kopia lustrzana https://github.com/gabrielegilardi/SignalFilters
update README.md and comments
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@ -22,7 +22,7 @@ Characteristics
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- The code has been written and tested in Python 3.7.7.
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- Implementation of several digital signal filters and functions for the
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generation of synthetic (surrogate) time-series.
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- Filter list (file <filters.py>):
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- Filters (file <filters.py>):
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Generic Generic filter
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SMA Simple moving average
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EMA Exponential moving average
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Y
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Filtered dataset (output).
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X_synt
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Surrogate/synthetic generated time-series (output)
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Surrogate/synthetic generated time-series (output).
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n_reps
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Number of surrogates/synthetic time-series to generate.
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@ -87,14 +87,14 @@ results are shown in file <Results_Examples.pdf>.
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- Filter: example showing filtering using an EMA, a Butterworth modified
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filter, and a type 2 Zero-lag EMA.
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- Kalman: example showing filtering using the three types of Kalman filter,
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alpha, alpha-beta, and alpha-beta-gamma.
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- Kalman: example showing filtering using the three types of Kalman filter
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(alpha, alpha-beta, and alpha-beta-gamma).
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- FFT_boot: example showing the generation of surrogates time-series using
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the Fourier-transform algorithm and the discrete differences.
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the Fourier-transform algorithm and discrete differences.
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- ME_boot: example showing the generation of surrogates time-series using the
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using maximum entropy bootstrap algorithm and the discrete differences.
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maximum entropy bootstrap algorithm and discrete differences.
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- Response: example showing the frequency response and lag/group delay for a
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band-pass filter.
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README.md
12
README.md
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@ -12,7 +12,7 @@
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- The code has been written and tested in Python 3.7.7.
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- Implementation of several digital signal filters and functions for the generation of synthetic (surrogate) time-series.
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- Filter list (*filters.py*):
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- Filters (*filters.py*):
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- **Generic** Generic filter.
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- **SMA** Simple moving average.
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- **EMA** Exponential moving average.
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`Y` Filtered dataset (output).
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`X_synt` Surrogate/synthetic generated time-series (output)
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`X_synt` Surrogate/synthetic generated time-series (output).
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`n_reps` Number of surrogates/synthetic time-series to generate.
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## Examples
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There are five examples (all of them use the dataset in *spx.csv*). The results are shown [here](Results_Examples.pdf).
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There are five examples (all of them use the dataset in *spx.csv*). The results are shown [here](Result_Examples.pdf).
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- **Filter** Example showing filtering using an EMA, a Butterworth modified filter, and a type 2 Zero-lag EMA.
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- **Kalman** Example showing filtering using the three types of Kalman filter, alpha, alpha-beta, and alpha-beta-gamma.
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- **Kalman** Example showing filtering using the three types of Kalman filter (alpha, alpha-beta, and alpha-beta-gamma).
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- **FFT_boot** Example showing the generation of surrogates time-series using the Fourier-transform algorithm and the discrete differences.
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- **FFT_boot** Example showing the generation of surrogates time-series using the Fourier-transform algorithm and discrete differences.
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- **ME_boot** Example showing the generation of surrogates time-series using the using maximum entropy bootstrap algorithm and the discrete differences.
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- **ME_boot** Example showing the generation of surrogates time-series using the maximum entropy bootstrap algorithm and discrete differences.
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- **Response** Example showing the frequency response and lag/group delay for a band-pass filter.
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