## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(AIUQ)

## ----fig.height = 3.5, fig.width = 3.5----------------------------------------
set.seed(1)

sim_bm = simulation()
show(sim_bm)

## Plot simulated particle trajectory
plot_traj(sim_bm)

## ----fig.height = 3.5, fig.width = 7------------------------------------------
par(mfrow=c(1,2))
## Plot intensity profile for different frames 
plot_intensity(sim_bm@intensity, sz=sim_bm@sz) #first frame 
plot_intensity(sim_bm@intensity, sz=sim_bm@sz,frame=10, color=T) #10th frame, color image

## ----fig.height = 3.5, fig.width = 7------------------------------------------
## AIUQ method: use BM as fitted model 
sam = SAM(sim_object=sim_bm, model_name='BM')
show(sam)

par(mfrow=c(1,2))
## Plot true MSD and estimated MSD
plot_MSD(object=sam, msd_truth=sam@msd_truth) #in log10 scale

## Plot intensity in reciprocal space 
plot_I_q_1 = matrix(sam@I_q[,1], sam@sz[1],sam@sz[2]) #first frame 
plot3D::image2D(abs(fftshift(plot_I_q_1)),main="intensity in reciprocal space")


## -----------------------------------------------------------------------------
sam = SAM(sim_object=sim_bm, AIUQ_thr=c(0.99,0.6)) 
#Note: Default model_name is "BM", it's ok to not specify this argument if want to fit with BM model 
show(sam)

sam = SAM(sim_object=sim_bm, index_q_AIUQ=5:50)
show(sam)

## ----fig.height = 3.5, fig.width = 3.5----------------------------------------
set.seed(1)

## Simulation
sim_bm = simulation(sz=100,len_t=100,sigma_bm=0.5)
show(sim_bm)

## Plot simulated particle trajectory
plot_traj(sim_bm)

## ----fig.height = 3.5, fig.width = 7------------------------------------------
## AIUQ method: fitting using BM model with uncertainty quantification 
sam = SAM(sim_object=sim_bm, uncertainty=T)
show(sam)

par(mfrow=c(1,2))
## Plot true MSD and estimated MSD with uncertainty 
plot_MSD(object=sam, msd_truth=sam@msd_truth) #in log10 scale 
plot_MSD(object=sam, msd_truth=sam@msd_truth,log10=F) #in real scale

## ----fig.height = 3.5, fig.width = 7------------------------------------------
set.seed(1)

## Simulation
sim_ou = simulation(sigma_ou=4, model_name="OU")
show(sim_ou)

par(mfrow=c(1,2))
## Plot simulated particle trajectory
plot_traj(sim_ou)

## AIUQ method: fitting using OU model
sam_ou = SAM(sim_object=sim_ou, model_name=sim_ou@model_name) 
show(sam_ou)

## Plot true MSD and estimated MSD with uncertainty
plot_MSD(object=sam_ou, msd_truth=sam_ou@msd_truth) #in log10 scale

## -----------------------------------------------------------------------------
set.seed(1)

## Simulation
sim_bm = simulation(sz=100,len_t=100,sigma_bm=0.5)
show(sim_bm)

## User defined MSD structure: function of parameters and 
#                                          vector of lag times 
msd_fn = function(param, d_input){
  MSD = param[1]*d_input+param[2]*d_input^2
}

# show MSD and MSD gradient with a simple example 
theta = c(2,1)
d_input = 0:10
model_name = "user_defined"
MSD_list = get_MSD_with_grad(theta=theta,d_input=d_input,model_name=model_name,
                             msd_fn=msd_fn)
MSD_list$msd

## AIUQ method: fitting using user_defined model 
sam = SAM(sim_object=sim_bm, model_name=model_name, msd_fn=msd_fn, num_param=2)
show(sam)

## -----------------------------------------------------------------------------
set.seed(1)

## Simulation
sim_bm = simulation(sz=100,len_t=100,sigma_bm = 0.5)
sam_mf = SAM(sim_object = sim_bm, model_name = sim_bm@model_name, 
          model_free_estimation = TRUE)
show(sam_mf)

plot_MSD(object = sam_mf, msd_truth = sam_mf@msd_truth)

## ----warning=FALSE------------------------------------------------------------
tif_path = system.file("extdata", "example_video.tif", package = "AIUQ")

## Load the microscopy video which is a space-by-space-by-time array
intensity = bioimagetools::readTIF(tif_path)
## Specify the structure of the input intensity data
intensity_str = "SST_array"

## Provide the time interval between consecutive frames and the pixel size
mindt = 1
pxsz = 1

sam_mf = SAM(intensity = intensity, intensity_str = intensity_str,
             mindt = mindt, pxsz = pxsz, model_free_estimation = TRUE)

plot(log10(sam_mf@d_input), log10(sam_mf@msd_est), col = "blue", pch = 16,
     xlab = expression(log[10](Delta * t)), ylab = expression(log[10](MSD)))

## ----eval=FALSE---------------------------------------------------------------
#  ## MSD_unit, Temp, and a should be specified according to the experiment.
#  ## Modulus estimation requires uncertainty = TRUE so that MSD intervals can be used
#  ## for sampling. The medians of sampled Gp and Gpp are used as the estimate of
#  ## storage and loss moduli.
#  sam_mf = SAM(intensity = real_data, intensity_str = "SST_array",
#               pxsz = pxsz, mindt = mindt,
#               model_free_estimation = TRUE,
#               uncertainty = TRUE, M = M,
#               estimate_moduli = TRUE,
#               MSD_unit = MSD_unit, Temp = Temp, a = a)
#  
#  plot(log10(sam_mf@omega), log10(sam_mf@Gp), type = "l", col = "blue",
#       xlab = expression(log[10](omega)), ylab = expression(log[10](modulus)))
#  lines(log10(sam_mf@omega), log10(sam_mf@Gpp), col = "red")
#  legend("topright", legend = c("G'", "G''"), col = c("blue", "red"), lty = 1)