Hello,
I have a question to ask..
I have Look up table in my model dependant on <Time> such as:
proportion of municipal waste segregated: segregation training effectivieness(Time)
segregation training effectivieness:[(0,0)-(20,10)],(0,1.7),(20,1.7)
When I run the model, I see warning like this:
WARNING: At 20.625 Above-segregation training effictivieness-computing-proportion of municipal waste segregated-
I cannot figure out why i am receiving this warning?..any help appreciated,
thanks, nesli
warnings
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Re: warnings
The x axis for your lookup goes from 0 to 20. These warning usually appear if you are passing in a value to the lookup which is outside these bounds (so "Time" in your case probably goes above 20).
Advice to posters seeking help (it really helps us to help you)
http://www.ventanasystems.co.uk/forum/v ... f=2&t=4391
Units are important!
http://www.bbc.co.uk/news/magazine-27509559
http://www.ventanasystems.co.uk/forum/v ... f=2&t=4391
Units are important!
http://www.bbc.co.uk/news/magazine-27509559
Re: warnings
Thank you so much, when i changed the time period, i missed to add the values for the years 20-33 to the Lookup..
Yes it is ok now, many thanks..
nesli
Yes it is ok now, many thanks..
nesli
Re: warnings
Can I ask one more question,
When I click on units check, it gives another warning that: WARNING: Lookup used with dimensioned argument-Year,
I know I use lookup function dependant on time, so what is the point of this error message?
Thanks again..
When I click on units check, it gives another warning that: WARNING: Lookup used with dimensioned argument-Year,
I know I use lookup function dependant on time, so what is the point of this error message?
Thanks again..
Re: warnings
Normally it's good practice to normalize the units in your lookups. So, for example, instead of writing:
productivity = productivity_lookup(capital)
you would write
productivity = normal_productivity*productivity_lookup( capital/normal_capital )
This approach makes it much easier to rescale your model to new problems and permits sensitivity analysis on the lookup via parameters (constants) rather than manually changing the shape.
For lookups that contain time series, this normalization is not so important. If you want to suppress the warning, you can normalize like:
price_of_cheese = price_lookup( time/one_year )
one year == 1
Or, you can just ignore the warning.
Tom
productivity = productivity_lookup(capital)
you would write
productivity = normal_productivity*productivity_lookup( capital/normal_capital )
This approach makes it much easier to rescale your model to new problems and permits sensitivity analysis on the lookup via parameters (constants) rather than manually changing the shape.
For lookups that contain time series, this normalization is not so important. If you want to suppress the warning, you can normalize like:
price_of_cheese = price_lookup( time/one_year )
one year == 1
Or, you can just ignore the warning.
Tom
/*
Advice to posters (it really helps us to help you)
http://www.ventanasystems.co.uk/forum/v ... f=2&t=4391
Blog: http://blog.metasd.com
Model library: http://models.metasd.com
Bookmarks: http://delicious.com/tomfid/SystemDynamics
*/
Advice to posters (it really helps us to help you)
http://www.ventanasystems.co.uk/forum/v ... f=2&t=4391
Blog: http://blog.metasd.com
Model library: http://models.metasd.com
Bookmarks: http://delicious.com/tomfid/SystemDynamics
*/