2.4.6 Segment Transforms
Perform manipulations on an entire segment of data. Segment transforms modify the data in place, so if you use a segment transform be aware that your data will be modified.
- Offset Factor
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Adds an offset to each column in input data. This can be used in conjunction with scale factor to split multiple channels into distinct bands of data.
- Parameters
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input_columns – List of column names
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offset_factor – int; number by which input_columns are offset by
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- Returns
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DataFrame
- Pre-emphasis Filter
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Performs a pre-emphasis filter on the input columns and modifies the sensor streams in place. This is a first-order Fir filter that performs a weighted average of each sample with the previous sample.
- Parameters
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input_column (str) – sensor stream to apply pre_emphasis filter against
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alpha (float) – pre-emphasis factor (weight given to the previous sample)
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prior (int) – the value of the previous sample, default is 0
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- Returns
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input data after having been passed through a pre-emphasis filter
Examples
>>> client.pipeline.reset() >>> df = client.datasets.load_activity_raw_toy() >>> print df out: Subject Class Rep accelx accely accelz 0 s01 Crawling 1 377 569 4019 1 s01 Crawling 1 357 594 4051 2 s01 Crawling 1 333 638 4049 3 s01 Crawling 1 340 678 4053 4 s01 Crawling 1 372 708 4051 5 s01 Crawling 1 410 733 4028 6 s01 Crawling 1 450 733 3988 7 s01 Crawling 1 492 696 3947 8 s01 Crawling 1 518 677 3943 9 s01 Crawling 1 528 695 3988 10 s01 Crawling 1 -1 2558 4609 11 s01 Running 1 -44 -3971 843 12 s01 Running 1 -47 -3982 836 13 s01 Running 1 -43 -3973 832 14 s01 Running 1 -40 -3973 834 15 s01 Running 1 -48 -3978 844 16 s01 Running 1 -52 -3993 842 17 s01 Running 1 -64 -3984 821 18 s01 Running 1 -64 -3966 813 19 s01 Running 1 -66 -3971 826 20 s01 Running 1 -62 -3988 827 21 s01 Running 1 -57 -3984 843 >>> client.pipeline.set_input_data('test_data', df, force=True)>>> client.pipeline.add_transform("Pre-emphasis Filter", params={"input_column": 'accelx', "alpha": 0.97, "prior": 2})>>> results, stats = client.pipeline.execute() >>> print results out: Class Rep Subject accelx accely accelz 0 Crawling 1 s01 187 569 4019 1 Crawling 1 s01 -5 594 4051 2 Crawling 1 s01 -7 638 4049 3 Crawling 1 s01 8 678 4053 4 Crawling 1 s01 21 708 4051 5 Crawling 1 s01 24 733 4028 6 Crawling 1 s01 26 733 3988 7 Crawling 1 s01 27 696 3947 8 Crawling 1 s01 20 677 3943 9 Crawling 1 s01 12 695 3988 10 Crawling 1 s01 -257 2558 4609 11 Running 1 s01 -23 -3971 843 12 Running 1 s01 -3 -3982 836 13 Running 1 s01 1 -3973 832 14 Running 1 s01 0 -3973 834 15 Running 1 s01 -5 -3978 844 16 Running 1 s01 -3 -3993 842 17 Running 1 s01 -7 -3984 821 18 Running 1 s01 -1 -3966 813 19 Running 1 s01 -2 -3971 826 20 Running 1 s01 1 -3988 827 21 Running 1 s01 1 -3984 843
- Scale Factor
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Scale data by either a defined scalar, std or median.
- Parameters
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input_columns – list of column names
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scale_factor – float; number by which input_columns are divided.
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- Returns
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DataFrame
Examples
>>> client.pipeline.reset() >>> df = client.datasets.load_activity_raw_toy() >>> print df out: Subject Class Rep accelx accely accelz 0 s01 Crawling 1 377 569 4019 1 s01 Crawling 1 357 594 4051 2 s01 Crawling 1 333 638 4049 3 s01 Crawling 1 340 678 4053 4 s01 Crawling 1 372 708 4051 5 s01 Crawling 1 410 733 4028 6 s01 Crawling 1 450 733 3988 7 s01 Crawling 1 492 696 3947 8 s01 Crawling 1 518 677 3943 9 s01 Crawling 1 528 695 3988 10 s01 Crawling 1 -1 2558 4609 11 s01 Running 1 -44 -3971 843 12 s01 Running 1 -47 -3982 836 13 s01 Running 1 -43 -3973 832 14 s01 Running 1 -40 -3973 834 15 s01 Running 1 -48 -3978 844 16 s01 Running 1 -52 -3993 842 17 s01 Running 1 -64 -3984 821 18 s01 Running 1 -64 -3966 813 19 s01 Running 1 -66 -3971 826 20 s01 Running 1 -62 -3988 827 21 s01 Running 1 -57 -3984 843 >>> client.pipeline.set_input_data('test_data', df, force=True) >>> client.pipeline.add_transform('Scale Factor', params={'scale_factor':4096., 'input_columns':['accely']}) >>> results, stats = client.pipeline.execute()
- Strip
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Remove each signal’s mean or min from its values, while leaving specified passthrough columns unmodified. This function transforms a dataframe in such a way that the entire signal is shifted towards ‘mean’ or ‘min’.
- Parameters
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input_columns – The list of columns names to use.
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type – Possible values are ‘mean’ or ‘min’.
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- Returns
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If type = ‘mean’, mean of each qualified columns is calculated. Each value in a column will be subtracted by column-mean.
Example
>>> client.pipeline.reset() >>> df = client.datasets.load_activity_raw_toy() >>> df out: Subject Class Rep accelx accely accelz 0 s01 Crawling 1 377 569 4019 1 s01 Crawling 1 357 594 4051 2 s01 Crawling 1 333 638 4049 3 s01 Crawling 1 340 678 4053 4 s01 Crawling 1 372 708 4051 5 s01 Crawling 1 410 733 4028 6 s01 Crawling 1 450 733 3988 7 s01 Crawling 1 492 696 3947 8 s01 Crawling 1 518 677 3943 9 s01 Crawling 1 528 695 3988 10 s01 Crawling 1 -1 2558 4609 11 s01 Running 1 -44 -3971 843 12 s01 Running 1 -47 -3982 836 13 s01 Running 1 -43 -3973 832 14 s01 Running 1 -40 -3973 834 15 s01 Running 1 -48 -3978 844 16 s01 Running 1 -52 -3993 842 17 s01 Running 1 -64 -3984 821 18 s01 Running 1 -64 -3966 813 19 s01 Running 1 -66 -3971 826 20 s01 Running 1 -62 -3988 827 21 s01 Running 1 -57 -3984 843>>> client.pipeline.set_input_data('test_data', df, force=True, data_columns=['accelx', 'accely', 'accelz'], group_columns=['Subject', 'Class', 'Rep'], label_column='Class')>>> client.pipeline.add_transform("Strip", params={"input_columns": ['accelx'], "type": 'min' })>>> results, stats = client.pipeline.execute() >>> print results out: Class Rep Subject accelx accely accelz 0 Crawling 1 s01 378.0 569 4019 1 Crawling 1 s01 358.0 594 4051 2 Crawling 1 s01 334.0 638 4049 3 Crawling 1 s01 341.0 678 4053 4 Crawling 1 s01 373.0 708 4051 5 Crawling 1 s01 411.0 733 4028 6 Crawling 1 s01 451.0 733 3988 7 Crawling 1 s01 493.0 696 3947 8 Crawling 1 s01 519.0 677 3943 9 Crawling 1 s01 529.0 695 3988 10 Crawling 1 s01 0.0 2558 4609 11 Running 1 s01 22.0 -3971 843 12 Running 1 s01 19.0 -3982 836 13 Running 1 s01 23.0 -3973 832 14 Running 1 s01 26.0 -3973 834 15 Running 1 s01 18.0 -3978 844 16 Running 1 s01 14.0 -3993 842 17 Running 1 s01 2.0 -3984 821 18 Running 1 s01 2.0 -3966 813 19 Running 1 s01 0.0 -3971 826 20 Running 1 s01 4.0 -3988 827 21 Running 1 s01 9.0 -3984 843
- Vertical AutoScale Segment
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Scale the segment scale amplitude to MAX_INT_16 or as close to possible without overflowing. Scaling operation only applied for input_columns other sensor columns will be ignored.
- Parameters
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input_columns – The list of columns names to use.
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group_columns – <List>; List of columns on which grouping is to be done. Each group will go through scale one at a time
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- Returns
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The vertically scaled dataframe for each segment for input_columns.