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unserialize wont do the right


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#1 ame824

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Posted 18 January 2022 - 04:08 PM

Hello
Im writing an program and stuck on my safe and load function..
I try to unserialize a saved table and it wont work

the functions
function install()
file = fs.open("recipes","w")
tmp = textutils.serialize(rFirstEntries)
file.writeLine(tmp)
file.close()
end

function load(table, name)
local file = fs.open(name, "r")
while true do
	 local tRead = file.readAll()
	 print(tRead)
	 if tRead then
			 local recipe = textutils.unserialize(tRead)
			 if recipe then
					 table.insert(table, recipe)
			 end
	 else
		    break
	 end
end
file.close()
end

the call
if not fs.exists("recipes") then
    install()
else
    load(recipes, "recipes")
end

and the finished file
data:image/png;base64,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

so where is my mistake in this

#2 Lyqyd

    Lua Liquidator

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Posted 20 January 2022 - 03:26 AM

You can't use 'table' for both the table library and the name of the table passed into the function.

There are a number of other oddities with what you've got going on, like the unnecessary while loop, but this should be the core of what's not working.





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