I have a model and everything was working fine. i was just changing some values to try different scenarios and adding some graphs when I got the error msg:
Error while drawing animation frame. possibly created by dynamic properties of animation shapes
The only message I could get from the console window is:
Error during model creation:
java.base/java.lang.String cannot be cast to java.base/java.lang.Double
what could be the cause of this error?
Somewhere in your code, you might have attempted to convert a string into a double.
String Message = "1.20";
double res = (double)Message;
The above code is not allowed in Java
A condition like this might have occurred.
You are not allowed to put a string into a double variable:
double res = "1.2";
Related
When I run a Xcos model containing a scifunc_block_m block like shown below
I get an error message relating to data dimensions inconsistency:
"Data dimensions are inconsistent:"
" Variable size=[1,1]"
"Block output size=[100,1]."
But when I double click in the block in order to see what can I change to make the dimensions correct I get a message in the console saying
Undefined variable: scifunc_block_m
What bugs me is that scifunc_block_m is not the name of any variable, but rather the name of the block itself like can be seen in the official docs.
Of course I double checked that nowhere in my function phase_shifter neither anywhere else I have any variable named like that.
I tried with Scilab 6.1.1 and 6.1.0 believing that it might be a bug from apparently not.
In your phase_shifter.sce file generating the input variable,
the signalIn variable does not comply with the From Workspace block requirements, whose documentation says that the input variable
must be a structure with time and values fields
.time must be a column vector, and in your case
.values must also be a column
So,
t = (0:1/fs:Npp/fs - 1/fs); // time vector
signalIn = A*%e^(%i*w*t);
should be replaced with
t = (0:1/fs:Npp/fs - 1/fs)'; // time column vector
signalIn = struct("time",t, "values",A*%e^(%i*w*t));
This fixes the inconsistent dimensions message.
In addition, i am not able to reproduce your issue about Undefined variable: scifunc_block_m. The parameters interface opens as expected.
You may get this kind of messages if you try to run some xcos parts out of xcos, without beforehand loading xcos-related libraries.
Then, we get an unclear "Output should be of complex type." message on the From workspace block.
By the way, you try to plot some complex values. Please have a look to the MATMAGPHI block before entering MUX: https://help.scilab.org/docs/6.1.1/en_US/MATMAGPHI.html
I am new to sage and have got a code (link to code) which should run.
I am still getting an error message in the decoding part. The error trace looks like this:
in decode(y)
--> sigma[i+1+1] = sigma[i+1]*(z)\
-(delta[i+1]/delta[mu+1])*z^(i-mu)*sigma[mu+1]*(z);
in sage.structure.element.Element.__mul__
if BOTH_ARE_ELEMNT(cl):
--> return coercion_model.bin_op(left, right, mul)
in sage.structure.coerce.CoercionModel_cache_maps.bin_op
--> action = self.get_action(xp,yp,op,x,y)
...... some more traces (don't actually know if they are important)
TypeError: positive characteristics not allowed in symbolic computations
Does anybody know if there is something wrong in this code snipped? Due to previous errors, I changed the following to get to where I am at the moment:
.coeffs() changed to .coefficients(sparse=False) due to a warning message.
in the code line sigma[i+1+1] = sigma[i+1](z)\
-(delta[i+1]/delta[mu+1])*z^(i-mu)*sigma[mu+1](z); where the error occurs, i needed to insert * eg. sigma[i+1]*(z)
I would be grateful for any guess what could be wrong!
Your issue is that you are multiplying things not of characteristic zero (like elements related to Phi.<x> = GF(2^m)) with elements of symbolic computation like z which you have explicitly defined as a symbolic variable
Phi.<x> = GF(2^m)
PR = PolynomialRing(Phi,'z')
z = var('z')
Basically, the z you get from PR is not the same one as from var('z'). I recommend naming it something else. You should be able to access this with PR.gen() or maybe PR(z).
I'd be able to be more detailed, but I encourage you next time to paste a fully (non-)working example; trying to slog through a big worksheet is not the easiest thing to track all this down. Finally, good luck, hope Sage ends up being useful for you!
I apologize that I can not tell you what these functions are form the start.
I have a function CheckOutCell. It takes one argument and that is the number 764. So every time I run the function it looks like this in it's entirety: CheckOutCell(764).
Now many times the function will give me an error:
Error in checkInCell(764) :
The function is currently locked; try again in a minute.
Which is a custom error message and the details are not important to this question.
Now this function could be locked from anywhere from 30 seconds to an hour. I want to be able to automatically run CheckOutCell(764) till it goes through, and then stop running it. That is, run it till I do not get an error, then stop.
I think a start would be using
while(capture.output(checkInCell(764)) == "Error in checkInCell(764) :
The function is currently locked; try again in a minute."){
do something}
However this just produces
Error in checkInCell(764) :
The function is currently locked; try again in a minute.
because the function is still locked, so no output can be captured.
How would I test for while(error = T)
Assume the source code of the function cannot be modified.
Even is.error(CheckInCell(764)) will just produce the same error message
So it seems that this code works in a way
wrapcheck <- function(x){
repeatCheck =tryCatch(checkOutCell(764),
error = function(cond)"skip")
SudoCheck = ifelse(repeatCheck=="skip",repeatCheck, checkOutCell(764))
while(SudoCheck == "skip"){
repeatCheck
}
}
wrapcheck(764)
Basically this checks for an error and then keeps running the function till the error is not produced. In fact I am fairly confident that this would work with any funciton you wanted to put in place of CheckOutCell.
The main problem is that when the function is locked, that it not really an error, it is locked. Therefore this above block will not work. This above block will work when errors other than a lock are produced.
I've been trying to run some species deletion simulations using the cheddar package and have come across an error:
Error in RemoveNodes(new.community, new.remove, title = title, method = "cascade") :
Removing these nodes would result in an empty community
you can recreate the error as such:
library(cheddar)
data(SkipwithPond)
a<-RemoveNodes(SkipwithPond,c('Detritus','Corixidae nymphs','Agabus / Ilybius larvae'),method='cascade')
i was wondering if was possible to disable this feature so as to allow the removal to occur? If not would there be a way to return a certain value (the number of nodes in the web in this case) if this error occurs?
I don't know much about the cheddar package, but the second option you mention would be to "catch" the error after trying to evaluate the expression. Enter tryCatch. See the documentation for this function, but generally when you save result of tryCatch to a variable, you can redirect your flow to accommodate for the error. Something along the lines of
# spaces possibly make code easier to read
a <- tryCatch(RemoveNodes(SkipwithPond, c('Detritus','Corixidae nymphs','Agabus / Ilybius larvae'), method='cascade'), error = function(e) e)
# str(a) to see what the error is (message, class...) and act on that message
# or if you want a custom message to catch
a <- tryCatch(RemoveNodes(SkipwithPond, c('Detritus','Corixidae nymphs','Agabus / Ilybius larvae'), method='cascade'), error = function(e) "empty community?")
if (a$message == "empty community?") {
# ...do something
}
levels(train$Variable1)
While running the above line, it executes well and shows me the result.
levels(test$Variable1) = levels(train$Variable1)
But trying to assign the output like the one shown above results in the below error. Looking for help to understand why it's happening and overcome it. Btw it's a factor variable.
Error in levels(test$Variable1) = levels(train$Variable1) : attempt to set an attribute on NULL