Hi I am new to terraform and I am getting the following warning on my project when i run plan/apply.
Warning: output "cloudfront_distribution_id": must use splat syntax to access aws_cloudfront_distribution.cloudfront attribute "id", because it has "count" set;
This is usually a warning when you are using count to define the number of a specified resource you intend to create.
Use aws_cloudfront_distribution.cloudfront.*.id to obtain a list of the attributes across all instances
Example
output "cloudfront_distribution_id" {
value = "${join("", aws_cloudfront_distribution.cloudfront.*.id)}"
}
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 trying to learn how to manipulate microarrays for differential expression analysis. While I am trying to add some annotation I can not find the keytype related to:
select(hugene10sttranscriptcluster.db,
keys = my_keys,
columns = c("GENENAME", "SYMBOL"),
keytype = "PROBEID")
-------------------------------------------------------
Error in .testForValidKeys(x, keys, keytype, fks) :
None of the keys entered are valid keys for 'PROBEID'. Please use the keys method to see a listing of valid arguments.
Being the keys:
my_keys
---------------------------------------------------------------------
[1] "16650045" "16650047" "16650049" "16650051" "16650053" "16650055" "16650057" "16650059"
I tried every possible type from keytypes(hugene10sttranscriptcluster.db) with no successful result:
"16650045" %in% keys(hugene10sttranscriptcluster.db, "GENEID")
------------------------------------------------------------------
[1] FALSE
Is there any documentation/alternative where I can find it. I have been looking through the documentation (Array Express) but did not help me. I am also not sure; is it possible that I require a different package (hugene10sttranscriptcluster.db)?
Effectively, I did have a problem with the package. If anyone has the same problem just try to look for the annotation of the microarray in the documentation (pd.hugene.2.0.st in my case) to install and use the proper package (hugene20sttranscriptcluster.db)
I want to save my Spark DataFrame into directory using spark_write_* function like this:
spark_write_csv(df, "file:///home/me/dir/")
but if the directory is already there I will get error:
ERROR: org.apache.spark.sql.AnalysisException: path file:/home/me/dir/ already exists.;
When I'm working on the same data, I want to overwrite this dir - how can I achieve this? In documentation there is one parameter:
mode Specifies the behavior when data or table already exists.
but it doesn't say what value you should use.
Parameter mode should simply have value "overwrite":
spark_write_csv(df, "file:///home/me/dir/", mode = "overwrite")
In testing a Java API, I need to change a default setting. According to the API's document, it should be done using a method defined within the class using "public void setType". Suppose the class name is 'Node', which is referred using
library(rJava)
.jinit(classpath=jarPath)
Node <- J("Node")
In an Java example from its documents, it's called as
Node nodeX = new Node("X", new Variable[]{x});
nodeX.setType(Type.TEMP);
The default type of nodeX is 'CONTEMP'. How the "setType" method can be called in R through rJava to change its default value to another one? Let's assume 'Type' is an enum variable which has several options, including "CONTEMP","TEMP", etc.
I think you want
library(rJava)
.jinit(classpath=jarPath)
variable <- .jarray(new(J("package.name.Variable", input_arg))
Node <- new(J("package.name.Node"), variable)
Then you can do
type <- J("package.name.Type")$TEMP
Node$setType(type)
I am trying to build the inline segment to filter the pages (ex. to separate the pages for blogs and games) using the function BuildClassificationValueSegment() to get the data from Adobe Analytics API,
I have tried some thing like
report.data.visits <- QueueTrended(reportsuite.id,date.from,date.to,metrics,elements,
segment.inline = BuildClassificationValueSegment("evar2","blog","OR")).
Got error like :
Error in ApiRequest(body = report.description, func.name = "Report.Validate") :
ERROR: segment_invalid - Segment "evar2" not valid for this company
In addition: Warning message:
In if (segment.inline != "") { :
the condition has length > 1 and only the first element will be used
Please help on the same.Thanks in advance...
I recommend you to declare the InlineSegment in advance and store it in a variable. Then pass it to the QueueTrended function.
I've been using the following syntax to generate an inline segment:
InlineSegment <- list(container=list(type=unbox("hits"),
rules=data.frame(
name=c("Page Name(eVar48)"),
element=c("evar48"),
operator=c("equals"),
value=c(as.character("value1","value2"))
))
You can change the name and element arguments in order to personalize the query.
The next step is to pass the InlineSegment to the QueueRanked function:
Report <- as.data.frame(QueueRanked("reportsuite",
date.from = dateStart,
date.to = dateEnd,
metrics = c("pageviews"),
elements = c("element"),
segment.inline = InlineSegment,
max.attempts=500))
I borrowed that syntax from this thread some time ago: https://github.com/randyzwitch/RSiteCatalyst/issues/129
Please note that there might be easier ways to obtain this kind of report without using InlineSegmentation. Maybe you can use the selected argument from the QueueRanked function in order to narrow down the scope of the report.
Also, I'm purposefully avoiding the BuildClassificationValueSegment function as I found it a bit difficult to understand.
Hope this workaround helps...