an array of values (must be ≥ 1)
values = np.array([1, 1.5, 2, 3, 5])
# Compute the inverse hyperbolic cosine for each value
acosh_values = np.acosh(values)
# Print the results
print("Input values:", values)
print("acosh values:", acosh_values)Output:
Input values: [1. 1.5 2. 3. 5. ]
acosh values: [0. 0.96242365 1.3169579 1.76274717 2.29243167]
3. Using the out Parameter
Using an output array to store results instead of creating a new array.
import numpy as np
# Define an array of values (must be ≥ 1)
values = np.array([1, 2, 3, 4])
# Create an output array with the same shape
output_array = np.ndarray(shape=values.shape)
# Compute acosh and store the result in output_array
np.acosh(values, out=output_array)
# Print the results
print("Computed acosh values:", output_array)Output:
Computed acosh values: [0. 1.3169579 1.76274717 2.06343707]
4. Using the where Parameter
Using a condition to compute acosh only for selected elements.
import numpy as np
# Define an array of values (must be ≥ 1)
values = np.array([1, 2, 3, 4])
# Define a mask (compute acosh only where mask is True)
mask = np.array([False, True, True, False])
# Compute acosh values where mask is True
result = np.acosh(values, where=mask)
# Print the results
print("Computed acosh values with mask:", result)Output:
Computed acosh values with mask: [0. 1.3169579 1.76274717 0. ]
The inverse hyperbolic cosine values are computed only for elements where mask=True. The other values remain unchanged.