Workflow.R
In ctross/DieTryin: A package for running network-structured economic games
################################### Install and/or load
library(devtools)
#You might need to turn off warning-error-conversion, because the tiniest warning stops installation
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true")
install_github('ctross/DieTryin')
library(DieTryin)
################################### Setup directory
# We set up storage for the RICH games data and data from several other social networks
path = "C:\\Users\\cody_ross\\Desktop"
games_to_add = c("FriendshipsData", "TrustData", "WisdomData", "WorkData", "EmotionData", "ReputationData")
setup_folders(path, add=games_to_add)
################################### Standardize photos
# First paste the images in the RespodentsImages folder in this zip folder
# into the RawImages folder in the RICH folder created just above, then run:
standardize_photos(path=path, pattern=".jpg", start=1, stop=3,
size_out=1000, border_size=10, asr=1.6180,
id_range=NULL, id_names=NULL, spin=TRUE)
# Imagine you messed up somewhere and want to re-do a single image, then run:
standardize_photos(path=path, pattern=".jpg", start=1, stop=3,
size_out=1000, border_size=10, asr=1.6180,
id_range=NULL, id_names="EW1", spin=TRUE)
################################### Now create the survey tool
# To create a random order, run:
build_survey(path=path, pattern=".jpg", start=1, stop=3,
n_panels=2, n_rows=4, n_cols=5, seed=15, ordered = NULL)
# But, for now lets set a specific sorting order
sorted_ids = c("LF1", "CCM", "SW1", "KC1",
"DB1", "RKM", "AT1", "CT1",
"JK1", "AF1", "KW1", "MK1",
"AOC", "FN1", "LWA", "DJT",
"LCK", "MM1", "RBG", "EM1",
"AR1", "MR1", "BYC", "JLO",
"BS1", "SR1", "DB2", "EW1",
"TT1", "EDG", "JA1", "SS1",
"SK1", "MB1", "AY1", "ASF",
"JC1", "BO1", "FKA"
)
build_survey(path=path, pattern=".jpg", start=1, stop=3,
n_panels=2, n_rows=4, n_cols=5, seed=1, ordered = sorted_ids)
################################### Enter some data for the RICH games
# Repeat this line to enter RICH data for a few ID codes above (e.g., "JLO","AOC", and "FKA"), for each of the three games ("G", "L", and "R")
# You must input data for ID and Game in the header file. Other entries, like date, are optional.
enter_data(path=path, pattern=".jpg", start=1, stop=3, n_panels=2, n_rows=4, n_cols=5, seed=1, ordered = sorted_ids, add=games_to_add)
# In the event of a data entry error just re-run the above function, and it will overwrite the erroneous, person- and game-specific data file.
################################### Compile the data and check it for accuracy
compile_data(path=path, game="GivingData")
compile_data(path=path, game="LeavingData")
compile_data(path=path, game="ReducingData")
# Now check the results in the Results subfolder, and ensure that the summary tables look correct. If there are errors, then
# fix the csv files by hand, or re-run the enter_data() function for the specific inviduals with data entry errors. Then,
# run the above compile functions again. Repeat until all summary tables look correct.
################################### Calculate payoffs
calculate_payouts(path=path, pattern=".jpg", start=1, stop=3, game="GLR", GV=1, LV=0.5, KV=1, RV=-3)
###############################################################################################
###############################################################################################
################################### Now, for the other data, we can move into automatic coding
# First paste results photos from the CollectedDataImages folder from the zip file into the ResultsPhotos directory. Use properly formatted titles
# of form: "GAMEID_PERSONID_PANELID.jpg" then downsize the images
downsize(path=path, scaler=3) # set scaler=1 to keep same size
# In order to set the right paramters for: lower_hue_threshold and upper_hue_threshold, it is helpful to check out the actual hue of your tokens.
# So pick a photo with all your token colors and run:
get_hue(paste0(path,"/ResultsPhotosSmall/E_YEZ_A.jpg"))
# make sure to click on several tokens and several places on each token to get a good idea of the range of values each token may take.
# get_hue(file.choose()) # will allow you to select a pictures without having to input the file path directly
################################ Now enter the data
classify(
path=path,
PID = "SS1",
HHID = "SKA",
RID = "CR",
day = 12, month = 4, year = 2020,
name = "Cody",
panels = c("A", "B"),
questions = c("A"),
game = "PeerReports",
order = "AB",
revise = FALSE,
pattern = ".jpg",
start = 1, stop = 3,
seed = 1,
n_panels = 2,
n_rows = 4, n_cols = 5,
ordered_ids = sorted_ids,
thresh = c(0.065, 0.065, 0.065),
lower_hue_threshold = c(110, 285,175),
upper_hue_threshold = c(155, 341,215),
plot_colors = c("empty", "seagreen4", "purple", "navyblue"),
lower_saturation_threshold = 0.1,
lower_luminance_threshold = 0.12,
upper_luminance_threshold = 0.88,
border_size = 0.22, iso_blur = 0,
direction = "forward",
automate = TRUE,
d_x = 20, d_y = 20
)
############################################################################
#The code below is outdated. It was needed in the BRM paper, but the package now does all of these steps automatically via the classify function
################################### Pre-process the data needed for analysis
# These lines will open a window where corners must be clicked
blank1 = pre_process(path=path, ID="CTR", game="Blank", panels=c("A","B")) # control with no tokens
friend1 = pre_process(path=path, ID="CTR", game="FriendshipsData", panels=c("A","B")) # game play with tokens
game_images_all1 = list(blank1[[1]], friend1[[1]]) # Then organize into a list
game_locs_all1 = list(blank1[[2]], friend1[[2]])
game_ID_all1 = list("Blank", "FriendshipsData")
################################### Run the classifier
Game_all1 = auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=0.1, lower_hue_threshold = 135, upper_hue_threshold = 170,
plot_colors=c("empty","seagreen4"), img=game_images_all1, locs=game_locs_all1, ID="CTR",
game=game_ID_all1, ordered=sorted_ids,
lower_saturation_threshold=0.12,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
################################### Check the performance of the classifier
check_classification(path=path, Game_all1[[1]], n_panels = 2, n_rows=4, n_cols=5, ID="CTR", game="FriendshipsData")
# and if it looks good, then save the results
annotate_data(path = path, results=Game_all1, HHID="BPL", RID="CR", day=12, month=4, year=2020,
name = "Cory", ID="CTR", game="FriendshipsData", order="AB", seed = 1)
# Here we use the same data, and duplicate it with a new set of header data just to test the compile function below
annotate_data(path = path, results=Game_all1, HHID="LQL", RID="CR", day=12, month=4, year=2020,
name = "Faith", ID="AOC", game="FriendshipsData", order="BA", seed = 1)
compile_data(path=path, game="FriendshipsData")
#########################################################################################
################################### Now pre-process the data needed for a Likert-analysis
# These lines will open a window where corners must be clicked
blank2 = pre_process(path=path, ID="QQQ", game="Blank", panels=c("A","B"))
trust2 = pre_process(path=path, ID="QQQ", game="TrustData", panels=c("A","B"))
game_images_all2 = list(blank2[[1]], trust2[[1]])
game_locs_all2 = list(blank2[[2]], trust2[[2]])
game_ID_all2 = list("Blank", "TrustData")
################################### Run the classifier
Game_all2 = auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all2, locs=game_locs_all2, ID="QQQ",
game=game_ID_all2, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
################################### Check the performance of the classifier
check_classification(path=path, Game_all2[[1]], n_panels = 2, n_rows=4, n_cols=5, ID="QQQ", game="TrustData")
# and if it looks good, then save the results
annotate_data(path = path, results=Game_all2, HHID="BPL", RID="CR", day=12, month=4, year=2020,
name = "Shakira", ID="QQQ", game="TrustData", order="BA", seed = 1)
# Just to test compiler we use the same data with a new set of annotations
annotate_data(path = path, results=Game_all2, HHID="BPL", RID="CR", day=14, month=4, year=2020,
name = "Lowkey", ID="LKW", game="TrustData", order="BA", seed = 1)
compile_data(path=path, game="TrustData")
#########################################################################################
################################### If images are preproccessed to be squared and cropped then,
# user input and image correction can be skipped by using pre_processed=TRUE
blank3 = pre_process(path=path, ID="YEZ", game="Blank", panels=c("A","B"), pre_processed=TRUE)
trust3 = pre_process(path=path, ID="YEZ", game="WisdomData", panels=c("A","B"), pre_processed=TRUE)
game_images_all3 = list(blank3[[1]], trust3[[1]])
game_locs_all5 = list(blank3[[2]], trust3[[2]])
game_ID_all3 = list("Blank", "WisdomData")
################################### Run the classifier
Game_all3 = auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all3, locs=game_locs_all3, ID="YEZ",
game=game_ID_all3, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
pre_processed=TRUE)
################################### Check the performance of the classifier
check_classification(path=path, Game_all3[[1]], n_panels = 2, n_rows=4, n_cols=5, ID="YEZ", game="WisdomData")
# and if it looks good, then save the results
annotate_data(path = path, results=Game_all3, HHID="BPL", RID="CR", day=12, month=4, year=2020,
name = "Cody", ID="YEZ", game="WisdomData", order="BA", seed = 1)
# Just to test compiler we use the same data with a new set of annotations
annotate_data(path = path, results=Game_all3, HHID="BPL", RID="CR", day=14, month=4, year=2020,
name = "Dan", ID="DJR", game="WisdomData", order="BA", seed = 1)
compile_data(path=path, game="WisdomData")
#####################################################################################################
################################### This all works for a single game, now lets batch process binary data
################################ Binary data
filled = vector("list", 27)
filled[[1]] = pre_process(path=path, ID="CTR", game="Blank", panels=c("A","B"))
for(i in 1:26)
filled[[i+1]] = pre_process(path=path, ID="CTR", game=toupper(letters)[i], panels=c("A","B")) # use letters for "game" names
game_images_all4 = vector("list", 27)
game_locs_all4 = vector("list", 27)
game_ID_all4 = vector("list", 27)
game_images_all4[[1]] <- filled[[1]][[1]]
game_locs_all4[[1]] <- filled[[1]][[2]]
game_ID_all4[[1]] <- "Blank"
for(i in 2:27){
game_images_all4[[i]] <- filled[[i]][[1]]
game_locs_all4[[i]] <- filled[[i]][[2]]
game_ID_all4[[i]] <- toupper(letters)[i-1]
}
Game_all4 <- auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=0.1, lower_hue_threshold = 135, upper_hue_threshold = 170,
plot_colors=c("empty","seagreen4"), img=game_images_all4, locs=game_locs_all4, ID="CTR",
game=game_ID_all4, ordered=sorted_ids,
lower_saturation_threshold=0.12,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
for(i in 1:26)
check_classification(path=path, Game_all4[[i]], n_panels = 2, n_rows=4, n_cols=5, ID="CTR", game=toupper(letters)[i])
annotate_batch_data(path = path, results=Game_all4, HHID="BQL", RID="CR", day=12, month=4, year=2020,
name = "BobBarker", ID="CTR", game="WorkData", order="AB", seed = 1)
annotate_batch_data(path = path, results=Game_all4, HHID="LQL", RID="CR", day=12, month=4, year=2020,
name = "Faith", ID="AOC", game="WorkData", order="BA", seed = 1)
compile_data(path=path, game="WorkData", batch=TRUE)
##################################################################################
################################### And now a batch process script for Likert data
filled2 = vector("list", 27)
filled2[[1]] = pre_process(path=path, ID="QQQ", game="Blank", panels=c("A","B"))
for(i in 1:26)
filled2[[i+1]] = pre_process(path=path, ID="QQQ", game=toupper(letters)[i], panels=c("A","B"))
game_images_all5 = vector("list", 27)
game_locs_all5= vector("list", 27)
game_ID_all5 = vector("list", 27)
game_images_all5[[1]] <- filled2[[1]][[1]]
game_locs_all5[[1]] <- filled2[[1]][[2]]
game_ID_all5[[1]] <- "Blank"
for(i in 2:27){
game_images_all5[[i]] <- filled2[[i]][[1]]
game_locs_all5[[i]] <- filled2[[i]][[2]]
game_ID_all5[[i]] <- toupper(letters)[i-1]
}
Game_all5 <- auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all5, locs=game_locs_all5, ID="QQQ",
game=game_ID_all5, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
for(i in 1:26)
check_classification(path=path, Game_all5[[i]], n_panels = 2, n_rows=4, n_cols=5, ID="QQQ", game=toupper(letters)[i])
annotate_batch_data(path = path, results=Game_all5, HHID="BQL", RID="CR", day=12, month=4, year=2020,
name = "Quark and Beans", ID="QQQ", game="EmotionData", order="AB", seed = 1)
annotate_batch_data(path = path, results=Game_all5, HHID="JKL", RID="CR", day=1, month=4, year=2020,
name = "Walter White", ID="XAS", game="EmotionData", order="AB", seed = 1)
compile_data(path=path, game="EmotionData", batch=TRUE)
####################################################### And Again in batch mode
################################### If images are preproccessed to be squared and cropped then,
# user input and image correction can be skipped by using pre_processed=TRUE
filled3 = vector("list", 27)
filled3[[1]] = pre_process(path=path, ID="YEZ", game="Blank", panels=c("A","B"), pre_processed=TRUE)
for(i in 1:26)
filled3[[i+1]] = pre_process(path=path, ID="YEZ", game=toupper(letters)[i], panels=c("A","B"), pre_processed=TRUE)
game_images_all6 = vector("list", 27)
game_locs_all6 = vector("list", 27)
game_ID_all6 = vector("list", 27)
game_images_all6[[1]] <- filled3[[1]][[1]]
game_locs_all6[[1]] <- filled3[[1]][[2]]
game_ID_all6[[1]] <- "Blank"
for(i in 2:27){
game_images_all6[[i]] <- filled3[[i]][[1]]
game_locs_all6[[i]] <- filled3[[i]][[2]]
game_ID_all6[[i]] <- toupper(letters)[i-1]
}
Game_all6 <- auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all6, locs=game_locs_all6, ID="YEZ",
game=game_ID_all6, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
pre_processed=TRUE)
for(i in 1:26)
check_classification(path=path, Game_all6[[i]], n_panels = 2, n_rows=4, n_cols=5, ID="YEZ", game=toupper(letters)[i])
annotate_batch_data(path = path, results=Game_all6, HHID="YEP", RID="CR", day=19, month=7, year=2020,
name = "Jim LaughAgain", ID="YEZ", game="ReputationData", order="AB", seed = 1)
annotate_batch_data(path = path, results=Game_all6, HHID="JKF", RID="CR", day=11, month=3, year=2020,
name = "Sunny Shade", ID="CVD", game="ReputationData", order="AB", seed = 1)
compile_data(path=path, game="ReputationData", batch=TRUE)
################################################################################## For Figure 9
par(mfrow = c(4, 5), # 2x2 layout
oma = c(1, 1, 1, 1), # two rows of text at the outer left and bottom margin
mar = c(1, 1, 1, 1)) # space for one row of text at ticks and to separate plots
pruned_image=(Game_all5[[15]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="navyblue",lwd=2)
abline(v=175,col="navyblue",lwd=2)
abline(v=350,col="purple",lwd=2)
abline(v=280,col="purple",lwd=2)
abline(v=165,col="seagreen4",lwd=2)
abline(v=110,col="seagreen4",lwd=2)
pruned_image=(Game_all5[[16]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="navyblue",lwd=2)
abline(v=175,col="navyblue",lwd=2)
pruned_image=(Game_all5[[19]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=165,col="seagreen4",lwd=2)
abline(v=110,col="seagreen4",lwd=2)
pruned_image=(Game_all5[[20]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=350,col="purple",lwd=2)
abline(v=280,col="purple",lwd=2)
################################################################################## For Figure 9 v2
par(mfrow = c(2, 5))
pruned_image=(Game_all5[[15]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="slateblue",lwd=2)
abline(v=175,col="indianred",lwd=2)
pruned_image=(Game_all5[[16]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="slateblue",lwd=2)
abline(v=175,col="indianred",lwd=2)
ctross/DieTryin documentation built on Sept. 8, 2024, 8:07 p.m.
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################################### Install and/or load
library(devtools)
#You might need to turn off warning-error-conversion, because the tiniest warning stops installation
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true")
install_github('ctross/DieTryin')
library(DieTryin)
################################### Setup directory
# We set up storage for the RICH games data and data from several other social networks
path = "C:\\Users\\cody_ross\\Desktop"
games_to_add = c("FriendshipsData", "TrustData", "WisdomData", "WorkData", "EmotionData", "ReputationData")
setup_folders(path, add=games_to_add)
################################### Standardize photos
# First paste the images in the RespodentsImages folder in this zip folder
# into the RawImages folder in the RICH folder created just above, then run:
standardize_photos(path=path, pattern=".jpg", start=1, stop=3,
size_out=1000, border_size=10, asr=1.6180,
id_range=NULL, id_names=NULL, spin=TRUE)
# Imagine you messed up somewhere and want to re-do a single image, then run:
standardize_photos(path=path, pattern=".jpg", start=1, stop=3,
size_out=1000, border_size=10, asr=1.6180,
id_range=NULL, id_names="EW1", spin=TRUE)
################################### Now create the survey tool
# To create a random order, run:
build_survey(path=path, pattern=".jpg", start=1, stop=3,
n_panels=2, n_rows=4, n_cols=5, seed=15, ordered = NULL)
# But, for now lets set a specific sorting order
sorted_ids = c("LF1", "CCM", "SW1", "KC1",
"DB1", "RKM", "AT1", "CT1",
"JK1", "AF1", "KW1", "MK1",
"AOC", "FN1", "LWA", "DJT",
"LCK", "MM1", "RBG", "EM1",
"AR1", "MR1", "BYC", "JLO",
"BS1", "SR1", "DB2", "EW1",
"TT1", "EDG", "JA1", "SS1",
"SK1", "MB1", "AY1", "ASF",
"JC1", "BO1", "FKA"
)
build_survey(path=path, pattern=".jpg", start=1, stop=3,
n_panels=2, n_rows=4, n_cols=5, seed=1, ordered = sorted_ids)
################################### Enter some data for the RICH games
# Repeat this line to enter RICH data for a few ID codes above (e.g., "JLO","AOC", and "FKA"), for each of the three games ("G", "L", and "R")
# You must input data for ID and Game in the header file. Other entries, like date, are optional.
enter_data(path=path, pattern=".jpg", start=1, stop=3, n_panels=2, n_rows=4, n_cols=5, seed=1, ordered = sorted_ids, add=games_to_add)
# In the event of a data entry error just re-run the above function, and it will overwrite the erroneous, person- and game-specific data file.
################################### Compile the data and check it for accuracy
compile_data(path=path, game="GivingData")
compile_data(path=path, game="LeavingData")
compile_data(path=path, game="ReducingData")
# Now check the results in the Results subfolder, and ensure that the summary tables look correct. If there are errors, then
# fix the csv files by hand, or re-run the enter_data() function for the specific inviduals with data entry errors. Then,
# run the above compile functions again. Repeat until all summary tables look correct.
################################### Calculate payoffs
calculate_payouts(path=path, pattern=".jpg", start=1, stop=3, game="GLR", GV=1, LV=0.5, KV=1, RV=-3)
###############################################################################################
###############################################################################################
################################### Now, for the other data, we can move into automatic coding
# First paste results photos from the CollectedDataImages folder from the zip file into the ResultsPhotos directory. Use properly formatted titles
# of form: "GAMEID_PERSONID_PANELID.jpg" then downsize the images
downsize(path=path, scaler=3) # set scaler=1 to keep same size
# In order to set the right paramters for: lower_hue_threshold and upper_hue_threshold, it is helpful to check out the actual hue of your tokens.
# So pick a photo with all your token colors and run:
get_hue(paste0(path,"/ResultsPhotosSmall/E_YEZ_A.jpg"))
# make sure to click on several tokens and several places on each token to get a good idea of the range of values each token may take.
# get_hue(file.choose()) # will allow you to select a pictures without having to input the file path directly
################################ Now enter the data
classify(
path=path,
PID = "SS1",
HHID = "SKA",
RID = "CR",
day = 12, month = 4, year = 2020,
name = "Cody",
panels = c("A", "B"),
questions = c("A"),
game = "PeerReports",
order = "AB",
revise = FALSE,
pattern = ".jpg",
start = 1, stop = 3,
seed = 1,
n_panels = 2,
n_rows = 4, n_cols = 5,
ordered_ids = sorted_ids,
thresh = c(0.065, 0.065, 0.065),
lower_hue_threshold = c(110, 285,175),
upper_hue_threshold = c(155, 341,215),
plot_colors = c("empty", "seagreen4", "purple", "navyblue"),
lower_saturation_threshold = 0.1,
lower_luminance_threshold = 0.12,
upper_luminance_threshold = 0.88,
border_size = 0.22, iso_blur = 0,
direction = "forward",
automate = TRUE,
d_x = 20, d_y = 20
)
############################################################################
#The code below is outdated. It was needed in the BRM paper, but the package now does all of these steps automatically via the classify function
################################### Pre-process the data needed for analysis
# These lines will open a window where corners must be clicked
blank1 = pre_process(path=path, ID="CTR", game="Blank", panels=c("A","B")) # control with no tokens
friend1 = pre_process(path=path, ID="CTR", game="FriendshipsData", panels=c("A","B")) # game play with tokens
game_images_all1 = list(blank1[[1]], friend1[[1]]) # Then organize into a list
game_locs_all1 = list(blank1[[2]], friend1[[2]])
game_ID_all1 = list("Blank", "FriendshipsData")
################################### Run the classifier
Game_all1 = auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=0.1, lower_hue_threshold = 135, upper_hue_threshold = 170,
plot_colors=c("empty","seagreen4"), img=game_images_all1, locs=game_locs_all1, ID="CTR",
game=game_ID_all1, ordered=sorted_ids,
lower_saturation_threshold=0.12,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
################################### Check the performance of the classifier
check_classification(path=path, Game_all1[[1]], n_panels = 2, n_rows=4, n_cols=5, ID="CTR", game="FriendshipsData")
# and if it looks good, then save the results
annotate_data(path = path, results=Game_all1, HHID="BPL", RID="CR", day=12, month=4, year=2020,
name = "Cory", ID="CTR", game="FriendshipsData", order="AB", seed = 1)
# Here we use the same data, and duplicate it with a new set of header data just to test the compile function below
annotate_data(path = path, results=Game_all1, HHID="LQL", RID="CR", day=12, month=4, year=2020,
name = "Faith", ID="AOC", game="FriendshipsData", order="BA", seed = 1)
compile_data(path=path, game="FriendshipsData")
#########################################################################################
################################### Now pre-process the data needed for a Likert-analysis
# These lines will open a window where corners must be clicked
blank2 = pre_process(path=path, ID="QQQ", game="Blank", panels=c("A","B"))
trust2 = pre_process(path=path, ID="QQQ", game="TrustData", panels=c("A","B"))
game_images_all2 = list(blank2[[1]], trust2[[1]])
game_locs_all2 = list(blank2[[2]], trust2[[2]])
game_ID_all2 = list("Blank", "TrustData")
################################### Run the classifier
Game_all2 = auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all2, locs=game_locs_all2, ID="QQQ",
game=game_ID_all2, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
################################### Check the performance of the classifier
check_classification(path=path, Game_all2[[1]], n_panels = 2, n_rows=4, n_cols=5, ID="QQQ", game="TrustData")
# and if it looks good, then save the results
annotate_data(path = path, results=Game_all2, HHID="BPL", RID="CR", day=12, month=4, year=2020,
name = "Shakira", ID="QQQ", game="TrustData", order="BA", seed = 1)
# Just to test compiler we use the same data with a new set of annotations
annotate_data(path = path, results=Game_all2, HHID="BPL", RID="CR", day=14, month=4, year=2020,
name = "Lowkey", ID="LKW", game="TrustData", order="BA", seed = 1)
compile_data(path=path, game="TrustData")
#########################################################################################
################################### If images are preproccessed to be squared and cropped then,
# user input and image correction can be skipped by using pre_processed=TRUE
blank3 = pre_process(path=path, ID="YEZ", game="Blank", panels=c("A","B"), pre_processed=TRUE)
trust3 = pre_process(path=path, ID="YEZ", game="WisdomData", panels=c("A","B"), pre_processed=TRUE)
game_images_all3 = list(blank3[[1]], trust3[[1]])
game_locs_all5 = list(blank3[[2]], trust3[[2]])
game_ID_all3 = list("Blank", "WisdomData")
################################### Run the classifier
Game_all3 = auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all3, locs=game_locs_all3, ID="YEZ",
game=game_ID_all3, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
pre_processed=TRUE)
################################### Check the performance of the classifier
check_classification(path=path, Game_all3[[1]], n_panels = 2, n_rows=4, n_cols=5, ID="YEZ", game="WisdomData")
# and if it looks good, then save the results
annotate_data(path = path, results=Game_all3, HHID="BPL", RID="CR", day=12, month=4, year=2020,
name = "Cody", ID="YEZ", game="WisdomData", order="BA", seed = 1)
# Just to test compiler we use the same data with a new set of annotations
annotate_data(path = path, results=Game_all3, HHID="BPL", RID="CR", day=14, month=4, year=2020,
name = "Dan", ID="DJR", game="WisdomData", order="BA", seed = 1)
compile_data(path=path, game="WisdomData")
#####################################################################################################
################################### This all works for a single game, now lets batch process binary data
################################ Binary data
filled = vector("list", 27)
filled[[1]] = pre_process(path=path, ID="CTR", game="Blank", panels=c("A","B"))
for(i in 1:26)
filled[[i+1]] = pre_process(path=path, ID="CTR", game=toupper(letters)[i], panels=c("A","B")) # use letters for "game" names
game_images_all4 = vector("list", 27)
game_locs_all4 = vector("list", 27)
game_ID_all4 = vector("list", 27)
game_images_all4[[1]] <- filled[[1]][[1]]
game_locs_all4[[1]] <- filled[[1]][[2]]
game_ID_all4[[1]] <- "Blank"
for(i in 2:27){
game_images_all4[[i]] <- filled[[i]][[1]]
game_locs_all4[[i]] <- filled[[i]][[2]]
game_ID_all4[[i]] <- toupper(letters)[i-1]
}
Game_all4 <- auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=0.1, lower_hue_threshold = 135, upper_hue_threshold = 170,
plot_colors=c("empty","seagreen4"), img=game_images_all4, locs=game_locs_all4, ID="CTR",
game=game_ID_all4, ordered=sorted_ids,
lower_saturation_threshold=0.12,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
for(i in 1:26)
check_classification(path=path, Game_all4[[i]], n_panels = 2, n_rows=4, n_cols=5, ID="CTR", game=toupper(letters)[i])
annotate_batch_data(path = path, results=Game_all4, HHID="BQL", RID="CR", day=12, month=4, year=2020,
name = "BobBarker", ID="CTR", game="WorkData", order="AB", seed = 1)
annotate_batch_data(path = path, results=Game_all4, HHID="LQL", RID="CR", day=12, month=4, year=2020,
name = "Faith", ID="AOC", game="WorkData", order="BA", seed = 1)
compile_data(path=path, game="WorkData", batch=TRUE)
##################################################################################
################################### And now a batch process script for Likert data
filled2 = vector("list", 27)
filled2[[1]] = pre_process(path=path, ID="QQQ", game="Blank", panels=c("A","B"))
for(i in 1:26)
filled2[[i+1]] = pre_process(path=path, ID="QQQ", game=toupper(letters)[i], panels=c("A","B"))
game_images_all5 = vector("list", 27)
game_locs_all5= vector("list", 27)
game_ID_all5 = vector("list", 27)
game_images_all5[[1]] <- filled2[[1]][[1]]
game_locs_all5[[1]] <- filled2[[1]][[2]]
game_ID_all5[[1]] <- "Blank"
for(i in 2:27){
game_images_all5[[i]] <- filled2[[i]][[1]]
game_locs_all5[[i]] <- filled2[[i]][[2]]
game_ID_all5[[i]] <- toupper(letters)[i-1]
}
Game_all5 <- auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all5, locs=game_locs_all5, ID="QQQ",
game=game_ID_all5, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
histogram_balancing=FALSE,
direction="backward")
for(i in 1:26)
check_classification(path=path, Game_all5[[i]], n_panels = 2, n_rows=4, n_cols=5, ID="QQQ", game=toupper(letters)[i])
annotate_batch_data(path = path, results=Game_all5, HHID="BQL", RID="CR", day=12, month=4, year=2020,
name = "Quark and Beans", ID="QQQ", game="EmotionData", order="AB", seed = 1)
annotate_batch_data(path = path, results=Game_all5, HHID="JKL", RID="CR", day=1, month=4, year=2020,
name = "Walter White", ID="XAS", game="EmotionData", order="AB", seed = 1)
compile_data(path=path, game="EmotionData", batch=TRUE)
####################################################### And Again in batch mode
################################### If images are preproccessed to be squared and cropped then,
# user input and image correction can be skipped by using pre_processed=TRUE
filled3 = vector("list", 27)
filled3[[1]] = pre_process(path=path, ID="YEZ", game="Blank", panels=c("A","B"), pre_processed=TRUE)
for(i in 1:26)
filled3[[i+1]] = pre_process(path=path, ID="YEZ", game=toupper(letters)[i], panels=c("A","B"), pre_processed=TRUE)
game_images_all6 = vector("list", 27)
game_locs_all6 = vector("list", 27)
game_ID_all6 = vector("list", 27)
game_images_all6[[1]] <- filled3[[1]][[1]]
game_locs_all6[[1]] <- filled3[[1]][[2]]
game_ID_all6[[1]] <- "Blank"
for(i in 2:27){
game_images_all6[[i]] <- filled3[[i]][[1]]
game_locs_all6[[i]] <- filled3[[i]][[2]]
game_ID_all6[[i]] <- toupper(letters)[i-1]
}
Game_all6 <- auto_enter_all(path=path, pattern=".jpg", start=1, stop=3, seed=1, n_panels=2, n_rows=4, n_cols=5,
thresh=c(0.075, 0.075, 0.075), lower_hue_threshold = c(135, 280, 170), upper_hue_threshold = c(170, 350, 215),
plot_colors=c("empty","seagreen4", "purple", "navyblue"), img=game_images_all6, locs=game_locs_all6, ID="YEZ",
game=game_ID_all6, ordered=sorted_ids,
lower_saturation_threshold=0.09,
lower_luminance_threshold=0.12,
upper_luminance_threshold=0.88,
border_size=0.22,
iso_blur=1,
pre_processed=TRUE)
for(i in 1:26)
check_classification(path=path, Game_all6[[i]], n_panels = 2, n_rows=4, n_cols=5, ID="YEZ", game=toupper(letters)[i])
annotate_batch_data(path = path, results=Game_all6, HHID="YEP", RID="CR", day=19, month=7, year=2020,
name = "Jim LaughAgain", ID="YEZ", game="ReputationData", order="AB", seed = 1)
annotate_batch_data(path = path, results=Game_all6, HHID="JKF", RID="CR", day=11, month=3, year=2020,
name = "Sunny Shade", ID="CVD", game="ReputationData", order="AB", seed = 1)
compile_data(path=path, game="ReputationData", batch=TRUE)
################################################################################## For Figure 9
par(mfrow = c(4, 5), # 2x2 layout
oma = c(1, 1, 1, 1), # two rows of text at the outer left and bottom margin
mar = c(1, 1, 1, 1)) # space for one row of text at ticks and to separate plots
pruned_image=(Game_all5[[15]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="navyblue",lwd=2)
abline(v=175,col="navyblue",lwd=2)
abline(v=350,col="purple",lwd=2)
abline(v=280,col="purple",lwd=2)
abline(v=165,col="seagreen4",lwd=2)
abline(v=110,col="seagreen4",lwd=2)
pruned_image=(Game_all5[[16]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="navyblue",lwd=2)
abline(v=175,col="navyblue",lwd=2)
pruned_image=(Game_all5[[19]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=165,col="seagreen4",lwd=2)
abline(v=110,col="seagreen4",lwd=2)
pruned_image=(Game_all5[[20]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=350,col="purple",lwd=2)
abline(v=280,col="purple",lwd=2)
################################################################################## For Figure 9 v2
par(mfrow = c(2, 5))
pruned_image=(Game_all5[[15]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="slateblue",lwd=2)
abline(v=175,col="indianred",lwd=2)
pruned_image=(Game_all5[[16]][[3]][[1]])
rows_image = imsplit(pruned_image,"y",4)
slice_image = imsplit(rows_image[[3]],"x",5)
slice = slice_image[2]
slice = slice[[1]]
plot(slice)
border_size = 10
px = Xc(slice) <= border_size
slice[px] = 0
px = Xc(slice) >= dim(slice)[1]-border_size
slice[px] = 0
px = Yc(slice) <= border_size*2
slice[px] = 0
px = Yc(slice) >= dim(slice)[2]-border_size*2
slice[px] = 0
plot(slice)
iso_blur = 2
slice = isoblur(slice, iso_blur)
plot(slice)
lower_saturation_threshold = 0.1
lower_luminance_threshold = 0.1
upper_luminance_threshold = 0.9
X = RGBtoHSL(slice)
S = ifelse(X[,,1,2]>lower_saturation_threshold,1,0)
L = ifelse(X[,,1,3]>lower_luminance_threshold & X[,,1,3]<upper_luminance_threshold ,0.5,0)
X[,,1,1] = X[,,1,1]
X[,,1,2] = S
X[,,1,3] = L
plot(HSLtoRGB(X))
plot(density(X[,,1,1]), col="darkred",lwd=2,xlim=c(0,365),main="")
abline(v=230,col="slateblue",lwd=2)
abline(v=175,col="indianred",lwd=2)
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