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https://github.com/happybono/sonatasmooth
Provides three different noise reduction algorithms for smoothing out data : Rectangular Averaging, Binomial Median Filtering, and Binomial Averaging. It processes data from a list and displays the results in another list.
https://github.com/happybono/sonatasmooth
algorithms average binomial binomial-coefficient binomial-theorem calibration csharp data-analysis data-calibration dynamic-noise-reduction median noise-algorithms noise-reduction noise-reduction-kernel outliers rectangular-averaging windows-desktop windows-desktop-application windows-forms winforms
Last synced: about 4 hours ago
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Provides three different noise reduction algorithms for smoothing out data : Rectangular Averaging, Binomial Median Filtering, and Binomial Averaging. It processes data from a list and displays the results in another list.
- Host: GitHub
- URL: https://github.com/happybono/sonatasmooth
- Owner: happybono
- License: mit
- Created: 2025-01-18T14:33:54.000Z (24 days ago)
- Default Branch: main
- Last Pushed: 2025-02-11T12:29:02.000Z (about 7 hours ago)
- Last Synced: 2025-02-11T13:25:46.237Z (about 6 hours ago)
- Topics: algorithms, average, binomial, binomial-coefficient, binomial-theorem, calibration, csharp, data-analysis, data-calibration, dynamic-noise-reduction, median, noise-algorithms, noise-reduction, noise-reduction-kernel, outliers, rectangular-averaging, windows-desktop, windows-desktop-application, windows-forms, winforms
- Language: C#
- Homepage:
- Size: 227 KB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SonataSmooth
This tool implements three different noise reduction algorithms for smoothing data: Rectangular Averaging, Binomial Median Filtering, and Binomial Averaging. It processes data from a list and displays the results in another list.
## What's New
Click to Expand
### v1.0.0.0
#### January 19, 2025
>[Initial release.](https://github.com/happybono/SonataSmooth/commit/1c9911992e2b0ec6b984828519ac78cbcb5a0a51)
### v1.0.1.0
#### January 19, 2025
> [Minor bugs fixed.](https://github.com/happybono/SonataSmooth/commit/a8a9cfd481aa7616bdbc14e27d71a9a6616d171b)
> [Explained NoiseReductionKernelWidth and updated algorithm details in README.md.](https://github.com/happybono/SonataSmooth/commit/dbad0337d5c7534902db7f22f6dc23ff60a54a4e)
### v1.0.2.0
#### January 20, 2025
> [Bugs fixed.](https://github.com/happybono/SonataSmooth/commit/f7d0568b4ebf30ed7868885a9bff92960e757b13)
## Features & Algorithms
1. **Rectangular Method**
- **How it works**: This method calculates the average of a window of values centered around each data point. The width of the window (kernel width) determines the number of neighboring data points included in the averaging process.
- **Principle**: By averaging the values within the window, the noise (random fluctuations) is smoothed out, resulting in a clearer, more stable signal.
- **Code Implementation**:
```csharp
// Rectangular
if (rbtnRect.Checked == true)
{
for (int i = 0; i < listBox1.Items.Count; i++)
{
double sum = 0;
int count = 0;
for (int kernel_index = -NoiseReductionKernelWidth; kernel_index <= NoiseReductionKernelWidth; kernel_index++)
{
int dataIndex = i + kernel_index;
if (dataIndex >= 0 && dataIndex < listBox1.Items.Count)
{
count++;
sum += Convert.ToDouble(listBox1.Items[dataIndex]);
}
}
listBox2.Items.Add(sum / count);
lblCnt2.Text = "Count : " + listBox2.Items.Count;
}
}
```
2. **Binomial Coefficients Method (Median)**
- **How it works**: This method uses the binomial coefficients to weight the data points within the window. The median of the weighted values is then calculated.
- **Principle**: The median is less sensitive to extreme values (outliers) than the average, making it effective for noise reduction while preserving the overall trend of the data.
- **Code Implementation**:
```csharp
// Binomial coefficient (median)
else if (rbtnMed.Checked == true)
{
for (int i = 0; i < listBox1.Items.Count; i++)
{
List> weightedValues = new List>();
for (int kernel_index = -NoiseReductionKernelWidth; kernel_index <= NoiseReductionKernelWidth; kernel_index++)
{
int dataIndex = i + kernel_index;
if (dataIndex >= 0 && dataIndex < listBox1.Items.Count && (NoiseReductionKernelWidth + kernel_index) >= 0 && (NoiseReductionKernelWidth + kernel_index) < binomialCoefficients.Length)
{
double value = Convert.ToDouble(listBox1.Items[dataIndex]);
int weight = binomialCoefficients[Math.Abs(kernel_index)];
for (int w = 0; w < weight; w++)
{
weightedValues.Add(new Tuple(value, weight));
}
}
}
if (weightedValues.Count > 0)
{
weightedValues.Sort((x, y) => x.Item1.CompareTo(y.Item1));
double weightedMedian;
int midIndex = weightedValues.Count / 2;
if (weightedValues.Count % 2 == 0)
{
weightedMedian = (weightedValues[midIndex - 1].Item1 + weightedValues[midIndex].Item1) / 2.0;
}
else
{
weightedMedian = weightedValues[midIndex].Item1;
}
listBox2.Items.Add(weightedMedian);
lblCnt2.Text = "Count : " + listBox2.Items.Count;
}
}
}
```
3. **Binomial Coefficients Method (Average)**
- **How it works**: This method uses binomial coefficients to weight the values within the kernel. The weighted average of these values is then calculated.
- **Principle**: Binomial coefficients give more weight to the central values in the window, which helps to preserve the central trend while smoothing out noise.
- **Code Implementation**:
```csharp
// Binomial coefficient (average)
else if (rbtnAvg.Checked == true)
{
binomialCoefficients = AvgCalcBinomialCoefficients(NoiseReductionKernelWidth * 2 + 1);
for (int i = 0; i < listBox1.Items.Count; i++)
{
List values = new List();
int coefficientSum = 0;
for (int kernel_index = -NoiseReductionKernelWidth; kernel_index <= NoiseReductionKernelWidth; kernel_index++)
{
int dataIndex = i + kernel_index;
if (dataIndex >= 0 && dataIndex < listBox1.Items.Count)
{
values.Add(Convert.ToDouble(listBox1.Items[dataIndex]) * binomialCoefficients[NoiseReductionKernelWidth + kernel_index]);
coefficientSum += binomialCoefficients[NoiseReductionKernelWidth + kernel_index];
}
}
if (values.Count > 0)
{
double sum = values.Sum();
double average = sum / coefficientSum;
listBox2.Items.Add(average);
lblCnt2.Text = "Count : " + listBox2.Items.Count;
}
}
}
```
### Binomial Coefficients Calculation
1. **Calculating Binomial Coefficients**
- **Principle**: Binomial coefficients are derived from Pascal's triangle and represent the coefficients in the expansion of a binomial expression.
- **Code Implementation**:
```csharp
private int[] AvgCalcBinomialCoefficients(int windowSize)
{
int[] coefficients = new int[windowSize];
coefficients[0] = 1;
for (int i = 1; i < windowSize; i++)
{
coefficients[i] = coefficients[i - 1] * (windowSize - i) / i;
}
return coefficients;
}
```
These algorithms help smooth out noise in data by averaging or filtering based on the chosen method.
### Data Handling and Processing
- Implemented drag-and-drop functionality to allow users to easily add data to the application.
- Used regular expressions to extract and parse numerical data from various formats.
### User Interface and Interaction
- Designed and developed a user-friendly interface with interactive elements like buttons and list boxes.
- Provided real-time feedback to the user by updating counts and results dynamically.
### Customization and Configuration
- Allowed users to select the noise reduction method and kernel width through the interface.
- Enabled users to calibrate and fine-tune the noise reduction process based on their specific needs.
## Conclusion
By implementing these techniques, this project effectively reduces noise from the given data, providing clearer and more reliable results.
## Demonstation
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Copyright
Copyright ⓒ HappyBono 2025. All rights Reserved.