Lua get index name of table as table - indexing

Is there any way to get every index value of a table?
Example:
local mytbl = {
["Hello"] = 123,
["world"] = 321
}
I want to get this:
{"Hello", "world"}


local t = {}
for k, v in pairs(mytbl) do
table.insert(t, k) -- or t[#t + 1] = k
end
Note that the order of how pairs iterates a table is not specified. If you want to make sure the elements in the result are in a certain order, use:
table.sort(t)

Related

Return table value index via metatable

I'm looking for a way to retrive index value via metatable. This is my attempt:
local mt = { __index =
{
index = function(t, value)
local value = 0
for k, entry in ipairs(t) do
if (entry == value) then
value = k
end
end
return value
end
}
}
t = {
"foo", "bar"
}
setmetatable(t,mt)
print(t.index(t,"foo"))
Result is 0 instead of 1. Where I'm wrong?

My attempt:
local mt = {
__index = function(t,value)
for index, val in pairs(t) do
if value == val then
return index
end
end
end
}
t = {
"foo",
"bar",
"aaa",
"bbb",
"aaa"
}
setmetatable(t,mt)
print(t["aaa"]) -- 3
print(t["asd"]) -- nil
print(t["bbb"]) -- 4
print(t["aaa"]) -- 3
print(t["bar"]) -- 2
print(t["foo"]) -- 1

Result is 0 instead of 1. Where [am I] wrong?
The code for the index function is wrong; the problem is not related to the (correct) metatable usage. You're shadowing the parameter value when you declare local value = 0. Subsequent entry == value comparisons yield false as the strings don't equal 0. Rename either the parameter or the local variable:
index = function(t, value)
local res = 0
for k, entry in ipairs(t) do
if entry == value then
res = k
end
end
return res
end
An early return instead of using a local variable in the first place works as well and helps improve performance.
To prevent such errors from happening again, consider getting a linter like Luacheck, which will warn you if you shadow variables. Some editors support Luacheck out of the box; otherwise there are usually decent plugins available.

compare values in an table in lua

I need help. I am having an table like this:
local dict = {}
dict[1] = {achan = '7f', aseq='02'} --ACK
dict[2] = {dchan = '7f', dseq='03'} --DATA
dict[3] = {achan = '7f', aseq='03'} --ACK
dict[4] = {dchan = '7f', dseq='04'} --DATA
dict[5] = {achan = '7f', aseq='04'} --ACK
dict[6] = {dchan = '7f', dseq='02'} --DATA
Basically I am using this in an Dissector so I don't know the Index except the one I am actually "standing" at the moment.
So what I want to have is:
if the "achan" and the "dchan" is the same and the "aseq" i am standing at the moment is the same as an "dseq" value on positions from the past which are already saved into the table then it should give me back the index from the same "dseq" value from the past.
if (dict[position at the moment].achan == dict[?].dchan) and (dict[position at the moment].aseq == dict[?].dseq) then
return index
end
for example: dchan from position 6 is the same es achan from position 1 and dseq from position 6 is the same as aseq from position 1. So I want to get the position 1 back

You can use a numeric for loop with a negative step size to go back in your table, starting from the previous element. Check wether the achan and aseq fields exist, then compare them vs the dchan and dseq fields of your current entry.
function getPreviousIndex(dict, currentIndex)
for i = currentIndex - 1, 1, -1 do
if dict[i].achan and dict[currentIndex].dchan
and dict[i].achan == dict[currentIndex].dchan
and dict[i].aseq and dict[currentIndex].dseq
and dict[i].aseq == dict[currentIndex].dseq then
return i
end
end
end
This code assumes you have no gaps in your table. You should also add some error handling that makes sure you actually are at a dchan entry and that your index is in range and so on...

Compact way to save JuMP optimization results in DataFrames

I would like to save all my variables and dual variables of my finished lp-optimization in an efficient manner. My current solution works, but is neither elegant nor suited for larger optimization programs with many variables and constraints because I define and push! every single variable into DataFrames separately. Is there a way to iterate through the variables using all_variables() and all_constraints() for the duals? While iterating, I would like to push the results into DataFrames with the variable index name as columns and save the DataFrame in a Dict().
A conceptual example would be for variables:
Result_vars = Dict()
for vari in all_variables(Model)
Resul_vars["vari"] = DataFrame(data=[indexval(vari),value(vari)],columns=[index(vari),"Value"])
end
An example of the appearance of the declared variable in JuMP and DataFrame:
#variable(Model, p[t=s_time,n=s_n,m=s_m], lower_bound=0,base_name="Expected production")
And Result_vars[p] shall approximately look like:
t,n,m,Value
1,1,1,50
2,1,1,60
3,1,1,145

Presumably, you could go something like:
x = all_variables(model)
DataFrame(
name = variable_name.(x),
Value = value.(x),
)
If you want some structure more complicated, you need to write custom code.
T, N, M, primal_solution = [], [], [], []
for t in s_time, n in s_n, m in s_m
push!(T, t)
push!(N, n)
push!(M, m)
push!(primal_solution, value(p[t, n, m]))
end
DataFrame(t = T, n = N, m = M, Value = primal_solution)
See here for constraints: https://jump.dev/JuMP.jl/stable/constraints/#Accessing-constraints-from-a-model-1. You want something like:
for (F, S) in list_of_constraint_types(model)
for con in all_constraints(model, F, S)
#show dual(con)
end
end

Thanks to Oscar, I have built a solution that could help to automatize the extraction of results.
The solution is build around a naming convention using base_name in the variable definition. One can copy paste the variable definition into base_name followed by :. E.g.:
#variable(Model, p[t=s_time,n=s_n,m=s_m], lower_bound=0,base_name="p[t=s_time,n=s_n,m=s_m]:")
The naming convention and syntax can be changed, comments can e.g. be added, or one can just not define a base_name. The following function divides the base_name into variable name, sets (if needed) and index:
function var_info(vars::VariableRef)
split_conv = [":","]","[",","]
x_str = name(vars)
if occursin(":",x_str)
x_str = replace(x_str, " " => "") #Deletes all spaces
x_name,x_index = split(x_str,split_conv[1]) #splits raw variable name+ sets and index
x_name = replace(x_name, split_conv[2] => "")
x_name,s_set = split(x_name,split_conv[3])#splits raw variable name and sets
x_set = split(s_set,split_conv[4])
x_index = replace(x_index, split_conv[2] => "")
x_index = replace(x_index, split_conv[3] => "")
x_index = split(x_index,split_conv[4])
return (x_name,x_set,x_index)
else
println("Var base_name not properly defined. Special Syntax required in form var[s=set]: ")
end
end
The next functions create the columns and the index values plus columns for the primal solution ("Value").
function create_columns(x)
col_ind=[String(var_info(x)[2][col]) for col in 1:size(var_info(x)[2])[1]]
cols = append!(["Value"],col_ind)
return cols
end
function create_index(x)
col_ind=[String(var_info(x)[3][ind]) for ind in 1:size(var_info(x)[3])[1]]
index = append!([string(value(x))],col_ind)
return index
end
function create_sol_matrix(varss,model)
nested_sol_array=[create_index(xx) for xx in all_variables(model) if varss[1]==var_info(xx)[1]]
sol_array=hcat(nested_sol_array...)
return sol_array
end
Finally, the last function creates the Dict which holds all results of the variables in DataFrames in the previously mentioned style:
function create_var_dict(model)
Variable_dict=Dict(vars[1]
=>DataFrame(Dict(vars[2][1][cols]
=>create_sol_matrix(vars,model)[cols,:] for cols in 1:size(vars[2][1])[1]))
for vars in unique([[String(var_info(x)[1]),[create_columns(x)]] for x in all_variables(model)]))
return Variable_dict
end
When those functions are added to your script, you can simply retrieve all the solutions of the variables after the optimization by calling create_var_dict():
var_dict = create_var_dict(model)
Be aware: they are nested functions. When you change the naming convention, you might have to update the other functions as well. If you add more comments you have to avoid using [, ], and ,.
This solution is obviously far from optimal. I believe there could be a more efficient solution falling back to MOI.

Iterate through a column in Dataset which have array of key value pairs and find out a pair with max value

I have data in a dataframe , which was obtained from azure eventhub.
Then I convert this data to json object and stored the required data into a dataset as shown below.
Code for obtaining data from eventhub and store it into a dataframe.
val connectionString = ConnectionStringBuilder(<ENDPOINT URL>)
.setEventHubName(<EVENTHUB NAME>).build
val currTime = Instant.now
val ehConf = EventHubsConf(connectionString)
.setConsumerGroup("<CONSUMER GRP>")
.setStartingPosition(EventPosition
.fromEnqueuedTime(currTime.minus(Duration.ofMinutes(30))))
.setEndingPosition(EventPosition.fromEnqueuedTime(currTime))
val reader = spark.read.format("eventhubs").options(ehConf.toMap).load()
var SIGNALS = reader
.select(get_json_object(($"body").cast("string"),"$.NUM").alias("NUM"),
get_json_object(($"body").cast("string"),"$.SIG1").alias("SIG1"),
get_json_object(($"body").cast("string"),"$.SIG2").alias("SIG2"),
get_json_object(($"body").cast("string"),"$.SIG3").alias("SIG3"),
get_json_object(($"body").cast("string"),"$.SIG4").alias("SIG4")
)
val SIGNALSFiltered = SIGNALS.filter(col("SIG1").isNotNull &&
col("SIG2").isNotNull && col("SIG3").isNotNull && col("SIG4").isNotNull)
The data obtained at SIGNALSFiltered is shown below.
+-----------------+--------------------+--------------------+--------------------+--------------------+
| NUM| SIG1| SIG2| SIG3| SIG4|
+-----------------+--------------------+--------------------+--------------------+--------------------+
|XXXXX01|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|
|XXXXX02|[{"TIME":15695604780...|[{"TIME":15695604780...|[{"TIME":15695604780...|[{"TIME":15695604780...|
|XXXXX03|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|
|XXXXX04|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|
|XXXXX05|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|
|XXXXX06|[{"TIME":15695605340...|[{"TIME":15695605340...|[{"TIME":15695605340...|[{"TIME":15695605340...|
|XXXXX07|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|
|XXXXX08|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|[{"TIME":15695605310...|
If we check entire data for a single row it will be as below.
|XXXXX01|[{"TIME":1569560531000,"VALUE":3.7825},{"TIME":1569560475000,"VALUE":3.7812},{"TIME":1569560483000,"VALUE":3.7812},{"TIME":1569560491000,"VALUE":34.7875}]|
[{"TIME":1569560537000,"VALUE":3.7825},{"TIME":1569560481000,"VALUE":34.7825},{"TIME":1569560489000,"VALUE":34.7825},{"TIME":1569560497000,"VALUE":34.7825}]|
[{"TIME":1569560505000,"VALUE":34.7825},{"TIME":1569560513000,"VALUE":34.7825},{"TIME":1569560521000,"VALUE":34.7825},{"TIME":1569560527000,"VALUE":34.7825}]|
[{"TIME":1569560535000,"VALUE":34.7825},{"TIME":1569560479000,"VALUE":34.7825},{"TIME":1569560487000,"VALUE":34.7825}]
I want only the highest TIME pair from each column, not the entire TIME VALUE pairs. Output should be as shown below.
+-----------------+-----------------------------+---------------------------------------+---------------------------------------+----------------------------------------+
| NUM| SIG1| SIG2| SIG3| SIG4|
+-----------------+-----------------------------+---------------------------------------+---------------------------------------+----------------------------------------+
|XXXXX01|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":4.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":5.7825}]|
|XXXXX02|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":6.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":7.7825}]|
|XXXXX03|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":9.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":8.7825}]|
How to Iterate through each column in each row and get the highest TIME-VALUE pair?
After getting highest in each columns (SIG1,....SIG4) have to update only the value of TIME in all columns with highest among them.
Is there Any way to convert the base dataset as below?. Each elements in a column should be converted to a new row.
+-----------------+-----------------------------+---------------------------------------+---------------------------------------+----------------------------------------+
| NUM| SIG1| SIG2| SIG3| SIG4|
+-----------------+-----------------------------+---------------------------------------+---------------------------------------+----------------------------------------+
|XXXXX01|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|
|XXXXX01|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|
|XXXXX01|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]| null |[{"TIME":1569560531000,"VALUE":3.7825}]|
|XXXXX01|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|
|XXXXX02|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|
|XXXXX02|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|
|XXXXX02|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|
|XXXXX02|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|[{"TIME":1569560531000,"VALUE":3.7825}]|```
Any leads or help is appreciated! Thanks in Advance.

You have to write one user defined function like below. which will loop your data and get Max Time Value.
Note: UDF is just for reference, you can change it as per requirement
How to Iterate through each column in each row and get the highest TIME-VALUE pair?
scala> import org.apache.spark.sql.expressions.{UserDefinedFunction}
scala> def MaxTime:UserDefinedFunction = udf((json:String) => {
val pars = JSON.parseFull(json)
var output=""
pars.foreach{ x => val y = x.asInstanceOf[List[Any]]
var i = 1
var TimeMap = scala.collection.mutable.Map[String, Long]()
var ValueMap = scala.collection.mutable.Map[String, Double]()
y.foreach{ zz => val z = zz.asInstanceOf[Map[String,Double]]
TimeMap(i.toString) = z("TIME").toLong
ValueMap(i.toString) = z("VALUE")
i = i + 1
}
output = """[{"TIME" : """ + TimeMap.maxBy(_._2)._2.toString + """ ,"VALUE": """ + ValueMap(TimeMap.maxBy(_._2)._1) + """}]"""
}
output})
scala> SIGNALSFiltered.withColumn("SIG1", MaxTime(col("SIG1")).withColumn("SIG2", MaxTime(col("SIG2")))).withColumn("SIG3", MaxTime(col("SIG3"))).withColumn("SIG4", MaxTime(col("SIG4"))).show(false)
After getting highest in each columns (SIG1,....SIG4) have to update only the value of TIME in all columns with highest among them.
Write same UDF like above and pass complete row as a parameter. Then parse each column value into Map and get Maximum among all columns.

how to iterate on selected rows with datables + select extension

I use the select extension an d try to 'alert' with the id of the selected rows.
the following code fails:
let sels = jqTable.api().rows({ selected: true });
let st = '';
sels.each(function (value, index) {
st += ',' + sels.row(value).id();
});
alert(st);
The function is called once independently of selected rows:
0 row: value = [], index = 0
>=1 : value = [0, 2], index = 0
The following code succeeds:
let sels = jqTable.api().rows({ selected: true });
let st = '';
for (let i = 0; i < sels.count(); i++) {
st += ',' + sels.row(sels[0][i]).id();
}
alert(st);
what do I missunderstand with each() :
Iterate over the contents of the API result set.
I notice that the following code runs:
sels.data().each(function (value, index) {
st += ',' + value.IdFile;
});
But using it cancels the advantage of rowId : 'IdFile' in the datatable configuration.

each() is used when the dataset returns an array of results within the API objects - in the case of rows() this isn't the case - it returns a single result, which happen to be an array containing the rowIDs of the selected rows.
Your first code block fails as there's only one iteration (the results are a single array).
Your second block works, because you're iterating over that single array (sels[0]).
And your third also works, as the rows().data() does generate an array containing the data of all the selected rows.
This example will hopefully help!