-
Notifications
You must be signed in to change notification settings - Fork 0
/
grab_data_step_6_exp_without_firstpoke.R
330 lines (278 loc) · 15.9 KB
/
grab_data_step_6_exp_without_firstpoke.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
# Get time productions
library(RcppCNPy)
library(R.utils)
library(scales)
library(LambertW)
library(ggplot2)
library(ggExtra)
library(mgcv)
library(lme4)
library(lmerTest)
library(dplyr)
library(itsadug)
library(plyr)
library(stringr)
#=======================================================================================
# UTILS & functions
#=======================================================================================
make_trial_index <- function(d) {
trial_start_stamp <- match(d$V5, 'StartPoke') + match(d$V3, 'Entry') - 1
sum(trial_start_stamp, na.rm=T)
time_stamps <- which(trial_start_stamp==1)
time_stamps <- c(1,time_stamps, length(trial_start_stamp))
trial_index <- c()
for (i in 2:length(time_stamps)) {
trial_index <- c(trial_index, rep(i-1, time_stamps[i]-(time_stamps[i-1])))
}
trial_index <- c(trial_index, i)
d <- cbind(d, trial_index)
d <- subset(d, trial_index!=1)
return(d)
}
#=======================================================================================
# UTILS & functions
#=======================================================================================
dat <- data.frame()
for (ss in c(1:15)) { # sessions number
dir <- paste('/Users/PC/Dropbox/eloras_rats/data/step6/S', toString(ss),'/', sep='')
ls <- list.files(dir)
csv_list <- c()
for (i in c(1:length(ls))) {
if (grepl('.csv', ls[i])) {
csv_list <- c(csv_list, ls[i])
}}
for (n in c(1:length(csv_list))) {
d <- read.table(paste(dir, csv_list[n], sep=''), header=F, nrows=4, sep=',', fill=T)
rat_nr <- toString(d$V4[1])
group_width <- substr(toString(d$V4[2]), start=1, stop=4)
group_dir <- substr(toString(d$V4[2]), start=6, stop=7)
print(rat_nr)
print(group_width)
print(group_dir)
d <- read.table(paste(dir, csv_list[n], sep=''), header=F, skip=7, sep=',', fill=T)
d <- d[-c((nrow(d)-3):nrow(d)), ] # remove last 4 line which is trash
d$time_stamp <- d$V2 #+ d$V3/10^3
d <- make_trial_index(d)
d_len <- dim(d)[1]
d <- d[-c(nrow(d)), ]
d <- cbind(d, rep(ss,length(d_len)), rep(group_width, length(d_len)), rep(group_dir, length(d_len)), rep(rat_nr,length(d_len)))
dat <- rbind(dat, d)
}}
colnames(dat) <- c('V1', 'V2', 'V3', 'V4', 'V5','V6','V7','time_stamp', 'trial_index', 'session', 'group_width', 'group_dir', 'rat_nr')
dat$V6 <- as.numeric(as.character(dat$V6))
write.table(dat, '/Users/PC/Dropbox/eloras_rats/data/dat_step6expp.csv')
#=======================================================================================
# GET the trial data + UTILS & functions
#=======================================================================================
dat <- read.table('/Users/PC/Dropbox/eloras_rats/data/dat_step6expp.csv')
all_data <- ddply(dat, .(rat_nr, session, trial_index, group_width, group_dir), function(z){ #int_offset
#z <- subset(dat, rat_nr==10 & session==3 & trial_index==15)
tmp1 <- subset(z, V5=='StartPoke' & V3=='Entry')
tmp2 <- subset(z, V5=='StartPort' & V3=='Input')
start_light = tmp1$time_stamp[1] # nose poke start
init_entry = tmp2$time_stamp[1] # initial entry in start port
cond=0
if (dim(subset(z, V5=='Duration_1s' & V3=='Exit'))[1]>0) {cond=1 # find out group_condition
} else if (dim(subset(z, V5=='Duration_2s' & V3=='Exit'))[1]>0) {cond=2
} else if (dim(subset(z, V5=='Duration_4s' & V3=='Exit'))[1]>0) {cond=4}
int_offset = subset(z, grepl('Duration_', V5) & V3=='Exit')$time_stamp[1]
first_check = subset(z, grepl('NosePoke', V5) & time_stamp>int_offset)$V4[1] # check first state
first_check_lat = subset(z, grepl('NosePoke', V5) & time_stamp>int_offset)$time_stamp[1]
sumNP1 = sum(z$V5 == 'NosePoke1' & z$V3 == 'Input')
sumNP2 = sum(z$V5 == 'NosePoke2' & z$V3 == 'Input')
sumNP3 = sum(z$V5 == 'NosePoke3' & z$V3 == 'Input')
sumNP4 = sum(z$V5 == 'NosePoke4' & z$V3 == 'Input')
sumNP5 = sum(z$V5 == 'NosePoke5' & z$V3 == 'Input')
time_NP1 <- z$V2[z$V5 == "NosePoke1" & z$V3 == "Input" & z$V2 > 0]
time_NP2 <- z$V2[z$V5 == "NosePoke2" & z$V3 == "Input" & z$V2 > 0]
time_NP3 <- z$V2[z$V5 == "NosePoke3" & z$V3 == "Input" & z$V2 > 0]
time_NP4 <- z$V2[z$V5 == "NosePoke4" & z$V3 == "Input" & z$V2 > 0]
time_NP5 <- z$V2[z$V5 == "NosePoke5" & z$V3 == "Input" & z$V2 > 0]
start_lat <- z$V2[max(which(grepl("Duration_", z$V5)& z$V3 == "Entry"))]
lat_NP1 <- min(time_NP1[time_NP1 > start_lat]) - start_lat
lat_NP2 <- min(time_NP2[time_NP2 > start_lat]) - start_lat
lat_NP3 <- min(time_NP3[time_NP3 > start_lat]) - start_lat
lat_NP4 <- min(time_NP4[time_NP4 > start_lat]) - start_lat
lat_NP5 <- min(time_NP5[time_NP5 > start_lat]) - start_lat
return(c(cond, start_light, init_entry, int_offset, first_check, first_check_lat, sumNP1, sumNP2, sumNP3, sumNP4, sumNP5, lat_NP1, lat_NP2, lat_NP3, lat_NP4, lat_NP5))
}, .progress='text')
colnames(all_data)[c(6:21)] <- c('cond','start_light', 'init_entry', 'int_offset', 'first_check', 'first_check_lat', 'sumNP1','sumNP2','sumNP3','sumNP4','sumNP5','lat_NP1','lat_NP2','lat_NP3','lat_NP4','lat_NP5')
# wide group
all_data$correct_entry <-
ifelse(all_data$group_width=='WIDE' & all_data$group_dir=='LM' & all_data$cond==1 & all_data$first_check==3, 1,
ifelse(all_data$group_width=='WIDE' & all_data$group_dir=='LM' & all_data$cond==2 & all_data$first_check==5, 1,
ifelse(all_data$group_width=='WIDE' & all_data$group_dir=='LM' & all_data$cond==4 & all_data$first_check==1, 1,
ifelse(all_data$group_width=='WIDE' & all_data$group_dir=='ML' & all_data$cond==1 & all_data$first_check==3, 1,
ifelse(all_data$group_width=='WIDE' & all_data$group_dir=='ML' & all_data$cond==2 & all_data$first_check==1, 1,
ifelse(all_data$group_width=='WIDE' & all_data$group_dir=='ML' & all_data$cond==4 & all_data$first_check==5, 1,
ifelse(all_data$group_width=='NARO' & all_data$group_dir=='LM' & all_data$cond==1 & all_data$first_check==3, 1,
ifelse(all_data$group_width=='NARO' & all_data$group_dir=='LM' & all_data$cond==2 & all_data$first_check==4, 1,
ifelse(all_data$group_width=='NARO' & all_data$group_dir=='LM' & all_data$cond==4 & all_data$first_check==2, 1,
ifelse(all_data$group_width=='NARO' & all_data$group_dir=='ML' & all_data$cond==1 & all_data$first_check==3, 1,
ifelse(all_data$group_width=='NARO' & all_data$group_dir=='ML' & all_data$cond==2 & all_data$first_check==2, 1,
ifelse(all_data$group_width=='NARO' & all_data$group_dir=='ML' & all_data$cond==4 & all_data$first_check==4, 1, 0))))))))))))
write.table(all_data, 'C:/Users/PC/Dropbox/eloras_rats/data/all_data_step6exp.csv')
# Load the required packages
library(tidyr)
library(stringr)
# Read in the CSV file
df <- read.csv("/Users/PC/Dropbox/eloras_rats/data/all_data_step6exp.csv", header = FALSE, col.names = "data")
# Split the single column into multiple columns
df <- separate(df, data, into = c("col1", "col2", "col3", "col4", "col5", "col6", "col7", "col8", "col9", "col10", "col11", "col12", "col13", "col14", "col15", "col16","col17","col18","col19","col20","col21","col22", "col23"), sep = " ")
# Convert columns to their appropriate data types
df$col1 <- as.integer(df$col1)
df$col2 <- as.character(df$col2)
df$col3 <- as.integer(df$col3)
df$col4 <- as.integer(df$col4)
df$col5 <- as.character(df$col5)
df$col6 <- as.character(df$col6)
df$col7 <- as.integer(df$col7)
df$col8 <- as.numeric(df$col8)
df$col9 <- as.numeric(df$col9)
df$col10 <- as.numeric(df$col10)
df$col11 <- as.numeric(df$col11)
df$col12 <- as.numeric(df$col12)
df$col13 <- as.numeric(df$col13)
df$col14 <- as.numeric(df$col14)
df$col15 <- as.numeric(df$col15)
df$col16 <- as.numeric(df$col16)
df$col17 <- as.numeric(df$col17)
df$col18 <- as.numeric(df$col18)
df$col19 <- as.numeric(df$col19)
df$col20 <- as.numeric(df$col20)
df$col21 <- as.numeric(df$col21)
df$col22 <- as.numeric(df$col22)
df$col23 <- as.numeric(df$col23)
# View the resulting data frame
write.csv(df, file = "all_data_step6expcolumned.csv", row.names = FALSE)
# read in the data from the CSV file and exclude lat_NP values greater than 20
all_data <- subset(read.csv('C:/Users/PC/Dropbox/eloras_rats/data/all_data_step6expcolumned.csv'))
# create four subsets based on the given conditions
subset1 <- all_data[all_data$cond == 4 & all_data$group_width == "NARO" & all_data$group_dir == "LM", ]
subset2 <- all_data[all_data$cond == 4 & all_data$group_width == "NARO" & all_data$group_dir == "ML", ]
subset3 <- all_data[all_data$cond == 4 & all_data$group_width == "WIDE" & all_data$group_dir == "LM", ]
subset4 <- all_data[all_data$cond == 4 & all_data$group_width == "WIDE" & all_data$group_dir == "ML", ]
# create SE and ME columns for each subset
subset1$SE <- ifelse(is.infinite(subset1$lat_NP3), NA, subset1$lat_NP3)
subset1$ME <- ifelse(is.infinite(subset1$lat_NP4), NA, subset1$lat_NP4)
subset2$SE <- ifelse(is.infinite(subset2$lat_NP3), NA, subset2$lat_NP3)
subset2$ME <- ifelse(is.infinite(subset2$lat_NP2), NA, subset2$lat_NP2)
subset3$SE <- ifelse(is.infinite(subset3$lat_NP3), NA, subset3$lat_NP3)
subset3$ME <- ifelse(is.infinite(subset3$lat_NP5), NA, subset3$lat_NP5)
subset4$SE <- ifelse(is.infinite(subset4$lat_NP3), NA, subset4$lat_NP3)
subset4$ME <- ifelse(is.infinite(subset4$lat_NP1), NA, subset4$lat_NP1)
# combine the subsets back into one data frame
all_data <- rbind(subset1, subset2, subset3, subset4)
# write the modified data frame to a new CSV file with lat_NP columns
write.csv(all_data, 'C:/Users/PC/Dropbox/eloras_rats/data/all_data_step6exp_filtered.csv', row.names = FALSE)
# #=======================================================================================
# # Grab all of the data in one data frame first.
# #=======================================================================================
# dat <- data.frame()
# for (b in c(1:40)) {
# dir <- paste('/Users/tadeusz/Dropbox/Metacognition rat orsay DATA/metacognition_rat_orsay/data_testing/data_testing_step5_ss', toString(b),'/', sep='')
# ls <- list.files(dir)
# csv_list <- c()
# for (i in c(1:length(ls))) {
# if (grepl('.csv', ls[i])) {
# csv_list <- c(csv_list, ls[i])
# }
# }
# for (n in c(1:length(csv_list))) {
# d <- read.table(paste(dir, csv_list[n], sep=''), header=F, nrows=2, sep=',', fill=T)
# rat_nr <- toString(d$V4[1])
# group <- substr(toString(d$V4[2]), start=4, stop=7)
# if (substr(toString(d$V4[2]), start=4, stop=4) == 'P') {
# interval <- as.numeric(substr(toString(d$V4[2]), start=10, stop=11)) / 10
# } else {
# interval <- as.numeric(substr(toString(d$V4[2]), start=9, stop=10)) / 10
# }
# print(group)
# print(rat_nr)
# d <- read.table(paste(dir, csv_list[n], sep=''), header=F, skip=7, sep=',', fill=T)
# d <- d[-c((nrow(d)-3):nrow(d)), ] # remove last 4 line which is trash
# d$time_stamp <- d$V2 + d$V3/10^3
# d <- make_trial_index(d)
# d_len <- dim(d)[1]
# d <- d[-c(nrow(d)), ] # last line TO DO should be fixed in make_trial_index.R
# d <- cbind(d, rep(b,length(d_len)), rep(group, length(d_len)), rep(rat_nr,length(d_len)))
# dat <- rbind(dat, d)
# }
# }
# colnames(dat) <- c('V1', 'V2', 'V3', 'V4', 'V5','V6','V7','V8','V9','time_stamp', 'trial_index', 'session', 'group', 'rat_nr')
# dat$rat_nr <- as.numeric(as.character(dat$rat_nr))
# # TO DO: Remove col 'V2, V3, V9, V8'
#
#
# #=======================================================================================
# # Debug >>>
# #z <- subset(dat, rat_nr==1 & trial_index==4 & session==6)
#
#
# all_data <- ddply(dat, .(rat_nr, group, session, trial_index), function(z){
# rts<-0
# pellet<-0
# pellet_test<-0
# if (z$group[1]=='HOLD') {
# tmp1 <- subset(z, V6=='Lever' & V4=='Input')
# tmp2 <- subset(z, V6=='Lever off' & V4=='Input')
# tp <- tmp2$time_stamp - tmp1$time_stamp
# } else if (z$group[1]=='PRES') {
# tmp <- subset(z, V6=='Lever' & V4=='Input')
# if (dim(tmp)[1]!=2) {tp<-0 # It catches last trial when program stoped due to time limit
# } else {
# tmp1 <- tmp[seq(1, dim(tmp)[1], 2), ]
# tmp2 <- tmp[seq(2, dim(tmp)[1], 2), ]
# tp <- tmp2$time_stamp - tmp1$time_stamp
# }
# }
# if (length(tp)>1) {tp<-0} else if (length(tp)==0) {tp<-0}
# # EXTRACT port side
# if (sum(z$V6=='FDR 1 0p Light' & z$V4=='Entry')) {reward_port=1
# } else if (sum(z$V6=='FDR 1 1p Light' & z$V4=='Entry')) {reward_port=1
# } else if (sum(z$V6=='FDR 1 2p Light' & z$V4=='Entry')) {reward_port=1
# } else if (sum(z$V6=='FDR 2 0p Light' & z$V4=='Entry')) {reward_port=2
# } else if (sum(z$V6=='FDR 2 1p Light' & z$V4=='Entry')) {reward_port=2
# } else if (sum(z$V6=='FDR 2 2p Light' & z$V4=='Entry')) {reward_port=2
# } else if (sum(z$V6=='FDR 1&2 2p Light' & z$V4=='Entry')) {reward_port=32 # ?????
# } else if (sum(z$V6=='FDR 1&2 1p Light' & z$V4=='Entry')) {reward_port=31
# } else {reward_port=0}
# # EXTRACT RT: left and right, if no rt than assign 0.
# subset_to_eval <- subset(z, grepl('Light',V6) & grepl('Entry',V4))
# if (!empty(subset_to_eval)) {
# rts <- subset(z, grepl('Port',V6))$time_stamp - subset_to_eval$time_stamp
# # EXTRACT pellet number on a given trial:
# switch(as.numeric(substr(as.character(subset_to_eval$V6), start=7, stop=7))+1,
# {pellet=0}, {pellet=1}, {pellet=2})
# # EXTRACT pellet number on a given trial in "1&2" test trials:
# if (!empty(subset(z, grepl('FDR 1&2 1p Pellet',V6) & grepl('Entry',V4)))) {pellet_test=1
# } else if (!empty(subset(z, grepl('FDR 1&2 2p Pellet',V6) & grepl('Entry',V4)))) {pellet_test=2
# } else if (empty(subset(z, grepl('FDR 1&2 2p Pellet',V6) & grepl('Entry',V4))) & empty(subset(z, grepl('FDR 1&2 2p Pellet',V6) & grepl('Entry',V4)))) {pellet_test=0}
# }
# # Rewritten to 3 lines + pellet extraction -->
# # if (sum(z$V6=='FDR 1 0p Light' & z$V4=='Entry')) {
# # rts <- subset(z, grepl('Port',V6))$time_stamp - subset(z, V6=='FDR 1 0p Light' & V4=='Entry')$time_stamp
# # } else if (sum(z$V6=='FDR 1 1p Light' & z$V4=='Entry')) {
# # rts <- subset(z, grepl('Port',V6))$time_stamp - subset(z, V6=='FDR 1 1p Light' & V4=='Entry')$time_stamp
# # } else if (sum(z$V6=='FDR 1 0p Light' & z$V4=='Entry')) {
# # rts <- subset(z, grepl('Port',V6))$time_stamp - subset(z, V6=='FDR 1 2p Light' & V4=='Entry')$time_stamp
# # }
# # EXTRACT first port entry
# rt_position <- min(which(rts>0))
# # if length(subset(z, grepl('Port',V6))[1,]==0) {first_port_entry<-0}
# first_port_entry <- as.character(subset(z, grepl('Port',V6))$V6[rt_position])
# rts <- rts[which(rts>0)] # exclude the early pokes >> animal collecting the previous reward.
# if (length(rts)>1) {rts<-rts[1]}
# if (length(rts)==0) {rts<-0} # assign 0 to trials with port pickup error.
# if (length(first_port_entry)==0) {first_port_entry<-0}
# return(c(tp, reward_port, first_port_entry, rts, pellet, pellet_test))
# }, .progress='text')
# colnames(all_data)[c(4:10)] <- c('trial_nr','tp', 'reward_port', 'first_port_entry', 'rt', 'pellet', 'pellet_test')
# all_data$first_port_entry <- ifelse(all_data$first_port_entry=='Port1', 1, ifelse(all_data$first_port_entry=='Port2', 2, 0))
# # combine pellet from 'trianing' and 'test' trials
# #tmp<-all_data
# all_data$pellet <-ifelse(all_data$reward_port==31 & all_data$pellet_test==1, 1,
# ifelse(all_data$reward_port==31 & all_data$pellet_test==0, 0,
# ifelse(all_data$reward_port==32 & all_data$pellet_test==2, 2,
# ifelse(all_data$reward_port==32 & all_data$pellet_test==0, 0, all_data$pellet))))
# write.table(all_data, '/Users/tadeusz/Data/metacog_rat_orsay/data_frame/data_training_step_5.csv')