hampel 1.0.2

Python implementation of the Hampel Filter

Hampel

Warning: Starting from version 1.0.1, there are breaking changes in this library. Please review the 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 section.

Table of Contents

• Installation
• Usage
• Parameters
• Testing
• License
• Contributing
• 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:

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:

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:

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 file for details.

Contributing

Contributions are welcome! Feel free to open issues, submit pull requests, or suggest improvements.

Related Links

https://medium.com/wwblog/clean-up-your-time-series-data-with-a-hampel-filter-58b0bb3ebb04

https://en.wikipedia.org/wiki/Median_absolute_deviation