Version 0.7 released
New features
- implemented imports so that simulation files can be split and reused. This can be used to simply split a large model file into smaller files, or (more interestingly) to create simulation variants without having to duplicate the common parts. This feature was inspired by some code from Alexis Eidelman. For details see the Importing other models section.
- added new logit and logistic functions. They were previously used internally but not available to modellers.
- added two new debugging features: autodump and autodiff. autodump will dump all (non-scalar) variables (including temporaries) at the end of each procedure in a separate hdf5 file. It can be used stand-alone for debugging, or in combination with autodiff. Autodiff will gather all variables at the end of each procedure and compare them with the values stored previously by autodump in another run of the model (or a variant of it). This can be used to precisely compare two versions/variants of a model and see exactly where they start to differ.
- added new assert functions:
- assertIsClose to check that two results are “almost” equal tolerating small value differences (for example due to rounding differences).
- assertEquiv to check that two results are equal tolerating differences in shape (though they must be compatible).
- assertNanEqual to check that two arrays are equal even in the presence of nans (because normally nan != nan).
- added a new “timings” option to hide timings from the simulation log, so that two simulation logs are more easily comparable (for example with “diff” tools like WinMerge).
- added a menu entry in notepad++ to run a simulation in “debug mode”.
Miscellaneous improvements
improved the performance and memory usage by changing the internal memory layout. Most operations are now faster. new(), remove(), “merging data” (for retrospective simulations) and writing data at the end of each period are now slower. In our model, this translates to a peak memory usage 20% smaller and a 35% overall speed increase. However, if your model has a low processes/variables ratio, it may very well be slower overall with this version. If it is your case, please contact us.
changed the syntax for all aggregate functions: grpxxx(...) should now be xxx(...). For example, grpsum(age) should now be: sum(age). The old syntax is still valid but it is deprecated (it will be removed in a later version). A special note for grpmin() and grpmax() which becomes min() and max() respectively even though those functions already existed. The meaning is deduced from the number of “non-keyword” arguments:
- min(expr1, expr2)
minimum between expr1 and expr2 (for each individual)
- min(expr)
(aggregate) minimum value of “expr” over all individuals
- min(expr1, filter=expr2)
(aggregate) minimum value of “expr” over individuals satisfying the filter
A tool to automatically upgrade models to the new syntax is provided. In notepad++, you should use the LIAM2: upgrade model command in the Macro menu.
You can also run it via the command line:
main upgrade model.yml [output.yml]
see main upgrade –help for details.
changed the syntax for all one2many link functions: xxxlink(link_name, ...) should now be link_name.xxx(...). For example, countlink(persons) should now be: persons.count(). The old syntax is still valid but it is deprecated (it will be removed in a later version). As for aggregate functions, one can upgrade its models automatically with the “upgrade” command.
the “period” argument of value_for_period can now be a scalar expression (it must have the same value for all individuals).
when the output directory does not exist, LIAM2 will now try to create it.
when debug mode is on, print the position in the random sequence before and after operations which use random numbers.
entities are loaded/stored for each period in alphabetical order instead of randomly. This has no influence on the results but produces nicer log files.
deprecated the “predictor” keyword. If you need several processes to write to the same variable, you should use procedures instead.
Fixes
using invalid indexes in “global arrays” do not crash anymore if they are properly enclosed in an if() expression. For example if you have an array “by_age” with values for indices from 0 to 99, the following code will now work as expected:
if(age < 50, by_age[age + 50], 0.5)
Periodic globals are unaffected (they always return “missing” when out of bounds).
fixed link expressions which span 3 (or more) different entities.
fixed using show() on a scalar created by summing a “global array”.
fixed the progress bar of matching() when the number of individuals is different in the two sets.
