#13559: loglog plots of "large" function fail to find good evaluation points
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Reporter: nthiery | Owner: jason, was
Type: defect | Status: new
Priority: major | Milestone: sage-5.5
Component: graphics | Resolution:
Keywords: agregation | Work issues:
Report Upstream: N/A | Reviewers:
Authors: | Merged in:
Dependencies: | Stopgaps:
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Comment (by ppurka):
Replying to [comment:5 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.
--
Ticket URL: <http://trac.sagemath.org/sage_trac/ticket/13559#comment:6>
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