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https://github.com/michaelistrofficus/hampel_filter
Python implementation of the Hampel Filter
https://github.com/michaelistrofficus/hampel_filter
Last synced: about 2 months ago
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Python implementation of the Hampel Filter
- Host: GitHub
- URL: https://github.com/michaelistrofficus/hampel_filter
- Owner: MichaelisTrofficus
- License: mit
- Created: 2020-09-18T11:11:35.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-02-06T11:35:24.000Z (11 months ago)
- Last Synced: 2024-03-23T09:20:29.705Z (10 months ago)
- Language: Python
- Size: 173 KB
- Stars: 41
- Watchers: 3
- Forks: 8
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Hampel
> **Warning:** Starting from version 1.0.2, there are **breaking changes** in this library. Please review the [Usage](#usage) section for details and update your code accordingly.
The Hampel filter is generally used to detect anomalies in data with a timeseries structure.
It basically consists of a sliding window of a parameterizable size.
For each window, each observation will be compared with the Median Absolute Deviation (MAD).
The observation will be considered an outlier in the case in which it exceeds the MAD by n times (the parameter n is also parameterizable).
For more details, see the [Related Links](#related-links) section.
You can also take a look at my [Medium post](https://medium.com/@miguel.otero.pedrido.1993/hampel-filter-with-python-17db1d265375) about this library!! 😁😁😁
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Parameters](#parameters)
- [Testing](#testing)
- [License](#license)
- [Contributing](#contributing)
- [Related Links](#related-links)
## Installation
To use the Hampel filter in your Python project, you can install it via pip:
```
pip install hampel
```
## Usage
Here's a simple example of how to use the Hampel filter:
```python
import pandas as pd
from hampel import hampel
# Sample data as a pandas.Series
data = pd.Series([1.0, 2.0, 3.0, 100.0, 4.0, 5.0, 6.0])
# Apply the Hampel filter
result = hampel(data, window_size=3, n_sigma=3.0)
print(result.filtered_data)
```
When you apply the Hampel filter, it returns a `Result` object with the following attributes:
- `filtered_data`: The data with outliers replaced.
- `outlier_indices`: Indices of the detected outliers.
- `medians`: Median values within the sliding window.
- `median_absolute_deviations`: Median Absolute Deviation (MAD) values within the sliding window.
- `thresholds`: Threshold values for outlier detection.
You can access these attributes as follows:
```python
result = hampel(data, window_size=3, n_sigma=3.0)
filtered_data = result.filtered_data
outlier_indices = result.outlier_indices
medians = result.medians
mad_values = result.median_absolute_deviations
thresholds = result.thresholds
```
If you want to directly apply hampel filter to multiple columns in a `pandas.Dataframe`,
follow this code:
```python
import pandas as pd
from hampel import hampel
df = pd.DataFrame({
'A': [1.0, 2.0, 3.0, 100.0, 4.0, 5.0, 6.0],
'B': [7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0]
})
# We are just getting the filtered data in this case
filtered_df = df.apply(lambda x: hampel(x).filtered_data, axis=0)
print(df)
```
## Parameters
* `data`: The input 1-dimensional data to be filtered (pandas.Series or numpy.ndarray).
* `window_size` (optional): The size of the moving window for outlier detection (default is 5).
* `n_sigma` (optional): The number of standard deviations for outlier detection (default is 3.0).
## Testing
If you want to run the tests, simple run:
```
make test
```
## License
See the [LICENSE](LICENSE) file for details.
## Contributing
Contributions are welcome! Feel free to open issues, submit pull requests, or suggest improvements.
## Related Links
https://medium.com/@miguel.otero.pedrido.1993/hampel-filter-with-python-17db1d265375
https://medium.com/wwblog/clean-up-your-time-series-data-with-a-hampel-filter-58b0bb3ebb04
https://en.wikipedia.org/wiki/Median_absolute_deviation