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Method argpartition
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NumPy | interp method

schedule Aug 12, 2023
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Numpy's interp(~) method computes the linear interpolated values given data points.

Parameters

1. a | array-like

The x-values you want to evaluate at.

2. xp | array-like

A one-dimensional array of x-values.

3. fp | array-like

A one-dimensional array of y-values.

4. left | float | optional

The value to return when a value in a is less than xp[0], that is, we're to the left of the bounds. By default, the first value of fp is returned.

5. right | float | optional

The value to return when a value in a is larger than xp[len(xp)-1], that is, we're to the right of the bounds. By default, the last value of fp is returned.

Return value

A Numpy array holding the y-values that have been linearly interpolated given the data points.

Examples

Basic usage

x_vals_to_evaluate = [1.5, 2.5, 4]
x = [1,2,3]
y = [1,4,2]
interp_y = np.interp(x_vals_to_evaluate, x, y)

# Let's graph the interpolated y-values
plt.plot(x,y, marker="*")
plt.plot(x_vals_to_evaluate, interp_y, "o", color="red")
plt.show()

This gives us the following:

Here, the stars are the data-points we used to construct the linear relationship. The red dots represent the data points that were interpolated at the specified x=[1.5, 2.5, 4]. The corresponding interpolated y-values are as follows:

interp_y
array([2.5, 3. , 2. ])

Finally, look at the red dot on the far right. We tried to interpolate at a x-value that was outside our specified x-bounds (xp), and by default, the returned y-value in this case is the last value of fp (2 in this case).

We could return an arbitrary value in this case by specifying the right parameter.

robocat
Published by Isshin Inada
Edited by 0 others
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