Obtain the continuous wavelet transform of the signal, and the frequencies of the CWT.,Obtain the continuous wavelet transform of a speech sample using default values.,Create a signal composed of two sinusoids with disjoint support in time. One sinusoid has a frequency of 32 Hz and amplitude equal to 1. The other sinusoid has a frequency of 64 Hz and amplitude equal to 2. The signal is sampled for one second at 1000 Hz. Plot the signal.,This example shows how to generate a MEX file to perform the continuous wavelet transform (CWT) using generated CUDA code.

load mtlb; w = cwt(mtlb);

load mtlb

```
load mtlb
cwt(mtlb, "bump", Fs)
```

cwt(mtlb, Fs)

load kobe

```
plot((1: numel(kobe)). / 60, kobe)
xlabel("Time (mins)")
ylabel("Vertical Acceleration (nm/s^2)")
title("Kobe Earthquake Data")
grid on
axis tight
```

Here's a link to the documentation, github and a basic snippet for usage. It's pretty intuitive to use and has a pretty extended library of implemented wavelets.

```
import pywt
import numpy as np
import matplotlib.pyplot as plt
num_steps = 512
x = np.arange(num_steps)
y = np.sin(2 * np.pi * x / 32)
delta_t = x[1] - x[0]
scales = np.arange(1, num_steps + 1)
wavelet_type = 'morl'
coefs, freqs = pywt.cwt(y, scales, wavelet_type, delta_t)
plt.matshow(coefs)
plt.show()
```

Here's a link to the documentation, github and a basic snippet for usage. This library has a steeper learning curve and the api is not as nice, but supports functionalities such as `cone of influence`

or `significance testing`

.

```
import pycwt as wavelet
import numpy as np
import matplotlib.pyplot as plt
num_steps = 512
x = np.arange(num_steps)
y = np.sin(2 * np.pi * x / 32)
delta_t = x[1] - x[0]
scales = np.arange(1, num_steps + 1)
freqs = 1 / (wavelet.Morlet().flambda() * scales)
wavelet_type = 'morlet'
coefs, scales, freqs, coi, fft, fftfreqs = wavelet.cwt(y, delta_t, wavelet = wavelet_type, freqs = freqs)
plt.matshow(coefs.real)
plt.show()
```

In Matlab using the cwt() function (Continuous 1-D wavelet transform) provided in the Wavelet Toolbox I can specify the scale(s) I want as a parameter to cwt(), and it will return all possible timeshifts:,I want to compute the wavelet of a signal with different scales and timeshifts., The fft method is O (N * log2 (N)) with N = len (scale) + len (data) - 1. It is well suited for large size signals but slightly slower than conv on small ones. Axis over which to compute the CWT. If not given, the last axis is used. Continuous wavelet transform of the input signal for the given scales and wavelet. , 1 week ago Also, Python and MATLAB implementation are shown to compute continuous wavelet transform coefficients in the form of beautiful Scalograms. These Scalograms are very important for the study of CWT of 1-D signals, highlighting their properties such as frequency break, time discontinuity, burst etc.

```
x = [1, 2, 3, 4];
scales = [1, 2, 3];
wavelet_name = 'db1';
coefs = cwt(x, scales, wavelet_name); >> coefs = -0.0000 - 0.0000 - 0.0000 0.0000 - 0.7071 - 0.7071 - 0.7071 - 0.7071 - 1.1553 - 1.1553 - 1.1553 1.7371
```

```
import pywt
import numpy as np
import matplotlib.pyplot as plt num_steps = 512 x = np.arange(num_steps) y = np.sin(2 * np.pi * x / 32) delta_t = x[1] - x[0] scales = np.arange(1, num_steps + 1) wavelet_type = 'morl'
coefs, freqs = pywt.cwt(y, scales, wavelet_type, delta_t) plt.matshow(coefs) plt.show()
```

```
s0 = 6 / fs; % smallest scale ds = 0.001; % spacing between scales NbSc = 3000; % number of scales SCA = {
s0,
ds,
NbSc,
'lin'
}; % specify scales cwtstruct = cwtft({
data,
1 / fs
}, 'scales', SCA);
```

```
import pywt
import numpy as np fs = 1e3 s0 = 6 / fs smax = 3000 wave = 'morl'
scales = np.arange(s0, smax, 1 / fs) Wxx, freq = pywt.cwt(data, scales, wave, sampling_period = 1 / fs) freq = freq * fs
```