today I wanted to run TropFishR package, the problem is (to me), every data must be arranged in list. So I tried to reconstruct the alba dataset in order to replicate with my own data in the future. Here is what I have done:
library(TropFishR)
data("alba")
str(alba) #the list contain 4 variables
List of 4
$ sample.no : int [1:14] 1 2 3 4 5 6 7 8 9 10 ...
$ midLengths: num [1:14] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 ...
$ dates : Date[1:7], format: "1976-04-17" "1976-07-02" "1976-09-19" ...
$ catch : num [1:14, 1:7] 0 0 0 1 1 1 3 9 5 0 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:7] "1976.29315068493" "1976.50136986301" "1976.71780821918" "1976.95616438356" ...
- attr(*, "class")= chr "lfq"
And this is what I did:
#1 We create sample.no
sample.no <- c(1:14)
sample.no
#2 We create "midlengths"
midlengths <- seq(from = 1.5, to = 14.5, by = 1)
midlengths
#3 We create "dates"
dates <- as.Date(c("1976-04-17","1976-07-02", "1976-09-19", "1976-12-15", "1977-02-18",
"1977-04-30", "1977-06-24"))
dates
#4 We create "catch"
catch <- as.matrix(read.csv(file.choose(), header=T))
#I copied the alba length freq data, move it to excel and imported as csv file
colnames(catch)<-NULL
print(catch)
#5 create list files
synLFQb <- list(sample.no,midlengths,dates,catch)
synLFQb #just checked if it turned out to be as desired format
#6 create a name for the data list
names(synLFQb) <- c("sample.no","midlengths","dates","catch")
#Finally, we need to assign the class lfq to our new object in order to allow it to be recognized by other TropFishR functions, e.g. plot.lfq:
class(synLFQb) <- "lfq"
it will produce "similar" data list
str(synLFQb)
List of 4
$ sample.no : int [1:14] 1 2 3 4 5 6 7 8 9 10 ...
$ midlengths: num [1:14] 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 ...
$ dates : Date[1:7], format: "1976-04-17" "1976-07-02" "1976-09-19" ...
$ catch : int [1:14, 1:7] 0 0 0 1 1 1 3 9 5 0 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : NULL
- attr(*, "class")= chr "lfq"
However, when everytime I tried to do this simple command:
plot(synLFQb, Fname="catch", hist.sc = 1)
It resulted in error:
> plot(synLFQb, Fname="catch", hist.sc = 1)
Error in plot.window(...) : need finite 'ylim' values
In addition: Warning messages:
1: In min(x, na.rm = na.rm) :
no non-missing arguments to min; returning Inf
2: In max(x, na.rm = na.rm) :
no non-missing arguments to max; returning -Inf
Any help will be much appreciated.
Please make sure that you call the mid lengths vector in your list "midLengths" with a capital "L". I hope that will does the trick in your example.
I want to count areas of interest in my dataframe column 'which_AOI' (ranging from 0 -9). I would like to have a new column with the results added to a dataframe depending on a variable 'marker' (ranging from 0 - x) which tells me when one 'picture' is done and the next begins (one marker can go on for a variable length of rows). This is my code so far but it seems to be stuck and runs on without giving output. I tried reconstructing it from the beginning once but as soon as i get to 'if df.marker == num' it doesn't stop. What am I missing?
(example dataframe below)
## AOI count of spec. type function (in progress):
import numpy as np
import pandas as pd
path_i = "/Users/Desktop/Pilot/results/gazedata_filename.csv"
df = pd.read_csv(path_i, sep =",")
#create a new dataframe for AOIs:
d = {'marker': []}
df_aoi = pd.DataFrame(data=d)
### Creating an Aoi list
item = df.which_AOI
aoi = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] #list for search
aoi_array = [0, 0 , 0, 0, 0, 0, 0, 0, 0, 0] #list for filling
num = 0
for i in range (0, len (df.marker)): #loop through the dataframe
if df.marker == num: ## if marker = num its one picture
for index, item in enumerate(aoi): #look for item (being a number in which_AOI) in aoi list
if (item == aoi[index]):
aoi_array[index] += 1
print (aoi)
print (aoi_array)
se = pd.Series(aoi_array) # make list into a series to attach to dataframe
df_aoi['new_col'] = se.values #add list to dataframe
aoi_array.clear() #clears list before next picture
else:
num +=1
index pos_time pos_x pos_y pup_time pup_diameter marker which_AOI fixation Picname shock
1 16300 168.608779907227 -136.360855102539 16300 2.935715675354 0 7 18 5 save
2 16318 144.97673034668 -157.495513916016 16318 3.08838820457459 0 8 33 5 save
3 16351 152.92560577392598 -156.64172363281298 16351 3.0895299911499 0 7 17 5 save
4 16368 152.132453918457 -157.989685058594 16368 3.111008644104 0 7 18 5 save
5 16386 151.59835815429702 -157.55587768554702 16386 3.09514689445496 0 7 18 5 save
6 16404 150.88092803955098 -152.69479370117202 16404 3.10009074211121 1 7 37 5 save
7 16441 152.76554107666 -142.06188964843798 16441 3.0821495056152304 1 7 33 5 save
Not 100% clear based on your question but it sounds like you want to count the number of rows for each which_AOI value in each marker.
You can accomplish this using groupby
df_aoi = df.groupby(['marker','which_AOI']).size().unstack('which_AOI',fill_value=0)
In:
pos_time pos_x pos_y pup_time pup_diameter marker \
0 16300 168.608780 -136.360855 16300 2.935716 0
1 16318 144.976730 -157.495514 16318 3.088388 0
2 16351 152.925606 -156.641724 16351 3.089530 0
3 16368 152.132454 -157.989685 16368 3.111009 0
4 16386 151.598358 -157.555878 16386 3.095147 0
5 16404 150.880928 -152.694794 16404 3.100091 1
6 16441 152.765541 -142.061890 16441 3.082150 1
which_AOI fixation Picname shock
0 7 18 5 save
1 8 33 5 save
2 7 17 5 save
3 7 18 5 save
4 7 18 5 save
5 7 37 5 save
6 7 33 5 save
Out:
which_AOI 7 8
marker
0 4 1
1 2 0
I have a two dimensional list of values:
[
[[12.2],[5325]],
[[13.4],[235326]],
[[15.9],[235326]],
[[17.7],[53521]],
[[21.3],[42342]],
[[22.6],[6546]],
[[25.9],[34634]],
[[27.2],[523523]],
[[33.4],[235325]],
[[36.2],[235352]]
]
I would like to get a list of averages defined by a given step so that for a step=10 it would like like this:
[
[[10],[average of all 10-19]],
[[20],[average of all 20-29]],
[[30],[average of all 30-39]]
]
How can I achieve that? Please note that the number of 10s, 20s, 30s and so on is not always the same.
import pandas as pd
df = pd.DataFrame((q[0][0], q[1][0]) for q in thelist)
df['group'] = (df[0] / 10).astype(int)
Now df is:
0 1 group
0 12.2 5325 1
1 13.4 235326 1
2 15.9 235326 1
3 17.7 53521 1
4 21.3 42342 2
5 22.6 6546 2
6 25.9 34634 2
7 27.2 523523 2
8 33.4 235325 3
9 36.2 235352 3
Then:
df.groupby('group').mean()
Gives you the answers you seek:
0 1
group
1 14.80 132374
2 24.25 151761
3 34.80 235338
UPDATE 2
*I've added some code (and explanation) I wrote myself at the end of this question, this is however a suboptimal solution (both in coding efficiency as resulting output) but kind of manages to make a selection of items that adhere to the constraints. If you have any ideas on how to improve it (again both in efficiency as resulting output) please let me know.
1. Updated Post
Please look below for the initial question and sample code. Thx to alexis_laz his answer the problem was solved for a small number of items. However when the number of items becomes to large the combn function in R cannot calculate it anymore because of the invalid 'ncol' value (too large or NA) error. Since my dataset has indeed a lot of items, I was wondering whether replacing some of his code (shown after this) with C++ provides a solution to this, and if this is the case what code I should use for this? Tnx!
This is the code as provided by alexis_laz;
ff = function(x, No_items, No_persons)
{
do.call(rbind,
lapply(No_items:ncol(x),
function(n) {
col_combs = combn(seq_len(ncol(x)), n, simplify = F)
persons = lapply(col_combs, function(j) rownames(x)[rowSums(x[, j, drop = F]) == n])
keep = unlist(lapply(persons, function(z) length(z) >= No_persons))
data.frame(persons = unlist(lapply(persons[keep], paste, collapse = ", ")),
items = unlist(lapply(col_combs[keep], function(z) paste(colnames(x)[z], collapse = ", "))))
}))
}
2. Initial Post
Currently I'm working on a set of data coming from adaptive measurement, which means that not all persons have made all of the same items. For my analysis however I need a dataset that contains only items that have been made by all persons (or a subset of these persons).
I have a matrix object in R with rows = persons (100000), and columns = items(220), and a 1 in a cell if the person has made the item and a 0 if the person has not made the item.
How can I use R to determine which combination of at least 15 items, is made by the highest amount of persons?
Hopefully the question is clear (if not please ask me for more details and I will gladly provide those).
Tnx in advance.
Joost
Edit:
Below is a sample matrix with the items (A:E) as columns and persons (1:5) as rows.
mat <- matrix(c(1,1,1,0,0,1,1,0,1,1,1,1,1,0,1,0,1,1,0,0,1,1,1,1,0),5,5,byrow=T)
colnames(mat) <- c("A","B","C","D","E")
rownames(mat) <- 1:5
> mat
A B C D E
"1" 1 1 1 0 0
"2" 1 1 0 1 1
"3" 1 1 1 0 1
"4" 0 1 1 0 0
"5" 1 1 1 1 0
mat[1,1] = 1 means that person 1 has given a response to item 1.
Now (in this example) I'm interested in finding out which set of at least 3 items is made by at least 3 people. So here I can just go through all possible combinations of 3, 4 and 5 items to check how many people have a 1 in the matrix for each item in a combination.
This will result in me choosing the item combination A, B and C, since it is the only combination of items that has been made by 3 people (namely persons 1, 3 and 5).
Now for my real dataset I want to do this but then for a combination of at least 10 items that a group of at least 75 people all responded to. And since I have a lot of data preferably not by hand as in the example data.
I'm thus looking for a function/code in R, that will let me select the minimal amount of items, and questions, and than gives me all combinations of items and persons that adhere to these constraints or have a greater number of items/persons than the constrained.
Thus for the example matrix it would be something like;
f <- function(data,no.items,no.persons){
#code
}
> f(mat,3,3)
no.item no.pers items persons
1 3 3 A, B, C 1, 3, 5
Or in case of at least 2 items that are made by at least 3 persons;
> f(mat,2,3)
no.item no.pers items persons
1 2 4 A, B 1, 2, 3, 5
2 2 3 A, C 1, 3, 5
3 2 4 B, C 1, 3, 4, 5
4 3 3 A, B, C 1, 3, 5
Hopefully this clears up what my question actually is about. Tnx for the quick replies that I already received!
3. Written Code
Below is the code I've written today. It takes each item once as a starting point and then looks to the item that has been answered most by people who also responded to the start item. It the takes these two items and looks to a third item, and repeats this until the number of people that responded to all selected questions drops below the given limit. One drawback of the code is that it takes some time to run, (it goes up somewhat exponentially when the number of items grows). The second drawback is that this still does not evaluate all possible combinations of items, in the sense that the start item, and the subsequently chosen item may have a lot of persons that answered to these items in common, however if the chosen item has almost no similarities with the other (not yet chosen) items, the sample might shrink very fast. While if an item was chosen with somewhat less persons in common with the start item, and this item has a lot of connections to other items, the final collection of selected items might be much bigger than the one based on the code used below. So again suggestions and improvements in both directions are welcome!
set.seed(512)
mat <- matrix(rbinom(1000000, 1, .6), 10000, 100)
colnames(mat) <- 1:100
fff <- function(data,persons,items){
xx <- list()
for(j in 1:ncol(data)){
d <- matrix(c(j,length(which(data[,j]==1))),1,2)
colnames(d) <- c("item","n")
t = persons+1
a <- j
while(t >= persons){
b <- numeric(0)
for(i in 1:ncol(data)){
z <- c(a,i)
if(i %in% a){
b[i] = 0
} else {
b[i] <- length(which(rowSums(data[,z])==length(z)))
}
}
c <- c(which.max(b),max(b))
d <- rbind(d,c)
a <- c(a,c[1])
t <- max(b)
}
print(j)
xx[[j]] = d
}
x <- y <- z <- numeric(0)
zz <- matrix(c(0,0,rep(NA,ncol(data))),length(xx),ncol(data)+2,byrow=T)
colnames(zz) <- c("n.pers", "n.item", rep("I",ncol(data)))
for(i in 1:length(xx)){
zz[i,1] <- xx[[i]][nrow(xx[[i]])-1,2]
zz[i,2] <- length(unname(xx[[i]][1:nrow(xx[[i]])-1,1]))
zz[i,3:(zz[i,2]+2)] <- unname(xx[[i]][1:nrow(xx[[i]])-1,1])
}
zz <- zz[,colSums(is.na(zz))<nrow(zz)]
zz <- zz[which((rowSums(zz,na.rm=T)/rowMeans(zz,na.rm=T))-2>=items),]
zz <- as.data.frame(zz)
return(zz)
}
fff(mat,110,8)
> head(zz)
n.pers n.item I I I I I I I I I I
1 156 9 1 41 13 80 58 15 91 12 39 NA
2 160 9 2 27 59 13 81 16 15 6 92 NA
3 158 9 3 59 83 32 25 80 14 41 16 NA
4 160 9 4 24 27 71 32 10 63 42 51 NA
5 114 10 5 59 66 27 47 13 44 63 30 52
6 158 9 6 13 56 61 12 59 8 45 81 NA
#col 1 = number of persons in sample
#col 2 = number of items in sample
#col 3:12 = which items create this sample (NA if n.item is less than 10)
to follow up on my comment, something like:
set.seed(1618)
mat <- matrix(rbinom(1000, 1, .6), 100, 10)
colnames(mat) <- sample(LETTERS, 10)
rownames(mat) <- sprintf('person%s', 1:100)
mat1 <- mat[rowSums(mat) > 5, ]
head(mat1)
# A S X D R E Z K P C
# person1 1 1 1 0 1 1 1 1 1 1
# person3 1 0 1 1 0 1 0 0 1 1
# person4 1 0 1 1 1 1 1 0 1 1
# person5 1 1 1 1 1 0 1 1 0 0
# person6 1 1 1 1 0 1 0 1 1 0
# person7 0 1 1 1 1 1 1 1 0 0
table(rowSums(mat1))
# 6 7 8 9
# 24 23 21 5
tab <- table(sapply(1:nrow(mat1), function(x)
paste(names(mat1[x, ][mat1[x, ] == 1]), collapse = ',')))
data.frame(tab[tab > 1])
# tab.tab...1.
# A,S,X,D,R,E,P,C 2
# A,S,X,D,R,E,Z,P,C 2
# A,S,X,R,E,Z,K,C 3
# A,S,X,R,E,Z,P,C 2
# A,S,X,Z,K,P,C 2
Here is another idea that matches your output:
ff = function(x, No_items, No_persons)
{
do.call(rbind,
lapply(No_items:ncol(x),
function(n) {
col_combs = combn(seq_len(ncol(x)), n, simplify = F)
persons = lapply(col_combs, function(j) rownames(x)[rowSums(x[, j, drop = F]) == n])
keep = unlist(lapply(persons, function(z) length(z) >= No_persons))
data.frame(persons = unlist(lapply(persons[keep], paste, collapse = ", ")),
items = unlist(lapply(col_combs[keep], function(z) paste(colnames(x)[z], collapse = ", "))))
}))
}
ff(mat, 3, 3)
# persons items
#1 1, 3, 5 A, B, C
ff(mat, 2, 3)
# persons items
#1 1, 2, 3, 5 A, B
#2 1, 3, 5 A, C
#3 1, 3, 4, 5 B, C
#4 1, 3, 5 A, B, C
I am tryng to build a link between my simulink model or just an m file and my C++ application but I'm having an issue. I don't really understand how matlab receives the data, and for simulink in which form should I send the data to the block? I would like to send coordinates like xyz to matlab and with matlab scatter3 the stream of coordinates in real time. Is it possible ?
Here is the C++ extract code : (sorry comments are in french)
/* Démarrage du listage (mode server) */
sock_err = listen(sock, 5);
printf("Listage du port %d...\n", PORT);
/* Si la socket fonctionne */
if(sock_err != SOCKET_ERROR)
{
/* Attente pendant laquelle le client se connecte */
printf("Patientez pendant que le client se connecte sur le port %d...\n", PORT);
csock = accept(sock, (SOCKADDR*)&csin, &recsize);
printf("Un client se connecte avec la socket %d de %s:%d\n", csock, inet_ntoa(csin.sin_addr), htons(csin.sin_port));
// double buff = 25;
mxArray *datasend;
datasend = mxCreateDoubleMatrix(1, 1, mxREAL);
mlfPrintmatrix(datasend);
sock_err = send(csock, T, 3*sizeof(double), 0);
if(sock_err != SOCKET_ERROR)
printf("Send : %s\n", buffer[0]);
//printf("send : [10 20 30]");
else
printf("Erreur de transmission\n");
/* Il ne faut pas oublier de fermer la connexion (fermée dans les deux sens) */
shutdown(csock, 2);
}
Simulink errors:
Error evaluating registered method 'Outputs' of M-S-Function 'stcpiprb' in 'trajectory2/TCP//IP Receive'. The specified amount of data was not returned within the Timeout period.
Please ensure that data is being sent to the specified port or specify a greater timeout value.
The specified amount of data was not returned within the Timeout period.
Please ensure that data is being sent to the specified port or specify a greater timeout value.
simulink model :
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I can't see creating socket and binding to it in your C code. Are you sure that you're binding on 127.0.0.1 and port 80? Are you sure that there's no some apache running on the server on port 80?
http://www.linuxhowtos.org/C_C++/socket.htm
Also:
sock_err = send(csock, T, 3*sizeof(double), 0);
I don't also see T in your code, which is probably not the issue, but it doesn't seem to be the datasend pointer to mxArray.