NumPy arccosh()
The numpy.arccosh() function computes the inverse hyperbolic cosine (arccosh) of each element in an input array.
The function requires input values to be greater than or equal to 1, as arccosh(x) is only defined for x ≥ 1.
Syntax
numpy.arccosh(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 arccosh 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.arccosh(x)
# Print the result
print("arccosh(2.0):", result)Output:
arccosh(2.0): 1.3169578969248166
2. Computing arccosh 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
arccosh_values = np.arccosh(values)
# Print the results
print("Input values:", values)
print("arccosh values:", arccosh_values)Output:
Input values: [1. 1.5 2. 3. 5. ]
arccosh 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 arccosh and store the result in output_array
np.arccosh(values, out=output_array)
# Print the results
print("Computed arccosh values:", output_array)Output:
Computed arccosh values: [0. 1.3169579 1.76274717 2.06343707]
4. Using the where Parameter
Using a condition to compute arccosh 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 arccosh only where mask is True)
mask = np.array([False, True, True, False])
# Compute arccosh values where mask is True
result = np.arccosh(values, where=mask)
# Print the results
print("Computed arccosh values with mask:", result)Output:
Computed arccosh 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.