Ideally implitic_plot should be able to do it. The plot comes from a
paper whose algorithm was implemented in a GSoC project
https://github.com/sympy/sympy/wiki/GSoC-2012-Report-Bharath-M-R:-Implicit-plotting.

Aaron Meurer

On Tue, May 12, 2015 at 12:45 PM, Sumith 1896 <[email protected]> wrote:
> Is there a need for an issue to be opened?
>
>
> On Tue, May 12, 2015 at 10:59 PM Peter Brady <[email protected]> wrote:
>>
>> Just tried:
>>
>> In [1]: from sympy import *
>>
>> In [3]: x,y = symbols("x y")
>>
>> In [11]: rhs =
>> floor(Mod(floor(y/17)*2**(-17*floor(x)-Mod(floor(y),17)),2))
>>
>> In [14]: s = "960 939 379 918 958 884 971 672 962 127 852 754 715 004 339
>> 660 129 306 651 505 519 271 702 802 395 266 424 689 642 842 174 350 718 121
>> 267 153 782 770 623 355 993 237 280 874 144 307 891 325 963 941 337 723 487
>> 857 735 749 823 926 629 715 517 173 716 995 165 232 890 538 221 612 403 238
>> 855 866 184 013 235 585 136 048 828 693 337 902 491 454 229 288 667 081 096
>> 184 496 091 705 183 454 067 827 731 551 705 405 381 627 380 967 602 565 625
>> 016 981 482 083 418 783 163 849 115 590 225 610 003 652 351 370 343 874 461
>> 848 378 737 238 198 224 849 863 465 033 159 410 054 974 700 593 138 339 226
>> 497 249 461 751 545 728 366 702 369 745 461 014 655 997 933 798 537 483 143
>> 786 841 806 593 422 227 898 388 722 980 000 748 404 719".replace(" ", "")
>>
>> In [16]: k = S(s)
>>
>> In [17]: plot_
>> plot_backends  plot_implicit
>>
>> In [17]: plot_implicit(S(1/2) < rhs, (x, 0, 106), (y, k, k+17))
>> /home/ptb/gitrepos/sympy/sympy/plotting/plot_implicit.py:84: UserWarning:
>> Adaptive meshing could not be applied to the expression. Using uniform
>> meshing.
>>   warnings.warn("Adaptive meshing could not be applied to the"
>>
>> I then got a lot of errors that started with:
>>
>>
>> ---------------------------------------------------------------------------
>> ValueError                                Traceback (most recent call
>> last)
>> /home/ptb/gitrepos/sympy/sympy/plotting/experimental_lambdify.py in
>> __call__(self, *args)
>>     118             temp_args = (np.array(a, dtype=np.complex) for a in
>> args)
>> --> 119             results = self.vector_func(*temp_args)
>>     120             results = np.ma.masked_where(
>>
>> <string> in <lambda>(x0, x1)
>>
>> and ended with
>>
>> /home/ptb/gitrepos/sympy/sympy/sets/sets.py in __new__(cls, *args,
>> **kwargs)
>>    1684         evaluate = kwargs.get('evaluate', global_evaluate[0])
>>    1685         if evaluate:
>> -> 1686             args = list(map(sympify, args))
>>    1687
>>    1688             if len(args) == 0:
>>
>> RuntimeError: maximum recursion depth exceeded
>>
>>
>> On Tuesday, May 12, 2015 at 11:22:14 AM UTC-6, Ondřej Čertík wrote:
>>>
>>> On Tue, May 12, 2015 at 10:00 AM, Sumith 1896 <[email protected]> wrote:
>>> > Hi there,
>>> > I just happened to come across this very interesting formula known as
>>> > Tupper’s self-referential formula.
>>> > The wiki article says that is a formula defined by Jeff Tupper that,
>>> > when graphed in two dimensions at a very specific location in the
>>> > plane, can
>>> > be “programmed” to visually reproduce the formula itself.
>>> > Matlab is capable to plot this. It was very interesting to see the
>>> > plot.
>>> > Is our plotting module capable to plot this?
>>> > If yes, could you say how?
>>>
>>>
>>> Good question, we should be able to do it. I found more info about this:
>>>
>>>
>>> http://www.quora.com/How-did-Jeff-Tupper-come-up-with-his-%E2%80%9Cself-referential%E2%80%9D-formula
>>>
>>> With some other examples.
>>>
>>> Ondrej
>>
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