matplotlib-users Mailing List for matplotlib (Page 2)

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年06月18日, 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年06月18日, 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: 2014年6月18日 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年06月19日 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
> > <‘s" rel=nofollow>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
> <‘s" rel=nofollow>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年06月19日 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
<‘s" rel=nofollow>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年06月17日 21:20 GMT+02:00 Eric Firing <ef...@ha...>:
> On 2014年06月17日, 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
> >
> >
> >
> ------------------------------------------------------------------------------
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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年06月17日, 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
>

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??
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

Based on the example you posted, you need like:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
ax.contour(data)
ax.axhline(magic_value)
On Mon, Jun 16, 2014 at 1:30 AM, dydy2014 <dya...@gm...> wrote:
> Hello all,
>
> I have contour plot like this and I have problem to pick a particular data
> along red line and save it.
> How do I make it with python program?
>
> <http://matplotlib.1069221.n5.nabble.com/file/n43532/190311.png>
>
> Thank you in advance.
>
> Dydy
>
>
>
> --
> View this message in context:
> http://matplotlib.1069221.n5.nabble.com/Pick-a-particular-data-from-array-tp43532.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
>
>
> ------------------------------------------------------------------------------
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Perfect, many thanks!
So the trick was _not_ to do "show()" in A.py
(Moreover, doing "draw()" in A.py also seems necessary... even though I
don't really get why -- actually in my own more complicated program, it
works also without this draw...)
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Hi.
The short answer is yes.
orion:~ % cat A.py
from matplotlib.pyplot import *
print "A"
plot([0,1],[0,1])
draw()
orion:~ % cat B.py
from matplotlib.pyplot import *
import A
print "B"
plot([0.5,0.75],[0,1])
draw()
show()
Using ipython:
In [2]: run -i B.py
A
B
and the figure shows both plots.
M
On 6/16/14, 12:12 PM, felix_werner wrote:
> Hello,
>
> I am plotting something in a file A.py
>
> In another file (B.py), I wish to do
> import A
> and then add a curve to that same plot (and replot it).
>
> Is that possible?
>
> Thanks!
>
>
>
> --
> View this message in context: http://matplotlib.1069221.n5.nabble.com/modifying-a-plot-from-an-imported-module-tp43533.html
> Sent from the matplotlib - users mailing list archive at Nabble.com.
>
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> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
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>

Hi Andruska,
The Basemap.colorbar has a "size" keyword to allow you have the shrink-like
function to adjust the size of the colorbar.
Otherwise you can creat an axes on the exact position you want to hold the
colorbar, like below I have prepared an example for you:
arr = np.arange(100).reshape(10,10)
fig,ax = plt.subplots(1,1)
cs = ax.imshow(arr)
ax.set_position([0.2, 0.3, 0.6, 0.6])
axt = fig.add_axes([0.4,0.2,0.4,0.05])
cbar = plt.colorbar(cs,cax=axt,orientation='horizontal')
fig.text(0.25,0.22,'I am label',va='center',size=13)
draw()
I think it's hard to use the colorbar.set_label put the label directly on
the left of your colorbar, I rather suggest you to use fig.text to
position exactly a text for your label.
At the beginning of matplotlib you might feel confused, but after investing
a significant amount of time you feel it extremely flexible, and going to
like it :)
Cheers,
Chao
On Mon, Jun 16, 2014 at 6:32 PM, Andruska, Michael [via matplotlib] <
ml-...@n5...> wrote:
> Hi all,
>
>
>
> I am having great difficulty understanding how to change the size of my
> basemap colorbar, altering its position and moving the text label all at
> the same time. I would like to:
>
> 1. Shrink the size of the colorbar (there doesn’t seem to be a
> shrink property in the basemap.colorbar() method (only plt.colorbar() or
> fig.colorbar())
>
> 2. Move the bar so it is not centered but instead so its right edge
> is aligned vertically with the right end of the basemap.
>
> 3. Move the colorbar W/m^2 text label so it is not below the
> colorbar but is instead directly to its left.
>
>
>
> I looked up several other responses online that mentioned doing things
> such as adding a second axes, or using the shrink command from
> plt.colorbar(), and changing some other properties such as padding, but in
> the end, most of these alterations seem to introduce another problem when I
> try them. Even after viewing their documentation, I still do not fully
> understand their proper usage. Also, I tried a few properties listed in the
> matplotlib documentation such as anchor and panchor in my the
> fig.colorbar() method in attempt to move the bar around but when I tried to
> run it, the keyword was not recognized by the interpreter and produced an
> error (it seems strange that some of the keywords listed in the docs aren’t
> being recognized; and I’m pretty sure I have the most current matplotlib
> version too). You can see some of the commented commands I tried in the
> code below (not all at once, of course, but just in various conjunctions
> with one another). Here is an example of my code and an attached example of
> what the plot currently looks like after running said code. Any helpful
> advice would be greatly appreciated. So confused right now and I feel like
> I’ve read the docs over and over to little avail (P.S. Getting down to the
> nitty gritty of working with matplotlib objects and understanding its inner
> workings to customize my plots better is really confusing, even with the
> docs, (sigh)):
>
>
>
> swi = swi.reshape(1059, 1799)
>
> lat = lat.reshape(1059, 1799)
>
> lon = lon.reshape(1059, 1799)
>
>
>
> def plot_conus():
>
> m = mpl_toolkits.basemap.Basemap(
>
> llcrnrlon=-135.0,
>
> llcrnrlat=19.0,
>
> urcrnrlon=-60.0,
>
> urcrnrlat=54.0,
>
> projection='mill',
>
> resolution='c')
>
> m.drawcoastlines()
>
> m.drawcountries()
>
> m.drawstates()
>
> # draw parallels
>
> parallels = np.arange(0.,90,10.)
>
> m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
>
> # draw meridians
>
> meridians = np.arange(180.,360.,10.)
>
> m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
>
> return m
>
>
>
> # find hex color values at http://www.colorpicker.com
>
> swi_colors = [
>
> #"#f800fd", # light purple
>
> #"#9854c6", # dark purple
>
> "#04e9e7",
>
> "#019ff4",
>
> "#0300f4",
>
> "#02fd02",
>
> "#01c501",
>
> "#008e00",
>
> "#fdf802",
>
> "#e5bc00",
>
> "#fd9500",
>
> "#fd0000",
>
> "#d40000",
>
> "#bc0000",
>
> "#A10505" # brick
>
> ]
>
>
>
> swi_colormap = matplotlib.colors.ListedColormap(swi_colors)
>
>
>
> m = plot_conus()
>
>
>
> levels = []
>
> for i in range(13):
>
> levels.append(i*90.0)
>
>
>
> # create black and white cross at observatory location on map
>
> site_lon = -87.99495
>
> site_lat = 41.70121
>
> x_site, y_site = m(site_lon, site_lat)
>
> m.plot(x_site, y_site, 'w+', markersize=30, markeredgewidth=8) # white
> cross
>
> m.plot(x_site, y_site, 'k+', markersize=25, markeredgewidth=3) # black
> cross
>
>
>
> norm = matplotlib.colors.BoundaryNorm(levels, 13)
>
> cax = m.pcolormesh(lon, lat, swi, latlon=True, norm=norm,
>
> cmap=swi_colormap)
>
>
>
> #cbar = m.colorbar(cax)
>
> fig = plt.gcf()
>
> #ax = plt.gca()
>
> #cbar = fig.colorbar(cax, orientation='horizontal', shrink=0.75)
>
> #cbaxes = fig.add_axes([0.8, 0.1, 0.03, 0.8])
>
> #cb = fig.colorbar(cax)
>
> cbar = m.colorbar(cax, location='bottom', pad='6%')
>
> cbar.set_label('$W/m^2$', fontsize=18)
>
>
>
> plt.title('NOAA LAPS GHI, RT ' + modelrun_time_label + ', VT ' +
> fcst_time_label)
>
> plt.show()
>
>
>
>
>
> ------------------------------------------------------------------------------
>
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> Find What Matters Most in Your Big Data with HPCC Systems
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Hi all,
I am having great difficulty understanding how to change the size of my basemap colorbar, altering its position and moving the text label all at the same time. I would like to:
1. Shrink the size of the colorbar (there doesn't seem to be a shrink property in the basemap.colorbar() method (only plt.colorbar() or fig.colorbar())
2. Move the bar so it is not centered but instead so its right edge is aligned vertically with the right end of the basemap.
3. Move the colorbar W/m^2 text label so it is not below the colorbar but is instead directly to its left.
I looked up several other responses online that mentioned doing things such as adding a second axes, or using the shrink command from plt.colorbar(), and changing some other properties such as padding, but in the end, most of these alterations seem to introduce another problem when I try them. Even after viewing their documentation, I still do not fully understand their proper usage. Also, I tried a few properties listed in the matplotlib documentation such as anchor and panchor in my the fig.colorbar() method in attempt to move the bar around but when I tried to run it, the keyword was not recognized by the interpreter and produced an error (it seems strange that some of the keywords listed in the docs aren't being recognized; and I'm pretty sure I have the most current matplotlib version too). You can see some of the commented commands I tried in the code below (not all at once, of course, but just in various conjunctions with one another). Here is an example of my code and an attached example of what the plot currently looks like after running said code. Any helpful advice would be greatly appreciated. So confused right now and I feel like I've read the docs over and over to little avail (P.S. Getting down to the nitty gritty of working with matplotlib objects and understanding its inner workings to customize my plots better is really confusing, even with the docs, (sigh)):
swi = swi.reshape(1059, 1799)
lat = lat.reshape(1059, 1799)
lon = lon.reshape(1059, 1799)
def plot_conus():
m = mpl_toolkits.basemap.Basemap(
llcrnrlon=-135.0,
llcrnrlat=19.0,
urcrnrlon=-60.0,
urcrnrlat=54.0,
projection='mill',
resolution='c')
m.drawcoastlines()
m.drawcountries()
m.drawstates()
# draw parallels
parallels = np.arange(0.,90,10.)
m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
# draw meridians
meridians = np.arange(180.,360.,10.)
m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
return m
# find hex color values at http://www.colorpicker.com
swi_colors = [
#"#f800fd", # light purple
#"#9854c6", # dark purple
"#04e9e7",
"#019ff4",
"#0300f4",
"#02fd02",
"#01c501",
"#008e00",
"#fdf802",
"#e5bc00",
"#fd9500",
"#fd0000",
"#d40000",
"#bc0000",
"#A10505" # brick
]
swi_colormap = matplotlib.colors.ListedColormap(swi_colors)
m = plot_conus()
levels = []
for i in range(13):
levels.append(i*90.0)
# create black and white cross at observatory location on map
site_lon = -87.99495
site_lat = 41.70121
x_site, y_site = m(site_lon, site_lat)
m.plot(x_site, y_site, 'w+', markersize=30, markeredgewidth=8) # white cross
m.plot(x_site, y_site, 'k+', markersize=25, markeredgewidth=3) # black cross
norm = matplotlib.colors.BoundaryNorm(levels, 13)
cax = m.pcolormesh(lon, lat, swi, latlon=True, norm=norm,
cmap=swi_colormap)
#cbar = m.colorbar(cax)
fig = plt.gcf()
#ax = plt.gca()
#cbar = fig.colorbar(cax, orientation='horizontal', shrink=0.75)
#cbaxes = fig.add_axes([0.8, 0.1, 0.03, 0.8])
#cb = fig.colorbar(cax)
cbar = m.colorbar(cax, location='bottom', pad='6%')
cbar.set_label('$W/m^2$', fontsize=18)
plt.title('NOAA LAPS GHI, RT ' + modelrun_time_label + ', VT ' + fcst_time_label)
plt.show()

Hello,
I am plotting something in a file A.py
In another file (B.py), I wish to do
 import A
and then add a curve to that same plot (and replot it).
Is that possible?
Thanks!
--
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Hello all,
I have contour plot like this and I have problem to pick a particular data
along red line and save it.
How do I make it with python program?
<http://matplotlib.1069221.n5.nabble.com/file/n43532/190311.png> 
Thank you in advance.
Dydy
--
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On 16-Jun-14 01:12, Eric Firing wrote:
> On 2014年06月15日, 12:17 PM, Virgil Stokes wrote:
>> There are some rather nice and useful matplotlib examples for colormaps
>> that are shown at:
>>
>> http://nbviewer.ipython.org/github/dpsanders/matplotlib-examples/blob/master/colorline.ipynb
>>
>> In*Example 1. Sine wave colored by time (uses the defaults for
>> colorline)*, how can one add a colorbar?
> lc = colorline(x, y)
> cbar = fig.colorbar(lc)
>
> Eric
>
>
This works fine --- thanks very much Eric.
Have a good day