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From: John Hunter <jdhunter@ac...>  20060525 21:49:55

>>>>> "JUAN" == JUAN ERNESTO FLORES BELTRAN <juanernestof@...> writes: JUAN> Hi you all, I am trying to get the mouse location in JUAN> coordenates, i have been able to get the X and Y position in JUAN> pixels however getting the location in coordinates has not JUAN> been possible. My code as follows: JUAN> self.figura = Figure(figsize=(8,4), facecolor=0.99, dpi=70, JUAN> frameon=True) self.vista=self.figura.add_subplot(111) JUAN> self.canvas = FigureCanvasGTK(self.figura) JUAN> self.canvas.connect("button_press_event", self.location) You want matplotlib events, not gtk events self.canvas.mpl_connect("button_press_event", self.location) ^^^^ JDH 
From: JUAN ERNESTO FLORES BELTRAN <juanernestof@ho...>  20060525 21:46:15

Hi you all, I am trying to get the mouse location in coordenates, i have been able to get the X and Y position in pixels however getting the location in coordinates has not been possible. My code as follows:  self.figura = Figure(figsize=(8,4), facecolor=0.99, dpi=70, frameon=True) self.vista=self.figura.add_subplot(111) self.canvas = FigureCanvasGTK(self.figura) self.canvas.connect("button_press_event", self.location)  def location(self,widget, event): print event.x, event.y  however when i try:  def location(self,widget, event): print event.x, event.y  i do get the folowing: AttributeError: xdata Any sugestion to get the xdata value?? Thanks for your answers, any help highly appretiated Juan 
From: Graeme O'Keefe <gjok@ne...>  20060525 15:39:59

Seems the last email didn't deliver the attachement (even though I =20 received it back?) The attachment is here again but just in case, here's the code as =20 plain text: #!/usr/bin/env python # # ported fnnls.m to fnnls.py # # gjok  20050816 # import sys, math import numpy, numpy.linalg.linalg as la def any(X) : return len(numpy.nonzero(X)) !=3D 0 def find(X) : return numpy.nonzero(X) def norm(X, d) : return max(numpy.sum(abs(X))) # # x, w =3D fnnls(XtX, Xty, tol) # def fnnls(XtX, Xty, tol =3D 0) : #FNNLS Nonnegative leastsquares. # # Adapted from NNLS of Mathworks, Inc. # [x,w] =3D nnls(X, y) # # x, w =3D fnnls(XtX,Xty) returns the vector X that solves x =3D = pinv(XtX)=20 *Xty # in a least squares sense, subject to x >=3D 0. # Differently stated it solves the problem min y  Xx if # XtX =3D X'*X and Xty =3D X'*y. # # A default tolerance of TOL =3D MAX(SIZE(XtX)) * NORM(XtX,1) * = EPS # is used for deciding when elements of x are less than zero. # This can be overridden with x =3D fnnls(XtX,Xty,TOL). # # [x,w] =3D fnnls(XtX,Xty) also returns dual vector w where # w(i) < 0 where x(i) =3D 0 and w(i) =3D 0 where x(i) > 0. # # See also NNLS and FNNLSb # L. Shure 5887 # Revised, 121588,83189 LS. # (Partly) Copyright (c) 198494 by The MathWorks, Inc. # Modified by R. Bro 5796 according to # Bro R., de Jong S., Journal of Chemometrics, 1997, 11, 393401 # Corresponds to the FNNLSa algorithm in the paper #=09 # Rasmus bro # Chemometrics Group, Food Technology # Dept. Dairy and Food Science # Royal Vet. & Agricultural # DK1958 Frederiksberg C # Denmark # rb@... # http://newton.foodsci.kvl.dk/users/rasmus.html # Reference: # Lawson and Hanson, "Solving Least Squares Problems", Prentice=20 Hall, 1974. # # initialize variables m =3D XtX.shape[0] n =3D XtX.shape[1] if tol =3D=3D 0 : eps =3D 2.2204e16 tol =3D 10 * eps * norm(XtX,1)*max(m, n); #end P =3D numpy.zeros(n, numpy.Int16) P[:] =3D 1 Z =3D numpy.arange(0,n) z =3D numpy.zeros(m, numpy.Float) x =3D numpy.array(P) ZZ =3D numpy.array(Z) w =3D Xty  numpy.dot(XtX, x) # set up iteration criterion iter =3D 0 itmax =3D 30 * n # outer loop to put variables into set to hold positive coefficients while any(Z) and any(w[ZZ] > tol) : wt =3D w[ZZ].max() t =3D find(w[ZZ] =3D=3D wt) t =3D t[1:][0] t =3D ZZ[t] P[t] =3D t Z[t] =3D 1 PP =3D find(P !=3D 1) ZZ =3D find(Z !=3D 1) if len(PP) =3D=3D 1 : XtyPP =3D Xty[PP] XtXPP =3D XtX[PP, PP] z[PP] =3D XtyPP / XtXPP else : XtyPP =3D numpy.array(Xty[PP]) XtXPP =3D numpy.array(XtX[PP, numpy.array(PP)[:, =20 numpy.NewAxis]]) z[PP] =3D numpy.dot(XtyPP, la.generalized_inverse(XtXPP)) #end z[ZZ] =3D 0 # inner loop to remove elements from the positive set which no longer =20= belong while any(z[PP] <=3D tol) and (iter < itmax) : iter +=3D 1 iztol =3D find(z <=3D tol) ip =3D find(P[iztol] !=3D 1) QQ =3D iztol[ip] if len(QQ) =3D=3D 1 : alpha =3D x[QQ] / (x[QQ]  z[QQ]) else : x_xz =3D x[QQ] / (x[QQ]  z[QQ]) alpha =3D x_xz.min() #end x +=3D alpha * (z  x) iabs =3D find(abs(x) < tol) ip =3D find(P[iabs] !=3D 1) ij =3D iabs[ip] Z[ij] =3D numpy.array(ij) P[ij] =3D 1 PP =3D find(P !=3D 1) ZZ =3D find(Z !=3D 1) if len(PP) =3D=3D 1 : XtyPP =3D Xty[PP] XtXPP =3D XtX[PP, PP] z[PP] =3D XtyPP / XtXPP else : XtyPP =3D numpy.array(Xty[PP]) XtXPP =3D numpy.array(XtX[PP, numpy.array(PP)[:, =20 numpy.NewAxis]]) z[PP] =3D numpy.dot(XtyPP, = la.generalized_inverse(XtXPP)) #endif z[ZZ] =3D 0 #end while x =3D numpy.array(z) w =3D Xty  numpy.dot(XtX, x) #end while return x, w #end def if __name__ =3D=3D '__main__' : # # test [x, w] =3D fnnls(Xt.X, Xt.y, tol) # to solve min y  X.x s.t. x >=3D 0 # # matlab:lsqnonneg # X =3D [1, 10, 4, 10; 4, 5, 1, 12; 5, 1, 9, 20]; # y =3D [4; 7; 4] # x =3D lsqnonneg(X, y) =3D> x =3D [0.9312; 0.3683; 0; 0]; # X =3D numpy.array([[1, 10, 4, 10], [4, 5, 1, 12], [5, 1, 9, 20]], =20= numpy.Float) y =3D numpy.array([4, 7, 4], numpy.Float) if False : X =3D numpy.array([[1, 10, 4, 10], [4, 5, 1, 12], [5, 1, 9, 20], [4, 3, 2, 1]], numpy.Float) y =3D numpy.array([4, 7, 4, 1], numpy.Float) #end if False : X =3D numpy.zeros((20, 20), numpy.Float) for n in range(20) : X[n,:] =3D numpy.arange(0.0, 400.0, step =20= =3D 20) y =3D numpy.arange(0.0, 20.0) #end Xt =3D numpy.transpose(numpy.array(X)) x, w =3D fnnls(numpy.dot(Xt, X), numpy.dot(Xt, y)) print 'X =3D ', X print 'y =3D ', y print 'x =3D ', x #end if __name__ =3D=3D '__main__' Begin forwarded message: > From: Graeme O'Keefe <gjok@...> > Date: 25 May 2006 8:46:07 PM > To: matplotlib user list <matplotlibusers@...> > Subject: [Matplotlibusers] Fwd: python version of nnls/fnnls > > > Dear matplotlibers, > > I seem to use Non Negative Least Squares every couple of months and =20= > the most recent problem I solved reminded me that I meant to post =20 > this last November. > > fnnls.py is a port of fnnls.m which was written by Rasmus Bro =20 > (http://newton.foodsci.kvl.dk/users/rasmus.html, code available at =20 > http://www.ub.es/gesq/mcr/als/fnnls.m, and also available on Matlab =20= > Central). > > I've retained Rasmus' comments and translated the matlab to python/=20 > numpy. > =EF=BF=BC > > I've used this to linearise problems such as: > fitting Neutron TOF data with a E0 energy spectrum of gaussians =20= > (with energyresolutions a function of TOF =3D> E): exp(((E  E0) / =20= > deltaE)^2) > fitting Cerebral Blood Flow data with a kspectrum set of =20 > exponentials: exp(kt) > fitting BGO detector pulses (to detect/correct pileup) with =20 > Tdelayspectrum of exponentials: exp((t  Tdelay) / tau) > > Hope others find it as useful as I have. > > regards, > > > Graeme > > > > Graeme O'Keefe, PhD, MACPSEM > Principal Medical Physicist > Centre for PET > Austin Hospital > Tel: (613)94965767 > Fax: (613) 94585023 > > 
From: Alan G Isaac <aisaac@am...>  20060525 15:18:27

On Thu, 25 May 2006, Graeme O'Keefe apparently wrote: > fnnls.py And it is ... where? Cheers, Alan Isaac 
From: Graeme O'Keefe <gjok@ne...>  20060525 10:44:47

Dear matplotlibers, I seem to use Non Negative Least Squares every couple of months and =20 the most recent problem I solved reminded me that I meant to post =20 this last November. fnnls.py is a port of fnnls.m which was written by Rasmus Bro (http://=20= newton.foodsci.kvl.dk/users/rasmus.html, code available at http://=20 http://www.ub.es/gesq/mcr/als/fnnls.m, and also available on Matlab Central). I've retained Rasmus' comments and translated the matlab to python/=20 numpy. =EF=BF=BC I've used this to linearise problems such as: fitting Neutron TOF data with a E0 energy spectrum of gaussians =20= (with energyresolutions a function of TOF =3D> E): exp(((E  E0) / =20 deltaE)^2) fitting Cerebral Blood Flow data with a kspectrum set of =20 exponentials: exp(kt) fitting BGO detector pulses (to detect/correct pileup) with = Tdelay=20 spectrum of exponentials: exp((t  Tdelay) / tau) Hope others find it as useful as I have. regards, Graeme Graeme O'Keefe, PhD, MACPSEM Principal Medical Physicist Centre for PET Austin Hospital Tel: (613)94965767 Fax: (613) 94585023 
From: Bryan Cole <bryan@co...>  20060525 08:10:12

On Wed, 20060524 at 17:39 +0800, Allan Noriel Estrella wrote: > is there a way to modify the toolbars in matplotlib and add more > functions, like a button for averaging the data displayed then display > it; draw threshold levels and have a spin up button to change the > level while drawing it? To accomplish similar goals, I embed a matplotlib panel (including toolbar) in a wxPython application (and use the 'WXAgg' backend). Additional GUI elements (menus, controls etc.) can be added around the matplotlib panel for all the additional functionality you might need. With wxPython, the NavigationalToolbar object could easily be subclassed and additional elements added, if need be. You could equally well do this using an alternative toolkitofchoice (Tk, gtk, Qt etc.). See the "embedding_in_*.py" files from the matplotlib examples set. Bryan 
From: Graeme O'Keefe <graeme.okeefe@pe...>  20060525 03:40:33

Dear matplotlibers, I seem to use Non Negative Least Squares every couple of months and =20 the most recent problem I solved reminded me that I meant to post =20 this last November. fnnls.py is a port of fnnls.m which was written by Rasmus Bro (http://=20= newton.foodsci.kvl.dk/users/rasmus.html, code available at http://=20 http://www.ub.es/gesq/mcr/als/fnnls.m, and also available on Matlab Central). I've retained Rasmus' comments and translated the matlab to python/=20 numpy. =EF=BF=BC I've used this to linearise problems such as: fitting Neutron TOF data with a E0 energy spectrum of gaussians =20= (with energyresolutions a function of TOF =3D> E): exp(((E  E0) / =20 deltaE)^2) fitting Cerebral Blood Flow data with a kspectrum set of =20 exponentials: exp(kt) fitting BGO detector pulses (to detect/correct pileup) with = Tdelay=20 spectrum of exponentials: exp((t  Tdelay) / tau) Hope others find it as useful as I have. regards, Graeme Graeme O'Keefe, PhD, MACPSEM Principal Medical Physicist Centre for PET Austin Hospital Tel: (613)94965767 Fax: (613) 94585023 
From: Ramon Felciano <felciano@in...>  20060525 02:28:34

Hi  I'm trying to create a scatter plot that represents a matrix crossreferencing two categorical lists. For example, given the following: Joe San Francisco Sam Seattle Frank New York Frank Boston Frank Miami Andrew Miami I would like a scatter plot with names on one axis and cities on the other, with a plot mark at the axis intersections where there is a match. Is there a way to map directly from such categories to matplotlib datasets, or do I first need to map this to a numerical indexing scheme and then visualize it? Is there a simple way to at least have the categories used as value labels on the axes? I'm new to matplotlib, so if this turns out to be a poor fit for this tool, any pointers to better tools (preferrably pythonbased) would be appreciated. Thank you in advance for your time, Ramon + This email message (and any attached document) contains information from = Ingenuity Systems Inc. which may be considered confidential by = Ingenuity, or which may be privileged or otherwise exempt from = disclosure under law, and is for the sole use of the individual or = entity to whom it is addressed. Any other dissemination, distribution = or copying of this message is strictly prohibited. If you receive this = message in error, please notify me and destroy the attached message (and = all attached documents) immediately.=20 