[Numpy-discussion] PSF GSoC 2010 (Py3K focus)
Hello, Given the interest in participating in the GSoC this summer, I am forwarding a very interesting email from Titus Brown. If you are interested in doing a GSoC or mentoring, please read his email carefully. Basically, the PSF will be focuing on Py3K-related projects. Given Pauli's work on Py3K support for NumPy, I think we might be in a good position to move forward on porting the rest of our stack to Py3K. So we should focus on projects to: 1. finish porting and testing NumPy with Py3K 2. port and test SciPy with Py3K 3. port and test matplotlib with Py3K 4. port and test ipython with Py3K 5. etc. Given the PSF's stated emphasis this year, it probably doesn't make sense to pursue any non-Py3K projects. Jarrod -- Forwarded message -- From: C. Titus Brown c...@msu.edu Date: Tue, Mar 2, 2010 at 6:12 AM Subject: [SoC2009-mentors] [...@msu.edu: GSoC 2010 - it's on!] To: soc2009-ment...@python.org - Forwarded message from C. Titus Brown c...@msu.edu - Date: Wed, 24 Feb 2010 12:54:52 -0800 From: C. Titus Brown c...@msu.edu To: psf-memb...@python.org Cc: gsoc2010-ment...@python.org Subject: GSoC 2010 - it's on! Hi all, it's that time of year again, and Google has decided to run the Google Summer of Code again! http://groups.google.com/group/google-summer-of-code-discuss/browse_thread/thread/d839c0b02ac15b3f http://socghop.appspot.com/ Arc Riley has stepped up to run it for the PSF again this year, and I'm backstopping him. If you are interested in mentoring or kibbitzing on those who are, please sign up for the soc2010-mentors mailing list here, http://mail.python.org/mailman/listinfo/soc2010-mentors This year we're proposing to solicit and prioritize applications for Python 3.x -- 3K tools, porting old projects, etc. Python 2.x projects will be a distinct second. There will be no core category this year, although obviously if someone on one of the core teams wants to push a project it'll help! If you have an idea for a project, please send it to the -mentors list and add it to the wiki at http://wiki.python.org/moin/SummerOfCode/2010 We're also going to change a few things up to make it more useful to the PSF. Specifically, - the foundation is going to *require* 1 blog post/wk from each student. - we're going to hire an administrative assistant to monitor the students. - the student application process will be a bit more rigorous and job-app like; the Django SF has been doing this for at least one round and they claim that it results in much better and more serious students. - we'll be focusing on student quality more than on project egalitarianism. If project X can recruit three fantastic students to one fantastic and one mediocre student for project Y, then project X gets three and project Y gets one. The hope is that this will make the GSoC much more useful for Python than it has been in the past. Arc will be posting something to the www.python.org site and python-announce soon, too. Followups to soc2010-mentors. cheers, --titus -- C. Titus Brown, c...@msu.edu - End forwarded message - ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Is this a bug in numpy.ma.reduce?
On 03/08/2010 01:30 AM, David Goldsmith wrote:
On Sun, Mar 7, 2010 at 4:41 AM, Friedrich Romstedt
friedrichromst...@gmail.com mailto:friedrichromst...@gmail.com wrote:
2010/3/5 Pierre GM pgmdevl...@gmail.com
mailto:pgmdevl...@gmail.com:
'm'fraid no. I gonna have to investigate that. Please open a
ticket with a self-contained example that reproduces the issue.
Thx in advance...
P.
I would like to stress the fact that imo this is maybe not ticket
and not a bug.
The issue arises when calling a.max() or similar of empty arrays
a, i.e., with:
0 in a.shape
True
Opposed to the .prod() of an empty array, such a .max() or .min()
cannot be defined, because the set is empty. So it's fully correct to
let such calls fail. Just the failure is a bit deep in numpy, and
only the traceback gives some hint what went wrong.
I posted something similar also on the matplotlib-users list, sorry
for cross-posting thus.
Any suggestions, then, how to go about figuring out what's happening
in my code that's causing this feature to manifest itself?
DG
Perhaps providing the code with specific versions of Python, numpy etc.
would help.
I would guess that aquarius_test.py has not correctly setup the
necessary inputs (or has invalid inputs) required by matplotlib (which I
have no knowledge about). Really you have to find if the _A in cmp.py
used by 'self.norm.autoscale_None(self._A)' is valid. You may be missing
a valid initialization step because the TypeError exception in
autoscale_None ('You must first set_array for mappable') implies
something need to be done first.
Bruce
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Re: [Numpy-discussion] Is this a bug in numpy.ma.reduce?
On Mon, Mar 8, 2010 at 6:52 AM, Bruce Southey bsout...@gmail.com wrote:
On 03/08/2010 01:30 AM, David Goldsmith wrote:
On Sun, Mar 7, 2010 at 4:41 AM, Friedrich Romstedt
friedrichromst...@gmail.com wrote:
I would like to stress the fact that imo this is maybe not ticket and not
a bug.
The issue arises when calling a.max() or similar of empty arrays a, i.e.,
with:
0 in a.shape
True
Opposed to the .prod() of an empty array, such a .max() or .min()
cannot be defined, because the set is empty. So it's fully correct to
let such calls fail. Just the failure is a bit deep in numpy, and
only the traceback gives some hint what went wrong.
I posted something similar also on the matplotlib-users list, sorry
for cross-posting thus.
Any suggestions, then, how to go about figuring out what's happening in my
code that's causing this feature to manifest itself?
DG
Perhaps providing the code with specific versions of Python, numpy etc.
would help.
I would guess that aquarius_test.py has not correctly setup the necessary
inputs (or has invalid inputs) required by matplotlib (which I have no
knowledge about). Really you have to find if the _A in cmp.py used by
'self.norm.autoscale_None(self._A)' is valid. You may be missing a valid
initialization step because the TypeError exception in autoscale_None ('You
must first set_array for mappable') implies something need to be done first.
Bruce
Python 2.5.4, Numpy 1.4.0, Matplotlib 0.99.0, Windows 32bit Vista Home
Premium SP2
# Code copyright 2010 by David Goldsmith
# Comments and unnecessaries edited for brevity
import numpy as N
import matplotlib as MPL
from matplotlib import pylab
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.cm as cm
J = complex(0,1); tol = 1e-6; maxiter = 20;
roots = (-2 + J/2, -1 + J, -0.5 + J/2, 0.5 + J, 1 + J/2, 2 + J, 2.5 + J/2,
-2 - J, -1 - J/2, -0.5 - J, 0.5 - J/2, 1 - J, 2 - J/2, 2.5 - J)
def ffp(z):
w, wp = (0J, 0J)
for root in roots:
z0 = z - root
w += N.sin(1/z0)
wp -= N.cos(1/z0)/(z0*z0)
return (w, wp)
def iter(z):
w, wp = ffp(z)
return z - w/wp
def find_root(z0):#, k, j):
count = 0
z1 = iter(z0)
if N.isnan(z1):
return N.complex64(N.inf)
while (N.abs(z1 - z0) tol) and \
(count maxiter):
count += 1
z0 = z1
z1 = iter(z0)
if N.abs(z1 - z0) tol:
result = 0
else:
result = z1
return N.complex64(result)
w, h, DPI = (3.2, 2.0, 100)
fig = Figure(figsize=(w, h),
dpi=DPI,
frameon=False)
ax = fig.add_subplot(1,1,1)
canvas = FigureCanvas(fig)
nx, xmin, xmax = (int(w*DPI), -0.5, 0.5)
ny, ymin, ymax = (int(h*DPI), 0.6, 1.2)
X, xincr = N.linspace(xmin,xmax,nx,retstep=True)
Y, yincr = N.linspace(ymin,ymax,ny,retstep=True)
W = N.zeros((ny,nx), dtype=N.complex64)
for j in N.arange(nx):
if not (j%100): # report progress
print j
for k in N.arange(ny):
x, y = (X[j], Y[k])
z0 = x + J*y
W[k,j] = find_root(z0)#,k,j)
print N.argwhere(N.logical_not(N.isfinite(W.real)))
print N.argwhere(N.logical_not(N.isfinite(W.imag)))
W = W.T
argW = N.angle(W)
print N.argwhere(N.logical_not(N.isfinite(argW)))
cms = (Blues,)# Blues_r, cool, cool_r,
def all_ticks_off(ax):
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
for cmap_name in cms:
all_ticks_off(ax)
ax.hold(True)
for i in range(4):
for j in range(4):
part2plot = argW[j*ny/4:(j+1)*ny/4, i*nx/4:(i+1)*nx/4]
if N.any(N.logical_not(N.isfinite(part2plot))):
print i, j,
print N.argwhere(N.logical_not(N.isfinite(part2plot)))
extent = (i*nx/4, (i+1)*nx/4, (j+1)*ny/4, j*ny/4)
ax.imshow(part2plot, cmap_name, extent = extent)
ax.set_xlim(0, nx)
ax.set_ylim(0, ny)
canvas.print_figure('../../Data-Figures/Zodiac/Aquarius/'+ cmap_name +
'Aquarius_test.png', dpi=DPI)
# End Aquarius_test.png
DG
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Re: [Numpy-discussion] printing structured arrays
Hello, I am also looking into the convertsion from strcutured arrays to ndarray. I've just started playing with numpy and have noticed that when printing a structured array that the output is not nicely formatted. Is there a way to make the formatting look the same as it does for an unstructured array? Output is: ### ndarray [[ 1. 2. ] [ 3. 4.1]] ### structured array [(1.0, 2.0) (3.0, 4.0996)] How could we make this structured array look like the above shown ndarray with shape (2, 2)? Thanks for any additional hint, Timmie ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] printing structured arrays
On Mon, Mar 8, 2010 at 1:55 PM, Tim Michelsen timmichel...@gmx-topmail.de wrote: Hello, I am also looking into the convertsion from strcutured arrays to ndarray. I've just started playing with numpy and have noticed that when printing a structured array that the output is not nicely formatted. Is there a way to make the formatting look the same as it does for an unstructured array? Output is: ### ndarray [[ 1. 2. ] [ 3. 4.1]] ### structured array [(1.0, 2.0) (3.0, 4.0996)] How could we make this structured array look like the above shown ndarray with shape (2, 2)? .view(float) should do it, to created a ndarray view of the structured array data Josef Thanks for any additional hint, Timmie ___ 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] printing structured arrays
On Mon, Mar 8, 2010 at 2:01 PM, josef.p...@gmail.com wrote: On Mon, Mar 8, 2010 at 1:55 PM, Tim Michelsen timmichel...@gmx-topmail.de wrote: Hello, I am also looking into the convertsion from strcutured arrays to ndarray. I've just started playing with numpy and have noticed that when printing a structured array that the output is not nicely formatted. Is there a way to make the formatting look the same as it does for an unstructured array? Output is: ### ndarray [[ 1. 2. ] [ 3. 4.1]] ### structured array [(1.0, 2.0) (3.0, 4.0996)] How could we make this structured array look like the above shown ndarray with shape (2, 2)? .view(float) should do it, to created a ndarray view of the structured array data Plus a reshape. I usually know how many columns I have, so I put in axis 1 and leave axis 0 as -1. In [21]: a.view(float).reshape(-1,2) Out[21]: array([[ 1. , 2. ], [ 3. , 4.1]]) Skipper ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] printing structured arrays
On Mon, Mar 8, 2010 at 2:04 PM, Skipper Seabold jsseab...@gmail.com wrote: On Mon, Mar 8, 2010 at 2:01 PM, josef.p...@gmail.com wrote: On Mon, Mar 8, 2010 at 1:55 PM, Tim Michelsen timmichel...@gmx-topmail.de wrote: Hello, I am also looking into the convertsion from strcutured arrays to ndarray. I've just started playing with numpy and have noticed that when printing a structured array that the output is not nicely formatted. Is there a way to make the formatting look the same as it does for an unstructured array? Output is: ### ndarray [[ 1. 2. ] [ 3. 4.1]] ### structured array [(1.0, 2.0) (3.0, 4.0996)] How could we make this structured array look like the above shown ndarray with shape (2, 2)? .view(float) should do it, to created a ndarray view of the structured array data Plus a reshape. I usually know how many columns I have, so I put in axis 1 and leave axis 0 as -1. In [21]: a.view(float).reshape(-1,2) Out[21]: array([[ 1. , 2. ], [ 3. , 4.1]]) a.view(float).reshape(len(a),-1) #if you don't want to count columns I obviously haven't done this in a while. And of course, it only works if all elements of the structured array have the same type. Josef Skipper ___ 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] printing structured arrays
On Mon, Mar 8, 2010 at 2:17 PM, josef.p...@gmail.com wrote:
On Mon, Mar 8, 2010 at 2:04 PM, Skipper Seabold jsseab...@gmail.com wrote:
On Mon, Mar 8, 2010 at 2:01 PM, josef.p...@gmail.com wrote:
On Mon, Mar 8, 2010 at 1:55 PM, Tim Michelsen
timmichel...@gmx-topmail.de wrote:
Hello,
I am also looking into the convertsion from strcutured arrays to ndarray.
I've just started playing with numpy and have noticed that when printing
a structured array that the output is not nicely formatted. Is there a
way to make the formatting look the same as it does for an unstructured
array?
Output is:
### ndarray
[[ 1. 2. ]
[ 3. 4.1]]
### structured array
[(1.0, 2.0) (3.0, 4.0996)]
How could we make this structured array look like the above shown
ndarray with shape (2, 2)?
.view(float) should do it, to created a ndarray view of the structured
array data
Plus a reshape. I usually know how many columns I have, so I put in
axis 1 and leave axis 0 as -1.
In [21]: a.view(float).reshape(-1,2)
Out[21]:
array([[ 1. , 2. ],
[ 3. , 4.1]])
a.view(float).reshape(len(a),-1) #if you don't want to count columns
I obviously haven't done this in a while.
And of course, it only works if all elements of the structured array
have the same type.
For the archives with heterogeneous dtype.
import numpy as np
b = np.array([(1.0, 'string1', 2.0), (3.0, 'string2', 4.1)],
dtype=[('x', float),('str_var', 'a7'),('y',float)])
b[['x','y']].view(float).reshape(len(b),-1) # note the list within list syntax
#array([[ 1. , 2. ],
# [ 3. , 4.1]])
Skipper
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Re: [Numpy-discussion] Is this a bug in numpy.ma.reduce?
On Mon, Mar 8, 2010 at 10:17 AM, David Goldsmith d.l.goldsm...@gmail.comwrote:
On Mon, Mar 8, 2010 at 6:52 AM, Bruce Southey bsout...@gmail.com wrote:
On 03/08/2010 01:30 AM, David Goldsmith wrote:
On Sun, Mar 7, 2010 at 4:41 AM, Friedrich Romstedt
friedrichromst...@gmail.com wrote:
I would like to stress the fact that imo this is maybe not ticket and not
a bug.
The issue arises when calling a.max() or similar of empty arrays a, i.e.,
with:
0 in a.shape
True
Opposed to the .prod() of an empty array, such a .max() or .min()
cannot be defined, because the set is empty. So it's fully correct to
let such calls fail. Just the failure is a bit deep in numpy, and
only the traceback gives some hint what went wrong.
I posted something similar also on the matplotlib-users list, sorry
for cross-posting thus.
Any suggestions, then, how to go about figuring out what's happening in my
code that's causing this feature to manifest itself?
DG
Perhaps providing the code with specific versions of Python, numpy etc.
would help.
I would guess that aquarius_test.py has not correctly setup the necessary
inputs (or has invalid inputs) required by matplotlib (which I have no
knowledge about). Really you have to find if the _A in cmp.py used by
'self.norm.autoscale_None(self._A)' is valid. You may be missing a valid
initialization step because the TypeError exception in autoscale_None ('You
must first set_array for mappable') implies something need to be done first.
Bruce
Python 2.5.4, Numpy 1.4.0, Matplotlib 0.99.0, Windows 32bit Vista Home
Premium SP2
# Code copyright 2010 by David Goldsmith
# Comments and unnecessaries edited for brevity
import numpy as N
import matplotlib as MPL
from matplotlib import pylab
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.cm as cm
J = complex(0,1); tol = 1e-6; maxiter = 20;
roots = (-2 + J/2, -1 + J, -0.5 + J/2, 0.5 + J, 1 + J/2, 2 + J, 2.5 +
J/2,
-2 - J, -1 - J/2, -0.5 - J, 0.5 - J/2, 1 - J, 2 - J/2, 2.5 -
J)
def ffp(z):
w, wp = (0J, 0J)
for root in roots:
z0 = z - root
w += N.sin(1/z0)
wp -= N.cos(1/z0)/(z0*z0)
return (w, wp)
def iter(z):
w, wp = ffp(z)
return z - w/wp
def find_root(z0):#, k, j):
count = 0
z1 = iter(z0)
if N.isnan(z1):
return N.complex64(N.inf)
while (N.abs(z1 - z0) tol) and \
(count maxiter):
count += 1
z0 = z1
z1 = iter(z0)
if N.abs(z1 - z0) tol:
result = 0
else:
result = z1
return N.complex64(result)
w, h, DPI = (3.2, 2.0, 100)
fig = Figure(figsize=(w, h),
dpi=DPI,
frameon=False)
ax = fig.add_subplot(1,1,1)
canvas = FigureCanvas(fig)
nx, xmin, xmax = (int(w*DPI), -0.5, 0.5)
ny, ymin, ymax = (int(h*DPI), 0.6, 1.2)
X, xincr = N.linspace(xmin,xmax,nx,retstep=True)
Y, yincr = N.linspace(ymin,ymax,ny,retstep=True)
W = N.zeros((ny,nx), dtype=N.complex64)
for j in N.arange(nx):
if not (j%100): # report progress
print j
for k in N.arange(ny):
x, y = (X[j], Y[k])
z0 = x + J*y
W[k,j] = find_root(z0)#,k,j)
print N.argwhere(N.logical_not(N.isfinite(W.real)))
print N.argwhere(N.logical_not(N.isfinite(W.imag)))
W = W.T
argW = N.angle(W)
print N.argwhere(N.logical_not(N.isfinite(argW)))
cms = (Blues,)# Blues_r, cool, cool_r,
def all_ticks_off(ax):
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
for cmap_name in cms:
all_ticks_off(ax)
ax.hold(True)
for i in range(4):
for j in range(4):
part2plot = argW[j*ny/4:(j+1)*ny/4, i*nx/4:(i+1)*nx/4]
if N.any(N.logical_not(N.isfinite(part2plot))):
print i, j,
print N.argwhere(N.logical_not(N.isfinite(part2plot)))
extent = (i*nx/4, (i+1)*nx/4, (j+1)*ny/4, j*ny/4)
ax.imshow(part2plot, cmap_name, extent = extent)
ax.set_xlim(0, nx)
ax.set_ylim(0, ny)
canvas.print_figure('../../Data-Figures/Zodiac/Aquarius/'+ cmap_name +
'Aquarius_test.png', dpi=DPI)
# End Aquarius_test.png
DG
Oh, and here's fresh output (i.e., I just reran it to confirm that I'm
still having the problem).
0
100
200
300
[[133 319]]
[]
[]
Traceback (most recent call last):
File
C:\Users\Fermat\Documents\Fractals\Python\Source\Zodiac\aquarius_test.py,
line 108, in module
ax.imshow(part2plot, cmap_name, extent = extent)
File C:\Python254\lib\site-packages\matplotlib\axes.py, line 6261, in
imshow
im.autoscale_None()
File C:\Python254\lib\site-packages\matplotlib\cm.py, line 236, in
autoscale_None
self.norm.autoscale_None(self._A)
File C:\Python254\lib\site-packages\matplotlib\colors.py, line 792, in
autoscale_None
if self.vmin is None: self.vmin =
Re: [Numpy-discussion] Is this a bug in numpy.ma.reduce?
It's pretty simple, but I was stunned myself how simple. Have a look at line 65 of your script you provided: W = W.T This means, x - y. But in the for loops, you still act as if W wasn't transposed. I added some prints, the positions should be clear for you: argW.shape = (320, 200) i, j = (0, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 0, 80) part2plot.shape = (50, 80) i, j = (0, 1) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (50, 100, 0, 80) part2plot.shape = (50, 80) i, j = (0, 2) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (100, 150, 0, 80) part2plot.shape = (50, 80) i, j = (0, 3) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (150, 200, 0, 80) part2plot.shape = (50, 80) i, j = (1, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 80, 160) part2plot.shape = (50, 80) i, j = (1, 1) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (50, 100, 80, 160) part2plot.shape = (50, 80) i, j = (1, 2) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (100, 150, 80, 160) part2plot.shape = (50, 80) i, j = (1, 3) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (150, 200, 80, 160) part2plot.shape = (50, 80) i, j = (2, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 160, 240) part2plot.shape = (50, 40) i, j = (2, 1) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (50, 100, 160, 240) part2plot.shape = (50, 40) i, j = (2, 2) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (100, 150, 160, 240) part2plot.shape = (50, 40) i, j = (2, 3) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (150, 200, 160, 240) part2plot.shape = (50, 40) i, j = (3, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 240, 320) part2plot.shape = (50, 0) Traceback (most recent call last): File D:\Home\Friedrich\Entwicklung\2010\David\aquarius.py, line 91, in ? ax.imshow(part2plot, extent = extent) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\matplotlib\ax es.py, line 5471, in imshow im.autoscale_None() File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\matplotlib\cm .py, line 148, in autoscale_None self.norm.autoscale_None(self._A) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\matplotlib\co lors.py, line 682, in autoscale_None if self.vmin is None: self.vmin = ma.minimum(A) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\numpy\ma\core .py, line 3042, in __call__ return self.reduce(a) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\numpy\ma\core .py, line 3057, in reduce t = self.ufunc.reduce(target, **kargs) ValueError: zero-size array to ufunc.reduce without identity So you simply have to exchange the role of x and y in your slice indicing expression, and everything will work out fine, I suspect :-) Or simpy leave out the transposition? Note that in the other case, you also may have to consider to change to extent's axes to get it properly reflected. NB: With my version of matplotlib, it didn't accept the colormap, but when yours does, it doesn't matter. Friedrich ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] printing structured arrays
On Mon, Mar 8, 2010 at 2:24 PM, Skipper Seabold jsseab...@gmail.com wrote:
On Mon, Mar 8, 2010 at 2:17 PM, josef.p...@gmail.com wrote:
On Mon, Mar 8, 2010 at 2:04 PM, Skipper Seabold jsseab...@gmail.com wrote:
On Mon, Mar 8, 2010 at 2:01 PM, josef.p...@gmail.com wrote:
On Mon, Mar 8, 2010 at 1:55 PM, Tim Michelsen
timmichel...@gmx-topmail.de wrote:
Hello,
I am also looking into the convertsion from strcutured arrays to ndarray.
I've just started playing with numpy and have noticed that when printing
a structured array that the output is not nicely formatted. Is there a
way to make the formatting look the same as it does for an unstructured
array?
Output is:
### ndarray
[[ 1. 2. ]
[ 3. 4.1]]
### structured array
[(1.0, 2.0) (3.0, 4.0996)]
How could we make this structured array look like the above shown
ndarray with shape (2, 2)?
.view(float) should do it, to created a ndarray view of the structured
array data
Plus a reshape. I usually know how many columns I have, so I put in
axis 1 and leave axis 0 as -1.
In [21]: a.view(float).reshape(-1,2)
Out[21]:
array([[ 1. , 2. ],
[ 3. , 4.1]])
a.view(float).reshape(len(a),-1) #if you don't want to count columns
I obviously haven't done this in a while.
And of course, it only works if all elements of the structured array
have the same type.
For the archives with heterogeneous dtype.
import numpy as np
b = np.array([(1.0, 'string1', 2.0), (3.0, 'string2', 4.1)],
dtype=[('x', float),('str_var', 'a7'),('y',float)])
b[['x','y']].view(float).reshape(len(b),-1) # note the list within list syntax
#array([[ 1. , 2. ],
# [ 3. , 4.1]])
nice, I've never seen selection of multiple columns before. I didn't
know it is possible to get a subset of columns this way
b[['x','y']]
array([(1.0, 2.0), (3.0, 4.0996)],
dtype=[('x', 'f8'), ('y', 'f8')])
b['x']
array([ 1., 3.])
b[['x']]
array([(1.0,), (3.0,)],
dtype=[('x', 'f8')])
Josef
Skipper
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Re: [Numpy-discussion] Is this a bug in numpy.ma.reduce?
On 03/08/2010 12:17 PM, David Goldsmith wrote:
On Mon, Mar 8, 2010 at 6:52 AM, Bruce Southey bsout...@gmail.com
mailto:bsout...@gmail.com wrote:
On 03/08/2010 01:30 AM, David Goldsmith wrote:
On Sun, Mar 7, 2010 at 4:41 AM, Friedrich Romstedt
friedrichromst...@gmail.com
mailto:friedrichromst...@gmail.com wrote:
I would like to stress the fact that imo this is maybe not
ticket and not a bug.
The issue arises when calling a.max() or similar of empty
arrays a, i.e., with:
0 in a.shape
True
Opposed to the .prod() of an empty array, such a .max() or .min()
cannot be defined, because the set is empty. So it's fully
correct to
let such calls fail. Just the failure is a bit deep in
numpy, and
only the traceback gives some hint what went wrong.
I posted something similar also on the matplotlib-users list,
sorry
for cross-posting thus.
Any suggestions, then, how to go about figuring out what's
happening in my code that's causing this feature to manifest
itself?
DG
Perhaps providing the code with specific versions of Python, numpy
etc. would help.
I would guess that aquarius_test.py has not correctly setup the
necessary inputs (or has invalid inputs) required by matplotlib
(which I have no knowledge about). Really you have to find if the
_A in cmp.py used by 'self.norm.autoscale_None(self._A)' is valid.
You may be missing a valid initialization step because the
TypeError exception in autoscale_None ('You must first set_array
for mappable') implies something need to be done first.
Bruce
Python 2.5.4, Numpy 1.4.0, Matplotlib 0.99.0, Windows 32bit Vista Home
Premium SP2
# Code copyright 2010 by David Goldsmith
# Comments and unnecessaries edited for brevity
import numpy as N
import matplotlib as MPL
from matplotlib import pylab
from matplotlib.backends.backend_agg import FigureCanvasAgg as
FigureCanvas
from matplotlib.figure import Figure
import matplotlib.cm http://matplotlib.cm as cm
J = complex(0,1); tol = 1e-6; maxiter = 20;
roots = (-2 + J/2, -1 + J, -0.5 + J/2, 0.5 + J, 1 + J/2, 2 + J, 2.5
+ J/2,
-2 - J, -1 - J/2, -0.5 - J, 0.5 - J/2, 1 - J, 2 - J/2,
2.5 - J)
def ffp(z):
w, wp = (0J, 0J)
for root in roots:
z0 = z - root
w += N.sin(1/z0)
wp -= N.cos(1/z0)/(z0*z0)
return (w, wp)
def iter(z):
w, wp = ffp(z)
return z - w/wp
def find_root(z0):#, k, j):
count = 0
z1 = iter(z0)
if N.isnan(z1):
return N.complex64(N.inf)
while (N.abs(z1 - z0) tol) and \
(count maxiter):
count += 1
z0 = z1
z1 = iter(z0)
if N.abs(z1 - z0) tol:
result = 0
else:
result = z1
return N.complex64(result)
w, h, DPI = (3.2, 2.0, 100)
fig = Figure(figsize=(w, h),
dpi=DPI,
frameon=False)
ax = fig.add_subplot(1,1,1)
canvas = FigureCanvas(fig)
nx, xmin, xmax = (int(w*DPI), -0.5, 0.5)
ny, ymin, ymax = (int(h*DPI), 0.6, 1.2)
X, xincr = N.linspace(xmin,xmax,nx,retstep=True)
Y, yincr = N.linspace(ymin,ymax,ny,retstep=True)
W = N.zeros((ny,nx), dtype=N.complex64)
for j in N.arange(nx):
if not (j%100): # report progress
print j
for k in N.arange(ny):
x, y = (X[j], Y[k])
z0 = x + J*y
W[k,j] = find_root(z0)#,k,j)
print N.argwhere(N.logical_not(N.isfinite(W.real)))
print N.argwhere(N.logical_not(N.isfinite(W.imag)))
W = W.T
argW = N.angle(W)
print N.argwhere(N.logical_not(N.isfinite(argW)))
cms = (Blues,)# Blues_r, cool, cool_r,
def all_ticks_off(ax):
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
for cmap_name in cms:
all_ticks_off(ax)
ax.hold(True)
for i in range(4):
for j in range(4):
part2plot = argW[j*ny/4:(j+1)*ny/4, i*nx/4:(i+1)*nx/4]
if N.any(N.logical_not(N.isfinite(part2plot))):
print i, j,
print N.argwhere(N.logical_not(N.isfinite(part2plot)))
extent = (i*nx/4, (i+1)*nx/4, (j+1)*ny/4, j*ny/4)
ax.imshow(part2plot, cmap_name, extent = extent)
ax.set_xlim(0, nx)
ax.set_ylim(0, ny)
canvas.print_figure('../../Data-Figures/Zodiac/Aquarius/'+ cmap_name +
'Aquarius_test.png', dpi=DPI)
# End Aquarius_test.png
DG
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Hmm,
Appears that you have mixed your indices when creating part2plot. If you
this line instead it works:
part2plot = argW[j*nx/4:(j+1)*nx/4, i*ny/4:(i+1)*ny/4]
I found that by looking the shape of the part2plot array that is
component of the argW array.
The shape of argW is (320, 200). So in your loops to find part2plot you
Re: [Numpy-discussion] Is this a bug in numpy.ma.reduce?
2010/3/8 Bruce Southey bsout...@gmail.com: Hmm, Appears that you have mixed your indices when creating part2plot. If you this line instead it works: part2plot = argW[j*nx/4:(j+1)*nx/4, i*ny/4:(i+1)*ny/4] I found that by looking the shape of the part2plot array that is component of the argW array. The shape of argW is (320, 200). So in your loops to find part2plot you eventually exceed 200 and eventually the index to the second axis is greater than 200 causing everything to crash: When i=0 or 1 then the shape of part2plot is (50, 80) when i=2 then the shape of part2plot is (50, 40) when i=3 then the shape of part2plot is (50, 0) # crash Nice that we have found this out both at the same instance of time (with 5min precision) :-) Friedrich ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Is this a bug in numpy.ma.reduce?
How embarrassing! :O Well, as they say, 'nother set of eyes... Thanks! DG On Mon, Mar 8, 2010 at 11:25 AM, Friedrich Romstedt friedrichromst...@gmail.com wrote: It's pretty simple, but I was stunned myself how simple. Have a look at line 65 of your script you provided: W = W.T This means, x - y. But in the for loops, you still act as if W wasn't transposed. I added some prints, the positions should be clear for you: argW.shape = (320, 200) i, j = (0, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 0, 80) part2plot.shape = (50, 80) i, j = (0, 1) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (50, 100, 0, 80) part2plot.shape = (50, 80) i, j = (0, 2) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (100, 150, 0, 80) part2plot.shape = (50, 80) i, j = (0, 3) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (150, 200, 0, 80) part2plot.shape = (50, 80) i, j = (1, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 80, 160) part2plot.shape = (50, 80) i, j = (1, 1) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (50, 100, 80, 160) part2plot.shape = (50, 80) i, j = (1, 2) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (100, 150, 80, 160) part2plot.shape = (50, 80) i, j = (1, 3) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (150, 200, 80, 160) part2plot.shape = (50, 80) i, j = (2, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 160, 240) part2plot.shape = (50, 40) i, j = (2, 1) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (50, 100, 160, 240) part2plot.shape = (50, 40) i, j = (2, 2) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (100, 150, 160, 240) part2plot.shape = (50, 40) i, j = (2, 3) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (150, 200, 160, 240) part2plot.shape = (50, 40) i, j = (3, 0) j*ny/4, (j+1)*ny/4, i*nx/4, (i+1)*nx/4 = (0, 50, 240, 320) part2plot.shape = (50, 0) Traceback (most recent call last): File D:\Home\Friedrich\Entwicklung\2010\David\aquarius.py, line 91, in ? ax.imshow(part2plot, extent = extent) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\matplotlib\ax es.py, line 5471, in imshow im.autoscale_None() File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\matplotlib\cm .py, line 148, in autoscale_None self.norm.autoscale_None(self._A) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\matplotlib\co lors.py, line 682, in autoscale_None if self.vmin is None: self.vmin = ma.minimum(A) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\numpy\ma\core .py, line 3042, in __call__ return self.reduce(a) File D:\Programme\Programmierung\python-2.4.1\lib\site-packages\numpy\ma\core .py, line 3057, in reduce t = self.ufunc.reduce(target, **kargs) ValueError: zero-size array to ufunc.reduce without identity So you simply have to exchange the role of x and y in your slice indicing expression, and everything will work out fine, I suspect :-) Or simpy leave out the transposition? Note that in the other case, you also may have to consider to change to extent's axes to get it properly reflected. NB: With my version of matplotlib, it didn't accept the colormap, but when yours does, it doesn't matter. Friedrich ___ 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] printing structured arrays
On Mar 8, 2010, at 1:55 PM, Tim Michelsen wrote: Hello, I am also looking into the convertsion from strcutured arrays to ndarray. I've just started playing with numpy and have noticed that when printing a structured array that the output is not nicely formatted. Is there a way to make the formatting look the same as it does for an unstructured array? Output is: ### ndarray [[ 1. 2. ] [ 3. 4.1]] ### structured array [(1.0, 2.0) (3.0, 4.0996)] How could we make this structured array look like the above shown ndarray with shape (2, 2)? if you're 100% sure all your fields have the same dtype (float), ``a.view((float,2))`` is the simplest. Note the tuple (dtype, len(a.dtype.names)). ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] PSF GSoC 2010 (Py3K focus)
I added Titus' email regarding the PSF's focus on Py3K-related projects to our SoC ideas wiki page: http://projects.scipy.org/scipy/wiki/SummerofCodeIdeas Given Titus' email, this is the most likely list of projects we will get accepted this year: - finish porting NumPy to Py3K - port SciPy to Py3K - port matplotlib to Py3K - port ipython to Py3K Given that we know what projects we will likely have accepted, it is worth starting to flesh these proposals out in detail. Also, we should start discussing how we will choose which student's we want to work on these ports. In particular, we should list what skills and background will be necessary to successfully complete these ports. Thoughts? Ideas? Best, Jarrod ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] PSF GSoC 2010 (Py3K focus)
On Mon, Mar 8, 2010 at 10:29 PM, Jarrod Millman mill...@berkeley.eduwrote: I added Titus' email regarding the PSF's focus on Py3K-related projects to our SoC ideas wiki page: http://projects.scipy.org/scipy/wiki/SummerofCodeIdeas Given Titus' email, this is the most likely list of projects we will get accepted this year: - finish porting NumPy to Py3K I think Numpy is pretty much done. It needs use testing to wring out any small oddities, but it doesn't look to me like a GSOC project at the moment. Maybe Pauli can weigh in here. - port SciPy to Py3K This project might be more appropriate, although I'm not clear on what needs to be done. - port matplotlib to Py3K - port ipython to Py3K Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
