| Title: | Large Amplitude Oscillatory Shear (LAOS) |
|---|---|
| Description: | The Sequence of Physical Processes (SPP) framework is a way of interpreting the transient data derived from oscillatory rheological tests. It is designed to allow both the linear and non-linear deformation regimes to be understood within a single unified framework. This code provides a convenient way to determine the SPP framework metrics for a given sample of oscillatory data. It will produce a text file containing the SPP metrics, which the user can then plot using their software of choice. It can also produce a second text file with additional derived data (components of tangent, normal, and binormal vectors), as well as pre-plotted figures if so desired. It is the R version of the Package SPP by Simon Rogers Group for Soft Matter (Simon A. Rogers, Brian M. Erwin, Dimitris Vlassopoulos, Michel Cloitre (2011) <doi:10.1122/1.3544591>). |
| Authors: | Serena Berretta [aut, cre], Giorgio Luciano [aut], Kristian Hovde Liland [ctb], Simon Rogers [ctb] |
| Maintainer: | Serena Berretta <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0 |
| Built: | 2024-12-19 06:49:22 UTC |
| Source: | CRAN |
The data is arranged into four columns: Time (s), Strain (-), Rate (1/s) and Stress (Pa). reflecting the applied strain- control
data(mydata)data(mydata)
A data frame with 1024 rows and 4 columns
Time
Strain
Rate
Stress
ppp
create Cole-Cole plot
create Cole-Cole plot
plotColeCole(Gp_t, Gpp_t, ...) plotColeCole(Gp_t, Gpp_t, ...)plotColeCole(Gp_t, Gpp_t, ...) plotColeCole(Gp_t, Gpp_t, ...)
Gp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
Gpp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t= out$spp_data_out$Gp_t Gpp_t= out$spp_data_out$Gpp_t plotColeCole(Gp_t,Gpp_t)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t= out$spp_data_out$Gp_t Gpp_t= out$spp_data_out$Gpp_t plotColeCole(Gp_t,Gpp_t)
create Strain Delta Plot
create Strain Delta Plot
plotDeltaStrain(strain, delta_t, ...) plotDeltaStrain(strain, delta_t, ...)plotDeltaStrain(strain, delta_t, ...) plotDeltaStrain(strain, delta_t, ...)
strain |
from the output matrix from fft analysis or numerical differentiation analysis |
delta_t |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain delta_t= out$spp_data_out$delta_t plotDeltaStrain(strain,delta_t)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain delta_t= out$spp_data_out$delta_t plotDeltaStrain(strain,delta_t)
Strain Displacement Stress
Strain Displacement Stress
plotDisp(strain, disp_stress, ...) plotDisp(strain, disp_stress, ...)plotDisp(strain, disp_stress, ...) plotDisp(strain, disp_stress, ...)
strain |
from the output matrix from fft analysis or numerical differentiation analysis |
disp_stress |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain disp_stress= out$spp_data_out$disp_stress plotDisp(strain,disp_stress)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain disp_stress= out$spp_data_out$disp_stress plotDisp(strain,disp_stress)
create Fourier Harmonic Magnitudes plot
create Fourier Harmonic Magnitudes plot
plotFft(ft_amp, fft_resp, spp_params, ...) plotFft(ft_amp, fft_resp, spp_params, ...)plotFft(ft_amp, fft_resp, spp_params, ...) plotFft(ft_amp, fft_resp, spp_params, ...)
ft_amp |
from the output matrix from fft analysis or numerical differentiation analysis |
fft_resp |
from the output matrix from fft analysis or numerical differentiation analysis |
spp_params |
input parameters used for the fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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) ft_amp= out$ft_out$ft_amp fft_resp= out$ft_out$fft_resp spp_params= out$spp_params plotFft(ft_amp,fft_resp,spp_params)data(mydata) df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0)) time_wave <- df$raw_time resp_wave <- data.frame(df$strain,df$strain_rate,df$stress) out <- rpp_fft(time_wave,resp_wave,L=1024,omega=3.16 , M=15,p=1) ft_amp= out$ft_out$ft_amp fft_resp= out$ft_out$fft_resp spp_params= out$spp_params plotFft(ft_amp,fft_resp,spp_params)
create Gp_t_dot vs Gpp_t_dot
create Gp_t_dot vs Gpp_t_dot
plotGpdot(Gp_t_dot, Gpp_t_dot, ...) plotGpdot(Gp_t_dot, Gpp_t_dot, ...)plotGpdot(Gp_t_dot, Gpp_t_dot, ...) plotGpdot(Gp_t_dot, Gpp_t_dot, ...)
Gp_t_dot |
from the output matrix from fft analysis or numerical differentiation analysis |
Gpp_t_dot |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t_dot= out$spp_data_out$Gp_t_dot Gpp_t_dot= out$spp_data_out$Gpp_t_dot plotGpdot(Gp_t_dot,Gpp_t_dot)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t_dot= out$spp_data_out$Gp_t_dot Gpp_t_dot= out$spp_data_out$Gpp_t_dot plotGpdot(Gp_t_dot,Gpp_t_dot)
create Strain Delta Plot
create Strain Delta Plot
plotPAV(strain, delta_t_dot, ...) plotPAV(strain, delta_t_dot, ...)plotPAV(strain, delta_t_dot, ...) plotPAV(strain, delta_t_dot, ...)
strain |
from the output matrix from fft analysis or numerical differentiation analysis |
delta_t_dot |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain delta_t_dot= out$spp_data_out$delta_t_dot plotPAV(strain,delta_t_dot)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain delta_t_dot= out$spp_data_out$delta_t_dot plotPAV(strain,delta_t_dot)
create Speed-G'_t plot
create Speed-G'_t plot
plotSpeedGp(Gp_t, G_speed, ...) plotSpeedGp(Gp_t, G_speed, ...)plotSpeedGp(Gp_t, G_speed, ...) plotSpeedGp(Gp_t, G_speed, ...)
Gp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
G_speed |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t= out$spp_data_out$Gp_t G_speed= out$spp_data_out$G_speed plotSpeedGp(Gp_t,G_speed)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t= out$spp_data_out$Gp_t G_speed= out$spp_data_out$G_speed plotSpeedGp(Gp_t,G_speed)
create Speed-G”_t plot
create Speed-G”_t plot
plotSpeedGpp(G_speed, Gpp_t, ...) plotSpeedGpp(G_speed, Gpp_t, ...)plotSpeedGpp(G_speed, Gpp_t, ...) plotSpeedGpp(G_speed, Gpp_t, ...)
G_speed |
from the output matrix from fft analysis or numerical differentiation analysis |
Gpp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) G_speed= out$spp_data_out$G_speed Gpp_t= out$spp_data_out$Gpp_t plotSpeedGpp(G_speed,Gpp_t)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) G_speed= out$spp_data_out$G_speed Gpp_t= out$spp_data_out$Gpp_t plotSpeedGpp(G_speed,Gpp_t)
Strain Gp_t,eq_strain_est
Strain Gp_t,eq_strain_est
plotStrain(Gp_t, eq_strain_est, ...) plotStrain(Gp_t, eq_strain_est, ...)plotStrain(Gp_t, eq_strain_est, ...) plotStrain(Gp_t, eq_strain_est, ...)
Gp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
eq_strain_est |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t= out$spp_data_out$Gp_t eq_strain_est= out$spp_data_out$eq_strain_est plotStrain(Gp_t,eq_strain_est)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) Gp_t= out$spp_data_out$Gp_t eq_strain_est= out$spp_data_out$eq_strain_est plotStrain(Gp_t,eq_strain_est)
create Stress Rate Plot
create Stress Rate Plot
plotStressRate(stress, rate, ...) plotStressRate(stress, rate, ...)plotStressRate(stress, rate, ...) plotStressRate(stress, rate, ...)
stress |
data the output matrix from fft analysis or numerical differentiation analysis |
rate |
data the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) rate= out$spp_data_out$rate stress= out$spp_data_out$stress plotStressRate(stress, rate)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) rate= out$spp_data_out$rate stress= out$spp_data_out$stress plotStressRate(stress, rate)
create Stress Strain Plot
create Stress Strain Plot
plotStressStrain(stress, strain, strain_in, stress_in, ...) plotStressStrain(stress, strain, strain_in, stress_in, ...)plotStressStrain(stress, strain, strain_in, stress_in, ...) plotStressStrain(stress, strain, strain_in, stress_in, ...)
stress |
data the output matrix from fft analysis or numerical differentiation analysis |
strain |
data the output matrix from fft analysis or numerical differentiation analysis |
strain_in |
data the input matrix from fft analysis or numerical differentiation analysis |
stress_in |
data the input matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain stress= out$spp_data_out$stress strain_in= out$spp_data_in$strain stress_in= out$spp_data_in$stress plotStressStrain(stress, strain,strain_in,stress_in)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) strain= out$spp_data_out$strain stress= out$spp_data_out$stress strain_in= out$spp_data_in$strain stress_in= out$spp_data_in$stress plotStressStrain(stress, strain,strain_in,stress_in)
create Stress-Time plot
create Stress-Time plot
plotStressTime(time_wave_in, stress_in, time_wave, stress) plotStressTime(time_wave_in, stress_in, time_wave, stress)plotStressTime(time_wave_in, stress_in, time_wave, stress) plotStressTime(time_wave_in, stress_in, time_wave, stress)
time_wave_in |
raw time from input data |
stress_in |
stress from input data |
time_wave |
from the output matrix from fft analysis or numerical differentiation analysis |
stress |
from the output matrix from fft analysis or numerical differentiation analysis |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave_in= out$spp_data_in$time_wave stress_in= out$spp_data_in$stress time_wave= out$spp_data_out$time_wave stress= out$spp_data_out$stress plotStressTime(time_wave_in,stress_in,time_wave,stress)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave_in= out$spp_data_in$time_wave stress_in= out$spp_data_in$stress time_wave= out$spp_data_out$time_wave stress= out$spp_data_out$stress plotStressTime(time_wave_in,stress_in,time_wave,stress)
create Rate, time_wave plot
create Rate, time_wave plot
plotTimeRate(time_wave, rate, time_wave_in, strain_rate, ...) plotTimeRate(time_wave, rate, time_wave_in, strain_rate, ...)plotTimeRate(time_wave, rate, time_wave_in, strain_rate, ...) plotTimeRate(time_wave, rate, time_wave_in, strain_rate, ...)
time_wave |
from the output matrix from fft analysis or numerical differentiation analysis |
rate |
from the output matrix from fft analysis or numerical differentiation analysis |
time_wave_in |
raw time from input data |
strain_rate |
strain rate from input data |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave= out$spp_data_out$time_wave rate= out$spp_data_out$rate time_wave_in= out$spp_data_in$time_wave strain_rate= out$spp_data_in$strain_rate plotTimeRate(time_wave,rate,time_wave_in,strain_rate)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave= out$spp_data_out$time_wave rate= out$spp_data_out$rate time_wave_in= out$spp_data_in$time_wave strain_rate= out$spp_data_in$strain_rate plotTimeRate(time_wave,rate,time_wave_in,strain_rate)
Strain time_wave, strain
Strain time_wave, strain
plotTimeStrain(time_wave, strain, time_wave_in, strain_in, ...) plotTimeStrain(time_wave, strain, time_wave_in, strain_in, ...)plotTimeStrain(time_wave, strain, time_wave_in, strain_in, ...) plotTimeStrain(time_wave, strain, time_wave_in, strain_in, ...)
time_wave |
time from output data |
strain |
from the output matrix from fft analysis or numerical differentiation analysis |
time_wave_in |
time from input data |
strain_in |
from the input matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave= out$spp_data_out$time_wave strain= out$spp_data_out$strain time_wave_in= out$spp_data_in$time_wave strain_in= out$spp_data_in$strain plotTimeStrain(time_wave,strain,time_wave_in,strain_in)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave= out$spp_data_out$time_wave strain= out$spp_data_out$strain time_wave_in= out$spp_data_in$time_wave strain_in= out$spp_data_in$strain plotTimeStrain(time_wave,strain,time_wave_in,strain_in)
create Stress-Time plot
create Stress-Time plot
plotTimeStress(time_wave, stress, time_wave_in, strain_rate, ...) plotTimeStress(time_wave, stress, time_wave_in, strain_rate, ...)plotTimeStress(time_wave, stress, time_wave_in, strain_rate, ...) plotTimeStress(time_wave, stress, time_wave_in, strain_rate, ...)
time_wave |
from the output matrix from fft analysis or numerical differentiation analysis |
stress |
from the output matrix from fft analysis or numerical differentiation analysis |
time_wave_in |
raw time from input data |
strain_rate |
strain rate from input data |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave= out$spp_data_out$time_wave stress= out$spp_data_out$stress time_wave_in= out$spp_data_in$time_wave strain_rate= out$spp_data_in$strain_rate plotTimeStress(time_wave,stress,time_wave_in,strain_rate)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) time_wave= out$spp_data_out$time_wave stress= out$spp_data_out$stress time_wave_in= out$spp_data_in$time_wave strain_rate= out$spp_data_in$strain_rate plotTimeStress(time_wave,stress,time_wave_in,strain_rate)
create VGP plot
create VGP plot
plotVGP(G_star_t, delta_t, ...) plotVGP(G_star_t, delta_t, ...)plotVGP(G_star_t, delta_t, ...) plotVGP(G_star_t, delta_t, ...)
G_star_t |
from the output matrix from fft analysis or numerical differentiation analysis |
delta_t |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
No return value
No return value
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
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
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) G_star_t= out$spp_data_out$G_star_t delta_t= out$spp_data_out$delta_t plotVGP(G_star_t,delta_t)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1) G_star_t= out$spp_data_out$G_star_t delta_t= out$spp_data_out$delta_t plotVGP(G_star_t,delta_t)
applies the SPP Analysis by means of a fourier series.
rpp_fft(time_wave, resp_wave, L, omega, M, p)rpp_fft(time_wave, resp_wave, L, omega, M, p)
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 |
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)
Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)
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
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)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)
applies the SPP Analysis by means of a numerical differentiation.
Rpp_num(time_wave, resp_wave, L, k, num_mode)Rpp_num(time_wave, resp_wave, L, k, num_mode)
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 |
k |
step size for numerical differentiation |
num_mode |
numerical method |
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)
Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)
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
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
# This function export the output the SPP analysis (performed via FFT or Numeric Analysis) and export it to csv files
rpp_out_csv(out, myfilename = "my_models.xlsx")rpp_out_csv(out, myfilename = "my_models.xlsx")
out |
output of the SPP analysis (performed via FFT or Numeric Analysis) |
myfilename |
name of the file where to save results (csv) |
No return value
Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)
# This function export the output the SPP analysis (performed via FFT or Numeric Analysis) and export it to xls files
rpp_out_excel(out, myfilename = "my_models.xlsx")rpp_out_excel(out, myfilename = "my_models.xlsx")
out |
output of the SPP analysis (performed via FFT or Numeric Analysis) |
myfilename |
name of the file where to save results in xls format |
No return value
Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)
This function reads data from the selected file, and assign it to a dataframe
rpp_read(filename, header = TRUE, selected = c(2, 3, 4, 0, 0, 1, 0, 0), ...)rpp_read(filename, header = TRUE, selected = c(2, 3, 4, 0, 0, 1, 0, 0), ...)
filename |
the name of the file to read |
header |
TRUE if colnames are present FALSE if colnames are not present |
selected |
the user should input the number of the columns that represent strain-smoothed (gamma), strain rate-smoothed (gamma dot), stress smoothed (tau recon), Elast-Stress (FTtau_e), Visco-Stress (FTtau_v), raw time (time), raw stress (tau), raw strain (gamma) i.e. selected=c(2, 3, 4, 0, 0, 1, 0, 0) means that the second column of your data is the strain rate smoothed, the third column is the stress smoothed, the stress smoothed is the fourth column in the original data, and finally that we do not have data for the raw stress and raw strain |
... |
parameters of read.csv |
a dataframe with all the columns assigned
Giorgio Luciano and Serena Berretta, Simon Rogers Group for Soft Matter (matlab version)
This function reads data from a dataframe
rpp_read2(dat, selected = c(2, 3, 4, 0, 0, 1, 0, 0), ...)rpp_read2(dat, selected = c(2, 3, 4, 0, 0, 1, 0, 0), ...)
dat |
dataframe of input |
selected |
the user should input the number of the columns that represent strain-smoothed (gamma), strain rate-smoothed (gamma dot), stress smoothed (tau recon), Elast-Stress (FTtau_e), Visco-Stress (FTtau_v), raw time (time), raw stress (tau), raw strain (gamma) i.e. selected=c(2, 3, 4, 0, 0, 1, 0, 0) means that the second column of your data is the strain rate smoothed, the third column is the stress smoothed, the stress smoothed is the fourth column in the original data, and finally that we do not have data for the raw stress and raw strain |
... |
parameters of read.csv |
a dataframe with all the columns assigned
Giorgio Luciano and Serena Berretta, Simon Rogers Group for Soft Matter (matlab version)
data(mydata) df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))data(mydata) df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))