# NumPy arctan2()

The `numpy.arctan2()` method computes the element-wise arc tangent (inverse tangent) of `y / x`, where `y` and `x` are arrays.

### Example

``````import numpy as np

# create arrays for y and x coordinates
y = np.array([1, -1, 1, -1])
x = np.array([1, 1, -1, -1])

# compute the element-wise arc tangent of y / x
result = np.arctan2(y, x)

# print the resulting angles
print(result)

# Output: [ 0.78539816 -0.78539816  2.35619449 -2.35619449]``````

## arctan2() Syntax

The syntax of the `numpy.arctan2()` method is:

``numpy.arctan2(y, x, out = None, where = True, order = 'K', dtype = None)``

## arctan2() Arguments

The `numpy.arctan2()` method takes the following arguments:

• `y` - an array containing y-coordinate values
• `x` - an array containing x-coordinate values
• `out` (optional) - the output array where the result will be stored
• `where` (optional) - a boolean array or condition specifying which elements should be updated
• `order` (optional) - specifies the order of the output array
• `dtype` (optional) - the data type of the returned output

## arctan2() Return Value

The `numpy.arctan2()` method returns an array with the same shape as y and x, containing the element-wise arc tangent of `y / x`.

## Example 1: Optional out and where Arguments in arctan2()

``````import numpy as np

# create arrays for y and x coordinates
y = np.array([1, -1, 1, -1])
x = np.array([1, 1, -1, -1])

# create a condition array
condition = np.array([True, False, True, False])

# create an array of zeros with the same shape as y, using float data type
result = np.zeros_like(y, dtype = float)

# compute the element-wise arc tangent
# of y / x, using the provided condition
# and store the result in the result array
np.arctan2(y, x, out = result, where = condition)

# print the resulting angles
print(result)``````

Output

`[0.78539816 0.         2.35619449 0.        ]`

In the above example, `arctan2()` calculates the element-wise arc tangent of the division between y and x for elements where the corresponding value in the condition array is `True`.

And by setting `out = result`, the output is stored in the result array.

## Example 2: Use of dtype Argument in arctan2()

``````import numpy as np

# create arrays for y and x coordinates
y = np.array([2.5, -3.8, 1.2, -4.6])
x = np.array([-1.5, 2.7, -3.1, 0.9])

# compute arctan2 with float32 dtype
resultFloat = np.arctan2(y, x, dtype = np.float32)

# compute arctan2 with float64 dtype
resultDouble = np.arctan2(y, x, dtype = np.float64)

# print results
print("Result with float32 dtype:")
print(resultFloat)
print("\nResult with float64 dtype:")
print(resultDouble)``````

Output

```Result with float32 dtype:
[ 2.1112158 -0.9530406  2.772259  -1.3775848]

Result with float64 dtype:
[ 2.11121583 -0.9530406   2.772259   -1.37758484]```

By specifying different `dtype` values, we can control the precision and memory usage of the resulting array.

In this example, resultFloat has the `float32` data type, while resultDouble has the `float64` data type.

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