Opened 9 years ago
Last modified 7 years ago
#13559 new defect
fast_float yields infinity when Python does, but should handle bigger numbers
Reported by: | nthiery | Owned by: | AlexGhitza |
---|---|---|---|
Priority: | major | Milestone: | sage-6.4 |
Component: | basic arithmetic | Keywords: | agregation |
Cc: | eviatarbach | Merged in: | |
Authors: | Reviewers: | ||
Report Upstream: | N/A | Work issues: | |
Branch: | Commit: | ||
Dependencies: | #15030 | Stopgaps: |
Description
It sounds like the adaptative algorithm fails to find suitable evaluation points when plotting large functions (like exp) in loglog scale. In the following example, the exp function gets drawn with only three points:
sage: plot([n^2,exp(n)], xmin=1, xmax=10^5, ymin=1,ymax=10^10, scale="loglog") verbose 0 (2397: plot.py, generate_plot_points) WARNING: When plotting, failed to evaluate function at 198 points. verbose 0 (2397: plot.py, generate_plot_points) Last error message: ''
If xmax is replaced by 10^{10, the function is not even drawn: }
sage: sage: plot([n^2,exp(n)], xmin=1, xmax=10^10, ymin=1,ymax=10^10, scale="loglog") verbose 0 (2397: plot.py, generate_plot_points) WARNING: When plotting, failed to evaluate function at 199 points. verbose 0 (2397: plot.py, generate_plot_points) Last error message: ''
On the other hand, the equivalent semilogy plot works smoothly:
sage: plot([10^n,exp(10^n)], xmin=0, xmax=5, ymin=1,ymax=10^10, scale="semilogy") verbose 0 (2397: plot.py, generate_plot_points) WARNING: When plotting, failed to evaluate function at 86 points. verbose 0 (2397: plot.py, generate_plot_points) Last error message: ''
(Such plots are typically useful in classes about algorithmic complexity http://combinat.sagemath.org/doc/thematic_tutorials/agregation-option-calcul-formel/tris_et_complexite.html)
Change History (15)
comment:1 Changed 9 years ago by
- Keywords agregation added
comment:2 follow-up: ↓ 3 Changed 9 years ago by
comment:3 in reply to: ↑ 2 ; follow-up: ↓ 4 Changed 9 years ago by
Replying to ppurka:
FYI, the change to loglog/semilog* scale happens only during the very end when
show()
is called. This happens after the generation of the plot points.
Really??? Ouch! In a x log scale one certainly would want to disperse ploting points differently.
comment:4 in reply to: ↑ 3 ; follow-up: ↓ 5 Changed 9 years ago by
FYI, the change to loglog/semilog* scale happens only during the very end when
show()
is called. This happens after the generation of the plot points.Really??? Ouch! In a x log scale one certainly would want to disperse ploting points differently.
True, but that would be another ticket. And sometimes one would want to plot the same data in two different ways, so we wouldn't want to remove that entirely.
comment:5 in reply to: ↑ 4 ; follow-up: ↓ 6 Changed 9 years ago by
Hi!
Replying to kcrisman:
True, but that would be another ticket.
Well, unless there is a quick solution for just the issue stated in this ticket, I am happy recycling it to whatever the right fix should be (taking into account the log scale early or delaying the generation of the evaluation points to show
).
And sometimes one would want to plot the same data in two different ways, so we wouldn't want to remove that entirely.
I don't know the current implementation, so there might be technical obstructions I can't see; however, in principle, isn't the data really the function rather than the points? In that case, should'nt the points just be recalculated as needed?
comment:6 in reply to: ↑ 5 Changed 9 years ago by
Replying to nthiery:
I don't know the current implementation, so there might be technical obstructions I can't see; however, in principle, isn't the data really the function rather than the points? In that case, should'nt the points just be recalculated as needed?
Suppose you want to plot the points (0, 1), (1, 10), (2, 100), (3, 1000)
. Then what you could do is send these points to matplotlib and ask it to plot it in a linear scale by using, say, pyplot.plot(x, y)
. Alternatively, if you want semilogy plot, you could do pyplot.semilogy(x, y)
, where x
and y
are the data points along the x and y axes. Note that we do not send the "linearized" data points [0, 1, 2, 3]
(obtained by taking log of [1, 10, 100, 1000]
to the base 10) as the y
list to matplotlib.
Now, suppose you want to plot 10**n
for large values of n
. You would still do the same thing. Find the values of this function in the linear scale and then pass on the computed values to matplotlib to plot it on the logarithmic scale. In either case, the computation of the values of the function is being done on the linear scale. And it is this computation that is failing in your examples. As of now, this problem needs a fix even on the linear scale, let alone the log scale.
comment:7 follow-up: ↓ 8 Changed 9 years ago by
In fact, I just realized why you are getting the errors. The problem is with fast_float
.
sage: set_verbose(1) sage: p = plot_loglog(exp(x), (1, 10^5), plot_points=2) verbose 1 (2397: plot.py, generate_plot_points) Unable to compute f(100000.0) (time = 19.237264) sage: exp(100000.0).n() 2.80666336042612e43429 sage: from sage.ext.fast_eval import fast_float sage: f(x) = exp(x) sage: v = f.variables() sage: F = fast_float(f, *v) sage: F(100000.0) inf
Maybe you are better off generating the list of data points by using exact arithmetic in Sage and then passing off the list to list_plot
.
comment:8 in reply to: ↑ 7 Changed 9 years ago by
- Summary changed from loglog plots of "large" function fail to find good evaluation points to fast_float yields infinity when Python does, but should handle bigger numbers
In fact, I just realized why you are getting the errors. The problem is with
fast_float
.sage: from sage.ext.fast_eval import fast_float sage: f(x) = exp(x) sage: v = f.variables() sage: F = fast_float(f, *v) sage: F(100000.0) inf
Huh, that is not good.
sage: F(709.7) 1.6549840276802644e+308 sage: F(709.8) inf
That's as much bisecting as I want to do. And really, here is what is going on, I suspect.
In [3]: import math In [6]: math.exp(709.8) --------------------------------------------------------------------------- OverflowError Traceback (most recent call last) /Users/.../<ipython console> in <module>() OverflowError: math range error In [7]: math.exp(709.7) Out[7]: 1.6549840276802644e+308
So fast_float
really is doing floats, but we need something better than that.
comment:9 Changed 9 years ago by
- Component changed from graphics to basic arithmetic
- Owner changed from jason, was to AlexGhitza
comment:10 Changed 8 years ago by
- Milestone changed from sage-5.11 to sage-5.12
comment:11 Changed 8 years ago by
- Dependencies set to #15030
The solution of this ticket depends on #15030 and this ask.sagemath thread.
How? I suppose we can introduce a plot keyword precision=53
that gets passed on to fast_callable
and one can increase that to get higher precision but slower plots.
comment:12 Changed 8 years ago by
- Cc eviatarbach added
comment:13 Changed 7 years ago by
- Milestone changed from sage-6.1 to sage-6.2
comment:14 Changed 7 years ago by
- Milestone changed from sage-6.2 to sage-6.3
comment:15 Changed 7 years ago by
- Milestone changed from sage-6.3 to sage-6.4
It is not a problem with loglog. There is the same problem with normal plots.
FYI, the change to loglog/semilog* scale happens only during the very end when
show()
is called. This happens after the generation of the plot points. Though I can't understand why semilogy is working fine for you (you still get the warnings though).