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# How to interpolate a single ("non-piecewise") cubic spline from a set of data points?

By : user3099812
Date : January 11 2021, 03:34 PM
fixed the issue. Will look into that further If you really want a single cubic parabola, you can use numpy.polyfit.
If what you need is a continuous 2nd derivative, then you're indeed after a cubic spline, which is nothing but a collection of cubics on successive intervals (hence piecewise), which are matched up to the 2nd derivative.
code :

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## How to set first and last slope of cubic spline interpolation in scipy.interpolate?

By : Tymianek
Date : March 29 2020, 07:55 AM
hope this fix your issue This is not possible with interp1d. You can get the spline coefficients from splrep though.

## Getting coefficients of a cubic spline from scipy.interpolate.splrep

By : TheQuantumLeviathan
Date : March 29 2020, 07:55 AM
hop of those help? If you want to have the knots in specific locations along the curves you need to use the argument task=-1 of splrep and give an array of interior knots as the t argument.
The knots in t must satisfy the following condition:
code :
``````import numpy as np
import scipy.interpolate

x = np.linspace(0, 10, 20)
y = np.sin(x)

t = np.linspace(0, 10, 10)

tck = scipy.interpolate.splrep(x, y, t=t[1:-1])

F   = scipy.interpolate.PPoly.from_spline(tck)

print(F.x)
print(F.c)

# Accessing coeffs of nth segment: index = k + n - 1
# Eg. for second segment:
print(F.c[:,4])
``````

## How to interpolate semilogx plot with cubic spline or pchip

By : user2294938
Date : March 29 2020, 07:55 AM
may help you . So I have been stuck on this for a while. I am wondering how to interpolate on a semilogx plot using different methods like pchip or cubic spline. So far this is the code that I have. ,
Should I be transforming the data before interpolation?
code :
``````interp_obj = PchipInterpolator(np.log10(x), y)

new_x_vals = np.arange(0.0015, 0.42501, 0.0001)
new_y_vals = interp_obj(np.log10(new_x_vals))
``````

## "Invalid input data" from SciPy's cublic spline interpolation process; bad results from interpolate.bisplrep?

By : ShenI Yang
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further From my past experience with this problem, features[0] and features[1] must be sorted in ascending order for bisplev to work.

## Writing B-spline as Piecewise Cubic

By : user4865938
Date : March 29 2020, 07:55 AM
I wish did fix the issue. B-splines can be converted to piecewise polynomials efficiently.
This can be easily done in Scipy 0.14.0 (to be released in a couple of months) which has scipy.interpolate.PPoly.from_spline.