Sensor Filters
Modifies a sensor in place and performs some sort of filtering (ie. moving average). This acts on a Sensor source that was either input or created using a sensor transform. This does not create a new source.
-
Moving Average Sensor Transform Performs a symmetric moving average filter on the input column, creates a new column with the filtered data.
- Parameters
input_data – DataFrame containing the time series data.
group_columns – columns to group data by before processing.
input_column – sensor stream to apply moving average filter on.
order (filter) – the number of samples to average to the left and right.
- Returns
input data after having been passed through symmetric moving average filter
-
Streaming Downsample Downsample the entire dataframe into a dataframe of size filter_length by taking the average over the samples within the filter length.
- Parameters
input_data – dataframe
group_columns (a list) – List of columns on which grouping is to be done. Each group will go through downsampling one at a time
input_columns – List of columns to be downsampled
filter_length – Number of samples in each new filter length
- Returns
The downsampled dataframe.
- Return type
DataFrame
-
Streaming Downsample by Decimation Decrease the sample rate by a factor of filter_length, this will only keep one samples for every length of the filter.
- Parameters
input_data – dataframe
columns – List of columns to be downsampled
group_columns (a list) – List of columns on which grouping is to be done. Each group will go through downsampling one at a time
filter_length – integer; Number of samples in each new filter length
- Returns
The downsampled dataframe.
- Return type
DataFrame
Examples
>>> from pandas import DataFrame >>> df = DataFrame([[3, 3], [4, 5], [5, 7], [4, 6], [3, 1], [3, 1], [4, 3], [5, 5], [4, 7], [3, 6]], columns=['accelx', 'accely']) >>> df Out: accelx accely 0 3 3 1 4 5 2 5 7 3 4 6 4 3 1 5 3 1 6 4 3 7 5 5 8 4 7 9 3 6 >>> client.pipeline.reset() >>> client.pipeline.set_input_data('test_data', df, force = True) >>> client.pipeline.add_transform('Streaming Downsample by Decimation', params={'group_columns':[], 'columns' : ['accelx', 'accely'], 'filter_length' : 5 }) >>> results, stats = client.pipeline.execute() >>> print results Out: accelx accely 0 3 3 1 3 1 2 3 6
-
High Pass Filter This is a simple IIR Filter that is useful for removing drift from sensors by subtracting the attentuated running average.
\[y_{i}= y_{i-1} + \alpha (x_{i} - y_{i-1})\]- Parameters
input_data – DataFrame containing the time series data
input_columns – sensor streams to apply the filter to
alpha – attenuation coefficient
- Returns
input data after having been passed through the IIR filter