Type: | Package |
Title: | Large Amplitude Oscillatory Shear (LAOS) |
Version: | 1.0 |
Date: | 2021-03-01 |
Maintainer: | Serena Berretta <serena.berretta@ge.imati.cnr.it> |
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>). |
Imports: | gridExtra,ggplot2,openxlsx,tools,spectral,pracma,fftwtools,scales |
Depends: | R (≥ 3.5.0) |
License: | GPL-2 |
Encoding: | UTF-8 |
RoxygenNote: | 7.1.1 |
NeedsCompilation: | no |
Packaged: | 2021-03-08 09:23:35 UTC; seren |
Author: | Serena Berretta [aut, cre], Giorgio Luciano [aut], Kristian Hovde Liland [ctb], Simon Rogers [ctb] |
Repository: | CRAN |
Date/Publication: | 2021-03-10 19:20:07 UTC |
SPP Analysis via numerical differentiation
Description
applies the SPP Analysis by means of a numerical differentiation.
Usage
Rpp_num(time_wave, resp_wave, L, k, num_mode)
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 |
k |
step size for numerical differentiation |
num_mode |
numerical method |
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_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
Data from the Giesikus model
Description
The data is arranged into four columns: Time (s), Strain (-), Rate (1/s) and Stress (Pa). reflecting the applied strain- control
Usage
data(mydata)
Format
A data frame with 1024 rows and 4 columns
- V1
Time
- V2
Strain
- V3
Rate
- V4
Stress
References
ppp
Cole-Cole plot
Description
create Cole-Cole plot
create Cole-Cole plot
Usage
plotColeCole(Gp_t, Gpp_t, ...)
plotColeCole(Gp_t, Gpp_t, ...)
Arguments
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() |
Value
No return value
No return value
Author(s)
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
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
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_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)
Strain Delta Plot
Description
create Strain Delta Plot
create Strain Delta Plot
Usage
plotDeltaStrain(strain, delta_t, ...)
plotDeltaStrain(strain, delta_t, ...)
Arguments
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() |
Value
No return value
No return value
Author(s)
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
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
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_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
Description
Strain Displacement Stress
Strain Displacement Stress
Usage
plotDisp(strain, disp_stress, ...)
plotDisp(strain, disp_stress, ...)
Arguments
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() |
Value
No return value
No return value
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
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_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)
Fourier Harmonic Magnitudes plot
Description
create Fourier Harmonic Magnitudes plot
create Fourier Harmonic Magnitudes plot
Usage
plotFft(ft_amp, fft_resp, spp_params, ...)
plotFft(ft_amp, fft_resp, spp_params, ...)
Arguments
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() |
Value
No return value
No return value
Author(s)
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
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)
ft_amp= out$ft_out$ft_amp
fft_resp= out$ft_out$fft_resp
spp_params= out$spp_params
plotFft(ft_amp,fft_resp,spp_params)
Gp_t_dot vs Gpp_t_dot
Description
create Gp_t_dot vs Gpp_t_dot
create Gp_t_dot vs Gpp_t_dot
Usage
plotGpdot(Gp_t_dot, Gpp_t_dot, ...)
plotGpdot(Gp_t_dot, Gpp_t_dot, ...)
Arguments
Gp_t_dot |
from the output matrix from fft analysis or numerical differentiation analysis |
Gpp_t_dot |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
Gp_t_dot= out$spp_data_out$Gp_t_dot
Gpp_t_dot= out$spp_data_out$Gpp_t_dot
plotGpdot(Gp_t_dot,Gpp_t_dot)
Strain Delta Plot
Description
create Strain Delta Plot
create Strain Delta Plot
Usage
plotPAV(strain, delta_t_dot, ...)
plotPAV(strain, delta_t_dot, ...)
Arguments
strain |
from the output matrix from fft analysis or numerical differentiation analysis |
delta_t_dot |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
strain= out$spp_data_out$strain
delta_t_dot= out$spp_data_out$delta_t_dot
plotPAV(strain,delta_t_dot)
Speed-G'_t plot
Description
create Speed-G'_t plot
create Speed-G'_t plot
Usage
plotSpeedGp(Gp_t, G_speed, ...)
plotSpeedGp(Gp_t, G_speed, ...)
Arguments
Gp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
G_speed |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
Gp_t= out$spp_data_out$Gp_t
G_speed= out$spp_data_out$G_speed
plotSpeedGp(Gp_t,G_speed)
Speed-G”_t plot
Description
create Speed-G”_t plot
create Speed-G”_t plot
Usage
plotSpeedGpp(G_speed, Gpp_t, ...)
plotSpeedGpp(G_speed, Gpp_t, ...)
Arguments
G_speed |
from the output matrix from fft analysis or numerical differentiation analysis |
Gpp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
G_speed= out$spp_data_out$G_speed
Gpp_t= out$spp_data_out$Gpp_t
plotSpeedGpp(G_speed,Gpp_t)
Strain Gp_t,eq_strain_est
Description
Strain Gp_t,eq_strain_est
Strain Gp_t,eq_strain_est
Usage
plotStrain(Gp_t, eq_strain_est, ...)
plotStrain(Gp_t, eq_strain_est, ...)
Arguments
Gp_t |
from the output matrix from fft analysis or numerical differentiation analysis |
eq_strain_est |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
Gp_t= out$spp_data_out$Gp_t
eq_strain_est= out$spp_data_out$eq_strain_est
plotStrain(Gp_t,eq_strain_est)
Stress-Rate plot
Description
create Stress Rate Plot
create Stress Rate Plot
Usage
plotStressRate(stress, rate, ...)
plotStressRate(stress, rate, ...)
Arguments
stress |
data the output matrix from fft analysis or numerical differentiation analysis |
rate |
data the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
rate= out$spp_data_out$rate
stress= out$spp_data_out$stress
plotStressRate(stress, rate)
Stress-Strain plot
Description
create Stress Strain Plot
create Stress Strain Plot
Usage
plotStressStrain(stress, strain, strain_in, stress_in, ...)
plotStressStrain(stress, strain, strain_in, stress_in, ...)
Arguments
stress |
data the output matrix from fft analysis or numerical differentiation analysis |
strain |
data the output matrix from fft analysis or numerical differentiation analysis |
strain_in |
data the input matrix from fft analysis or numerical differentiation analysis |
stress_in |
data the input matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
strain= out$spp_data_out$strain
stress= out$spp_data_out$stress
strain_in= out$spp_data_in$strain
stress_in= out$spp_data_in$stress
plotStressStrain(stress, strain,strain_in,stress_in)
Stress-Time plot
Description
create Stress-Time plot
create Stress-Time plot
Usage
plotStressTime(time_wave_in, stress_in, time_wave, stress)
plotStressTime(time_wave_in, stress_in, time_wave, stress)
Arguments
time_wave_in |
raw time from input data |
stress_in |
stress from input data |
time_wave |
from the output matrix from fft analysis or numerical differentiation analysis |
stress |
from the output matrix from fft analysis or numerical differentiation analysis |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
time_wave_in= out$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$stress
plotStressTime(time_wave_in,stress_in,time_wave,stress)
Rate, time_wave plot
Description
create Rate, time_wave plot
create Rate, time_wave plot
Usage
plotTimeRate(time_wave, rate, time_wave_in, strain_rate, ...)
plotTimeRate(time_wave, rate, time_wave_in, strain_rate, ...)
Arguments
time_wave |
from the output matrix from fft analysis or numerical differentiation analysis |
rate |
from the output matrix from fft analysis or numerical differentiation analysis |
time_wave_in |
raw time from input data |
strain_rate |
strain rate from input data |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
time_wave= out$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$strain_rate
plotTimeRate(time_wave,rate,time_wave_in,strain_rate)
Strain time_wave,strain
Description
Strain time_wave, strain
Strain time_wave, strain
Usage
plotTimeStrain(time_wave, strain, time_wave_in, strain_in, ...)
plotTimeStrain(time_wave, strain, time_wave_in, strain_in, ...)
Arguments
time_wave |
time from output data |
strain |
from the output matrix from fft analysis or numerical differentiation analysis |
time_wave_in |
time from input data |
strain_in |
from the input matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
time_wave= out$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$strain
plotTimeStrain(time_wave,strain,time_wave_in,strain_in)
Stress-Time plot
Description
create Stress-Time plot
create Stress-Time plot
Usage
plotTimeStress(time_wave, stress, time_wave_in, strain_rate, ...)
plotTimeStress(time_wave, stress, time_wave_in, strain_rate, ...)
Arguments
time_wave |
from the output matrix from fft analysis or numerical differentiation analysis |
stress |
from the output matrix from fft analysis or numerical differentiation analysis |
time_wave_in |
raw time from input data |
strain_rate |
strain rate from input data |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
time_wave= out$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$strain_rate
plotTimeStress(time_wave,stress,time_wave_in,strain_rate)
VGP plot
Description
create VGP plot
create VGP plot
Usage
plotVGP(G_star_t, delta_t, ...)
plotVGP(G_star_t, delta_t, ...)
Arguments
G_star_t |
from the output matrix from fft analysis or numerical differentiation analysis |
delta_t |
from the output matrix from fft analysis or numerical differentiation analysis |
... |
parameters of plot() |
Value
No return value
No return value
Author(s)
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
Giorgio Luciano and Serena Beretta, based on the Plotting functions created by Simon Rogers Group for Soft Matter
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$raw_time
resp_wave <- data.frame(df$strain,df$strain_rate,df$stress)
out <- Rpp_num(time_wave, resp_wave , L=1024, k=8, num_mode=1)
G_star_t= out$spp_data_out$G_star_t
delta_t= out$spp_data_out$delta_t
plotVGP(G_star_t,delta_t)
SPP Analysis via fourier
Description
applies the SPP Analysis by means of a fourier series.
Usage
rpp_fft(time_wave, resp_wave, L, omega, M, p)
Arguments
time_wave |
Lx1 vector of time at each measurement point |
resp_wave |
Lx3 matrix of the strain, rate and stress data,with each row representing a measuring point |
L |
number of measurement points in the extracted data |
omega |
frequency of oscilation (rad/s) |
M |
number of harmonics for stress |
p |
number of cycles |
Value
a list with the following data frame spp_data_in= the data frame with the data spp_params=spp_params, spp_data_out= Length,frequency,harmonics,cycles,max_harmonics,step_size fsf_data_out= Tx,Ty,Tz,Nx,Ny,Nz,Bx,By,Bz coordinates of the trajectory (T=tangent,N=principal Normal,B=Binormal Vectors) ft_out=data frame with that includes time_wave,strain,rate,stress,Gp_t,Gpp_t,G_star_t,tan_delta_t,delta_t,disp_stress,eq_strain_est,Gp_t_dot,Gpp_t_dot,G_speed,delta_t_dot)
Author(s)
Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)
References
Simon A. Rogersa, M. Paul Letting, A sequence of physical processes determined and quantified in large-amplitude oscillatory shear (LAOS): Application to theoretical nonlinear models Journal of Rheology 56:1, 1-25
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))
time_wave <- df$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)
Export results of the performed SPP analysis in csv format
Description
# This function export the output the SPP analysis (performed via FFT or Numeric Analysis) and export it to csv files
Usage
rpp_out_csv(out, myfilename = "my_models.xlsx")
Arguments
out |
output of the SPP analysis (performed via FFT or Numeric Analysis) |
myfilename |
name of the file where to save results (csv) |
Value
No return value
Author(s)
Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)
Export results of the performed SPP analysis in xls format
Description
# This function export the output the SPP analysis (performed via FFT or Numeric Analysis) and export it to xls files
Usage
rpp_out_excel(out, myfilename = "my_models.xlsx")
Arguments
out |
output of the SPP analysis (performed via FFT or Numeric Analysis) |
myfilename |
name of the file where to save results in xls format |
Value
No return value
Author(s)
Simon Rogers Group for Soft Matter (matlab version), Giorgio Luciano and Serena Berretta (R version)
Read function
Description
This function reads data from the selected file, and assign it to a dataframe
Usage
rpp_read(filename, header = TRUE, selected = c(2, 3, 4, 0, 0, 1, 0, 0), ...)
Arguments
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 |
Value
a dataframe with all the columns assigned
Author(s)
Giorgio Luciano and Serena Berretta, Simon Rogers Group for Soft Matter (matlab version)
Read function
Description
This function reads data from a dataframe
Usage
rpp_read2(dat, selected = c(2, 3, 4, 0, 0, 1, 0, 0), ...)
Arguments
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 |
Value
a dataframe with all the columns assigned
Author(s)
Giorgio Luciano and Serena Berretta, Simon Rogers Group for Soft Matter (matlab version)
Examples
data(mydata)
df <- rpp_read2(mydata , selected=c(2, 3, 4, 0, 0, 1, 0, 0))