I can use 'r' to get the info of CPU register FLAG.
1.Can I understand by this?
eflags 0x00000082: id vip vif ac vm rf nt IOPL=0 of df if tf SF zf af pf cf
0x00000082= 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0
2.How to change the FLAG? By 'set' command?
<bochs:5> set eflags=0x03
:5: syntax error at 'eflags'
Thank you~
If the flag name is in capitals, then the flag is set. E.g. 'SF' means that sign flag is set, while 'sf' means it is not set. Did you mean this, or something else in your question?
The bochs manual says: "Currently only general purpose registers are supported, you may not change: eflags, eip, cs, ss, ds, es, fs, gs" (http://bochs.sourceforge.net/doc/docbook/user/internal-debugger.html#AEN3098).
Regards
Related
I setup a constraint that does not constraint the solver in pyomo.
The constraint is the following:
def revenue_positive(model,t):
for t in model.T:
return (model.D[t] * model.P[t]) >= 0
model.positive_revenue = Constraint(model.T, rule=revenue_positive)
while the model parameters are:
model = ConcreteModel()
model.T = Set(doc='quarter of year', initialize=df.index.tolist(), ordered=True)
model.P = Param(model.T, initialize=df['price'].to_dict(), within=Any, doc='Price for each quarter')
model.C = Var(model.T, domain=NonNegativeReals)
model.D = Var(model.T, domain=NonNegativeReals)
income = sum(df.loc[t, 'price'] * model.D[t] for t in model.T)
expenses = sum(df.loc[t, 'price'] * model.C[t] for t in model.T)
profit = income - expenses
model.objective = Objective(expr=profit, sense=maximize)
# Solve the model
solver = SolverFactory('cbc')
solver.solve(model)
df dataframe is:
df time_stamp price Status imbalance Difference Situation ... week month hour_of_day day_of_week day_of_year yearly_quarter
quarter ...
0 2021-01-01 00:00:00 64.84 Final 16 -3 Deficit ... 00 1 0 4 1 1
1 2021-01-01 00:15:00 13.96 Final 38 2 Surplus ... 00 1 0 4 1 1
2 2021-01-01 00:30:00 12.40 Final 46 1 Surplus ... 00 1 0 4 1 1
3 2021-01-01 00:45:00 7.70 Final 65 14 Surplus ... 00 1 0 4 1 1
4 2021-01-01 01:00:00 64.25 Final 3 -9 Deficit ... 00 1 1 4 1 1
The objective is to constraint the solver not to accept a negative revenue. As such it does not work as the solver passes 6 negative revenue values through. Looking at the indices with negative revenue, it appears the system chooses to sell at a negative price to buy later at a price even "more" negative, so from an optimization standpoint, it is ok. I would like to check the difference in results if we prohibit the solver to do that. Any input is welcome as after many searches on the web, still not the right way to write it correctly.
I did a pprint() of the constraint that returned:
positive_revenue : Size=35040, Index=T, Active=True
UPDATE following new constraint code:
def revenue_positive(model,t):
return model.D[t] * model.P[t] >= 0
model.positive_revenue = Constraint(model.T, rule=revenue_positive)
Return the following error:
ERROR: Rule failed when generating expression for constraint positive_revenue
with index 283: ValueError: Invalid constraint expression. The constraint
expression resolved to a trivial Boolean (True) instead of a Pyomo object.
Please modify your rule to return Constraint.Feasible instead of True.
Error thrown for Constraint 'positive_revenue[283]'
ERROR: Constructing component 'positive_revenue' from data=None failed:
ValueError: Invalid constraint expression. The constraint expression
resolved to a trivial Boolean (True) instead of a Pyomo object. Please
modify your rule to return Constraint.Feasible instead of True.
Error thrown for Constraint 'positive_revenue[283]'
Traceback (most recent call last):
File "/home/olivier/Desktop/Elia - BESS/run_imbalance.py", line 25, in <module>
results_df = optimize_year(df)
File "/home/olivier/Desktop/Elia - BESS/battery_model_imbalance.py", line 122, in optimize_year
model.positive_revenue = Constraint(model.T, rule=revenue_positive)
File "/home/olivier/anaconda3/lib/python3.9/site-packages/pyomo/core/base/block.py", line 542, in __setattr__
self.add_component(name, val)
File "/home/olivier/anaconda3/lib/python3.9/site-packages/pyomo/core/base/block.py", line 1087, in add_component
val.construct(data)
File "/home/olivier/anaconda3/lib/python3.9/site-packages/pyomo/core/base/constraint.py", line 781, in construct
self._setitem_when_not_present(
File "/home/olivier/anaconda3/lib/python3.9/site-packages/pyomo/core/base/indexed_component.py", line 778, in _setitem_when_not_present
obj.set_value(value)
File "/home/olivier/anaconda3/lib/python3.9/site-packages/pyomo/core/base/constraint.py", line 506, in set_value
raise ValueError(
ValueError: Invalid constraint expression. The constraint expression resolved to a trivial Boolean (True) instead of a Pyomo object. Please modify your rule to return Constraint.Feasible instead of True.
Error thrown for Constraint 'positive_revenue[283]'
So there are 2 issues w/ your constraint. It isn't clear if one is a cut & paste issue or not.
The function call to make the constraint appears to be indented and inside of your function after the return statement, making it unreachable code. Could be just the spacing in your post.
You are incorrectly adding a loop inside of your function. You are passing in the parameter t as a function argument and then you are blowing it away with the for loop, which only executes for the first value of t in T then hits the return statement. Remove the loop. When you use the rule= structure in pyomo it will call the rule for each instance of the set that you are using in the Constraint(xx, rule=) structure.
So I think you should have:
def revenue_positive(model, t):
return model.D[t] * model.P[t] >= 0
model.positive_revenue = Constraint(model.T, rule=revenue_positive)
Updated re: the error you added.
The error cites the 283rd index. My bet is that price[283] is zero, so you are multiplying by a zero and killing your variable.
You could add a check within the function that checks if the price is zero, and in that case, just return pyo.Constraint.Feasible, which is the trivial return that doesn't influence the model (or crash)
I'm pretty new in statistics:
fisher = function(idxToTest, idxATI){
idxDependent=c()
dependent=c()
p = c()
for(i in c(1:length(idxToTest)))
{
tbl = table(data[[idxToTest[i]]], data[[idxATI]])
rez = fisher.test(tbl, workspace = 20000000000)
if(rez$p.value<0.1){
dependent=c(dependent, TRUE)
if(rez$p.value<0.1){
idxDependent = c(idxDependent, idxToTest[i])
}
}
else{
dependent = c(dependent, FALSE)
}
p = c(p, rez$p.value)
}
}
This is the function I use. It seems to work.
What I understood until now is that I have to pass as first parameter data like:
Men Women
Dieting 10 30
Non-dieting 5 60
My data comes from a CSV:
data = read.csv('***.csv', header = TRUE, sep=',');
My first problem is that I don't know how to converse from:
Loan.Purpose Home.Ownership
lp_value_1 ho_value_2
lp_value_1 ho_value_2
lp_value_2 ho_value_1
lp_value_3 ho_value_2
lp_value_2 ho_value_3
lp_value_4 ho_value_2
lp_value_3 ho_value_3
to:
ho_value_1 ho_value_2 ho_value_3
lp_value1 0 2 0
lp_value2 1 0 1
lp_value3 0 1 1
lp_value4 0 1 0
The second issue is that I don't know what the second parameter should be
POST UPDATE: This is what I get using fisher.test(myTable):
Error in fisher.test(test) : FEXACT error 501.
The hash table key cannot be computed because the largest key
is larger than the largest representable int.
The algorithm cannot proceed.
Reduce the workspace size or use another algorithm.
where myTable is:
MORTGAGE NONE OTHER OWN RENT
car 18 0 0 5 27
credit_card 190 0 2 38 214
debt_consolidation 620 0 2 87 598
educational 5 0 0 3 7
...
Basically, fisher tests only work on smallish data sets because they require alot of memory. But all is good because chi-square tests make minimal additional assumptions and are easier on the computer. Just do:
chisq.test(Loan.Purpose,Home.Ownership)
to get your p-values.
Make sure you read through and understand the help page for chisq.test, especially the examples at the bottom.
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/chisq.test.html
Then look at a mosaicplot to see the quantities like:
mosaicplot(Loan.Purpose,Home.Ownership)
this reference explains how mosaicplots work.
http://alumni.media.mit.edu/~tpminka/courses/36-350.2001/lectures/day12/
i was wondering how to make something that checks if column Lair in the data
is below or above an certain threshold lets say below 0.5 is called LOH en
above is called imbalance. So the calls LOH and INBALANCE should be written in a new column. I tried something as the code below.
detection<-function(assay,method,thres){
if(method=="threshold"){
idx<-ifelse(segmenten["intensity"]<1.1000000 & segmenten["intensity"]>0.900000 & segmenten["Lair"]>thres,TRUE,FALSE)
}
if(method=="cnloh"){
idx<-ifelse(segmenten["intensity"]<1.1000000 & segmenten["intensity"]>0.900000 & segmenten["Lair"]<thres,TRUE,FALSE)
}
if(method=="gain"){
idx<-ifelse(segmenten["intensity"]>1.1000000 & segmenten["Lair"]<thres,TRUE,FALSE)
}
if(method=="loss"){
idx<-ifelse(segmenten["intensity"]<0.900000 & segmenten["Lair"]<thres,TRUE,FALSE)
}
if(method=="bloss"){
idx<-ifelse(segmenten["intensity"]<0.900000 & segmenten["Lair"]>thres,TRUE,FALSE)
}
if(method=="bgain"){
idx<-ifelse(segmenten["intensity"]>1.100000 & segmenten["Lair"]>thres,TRUE,FALSE)
}
return(idx)
}
After this part the next step is to write the data from the function to the existing table.
Anyone has an idea
Since your desired result is not clear enough I made some assumptions and wrote something that might be useful or not.
First at all, inside your function there is an object segmenten which is not defined, I suppose this is the data set supplied as an input, then you used ifelse and the returning results are TRUE or FALSE but you want either LOH or INBALANCE when some conditions are met.
You want INBALANCE when ... & segmenten["Lair"]>thres and LOH otherwise (here ... means the other part of the condition) this will give a vector, but you want it in the main dataset as an addional column, don't you? So maybe this could be a new starting point for you to improve your code.
detection <- function(assay, method=c('threshold', 'cnloh', 'gain', 'loss', 'bloss', 'bgain'),
thres=0.5){
x <- assay
idx <- switch(match.arg(method),
threshold = ifelse(x["intensity"]<1.1 & x["intensity"]>0.9 & x["Lair"]>thres, 'INBALANCE', 'LOH'),
cnloh = ifelse(x["intensity"]<1.1 & x["intensity"]>0.9 & x["Lair"]<thres, 'LOH', 'INBALANCE'),
gain = ifelse(x["intensity"]>1.1 & x["Lair"]<thres, 'LOH', 'INBALANCE'),
loss = ifelse(x["intensity"]<0.9 & x["Lair"]<thres,'LOH', 'INBALANCE'),
bloss = ifelse(x["intensity"]<0.9 & x["Lair"]>thres, 'INBALANCE', 'LOH'),
bgain = ifelse(x["intensity"]>1.1 & x["Lair"]>thres, 'INBALANCE', 'LOH'))
colnames(idx) <- 'Checking'
return(cbind(x, as.data.frame(idx)))
}
Example:
Data <- read.csv("japansegment data.csv", header=T)
result <- detection(Data, method='threshold', thres=0.5) # 'threshold' is the default value for method
head(result)
SNP_NAME x0 x1 y pos.start pos.end chrom count copynumber intensity allele.B Lair uncertain sample_id
1 SNP_A-1656705 0 0 0 836727 27933161 1 230 2 1.0783 1 0.9218 FALSE GSM288035
2 SNP_A-1677548 0 0 0 28244579 246860994 1 4408 2 0.9827 1 0.9236 FALSE GSM288035
3 SNP_A-1669537 0 0 0 100819 159783145 2 3480 2 0.9806 1 0.9193 FALSE GSM288035
4 SNP_A-1758569 0 0 0 159783255 159791136 2 5 2 1.7244 1 0.9665 FALSE GSM288035
5 SNP_A-1662168 0 0 0 159817465 168664268 2 250 2 0.9786 1 0.9197 FALSE GSM288035
6 SNP_A-1723506 0 0 0 168721411 168721920 2 2 2 1.8027 -4 NA FALSE GSM288035
Checking
1 INBALANCE
2 INBALANCE
3 INBALANCE
4 LOH
5 INBALANCE
6 LOH
Using match.arg and switch functions will help you to avoid a lot of if statements.
I have two files
File 1 contains 3 fields
File 2 contains 4 fields
The number of rows of File 1 is much smaller than that of File 2
I would like to compare between two files based on 1st field with the following operation
If the first field in any row of file 1 appears in the first field of a row in file 2, don't print that row for file 2.
Any advice would be grateful.
Input File 1
S13109 3739 31082
S45002 3800 31873
S43722 3313 26638
Input File 2
S13109 3738 31081 0
S13109 3737 31080 0
S00033 3008 29985 0
S00033 3007 29984 0
S00022 4130 31838 0
S00022 4129 31837 0
S00188 3317 27372 0
S45002 3759 31832 0
S45002 3758 31831 0
S45002 3757 31830 0
S43722 3020 26345 0
S43722 3019 26344 0
S00371 3737 33636 0
S00371 3736 33635 0
Desired Output
S00033 3008 29985 0
S00033 3007 29984 0
S00022 4130 31838 0
S00022 4129 31837 0
S00188 3317 27372 0
S00371 3737 33636 0
S00371 3736 33635 0
awk 'FNR==NR{a[$1]++;next}!a[$1]' file1 file2
How it works:
FNR==NR
When you have two (or more) input files to awk, NR will reset back to 1 on the first line of the next file whereas FNR will continuing incrementing from where it left off. By checking FNR==NR we are essentially checking to see if we are currently parsing the first file.
a[$1]++
If we are parsing the first file (see above) then create an associative array with the first field $1 as the key and post increment the value by 1. This essentially lets us create a 'seen' list.
next
This command tells awk not to process any further commands and to read in the next record and start over. We do this because file1 is only meant to set the associative array
!a[$1]
This line only executes when FNR==NR is false, i.e. we are not parsing file1 and thus must be parsing file2. We then use the first field $1 of file2 as the key to index into our 'seen' list created earlier. If the value returned is 0 it means we didn't see it in file1 and therefore we should print this line. Conversely, if the value is non-zero then we did see it in file1 and thus we should not print its value. Note that !a[$1] is equivalent to !a[$1]{print} because the default action when one is not given is to print the entire line.
If you don't need to preserve the order of the lines, you can use process substitution in Bash, Korn shell or Z shell along with the join and sort utilities:
join -v 2 <(sort file_1) <(sort file_2)
If you're using a shell without process substitution you would have to pre-sort the files.
Does anyone know the api for adding users and groups in unix and removing them ? I want to do this programatically.
Thanks,
Frank
I started looking at some system calls and found the following. Note that they are of varying standards, so not all may work on your Unix version:
getpwent
setpwent
putpwent
These however, all assume a password file. Out of curiosity, I straced useradd to find out what he did. Here's a small section of it's output:
# grep -E 'passwd|shadow' useradd.log.20283
...
open("/etc/shadow", O_RDONLY|O_CLOEXEC) = 3
open("/etc/passwd", O_RDONLY|O_CLOEXEC) = 3
open("/etc/passwd.20283", O_WRONLY|O_CREAT|O_EXCL, 0600) = 4
link("/etc/passwd.20283", "/etc/passwd.lock") = 0
stat("/etc/passwd.20283", {st_mode=S_IFREG|0600, st_size=6, ...}) = 0
unlink("/etc/passwd.20283") = 0
open("/etc/passwd", O_RDWR) = 4
open("/etc/shadow.20283", O_WRONLY|O_CREAT|O_EXCL, 0600) = 5
link("/etc/shadow.20283", "/etc/shadow.lock") = 0
stat("/etc/shadow.20283", {st_mode=S_IFREG|0600, st_size=6, ...}) = 0
unlink("/etc/shadow.20283") = 0
open("/etc/shadow", O_RDWR) = 5
open("/etc/gshadow.20283", O_WRONLY|O_CREAT|O_EXCL, 0600) = 7
link("/etc/gshadow.20283", "/etc/gshadow.lock") = 0
stat("/etc/gshadow.20283", {st_mode=S_IFREG|0600, st_size=6, ...}) = 0
unlink("/etc/gshadow.20283") = 0
open("/etc/gshadow", O_RDWR) = 7
open("/etc/passwd", O_RDONLY|O_CLOEXEC) = 8
open("/etc/passwd", O_RDONLY|O_CLOEXEC) = 9
open("/etc/passwd-", O_WRONLY|O_CREAT|O_TRUNC, 0666) = 9
utime("/etc/passwd-", [2010/09/02-07:07:34, 2010/09/02-07:07:34]) = 0
open("/etc/passwd+", O_WRONLY|O_CREAT|O_TRUNC, 0666) = 4
lstat("/etc/passwd", {st_mode=S_IFREG|0644, st_size=2479, ...}) = 0
rename("/etc/passwd+", "/etc/passwd") = 0
open("/etc/shadow-", O_WRONLY|O_CREAT|O_TRUNC, 0666) = 4
utime("/etc/shadow-", [2010/09/02-07:07:34, 2010/09/02-07:07:34]) = 0
open("/etc/shadow+", O_WRONLY|O_CREAT|O_TRUNC, 0666) = 4
lstat("/etc/shadow", {st_mode=S_IFREG|0600, st_size=1429, ...}) = 0
r ename("/etc/shadow+", "/etc/shadow") = 0
open("/etc/gshadow-", O_WRONLY|O_CREAT|O_TRUNC, 0666) = 4
utime("/etc/gshadow-", [2010/09/02-07:07:34, 2010/09/02-07:07:34]) = 0
open("/etc/gshadow+", O_WRONLY|O_CREAT|O_TRUNC, 0666) = 4
lstat("/etc/gshadow", {st_mode=S_IFREG|0400, st_size=1069, ...}) = 0
rename("/etc/gshadow+", "/etc/gshadow") = 0
unlink("/etc/shadow.lock") = 0
unlink("/etc/passwd.lock") = 0
unlink("/etc/gshadow.lock") = 0
Although you get a better idea what's going on with the full context, note that it links a temporary file it created (/etc/passwd.20283) to /etc/passwd.lock. useradd does similarly with the shadow, and gshadow files as well.
It's also important to note that useradd made four calls out to nscd. Two of these were for passwd, and two were for group:
execve("/usr/sbin/nscd", ["/usr/sbin/nscd", "nscd", "-i", "passwd"], [/* 0 vars */]) = 0
If there isn't an API (and I can't seem to find one), it may be because there's many more ways to store users than simple passwd files. Indeed, it's possible that the machine has no control at all over the users.
EDIT: I suppose it's also important to note that useradd consulted /etc/nsswitch.conf as well, likely to verify the origin of the user database. Furthermore, userdel behaved almost identically, creating similarly named temporary and lock files.
I tested under Linux using the following command:
strace -o useradd.log -f -ff -s 1024 useradd tempuser
strace may also appear as truss and ktrace on other unix systems.
I found this question while looking for a way to list all groups on a Unix.
But to create and delete users you can programatically call the utility programs useradd, userdel, groupadd, groupdel since you would know the user name been sent. But I guess you will need superuser rights to invoke them. Anyway you can check the process exit code for command status.
useradd xxx // status zero operation completed
useradd xxx // status nine user already exists
Hope it helps.