> In this case, real and positive assumptions don't help for the lambdify call. > I guess making lambdify assumption aware could be an option.
I was actually expecting that one would always use `simplify` before `lambdify`. This would make a big difference, because with the appropriate assumptions `simplify` would remove all such removable singularities[1], which is the technical term for these point. Unfortunately a quick google search for "numerical removable singularity" does not show anything promising. [1]: https://en.wikipedia.org/wiki/Removable_singularity On 10 September 2013 14:19, Jason Moore <[email protected]> wrote: > In this case, real and positive assumptions don't help for the lambdify > call. I guess making lambdify assumption aware could be an option. > > I think we should define dynamicsymbols in mechanics to have a default real > assumption, this could help in simplification. > > The different basis is an interesting idea. Do you have an example of that? > > > Jason > moorepants.info > +01 530-601-9791 > > > On Tue, Sep 10, 2013 at 2:01 PM, Stefan Krastanov > <[email protected]> wrote: >> >> The poorman's solution that I used when I had this problem was to do >> the calculations in a different basis. I do not know how >> general/automatic this solution would be. >> >> However, a common reason for bad/no simplification is lack of >> appropriate assumptions. Define all you symbols as real/positive and >> half of the issues will go away. >> >> On 10 September 2013 12:17, Jason Moore <[email protected]> wrote: >> > In the mechanics package we sometimes end up with symbolic expressions >> > that >> > can give a divide by zero error if we use the expression in a numerical >> > function. Below is a simple example of an expression that could be >> > generated >> > by the mechanics package. It should evaluate to zero (i.e the limit as >> > vx -> >> > zero. evalf says it's zero but if I use lambdify (or probably any code >> > generator) to transform the expression to something that is purely >> > numeric >> > we get divide by zeros. I feel like this must be a common issue when >> > transforming symbolic expressions to numerical functions. Does anyone >> > know >> > how or if this is dealt with? It be nice if we could generate equations >> > of >> > motion in the mechanics package and generate code from them that >> > wouldn't >> > have divide by zero errors, requiring some fix on the numerical side. >> > >> > In [1]: from sympy import symbols, sqrt, lambdify >> > >> > In [2]: k, vx, vy, vz = symbols('k vx vy vz') >> > >> > In [3]: f = k * sqrt(vx ** 2 + vy ** 2 + vz ** 2) >> > >> > In [4]: dfdvx = f.diff(vx) >> > >> > In [5]: dfdvx >> > Out[5]: k*vx/sqrt(vx**2 + vy**2 + vz**2) >> > >> > In [6]: dfdvx.evalf(subs={k: 1.0, vx: 0.0, vy: 0.0, vz: 0.0}) >> > Out[6]: 0 >> > >> > In [7]: numeric_dfdvx = lambdify((k, vx, vy, vz), dfdvx) >> > >> > In [8]: numeric_dfdvx(1.0, 0.0, 0.0, 0.0) >> > >> > --------------------------------------------------------------------------- >> > ZeroDivisionError Traceback (most recent call >> > last) >> > <ipython-input-8-55e44f8ab1bf> in <module>() >> > ----> 1 numeric_dfdvx(1.0, 0.0, 0.0, 0.0) >> > >> > /usr/local/lib/python2.7/dist-packages/numpy/__init__.pyc in >> > <lambda>(_Dummy_26, _Dummy_27, _Dummy_28, _Dummy_29) >> > >> > ZeroDivisionError: float division by zero >> > >> > In [9]: numeric_dfdvx = lambdify((k, vx, vy, vz), dfdvx, >> > modules='numpy') >> > >> > In [10]: numeric_dfdvx(1.0, 0.0, 0.0, 0.0) >> > /usr/local/lib/python2.7/dist-packages/numpy/__init__.py:1: >> > RuntimeWarning: >> > invalid value encountered in double_scalars >> > """ >> > Out[10]: nan >> > >> > In [11]: numeric_dfdvx = lambdify((k, vx, vy, vz), dfdvx, >> > modules='mpmath') >> > >> > In [12]: numeric_dfdvx(1.0, 0.0, 0.0, 0.0) >> > >> > --------------------------------------------------------------------------- >> > ZeroDivisionError Traceback (most recent call >> > last) >> > <ipython-input-12-55e44f8ab1bf> in <module>() >> > ----> 1 numeric_dfdvx(1.0, 0.0, 0.0, 0.0) >> > >> > <string> in <lambda>(k, vx, vy, vz) >> > >> > /usr/local/lib/python2.7/dist-packages/sympy/mpmath/ctx_mp_python.pyc in >> > __rdiv__(s, t) >> > 206 if t is NotImplemented: >> > 207 return t >> > --> 208 return t / s >> > 209 >> > 210 def __rpow__(s, t): >> > >> > /usr/local/lib/python2.7/dist-packages/sympy/mpmath/ctx_mp_python.pyc in >> > __div__(self, other) >> > >> > /usr/local/lib/python2.7/dist-packages/sympy/mpmath/libmp/libmpf.pyc in >> > mpf_div(s, t, prec, rnd) >> > 928 if not sman or not tman: >> > 929 if s == fzero: >> > --> 930 if t == fzero: raise ZeroDivisionError >> > 931 if t == fnan: return fnan >> > 932 return fzero >> > >> > ZeroDivisionError: >> > >> > In [19]: dfdvx.limit(vx, 0.0) >> > Out[19]: 0 >> > >> > >> > Jason >> > moorepants.info >> > +01 530-601-9791 >> > >> > -- >> > You received this message because you are subscribed to the Google >> > Groups >> > "sympy" group. >> > To unsubscribe from this group and stop receiving emails from it, send >> > an >> > email to [email protected]. >> > To post to this group, send email to [email protected]. >> > Visit this group at http://groups.google.com/group/sympy. >> > For more options, visit https://groups.google.com/groups/opt_out. >> >> -- >> You received this message because you are subscribed to the Google Groups >> "sympy" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> To post to this group, send email to [email protected]. >> Visit this group at http://groups.google.com/group/sympy. >> For more options, visit https://groups.google.com/groups/opt_out. > > > -- > You received this message because you are subscribed to the Google Groups > "sympy" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at http://groups.google.com/group/sympy. > For more options, visit https://groups.google.com/groups/opt_out. -- You received this message because you are subscribed to the Google Groups "sympy" group. 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