rpp_fft {oreo}

SPP Analysis via fourier

Description

applies the SPP Analysis by means of a fourier series.

Usage

rpp_fft(time_wave, resp_wave, L, omega, M, p)

Arguments

time_wave

Lx1 vector of time at each measurement point

resp_wave

Lx3 matrix of the strain, rate and stress data,with each row representing a measuring point

L

number of measurement points in the extracted data

omega

frequency of oscilation (rad/s)

M

number of harmonics for stress

p

number of cycles

Value

a list with the following data frame spp_data_in= the data frame with the data spp_params=spp_params, spp_data_out= Length,frequency,harmonics,cycles,max_harmonics,step_size fsf_data_out= Tx,Ty,Tz,Nx,Ny,Nz,Bx,By,Bz coordinates of the trajectory (T=tangent,N=principal Normal,B=Binormal Vectors) ft_out=data frame with that includes time_wave,strain,rate,stress,Gp_t,Gpp_t,G_star_t,tan_delta_t,delta_t,disp_stress,eq_strain_est,Gp_t_dot,Gpp_t_dot,G_speed,delta_t_dot)

Author(s)

Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)

References

Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25

Examples

data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress) 
out <- rpp_fft(time_wave,resp_wave,L=1024,omega=3.16 , M=15,p=1)