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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -65,7 +65,7 @@ plt.title(f"filter {wavelet_name}")
## Building a frequency scale for the complex Morlet wavelet
We don't want to waste computations power. Thus we want to put the frequency band for higher frequencies further away than for smaller frequencies. Thus we will use a $2^{N \cdot Scale}$ scale.
We don't want to waste computations power. Thus we want to put the frequency band for higher frequencies further away than for smaller frequencies. Thus we will use a $`2^{N \cdot Scale}`$ scale.
```python
import numpy as np
@ -104,7 +104,7 @@ plt.show()
## Cone of influence for the complex Morlet wavelet
At the edges of the time series, the wavelet is dangling out of the allowed time axis. Thus these values are nonsense and need to be removed. The size of the wavelet is connected to its scale, hence for different scales the bad zone has different sizes. For the complex Morlet wavelet the number of samples are defined by the equation $\sqrt(2) \cdot scale$ ( [A Practical Guide to Wavelet Analysis](https://paos.colorado.edu/research/wavelets/) -> [PDF](https://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf) ). Which looks like this: 
At the edges of the time series, the wavelet is dangling out of the allowed time axis. Thus these values are nonsense and need to be removed. The size of the wavelet is connected to its scale, hence for different scales the bad zone has different sizes. For the complex Morlet wavelet the number of samples are defined by the equation $`\sqrt(2) \cdot scale`$ ( [A Practical Guide to Wavelet Analysis](https://paos.colorado.edu/research/wavelets/) -> [PDF](https://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf) ). Which looks like this: 
```python
cone_of_influence = np.ceil(np.sqrt(2) * wave_scales).astype(dtype=np.int64)