## Methods for Single-Level Discrete Wavelet Transform of 1D Signals in Python (I): Wavelet Decomposition

Master the discrete wavelet decomposition methods using easily understanding examples

The methods for 1D Single-Level Discrete Wavelet Transform in Python will be discussed in the following 3 parts.

(I): Wavelet Decomposition

The decomposition process of Discrete wavelet transform (DWT) has been discussed in a previous post. In this tutorial post, we will talk about two concrete methods to decompose a 1D signal into the first level, or one stage using discrete wavelet transform (DWT). For the method parts, simple and easily understanding examples will be used so that we can master the methods quickly, since we can clearly see what results the methods will generate from simple examples. In the future, we will use these methods to solve complicated real-world cases. You will feel everything very easily to understand if you use times to learn these methods here and the concepts of wavelet transform in previous posts.

### 1. DWT Method

The first method for one stage discrete wavelet decomposition of 1D signal is discrete wavelet transform method (`dwt`).

#### (1) DWT method

A single level (or one stage) discrete wavelet decomposition of 1D signal can be easily realized in the PyWavelets using `dwt` method.

`pywt.dwt(signal, wavelet, mode='symmetric', axis=-1)`

There are 4 parameters in the function, and it returns approximation and detail coefficients at the first level.

Parameters:

• signal: [array_like] Input signal, or data
• wavelet: [Wavelet object or name] Wavelet to use
• mode: [str, optional] Signal extension mode, see Modes.
• axis: [int, optional] Axis over which to compute the DWT. If not given, the last axis is used.

Returns:
(cA, cD): [tuple] Approximation and detail coefficients

For the signal extension modes, we have already discussed it in the previous post.

#### (2) An Example

First let’s import the PyWavelets, and next create a simple signal. Then we decompose it.

#### (i) import pywavelets

`import pywt`

#### (ii) create a signal

Here, suppose we have a signal (S) with values only from 0 to 10.

`S = [1,2,3,4,5,6,7,8,9,10]`

#### (iii) decompose the signal

Here, we use `db2` discrete wavelet and `symmetric` mode. We have already talked about the built-in wavelet families and members in each family of PyWavelets and signal extension modes in previous posts. If you are not familiar with them, please read them first.

`(cA,cD) = pywt.dwt(S,'db2','symmetric')`

In many situations, we also use a variable to pack the approximation and detail coefficients. We unpack them when we use each of them.

`coeffs = pywt.dwt(S,'db2','symmetric')`
`(cA, cD) = coeffs`

Or we unpacked each of them using:

`cA = coeffs[0]cD = coeffs[1]`

#### (iv) display the decomposed coefficients

Now, we can display the approximation and detail coefficients

`print('Approximation Coefficient: \n',cA)print('Detail Coefficient: \n',cD)`

The results look as follows:

`Approximation Coefficient:  [ 1.76776695  2.31078903  5.13921616  7.96764328 10.79607041 13.78858223]Detail Coefficient:  [-6.12372436e-01  1.66533454e-16  3.33066907e-16  2.22044605e-162.22044605e-16  6.12372436e-01]`

### 2. Downcoef Method

In many cases, however, we probably only care about the approximation coefficient or detail coefficient rather than both of them. This process can be called as `Partial decomposition`, which is realized using `downcoef` method.

#### (1) Downcoef Method

`pywt.downcoef(part, data, wavelet, mode='symmetric', level=1)`

#### Parameters:

part: str Coefficients type:

• ‘a’ — approximations coefficient decomposition
• ‘d’ — details coefficient decomposition

data: array_like, Input signal

wavelet: Wavelet name

mode: str, optional. Signal extension mode

level: int, optional. Decomposition level. Default is 1.

#### Returns:

• coeffs: array of coefficients

#### (2) An example

We still use the simple signal (S) with values only from 0 to 10 in the above example.

`import pywt# the signalS = [1,2,3,4,5,6,7,8,9,10]`

Suppose we only need the approximation at the first level.

`cA = pywt.downcoef('a', S, 'db2', 'symmetric',level=1)print(cA)`

The result is as followsL

`[ 1.76776695  2.31078903  5.13921616  7.96764328 10.79607041 13.78858223]`

If you require the detail coefficient, you can use the following method.

`cD = pywt.downcoef('d', S, 'db2', 'symmetric',level=1)print(cD)`

It produces the following result.

`[-6.12372436e-01  1.66533454e-16  3.33066907e-16  2.22044605e-162.22044605e-16  6.12372436e-01]`

If comparing it with the first method, we find that two methods produce the same results.

### 3. Calculate Coefficients Length

Length of coefficients arrays are not same, which depend on the selected mode. From the above results of the example, we can clearly see that the lengths of approximation and detail coefficients are both 6. As you know, for a long signal, we can calculate it easily using function `len()`.

We can see or calculate the decompose length easily after we get the decomposition results. But, can we calculate them before decomposition? The answer is ‘yes’?

I would like to introduce the Pywavelets built-in method to calculate them rather than the mathematic equations.

#### (1) Method

`pywt.dwt_coeff_len(data_len, filter_len, mode='symmetric')`

Parameters:

• data_len: [int] Data length.
• filter_len: [int] Filter length.
• mode: [str, optional] Signal extension mode

Returns:
len: [int] Length of dwt output.

#### (2) An example

We still use the simple signal above.

`w = pywt.Wavelet(‘db2’)pywt.dwt_coeff_len(len(S), filter_len=w.dec_len, mode=’symmetric’)`

The result will be 6.

You can change the modes, and you will find that `periodization` mode (`per`) produces different coefficient length.

`w = pywt.Wavelet(‘db2’)pywt.dwt_coeff_len(len(S), filter_len=w.dec_len, mode=’per’)`

The result is 5.