matplotlib-users Mailing List for matplotlib (Page 69)

Not directly related, but considering the similarity of the mail I've
decided to just append my issue here.
I'm using python 3.4 x86 in virtualenv on win7 x64 with full copy of
Visual Studio 2010. This is my Exception message:

Exception information:
____________________________________
C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\BIN\cl.exe /c
/nologo /Ox /MD /W3 /GS- /DNDEBUG
-DPY_ARRAY_UNIQUE_SYMBOL=MPL_matplotlib_ft2font_ARRAY_API
-DPYCXX_ISO_CPP_LIB=1 -DPYCXX_PYTHON_2TO3=1
-IC:\Users\Dino\Desktop\Virtual\lib\site-packages\numpy\core\include
-I. -IC:\Users\Dino\Desktop\Virtual\include -IC:\python34\include
-IC:\python34\include /Tpsrc/ft2font.cpp
/Fobuild\temp.win32-3.4\Release\src/ft2font.obj

ft2font.cpp

C:\Program Files (x86)\Microsoft Visual Studio
10.0\VC\INCLUDE\xlocale(323) : warning C4530: C++ exception handler
used, but unwind semantics are not enabled. Specify /EHsc

c:\users\dino\desktop\virtual\build\matplotlib\src\ft2font.h(16) :
fatal error C1083: Cannot open include file: 'ft2build.h': No such
file or directory

error: command 'C:\\Program Files (x86)\\Microsoft Visual Studio
10.0\\VC\\BIN\\cl.exe' failed with exit status 2
________________________________


2014-06-26 2:52 GMT+02:00 Alan Ezust <ala...@gm...>:
> Hi, I'm trying to build matplotlib (git master) on windows 7, Python 3.3,
> with ms visual C++ 2010 SP1
> The error I am getting is related to how it can't find ft2build.h but
> after running freetype-2.3.5-1.setup.exe, there is now a copy of it under
> C:\Program Files (x86)\GnuWin32\include
> How do I tell setup.py to look there for the header file?
> Any other hints building this building on Windows 7 would be appreciated.
>
> [C:\Workspace\matplotlib]python setup.py build
> ============================================================================
> Edit setup.cfg to change the build options
>
> BUILDING MATPLOTLIB
>             matplotlib: yes [1.4.x]
>                 python: yes [3.3.5 (v3.3.5:62cf4e77f785, Mar  9 2014,
>                         10:35:05) [MSC v.1600 64 bit (AMD64)]]
>               platform: yes [win32]
>
> REQUIRED DEPENDENCIES AND EXTENSIONS
>                  numpy: yes [version 1.8.1]
>                    six: yes [using six version 1.7.2]
>               dateutil: yes [using dateutil version 2.2]
>                tornado: yes [tornado was not found. It is required for the
>                         WebAgg backend. pip/easy_install may attempt to
>                         install it after matplotlib.]
>              pyparsing: yes [using pyparsing version 2.0.2]
>                  pycxx: yes [Official versions of PyCXX are not compatible
>                         with Python 3.x.  Using local copy]
>                 libagg: yes [pkg-config information for 'libagg' could not
>                         be found. Using local copy.]
>               freetype: yes [Unknown version]
>                    png: yes [pkg-config information for 'libpng' could not
>                         be found. Using unknown version.]
>                  qhull: yes [pkg-config information for 'qhull' could not be
>                         found. Using local copy.]
>
> OPTIONAL SUBPACKAGES
>            sample_data: yes [installing]
>               toolkits: yes [installing]
>                  tests: yes [nose 0.11.1 or later is required to run the
>                         matplotlib test suite.  pip/easy_install may attempt
>                         to install it after matplotlib. / using
>                         unittest.mock]
>
> OPTIONAL BACKEND EXTENSIONS
>                 macosx: no  [Mac OS-X only]
>                 qt5agg: yes [installing, Qt: 5.2.1, PyQt: 5.2.1]
>                 qt4agg: no  [PyQt4 not found]
>                 pyside: no  [PySide not found]
>                gtk3agg: no  [gtk3agg backend does not work on Python 3]
>              gtk3cairo: no  [Requires cairocffi or pycairo to be installed.]
>                 gtkagg: no  [Requires pygtk]
>                  tkagg: no  [The C/C++ header for Tk (tk.h) could not be
>                         found.  You may need to install the development
>                         package.]
>                  wxagg: no  [requires wxPython]
>                    gtk: no  [Requires pygtk]
>                    agg: yes [installing]
>                  cairo: no  [cairocffi or pycairo not found]
>              windowing: yes [installing, installing]
>
> OPTIONAL LATEX DEPENDENCIES
>                 dvipng: no
>            ghostscript: no
>                  latex: no
>                pdftops: no
>
> running build
> running build_py
> copying lib\matplotlib\mpl-data\matplotlibrc ->
> build\lib.win-amd64-3.3\matplotlib\mpl-data
> running build_ext
> building 'matplotlib.ft2font' extension
> C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\Bin\cl.exe /c /nologo
> /Ox /MD /W3 /GS- /DNDEBUG -DPY_ARRAY_UN
> IQUE_SYMBOL=MPL_matplotlib_ft2font_ARRAY_API -DPYCXX_ISO_CPP_LIB=1
> -DPYCXX_PYTHON_2TO3=1 -IC:\Python33\lib\site-pack
> ages\numpy\core\include -I. -Iextern -IC:\Python33\include
> -IC:\Python33\include /Tpsrc/ft2font.cpp /Fobuild\temp.wi
> n-amd64-3.3\Release\src/ft2font.obj
> ft2font.cpp
> C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\INCLUDE\xlocale(323)
> : warning C4530: C++ exception handler u
> sed, but unwind semantics are not enabled. Specify /EHsc
> c:\users\r4hr-dev\workspace\matplotlib\src\ft2font.h(16) : fatal error
> C1083: Cannot open include file: 'ft2build.h'
> : No such file or directory
> error: command '"C:\Program Files (x86)\Microsoft Visual Studio
> 10.0\VC\Bin\cl.exe"' failed with exit status 2
>
>
> ------------------------------------------------------------------------------
> Open source business process management suite built on Java and Eclipse
> Turn processes into business applications with Bonita BPM Community Edition
> Quickly connect people, data, and systems into organized workflows
> Winner of BOSSIE, CODIE, OW2 and Gartner awards
> http://p.sf.net/sfu/Bonitasoft
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

Hi, I'm trying to build matplotlib (git master) on windows 7, Python 3.3,
with ms visual C++ 2010 SP1
The error I am getting is related to how it can't find ft2build.h but
after running freetype-2.3.5-1.setup.exe, there is now a copy of it under
C:\Program Files (x86)\GnuWin32\include
How do I tell setup.py to look there for the header file?
Any other hints building this building on Windows 7 would be appreciated.

[C:\Workspace\matplotlib]python setup.py build
============================================================================
Edit setup.cfg to change the build options

BUILDING MATPLOTLIB
            matplotlib: yes [1.4.x]
                python: yes [3.3.5 (v3.3.5:62cf4e77f785, Mar  9 2014,
                        10:35:05) [MSC v.1600 64 bit (AMD64)]]
              platform: yes [win32]

REQUIRED DEPENDENCIES AND EXTENSIONS
                 numpy: yes [version 1.8.1]
                   six: yes [using six version 1.7.2]
              dateutil: yes [using dateutil version 2.2]
               tornado: yes [tornado was not found. It is required for the
                        WebAgg backend. pip/easy_install may attempt to
                        install it after matplotlib.]
             pyparsing: yes [using pyparsing version 2.0.2]
                 pycxx: yes [Official versions of PyCXX are not compatible
                        with Python 3.x.  Using local copy]
                libagg: yes [pkg-config information for 'libagg' could not
                        be found. Using local copy.]
              freetype: yes [Unknown version]
                   png: yes [pkg-config information for 'libpng' could not
                        be found. Using unknown version.]
                 qhull: yes [pkg-config information for 'qhull' could not be
                        found. Using local copy.]

OPTIONAL SUBPACKAGES
           sample_data: yes [installing]
              toolkits: yes [installing]
                 tests: yes [nose 0.11.1 or later is required to run the
                        matplotlib test suite.  pip/easy_install may attempt
                        to install it after matplotlib. / using
                        unittest.mock]

OPTIONAL BACKEND EXTENSIONS
                macosx: no  [Mac OS-X only]
                qt5agg: yes [installing, Qt: 5.2.1, PyQt: 5.2.1]
                qt4agg: no  [PyQt4 not found]
                pyside: no  [PySide not found]
               gtk3agg: no  [gtk3agg backend does not work on Python 3]
             gtk3cairo: no  [Requires cairocffi or pycairo to be installed.]
                gtkagg: no  [Requires pygtk]
                 tkagg: no  [The C/C++ header for Tk (tk.h) could not be
                        found.  You may need to install the development
                        package.]
                 wxagg: no  [requires wxPython]
                   gtk: no  [Requires pygtk]
                   agg: yes [installing]
                 cairo: no  [cairocffi or pycairo not found]
             windowing: yes [installing, installing]

OPTIONAL LATEX DEPENDENCIES
                dvipng: no
           ghostscript: no
                 latex: no
               pdftops: no

running build
running build_py
copying lib\matplotlib\mpl-data\matplotlibrc ->
build\lib.win-amd64-3.3\matplotlib\mpl-data
running build_ext
building 'matplotlib.ft2font' extension
C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\Bin\cl.exe /c
/nologo /Ox /MD /W3 /GS- /DNDEBUG -DPY_ARRAY_UN
IQUE_SYMBOL=MPL_matplotlib_ft2font_ARRAY_API -DPYCXX_ISO_CPP_LIB=1
-DPYCXX_PYTHON_2TO3=1 -IC:\Python33\lib\site-pack
ages\numpy\core\include -I. -Iextern -IC:\Python33\include
-IC:\Python33\include /Tpsrc/ft2font.cpp /Fobuild\temp.wi
n-amd64-3.3\Release\src/ft2font.obj
ft2font.cpp
C:\Program Files (x86)\Microsoft Visual Studio 10.0\VC\INCLUDE\xlocale(323)
: warning C4530: C++ exception handler u
sed, but unwind semantics are not enabled. Specify /EHsc
c:\users\r4hr-dev\workspace\matplotlib\src\ft2font.h(16) : fatal error
C1083: Cannot open include file: 'ft2build.h'
: No such file or directory
error: command '"C:\Program Files (x86)\Microsoft Visual Studio
10.0\VC\Bin\cl.exe"' failed with exit status 2

What it means, specifically, is that one (or more) figure objects still in
memory at the time of exiting the python interpretor are missing the
expected canvas attribute. I see this happen in various backends from time
to time. The best I can figure is that somehow, the destructor for the
figure has already been called (as it should), which should eliminate its
"FigureManager" instance, but for some reason, that figure manager is still
around and its destructor finally tries to destroy the canvas, which is
already gone.

It is really an odd bug, and I am fairly certain my diagnostic isn't quite
right because I never was able to make a SSCE for
testing/debugging/traceback purposes. The closest I was able to get was
with a preliminary version of the animation framework Ryan May and I were
working on a few years back. His code was holding some extra references,
while destroying other objects... it was weird.

Sorry, I don't have any solutions for you. Maybe someone else will.

Ben Root


On Wed, Jun 25, 2014 at 8:29 AM, <kei...@bt...> wrote:

> I am running matplotlib 1.3.1 under Ubuntu 13 and python3.
> Everything works fine, but I get this message every time:
>
>   Error in atexit._run_exitfuncs:
>   AttributeError: 'FigureManagerGTK3Cairo' object has no attribute 'canvas'
>
> What does it mean, and how do I stop it?
>
> Keith
>
>
>
> ------------------------------------------------------------------------------
> Open source business process management suite built on Java and Eclipse
> Turn processes into business applications with Bonita BPM Community Edition
> Quickly connect people, data, and systems into organized workflows
> Winner of BOSSIE, CODIE, OW2 and Gartner awards
> http://p.sf.net/sfu/Bonitasoft
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

It is because the polar projection expects theta to be in radians, not
degrees. This is a somewhat common mistake as the default labelling of a
polar plot is in degrees, and so it tends to confuse new users.

Also, don't forget that your radar data is (most likely) describing
"bearing" (so, 0 azimuth is North rather than East).

Cheers!
Ben Root



On Wed, Jun 25, 2014 at 9:14 AM, Alexander Borghgraef <
ale...@gm...> wrote:

> Hi all,
>
>  I'm trying to plot some radar data I'm working on, which is presented as
> a 2048x1440 2D array of ints. This means 2048 azimuth angle values between
> 0° and 360°, and 1440 range increments. Array values represent signal
> return in that angle/range coordinate. Due to hardware problems, some angle
> ranges are missing, there the signal value is zero in the grid.
>  The obvious way of plotting this would be a polar grid, so I used a
> pcolormesh, like this:
>
>
>
>
> * import numpy as np  import matplotlib.pyplot as plt  *
>
> *  plt.ion()*
>
>
>
>
> *  fig = plt.figure()  ax = fig.add_subplot(111, projection='polar')
> theta,rad = np.meshgrid(np.arange(0., 360., 360./scan.shape[0]),
> np.arange(scan.shape[1]))   ax.pcolormesh(theta, rad, scan.T)   plt.draw()*
>
> I transposed scan because theta and rad are 1440x2048 arrays, where scan
> is a 2048x1440 array.
> The resulting image seemed ok, but there were no gaps, which I knew were
> there, so I ran the same code without the 'polar' option, and put them side
> to side. The result is this:
>
>
> ​In the rectangular plot, there is a gap between around 120° to 160°,
> which should be an empty wedge in the polar plot, but it isn't there. Same
> for the obvious echos at long range between 0° and 50°. OTOH the plot seems
> quite right, with the gating gap around the radar station in the center and
> the stronger echos at short range.
>  So I'm a bit baffled as to why this doesn't work the way it should. Does
> anyone here have an explanation?
>
> --
> Alex Borghgraef
>
>
> ------------------------------------------------------------------------------
> Open source business process management suite built on Java and Eclipse
> Turn processes into business applications with Bonita BPM Community Edition
> Quickly connect people, data, and systems into organized workflows
> Winner of BOSSIE, CODIE, OW2 and Gartner awards
> http://p.sf.net/sfu/Bonitasoft
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Hi all,

 I'm trying to plot some radar data I'm working on, which is presented as a
2048x1440 2D array of ints. This means 2048 azimuth angle values between 0°
and 360°, and 1440 range increments. Array values represent signal return
in that angle/range coordinate. Due to hardware problems, some angle ranges
are missing, there the signal value is zero in the grid.
 The obvious way of plotting this would be a polar grid, so I used a
pcolormesh, like this:




* import numpy as np  import matplotlib.pyplot as plt  *

*  plt.ion()*




*  fig = plt.figure()  ax = fig.add_subplot(111, projection='polar')
theta,rad = np.meshgrid(np.arange(0., 360., 360./scan.shape[0]),
np.arange(scan.shape[1]))   ax.pcolormesh(theta, rad, scan.T)  plt.draw()*

I transposed scan because theta and rad are 1440x2048 arrays, where scan is
a 2048x1440 array.
The resulting image seemed ok, but there were no gaps, which I knew were
there, so I ran the same code without the 'polar' option, and put them side
to side. The result is this:


​In the rectangular plot, there is a gap between around 120° to 160°, which
should be an empty wedge in the polar plot, but it isn't there. Same for
the obvious echos at long range between 0° and 50°. OTOH the plot seems
quite right, with the gating gap around the radar station in the center and
the stronger echos at short range.
 So I'm a bit baffled as to why this doesn't work the way it should. Does
anyone here have an explanation?

-- 
Alex Borghgraef

I am running matplotlib 1.3.1 under Ubuntu 13 and python3.
Everything works fine, but I get this message every time:

  Error in atexit._run_exitfuncs:  
  AttributeError: 'FigureManagerGTK3Cairo' object has no attribute 'canvas'

What does it mean, and how do I stop it?

Keith

i have read demo before. when not line not move auto, it can . but while my
case is need the diagram is dynamic move, i do not know how to modify my
code. i have tried many times.but failed.


2014-06-21 21:22 GMT+07:00 Eric Firing <ef...@ha...>:

> On 2014/06/21, 3:39 PM, 不坏阿峰 wrote:
> > is there someone can help me ?
>
> Posting a *simple*, self-contained example as a starting point would
> make it more likely that someone would understand your question.  Leave
> out everything that is irrelevant--I suspect all the gui and threading
> code is in that category.
>
> Also, although it is not directly related to your question, please note
> that you are using a horrible mixture of backend invocations, and even
> if it works now, it will do you no good in the long run.
>
> See the embedding examples: no matplotlib.use, no pylab, one and only
> one gui toolkit.
>
> I also suspect the answer to your question is at least partly in one of
> the examples of plotting with dates; dates and times are handled
> together.  Here is one such example:
>
> http://matplotlib.org/examples/pylab_examples/date_demo1.html
>
> Eric
>
> > many thanks
> >
> >
> > 2014-06-19 19:36 GMT+07:00 不坏阿峰 <onl...@gm...
> > <mailto:onl...@gm...>>:
> >
> >     Dear all
> >
> >     could some expert can help me.
> >     I have modify from one demo. but i do not how to change the x_lable
> >     to time like H:M:S, and can move it.  i have try some way, but
> failed.
> >
> >     hope some expert can do me a favor.
> >     thanks a lot
> >
> >     ######################
> >     # coding=utf-8
> >     import os
> >     import pprint
> >     import random, time
> >     import sys
> >     from PyQt4 import QtGui, QtCore
> >     from threading import *
> >     import time
> >     import datetime
> >
> >     import matplotlib
> >     matplotlib.use('WXAgg')
> >     from matplotlib.figure import Figure
> >     from matplotlib.backends.backend_qt4agg import \
> >          FigureCanvasQTAgg as FigCanvas, \
> >          NavigationToolbar2QT as NavigationToolbar
> >     import numpy as np
> >     import pylab
> >     class DataGen(object):
> >          """ A silly class that generates pseudo-random data for
> >              display in the plot.
> >          """
> >          def __init__(self, init=50):
> >              self.data = self.init = init
> >          def next(self):
> >              self._recalc_data()
> >              return self.data
> >          def _recalc_data(self):
> >              delta = random.uniform(-0.5, 0.5)
> >              r = random.random()
> >              if r > 0.9:
> >                  self.data += delta * 15
> >              elif r > 0.8:
> >                  # attraction to the initial value
> >                  delta += (0.5 if self.init > self.data else -0.5)
> >                  self.data += delta
> >              else:
> >                  self.data += delta
> >
> >     class myThing():
> >          class myThread(Thread):
> >              def __init__(self):
> >                  Thread.__init__(self)
> >                  self.running = True
> >                  self.vec = [0]
> >                  self.dg = DataGen()
> >
> >                  print "Initializing myThread..."
> >
> >              def run(self):
> >                  print "Running myThread..."
> >                  while self.running:
> >                      time.sleep(1)
> >                      self.vec.append(self.dg.next())
> >                      print "Splat"
> >              def getVec(self):
> >                  return self.vec
> >              def stop(self):
> >                  self.running = False
> >          def __init__(self):
> >              self.theThread = self.myThread()
> >              self.threadRunning = True
> >              print "initializing myThing..."
> >              self.theThread.start()
> >          def __del__(self):
> >              self.theThread.stop()
> >          def getVec(self):
> >              #print self.theThread.vec[:]
> >              return self.theThread.vec[:]
> >     class ApplicationWindow(QtGui.QMainWindow):
> >          """ The main window of the application
> >          """
> >          def __init__(self):
> >              QtGui.QMainWindow.__init__(self)
> >              self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
> >              self.setWindowTitle('Demo: dynamic matplotlib graph')
> >              self.thing1 = myThing()
> >              self.thing2 = myThing()
> >              self.starttime =  int(time.time())
> >
> >
> >              self.create_menu()
> >              #self.create_status_bar()
> >              self.create_main_panel()
> >              self.redraw_timer = QtCore.QTimer(self)
> >              QtCore.QObject.connect(self.redraw_timer,
> >     QtCore.SIGNAL("timeout()"), self.on_redraw_timer)
> >              self.redraw_timer.start(4000)
> >          def create_menu(self):
> >              menu_file = QtGui.QMenu("&File", self)
> >              #menu_file.addAction(u'&Save plot', self.on_save_plot,
> >              #                         QtCore.Qt.CTRL + QtCore.Qt.Key_S)
> >              menu_file.addSeparator()
> >              menu_file.addAction(u'E&xit', self.on_exit,
> >                                       QtCore.Qt.CTRL + QtCore.Qt.Key_X)
> >              self.menuBar().addMenu(menu_file)
> >          def create_main_panel(self):
> >              self.panel = QtGui.QFrame(self)
> >              self.setCentralWidget(self.panel)
> >              self.init_plot()
> >              self.canvas = FigCanvas(self.fig)
> >              self.canvas.setMinimumHeight(150)
> >              #self.toolbar = NavigationToolbar(self.canvas, None)
> >              self.vbox = QtGui.QVBoxLayout()
> >              self.vbox.addWidget(self.canvas)
> >
> >              self.panel.setLayout(self.vbox)
> >              #self.vbox.Fit(self)
> >              self.unit = 20
> >              width, height = self.geometry().width(),
> >     self.geometry().height()
> >              self.show()
> >          def init_plot(self):
> >              self.dpi = 100
> >              self.fig = Figure((5.0, 3.0), dpi=self.dpi)
> >              self.axes = self.fig.add_subplot(111, navigate=False)
> >              self.axes.set_axis_bgcolor('black')
> >
> >              self.axes.set_title('Very important random data', size=10)
> >              self.axes.set_xlabel('Time flies like an arrow',size=10)
> >              self.axes.set_ylabel('Random is just random',size=10)
> >              pylab.setp(self.axes.get_xticklabels(), fontsize=8)
> >              pylab.setp(self.axes.get_yticklabels(), fontsize=8)
> >              self.plot_data = self.axes.plot(
> >                  self.thing1.getVec(),
> >                  linewidth=0.5,
> >                  color=(1, 1, 0),
> >                  #marker='o',
> >                  label="set1",
> >                  )[0]
> >              print  self.thing1.getVec(), "<<>>"
> >              self.plot_data2 = self.axes.plot(
> >                  self.thing2.getVec(),
> >                  linewidth=1,
> >                  dashes=[.2, .4],
> >                  color=(0, 1, 1),
> >                  label="set2",
> >                  )[0]
> >
> >
> >          def draw_plot(self):
> >              """ Redraws the plot
> >              """
> >              self.data = self.thing1.getVec()
> >              self.data2 = self.thing2.getVec()
> >              def do_cal(urdata):
> >                  newdata = []
> >                  for x in range(len(urdata)):
> >                      urtime = x + self.starttime
> >                      newdata.append(urtime)
> >                  return newdata
> >
> >              xmax = len(self.data) if len(self.data) > 50 else 50
> >
> >              xmin = xmax - 50
> >
> >              min1 = min(self.data)
> >              min2 = min(self.data2)
> >              theMin = min(min1, min2)
> >
> >              ymin = round(theMin, 0) - 1
> >
> >              max1 = max(self.data)
> >              max2 = max(self.data2)
> >              theMax = max(max1, max2)
> >
> >              ymax = round(theMax, 0) + 1
> >
> >              self.axes.set_xbound(lower=xmin, upper=xmax)
> >              self.axes.set_ybound(lower=ymin, upper=ymax)
> >
> >              self.axes.grid(True, color='gray')
> >              pylab.setp(self.axes.get_xticklabels(),
> >                      visible=True)
> >
> >              self.plot_data.set_xdata(np.arange(len(self.data)))
> >              self.plot_data.set_ydata(np.array(self.data))
> >              self.plot_data2.set_xdata(np.arange(len(self.data2)))
> >              #self.plot_data2.set_xdata(np.array(newdata2))
> >              self.plot_data2.set_ydata(np.array(self.data2))
> >
> >              self.canvas.draw()
> >          def on_redraw_timer(self):
> >              self.draw_plot()
> >          def on_exit(self):
> >              self.close()
> >          def closeEvent(self, event):
> >              for thing in (self.thing1, self.thing2):
> >                  thing.theThread.stop()
> >                  thing.theThread.join()
> >     if __name__ == '__main__':
> >          app = QtGui.QApplication(sys.argv)
> >          aw = ApplicationWindow()
> >          aw.show()
> >          sys.exit(app.exec_())
> >
> >     #################################
> >     内嵌图片 1
> >
> >
> >
> >
> >
> ------------------------------------------------------------------------------
> > HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> > Find What Matters Most in Your Big Data with HPCC Systems
> > Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> > Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> > http://p.sf.net/sfu/hpccsystems
> >
> >
> >
> > _______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >
>
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

On 2014/06/21, 3:39 PM, 不坏阿峰 wrote:
> is there someone can help me ?

Posting a *simple*, self-contained example as a starting point would 
make it more likely that someone would understand your question.  Leave 
out everything that is irrelevant--I suspect all the gui and threading 
code is in that category.

Also, although it is not directly related to your question, please note 
that you are using a horrible mixture of backend invocations, and even 
if it works now, it will do you no good in the long run.

See the embedding examples: no matplotlib.use, no pylab, one and only 
one gui toolkit.

I also suspect the answer to your question is at least partly in one of 
the examples of plotting with dates; dates and times are handled 
together.  Here is one such example:

http://matplotlib.org/examples/pylab_examples/date_demo1.html

Eric

> many thanks
>
>
> 2014-06-19 19:36 GMT+07:00 不坏阿峰 <onl...@gm...
> <mailto:onl...@gm...>>:
>
>     Dear all
>
>     could some expert can help me.
>     I have modify from one demo. but i do not how to change the x_lable
>     to time like H:M:S, and can move it.  i have try some way, but failed.
>
>     hope some expert can do me a favor.
>     thanks a lot
>
>     ######################
>     # coding=utf-8
>     import os
>     import pprint
>     import random, time
>     import sys
>     from PyQt4 import QtGui, QtCore
>     from threading import *
>     import time
>     import datetime
>
>     import matplotlib
>     matplotlib.use('WXAgg')
>     from matplotlib.figure import Figure
>     from matplotlib.backends.backend_qt4agg import \
>          FigureCanvasQTAgg as FigCanvas, \
>          NavigationToolbar2QT as NavigationToolbar
>     import numpy as np
>     import pylab
>     class DataGen(object):
>          """ A silly class that generates pseudo-random data for
>              display in the plot.
>          """
>          def __init__(self, init=50):
>              self.data = self.init = init
>          def next(self):
>              self._recalc_data()
>              return self.data
>          def _recalc_data(self):
>              delta = random.uniform(-0.5, 0.5)
>              r = random.random()
>              if r > 0.9:
>                  self.data += delta * 15
>              elif r > 0.8:
>                  # attraction to the initial value
>                  delta += (0.5 if self.init > self.data else -0.5)
>                  self.data += delta
>              else:
>                  self.data += delta
>
>     class myThing():
>          class myThread(Thread):
>              def __init__(self):
>                  Thread.__init__(self)
>                  self.running = True
>                  self.vec = [0]
>                  self.dg = DataGen()
>
>                  print "Initializing myThread..."
>
>              def run(self):
>                  print "Running myThread..."
>                  while self.running:
>                      time.sleep(1)
>                      self.vec.append(self.dg.next())
>                      print "Splat"
>              def getVec(self):
>                  return self.vec
>              def stop(self):
>                  self.running = False
>          def __init__(self):
>              self.theThread = self.myThread()
>              self.threadRunning = True
>              print "initializing myThing..."
>              self.theThread.start()
>          def __del__(self):
>              self.theThread.stop()
>          def getVec(self):
>              #print self.theThread.vec[:]
>              return self.theThread.vec[:]
>     class ApplicationWindow(QtGui.QMainWindow):
>          """ The main window of the application
>          """
>          def __init__(self):
>              QtGui.QMainWindow.__init__(self)
>              self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
>              self.setWindowTitle('Demo: dynamic matplotlib graph')
>              self.thing1 = myThing()
>              self.thing2 = myThing()
>              self.starttime =  int(time.time())
>
>
>              self.create_menu()
>              #self.create_status_bar()
>              self.create_main_panel()
>              self.redraw_timer = QtCore.QTimer(self)
>              QtCore.QObject.connect(self.redraw_timer,
>     QtCore.SIGNAL("timeout()"), self.on_redraw_timer)
>              self.redraw_timer.start(4000)
>          def create_menu(self):
>              menu_file = QtGui.QMenu("&File", self)
>              #menu_file.addAction(u'&Save plot', self.on_save_plot,
>              #                         QtCore.Qt.CTRL + QtCore.Qt.Key_S)
>              menu_file.addSeparator()
>              menu_file.addAction(u'E&xit', self.on_exit,
>                                       QtCore.Qt.CTRL + QtCore.Qt.Key_X)
>              self.menuBar().addMenu(menu_file)
>          def create_main_panel(self):
>              self.panel = QtGui.QFrame(self)
>              self.setCentralWidget(self.panel)
>              self.init_plot()
>              self.canvas = FigCanvas(self.fig)
>              self.canvas.setMinimumHeight(150)
>              #self.toolbar = NavigationToolbar(self.canvas, None)
>              self.vbox = QtGui.QVBoxLayout()
>              self.vbox.addWidget(self.canvas)
>
>              self.panel.setLayout(self.vbox)
>              #self.vbox.Fit(self)
>              self.unit = 20
>              width, height = self.geometry().width(),
>     self.geometry().height()
>              self.show()
>          def init_plot(self):
>              self.dpi = 100
>              self.fig = Figure((5.0, 3.0), dpi=self.dpi)
>              self.axes = self.fig.add_subplot(111, navigate=False)
>              self.axes.set_axis_bgcolor('black')
>
>              self.axes.set_title('Very important random data', size=10)
>              self.axes.set_xlabel('Time flies like an arrow',size=10)
>              self.axes.set_ylabel('Random is just random',size=10)
>              pylab.setp(self.axes.get_xticklabels(), fontsize=8)
>              pylab.setp(self.axes.get_yticklabels(), fontsize=8)
>              self.plot_data = self.axes.plot(
>                  self.thing1.getVec(),
>                  linewidth=0.5,
>                  color=(1, 1, 0),
>                  #marker='o',
>                  label="set1",
>                  )[0]
>              print  self.thing1.getVec(), "<<>>"
>              self.plot_data2 = self.axes.plot(
>                  self.thing2.getVec(),
>                  linewidth=1,
>                  dashes=[.2, .4],
>                  color=(0, 1, 1),
>                  label="set2",
>                  )[0]
>
>
>          def draw_plot(self):
>              """ Redraws the plot
>              """
>              self.data = self.thing1.getVec()
>              self.data2 = self.thing2.getVec()
>              def do_cal(urdata):
>                  newdata = []
>                  for x in range(len(urdata)):
>                      urtime = x + self.starttime
>                      newdata.append(urtime)
>                  return newdata
>
>              xmax = len(self.data) if len(self.data) > 50 else 50
>
>              xmin = xmax - 50
>
>              min1 = min(self.data)
>              min2 = min(self.data2)
>              theMin = min(min1, min2)
>
>              ymin = round(theMin, 0) - 1
>
>              max1 = max(self.data)
>              max2 = max(self.data2)
>              theMax = max(max1, max2)
>
>              ymax = round(theMax, 0) + 1
>
>              self.axes.set_xbound(lower=xmin, upper=xmax)
>              self.axes.set_ybound(lower=ymin, upper=ymax)
>
>              self.axes.grid(True, color='gray')
>              pylab.setp(self.axes.get_xticklabels(),
>                      visible=True)
>
>              self.plot_data.set_xdata(np.arange(len(self.data)))
>              self.plot_data.set_ydata(np.array(self.data))
>              self.plot_data2.set_xdata(np.arange(len(self.data2)))
>              #self.plot_data2.set_xdata(np.array(newdata2))
>              self.plot_data2.set_ydata(np.array(self.data2))
>
>              self.canvas.draw()
>          def on_redraw_timer(self):
>              self.draw_plot()
>          def on_exit(self):
>              self.close()
>          def closeEvent(self, event):
>              for thing in (self.thing1, self.thing2):
>                  thing.theThread.stop()
>                  thing.theThread.join()
>     if __name__ == '__main__':
>          app = QtGui.QApplication(sys.argv)
>          aw = ApplicationWindow()
>          aw.show()
>          sys.exit(app.exec_())
>
>     #################################
>     内嵌图片 1
>
>
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
>
>
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

is there someone can help me ?
many thanks


2014-06-19 19:36 GMT+07:00 不坏阿峰 <onl...@gm...>:

> Dear all
>
> could some expert can help me.
> I have modify from one demo. but i do not how to change the x_lable to
> time like H:M:S, and can move it.  i have try some way, but failed.
>
> hope some expert can do me a favor.
> thanks a lot
>
> ######################
> # coding=utf-8
> import os
> import pprint
> import random, time
> import sys
> from PyQt4 import QtGui, QtCore
>
> from threading import *
> import time
> import datetime
>
> import matplotlib
> matplotlib.use('WXAgg')
> from matplotlib.figure import Figure
> from matplotlib.backends.backend_qt4agg import \
>     FigureCanvasQTAgg as FigCanvas, \
>     NavigationToolbar2QT as NavigationToolbar
> import numpy as np
> import pylab
>
>
> class DataGen(object):
>     """ A silly class that generates pseudo-random data for
>         display in the plot.
>     """
>     def __init__(self, init=50):
>         self.data = self.init = init
>
>     def next(self):
>         self._recalc_data()
>         return self.data
>
>     def _recalc_data(self):
>         delta = random.uniform(-0.5, 0.5)
>         r = random.random()
>
>         if r > 0.9:
>             self.data += delta * 15
>         elif r > 0.8:
>             # attraction to the initial value
>             delta += (0.5 if self.init > self.data else -0.5)
>             self.data += delta
>         else:
>             self.data += delta
>
> class myThing():
>     class myThread(Thread):
>         def __init__(self):
>             Thread.__init__(self)
>             self.running = True
>             self.vec = [0]
>             self.dg = DataGen()
>
>             print "Initializing myThread..."
>
>         def run(self):
>             print "Running myThread..."
>             while self.running:
>                 time.sleep(1)
>                 self.vec.append(self.dg.next())
>                 print "Splat"
>
>         def getVec(self):
>             return self.vec
>
>         def stop(self):
>             self.running = False
>
>     def __init__(self):
>         self.theThread = self.myThread()
>         self.threadRunning = True
>         print "initializing myThing..."
>         self.theThread.start()
>
>     def __del__(self):
>         self.theThread.stop()
>
>     def getVec(self):
>         #print self.theThread.vec[:]
>         return self.theThread.vec[:]
>
>
> class ApplicationWindow(QtGui.QMainWindow):
>     """ The main window of the application
>     """
>
>     def __init__(self):
>         QtGui.QMainWindow.__init__(self)
>         self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
>         self.setWindowTitle('Demo: dynamic matplotlib graph')
>
>         self.thing1 = myThing()
>         self.thing2 = myThing()
>         self.starttime =  int(time.time())
>
>
>         self.create_menu()
>         #self.create_status_bar()
>         self.create_main_panel()
>
>         self.redraw_timer = QtCore.QTimer(self)
>         QtCore.QObject.connect(self.redraw_timer,
> QtCore.SIGNAL("timeout()"), self.on_redraw_timer)
>         self.redraw_timer.start(4000)
>
>
>     def create_menu(self):
>         menu_file = QtGui.QMenu("&File", self)
>         #menu_file.addAction(u'&Save plot', self.on_save_plot,
>         #                         QtCore.Qt.CTRL + QtCore.Qt.Key_S)
>         menu_file.addSeparator()
>         menu_file.addAction(u'E&xit', self.on_exit,
>                                  QtCore.Qt.CTRL + QtCore.Qt.Key_X)
>
>         self.menuBar().addMenu(menu_file)
>
>     def create_main_panel(self):
>         self.panel = QtGui.QFrame(self)
>         self.setCentralWidget(self.panel)
>
>         self.init_plot()
>         self.canvas = FigCanvas(self.fig)
>         self.canvas.setMinimumHeight(150)
>
>         #self.toolbar = NavigationToolbar(self.canvas, None)
>         self.vbox = QtGui.QVBoxLayout()
>         self.vbox.addWidget(self.canvas)
>
>         self.panel.setLayout(self.vbox)
>         #self.vbox.Fit(self)
>
>         self.unit = 20
>         width, height = self.geometry().width(), self.geometry().height()
>         self.show()
>
>     def init_plot(self):
>         self.dpi = 100
>         self.fig = Figure((5.0, 3.0), dpi=self.dpi)
>
>         self.axes = self.fig.add_subplot(111, navigate=False)
>         self.axes.set_axis_bgcolor('black')
>
>         self.axes.set_title('Very important random data', size=10)
>         self.axes.set_xlabel('Time flies like an arrow',size=10)
>         self.axes.set_ylabel('Random is just random',size=10)
>
>         pylab.setp(self.axes.get_xticklabels(), fontsize=8)
>         pylab.setp(self.axes.get_yticklabels(), fontsize=8)
>
>         self.plot_data = self.axes.plot(
>             self.thing1.getVec(),
>             linewidth=0.5,
>             color=(1, 1, 0),
>             #marker='o',
>             label="set1",
>             )[0]
>         print  self.thing1.getVec(), "<<>>"
>         self.plot_data2 = self.axes.plot(
>             self.thing2.getVec(),
>             linewidth=1,
>             dashes=[.2, .4],
>             color=(0, 1, 1),
>             label="set2",
>             )[0]
>
>
>     def draw_plot(self):
>         """ Redraws the plot
>         """
>         self.data = self.thing1.getVec()
>         self.data2 = self.thing2.getVec()
>         def do_cal(urdata):
>             newdata = []
>             for x in range(len(urdata)):
>                 urtime = x + self.starttime
>                 newdata.append(urtime)
>             return newdata
>
>         xmax = len(self.data) if len(self.data) > 50 else 50
>
>         xmin = xmax - 50
>
>         min1 = min(self.data)
>         min2 = min(self.data2)
>         theMin = min(min1, min2)
>
>         ymin = round(theMin, 0) - 1
>
>         max1 = max(self.data)
>         max2 = max(self.data2)
>         theMax = max(max1, max2)
>
>         ymax = round(theMax, 0) + 1
>
>         self.axes.set_xbound(lower=xmin, upper=xmax)
>         self.axes.set_ybound(lower=ymin, upper=ymax)
>
>         self.axes.grid(True, color='gray')
>         pylab.setp(self.axes.get_xticklabels(),
>                 visible=True)
>
>         self.plot_data.set_xdata(np.arange(len(self.data)))
>         self.plot_data.set_ydata(np.array(self.data))
>         self.plot_data2.set_xdata(np.arange(len(self.data2)))
>         #self.plot_data2.set_xdata(np.array(newdata2))
>         self.plot_data2.set_ydata(np.array(self.data2))
>
>         self.canvas.draw()
>
>
>     def on_redraw_timer(self):
>         self.draw_plot()
>
>     def on_exit(self):
>         self.close()
>
>     def closeEvent(self, event):
>         for thing in (self.thing1, self.thing2):
>             thing.theThread.stop()
>             thing.theThread.join()
>
> if __name__ == '__main__':
>     app = QtGui.QApplication(sys.argv)
>     aw = ApplicationWindow()
>     aw.show()
>     sys.exit(app.exec_())
>
> #################################
> [image: 内嵌图片 1]
>

I have observed that the amount of time to draw a figure with a plot
depends heavily on the number of tick marks on the axes.  This appears
to be a major driver of perceived refresh performance on interactive
graphics in PySide (for example).  Somewhat tangentially this makes
log axes appear to perform slowly, but I think that is merely a
side-effect of the fact that log axes come with minor tick marks by
default.  I'm working with built-from-source matplotlib as of Apr 17,
2014; I think the observations here apply to any recent matplotlib.

I've published a full illustration at
https://gist.github.com/jbmohler/7c5c8cca39826ea8ede7 .  This small
PySide application lets you enter the number of points in a scatter
plot and the number of minor tick marks.  You can see for yourself
that increasing the number of points in the scatter plot has little
impact on performance, but increasing the number of tick marks has a
noticeable effect with only moderate increase.

Why does this matter if you have a sane number of tick marks?  It
points to tick marks being simply very expensive -- on my 2 year old
quad core, entirely removing tick marks results in 117 frames per
second, but with 7 (major) tick marks on x & y that drops to 38 frames
per second.  I think 100 tick marks falls with-in "sane" (in some
cases) and a graph with 100 tick marks has decidedly more lag in a gui
than 10 tick marks.  As mentioned above, log axes are particularly
likely place to have lots of tick marks.

How can I fix this?  I'm not sure, but I think there are reasonable
special cases that could be highly optimized.  The problem seems to me
to be that each tick mark is a Line2D artist and that has a marker
type (in fact, I think there is no "line" shown, the tick mark is the
single marker of the Line2D).  In the case of uniform sized tick
marks, I believe the tick marks for an axis could be all in one Line2D
with each tick mark being a marker in the single Line2D.  This is a
huge reduction of artists which seems likely to yield a speed up in
quite few places.

I'd love to hear your thoughts and/or fix suggestions on this topic.

Joel

Ok! I'm getting there! I've been trying to figure out, though, how to set
black - for example - for the zero values BUT interpolate also the colors
linearly from black to blue in the linear region (from zero to the linear
threshold). Is there a way to change the colormap like that?

Thanks a lot!
On 2014/06/18, 5:23 AM, Bruno Pace wrote:

> Ok, so using the norm=SymLogNorm I cannot distinguish the values that
> are exactly 0.0 from the really small ones, right? Would it be possible
>

Correct, the scale is linear for small values.

 to make use of the set_bad method without having to use masked arrays,
> just combining the SymLogNorm and the set_bad?
>

No, the mask is what identifies a point as bad.  If you want to distinguish
zero from non-zero, no matter how small, then this is the way to do it.

zm = np.ma.masked_equal(z, 0, copy=False)

Now you have a masked array where the points that are exactly zero are
masked.

The bad color won't show up on the colorbar, however.  There is no suitable
place for it.  It can show only the range from vmin to vmax, and a
"set_over" color for values greater than vmax, and a "set_under" color for
values less than vmin.

Eric

Hey Tom,

It looks like the only backend-agnostic file save function is save_figure()
(a toolbar method), which conflates choosing a filename and doing the
actual saving. The backend-specific code to choose a filename via a dialog
isn't uniform:

Qt4:
matplotlib.backends.backend_qt4._getSaveFileName

MacOS
matplotlib.backends.backend_osx._macosx.choose_save_file

Wx:
A bunch of code in matplotlib.backends.backend_wx.save_figure

TkAgg:
Tkinter.FileDialog

GtkAgg:
get_filechooser().get_filename_from_user()

It looks like, at a minimum, you would have to write your own wrapper code
to make a backend-agnostic interface for choosing a filename. Of course, if
you did that, it would also be nice to refactor that into MPL itself... :)

chris


On Fri, Jun 20, 2014 at 8:22 AM, Thomas Robitaille <
tho...@gm...> wrote:

> Hi everyone,
>
> I'm developing a simple GUI tool in Matplotlib that relies on the
> event framework to handle buttons/sliders. I am trying to avoid using
> a GUI toolkit directly to ensure maximum compatibility for users.
>
> One thing I would like is to be able to have a 'save' button that will
> open up a standard 'save file' dialog window (but not necessarily the
> plot itself). Matplotlib already has 'save file' GUI dialogs for the
> different backends, so I was wondering whether there is an easy and
> abstract way of asking matplotlib to open a 'save file' dialog and
> capturing the output? Or is this all handled separately in the
> different backends?
>
> Thanks!
> Tom
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
> --
> *************************************
> Chris Beaumont
> Senior Software Engineer
> Harvard Center for Astrophysics
> 60 Garden Street, MS 42
> Cambridge, MA 02138
> chrisbeaumont.org
> *************************************
>

Hi everyone,

I'm developing a simple GUI tool in Matplotlib that relies on the
event framework to handle buttons/sliders. I am trying to avoid using
a GUI toolkit directly to ensure maximum compatibility for users.

One thing I would like is to be able to have a 'save' button that will
open up a standard 'save file' dialog window (but not necessarily the
plot itself). Matplotlib already has 'save file' GUI dialogs for the
different backends, so I was wondering whether there is an easy and
abstract way of asking matplotlib to open a 'save file' dialog and
capturing the output? Or is this all handled separately in the
different backends?

Thanks!
Tom

On 2014/06/18, 5:23 AM, Bruno Pace wrote:
> Ok, so using the norm=SymLogNorm I cannot distinguish the values that
> are exactly 0.0 from the really small ones, right? Would it be possible

Correct, the scale is linear for small values.

> to make use of the set_bad method without having to use masked arrays,
> just combining the SymLogNorm and the set_bad?

No, the mask is what identifies a point as bad.  If you want to 
distinguish zero from non-zero, no matter how small, then this is the 
way to do it.

zm = np.ma.masked_equal(z, 0, copy=False)

Now you have a masked array where the points that are exactly zero are 
masked.

The bad color won't show up on the colorbar, however.  There is no 
suitable place for it.  It can show only the range from vmin to vmax, 
and a "set_over" color for values greater than vmax, and a "set_under" 
color for values less than vmin.

Eric

/usr/lib64/python2.7/site-packages/matplotlib/tight_layout.py:225: UserWarning: 
tight_layout : falling back to Agg renderer
  warnings.warn("tight_layout : falling back to Agg renderer")
Traceback (most recent call last):
  File "./plot_stuff2.py", line 10, in <module>
    plt.tight_layout()
  File "/usr/lib64/python2.7/site-packages/matplotlib/pyplot.py", line 1255, in 
tight_layout
    fig.tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad, rect=rect)
  File "/usr/lib64/python2.7/site-packages/matplotlib/figure.py", line 1605, in 
tight_layout
    rect=rect)
  File "/usr/lib64/python2.7/site-packages/matplotlib/tight_layout.py", line 
325, in get_tight_layout_figure
    max_nrows = max(nrows_list)
ValueError: max() arg is an empty sequence

The plotting script is quite long and complex, so I won't post it, but it 
begins:

#!/usr/bin/python
import matplotlib as mpl
mpl.use ('pdf')

import matplotlib.pyplot as plt
plt.tight_layout()


It produces multipage-pdf using
from matplotlib.backends.backend_pdf import PdfPages

It works without plt.tight_layout().  Any clues what I did wrong here?

​So do you want to find the particular row or column to plot
interactively?​  For that you should look at "Event handling and picking"
in the matplotlib docs (http://matplotlib.org/users/event_handling.html).
 It shows there how to return the values of the location of mouse click
events.  Once you have either the x or y value then you could find the
values in your array that correspond to that and plot them.  Or is how to
do the latter your question?

For more involved data exploration, you might want to look into glue (
www.glueviz.org).

Jon

On Thu, Jun 19, 2014 at 4:27 AM, <
mat...@li...> wrote:

> From: dydy2014 <dya...@gm...>
> To: mat...@li...
> Cc:
> Date: Wed, 18 Jun 2014 17:56:21 -0700 (PDT)
> Subject: Re: [Matplotlib-users] Pick a particular data from array
> Thank you Paul for your comment, but what I need not just put a line in the
> contour.
> I want to pick value along the red line, so which the data that placed on
> the red line.
> Then I will plot it in the other type of plot.
>

-- 
________________________________________________________
Jonathan D. Slavin                 Harvard-Smithsonian CfA
js...@cf...       60 Garden Street, MS 83
phone: (617) 496-7981       Cambridge, MA 02138-1516
fax: (617) 496-7577            USA
________________________________________________________

Dear all

could some expert can help me.
I have modify from one demo. but i do not how to change the x_lable to time
like H:M:S, and can move it.  i have try some way, but failed.

hope some expert can do me a favor.
thanks a lot

######################
# coding=utf-8
import os
import pprint
import random, time
import sys
from PyQt4 import QtGui, QtCore

from threading import *
import time
import datetime

import matplotlib
matplotlib.use('WXAgg')
from matplotlib.figure import Figure
from matplotlib.backends.backend_qt4agg import \
    FigureCanvasQTAgg as FigCanvas, \
    NavigationToolbar2QT as NavigationToolbar
import numpy as np
import pylab


class DataGen(object):
    """ A silly class that generates pseudo-random data for
        display in the plot.
    """
    def __init__(self, init=50):
        self.data = self.init = init

    def next(self):
        self._recalc_data()
        return self.data

    def _recalc_data(self):
        delta = random.uniform(-0.5, 0.5)
        r = random.random()

        if r > 0.9:
            self.data += delta * 15
        elif r > 0.8:
            # attraction to the initial value
            delta += (0.5 if self.init > self.data else -0.5)
            self.data += delta
        else:
            self.data += delta

class myThing():
    class myThread(Thread):
        def __init__(self):
            Thread.__init__(self)
            self.running = True
            self.vec = [0]
            self.dg = DataGen()

            print "Initializing myThread..."

        def run(self):
            print "Running myThread..."
            while self.running:
                time.sleep(1)
                self.vec.append(self.dg.next())
                print "Splat"

        def getVec(self):
            return self.vec

        def stop(self):
            self.running = False

    def __init__(self):
        self.theThread = self.myThread()
        self.threadRunning = True
        print "initializing myThing..."
        self.theThread.start()

    def __del__(self):
        self.theThread.stop()

    def getVec(self):
        #print self.theThread.vec[:]
        return self.theThread.vec[:]


class ApplicationWindow(QtGui.QMainWindow):
    """ The main window of the application
    """

    def __init__(self):
        QtGui.QMainWindow.__init__(self)
        self.setAttribute(QtCore.Qt.WA_DeleteOnClose)
        self.setWindowTitle('Demo: dynamic matplotlib graph')

        self.thing1 = myThing()
        self.thing2 = myThing()
        self.starttime =  int(time.time())


        self.create_menu()
        #self.create_status_bar()
        self.create_main_panel()

        self.redraw_timer = QtCore.QTimer(self)
        QtCore.QObject.connect(self.redraw_timer,
QtCore.SIGNAL("timeout()"), self.on_redraw_timer)
        self.redraw_timer.start(4000)


    def create_menu(self):
        menu_file = QtGui.QMenu("&File", self)
        #menu_file.addAction(u'&Save plot', self.on_save_plot,
        #                         QtCore.Qt.CTRL + QtCore.Qt.Key_S)
        menu_file.addSeparator()
        menu_file.addAction(u'E&xit', self.on_exit,
                                 QtCore.Qt.CTRL + QtCore.Qt.Key_X)

        self.menuBar().addMenu(menu_file)

    def create_main_panel(self):
        self.panel = QtGui.QFrame(self)
        self.setCentralWidget(self.panel)

        self.init_plot()
        self.canvas = FigCanvas(self.fig)
        self.canvas.setMinimumHeight(150)

        #self.toolbar = NavigationToolbar(self.canvas, None)
        self.vbox = QtGui.QVBoxLayout()
        self.vbox.addWidget(self.canvas)

        self.panel.setLayout(self.vbox)
        #self.vbox.Fit(self)

        self.unit = 20
        width, height = self.geometry().width(), self.geometry().height()
        self.show()

    def init_plot(self):
        self.dpi = 100
        self.fig = Figure((5.0, 3.0), dpi=self.dpi)

        self.axes = self.fig.add_subplot(111, navigate=False)
        self.axes.set_axis_bgcolor('black')

        self.axes.set_title('Very important random data', size=10)
        self.axes.set_xlabel('Time flies like an arrow',size=10)
        self.axes.set_ylabel('Random is just random',size=10)

        pylab.setp(self.axes.get_xticklabels(), fontsize=8)
        pylab.setp(self.axes.get_yticklabels(), fontsize=8)

        self.plot_data = self.axes.plot(
            self.thing1.getVec(),
            linewidth=0.5,
            color=(1, 1, 0),
            #marker='o',
            label="set1",
            )[0]
        print  self.thing1.getVec(), "<<>>"
        self.plot_data2 = self.axes.plot(
            self.thing2.getVec(),
            linewidth=1,
            dashes=[.2, .4],
            color=(0, 1, 1),
            label="set2",
            )[0]


    def draw_plot(self):
        """ Redraws the plot
        """
        self.data = self.thing1.getVec()
        self.data2 = self.thing2.getVec()
        def do_cal(urdata):
            newdata = []
            for x in range(len(urdata)):
                urtime = x + self.starttime
                newdata.append(urtime)
            return newdata

        xmax = len(self.data) if len(self.data) > 50 else 50

        xmin = xmax - 50

        min1 = min(self.data)
        min2 = min(self.data2)
        theMin = min(min1, min2)

        ymin = round(theMin, 0) - 1

        max1 = max(self.data)
        max2 = max(self.data2)
        theMax = max(max1, max2)

        ymax = round(theMax, 0) + 1

        self.axes.set_xbound(lower=xmin, upper=xmax)
        self.axes.set_ybound(lower=ymin, upper=ymax)

        self.axes.grid(True, color='gray')
        pylab.setp(self.axes.get_xticklabels(),
                visible=True)

        self.plot_data.set_xdata(np.arange(len(self.data)))
        self.plot_data.set_ydata(np.array(self.data))
        self.plot_data2.set_xdata(np.arange(len(self.data2)))
        #self.plot_data2.set_xdata(np.array(newdata2))
        self.plot_data2.set_ydata(np.array(self.data2))

        self.canvas.draw()


    def on_redraw_timer(self):
        self.draw_plot()

    def on_exit(self):
        self.close()

    def closeEvent(self, event):
        for thing in (self.thing1, self.thing2):
            thing.theThread.stop()
            thing.theThread.join()

if __name__ == '__main__':
    app = QtGui.QApplication(sys.argv)
    aw = ApplicationWindow()
    aw.show()
    sys.exit(app.exec_())

#################################
[image: 内嵌图片 1]

thanksfor ur reply.  after i send this mail . i have trie annotate, and
make it works.
if have good style, hope all of u can share it.
############
for i in range(len(ls)):
                circle_ls.append(pie(ax, ls[i], radius=r_len-width*i,
pctdistance=1-width/2, **kwargs))
                ax.annotate('test0' + str(i), xy=(0.1,0.5-i *
0.2),xytext=(0.6,0.5-i *
0.2),arrowprops=dict(arrowstyle="->",connectionstyle="arc3"))
############

[image: 内嵌图片 1]



2014-06-19 18:30 GMT+07:00 Mike Kaufman <mc...@gm...>:

> use annotate()
>
>
> http://matplotlib.org/users/annotations_guide.html#plotting-guide-annotation
>
> M
>
> On 6/19/14, 12:27 AM, 不坏阿峰 wrote:
> > thanks to Joe Kington
> > <https://plus.google.com/u/0/115087865729901776991?prsrc=4>‘s help, i
> > got this pie donuts
> > i have modified code to  generate pie base one the Num of list.
> > but i do not know how to draw the text label like below, i need label
> > inform of each pie . pls give me some guide.
> > thanks a lot
> > 内嵌图片 1
> >
>
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

use annotate()

http://matplotlib.org/users/annotations_guide.html#plotting-guide-annotation

M

On 6/19/14, 12:27 AM, 不坏阿峰 wrote:
> thanks to Joe Kington
> <https://plus.google.com/u/0/115087865729901776991?prsrc=4>‘s help, i
> got this pie donuts
> i have modified code to  generate pie base one the Num of list.
> but i do not know how to draw the text label like below, i need label
> inform of each pie . pls give me some guide.
> thanks a lot
> 内嵌图片 1
>

Just to clarify, do you actually want to be able to "pick" it, so by
selecting in interactively (and probably manually, i.e. with the mouse) or
are you only interested in displaying the "data underneath the line".

The second is straightforward: just plot in a new axes the relevant row of
your 2D data.
The former requires you to add events to your figure so that you can pick
values interactively. The matplotlib example [pick_event_demo][1] shows you
how it's done. I recomment studying it and then asking again if it doesn't
work.



1: http://matplotlib.org/examples/event_handling/pick_event_demo.html


2014-06-19 2:56 GMT+02:00 dydy2014 <dya...@gm...>:

> Thank you Paul for your comment, but what I need not just put a line in the
> contour.
> I want to pick value along the red line, so which the data that placed on
> the red line.
> Then I will plot it in the other type of plot.
>
>
>
>
>
>
>
> --
> View this message in context:
> http://matplotlib.1069221.n5.nabble.com/Pick-a-particular-data-from-array-tp43532p43545.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

thanks to Joe Kington
<https://plus.google.com/u/0/115087865729901776991?prsrc=4>‘s help, i got
this pie donuts
i have modified code to  generate pie base one the Num of list.
but i do not know how to draw the text label like below, i need label
inform of each pie . pls give me some guide.
thanks a lot
[image: 内嵌图片 1]

#################################
from __future__ import unicode_literals
import matplotlib.pyplot as plt
import numpy as np

import sys # os, random
from PyQt4 import QtGui, QtCore

#from numpy import arange, sin, pi
from matplotlib import font_manager as fm
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as
FigureCanvas
#from matplotlib.figure import Figure
from mychart_ui import Ui_Form


class MyMplCanvas(FigureCanvas):
    """Ultimately, this is a QWidget (as well as a FigureCanvasAgg,
etc.)."""
    def __init__(self, parent=None, width=5, height=4, dpi=100):
        #fig = Figure(figsize=(width, height), dpi=dpi)
        # self.axes = fig.add_subplot(111)
        # We want the axes cleared every time plot() is called
        #self.axes.hold(False)
        plt.rcParams['font.size'] = 9
        plt.rcParams['font.weight'] = 'normal'
        self.fig, self.axes = plt.subplots()
        self.compute_initial_figure()

        #
        FigureCanvas.__init__(self, self.fig)
        self.setParent(parent)

        FigureCanvas.setSizePolicy(self,
                                   QtGui.QSizePolicy.Expanding,
                                   QtGui.QSizePolicy.Expanding)
        FigureCanvas.updateGeometry(self)

    def compute_initial_figure(self):
        pass
class MyStaticMplCanvas(MyMplCanvas):
    """Simple canvas with a sine plot."""
    def compute_initial_figure(self):
        #fig, ax = plt.subplots()
        #ax.axis = ('equal')

        data = [[96, 124],[33, 64],[55, 96]]
        header = ['Hardware', 'Software']
        def pie_plot(myfig,myaxes,data):
            fig = myfig
            ax = myaxes
            ax.set_position([-0.12, 0.4, 0.6, 0.6])
            ax.axis('equal')
            ls = data
            r_len = 0.6
            width = r_len/(len(ls)+1)
            print width
            kwargs = dict(colors=['#66FF66', '#9999FF', '#FF9999'],
startangle=90)
            proptease = fm.FontProperties()
            proptease.set_size('xx-small')

            circle_ls = []
            for i in range(len(ls)):
                print i
                circle_ls.append(pie(ax, ls[i], radius=r_len-width*i,
pctdistance=1-width/2, **kwargs))
            # outside = pie(ax, ls[0], radius=r_len, pctdistance=1-width/2,
**kwargs)
            # middle = pie(ax,ls[1] , radius=r_len-width,
            #              pctdistance=1-width/2, **kwargs)
            # middle2 = pie(ax,ls[1] , radius=r_len-width*2,
            #              pctdistance=1-width/2, **kwargs)
            # inside = pie(ax,ls[2] , radius=r_len-width*3,
            #              pctdistance=1-width/2, **kwargs)

            plt.setp(circle_ls, width=width, edgecolor='white')

            ax.legend(circle_ls[0][::-1], header, frameon=False)

        pie_plot(self.fig,self.axes,data)
        kwargs = dict(size=13, color='white', va='center',
fontweight='bold')
        # ax.text(0, 0, 'Year 2005', ha='center',
        #         bbox=dict(boxstyle='round', facecolor='blue',
edgecolor='none'),
        #         **kwargs)
        # ax.annotate('Year 2006', (0, 0), xytext=(np.radians(-45), 1.1),
        #             bbox=dict(boxstyle='round', facecolor='green',
edgecolor='none'),
        #             textcoords='polar', ha='left', **kwargs)

        #ax.axes.plot()

def pie(ax, values, **kwargs):
    total = sum(values)
    def formatter(pct):
        return '{:0.0f}\n{:0.1f}%'.format(pct*total/100,pct)
    wedges, _, labels = ax.pie(values, autopct=formatter, **kwargs)
    return wedges


#plt.show()


class myWidget(QtGui.QWidget, Ui_Form):
    def __init__(self,parent=None):
        QtGui.QWidget.__init__(self, parent)
        self.setupUi(self)
        self.pushButton.clicked.connect(self.draw)

    def draw(self):
        print  '='
        sc = MyStaticMplCanvas(self.matwidget, width=2, height=3, dpi=100)
        sc.show()

qApp = QtGui.QApplication(sys.argv)

# aw = ApplicationWindow()
# aw.setWindowTitle("%s" % progname)
aw = myWidget()
aw.show()
sys.exit(qApp.exec_())
####################################

Thank you Paul for your comment, but what I need not just put a line in the
contour.
I want to pick value along the red line, so which the data that placed on
the red line.
Then I will plot it in the other type of plot.







--
View this message in context: http://matplotlib.1069221.n5.nabble.com/Pick-a-particular-data-from-array-tp43532p43545.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

Ok, so using the norm=SymLogNorm I cannot distinguish the values that are
exactly 0.0 from the really small ones, right? Would it be possible to make
use of the set_bad method without having to use masked arrays, just
combining the SymLogNorm and the set_bad?

Thanks!


2014-06-17 21:20 GMT+02:00 Eric Firing <ef...@ha...>:

> On 2014/06/17, 8:59 AM, Bruno Pace wrote:
> > Hi all,
> >
> > I'm trying to use imshow to plot some values which fall on the interval
> > [0,1]. I need to
> > use a logscale to emphasize the scales of the data. The solution I found
> > checking some discussions was like this
> >
> > plt.imshow(X, interpolation='none', norm=matplotlib.colors.LogNorm())
> >
> > However, I notice that the way these colors are assigned are not always
> > the same (although my data always contains the minimum value 0.0 and
> >   the maximum 1.0). I need to have a coherent color scale to indicate
> > the real values. Is it easier to do the color code myself? What is the
> > proper way of tackling this problem??
>
> Use the vmin and vmax kwargs to LogNorm, remembering that vmin must be
> greater than zero for a log scale.
>
> Eric
>
> >
> > It's pretty much the same problem described here, but with a logscale...
> >
> >
> http://stackoverflow.com/questions/7875688/how-can-i-create-a-standard-colorbar-for-a-series-of-plots-in-python
> >
> >
> > Thank you very much!
> >
> > Bruno
> >
> >
> >
> ------------------------------------------------------------------------------
> > HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> > Find What Matters Most in Your Big Data with HPCC Systems
> > Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> > Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> > http://p.sf.net/sfu/hpccsystems
> >
> >
> >
> > _______________________________________________
> > Matplotlib-users mailing list
> > Mat...@li...
> > https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> >
>
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

Hey all,

I am trying to produce an animation from several images generated with
imshow from a sequence of arrays in time, I have done that in several ways.
However, my animations consist of several frames (on the order of 10000
frames) and thus the simulation crashes when it's too large.

The solution I found was writing the png files and then animating. It is
very time and memory consuming, though, and I have the impression it is not
the best solution to tackle this problem. What is the best practice to deal
with this problem?

Thanks!

Bruno

P.S.: I'm using Ipython, would it change running from a terminal instead of
running it from the shell?

On 2014/06/17, 8:59 AM, Bruno Pace wrote:
> Hi all,
>
> I'm trying to use imshow to plot some values which fall on the interval
> [0,1]. I need to
> use a logscale to emphasize the scales of the data. The solution I found
> checking some discussions was like this
>
> plt.imshow(X, interpolation='none', norm=matplotlib.colors.LogNorm())
>
> However, I notice that the way these colors are assigned are not always
> the same (although my data always contains the minimum value 0.0 and
>   the maximum 1.0). I need to have a coherent color scale to indicate
> the real values. Is it easier to do the color code myself? What is the
> proper way of tackling this problem??

Use the vmin and vmax kwargs to LogNorm, remembering that vmin must be 
greater than zero for a log scale.

Eric

>
> It's pretty much the same problem described here, but with a logscale...
>
> http://stackoverflow.com/questions/7875688/how-can-i-create-a-standard-colorbar-for-a-series-of-plots-in-python
>
>
> Thank you very much!
>
> Bruno
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
>
>
>
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>