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Cleanup md files, move things to myst.yml
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approx.md

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template: arxiv_nips
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kernelspec:
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display_name: Python 3 (ipykernel)
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---
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# Approximation
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```{include} math.md
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```
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2013
```{code-cell}
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from pylab import *
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from scipy.interpolate import barycentric_interpolate

aquad.md

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---
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# Adaptive quadrature
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```{include} math.md
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```
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```{code-cell}
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#%config InlineBackend.figure_format = 'svg'
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from pylab import *

barycentric.md

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# Barycentric form
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```{include} math.md
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```
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```{code-cell}
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#%config InlineBackend.figure_format = 'svg'
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from pylab import *

best.md

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# Best approximation
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```{include} math.md
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```
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```{code-cell}
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#%config InlineBackend.figure_format = 'svg'
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from pylab import *

bisect.md

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# Bisection method
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```{include} math.md
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```
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```{code-cell}

chebintconv.md

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# Chebyshev approximation
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```{include} math.md
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```
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```{code-cell}
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from pylab import *
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from scipy.interpolate import barycentric_interpolate
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We see that $\log\norm{f - p_n}_\infty$ versus $n$ is a straight line with negative slope, indicating exponential decrease of the error wrt $n$.
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:::
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dft.md

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# Trigonometric interpolation
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```{include} math.md
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```
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```{code-cell}
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#%config InlineBackend.figure_format = 'svg'
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from pylab import *

extquad.md

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# Extrapolation methods
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```{include} math.md
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```
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Extrapolation methods exploit the error estimate to compute better approximations. Such methods can be used for any approximation methods but here we illustrate it for numerical integration.
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## Aitken extrapolation

float.md

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# Computer arithmetic
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```{include} math.md
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```
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```{code-cell}
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#%config InlineBackend.figure_format = 'svg'
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from pylab import *

fourier.md

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# Fourier transform
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```{include} math.md
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```
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```{code-cell}
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#%config InlineBackend.figure_format = 'svg'
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from pylab import *

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