matplotlib-users Mailing List for matplotlib

Hallöchen!

I visualize experimental data in quasi-realtime while it's acquired.
The x-axis is fixed right from the beginning.  However, the y-axis
must be autoscaled depending on the data that's coming in.

So far, I do the following on Windows:

#--------------------------------------------------
ion()

NaN = 0.0

x_values = arange(0, 2 * zyklen, 1.0 / number_of_values)
y_values = total_number_of_values * [NaN]
line, = plot(x_values, y_values)

for halfcycle in range(zyklen*2):
    keithley.write("F0B2M2G0T2Q%dI%dX" % (milliseconds, number_of_values))
    keithley.trigger()
    keithley.wait_for_srq()
    # Here I read the values
    voltages = keithley.read_floats()

    # Insert the values in the set of data values
    y_values[halfcycle * number_of_values : (halfcycle+1) * number_of_values] = \
                       voltages
    line.set_ydata(y_values)        # Plot update
    gcf().autoscale_view()          # This has no effect
    draw()
    time.sleep(1)
#--------------------------------------------------

I have three questions:

1. On Linux, I can say NaN=float("NaN"), which suppresses plot
   points in an area where no plot exists so far.  On Windows, I seem
   to be forced to set the un-measures interval to "0".  This is
   uglier, is there a better way?

2. The above loop is the last part of my program.  The program
   terminates with

       Fatal Python error: PyEval_RestoreThread: NULL tstate

   Apparently, this is done by my improper handling of matplotlib.
   What's going wrong?

3. How can I achieve that dynamic autoscaling of the y-axis?  As
   noted in the source, "gcf().autoscale_view" has no effect.

Thank you!

Tschö,
Torsten.

-- 
Torsten Bronger, aquisgrana, europa vetus

>>>>> "Peter" == Peter L Buschman <plb...@io...> writes:

    Peter> Another newbie question... I want to explicitly set the
    Peter> maximum x and y ranges for my plot and don't want them
    Peter> scaled down.  What is the best way to do this?

To control the x and y ranges, see the xlim, ylim and axis commands in
pylab, or the ax.set_xlim and ax.set_ylim commands in the API.

To turn off autoscaling, set the "autoscale_on" property of the axes
at axes creation time with a kwarg 

  ax = subplot(111, autoscale_on=False)
 
or after an axes is created with a set method

  ax.set_autoscale_on(False)

This property was introduced in matplotlib-0.82, so you need the
latest version for it to work.

JDH

Another newbie question... I want to explicitly set the maximum x and y 
ranges for my
plot and don't want them scaled down.  What is the best way to do this?

Thanks for your patience :)

>>>>> "Vidar" == Vidar Gundersen <vid...@37...> writes:

    Vidar> ===== Original message from John Hunter | Sat, 02 Jul 2005:
    >> props = dict(color='black', linestyle='--', linewidth=1)
    >> axvline(x=0, **props) axhline(y=0, **props)

    Vidar> this is nice, i like this.  the only problem with this
    Vidar> approach, compared to using actual gridlines, is that it
    Vidar> sets axis ranges, so it might be a problem when data are
    Vidar> not located around zero.

Yes, this is a potential problem with this approach, but you can
always set the xlim and ylim manually.  One could add a kwarg to the
various plot commands like noupdate to suppress updating the datalim,
but I don't know if there are enough use cases to justify this
convenience over simply setting the view limits manually.

    Vidar> i'm using this for plotting PCA Scores, calculated via the
    Vidar> ChemoPy package. in many chemometrics applications the
    Vidar> usual thing is to use the sample number, or variable names
    Vidar> (for the loadings plot), as a plotting symbol.  are there
    Vidar> currently a routine for this in matplotlib?

    Vidar> i think i can create such a plot using a for-loop and
    Vidar> figtext, and still be able to color the dot and the text
    Vidar> using a colormap?

Yes, you could do this, but it would take a bit of work to get
everything right.  Basically, you would like to add string symbol
markers to scatter, and have them colored with colormaps and support
variable sizeing as well, right?  The right way to do this, I think,
would be to implement a TextCollection, following the examples in
collections.py.  Otherwise it would be extremely slow for large
numbers of markers.  This would be a useful class anyhow to support
drawing of text with shared property (eg tick labels) since text
drawing is slow and is a bottleneck in some applications.

    Vidar> what approach do you recommend for scaling the marker size
    Vidar> relative to axis ranges and figure size?

I'm not sure what you are after here.  marker sizes are in points, so
you can pick the physical sizes you want.

    >> If for some reason you *really* want to use the actual
    >> gridlines functionality w/o affecting the ticks, you could
    >> selectively toggle the visible property of the gridlines you do
    >> not want to show.

    Vidar> how can i do this?

Here is the basic idea -- this is untested so it might have an error
in it

for gline in ax.get_xgridlines():
   xmin, xmax = gline.get_xdata()  # both should be the same
   gline.set_visible(xmin==0.0)    # visible only at x==0

and then do the same for y.  Of course you will need to make sure you
actually have a tick/grid at 0.  Let me know how this works.

>>>>> "Jesper" == Jesper Larsen <jl...@dm...> writes:

    >> 2) How do I make a CheckButtons class with only one button? The
    >> example below fails with a "TypeError: unsubscriptable object".

    Jesper> I still don't know the answer to this problem.

This was fixed in revision 1.15 of widgets.py in CVS, available at

  http://cvs.sourceforge.net/viewcvs.py/matplotlib/matplotlib/lib/matplotlib/widgets.py?rev=1.15&view=log

You should be able to just drop the updated file in
site-packages/matplotlib.

Cheers,
JDH

On Monday 04 July 2005 13:10, you wrote:
> 1) It seems like I can't initialize boolean variables prior to using them.
> The example below complains that bsub or bwin are referenced before
> assignment when I click on the checkbuttons.
>
> #!/usr/bin/env /usr/bin/python
> from pylab import *
> from matplotlib.widgets import *
>
> def test():
>   # Initialize values
>   bsub = False
>   bwin = False
>   print bsub,bwin
>
>   winax = axes([0.125, 0.025, 0.175, 0.1])
>   check = CheckButtons(winax, ('Subsample','Window'), (False, False))
>
>   def win(label):
>     if label == 'Subsample': bsub = not bsub
>     if label == 'Window': bwin = not bwin
>   check.on_clicked(win)
>
>   show()
>
> if __name__ == '__main__': test()

Note to self: Think before posting:-)

Of course the win function does not know about the bsub and bwin variables in 
the parent function.

> 2) How do I make a CheckButtons class with only one button? The example
> below fails with a "TypeError: unsubscriptable object".

I still don't know the answer to this problem.

Hi matplotlib-users,

I'm trying to make a very simple graphical user interface using the matplotlib 
widgets. While doing this I encountered two problems using the CheckButtons 
class:

1) It seems like I can't initialize boolean variables prior to using them. The 
example below complains that bsub or bwin are referenced before assignment 
when I click on the checkbuttons.

#!/usr/bin/env /usr/bin/python
from pylab import *
from matplotlib.widgets import *

def test():
  # Initialize values
  bsub = False
  bwin = False
  print bsub,bwin

  winax = axes([0.125, 0.025, 0.175, 0.1])
  check = CheckButtons(winax, ('Subsample','Window'), (False, False))

  def win(label):
    if label == 'Subsample': bsub = not bsub
    if label == 'Window': bwin = not bwin
  check.on_clicked(win)

  show()

if __name__ == '__main__': test()

2) How do I make a CheckButtons class with only one button? The example below 
fails with a "TypeError: unsubscriptable object".

#!/usr/bin/env /usr/bin/python
from pylab import *
from matplotlib.widgets import *

def test():
  # Initialize values
  bsub = False

  winax = axes([0.125, 0.025, 0.175, 0.1])
  check = CheckButtons(winax, ('Subsample'), (False))

  def win(label):
    if label == 'Subsample': bsub = not bsub
  check.on_clicked(win)

  show()

if __name__ == '__main__': test()

Kind regards,
Jesper