On Wed, Jun 6, 2012 at 6:12 PM, Tony Yu wrote:
>
>
> On Wed, Jun 6, 2012 at 3:32 PM, kamel maths wrote:
>
>> Hi,
>>
>> for this script:
>>
>> from pylab import *
>>
>> fig = figure()
>> ax = fig.add_subplot(111)
>> ax.axis('equal')
>>
>> x = linspace(-2, 3, 5
On Wed, Jun 6, 2012 at 3:32 PM, kamel maths wrote:
> Hi,
>
> for this script:
>
> from pylab import *
>
> fig = figure()
> ax = fig.add_subplot(111)
> ax.axis('equal')
>
> x = linspace(-2, 3, 50)
> ax.plot(x, sin(x))
>
> show()
> --
Hi,
for this script:
from pylab import *
fig = figure()
ax = fig.add_subplot(111)
ax.axis('equal')
x = linspace(-2, 3, 50)
ax.plot(x, sin(x))
show()
-
If I try to get ymax with ax.get_ylim(), i obtain 1.0 whereas I observe it
> From: Eric Firing [mailto:efir...@hawaii.edu]
> Sent: Wednesday, June 06, 2012 13:41
> To: matplotlib-users@lists.sourceforge.net
> Subject: Re: [Matplotlib-users] scatter plot with constant x
>
> On 06/06/2012 06:42 AM, Ethan Gutmann wrote:
> >> ...
> >> No, but you can do this:
> >>
> >> plt.
On Jun 6, 2012, at 11:41 AM, Eric Firing wrote:
> Since we end up needing float64 anyway:
>
> In [3]: %timeit l=np.empty(1,dtype=np.float64); l.fill(3)
> 10 loops, best of 3: 14.1 us per loop
nice, fill and empty seem to be responsible for about half the speed up each,
good tools to kno
On 06/06/2012 06:42 AM, Ethan Gutmann wrote:
>> ...
>> No, but you can do this:
>>
>> plt.plot([3] * 4, [60, 80, 120, 180], ...)
>
> This started from a simple enough question, but it got me thinking about what
> the fastest way to do this is (in case you have HUGE arrays, or many loops
> over th
On 06/06/2012 12:54 PM, Ethan Gutmann wrote:
> On Jun 6, 2012, at 10:49 AM, Michael Droettboom wrote:
>> Interesting result. Note, however, that matplotlib will eventually turn
>> all data arrays into float64 at rendering time, so any speed advantage
>> to using integers will be lost by the subseq
On Jun 6, 2012, at 10:49 AM, Michael Droettboom wrote:
> Interesting result. Note, however, that matplotlib will eventually turn
> all data arrays into float64 at rendering time, so any speed advantage
> to using integers will be lost by the subsequent conversion, I suspect.
I don't think it d
On 06/06/2012 12:42 PM, Ethan Gutmann wrote:
>> ...
>> No, but you can do this:
>>
>> plt.plot([3] * 4, [60, 80, 120, 180], ...)
> Using int16, int32, float32 get progressively slower to the default float64
> case listed on line [5], changing the datatype in other methods doesn't
> result in near
> ...
> No, but you can do this:
>
> plt.plot([3] * 4, [60, 80, 120, 180], ...)
This started from a simple enough question, but it got me thinking about what
the fastest way to do this is (in case you have HUGE arrays, or many loops over
them). This may be old news to some of you, but I though
On Tue, Jun 5, 2012 at 11:53 AM, Ulrich vor dem Esche <
ulrich.es...@googlemail.com> wrote:
> Hey! :o)
> This should be simple, but i cant manage: I need to plot many dots with
> the same x, like
>
> plt.plot([3,3,3,3],[60,80,120,180],'+',markersize=8,mec='k')
>
> The array for x values is silly,
Hey! :o)
This should be simple, but i cant manage: I need to plot many dots with the
same x, like
plt.plot([3,3,3,3],[60,80,120,180],'+',markersize=8,mec='k')
The array for x values is silly, especially since the number of y values
may be rather large. Is there a way to enter a constant there?
C
On Wed, Jun 6, 2012 at 8:01 AM, Guillaume Gay
wrote:
> Le 05/06/2012 16:25, Tom Dimiduk a écrit :
>> Is any of this stuff I should be looking to upstream or split off into
>> the start of a scientific imaging library for python?
> Have you had a look at skimage https://github.com/scikits-image ?
>
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