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From: Armin Moser <armin.moser@st...>  20090220 23:23:03

Eric Firing wrote: > Jeff Whitaker wrote: >> Armin Moser wrote: >>> Jeff Whitaker wrote: >>> >>>> Armin Moser wrote: >>>> >>>>> Hi, >>>>> >>>>> I would like to interpolate an array of shape (801,676) to regularily >>>>> spaced datapoints using griddata. This interpolation is quick if the >>>>> (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this >>>>> condition is not fullfilled the delaunay triangulation is extremely >>>>> slow, i.e. not useable. Is this a known property of the used >>>>> triangulation? The triangulation can be performed with matlab without >>>>> any problems. >>>>> >>>>> Armin >>>>> >>>> Armin: You could try installing the natgrid toolkit and see if that >>>> speeds up griddata at all. If not, please post a test script with data >>>> and maybe we can figure out what is going on. >>>> >>> I have already tried natgrid and it didn't improve the situation. As >>> suggested I append a script demonstrating the problem. >>> >>> Thanks >>> Armin >>> >> >> Armin: On my mac, your two benchmarks take 15 and 14 seconds. Do you >> consider that too slow? > > I got 10 s and 8 s on my Thinkpadbut that was without natgrid. When I > installed natgrid, the benchmark was taking so long I killed the window. Thats the behaviour I observed (on Windows and Linux) with and without natgrid. I'm very puzzled at the moment. Thanks a lot Armin 
From: bpklmd <rphelps@re...>  20090220 23:16:47

I have searched the web for a solution to the following problem and nothing works. I call matplotlib code from a python script (2.5) running on Apache2.2 and mod_python. When I get to the point where I the code is to save a figure in the website dir I get the following error. File "/usr/lib/python2.5/sitepackages/matplotlib/__init__.py", line 324, in _get_configdir raise RuntimeError("'%s' is not a writable dir; you must set environment variable HOME to be a writable dir "%h) RuntimeError: '/root' is not a writable dir; you must set environment variable HOME to be a writable dir The code that creates the fig is a follows: import matplotlib from matplotlib import pylab as plt import numpy as np matplotlib.use('AGG') # use a nongui backend matplotlib.rdParams['MPLCONFIGDIR']='/var/www/modtest/' ... plot code here ... plt.savefig('/var/www/modtest/plot_data/' + plotfile[0]) # use 1st generated random file name First can anyone tell me what "HOME" variable the error message is referring to? I have changed the $HOME env variable under linux Ubuntu Gutsy I have added a $HOME variable to httpd.conf, however I am not an experienced Apache user I can get the code to work from the interactive.py shell rlp  View this message in context: http://www.nabble.com/Problemswithsavingfigurefromwithinmod_pythonscripttp22129192p22129192.html Sent from the matplotlib  users mailing list archive at Nabble.com. 
From: Armin Moser <armin.moser@st...>  20090220 21:27:21

Jeff Whitaker wrote: > Armin Moser wrote: >> Jeff Whitaker wrote: >> >>> Armin Moser wrote: >>> >>>> Hi, >>>> >>>> I would like to interpolate an array of shape (801,676) to regularily >>>> spaced datapoints using griddata. This interpolation is quick if the >>>> (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this >>>> condition is not fullfilled the delaunay triangulation is extremely >>>> slow, i.e. not useable. Is this a known property of the used >>>> triangulation? The triangulation can be performed with matlab without >>>> any problems. >>>> >>>> Armin >>>> >>> Armin: You could try installing the natgrid toolkit and see if that >>> speeds up griddata at all. If not, please post a test script with data >>> and maybe we can figure out what is going on. >>> >> I have already tried natgrid and it didn't improve the situation. As >> suggested I append a script demonstrating the problem. >> >> Thanks >> Armin >> > > Armin: On my mac, your two benchmarks take 15 and 14 seconds. Do you > consider that too slow? No definitely not but up to now I always killed the process (after more than 10 minutes). I tried the script again and it worked... I have to check the original on monday at work. > > Perhaps this is just a toy example to test griddata, but I assume you > realize that you wouldn't normally use griddata to interpolate data on > one regular grid to another regular grid. griddata is strictly for > interpolating scatter data (not on a regular mesh) to a regular mesh. Yes I do, but the supporting points of the second example are not on a regular grid. Thanks a lot Armin 
From: Eric Firing <efiring@ha...>  20090220 21:11:04

Jeff Whitaker wrote: > Armin Moser wrote: >> Jeff Whitaker wrote: >> >>> Armin Moser wrote: >>> >>>> Hi, >>>> >>>> I would like to interpolate an array of shape (801,676) to regularily >>>> spaced datapoints using griddata. This interpolation is quick if the >>>> (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this >>>> condition is not fullfilled the delaunay triangulation is extremely >>>> slow, i.e. not useable. Is this a known property of the used >>>> triangulation? The triangulation can be performed with matlab without >>>> any problems. >>>> >>>> Armin >>>> >>>> >>> Armin: You could try installing the natgrid toolkit and see if that >>> speeds up griddata at all. If not, please post a test script with data >>> and maybe we can figure out what is going on. >>> >> I have already tried natgrid and it didn't improve the situation. As >> suggested I append a script demonstrating the problem. >> >> Thanks >> Armin >> > > Armin: On my mac, your two benchmarks take 15 and 14 seconds. Do you > consider that too slow? Jeff, I got 10 s and 8 s on my Thinkpadbut that was without natgrid. When I installed natgrid, the benchmark was taking so long I killed the window. I'm running 32bit Ubuntu. Eric > > Perhaps this is just a toy example to test griddata, but I assume you > realize that you wouldn't normally use griddata to interpolate data on > one regular grid to another regular grid. griddata is strictly for > interpolating scatter data (not on a regular mesh) to a regular mesh. > > Jeff >> 8< >> from numpy import * >> from pylab import * >> import time >> >> deg2rad = pi/180.0 >> ai = 0.12*deg2rad >> x = linspace(13,40,676) >> y = linspace(10,22,801) >> >> x = x*deg2rad >> y = y*deg2rad >> [x,y] = meshgrid(x,y) >> z = (x**2+y**2) >> >> xi = linspace(x.min(),x.max(),x.shape[1]) >> yi = linspace(y.min(),y.max(),y.shape[0]) >> tic= time.time() >> zi = griddata(x.flatten(),y.flatten(),z.flatten(),xi,yi) >> toc = time.time() >> print toctic >> >> fac = 2*pi/1.2681 >> nx = fac * (cos(y)*cos(x)  cos(ai)) >> ny = fac * (cos(y)*sin(x)) >> nz = fac * (sin(y) + sin(ai)) >> np = sqrt(nx**2 + ny**2) >> >> z = (np**2+nz**2)*exp(0.001*nz) >> >> xi = linspace(np.min(),np.max(),x.shape[1]) >> yi = linspace(nz.min(),nz.max(),y.shape[0]) >> tic = time.time() >> zi = griddata(np.flatten(),nz.flatten(),z.flatten(),xi,yi) >> toc = time.time() >> print toctic >> > > 
From: Jeff Whitaker <jswhit@fa...>  20090220 20:04:17

Armin Moser wrote: > Jeff Whitaker wrote: > >> Armin Moser wrote: >> >>> Hi, >>> >>> I would like to interpolate an array of shape (801,676) to regularily >>> spaced datapoints using griddata. This interpolation is quick if the >>> (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this >>> condition is not fullfilled the delaunay triangulation is extremely >>> slow, i.e. not useable. Is this a known property of the used >>> triangulation? The triangulation can be performed with matlab without >>> any problems. >>> >>> Armin >>> >>> >> Armin: You could try installing the natgrid toolkit and see if that >> speeds up griddata at all. If not, please post a test script with data >> and maybe we can figure out what is going on. >> > I have already tried natgrid and it didn't improve the situation. As > suggested I append a script demonstrating the problem. > > Thanks > Armin > Armin: On my mac, your two benchmarks take 15 and 14 seconds. Do you consider that too slow? Perhaps this is just a toy example to test griddata, but I assume you realize that you wouldn't normally use griddata to interpolate data on one regular grid to another regular grid. griddata is strictly for interpolating scatter data (not on a regular mesh) to a regular mesh. Jeff > 8< > from numpy import * > from pylab import * > import time > > deg2rad = pi/180.0 > ai = 0.12*deg2rad > x = linspace(13,40,676) > y = linspace(10,22,801) > > x = x*deg2rad > y = y*deg2rad > [x,y] = meshgrid(x,y) > z = (x**2+y**2) > > xi = linspace(x.min(),x.max(),x.shape[1]) > yi = linspace(y.min(),y.max(),y.shape[0]) > tic= time.time() > zi = griddata(x.flatten(),y.flatten(),z.flatten(),xi,yi) > toc = time.time() > print toctic > > fac = 2*pi/1.2681 > nx = fac * (cos(y)*cos(x)  cos(ai)) > ny = fac * (cos(y)*sin(x)) > nz = fac * (sin(y) + sin(ai)) > np = sqrt(nx**2 + ny**2) > > z = (np**2+nz**2)*exp(0.001*nz) > > xi = linspace(np.min(),np.max(),x.shape[1]) > yi = linspace(nz.min(),nz.max(),y.shape[0]) > tic = time.time() > zi = griddata(np.flatten(),nz.flatten(),z.flatten(),xi,yi) > toc = time.time() > print toctic >  Jeffrey S. Whitaker Phone : (303)4976313 Meteorologist FAX : (303)4976449 NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@... 325 Broadway Office : Skaggs Research Cntr 1D113 Boulder, CO, USA 803033328 Web : http://tinyurl.com/5telg 
From: Armin Moser <armin.moser@st...>  20090220 19:10:19

Jeff Whitaker wrote: > Armin Moser wrote: >> Hi, >> >> I would like to interpolate an array of shape (801,676) to regularily >> spaced datapoints using griddata. This interpolation is quick if the >> (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this >> condition is not fullfilled the delaunay triangulation is extremely >> slow, i.e. not useable. Is this a known property of the used >> triangulation? The triangulation can be performed with matlab without >> any problems. >> >> Armin >> > > Armin: You could try installing the natgrid toolkit and see if that > speeds up griddata at all. If not, please post a test script with data > and maybe we can figure out what is going on. I have already tried natgrid and it didn't improve the situation. As suggested I append a script demonstrating the problem. Thanks Armin 8< from numpy import * from pylab import * import time deg2rad = pi/180.0 ai = 0.12*deg2rad x = linspace(13,40,676) y = linspace(10,22,801) x = x*deg2rad y = y*deg2rad [x,y] = meshgrid(x,y) z = (x**2+y**2) xi = linspace(x.min(),x.max(),x.shape[1]) yi = linspace(y.min(),y.max(),y.shape[0]) tic= time.time() zi = griddata(x.flatten(),y.flatten(),z.flatten(),xi,yi) toc = time.time() print toctic fac = 2*pi/1.2681 nx = fac * (cos(y)*cos(x)  cos(ai)) ny = fac * (cos(y)*sin(x)) nz = fac * (sin(y) + sin(ai)) np = sqrt(nx**2 + ny**2) z = (np**2+nz**2)*exp(0.001*nz) xi = linspace(np.min(),np.max(),x.shape[1]) yi = linspace(nz.min(),nz.max(),y.shape[0]) tic = time.time() zi = griddata(np.flatten(),nz.flatten(),z.flatten(),xi,yi) toc = time.time() print toctic 
From: Ryan May <rmay31@gm...>  20090220 17:18:54

On Fri, Feb 20, 2009 at 8:11 AM, Armin Moser <armin.moser@...>wrote: > Hi, > > I would like to interpolate an array of shape (801,676) to regularily > spaced datapoints using griddata. This interpolation is quick if the > (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this > condition is not fullfilled the delaunay triangulation is extremely > slow, i.e. not useable. Is this a known property of the used > triangulation? The triangulation can be performed with matlab without > any problems. > If you're not using meshgrid, how do you compute x,y? A small complete example would be helpful. Ryan  Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma Sent from: Norman Oklahoma United States. 
From: Jeff Whitaker <jswhit@fa...>  20090220 15:52:56

Armin Moser wrote: > Hi, > > I would like to interpolate an array of shape (801,676) to regularily > spaced datapoints using griddata. This interpolation is quick if the > (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this > condition is not fullfilled the delaunay triangulation is extremely > slow, i.e. not useable. Is this a known property of the used > triangulation? The triangulation can be performed with matlab without > any problems. > > Armin > Armin: You could try installing the natgrid toolkit and see if that speeds up griddata at all. If not, please post a test script with data and maybe we can figure out what is going on. Jeff  Jeffrey S. Whitaker Phone : (303)4976313 Meteorologist FAX : (303)4976449 NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@... 325 Broadway Office : Skaggs Research Cntr 1D113 Boulder, CO, USA 803033328 Web : http://tinyurl.com/5telg 
From: Armin Moser <armin.moser@st...>  20090220 14:53:10

Hi, I would like to interpolate an array of shape (801,676) to regularily spaced datapoints using griddata. This interpolation is quick if the (x,y) supporting points are computed as X,Y = meshgrid(x,y). If this condition is not fullfilled the delaunay triangulation is extremely slow, i.e. not useable. Is this a known property of the used triangulation? The triangulation can be performed with matlab without any problems. Armin 
From: G. Allegri <giohappy@gm...>  20090220 11:02:20

Thanks Andrew, conceptually it's clear. Now I have to code it :) I will have a look to SimPy, and also to SciPy/NumPy I will let you know how it's going on. 2009/2/20 Andrew Straw <strawman@...>: > G. Allegri wrote: >> >> Hi Andrew. >> With dist(point_i,polynomial_curve) do you mean point_i belonging to >> the Line 2 set of points and pol_curve as Line 1? > > yes > >> In this case it >> could be reasonably ok for me. How can I derive the closed form for >> dist()? Excuse my ignorance with geometry.... >> > > Take the equation for line 1parameterized by s. Something like f(s) = (x,y) > = (as**2 + bs +c, ds**2 + es + f ) for your polynomial model. Now, the > distance for that point on line 1 from point i is dist(point_i, f(s)), where > dist can be Euclidean distance, for example. > > So, the question is what value of s minimizes the distance. Since this > function will be smallest at an inflection, just take the derivative of your > distance function and solve for it to be equal to zero. Hopefully this > function will be convex and you'll have only one zero, which will tell you > the value of s where distance is a minimum. Otherwise, pick the inflection > at the closest distance. Finally, repeat for all points i and sum the > results. > > Hopefully that helps on the conceptual side. Sympy will be more useful than > matplotlib on the coding side... > 
From: Andrew Straw <strawman@as...>  20090220 10:32:43

G. Allegri wrote: > Hi Andrew. > With dist(point_i,polynomial_curve) do you mean point_i belonging to > the Line 2 set of points and pol_curve as Line 1? yes > In this case it > could be reasonably ok for me. How can I derive the closed form for > dist()? Excuse my ignorance with geometry.... > Take the equation for line 1parameterized by s. Something like f(s) = (x,y) = (as**2 + bs +c, ds**2 + es + f ) for your polynomial model. Now, the distance for that point on line 1 from point i is dist(point_i, f(s)), where dist can be Euclidean distance, for example. So, the question is what value of s minimizes the distance. Since this function will be smallest at an inflection, just take the derivative of your distance function and solve for it to be equal to zero. Hopefully this function will be convex and you'll have only one zero, which will tell you the value of s where distance is a minimum. Otherwise, pick the inflection at the closest distance. Finally, repeat for all points i and sum the results. Hopefully that helps on the conceptual side. Sympy will be more useful than matplotlib on the coding side... 
From: G. Allegri <giohappy@gm...>  20090220 08:51:26

Hi Andrew. With dist(point_i,polynomial_curve) do you mean point_i belonging to the Line 2 set of points and pol_curve as Line 1? In this case it could be reasonably ok for me. How can I derive the closed form for dist()? Excuse my ignorance with geometry.... 
From: Andrew Straw <strawman@as...>  20090220 06:21:36

G. Allegri wrote: > Hello list, > I'm completely new to matplotlib and I'm not a computer scientist (not > a good starting point!) but I need to solve a geometric/graphical > problem. > I've been asked to find a method, in Python, to find the distance > between a 2D polynomial curve, derived from least squares > interpolation on a set of points, and a curve locallly interpolating > another set of points. Do you really need the distance to be relative to the interpolated curve? Why not to the points which are being interpolated? Then the answer is just: Sum_i dist(point_i,polynomial_curve) Where dist() can be arrived at in closed form... Otherwise, I guess it would depend on the interpolation, which you didn't really specify. > >  the starting line is a smooth line, while the second should > describe a path passing exactly thorugh the given points. >  the distance should be the one along the normal to the first line > > I attach a sketch to explain this. > > Is there an heuristic, an algorithm, to solve this problem in an > efficient way (I have to apply it to thousands couples of sets from > sonar and seismic acquisitions)? Is the mapltolip API useful for this? > > Thanks in advance, > Giovanni > > >  > > >  > >  > Open Source Business Conference (OSBC), March 2425, 2009, San Francisco, CA > OSBC tackles the biggest issue in open source: Open Sourcing the Enterprise > Strategies to boost innovation and cut costs with open source participation > Receive a $600 discount off the registration fee with the source code: SFAD > http://p.sf.net/sfu/XcvMzF8H > > >  > > _______________________________________________ > Matplotlibusers mailing list > Matplotlibusers@... > https://lists.sourceforge.net/lists/listinfo/matplotlibusers 
From: Nash, Brent R <brent.r.nash@jp...>  20090220 01:22:59

From: Eric Firing <efiring@ha...>  20090220 01:16:52

per freem wrote: > hi all, > > when plotting a simple scatter plot in matlab, points that overlap will > cross in each other  if i plot > > scatter(randn(1,1000),randn(1,1000)) > > then no point will be fully "on top" of the other  if they overlap, > then their edges will cross and they will look like tiny venn diagrams. > > in matplotlib, this is not the case, and points that overlap are placed > on top of each other. for example if i use: > x = randn(1,1000) > plot(x, x, 'bo') > > how can i fix it so that it looks like matlab and points cross? So you want the interiors of the points to be transparent? Add the keyword argument mfc='none'. I may be misunderstanding the desired effect, however. > > more importantly, the above command in matplotlib generates many many > line objects and takes forever to render. if i don't specify 'bo' and Again, you are using 2D arrays when you should be using 1D. In matlab, everything is a 2D matrix (or it used to be, until higher dimensions were clumsily patched in). Numpy is designed to use as many or as few dimensions as you need. Mpl plot(x, y) with x and y being MxN is assuming each of the N columns is a line, so it is plotting N linesas well as cycling through the colors, one per "line", if you don't explicitly give a color. > simply call plot(x, x, 'o') it makes every point in a *different color*. > why is that? how can i change that? i feel like i must be doing > something wrong here. It's just a Matlab hangovera common malady, painful but rarely fatal. Eric 
From: Eric Firing <efiring@ha...>  20090220 01:03:20

per freem wrote: > hi all, > > i'm trying to do something extremely simple, namely print a scatter plot > of two random arrays: > > import matplotlib.plt as plt > from numpy.random import * > > x = rand(1,10) > scatter(x, x) > > this fails with the error: > > ValueError: Offsets array must be Nx2 > > what is happening here? are arrays somehow weird? do they not behave > like lists? any info on this will be greatly appreciated. Your "x" is 2D; what you want is rand(10); or you can do scatter(x.ravel(), x.ravel()). Your example does point to a bug in scatter, however; it should not be failing with an obscure error message like that. (In mpl from svn trunk it still fails, but with a different obscure error message.) I don't know any good reason why it should not be able to handle the 2D inputs. I will take a look. Eric > > thank you. > > >  > >  > Open Source Business Conference (OSBC), March 2425, 2009, San Francisco, CA > OSBC tackles the biggest issue in open source: Open Sourcing the Enterprise > Strategies to boost innovation and cut costs with open source participation > Receive a $600 discount off the registration fee with the source code: SFAD > http://p.sf.net/sfu/XcvMzF8H > > >  > > _______________________________________________ > Matplotlibusers mailing list > Matplotlibusers@... > https://lists.sourceforge.net/lists/listinfo/matplotlibusers 
From: per freem <perfreem@gm...>  20090220 00:26:55

hi all, when plotting a simple scatter plot in matlab, points that overlap will cross in each other  if i plot scatter(randn(1,1000),randn(1,1000)) then no point will be fully "on top" of the other  if they overlap, then their edges will cross and they will look like tiny venn diagrams. in matplotlib, this is not the case, and points that overlap are placed on top of each other. for example if i use: x = randn(1,1000) plot(x, x, 'bo') how can i fix it so that it looks like matlab and points cross? more importantly, the above command in matplotlib generates many many line objects and takes forever to render. if i don't specify 'bo' and simply call plot(x, x, 'o') it makes every point in a *different color*. why is that? how can i change that? i feel like i must be doing something wrong here. thanks. 