Hi,

Is it possible to execute the Travis or SymPyBot tests without making a PR?

I would like to run the whole test set prior to make a PR, however, tests 
are too slow my poor laptop (and they seem to burn it :/ ).

On Friday, July 12, 2013 8:36:48 PM UTC+1, Ondřej Čertík wrote:
>
> Hi Cristóvão, 
>
> Excellent. Let us know if you need help with submitting a PR. 
>
> Ondrej 
>
> On Fri, Jul 12, 2013 at 1:32 PM, Cristóvão Sousa 
> <[email protected]<javascript:>> 
> wrote: 
> > Ok, I've added CSE of Add and Mul arguments (only commutative terms): 
> > http://nbviewer.ipython.org/5986996 
> > 
> > It runs faster compared to current sympy.cse, even more if applying it 
> to 
> > large expressions. 
> > However, it still lacks a lot of features, which I'll try to address 
> using 
> > sympy.cse unit tests. 
> > 
> > 
> > On Wednesday, July 3, 2013 2:52:13 PM UTC+1, Cristóvão Sousa wrote: 
> >> 
> >> Forwarded from PyDy mailing list, 
> >> https://groups.google.com/forum/#!topic/pydy/PjZ9SP8PYDA . 
> >> 
> >> 
> >> 
> >> Hi, 
> >> 
> >> I'm posting here because of one of GSoC 2013 ideas, "Efficient Code 
> >> Generation". 
> >> 
> >> You stated that "Common subexpression elimination (cse) takes a long 
> time 
> >> (>1 hour) to run on systems of equations that are derived in a very 
> short 
> >> period of time (< 1 minute). This needs to be improved." 
> >> [https://pydy.org/gsoc_2013_ideas#efficient_code_generation] 
> >> 
> >> Indeed, I've verified that myself on my work on SymPyBotics 
> >> (https://github.com/cdsousa/sympybotics), a tool I'm developing to 
> help me 
> >> on my PhD studies. 
> >> So, I've developed a kind of CSE, faster than SymPy CSE though less 
> >> general and with some quirks. 
> >> Such CSE functionality is implemented in SymCode package 
> >> (https://github.com/cdsousa/symcode). 
> >> 
> >> Be aware that both SymPyBotics and SymCode are badly documented and 
> >> probably reimplement some functionalities which could be taken from 
> >> SymPy/PyDy (and I probably implement them in worse ways :) 
> >> 
> >> The cse core function is "fast_cse()" which can be found in 
> >> symcode/subexprs.py 
> >> (https://github.com/cdsousa/symcode/blob/master/symcode/subexprs.py.) 
> >> (It uses Subexprs class, which can be used alone to store intermediate 
> >> variables in recursive computations). 
> >> 
> >> I've profiled SymPy cse and noticed that the main time consumption is 
> due 
> >> to "count" and "subs" functions, so I tried a different approach. 
> >> First, fast_cse function reversely parses the expression tree and 
> recreate 
> >> each unique operation with non-atom arguments substituted by temporary 
> >> symbols (this is the "collect" phase). 
> >> Each unique operation is stored on an "unique_op:tmp_symbol" 
> dictionary. 
> >> For example, 
> >> a + b*c + cos(b*c) 
> >> is transformed into 
> >> t2 
> >> while the dictionary holds 
> >> a + t0 + t1 : t2 
> >> cos(t0) : t1 
> >> b * c : t0 
> >> Additional, match of multiple argument Mul and Add operations is made, 
> in 
> >> a similar way to SymPy cse, although argument commutativity is always 
> >> assumed. 
> >> Then, in the "get" phase, the expression tree is recreated from the 
> >> dictionary while temporary "used more than once" symbols are 
> maintained. 
> >> The example output will be 
> >> ( [(t0, b*c)],  a + t0 + cos(t0) ) 
> >> This is much faster than SymPy CSE although output is generally 
> different. 
> >> Also, it still doesn't work with "iterable" arguments, and, as said, 
> >> non-commutativity is not respected . 
> >> 
> >> 
> >> I would love to have time to work on this, or to work on SymPy CSE 
> >> optimization directly, but I'm currently in work overload. 
> >> Nevertheless, I'm showing you this since some ideas can be useful. 
> >> 
> >> Best regards, 
> >> Cristóvão Sousa 
> >> 
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