Fixes unittest fail due to PyYAML upgrade #46
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New failures due to upgrade in Sympy's |
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@sanjayankur31 Will hopefully have them sorted out soon |
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I have fixed errors that were causing the tests to fail for Python 3.5-3.7. However, 2.7 is failing due to a function within Sympy not working with callable objects (instead of a function) when using PY2. Looks like it would be quite a bit of work to come up with a work around, which to my mind doesn't seem worth the trouble now that Python 2 is end-of-life. @apdavison, do you have any thoughts about dropping Python 2 support? Do you think it is worth the hassle at this stage? |
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Let's drop it. |
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Working around the PY2 issue turned out to be pretty straightforward in the end (came to me straight afterwards) so now all builds pass including Python 2.7. So we can keep it in until something else comes up. |
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Note that I have added numpy to the list of requirements to handle array values |
| .format(self.rhs_str)) | ||
| return val | ||
| return nineml_expression | ||
| return lambdify(list(self.rhs_symbol_names), self.rhs, 'numpy') |
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does this single line really achieve the same as the deleted code?
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Yes, and runs much faster for complex expressions as it uses Numpy under the hood :).
I think I must have missed lambdify when I was converting the library to Sympy, either that or it was added later. I have been using it in my ref_implementation branch so I am fairly familiar with how it works now. Also, if you have a look at that code it mostly just uses the evalf method but then has to handle the special cases when the expression is a relational (i.e. in Triggers) or has a built-in function in it (the eval at the bottom). Using Numpy avoids having to do all these things.
I had also been avoiding Numpy as a dependency as I liked the idea that the library was pure Python and then could be run in other languages (e.g. Java, Iron Python). But that is probably a very marginal gain and not worth the ease of handling array data with Numpy.
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