Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
2009/6/9 Stéfan van der Walt ste...@sun.ac.za: Hi Juan ---cut --- As you can see, it is very simple, but it takes several seconds running just to create a 200x200 plot. Fortran takes same time to create a 2000x2000 plot, around 100 times faster... So the question is, do you know how to programme this in a Python-like fashion in order to improve seriously the performance? Here is another version, similar to Robert's, that I wrote up for the documentation project last year: http://mentat.za.net/numpy/intro/intro.html We never used it, but I still like the pretty pictures :-) +1 Hi Stéfan, this does look really nice !! Could it be put somewhere more prominent !? How about onto the SciPy site with a newcomers start here-link right on the first page My 2 cents, Sebastian Haase ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] How to remove fortran-like loops with numpy?
Hi all,
I'm new in numpy. Actually, I'm new in Python. In order to learn a bit, I
want to create a program to plot the Mandelbrot set. This program is quite
simple, and I have already programmed it. The problem is that I come from
fortran, so I use to think in for loops. I know that it is not the best
way to use Python and in fact the performance of the program is more than
poor.
Here is the program:
*#!/usr/bin/python*
*import numpy as np*
*import matplotlib.pyplot as plt*
*# Some parameters*
*Xmin=-1.5*
*Xmax=0.5*
*Ymin=-1*
*Ymax=1*
*Ds = 0.01*
*# Initialization of varibles*
*X = np.arange(Xmin,Xmax,Ds)*
*Y = np.arange(Ymax,Ymin,-Ds)*
*N = np.zeros((X.shape[0],Y.shape[0]),'f')*
*## Here are inefficient the calculations *
*for i in range(X.shape[0]):*
* for j in range(Y.shape[0]):*
*z= complex(0.0, 0.0)*
*c = complex(X[i], Y[j])*
*while N[i, j] 30 and abs(z) 2:*
* N[i, j] += 1*
* z = z**2 + c*
*if N[i, j] == 29:*
* N[i, j]=0*
*# And now, just for ploting...*
*N = N.transpose()*
*fig = plt.figure()*
*plt.imshow(N,cmap=plt.cm.Blues)*
*plt.title('Mandelbrot set')*
*plt.xticks([]); plt.yticks([])*
*plt.show()*
*fig.savefig('test.png')*
As you can see, it is very simple, but it takes several seconds running just
to create a 200x200 plot. Fortran takes same time to create a 2000x2000
plot, around 100 times faster... So the question is, do you know how to
programme this in a Python-like fashion in order to improve seriously the
performance?
Thanks in advance
--
Juan José Gómez Navarro
Edificio CIOyN, Campus de Espinardo, 30100
Departamento de Física
Universidad de Murcia
Tfno. (+34) 968 398552
Email: juanjo.gomeznava...@gmail.com
Web: http://ciclon.inf.um.es/Inicio.html
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
I look forward to an instructive reply: the Pythonic way to do it would be to
take advantage of the facts that Numpy is pre-vectorized and uses
broadcasting, but so far I haven't been able to figure out (though I haven't
yet really buckled down and tried real hard) how to broadcast a
conditionally-terminated iteration where the number of iterations will vary
among the array elements. Hopefully someone else already has. :-)
DG
--- On Mon, 6/8/09, Juanjo Gomez Navarro juanjo.gomeznava...@gmail.com wrote:
From: Juanjo Gomez Navarro juanjo.gomeznava...@gmail.com
Subject: [Numpy-discussion] How to remove fortran-like loops with numpy?
To: numpy-discussion@scipy.org
Date: Monday, June 8, 2009, 2:52 PM
Hi all,
I'm new in numpy. Actually, I'm new in Python. In
order to learn a bit, I want to create a program to plot the
Mandelbrot set. This program is quite simple, and I have
already programmed it. The problem is that I come from
fortran, so I use to think in for loops. I know
that it is not the best way to use Python and in fact the
performance of the program is more than poor.
Here is the program:
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
# Some parameters
Xmin=-1.5
Xmax=0.5
Ymin=-1
Ymax=1
Ds = 0.01
# Initialization of varibles
X = np.arange(Xmin,Xmax,Ds)
Y = np.arange(Ymax,Ymin,-Ds)
N = np.zeros((X.shape[0],Y.shape[0]),'f')
## Here are inefficient the calculations
for i in range(X.shape[0]):
for j in range(Y.shape[0]):
z= complex(0.0, 0.0)
c = complex(X[i], Y[j])
while N[i, j] 30 and abs(z) 2:
N[i, j] += 1
z = z**2 + c
if N[i, j] == 29:
N[i, j]=0
# And now, just for ploting...
N = N.transpose()
fig = plt.figure()
plt.imshow(N,cmap=plt.cm.Blues)
plt.title('Mandelbrot set')
plt.xticks([]); plt.yticks([])
plt.show()
fig.savefig('test.png')
As you can see, it is very simple, but it takes several
seconds running just to create a 200x200 plot. Fortran takes
same time to create a 2000x2000 plot, around 100 times
faster... So the question is, do you know how to programme
this in a Python-like fashion in order to improve seriously
the performance?
Thanks in advance
--
Juan José Gómez Navarro
Edificio CIOyN, Campus de Espinardo, 30100
Departamento de Física
Universidad de Murcia
Tfno. (+34) 968 398552
Email: juanjo.gomeznava...@gmail.com
Web: http://ciclon.inf.um.es/Inicio.html
-Inline Attachment Follows-
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
On Mon, Jun 8, 2009 at 17:04, David Goldsmithd_l_goldsm...@yahoo.com wrote: I look forward to an instructive reply: the Pythonic way to do it would be to take advantage of the facts that Numpy is pre-vectorized and uses broadcasting, but so far I haven't been able to figure out (though I haven't yet really buckled down and tried real hard) how to broadcast a conditionally-terminated iteration where the number of iterations will vary among the array elements. Hopefully someone else already has. :-) You can't, really. What you can do is just keep iterating with the whole data set and ignore the parts that have already converged. Here is an example: z = np.zeros((201,201), dtype=complex) Y, X = np.mgrid[1:-1:-201j, -1.5:0.5:201j] c = np.empty_like(z) c.real = X c.imag = Y N = np.zeros(z.shape, dtype=int) while ((N30) | (abs(z)2)).all(): N += abs(z) 2 z = z ** 2 + c N[N=30] = 0 -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
2009/6/8 Robert Kern robert.k...@gmail.com: On Mon, Jun 8, 2009 at 17:04, David Goldsmithd_l_goldsm...@yahoo.com wrote: I look forward to an instructive reply: the Pythonic way to do it would be to take advantage of the facts that Numpy is pre-vectorized and uses broadcasting, but so far I haven't been able to figure out (though I haven't yet really buckled down and tried real hard) how to broadcast a conditionally-terminated iteration where the number of iterations will vary among the array elements. Hopefully someone else already has. :-) You can't, really. What you can do is just keep iterating with the whole data set and ignore the parts that have already converged. Here is an example: Well, yes and no. This is only worth doing if the number of problem points that require many iterations is small - not the case here without some sort of periodicity detection - but you can keep an array of not-yet-converged points, which you iterate. When some converge, you store them in a results array (with fancy indexing) and remove them from your still-converging array. It's also worth remembering that the overhead of for loops is large but not enormous, so you can often remove only the inner for loop, in this case perhaps iterating over the image a line at a time. Anne z = np.zeros((201,201), dtype=complex) Y, X = np.mgrid[1:-1:-201j, -1.5:0.5:201j] c = np.empty_like(z) c.real = X c.imag = Y N = np.zeros(z.shape, dtype=int) while ((N30) | (abs(z)2)).all(): N += abs(z) 2 z = z ** 2 + c N[N=30] = 0 -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
Thanks, Robert! DG --- On Mon, 6/8/09, Robert Kern robert.k...@gmail.com wrote: I haven't been able to figure out (though I haven't yet really buckled down and tried real hard) how to broadcast a conditionally-terminated iteration where the number of iterations will vary among the array elements. Hopefully someone else already has. :-) You can't, really. What you can do is just keep iterating with the whole data set and ignore the parts that have already converged. Here is an example: z = np.zeros((201,201), dtype=complex) Y, X = np.mgrid[1:-1:-201j, -1.5:0.5:201j] c = np.empty_like(z) c.real = X c.imag = Y N = np.zeros(z.shape, dtype=int) while ((N30) | (abs(z)2)).all(): N += abs(z) 2 z = z ** 2 + c N[N=30] = 0 -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
--- On Mon, 6/8/09, Anne Archibald peridot.face...@gmail.com wrote: You can't, really. What you can do is just keep iterating with the whole data set and ignore the parts that have already converged. Here is an example: Well, yes and no. This is only worth doing if the number of problem points that require many iterations is small - not the case here without some sort of periodicity detection - but you can keep an array of not-yet-converged points, which you iterate. When some converge, you store them in a results array (with fancy indexing) and remove them from your still-converging array. Thanks, Anne. This is the way I had anticipated implementing it myself eventually, but the fancy-indexing requirement has caused me to keep postponing it, waiting for some time when I'll have a hefty block of time to figure it out and then, inevitably, debug it. :( Also, the transfer of points from un-converged to converged - when that's a large number, might that not be a large time-suck compared to Rob's method? (Too bad this wasn't posted a couple weeks ago: I'd've had time then to implement your method and race it against Rob's, but alas, now I have this doc editing job...but that's a good thing, as my fractals are not yet making me any real money.) :-) It's also worth remembering that the overhead of for loops is large but not enormous, so you can often remove only the inner for loop, in this case perhaps iterating over the image a line at a time. Yes, definitely well worth remembering - thanks for reminding us! Thanks again, DG Anne ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
On Mon, Jun 8, 2009 at 18:01, David Goldsmithd_l_goldsm...@yahoo.com wrote: --- On Mon, 6/8/09, Anne Archibald peridot.face...@gmail.com wrote: You can't, really. What you can do is just keep iterating with the whole data set and ignore the parts that have already converged. Here is an example: Well, yes and no. This is only worth doing if the number of problem points that require many iterations is small - not the case here without some sort of periodicity detection - but you can keep an array of not-yet-converged points, which you iterate. When some converge, you store them in a results array (with fancy indexing) and remove them from your still-converging array. Thanks, Anne. This is the way I had anticipated implementing it myself eventually, but the fancy-indexing requirement has caused me to keep postponing it, waiting for some time when I'll have a hefty block of time to figure it out and then, inevitably, debug it. :( Also, the transfer of points from un-converged to converged - when that's a large number, might that not be a large time-suck compared to Rob's method? (Too bad this wasn't posted a couple weeks ago: I'd've had time then to implement your method and race it against Rob's, but alas, now I have this doc editing job...but that's a good thing, as my fractals are not yet making me any real money.) :-) The advantage of my implementation is that I didn't have to think too hard about it. -- Robert Kern I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth. -- Umberto Eco ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
Hi Juan
2009/6/8 Juanjo Gomez Navarro juanjo.gomeznava...@gmail.com:
I'm new in numpy. Actually, I'm new in Python. In order to learn a bit, I
want to create a program to plot the Mandelbrot set. This program is quite
simple, and I have already programmed it. The problem is that I come from
fortran, so I use to think in for loops. I know that it is not the best
way to use Python and in fact the performance of the program is more than
poor.
Here is the program:
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
# Some parameters
Xmin=-1.5
Xmax=0.5
Ymin=-1
Ymax=1
Ds = 0.01
# Initialization of varibles
X = np.arange(Xmin,Xmax,Ds)
Y = np.arange(Ymax,Ymin,-Ds)
N = np.zeros((X.shape[0],Y.shape[0]),'f')
## Here are inefficient the calculations
for i in range(X.shape[0]):
for j in range(Y.shape[0]):
z= complex(0.0, 0.0)
c = complex(X[i], Y[j])
while N[i, j] 30 and abs(z) 2:
N[i, j] += 1
z = z**2 + c
if N[i, j] == 29:
N[i, j]=0
# And now, just for ploting...
N = N.transpose()
fig = plt.figure()
plt.imshow(N,cmap=plt.cm.Blues)
plt.title('Mandelbrot set')
plt.xticks([]); plt.yticks([])
plt.show()
fig.savefig('test.png')
As you can see, it is very simple, but it takes several seconds running just
to create a 200x200 plot. Fortran takes same time to create a 2000x2000
plot, around 100 times faster... So the question is, do you know how to
programme this in a Python-like fashion in order to improve seriously the
performance?
Here is another version, similar to Robert's, that I wrote up for the
documentation project last year:
http://mentat.za.net/numpy/intro/intro.html
We never used it, but I still like the pretty pictures :-)
Cheers
Stéfan
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] How to remove fortran-like loops with numpy?
--- On Mon, 6/8/09, Robert Kern robert.k...@gmail.com wrote: Goldsmithd_l_goldsm...@yahoo.com wrote: The advantage of my implementation is that I didn't have to think too hard about it. -- Robert Kern Agreed. :-) DG ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
