Re: [Matplotlib-users] Heat Map
rachel-mikel_arcejae...@hmc.edu wrote: Hello, I'm trying to create a heat map from two lists of corresponding X and Y coordinates. I've tried both numpy.histogram2d() and pyplot.hexbin(). The histogram I get back doesn't correspond to the points I gave it. It seems as if it's sorting each X and Y list and then eliminating some points, which would definitely not give me the coordinates I want. (Also, I want to be able to switch the origin to the upper-left corner, but I can't figure out how to do this with imshow().) Histogram2d works fine for me, I use it more or less the same way you do, so I can't help there. You can specify the origin in imshow by saying: plt.imshow(heatmap, extent=axesExtent, origin='upper') The hexbin graph does correspond to my original points, but rather than having hexagons of equal size, some are super thin and tiny. (I'd also like to fill in the entire graph with background color (I want the axes to extend beyond the data) - is there a way to do that?) You can specify the bins in histogram2d to cover the whole screen (you can also specify bins in hexbin, which should be similar): xedges, yedges = linspace(0,1400,(1400/50)+1), linspace(0,600,(600/50)+1) npy.histogram2d(xList, yList, bins=[xedges,yedges]) Here's the code I wrote for the histogram and hexbin. Am I doing something wrong? I've attached three graphs - one is my coordinates as a scatter plot, one my histogram result, and the other the hexbin result. Thanks! ~Rachel def plotHistogram( coordsTuple ): plt.clf() # Clears any previous figure xList = coordsTuple[0] yList = coordsTuple[1] heatmap, xedges, yedges = npy.histogram2d(xList, yList) axesExtent = [0, xedges[-1], 0, yedges[-1]] plt.imshow(heatmap, extent = axesExtent) plt.title( Heat Map of User Clicks ) name = raw_input(\nImage name and extension: ) plt.savefig(name) def plotHexbins( coordsTuple ): plt.clf() # Clears any previous figure xList = coordsTuple[0] yList = coordsTuple[1] plt.hexbin(xList, yList) plt.title( Heat Map of User Clicks ) name = raw_input(\nImage name and extension: ) plt.savefig(name) -- Download Intel#174; Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
[Matplotlib-users] Basemap/ orthographic projection plot doesn't respect globe boundary
Hi forum/ mailing list,
When I plot in the orthographic projection I'm getting the large artefact
shown below extending away from the north east of the globe.
I'm not finding the same problem when plotting in a full globe projection so
I'm presuming the problem is with the way I'm projecting everything rather
than my data itself.
I've included my plotting code below, if anyone is able to spot some glaring
omissions/ errors I'd be most grateful (I've been using python/ matplotlib
for only a couple of weeks now!).
http://old.nabble.com/file/p28117655/binploterr.png
#!/usr/local/bin/python2.6
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import sys, glob
#input must be 3 col file of lons lats and data
#bins input values into half degree grid, ignores negative values
plts = glob.glob('*.plt')
x = np.arange(-180, 180, 0.5); y = np.arange(-90, 90, 0.5)
grid_lon, grid_lat = np.meshgrid(x,y) #regularly spaced 2D grid
n_vals = np.zeros((360,720)) #mean divisor
dat = np.zeros((360,720)) #2D grid of zeros
for pt in plts:
in_file = pt
data = np.loadtxt(in_file, comments = ';')
fname = in_file.split('.')[0]
lon = data[:,0] #original 1D list
lat = data[:,1] #original 1D list
slcol = data[:,2] #z data
lon = (np.around(lon*2))/2 #round to nearest .0 or 0.5
lat = (np.around(lat*2))/2 #round to nearest .0 or 0.5
##keep the below between files
j=0
for i in slcol:
if lon[j] 0:
grid_lon_ind = 360+(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
else:
grid_lon_ind = 360-(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
if i 0:
dat[grid_lat_ind, grid_lon_ind] += i #add i'th value
n_vals[grid_lat_ind, grid_lon_ind] += 1 #increase cell counter
by 1 for each extra value
j+=1
dat = np.nan_to_num(dat/n_vals)
#create map object
fig = plt.figure()
m = Basemap(projection='ortho', lon_0=lon[(len(lon)/2)], lat_0=0,
resolution='l', area_thresh=1.)
#m = Basemap(projection='moll',lon_0=0,resolution='c', area_thresh=1.)
X,Y = m(grid_lon, grid_lat)
#pass all 2d arrays to pcolor
im = m.pcolormesh(X,Y,dat)
#add coastlines, globe boundary and colourbar
m.drawcoastlines()
m.drawmapboundary()
m.drawparallels(np.arange(-90, 90,30))
m.drawmeridians(np.arange(-180,180,30))
fig.colorbar(im)
plt.title('CH20 and ting')
plt.savefig('binplot.png')
Thanks for your help,
Will.
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[Matplotlib-users] Basemap/ orthographic projection plot doesn't respect globe boundary
Hi forum/ mailing list,
When I plot in the orthographic projection I'm getting the large artefact
shown below extending away from the north east of the globe.
I'm not finding the same problem when plotting in a full globe projection so
I'm presuming the problem is with the way I'm projecting everything rather
than my data itself.
I've included my plotting code below, if anyone is able to spot some glaring
omissions/ errors I'd be most grateful (I've been using python/ matplotlib
for only a couple of weeks now!).
http://old.nabble.com/file/p28117654/binploterr.png
#!/usr/local/bin/python2.6
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import sys, glob
#input must be 3 col file of lons lats and data
#bins input values into half degree grid, ignores negative values
plts = glob.glob('*.plt')
x = np.arange(-180, 180, 0.5); y = np.arange(-90, 90, 0.5)
grid_lon, grid_lat = np.meshgrid(x,y) #regularly spaced 2D grid
n_vals = np.zeros((360,720)) #mean divisor
dat = np.zeros((360,720)) #2D grid of zeros
for pt in plts:
in_file = pt
data = np.loadtxt(in_file, comments = ';')
fname = in_file.split('.')[0]
lon = data[:,0] #original 1D list
lat = data[:,1] #original 1D list
slcol = data[:,2] #z data
lon = (np.around(lon*2))/2 #round to nearest .0 or 0.5
lat = (np.around(lat*2))/2 #round to nearest .0 or 0.5
##keep the below between files
j=0
for i in slcol:
if lon[j] 0:
grid_lon_ind = 360+(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
else:
grid_lon_ind = 360-(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
if i 0:
dat[grid_lat_ind, grid_lon_ind] += i #add i'th value
n_vals[grid_lat_ind, grid_lon_ind] += 1 #increase cell counter
by 1 for each extra value
j+=1
dat = np.nan_to_num(dat/n_vals)
#create map object
fig = plt.figure()
m = Basemap(projection='ortho', lon_0=lon[(len(lon)/2)], lat_0=0,
resolution='l', area_thresh=1.)
#m = Basemap(projection='moll',lon_0=0,resolution='c', area_thresh=1.)
X,Y = m(grid_lon, grid_lat)
#pass all 2d arrays to pcolor
im = m.pcolormesh(X,Y,dat)
#add coastlines, globe boundary and colourbar
m.drawcoastlines()
m.drawmapboundary()
m.drawparallels(np.arange(-90, 90,30))
m.drawmeridians(np.arange(-180,180,30))
fig.colorbar(im)
plt.title('CH20 and ting')
plt.savefig('binplot.png')
Thanks for your help,
Will.
--
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Re: [Matplotlib-users] Basemap/ orthographic projection plot doesn't respect globe boundary
On 4/2/10 4:27 AM, Will Hewson wrote:
Hi forum/ mailing list, When I plot in the orthographic projection I'm
getting the large artefact shown below extending away from the north
east of the globe. I'm not finding the same problem when plotting in a
full globe projection so I'm presuming the problem is with the way I'm
projecting everything rather than my data itself. I've included my
plotting code below, if anyone is able to spot some glaring omissions/
errors I'd be most grateful (I've been using python/ matplotlib for
only a couple of weeks now!).
Will: You'll have to provide the data so we can actually run the script.
-Jeff
#!/usr/local/bin/python2.6
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import sys, glob
#input must be 3 col file of lons lats and data
#bins input values into half degree grid, ignores negative values
plts = glob.glob('*.plt')
x = np.arange(-180, 180, 0.5); y = np.arange(-90, 90, 0.5)
grid_lon, grid_lat = np.meshgrid(x,y) #regularly spaced 2D grid
n_vals = np.zeros((360,720)) #mean divisor
dat = np.zeros((360,720)) #2D grid of zeros
for pt in plts:
in_file = pt
data = np.loadtxt(in_file, comments = ';')
fname = in_file.split('.')[0]
lon = data[:,0] #original 1D list
lat = data[:,1] #original 1D list
slcol = data[:,2] #z data
lon = (np.around(lon*2))/2 #round to nearest .0 or 0.5
lat = (np.around(lat*2))/2 #round to nearest .0 or 0.5
##keep the below between files
j=0
for i in slcol:
if lon[j] 0:
grid_lon_ind = 360+(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
else:
grid_lon_ind = 360-(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
if i 0:
dat[grid_lat_ind, grid_lon_ind] += i #add i'th value
n_vals[grid_lat_ind, grid_lon_ind] += 1 #increase cell counter by 1
for each extra value
j+=1
dat = np.nan_to_num(dat/n_vals)
#create map object
fig = plt.figure()
m = Basemap(projection='ortho', lon_0=lon[(len(lon)/2)], lat_0=0,
resolution='l', area_thresh=1.)
#m = Basemap(projection='moll',lon_0=0,resolution='c', area_thresh=1.)
X,Y = m(grid_lon, grid_lat)
#pass all 2d arrays to pcolor
im = m.pcolormesh(X,Y,dat)
#add coastlines, globe boundary and colourbar
m.drawcoastlines()
m.drawmapboundary()
m.drawparallels(np.arange(-90, 90,30))
m.drawmeridians(np.arange(-180,180,30))
fig.colorbar(im)
plt.title('CH20 and ting')
plt.savefig('binplot.png')
Thanks for your help,
Will.
View this message in context: Basemap/ orthographic projection plot
doesn't respect globe boundary
http://old.nabble.com/Basemap--orthographic-projection-plot-doesn%27t-respect-globe-boundary-tp28117654p28117654.html
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Re: [Matplotlib-users] Basemap/ orthographic projection plot doesn't respect globe boundary
On 4/2/10 4:27 AM, Will Hewson wrote:
Hi forum/ mailing list, When I plot in the orthographic projection I'm
getting the large artefact shown below extending away from the north
east of the globe. I'm not finding the same problem when plotting in a
full globe projection so I'm presuming the problem is with the way I'm
projecting everything rather than my data itself. I've included my
plotting code below, if anyone is able to spot some glaring omissions/
errors I'd be most grateful (I've been using python/ matplotlib for
only a couple of weeks now!).
Will: I think what's happening is that pcolormesh is having trouble
dealing with the higher curvlinear grid, which becomes nearly
pathological near the horizon of the projection. If you take a look at
the test.py file in the basemap examples directory, you'll see an
example orthographic plot that solves this problem by first
interpolating the data to a regular grid in projection coordinates (with
values over the plot horizon masked). The example uses imshow, but
pcolormesh works as well. A standalone version of the example using
pcolormesah is attached, which uses data files in the basemap examples
directory.
-Jeff
#!/usr/local/bin/python2.6
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import sys, glob
#input must be 3 col file of lons lats and data
#bins input values into half degree grid, ignores negative values
plts = glob.glob('*.plt')
x = np.arange(-180, 180, 0.5); y = np.arange(-90, 90, 0.5)
grid_lon, grid_lat = np.meshgrid(x,y) #regularly spaced 2D grid
n_vals = np.zeros((360,720)) #mean divisor
dat = np.zeros((360,720)) #2D grid of zeros
for pt in plts:
in_file = pt
data = np.loadtxt(in_file, comments = ';')
fname = in_file.split('.')[0]
lon = data[:,0] #original 1D list
lat = data[:,1] #original 1D list
slcol = data[:,2] #z data
lon = (np.around(lon*2))/2 #round to nearest .0 or 0.5
lat = (np.around(lat*2))/2 #round to nearest .0 or 0.5
##keep the below between files
j=0
for i in slcol:
if lon[j] 0:
grid_lon_ind = 360+(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
else:
grid_lon_ind = 360-(lon[j]*2)
grid_lat_ind = 180+(lat[j]*2)
if i 0:
dat[grid_lat_ind, grid_lon_ind] += i #add i'th value
n_vals[grid_lat_ind, grid_lon_ind] += 1 #increase cell counter by 1
for each extra value
j+=1
dat = np.nan_to_num(dat/n_vals)
#create map object
fig = plt.figure()
m = Basemap(projection='ortho', lon_0=lon[(len(lon)/2)], lat_0=0,
resolution='l', area_thresh=1.)
#m = Basemap(projection='moll',lon_0=0,resolution='c', area_thresh=1.)
X,Y = m(grid_lon, grid_lat)
#pass all 2d arrays to pcolor
im = m.pcolormesh(X,Y,dat)
#add coastlines, globe boundary and colourbar
m.drawcoastlines()
m.drawmapboundary()
m.drawparallels(np.arange(-90, 90,30))
m.drawmeridians(np.arange(-180,180,30))
fig.colorbar(im)
plt.title('CH20 and ting')
plt.savefig('binplot.png')
Thanks for your help,
Will.
View this message in context: Basemap/ orthographic projection plot
doesn't respect globe boundary
http://old.nabble.com/Basemap--orthographic-projection-plot-doesn%27t-respect-globe-boundary-tp28117654p28117654.html
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from mpl_toolkits.basemap import Basemap, shiftgrid
import numpy as np
import matplotlib.pyplot as plt
# read in topo data (on a regular lat/lon grid)
# longitudes go from 20 to 380.
topoin = np.loadtxt('etopo20data.gz')
lons = np.loadtxt('etopo20lons.gz')
lats = np.loadtxt('etopo20lats.gz')
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin,lons = shiftgrid(180.,topoin,lons,start=False)
m = Basemap(projection='ortho',lon_0=-105,lat_0=40,resolution='l')
# transform to nx x ny regularly spaced native projection grid
nx = int((m.xmax-m.xmin)/4.)+1; ny = int((m.ymax-m.ymin)/4.)+1
topodat,x,y =\
m.transform_scalar(topoin,lons,lats,nx,ny,returnxy=True,masked=True,order=1)
# create the figure.
fig=plt.figure(figsize=(8,8))
im = m.pcolormesh(x,y,topodat,cmap=plt.cm.jet)
m.drawcoastlines()
m.drawparallels(np.arange(0.,80,20.))
m.drawmeridians(np.arange(10.,360.,30.))
m.drawmapboundary()
plt.show()
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Re: [Matplotlib-users] Basemap/ orthographic projection plot doesn't respect globe boundary
This is great Jeff, thanks for the help - I'll give it a try over the weekend
(it's bank holiday here in the UK!) and get back to you, if I'm still having
trouble I'll stick up the plotting data too... thanks again.
Will
Jeff Whitaker wrote:
On 4/2/10 4:27 AM, Will Hewson wrote:
Hi forum/ mailing list, When I plot in the orthographic projection I'm
getting the large artefact shown below extending away from the north
east of the globe. I'm not finding the same problem when plotting in a
full globe projection so I'm presuming the problem is with the way I'm
projecting everything rather than my data itself. I've included my
plotting code below, if anyone is able to spot some glaring omissions/
errors I'd be most grateful (I've been using python/ matplotlib for
only a couple of weeks now!).
Will: I think what's happening is that pcolormesh is having trouble
dealing with the higher curvlinear grid, which becomes nearly
pathological near the horizon of the projection. If you take a look at
the test.py file in the basemap examples directory, you'll see an
example orthographic plot that solves this problem by first
interpolating the data to a regular grid in projection coordinates (with
values over the plot horizon masked). The example uses imshow, but
pcolormesh works as well. A standalone version of the example using
pcolormesah is attached, which uses data files in the basemap examples
directory.
-Jeff
from mpl_toolkits.basemap import Basemap, shiftgrid
import numpy as np
import matplotlib.pyplot as plt
# read in topo data (on a regular lat/lon grid)
# longitudes go from 20 to 380.
topoin = np.loadtxt('etopo20data.gz')
lons = np.loadtxt('etopo20lons.gz')
lats = np.loadtxt('etopo20lats.gz')
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin,lons = shiftgrid(180.,topoin,lons,start=False)
m = Basemap(projection='ortho',lon_0=-105,lat_0=40,resolution='l')
# transform to nx x ny regularly spaced native projection grid
nx = int((m.xmax-m.xmin)/4.)+1; ny = int((m.ymax-m.ymin)/4.)+1
topodat,x,y =\
m.transform_scalar(topoin,lons,lats,nx,ny,returnxy=True,masked=True,order=1)
# create the figure.
fig=plt.figure(figsize=(8,8))
im = m.pcolormesh(x,y,topodat,cmap=plt.cm.jet)
m.drawcoastlines()
m.drawparallels(np.arange(0.,80,20.))
m.drawmeridians(np.arange(10.,360.,30.))
m.drawmapboundary()
plt.show()
--
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Re: [Matplotlib-users] Basemap/ orthographic projection plot doesn't respect globe boundary
On 4/2/10 6:32 AM, Will Hewson wrote:
This is great Jeff, thanks for the help - I'll give it a try over the weekend
(it's bank holiday here in the UK!) and get back to you, if I'm still having
trouble I'll stick up the plotting data too... thanks again.
Will
Will: I forgot to mention that contourf will work on your data without
having to interpolate to projection coordinates.
-Jeff
Jeff Whitaker wrote:
On 4/2/10 4:27 AM, Will Hewson wrote:
Hi forum/ mailing list, When I plot in the orthographic projection I'm
getting the large artefact shown below extending away from the north
east of the globe. I'm not finding the same problem when plotting in a
full globe projection so I'm presuming the problem is with the way I'm
projecting everything rather than my data itself. I've included my
plotting code below, if anyone is able to spot some glaring omissions/
errors I'd be most grateful (I've been using python/ matplotlib for
only a couple of weeks now!).
Will: I think what's happening is that pcolormesh is having trouble
dealing with the higher curvlinear grid, which becomes nearly
pathological near the horizon of the projection. If you take a look at
the test.py file in the basemap examples directory, you'll see an
example orthographic plot that solves this problem by first
interpolating the data to a regular grid in projection coordinates (with
values over the plot horizon masked). The example uses imshow, but
pcolormesh works as well. A standalone version of the example using
pcolormesah is attached, which uses data files in the basemap examples
directory.
-Jeff
from mpl_toolkits.basemap import Basemap, shiftgrid
import numpy as np
import matplotlib.pyplot as plt
# read in topo data (on a regular lat/lon grid)
# longitudes go from 20 to 380.
topoin = np.loadtxt('etopo20data.gz')
lons = np.loadtxt('etopo20lons.gz')
lats = np.loadtxt('etopo20lats.gz')
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin,lons = shiftgrid(180.,topoin,lons,start=False)
m = Basemap(projection='ortho',lon_0=-105,lat_0=40,resolution='l')
# transform to nx x ny regularly spaced native projection grid
nx = int((m.xmax-m.xmin)/4.)+1; ny = int((m.ymax-m.ymin)/4.)+1
topodat,x,y =\
m.transform_scalar(topoin,lons,lats,nx,ny,returnxy=True,masked=True,order=1)
# create the figure.
fig=plt.figure(figsize=(8,8))
im = m.pcolormesh(x,y,topodat,cmap=plt.cm.jet)
m.drawcoastlines()
m.drawparallels(np.arange(0.,80,20.))
m.drawmeridians(np.arange(10.,360.,30.))
m.drawmapboundary()
plt.show()
--
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Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
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Re: [Matplotlib-users] Changing the font
Can you set verbose.level to debug-annoying in your matplotlibrc file, and then send the output to this list. That may help us track down where the font lookup is failing. Also, what platform and version of matplotlib are you running? Mike Alex S wrote: Hi, sorry I wasn't too clear... I changed that, but I don't seem to be able to choose between the different serif fonts, it just always gives me the default... Alex S wrote: Hi there, I'm trying to change the font default on my graph to New Century Schoolbook. I'm trying to do this by editing the matplotlibrc file. Unfortunately, although I'm able to change the font.family, I can't figure out how to make it use something other than the default in the family... I tried changing the list further down to only include the font I want, like this: font.serif : New Century Schoolbook #Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif (note I commented out the other fonts, just rearranging the list to put New Century Schoolbook first didn't seem to work either) Could anyone tell me what I'm doing wrong? Thanks a lot! Alex -- Michael Droettboom Science Software Branch Operations and Engineering Division Space Telescope Science Institute Operated by AURA for NASA -- Download Intel#174; Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] speed up imports?
My gut says it's probably the GUI framework import that is dominating
the time. Which backend are you using? Does importing it take a large
amount of time as well?
Can you provide a profiler output file we can examine to narrow it
down? The following from a command prompt should be sufficient to write
out a file called import.prof:
python.exe -c import cProfile; prof=cProfile.Profile();
prof.run('import pylab', 'import.prof')
Mike
C M wrote:
On Thu, Apr 1, 2010 at 7:17 PM, Eric Firing efir...@hawaii.edu wrote:
Andrew Kelly wrote:
Has anyone had any success in speeding up the mpl imports?
import matplotlib.pyplot as plt
( or from matplotlib.figure import Figure)
takes 6 full seconds to load. That seems excessive. Any ideas?
-Andy
Andy,
A couple replies came back directly to me (probably intended for the
list, though), and both reported results similar to yours, on Windows
machines only. What OS and version are you running?
Sorry Eric, that was indeed intended for the list. Just for the
list's sake, I'll repeat it:
It takes longer than any other Python module for me, too, about 5-6
seconds on a cold load (on Windows), though faster on a warm load.
I am running it locally on a laptop that is 1.7 GHz Intel Pentium
laptop with 1Meg RAM.
And I should add: I don't currently have Linux installed, but will
soon again I hope, and I will take note of how long it takes on Linux.
Thanks,
Che
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Re: [Matplotlib-users] MacOS 10.6 install dependency building fails (r8214)
Thomas Robitaille wrote: It looks like the zlib website removes previous version of its library that were previously available for download, so the part in make.osx where http://www.zlib.net/zlib-1.2.3.tar.gz is fetched now fails (since the current version is 1.2.4). The error in the matplotlib building is not explicit enough (incorrect archive type) - maybe one could catch such 404s and print out an error suggesting to increase the ZLIBVERSION variable? I tried changing ZLIBVERSION to 1.2.4 and the following occurs when building zlib: ... make[1]: *** No rule to make target `libz.dylib', needed by `install-libs'. Stop. make[1]: *** Waiting for unfinished jobs make: *** [zlib] Error 2 I have submitted a simple patch that fixes all these issues: https://sourceforge.net/tracker/?func=detailaid=2981126group_id=80706atid=560722 Matplotlib then compiles 'out of the box' for 10.6. Cheers, Thomas -- View this message in context: http://old.nabble.com/MacOS-10.6-install-dependency-building-fails-%28r8214%29-tp28069099p28119205.html Sent from the matplotlib - users mailing list archive at Nabble.com. -- Download Intel#174; Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev ___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] speed up imports?
On Fri, Apr 2, 2010 at 8:28 AM, Michael Droettboom md...@stsci.edu wrote:
My gut says it's probably the GUI framework import that is dominating
the time. Which backend are you using? Does importing it take a large
amount of time as well?
Can you provide a profiler output file we can examine to narrow it
down? The following from a command prompt should be sufficient to write
out a file called import.prof:
python.exe -c import cProfile; prof=cProfile.Profile();
prof.run('import pylab', 'import.prof')
Mike
Just for the records,
It reads as:
python -c import cProfile; cProfile.run('import pylab',
filename='test.out')
in Python 2.6.2
These helped me to load the profile output:
import pstats
stats = pstats.Stats(test.out)
stats.print_stats()
--
Gökhan
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Re: [Matplotlib-users] EPS files with LaTeX are invalid
It seems that removing 'restore' on line 1073 of the test_tex_r8216.eps file
fixes the problem, although I don't understand postscript well enough to
understand why that is.
Thomas
On Apr 2, 2010, at 9:30 AM, Michael Droettboom wrote:
Can you provide us with the EPS file? What version of LaTeX is this?
Mike
Thomas Robitaille wrote:
Hello,
I upgraded to the latest svn version of matplotlib today, and found that eps
files produced with the system latex now seem to be invalid. For example, if
I run the following script
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as mpl
mpl.rc('text', usetex=False)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_notex.eps')
mpl.rc('text', usetex=True)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_tex.eps')
and try running pstopdf on them (on MacOS 10.6) I get the following
air:air tom$ pstopdf test_tex.eps %%[ Warning: Empty job. No PDF file
produced. ] %%
air:air tom$ pstopdf test_notex.eps air:air tom$
So the file with the system LaTeX enabled no longer works. ps2pdf still
works, but the error with pstopdf is important, because for example
Preview.app on mac relies on pstopdf, not ps2pdf.
I tried this on two different computers under MacOS 10.6, and tried with
ghostscript 8.70 and 8.71 installed, and the problem occurs either way.
Does anyone know what might be causing this? I submitted a bug report a
little while back about this
https://sourceforge.net/tracker/?func=detailaid=2974953group_id=80706atid=560720
Thanks in advance for any help,
Thomas
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Re: [Matplotlib-users] Cmap creation
Below is the example script (sorry!). I've tried all three methods of
establishing a colormap to no avail. The most promising looked like
option 2, but that gave me the AttributeError: 'module' object has no
attribute 'register_cmap' error.
I'm getting this error with:
Python 2.4 (user requirement because this application I'm building
will live on a RHEL5 server)
matplotlib 0.99.1.1
numpy 1.3.0
Could this be a versioning issue?
Bruce
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
Example: suppose you want red to increase from 0 to 1 over the bottom
half, green to do the same over the middle half, and blue over the top
half. Then you would use:
cdict = {'red': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.25, 0.0, 0.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'blue': ((0.0, 0.0, 0.0),
(0.5, 0.0, 0.0),
(1.0, 1.0, 1.0))}
If, as in this example, there are no discontinuities in the r, g, and b
components, then it is quite simple: the second and third element of
each tuple, above, is the same--call it y. The first element (x)
defines interpolation intervals over the full range of 0 to 1, and it
must span that whole range. In other words, the values of x divide the
0-to-1 range into a set of segments, and y gives the end-point color
values for each segment.
Now consider the green. cdict['green'] is saying that for
0 = x = 0.25, y is zero; no green.
0.25 x = 0.75, y varies linearly from 0 to 1.
x 0.75, y remains at 1, full green.
If there are discontinuities, then it is a little more complicated.
Label the 3 elements in each row in the cdict entry for a given color as
(x, y0, y1). Then for values of x between x[i] and x[i+1] the color
value is interpolated between y1[i] and y0[i+1].
Going back to the cookbook example, look at cdict['red']; because y0 !=
y1, it is saying that for x from 0 to 0.5, red increases from 0 to 1,
but then it jumps down, so that for x from 0.5 to 1, red increases from
0.7 to 1. Green ramps from 0 to 1 as x goes from 0 to 0.5, then jumps
back to 0, and ramps back to 1 as x goes from 0.5 to 1.
row i: x y0 y1
/
/
row i+1: x y0 y1
Above is an attempt to show that for x in the range x[i] to x[i+1], the
interpolation is between y1[i] and y0[i+1]. So, y0[0] and y1[-1] are
never used.
cdict1 = {'red': ((0.0, 0.0, 0.0),
(0.5, 0.0, 0.1),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 1.0),
(0.5, 0.1, 0.0),
(1.0, 0.0, 0.0))
}
cdict2 = {'red': ((0.0, 0.0, 0.0),
(0.5, 0.0, 1.0),
(1.0, 0.1, 1.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.1),
(0.5, 1.0, 0.0),
(1.0, 0.0, 0.0))
}
cdict3 = {'red': ((0.0, 0.0, 0.0),
(0.25,0.0, 0.0),
(0.5, 0.8, 1.0),
(0.75,1.0, 1.0),
(1.0, 0.4, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.25,0.0, 0.0),
(0.5, 0.9, 0.9),
(0.75,0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.4),
(0.25,1.0, 1.0),
(0.5, 1.0, 0.8),
(0.75,0.0, 0.0),
(1.0, 0.0, 0.0))
}
# Now we will use this example to illustrate 3 ways of
# handling custom colormaps.
# First, the most direct and explicit:
blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)
# Second, create the map explicitly and register it.
# Like the first method, this method works with any kind
# of Colormap, not just
# a LinearSegmentedColormap:
blue_red2 = LinearSegmentedColormap('BlueRed2', cdict2)
plt.register_cmap(cmap=blue_red2)
# Third, for LinearSegmentedColormap only,
# leave everything to register_cmap:
plt.register_cmap(name='BlueRed3', data=cdict3) # optional lut kwarg
x = np.arange(0, np.pi, 0.1)
y = np.arange(0, 2*np.pi, 0.1)
X, Y = np.meshgrid(x,y)
Z = np.cos(X) * np.sin(Y)
plt.figure(figsize=(10,4))
plt.subplots_adjust(wspace=0.3)
plt.subplot(1,3,1)
plt.imshow(Z, interpolation='nearest', cmap=blue_red1)
plt.colorbar()
plt.subplot(1,3,2)
cmap = plt.get_cmap('BlueRed2')
plt.imshow(Z, interpolation='nearest', cmap=cmap)
plt.colorbar()
# Now we will set the third cmap as the default. One would
# not normally do this in the middle of a script like this;
# it is done here just to illustrate the method.
plt.rcParams['image.cmap'] = 'BlueRed3'
# Also see below for an alternative, particularly for
# interactive use.
Re: [Matplotlib-users] EPS files with LaTeX are invalid
I just had a quick look, but while extra restore could be a problem,
the erroneous one may not be the one at line 1073, but the one at line
1066.
I believe that the restore at 1073 is written by pstoeps function
in backend_ps.py, and this function did write a matching save at
line 11.
Regards,
-JJ
On Fri, Apr 2, 2010 at 10:35 AM, Thomas Robitaille
thomas.robitai...@gmail.com wrote:
It seems that removing 'restore' on line 1073 of the test_tex_r8216.eps file
fixes the problem, although I don't understand postscript well enough to
understand why that is.
Thomas
On Apr 2, 2010, at 9:30 AM, Michael Droettboom wrote:
Can you provide us with the EPS file? What version of LaTeX is this?
Mike
Thomas Robitaille wrote:
Hello,
I upgraded to the latest svn version of matplotlib today, and found that
eps files produced with the system latex now seem to be invalid. For
example, if I run the following script
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as mpl
mpl.rc('text', usetex=False)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_notex.eps')
mpl.rc('text', usetex=True)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_tex.eps')
and try running pstopdf on them (on MacOS 10.6) I get the following
air:air tom$ pstopdf test_tex.eps %%[ Warning: Empty job. No PDF file
produced. ] %%
air:air tom$ pstopdf test_notex.eps air:air tom$
So the file with the system LaTeX enabled no longer works. ps2pdf still
works, but the error with pstopdf is important, because for example
Preview.app on mac relies on pstopdf, not ps2pdf.
I tried this on two different computers under MacOS 10.6, and tried with
ghostscript 8.70 and 8.71 installed, and the problem occurs either way.
Does anyone know what might be causing this? I submitted a bug report a
little while back about this
https://sourceforge.net/tracker/?func=detailaid=2974953group_id=80706atid=560720
Thanks in advance for any help,
Thomas
--
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Re: [Matplotlib-users] EPS files with LaTeX are invalid
At least on my Linux box with gs 7.07, I have to use epstopdf (not
pstopdf) to convert an eps file to a pdf. ps2pdf does work for both .ps
and .eps files however.
It looks like the 0.99.1.1 file is not in fact an .eps file, but a .ps
file, (it certainly hasn't had the ps2eps function run on it) and I
think it was probably a bug (now fixed) that 0.99.1.1 was writing out
the wrong kind of file.
It seems the relevant change is in r8102: fix some issues in the bbox
after the postscript distiller is run. This change removed a commented
out call to ps2eps. I'm a bit out of my depth here as to why that
change was made, and why .eps files seemingly haven't been true .eps
files for a long time prior to that change. Anyone else?
Mike
Thomas Robitaille wrote:
It seems that removing 'restore' on line 1073 of the test_tex_r8216.eps file
fixes the problem, although I don't understand postscript well enough to
understand why that is.
Thomas
On Apr 2, 2010, at 9:30 AM, Michael Droettboom wrote:
Can you provide us with the EPS file? What version of LaTeX is this?
Mike
Thomas Robitaille wrote:
Hello,
I upgraded to the latest svn version of matplotlib today, and found that
eps files produced with the system latex now seem to be invalid. For
example, if I run the following script
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as mpl
mpl.rc('text', usetex=False)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_notex.eps')
mpl.rc('text', usetex=True)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_tex.eps')
and try running pstopdf on them (on MacOS 10.6) I get the following
air:air tom$ pstopdf test_tex.eps %%[ Warning: Empty job. No PDF file
produced. ] %%
air:air tom$ pstopdf test_notex.eps air:air tom$
So the file with the system LaTeX enabled no longer works. ps2pdf still
works, but the error with pstopdf is important, because for example
Preview.app on mac relies on pstopdf, not ps2pdf.
I tried this on two different computers under MacOS 10.6, and tried with
ghostscript 8.70 and 8.71 installed, and the problem occurs either way.
Does anyone know what might be causing this? I submitted a bug report a
little while back about this
https://sourceforge.net/tracker/?func=detailaid=2974953group_id=80706atid=560720
Thanks in advance for any help,
Thomas
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling, find bugs
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--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling, find bugs
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Re: [Matplotlib-users] EPS files with LaTeX are invalid
I just tried running epstopdf, and this does work correctly, so maybe now the
only issue is that Preview.app on mac always uses pstopdf, even for eps files?
(which then is not a matplotlib issue) I also checked that giving the file the
extension '.ps' produces a ps file, and that does open correctly in Preview.app.
Cheers,
Tom
On Apr 2, 2010, at 11:20 AM, Michael Droettboom wrote:
At least on my Linux box with gs 7.07, I have to use epstopdf (not pstopdf)
to convert an eps file to a pdf. ps2pdf does work for both .ps and .eps
files however.
It looks like the 0.99.1.1 file is not in fact an .eps file, but a .ps file,
(it certainly hasn't had the ps2eps function run on it) and I think it was
probably a bug (now fixed) that 0.99.1.1 was writing out the wrong kind of
file.
It seems the relevant change is in r8102: fix some issues in the bbox after
the postscript distiller is run. This change removed a commented out call
to ps2eps. I'm a bit out of my depth here as to why that change was made,
and why .eps files seemingly haven't been true .eps files for a long time
prior to that change. Anyone else?
Mike
Thomas Robitaille wrote:
It seems that removing 'restore' on line 1073 of the test_tex_r8216.eps file
fixes the problem, although I don't understand postscript well enough to
understand why that is.
Thomas
On Apr 2, 2010, at 9:30 AM, Michael Droettboom wrote:
Can you provide us with the EPS file? What version of LaTeX is this?
Mike
Thomas Robitaille wrote:
Hello,
I upgraded to the latest svn version of matplotlib today, and found that
eps files produced with the system latex now seem to be invalid. For
example, if I run the following script
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as mpl
mpl.rc('text', usetex=False)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_notex.eps')
mpl.rc('text', usetex=True)
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
fig.savefig('test_tex.eps')
and try running pstopdf on them (on MacOS 10.6) I get the following
air:air tom$ pstopdf test_tex.eps %%[ Warning: Empty job. No PDF file
produced. ] %%
air:air tom$ pstopdf test_notex.eps air:air tom$ So the file with the
system LaTeX enabled no longer works. ps2pdf still works, but the error
with pstopdf is important, because for example Preview.app on mac relies
on pstopdf, not ps2pdf.
I tried this on two different computers under MacOS 10.6, and tried with
ghostscript 8.70 and 8.71 installed, and the problem occurs either way.
Does anyone know what might be causing this? I submitted a bug report a
little while back about this
https://sourceforge.net/tracker/?func=detailaid=2974953group_id=80706atid=560720
Thanks in advance for any help,
Thomas
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
See why Intel Parallel Studio got high marks during beta.
http://p.sf.net/sfu/intel-sw-dev
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--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
See why Intel Parallel Studio got high marks during beta.
http://p.sf.net/sfu/intel-sw-dev
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Re: [Matplotlib-users] Cmap creation
Bruce Ford wrote:
Below is the example script (sorry!). I've tried all three methods of
establishing a colormap to no avail. The most promising looked like
option 2, but that gave me the AttributeError: 'module' object has no
attribute 'register_cmap' error.
I'm getting this error with:
Python 2.4 (user requirement because this application I'm building
will live on a RHEL5 server)
matplotlib 0.99.1.1
numpy 1.3.0
Could this be a versioning issue?
Yes, register_cmap is quite new--but it is just a convenience, and not
at all necessary. Use of a custom cmap without register_cmap is
illustrated in the first subplot of the example; you could modify the
example so that all of the subplots are made without register_cmap.
Eric
Bruce
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
Example: suppose you want red to increase from 0 to 1 over the bottom
half, green to do the same over the middle half, and blue over the top
half. Then you would use:
cdict = {'red': ((0.0, 0.0, 0.0),
(0.5, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.25, 0.0, 0.0),
(0.75, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'blue': ((0.0, 0.0, 0.0),
(0.5, 0.0, 0.0),
(1.0, 1.0, 1.0))}
If, as in this example, there are no discontinuities in the r, g, and b
components, then it is quite simple: the second and third element of
each tuple, above, is the same--call it y. The first element (x)
defines interpolation intervals over the full range of 0 to 1, and it
must span that whole range. In other words, the values of x divide the
0-to-1 range into a set of segments, and y gives the end-point color
values for each segment.
Now consider the green. cdict['green'] is saying that for
0 = x = 0.25, y is zero; no green.
0.25 x = 0.75, y varies linearly from 0 to 1.
x 0.75, y remains at 1, full green.
If there are discontinuities, then it is a little more complicated.
Label the 3 elements in each row in the cdict entry for a given color as
(x, y0, y1). Then for values of x between x[i] and x[i+1] the color
value is interpolated between y1[i] and y0[i+1].
Going back to the cookbook example, look at cdict['red']; because y0 !=
y1, it is saying that for x from 0 to 0.5, red increases from 0 to 1,
but then it jumps down, so that for x from 0.5 to 1, red increases from
0.7 to 1. Green ramps from 0 to 1 as x goes from 0 to 0.5, then jumps
back to 0, and ramps back to 1 as x goes from 0.5 to 1.
row i: x y0 y1
/
/
row i+1: x y0 y1
Above is an attempt to show that for x in the range x[i] to x[i+1], the
interpolation is between y1[i] and y0[i+1]. So, y0[0] and y1[-1] are
never used.
cdict1 = {'red': ((0.0, 0.0, 0.0),
(0.5, 0.0, 0.1),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 1.0),
(0.5, 0.1, 0.0),
(1.0, 0.0, 0.0))
}
cdict2 = {'red': ((0.0, 0.0, 0.0),
(0.5, 0.0, 1.0),
(1.0, 0.1, 1.0)),
'green': ((0.0, 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.1),
(0.5, 1.0, 0.0),
(1.0, 0.0, 0.0))
}
cdict3 = {'red': ((0.0, 0.0, 0.0),
(0.25,0.0, 0.0),
(0.5, 0.8, 1.0),
(0.75,1.0, 1.0),
(1.0, 0.4, 1.0)),
'green': ((0.0, 0.0, 0.0),
(0.25,0.0, 0.0),
(0.5, 0.9, 0.9),
(0.75,0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.4),
(0.25,1.0, 1.0),
(0.5, 1.0, 0.8),
(0.75,0.0, 0.0),
(1.0, 0.0, 0.0))
}
# Now we will use this example to illustrate 3 ways of
# handling custom colormaps.
# First, the most direct and explicit:
blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)
# Second, create the map explicitly and register it.
# Like the first method, this method works with any kind
# of Colormap, not just
# a LinearSegmentedColormap:
blue_red2 = LinearSegmentedColormap('BlueRed2', cdict2)
plt.register_cmap(cmap=blue_red2)
# Third, for LinearSegmentedColormap only,
# leave everything to register_cmap:
plt.register_cmap(name='BlueRed3', data=cdict3) # optional lut kwarg
x = np.arange(0, np.pi, 0.1)
y = np.arange(0, 2*np.pi, 0.1)
X, Y = np.meshgrid(x,y)
Z = np.cos(X) * np.sin(Y)
plt.figure(figsize=(10,4))
plt.subplots_adjust(wspace=0.3)
plt.subplot(1,3,1)
plt.imshow(Z,
[Matplotlib-users] visualizing colormaps for complex functions
Hi, Is there a way to generate colormaps for complex-valued functions using matplotlib? The type of plots I'm looking for are like the plots in: http://commons.wikimedia.org/wiki/User:Jan_Homann/Mathematics Thanks in advance, Guy http://www.guyrutenberg.com -- Download Intel#174; Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
Re: [Matplotlib-users] speed up imports?
Can you provide the actual saved profiler data? The output of the
command itself doesn't provide enough information to diagnose the
problem, since it doesn't have full file paths etc.
When you do (thanks Gökhan for the less verbose version):
python.exe -c import cProfile; cProfile.run('import pylab', 'test.out')
this should produce a binary file test.out that can be loaded with the
pstats module and used by GUI tools such as KCacheGrind to help us get
to the bottom of this.
Mike
Andrew Kelly wrote:
I'm back.
My backend is wx. Import wx does not really take much time to
import at all. In fact time.time() before and after = 0.0
Some computer details:
Processor: AMD Phenom IIx4 810 Processor 2.6 GHz
RAM: 8.00 GB
As for the cProfiler output on pylab, I have attached the output as
test.txt.
-Andy
On Fri, Apr 2, 2010 at 7:22 AM, Gökhan Sever gokhanse...@gmail.com
mailto:gokhanse...@gmail.com wrote:
On Fri, Apr 2, 2010 at 8:28 AM, Michael Droettboom
md...@stsci.edu mailto:md...@stsci.edu wrote:
My gut says it's probably the GUI framework import that is
dominating
the time. Which backend are you using? Does importing it
take a large
amount of time as well?
Can you provide a profiler output file we can examine to narrow it
down? The following from a command prompt should be
sufficient to write
out a file called import.prof:
python.exe -c import cProfile; prof=cProfile.Profile();
prof.run('import pylab', 'import.prof')
Mike
Just for the records,
It reads as:
python -c import cProfile; cProfile.run('import pylab',
filename='test.out')
in Python 2.6.2
These helped me to load the profile output:
import pstats
stats = pstats.Stats(test.out)
stats.print_stats()
--
Gökhan
--
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Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
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Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
--
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Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
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Re: [Matplotlib-users] speed up imports?
It looks like most of the time is being taken up by pytz (timezone
library), which opens ~500 files. How does the total time of import
pytz compare?
Mike
Andrew Kelly wrote:
I see. I was wondering why it spit out a binary file.
test.out is attached...
-Andy
On Fri, Apr 2, 2010 at 10:55 AM, Michael Droettboom md...@stsci.edu
mailto:md...@stsci.edu wrote:
Can you provide the actual saved profiler data? The output of the
command itself doesn't provide enough information to diagnose the
problem, since it doesn't have full file paths etc.
When you do (thanks Gökhan for the less verbose version):
python.exe -c import cProfile; cProfile.run('import pylab',
'test.out')
this should produce a binary file test.out that can be loaded
with the pstats module and used by GUI tools such as KCacheGrind
to help us get to the bottom of this.
Mike
Andrew Kelly wrote:
I'm back.
My backend is wx. Import wx does not really take much time
to import at all. In fact time.time() before and after = 0.0
Some computer details:
Processor: AMD Phenom IIx4 810 Processor 2.6 GHz
RAM: 8.00 GB
As for the cProfiler output on pylab, I have attached the
output as test.txt.
-Andy
On Fri, Apr 2, 2010 at 7:22 AM, Gökhan Sever
gokhanse...@gmail.com mailto:gokhanse...@gmail.com
mailto:gokhanse...@gmail.com mailto:gokhanse...@gmail.com
wrote:
On Fri, Apr 2, 2010 at 8:28 AM, Michael Droettboom
md...@stsci.edu mailto:md...@stsci.edu
mailto:md...@stsci.edu mailto:md...@stsci.edu wrote:
My gut says it's probably the GUI framework import that is
dominating
the time. Which backend are you using? Does importing it
take a large
amount of time as well?
Can you provide a profiler output file we can examine
to narrow it
down? The following from a command prompt should be
sufficient to write
out a file called import.prof:
python.exe -c import cProfile; prof=cProfile.Profile();
prof.run('import pylab', 'import.prof')
Mike
Just for the records,
It reads as:
python -c import cProfile; cProfile.run('import pylab',
filename='test.out')
in Python 2.6.2
These helped me to load the profile output:
import pstats
stats = pstats.Stats(test.out)
stats.print_stats()
-- Gökhan
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling,
find bugs
proactively, and fine-tune applications for parallel
performance.
See why Intel Parallel Studio got high marks during beta.
http://p.sf.net/sfu/intel-sw-dev
___
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mailto:Matplotlib-users@lists.sourceforge.net
mailto:Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
See why Intel Parallel Studio got high marks during beta.
http://p.sf.net/sfu/intel-sw-dev
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Re: [Matplotlib-users] speed up imports?
import pytz only took 0.0 seconds.
I actually just ran that pstats module and there is one line that stuck out
at me:
ncalls tottime percall cumtime percall filename:lineno(function)
10.0000.0000.0000.000
C:\Python26\lib\os.py:35(_get_exports_list)
5603.1070.0063.1070.006 {open}
That is ~50% of the load time. I have 0 idea what this is though.
Let me try this on my os machine.
-Andy
On Fri, Apr 2, 2010 at 12:31 PM, Michael Droettboom md...@stsci.edu wrote:
It looks like most of the time is being taken up by pytz (timezone
library), which opens ~500 files. How does the total time of import pytz
compare?
Mike
Andrew Kelly wrote:
I see. I was wondering why it spit out a binary file.
test.out is attached...
-Andy
On Fri, Apr 2, 2010 at 10:55 AM, Michael Droettboom md...@stsci.edumailto:
md...@stsci.edu wrote:
Can you provide the actual saved profiler data? The output of the
command itself doesn't provide enough information to diagnose the
problem, since it doesn't have full file paths etc.
When you do (thanks Gökhan for the less verbose version):
python.exe -c import cProfile; cProfile.run('import pylab',
'test.out')
this should produce a binary file test.out that can be loaded
with the pstats module and used by GUI tools such as KCacheGrind
to help us get to the bottom of this.
Mike
Andrew Kelly wrote:
I'm back.
My backend is wx. Import wx does not really take much time
to import at all. In fact time.time() before and after = 0.0
Some computer details:
Processor: AMD Phenom IIx4 810 Processor 2.6 GHz
RAM: 8.00 GB
As for the cProfiler output on pylab, I have attached the
output as test.txt.
-Andy
On Fri, Apr 2, 2010 at 7:22 AM, Gökhan Sever
gokhanse...@gmail.com mailto:gokhanse...@gmail.com
mailto:gokhanse...@gmail.com mailto:gokhanse...@gmail.com
wrote:
On Fri, Apr 2, 2010 at 8:28 AM, Michael Droettboom
md...@stsci.edu mailto:md...@stsci.edu
mailto:md...@stsci.edu mailto:md...@stsci.edu wrote:
My gut says it's probably the GUI framework import that is
dominating
the time. Which backend are you using? Does importing it
take a large
amount of time as well?
Can you provide a profiler output file we can examine
to narrow it
down? The following from a command prompt should be
sufficient to write
out a file called import.prof:
python.exe -c import cProfile; prof=cProfile.Profile();
prof.run('import pylab', 'import.prof')
Mike
Just for the records,
It reads as:
python -c import cProfile; cProfile.run('import pylab',
filename='test.out')
in Python 2.6.2
These helped me to load the profile output:
import pstats
stats = pstats.Stats(test.out)
stats.print_stats()
-- Gökhan
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling,
find bugs
proactively, and fine-tune applications for parallel
performance.
See why Intel Parallel Studio got high marks during beta.
http://p.sf.net/sfu/intel-sw-dev
___
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mailto:Matplotlib-users@lists.sourceforge.net
mailto:Matplotlib-users@lists.sourceforge.net
mailto:Matplotlib-users@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/matplotlib-users
-- Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling, find bugs
proactively, and fine-tune applications for parallel performance.
See why Intel Parallel Studio got high marks during beta.
http://p.sf.net/sfu/intel-sw-dev___
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Re: [Matplotlib-users] speed up imports?
Andrew Kelly wrote:
import pytz only took 0.0 seconds.
Sounds like it was already imported, so you were not really timing that
import.
On linux (ubuntu 9.10, Lenovo T60 laptop) importing pytz takes longer
than importing numpy:
efir...@manini:~$ time python -c import pytz
real0m0.203s
user0m0.144s
sys 0m0.052s
efir...@manini:~$ time python -c import pylab
real0m0.626s
user0m0.480s
sys 0m0.124s
efir...@manini:~$ time python -c import numpy
real0m0.113s
user0m0.088s
sys 0m0.020s
(Probably everything is in cache in these tests; repeats yielded similar
results.)
Eric
I actually just ran that pstats module and there is one line that stuck
out at me:
ncalls tottime percall cumtime percall filename:lineno(function)
10.0000.0000.0000.000
C:\Python26\lib\os.py:35(_get_exports_list)
5603.1070.0063.1070.006 {open}
That is ~50% of the load time. I have 0 idea what this is though.
Let me try this on my os machine.
-Andy
On Fri, Apr 2, 2010 at 12:31 PM, Michael Droettboom md...@stsci.edu
mailto:md...@stsci.edu wrote:
It looks like most of the time is being taken up by pytz (timezone
library), which opens ~500 files. How does the total time of
import pytz compare?
Mike
Andrew Kelly wrote:
I see. I was wondering why it spit out a binary file.
test.out is attached...
-Andy
On Fri, Apr 2, 2010 at 10:55 AM, Michael Droettboom
md...@stsci.edu mailto:md...@stsci.edu
mailto:md...@stsci.edu mailto:md...@stsci.edu wrote:
Can you provide the actual saved profiler data? The output
of the
command itself doesn't provide enough information to diagnose the
problem, since it doesn't have full file paths etc.
When you do (thanks Gökhan for the less verbose version):
python.exe -c import cProfile; cProfile.run('import pylab',
'test.out')
this should produce a binary file test.out that can be loaded
with the pstats module and used by GUI tools such as KCacheGrind
to help us get to the bottom of this.
Mike
Andrew Kelly wrote:
I'm back.
My backend is wx. Import wx does not really take much time
to import at all. In fact time.time() before and after = 0.0
Some computer details:
Processor: AMD Phenom IIx4 810 Processor 2.6 GHz
RAM: 8.00 GB
As for the cProfiler output on pylab, I have attached the
output as test.txt.
-Andy
On Fri, Apr 2, 2010 at 7:22 AM, Gökhan Sever
gokhanse...@gmail.com mailto:gokhanse...@gmail.com
mailto:gokhanse...@gmail.com mailto:gokhanse...@gmail.com
mailto:gokhanse...@gmail.com
mailto:gokhanse...@gmail.com mailto:gokhanse...@gmail.com
mailto:gokhanse...@gmail.com
wrote:
On Fri, Apr 2, 2010 at 8:28 AM, Michael Droettboom
md...@stsci.edu mailto:md...@stsci.edu
mailto:md...@stsci.edu mailto:md...@stsci.edu
mailto:md...@stsci.edu mailto:md...@stsci.edu
mailto:md...@stsci.edu mailto:md...@stsci.edu wrote:
My gut says it's probably the GUI framework import
that is
dominating
the time. Which backend are you using? Does
importing it
take a large
amount of time as well?
Can you provide a profiler output file we can examine
to narrow it
down? The following from a command prompt should be
sufficient to write
out a file called import.prof:
python.exe -c import cProfile;
prof=cProfile.Profile();
prof.run('import pylab', 'import.prof')
Mike
Just for the records,
It reads as:
python -c import cProfile; cProfile.run('import pylab',
filename='test.out')
in Python 2.6.2
These helped me to load the profile output:
import pstats
stats = pstats.Stats(test.out)
stats.print_stats()
-- Gökhan
--
Download Intel#174; Parallel Studio Eval
Try the new software tools for yourself. Speed compiling,
find bugs
proactively, and
[Matplotlib-users] Issues with Affine2D transform
Hi,
I have been trying to use the Affine2D transformation with pcolor and contour,
with no success. The following script and comments illustrates my problems:
matplotlib.use('Agg')
import matplotlib.pyplot as mpl
from matplotlib.transforms import Affine2D
import numpy as np
image = np.random.random((100,100))
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
ax.pcolor(image, transform=Affine2D()) # Does not work - the image is not there!
fig.savefig('test1.png')
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
ax.contour(image, transform=Affine2D()) # Ok, but transformation wouldn't
change anything anyway
fig.savefig('test2.png')
fig = mpl.figure()
ax = fig.add_subplot(1,1,1)
ax.contour(image, transform=Affine2D().scale(10.,10.)) # Does not work - the
image is unchanged
fig.savefig('test3.png')
Is there a reason why transform doesn't work for contour and pcolor?
Thanks for any help,
Thomas
--
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Re: [Matplotlib-users] visualizing colormaps for complex functions
Hi Guy, I am also interested in the answer to this. The cplot function in the mpmath module does exactly this using matplotlib, but very inefficiently, as it computes the colour of each pixel in the image in hls colour-space and generates the corresponding rgb value directly. I suspect this is how it has to be done, as colormaps in matplotlib are 1D sequences and the black-white (lightness) value is really another dimension. However mpmath's method can be improved by doing the mapping using array operations instead of computing it for each pixel. I've attached a function I wrote to reproduce the Sage cplot command in my own work. It's a bit old and can be improved. It takes the Arg and Abs of a complex array as the first two arguments - you can easily change this to compute these inside the function if you prefer. The line np.vectorize(hls_to_rgb) can be replaced - recent versions of matplotlib have a vectorized function called hsv_to_rgb() inside colors.py - so you replace the return line with the commented-out version if you first import hsv_to_rgb from colors. I hope this helps. I'm also curious: the plots you point to also show plots of the function extrema, which are the phase singularities - does mathematica have a function that gives you these, or did you write your own function to find them? regards, Gary Guy Rutenberg wrote: Hi, Is there a way to generate colormaps for complex-valued functions using matplotlib? The type of plots I'm looking for are like the plots in: http://commons.wikimedia.org/wiki/User:Jan_Homann/Mathematics Thanks in advance, Guy def cplot_like(ph, intens=None, int_exponent=1.0, s=1.0, l_bias=1.0, drape=0, is_like_mpmath=False): ''' Implements the mpmath cplot-like default_color_function The combined image is generated in hls colourspace then transformed to rgb *phase* A filename or 2D n x m array containing phase data in the range -pi-pi *intens* If None, set to 1.0 A filename or 2D n x m array containing intensity or amplitude data in the range 0-max *int_exponent* Default 1.0 applies the intens mask directly to the hls lightness-channel 0.6 works well when drape==0 *s* saturation. Defaults to 1.0. mpmath uses 0.8. *l_bias* biases the mean lightness value away from 0.5. mpmath uses 1.0. Examples are: l_bias=2 - mean=0.33 (ie darker), l_bias=0.5 - mean=0.66 (lighter) *drape* If 1, drapes a structured maximum filter of size drape x drape over the intensity data *is_like_mpmath* If True, sets int_exponent = 0.3, s = 0.8 ''' from colorsys import hls_to_rgb if type(ph) is str: cph = plt.imread(ph)/256.*2*pi-pi # -pi-pi if len(cph.shape) == 3: cph = cph[...,0] # if ph is RGB or RGBA, extract the R-plane else: cph = ph.copy() if intens is None: cintens = np.ones_like(cph) elif type(intens) is str: cintens = plt.imread(intens)/255. # 0-1 if len(cintens.shape) == 3: cintens = cintens[...,0] # if intens is RGB or RGBA, extract the R-plane else: cintens = intens.copy() cintens /= cintens.max() # autoscale intensity data to 0-1 if drape 1: # envelope the intensity cintens = maximum_filter(cintens, size=drape) h = ((cph + pi) / (2*pi)) % 1.0 if is_like_mpmath: # apply mpmath values int_exponent = 0.3 s = 0.8 l = 1.0 - l_bias/(l_bias+cintens**int_exponent) v_hls_to_rgb = np.vectorize(hls_to_rgb) #~ return hsv_to_rgb(dstack((h,np.ones_like(h),l))) return dstack(v_hls_to_rgb(h,l,s))-- Download Intel#174; Parallel Studio Eval Try the new software tools for yourself. Speed compiling, find bugs proactively, and fine-tune applications for parallel performance. See why Intel Parallel Studio got high marks during beta. http://p.sf.net/sfu/intel-sw-dev___ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
