| Type: | Package |
| Title: | Convolution of Data |
| Version: | 0.1.0 |
| Maintainer: | Federico Maria Vivaldi <federico-vivaldi@virgilio.it> |
| Description: | General functions for convolutions of data. Moving average, running median, and other filters are available. Bibliography regarding the functions can be found in the following text. Richard G. Brereton (2003) <ISBN:9780471489771>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2021-03-09 13:49:41 UTC; federico |
| Author: | Federico Maria Vivaldi [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2021-03-11 10:40:02 UTC |
Hamming window filter.
Description
This function return the data smoothed using the an Hamming window filter. Data are smoothed using a cosine window with particular coefficients.
Usage
Hamming(raw_data, buffer_size = 5)
Arguments
raw_data |
Data upon which the algorithm is applied |
buffer_size |
number of points the algorithm use to compute the coefficients of the Hann window |
Value
Smoothed data using Hann Window filter
Examples
raw_data = c(1:100)
smoothed_data = Hamming(raw_data)
Hann window filter.
Description
This function return the data smoothed using the an Hann window filter. Data are smoothed using a cosine window.
Usage
Hann(raw_data, buffer_size = 5)
Arguments
raw_data |
Data upon which the algorithm is applied |
buffer_size |
number of points the algorithm use to compute the coefficients of the Hann window |
Value
Smoothed data using Hann Window filter
Examples
raw_data = c(1:100)
smoothed_data = Hann(raw_data)
Moving average filter.
Description
This function return the data smoothed using the basic moving average algorithm. For each chunk of data of size equal to the buffer_size parameter is calculated the average and this value is used as the i term of the newly smoothed data. zero padding is applied for initial and final values
Usage
MA(raw_data, buffer_size = 5)
Arguments
raw_data |
Data upon which the algorithm is applied |
buffer_size |
number of points the algorithm use to compute the average |
Value
Smoothed data using moving average algorithm
Examples
raw_data = c(1:100)
smoothed_data = MA(raw_data)
Running median smoothing.
Description
This function return the data smoothed using the running median algorithm. For each chunk of data of size equal to the buffer_size parameter is calculated the median and this value is used as the i term of the newly smoothed data. For initial and final values zero padding is applied.
Usage
RMS(raw_data, buffer_size = 5)
Arguments
raw_data |
Data upon which the algorithm is applied |
buffer_size |
number of points the algorithm use to compute the median |
Value
Smoothed data using running median algorithm
Examples
raw_data = c(1:100)
smoothed_data = RMS(raw_data)
Sine window filter.
Description
This function return the data smoothed using the a sine window filter.
Usage
sine(raw_data, buffer_size = 5)
Arguments
raw_data |
Data upon which the algorithm is applied |
buffer_size |
number of points the algorithm use to compute the coefficients of the Hann window |
Value
Smoothed data using Hann Window filter
Examples
raw_data = c(1:100)
smoothed_data = sine(raw_data)
Test data generator
Description
Generate test data in order to test the filtering functions. To a signal function is added random noise contribution. V0.1 = noise is assumed gaussian
Usage
test_data(
amplitude = 1,
f = 100,
npoints = 1000,
type = "sinusoidal",
x0 = 0,
noise_contribution = 100
)
Arguments
amplitude |
amplitude of the signal, default = 1 |
f |
frequency of the sinusoidal signal, default = 100 |
npoints |
number of points of the time serie |
type |
type of signal, default = sinusoidal. Available types: sinusoidal, gaussian |
x0 |
signal position for gaussian type. Default = 0 |
noise_contribution |
percentage pointing the maximum wanted signal/noise ratio. Default = 10 |
Value
A time serie with added random noise.
Examples
test_data()