NumPy interp()
The numpy.interp() function performs one-dimensional linear interpolation for a set of given data points.
It estimates the value of a function at intermediate points based on known discrete values.
Syntax
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numpy.interp(x, xp, fp, left=None, right=None, period=None)Parameters
| Parameter | Type | Description |
|---|---|---|
x | array_like | The x-coordinates where interpolation is performed. |
xp | 1-D sequence of floats | The x-coordinates of known data points. Must be increasing unless period is specified. |
fp | 1-D sequence of float or complex | The corresponding y-values of the known data points. |
left | float or complex, optional | Value to return when x is less than the smallest value in xp. Default is fp[0]. |
right | float or complex, optional | Value to return when x is greater than the largest value in xp. Default is fp[-1]. |
period | None or float, optional | Defines a periodic cycle for xp, normalizing values before interpolation. |
Return Value
Returns an array of interpolated values corresponding to the given x values.
The output has the same shape as x.
Examples
1. Basic Linear Interpolation
Performing interpolation at a given point using known data points.
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import numpy as np
# Define known data points
xp = np.array([1, 2, 3, 4, 5]) # x-coordinates
fp = np.array([2, 4, 6, 8, 10]) # Corresponding y-values
# Interpolate for a new x-value
x = 2.5
result = np.interp(x, xp, fp)
print("Interpolated value at x =", x, ":", result)Output:
Interpolated value at x = 2.5 : 5.0
2. Interpolating Multiple Values
Interpolating multiple x-values at once.
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import numpy as np
# Define known data points
xp = np.array([0, 1, 2, 3, 4])
fp = np.array([0, 10, 20, 30, 40])
# Interpolate multiple x-values
x_values = np.array([0.5, 1.5, 2.5, 3.5])
interpolated_values = np.interp(x_values, xp, fp)
print("Interpolated values:", interpolated_values)Output:
Interpolated values: [ 5. 15. 25. 35.]
3. Using left and right Parameters
Handling values outside the interpolation range.
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import numpy as np
# Define known data points
xp = np.array([1, 2, 3, 4, 5])
fp = np.array([10, 20, 30, 40, 50])
# Define x-values, some are outside xp's range
x_values = np.array([0, 2.5, 6])
# Interpolate with left and right values specified
interpolated_values = np.interp(x_values, xp, fp, left=5, right=55)
print("Interpolated values with left and right parameters:", interpolated_values)Output:
Interpolated values with left and right parameters: [ 5. 25. 55.]
4. Using period Parameter for Cyclic Interpolation
Using the period parameter to handle periodic data.
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import numpy as np
# Define known data points for a periodic function
xp = np.array([0, 90, 180, 270, 360]) # Angles in degrees
fp = np.array([0, 1, 0, -1, 0]) # Corresponding sine values
# Define x-values outside the given range
x_values = np.array([450, 540]) # These should wrap within the 0-360 range
# Perform periodic interpolation
interpolated_values = np.interp(x_values, xp, fp, period=360)
print("Interpolated values with periodicity:", interpolated_values)Output:
Interpolated values with periodicity: [-1. 0.]
The period=360 ensures that values are wrapped within the range of known data points, allowing cyclic interpolation.