I used a list of a constraint numbers or a list of constraint names but this does not work with command m.remove_constraints().
m.add(sumbs[i] <= a[i], "ct_sum_%d" %i)
A.append("ct_sum_%d" %i)
Then later on when I want to change the model: m.remove_constraints(A)
What is the correct way of doing this?
Model.add_constraints() returns the list of newly added constraints.
Similarly, Model.remove_constraints takes a collection of constraint objects, not names , not indices. For example:
cts = mdl.add_constraints(...)
mdl.remove_constraints(cts[:3]) # remove the first three
Related
If I use the following yamldecode, I get the following output:
output "product" {
value = distinct(yamldecode(file("resource/lab/abc.yaml"))["account_list"]["resource_tags"][*]["TAG:product"])
}
Output:
+ product = [
+ "fargate",
+ "CRM",
]
I want fargate to be removed from my output and the expected output is this:
+ product = [
+ "CRM"
]
Please let me know how I can do this.
output "product" {
value = compact([for x in distinct(yamldecode(file("resource/lab/abc.yaml"))["account_list"]["resource_tags"][*]["TAG:product"]) : x == "fargate" ? "" : x])
}
I get this output:
test = [
"enter",
]
compact function solved the problem.
The Terraform language is based on functional principles rather than imperative principles and so it does not support directly modifying an existing data structure. Instead, the best way to think about goals like this is how to define a new value which differs from your existing value in some particular way.
For collection and structural types, the most common way to derive a new value with different elements is to use a for expression, which describes a rule for creating a new collection based on the elements of an existing collection. For each element in the source collection we can decide whether to use it in the result at all, and then if we do decide to use it we can also describe how to calculate a new element value based on the input element value.
In your case you seem to want to create a new list with fewer elements than an existing list. (Your question title mentions maps, but the question text shows a tuple which is behaving as a list.)
To produce a new collection with fewer elements we use the optional if clause of a for expression. For example:
[for x in var.example : x if x != "fargate"]
In the above expression, x refers to each element of var.example in turn. Terraform first evaluates the if clause, and expects it to return true if the element should be used. If so, it will then evaluate the expression immediately after the colon, which in this case is just x and so the result element is identical to the input element.
Your example expression also includes some unrelated work to decode a YAML string and then traverse to the list inside it. That expression replaces var.example in the above expression, as follows:
output "products" {
value = toset([
for x in yamldecode(file("${path.module}/resource/lab/abc.yaml"))["account_list"]["resource_tags"][*]["TAG:product"]: x
if x != "fargate"
])
}
I made some other small changes to the above compared to your example:
I named the output value "products" instead of "product", because it's returning a collection and it's Terraform language idiom to use plural names for collection values.
I wrapped the expression in toset instead of distinct, because from your description it seems like this is an unordered collection of unique strings rather than an ordered sequence of strings.
I added path.module to the front of the file path so that this will look for the file in the current module directory. If your output value is currently in a root module then this doesn't really matter because the module directory will always be the current working directory, but it's good practice to include this so that it's clear that the file belongs to the module.
Therefore returning a set is more appropriate than returning a list because it communicates to the caller of the module that they may not rely on the order of these items, and therefore allows the order to potentially change in future without it being a breaking change to your module.
I'm designing a Mastermind game, which basically compares 2 lists and marks the similarities. When a colour is found at the right place, a flag making the correct position is added and the item found on the reference list is marked off. The reference list is feeding off an array from another function. The problem is at the mark off, as any changes done to the reference list is changing also the original array, which i don't want it to happen
tempCode = mCode #mCode is the array combination randomly generated from another function
for i in range (len(uCode)): #user input array
for j in range (len(tempCode)): #temp array
if uCode[i] == tempCode[j]: # compare individual chars
if i == j: #compare position
flagMark = "*"
tempCode.insert(j+1, "x") #problem starts here
tempCode.remove(tempCode[j])
fCode.append(flagMark)
When the insert is reached both the tempCode and mCode change which it is not intended.
The code is written in a way should the user enter a combination of the same colours, thus checking the chras(the colours are just letters) and the position, and then mark them of with "x"
As it stands, when it gets to
tempCode.insert(j+1, "x")
the arrays will change to
mCode = ["B","R","x","G","Y"]
tempCode = ["B","R","x","G","Y"]
when I would just want
mCode = ["B","R","G","Y"]
tempCode = ["B","R","x","G","Y"]
See also this answer, which is a different presentation of the same problem.
Essentially, when you do tempCode = mCode, you're not making a copy of mCode, you're actually making another reference to it. Anything you do to tempCode thereafter affects the original as well, so at any given time the condition tempCode == mCode will be true (as they're the same object).
You probably want to make a copy of mCode, which could be done in either of the following ways:
tempCode = mCode.copy()
tempCode = mCode[:]
which produces a different list with the same elements, rather than the same list
I have a list of type:
ans=[(a,[b,c]),(x,[y,z]),(p,[q,r])]
I need to sort the list by using the following condition :
if (ans[j][1][1]>ans[j+1][1][1]) or (ans[j][1][1]==ans[j+1][1][1] and ans[j][1][0]<ans[j+1][1][0]):
# do something (like swap(ans[j],ans[j+1]))
I was able to implement using bubble sort, but I want a faster sorting method.
Is there a way to sort my list using the sort() or sorted() (Using comparator or something similar) functions while pertaining to my condition ?
You can create a comparator function that retuns a tuple; tuples are compared from left to right until one of the elements is "larger" than the other. Your input/output example is quite lacking, but I believe this will result into what you want:
def my_compare(x):
return x[1][1], x[1][0]
ans.sort(key=my_compare)
# ans = sorted(ans, key=my_compare)
Essentially this will first compare the x[1][1] value of both ans[j] and ans[j+1], and if it's the same then it will compare the x[1][0] value. You can rearrange and add more comparators as you wish if this didn't match your ues case perfectly.
I'm trying to create individual dictionary entries while looping through some input data. Part of the data is used for the key, while a different part is used as the value associated with that key. I'm running into a problem (due to Python's "everything is an object, and you reference that object" operations method) with this as ever iteration through my loop alters the key set in previous iterations, thus overwriting the previously set value, instead of creating a new dict key and setting it with its own value.
popcount = {}
for oneline of datafile:
if oneline[:3] == "POP":
dat1, dat2, dat3, dat4, dat5, dat6 = online.split(":")
datid = str.join(":", [dat2, dat3])
if datid in popcount:
popcount[datid] += int(dat4)
else:
popcount = { datid : int(dat4) }
This iterates over seven lines of data (datafile is a list containing that information) and should create four separate keys for datid, each with their own value. However, what ends up happening is that only the last value for datid exist in the dictionary when the code is run. That happens to be the one that has duplicates, and they get summed properly (so, at least i know that part of the code works, but the other key entries just are ... gone.
The data is read from a file, is colon (:) separated, and treated like a string even when its numeric (thus the int() call in the if datid in popcount).
What am I missing/doing wrong here? So far I haven't been able to find anything that helps me out on this one (though you folks have answered a lot of other Python questions i've run into, even if you didn't know it). I know why its failing; or, i think i do -- it is because when I update the value of datid the key gets pointed to the new datid value object even though I don't want it to, correct? I just don't know how to fix or work around this behavior. To be honest, its the one thing I dislike about working in Python (hopefully once I grok it, I'll like it better; until then...).
Simply change your last line
popcount = { datid : int(dat4) } # This does not do what you want
This creates a new dict and assignes it to popcount, throwing away your previous data.
What you want to do is add an entry to your dict instead:
popcount[datid] = int(dat4)
I'm trying to use get() to access a list element in R, but am getting an error.
example.list <- list()
example.list$attribute <- c("test")
get("example.list") # Works just fine
get("example.list$attribute") # breaks
## Error in get("example.list$attribute") :
## object 'example.list$attribute' not found
Any tips? I am looping over a vector of strings which identify the list names, and this would be really useful.
Here's the incantation that you are probably looking for:
get("attribute", example.list)
# [1] "test"
Or perhaps, for your situation, this:
get("attribute", eval(as.symbol("example.list")))
# [1] "test"
# Applied to your situation, as I understand it...
example.list2 <- example.list
listNames <- c("example.list", "example.list2")
sapply(listNames, function(X) get("attribute", eval(as.symbol(X))))
# example.list example.list2
# "test" "test"
Why not simply:
example.list <- list(attribute="test")
listName <- "example.list"
get(listName)$attribute
# or, if both the list name and the element name are given as arguments:
elementName <- "attribute"
get(listName)[[elementName]]
If your strings contain more than just object names, e.g. operators like here, you can evaluate them as expressions as follows:
> string <- "example.list$attribute"
> eval(parse(text = string))
[1] "test"
If your strings are all of the type "object$attribute", you could also parse them into object/attribute, so you can still get the object, then extract the attribute with [[:
> parsed <- unlist(strsplit(string, "\\$"))
> get(parsed[1])[[parsed[2]]]
[1] "test"
flodel's answer worked for my application, so I'm gonna post what I built on it, even though this is pretty uninspired. You can access each list element with a for loop, like so:
#============== List with five elements of non-uniform length ================#
example.list=
list(letters[1:5], letters[6:10], letters[11:15], letters[16:20], letters[21:26])
#===============================================================================#
#====== for loop that names and concatenates each consecutive element ========#
derp=c(); for(i in 1:length(example.list))
{derp=append(derp,eval(parse(text=example.list[i])))}
derp #Not a particularly useful application here, but it proves the point.
I'm using code like this for a function that calls certain sets of columns from a data frame by the column names. The user enters a list with elements that each represent different sets of column names (each set is a group of items belonging to one measure), and the big data frame containing all those columns. The for loop applies each consecutive list element as the set of column names for an internal function* applied only to the currently named set of columns of the big data frame. It then populates one column per loop of a matrix with the output for the subset of the big data frame that corresponds to the names in the element of the list corresponding to that loop's number. After the for loop, the function ends by outputting that matrix it produced.
Not sure if you're looking to do something similar with your list elements, but I'm happy I picked up this trick. Thanks to everyone for the ideas!
"Second example" / tangential info regarding application in graded response model factor scoring:
Here's the function I described above, just in case anyone wants to calculate graded response model factor scores* in large batches...Each column of the output matrix corresponds to an element of the list (i.e., a latent trait with ordinal indicator items specified by column name in the list element), and the rows correspond to the rows of the data frame used as input. Each row should presumably contain mutually dependent observations, as from a given individual, to whom the factor scores in the same row of the ouput matrix belong. Also, I feel I should add that if all the items in a given list element use the exact same Likert scale rating options, the graded response model may be less appropriate for factor scoring than a rating scale model (cf. http://www.rasch.org/rmt/rmt143k.htm).
'grmscores'=function(ColumnNameList,DataFrame) {require(ltm) #(Rizopoulos,2006)
x = matrix ( NA , nrow = nrow ( DataFrame ), ncol = length ( ColumnNameList ))
for(i in 1:length(ColumnNameList)) #flodel's magic featured below!#
{x[,i]=factor.scores(grm(DataFrame[, eval(parse(text= ColumnNameList[i]))]),
resp.patterns=DataFrame[,eval(parse(text= ColumnNameList[i]))])$score.dat$z1}; x}
Reference
*Rizopoulos, D. (2006). ltm: An R package for latent variable modelling and item response theory analyses, Journal of Statistical Software, 17(5), 1-25. URL: http://www.jstatsoft.org/v17/i05/