matplotlib-users Mailing List for matplotlib

Received from Adam Mercer on Tue, Nov 14, 2006 at 03:55:15PM EST:
> On 14/11/06, Lev Givon <le...@co...> wrote:
> 
> >I am also using Apple's X11 1.1.2 / Xfree86 4.4.0. Has anyone had any
> >success using the above Fink packages?
> 
> There is a font problem with X11 1.1.2, upgrade to X11 1.1.3 which was
> released yesterday and you should be OK.
> 
> Cheers
> 
> Adam

I upgraded X11 as indicated and rebuilt/reinstalled matplotlib, but
the problem persists. Should I rebuild/install the graphical libraries
matplotlib depends on (e.g., freetype, gtk)?

							Thanks,
							L.G.

On 14/11/06, Lev Givon <le...@co...> wrote:

> I am also using Apple's X11 1.1.2 / Xfree86 4.4.0. Has anyone had any
> success using the above Fink packages?

There is a font problem with X11 1.1.2, upgrade to X11 1.1.3 which was
released yesterday and you should be OK.

Cheers

Adam

I recently tried to build/install matplotlib 0.87.7 on an Intel iMac
running Mac OS X 10.4.8 via Fink. Although the installation succeeded,
any attempts to import pylab or other modules from matplotlib result
in a segfault. Tracing python2.4 while attempting to run a script that
imports pylab indicates that the crash occurs shortly after it
attempts to open and read data from some font file.

Here are the versions I am using:

matplotlib-py24-0.87.7-1
python24-2.4.3-1001
scipy-core-py24-1.0-4

I am also using Apple's X11 1.1.2 / Xfree86 4.4.0. Has anyone had any
success using the above Fink packages?

						L.G.

Thanks, Eric.  I will try it.

On 11/14/06, Eric Firing <ef...@ha...> wrote:
>
> Tom,
>
> I have made several changes in svn to improve the ability of colorbar to
> track changes in colormap and alpha.  It works with imshow, pcolor, and
> contourf.  The implementation could be improved--maybe later.  Here are
> minimal illustrations you can use with ipython -pylab:
>
> figure(); C = pcolor(rand(3,3)); colorbar(); C.set_alpha(0.5); draw()
>
> figure(); C = contourf(rand(3,3)); colorbar(); C.set_alpha(0.5); draw()
>
> figure(); C = imshow(rand(3,3)); colorbar(); C.set_alpha(0.4); draw()
>
> The agg backend has trouble rendering the "continuous" colorbar with
> non-unit alpha; it comes out somewhat striped.  Turning on antialiasing
> makes it better for small to moderate alpha, but causes similar
> artifacts to appear for alpha=1, so I don't know of any good solution.
> Good appearance is more important for the most common case of alpha=1,
> so I am leaving antialiasing off.  I haven't checked other backends that
> support alpha.
>
> Eric
>
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Tom,

I have made several changes in svn to improve the ability of colorbar to 
track changes in colormap and alpha.  It works with imshow, pcolor, and 
contourf.  The implementation could be improved--maybe later.  Here are 
minimal illustrations you can use with ipython -pylab:

figure(); C = pcolor(rand(3,3)); colorbar(); C.set_alpha(0.5); draw()

figure(); C = contourf(rand(3,3)); colorbar(); C.set_alpha(0.5); draw()

figure(); C = imshow(rand(3,3)); colorbar(); C.set_alpha(0.4); draw()

The agg backend has trouble rendering the "continuous" colorbar with 
non-unit alpha; it comes out somewhat striped.  Turning on antialiasing 
makes it better for small to moderate alpha, but causes similar 
artifacts to appear for alpha=1, so I don't know of any good solution. 
Good appearance is more important for the most common case of alpha=1, 
so I am leaving antialiasing off.  I haven't checked other backends that 
support alpha.

Eric

Hello at all
I have to do the following:
I have tu use as base grap the following:

 def calibrazione(self, massa, ampveliniziale, freqiniziale, freqfinale  =
                           , freqps, avps, freqss, avss):
        import math
        dati =3D []
        for freq in range(int(freqiniziale), int(freqfinale)):
            forza =3D float(massa) * ((2*math.pi*freq)**2)/10
            dati.append(forza)
        ax1 =3D subplot(111)
        plot(dati)
        xlabel('Frequenza HZ')
        ylabel('Forza daN')
        title('Grafico Calibrazione')
        ax2 =3D twinx()
        plot([0.1,0.2,0.3],'r-')
        ylabel('Gradi')
        ax2.yaxis.tick_right()
      #  savefig('test.pdf')
        show()

On this graf i have to draw others two signals that cam from two instrume=
ntes every 20 seconds for 115 ties.
Thare  are some sample or someone can explain it to me

Regards 

Luca=0A=0A=0A------------------------------------------------------=0AChi=
 punta sull=92inglese naturale Wall Street Institute, vince un English Bo=
x! Scopri come ritirare i tuoi premi, clicca qui!=0Ahttp://click.libero.i=
t/wallstreet14nov=0A

Ciao Sarah
> cross_correlate, but given two arrays, that function returns a single
> scalar value (whereas I was expecting a list of correlation
> coefficients corresponding to how well the two signals match on
> successive lags).

That's not really a mpl question, more a 'numerix' one: what does your numerix 
stand for ? numpy ? numarray ?

For numpy, I guess that the corresponding function is 'correlate', which you 
must use with the 'full' parameter:
>>>corxy = N.correlate(x, y, 'full')
Unless I'm mistaken, the lags are then 1-n, 2-n,..., -1, 0, 1, ..., n-1
Note that you may want to use anomalies (ie, x-x.mean() and y-y.mean()), and 
divide by the variances to get proper coefficients

A second possibility is to use FFTs,
>>>Fx = N.fft.fft(x, npad, axis=axis)
>>>Fy = N.fft.fft(y, npad, axis=axis)
>>>iFxy = N.fft.ifft(Fx.conj()*Fy,n=npad,axis=axis).real

with npad=x.size+y.size
The lags are then 0,1,2,...,n,1-n,...,-1

If you're interested, you can use these two functions that I had written

#..............................................................................
def ccf(x, y, axis=None):    
    """Computes the cross-correlation function of two series `x` and `y`.
Note that the computations are performed on anomalies (deviations from 
average).
Returns the values of the cross-correlation at different lags.
Lags are given as [0,1,2,...,n,n-1,n-2,...,-2,-1].
 
:Parameters:
    `x` : 1D MaskedArray
        Time series.
    `y` : 1D MaskedArray
        Time series.
    `axis` : integer *[None]*
        Axis along which to compute (0 for rows, 1 for cols).
        If `None`, the array is flattened first.
    """
    assert(x.ndim == y.ndim, "Inconsistent shape !")
#    assert(x.shape == y.shape, "Inconsistent shape !")   
    if axis is None:    
        if x.ndim > 1:
            x = x.ravel()
            y = y.ravel()
        npad = x.size + y.size
        xanom = (x - x.mean(axis=None))
        yanom = (y - y.mean(axis=None))
        Fx = N.fft.fft(xanom, npad, )
        Fy = N.fft.fft(yanom, npad, )
        iFxy = N.fft.ifft(Fx.conj()*Fy).real
        varxy = N.sqrt(N.inner(xanom,xanom) * N.inner(yanom,yanom))
    else:
        npad = x.shape[axis] + y.shape[axis]
        if axis == 1:
            if x.shape[0] != y.shape[0]:
                raise ValueError, "Arrays should have the same length!"
            xanom = (x - x.mean(axis=1)[:,None])
            yanom = (y - y.mean(axis=1)[:,None])
            varxy = N.sqrt((xanom*xanom).sum(1) * (yanom*yanom).sum(1))
[:,None]
        else:
            if x.shape[1] != y.shape[1]:
                raise ValueError, "Arrays should have the same width!"
            xanom = (x - x.mean(axis=0))
            yanom = (y - y.mean(axis=0))
            varxy = N.sqrt((xanom*xanom).sum(0) * (yanom*yanom).sum(0))
        Fx = N.fft.fft(xanom, npad, axis=axis)
        Fy = N.fft.fft(yanom, npad, axis=axis)
        iFxy = N.fft.ifft(Fx.conj()*Fy,n=npad,axis=axis).real
    #    
    return iFxy/varxy
#..............................................................................
def ccf1d(x,y):
    """Computes the crosscorrelation of two flat arrays `x` and `y`, with the
numpy.correlate function.
Note that the computations are performed on anomalies (deviations from 
average).
    """
    if x.ndim > 1:
        x = x.ravel()
    if y.ndim > 1:
        y = y.ravel()
    (xanom, yanom) = (x-x.mean(), y-y.mean())
    corxy = N.correlate(xanom, yanom, 'full')
    n = min(x.size, y.size)
    #    return N.r_[ xc[len(yf)-1:], 0, xc[:len(yf)-1] ]
    corxy = N.r_[ corxy[:n][::-1], 0, corxy[n:][::-1] ]
    varxy = N.sqrt(N.inner(xanom,xanom) * N.inner(yanom,yanom))
    return corxy/varxy

>
> So ... does matplotlib have something akin to what I'm after or is
> there an extention that might have it?
>
> Very many thanks,
>
> Sarah
>
> (*)
>
> http://www.mathworks.com/access/helpdesk/help/toolbox/garch/index.html?/acc
>ess/helpdesk/help/toolbox/garch/crosscorr.html
> http://www.mathworks.com/access/helpdesk/help/toolbox/signal/index.html?/ac
>cess/helpdesk/help/toolbox/signal/xcorr.html

> So ... does matplotlib have something akin to what I'm after or is
> there an extention that might have it?

Perhaps the 'correlate' function in NumPy is what you're looking for?

Joris


Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

Hi y'all.

I have two series of data, taken over a period of time and I want to
know whether one set of data contains a signal similar to that in the
other. I believe the way to do this is to use cross-correlation(*) and
I notice that dir(pylab) now contains a function called
cross_correlate, but given two arrays, that function returns a single
scalar value (whereas I was expecting a list of correlation
coefficients corresponding to how well the two signals match on
successive lags).

So ... does matplotlib have something akin to what I'm after or is
there an extention that might have it?

Very many thanks,

Sarah

(*)

http://www.mathworks.com/access/helpdesk/help/toolbox/garch/index.html?/access/helpdesk/help/toolbox/garch/crosscorr.html
http://www.mathworks.com/access/helpdesk/help/toolbox/signal/index.html?/access/helpdesk/help/toolbox/signal/xcorr.html

-- 
Work:   http://www.cogentcomputing.org
Blog:    http://varspool.blogspot.com
Photos: http://flickr.com/photos/sarahmount/