matplotlib-users Mailing List for matplotlib

The bar command makes rectangles whose bottom position is 0.
The reason that bars disappear when you set log scale is that bottom
position of the bar become -infinity.
You may
 * set log scale before calling the bar
or
 * create bar plot but with reasonable (positive) bottom value. Take
a look at the docs.
Regards,
-JJ
On Wed, Nov 11, 2009 at 5:25 PM, per freem <per...@gm...> wrote:
> hi all,
>
> I am trying to make a simple bar graph that has its yaxis scale set to
> log. I use the following code:
>
> import matplotlib
> matplotlib.use('PDF')
> import matplotlib.pyplot as plt
> from matplotlib import rc
> rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
> plt.rcParams['ps.useafm'] = True
> rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
> plt.rcParams['pdf.fonttype'] = 42
> plt.rcParams['font.size'] = 10
>
> x = range(1, 11)
> y = [5000, 900, 600, 500, 200, 110, 50, 20, 10, 5]
> plt.figure(figsize=(5, 5), dpi=300)
> plt.bar(x, y)
> plt.gca().set_yscale('log')
> plt.savefig('test_logscale.pdf')
>
> the problem is that the bar graphs do not appear -- instead, i simply
> get horizontal lines around where the top of the bar graph should
> appear. Any idea how to fix this?
>
> also, sometimes the x axis disappears when i try this. thanks.
>
> ------------------------------------------------------------------------------
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>

I'm not exactly sure what you're trying to do. but I think what you
want can be easily achieved by simply using the masked array. Is the
example below similar to what you need?
a = np.arange(100).reshape((10,10))
m1 = np.triu(a, 1) > 0
m2 = np.tril(a, -1) > 0
#m2 = ~m1
am1 = np.ma.array(a, mask=m1)
am2 = np.ma.array(a, mask=m2)
pcolormesh(am1, cmap=plt.cm.jet)
pcolormesh(am2, cmap=plt.cm.gray)
-JJ
On Wed, Nov 11, 2009 at 12:11 PM, Christian Meesters
<mee...@im...> wrote:
> Hi,
>
> Is there a way to somehow squeeze two datasets with two different color
> maps into one pcolormesh? Say one dataset occupies one triangle (e. g.
> numpy.triu(dataset1, 1)) and the second dataset a different area (e. g.
> numpy.tril(dataset2, -1)) and then paste the two datasets in one
> pcolormesh (e.g. pylab.pcolormesh(numpy.triu(dataset1, 1) +
> numpy.tril(dataset2, -1)) ). This will obviously share one colormap. But
> does anybody know a trick to use two different ones?
>
> TIA
> Christian
>
> PS Forgot to thank Eric Firing for his tip last time I asked. Thank you
> Eric: You solved my problem!
>
>
> ------------------------------------------------------------------------------
> Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day
> trial. Simplify your report design, integration and deployment - and focus on
> what you do best, core application coding. Discover what's new with
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> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>

On Wed, Nov 11, 2009 at 4:25 PM, per freem <per...@gm...> wrote:
> hi all,
>
> I am trying to make a simple bar graph that has its yaxis scale set to
> log. I use the following code:
>
> import matplotlib
> matplotlib.use('PDF')
> import matplotlib.pyplot as plt
> from matplotlib import rc
> rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
> plt.rcParams['ps.useafm'] = True
> rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
> plt.rcParams['pdf.fonttype'] = 42
> plt.rcParams['font.size'] = 10
>
> x = range(1, 11)
> y = [5000, 900, 600, 500, 200, 110, 50, 20, 10, 5]
> plt.figure(figsize=(5, 5), dpi=300)
> plt.bar(x, y)
It should work scaling from within the bar()
plt.bar(x, y, log=True)
# plt.gca().set_yscale('log')
> plt.savefig('test_logscale.pdf')
>
> the problem is that the bar graphs do not appear -- instead, i simply
> get horizontal lines around where the top of the bar graph should
> appear. Any idea how to fix this?
>
> also, sometimes the x axis disappears when i try this. thanks.
>
> ------------------------------------------------------------------------------
> Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day
> trial. Simplify your report design, integration and deployment - and focus on
> what you do best, core application coding. Discover what's new with
> Crystal Reports now. http://p.sf.net/sfu/bobj-july
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
-- 
Gökhan

2009年11月10日 23:45:10 -0800, shriv wrote:
> I am a very new user to both python and matplotlib so please beware that
> my questions might be a real no-brainer. Here goes: I have a 3D array of
> data that I isolated from a much larger dataset. The first two columns
> are the x and y positions while the third is the intensity at the
> position (xi, yi). I would like to produce a colour map of the intensity
> as a function of x and y positions. Numerous tries with pcolor have got
> me nowhere. I constantly get an error message saying "Too many indices".
You need to regrid your data on a regular grid, not at scattered
positions. See 
http://matplotlib.sourceforge.net/api/mlab_api.html#matplotlib.mlab.griddata
-- 
Pauli Virtanen

hi all,
I am trying to make a simple bar graph that has its yaxis scale set to
log. I use the following code:
import matplotlib
matplotlib.use('PDF')
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
plt.rcParams['ps.useafm'] = True
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['font.size'] = 10
x = range(1, 11)
y = [5000, 900, 600, 500, 200, 110, 50, 20, 10, 5]
plt.figure(figsize=(5, 5), dpi=300)
plt.bar(x, y)
plt.gca().set_yscale('log')
plt.savefig('test_logscale.pdf')
the problem is that the bar graphs do not appear -- instead, i simply
get horizontal lines around where the top of the bar graph should
appear. Any idea how to fix this?
also, sometimes the x axis disappears when i try this. thanks.

Ryan Neve wrote:
> Hello,
> In my filled contour plot: http://imgur.com/vXoCL.png
> There are faint lines between the contour levels. I think they are 
> yellow since they disappear in the yellow parts of the graph and are 
> most obvious in the red areas. Is there any way to get rid of these 
> lines? The number of contour levels is arbitrary, and I don't need them 
> emphasized with a moire pattern.
Try experimenting with the "antialiased" kwarg in your call to contourf. 
It is a boolean; see if a value of True or False gives a better result.
Eric
> 
> Thank you,
> 
> -Ryan
> 
> 
> ------------------------------------------------------------------------
> 
> ------------------------------------------------------------------------------
> Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day 
> trial. Simplify your report design, integration and deployment - and focus on 
> what you do best, core application coding. Discover what's new with
> Crystal Reports now. http://p.sf.net/sfu/bobj-july
> 
> 
> ------------------------------------------------------------------------
> 
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

On Wed, Nov 11, 2009 at 3:22 PM, Shrividya Ravi <pen...@gm...> wrote:
> Thanks for your reply, Giorgio. I had a look at the documentation for pcolor
> but unfortunately, I can't pick out where I have gone wrong. I have attached
> my data array as a txt file. In ipython, I use the 'loadtxt' command to load
> the dataset and then assigned the variables x,y, and z to the first, second
> and third columns respectively. I then specified the meshgrid X,Y from the x
> and y arrays of my data and used pcolor(X,Y,z) to try and plot the color
> map.
You have to interpolate your 1D data onto a 2D grid --
# first load the data
In [504]: x,y,z = np.loadtxt('195pt52_T6.txt', unpack=True)
# create evenly sampled x and y vectors to interpolate onto
In [505]: xi = np.linspace(x.min(), x.max(), 20)
In [506]: yi = np.linspace(y.min(), y.max(), 20)
# use griddata to do the 2D interoplation
In [507]: Z = mlab.griddata(x, y, z, xi, yi)
In [508]: Z.shape
Out[508]: (20, 20)
# use meshgrid to create 2D grids of your 1D x and 1 data
In [509]: X, Y = np.meshgrid(xi, yi)
# pass all the 2D arrays to pcolor
In [510]: pcolor(X, Y, Z)
Out[510]: <matplotlib.collections.PolyCollection object at 0x113e6d8c>
Hope this helps!

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37.3895 32.7789 87.0748
37.3895 35.4211 95.0816
37.3895 38.0632 84.0722
37.3895 40.7053 98.0842
37.3895 43.3474 92.0791
37.3895 45.9895 88.0756
37.3895 48.6316 96.0825
37.3895 51.2737 93.0799
37.3895 53.9158 82.0705
37.3895 56.5579 96.0825
37.3895 59.2 86.0739
40.2 9 89.0765
40.2 11.6421 103.089
40.2 14.2842 93.0799
40.2 16.9263 89.0765
40.2 19.5684 85.073
40.2 22.2105 88.0756
40.2 24.8526 97.0834
40.2 27.4947 97.0834
40.2 30.1368 90.0773
40.2 32.7789 87.0748
40.2 35.4211 101.087
40.2 38.0632 106.091
40.2 40.7053 107.092
40.2 43.3474 103.089
40.2 45.9895 100.086
40.2 48.6316 94.0808
40.2 51.2737 97.0834
40.2 53.9158 87.0748
40.2 56.5579 96.0825
40.2 59.2 128.11

Hello,
In my filled contour plot: http://imgur.com/vXoCL.png
There are faint lines between the contour levels. I think they are yellow
since they disappear in the yellow parts of the graph and are most obvious
in the red areas. Is there any way to get rid of these lines? The number of
contour levels is arbitrary, and I don't need them emphasized with a moire
pattern.
Thank you,
-Ryan

On Nov 11, 2009, at 1:33 PM, Michael Droettboom wrote:
> Which older revision (or release) worked as you expected?
After some digging, I think this issue is caused by an error in the 
macosx backend (everything works fine with tkagg, qt4agg, and agg). 
Oddly enough, calling savefig(), instead of show(), with the macosx 
backend creates the correct plot, but calling savefig() after show() 
does not.
I'm actually not sure if my code worked for older revisions of the 
macosx backend (I may have had a different default backend before). 
The transform code doesn't work with revisions between 7950 and 7625; 
older versions break the macosx backend on my system (OS X 10.6.1).
-Tony
>
> Mike
>
> Tony S Yu wrote:
>> Hi,
>>
>> Awhile back, Jae-Joon helped me transform collection sizes from 
>> points to data values by overriding the `get_transform` method of 
>> a RegularPolyCollection (see example code below).
>>
>> When I tried the code today, the collection didn't appear on the 
>> plot. Removing the get_transform method below brings the 
>> collection back, but of course, the sizes are not what I want.
>>
>> Some simple testing suggests that changing the transform returned 
>> by `get_transform` changes the offsets of the collection items. In 
>> other words, the transform originally (i.e. in some older 
>> revision) modified only the scale of collection items, but now 
>> appears to modify both the scale and offset.
>>
>> Is there a way to modify only the scale of collection items?
>>
>> Thanks,
>> -Tony
>>
>> BTW, I'm on svn r7950.
>>
>> #-----------------
>> # The squares will not show up with the code below
>> # Comment out the get_transform method to see squares
>> # Change scale_x and scale_y to values close to 1 to see how they 
>> change both scale and offset
>>
>> class SquareCollection(collections.RegularPolyCollection):
>> """Return a collection of squares."""
>>
>> def __init__(self, **kwargs):
>> super(SquareCollection, self).__init__(4, rotation=np.pi/ 
>> 4., **kwargs)
>>
>> def get_transform(self):
>> """Return transform scaling circle areas to data space."""
>> ax = self.axes
>> pts2pixels = 72.0 / ax.figure.dpi
>> scale_x = pts2pixels * ax.bbox.width / ax.viewLim.width
>> scale_y = pts2pixels * ax.bbox.height / ax.viewLim.height
>> return transforms.Affine2D().scale(scale_x, scale_y)
>>
>> ax = plt.gca()
>> circle_areas = np.pi * np.ones(4)
>> xy = [(0, 0), (0, 1), (1, 0), (1, 1)]
>> squares = SquareCollection(sizes=circle_areas,
>> offsets=xy, transOffset=ax.transData,
>> facecolor='black')
>> ax.add_collection(squares, autolim=True)
>> plt.axis([-0.5, 1.5, -0.5, 1.5])
>> plt.show()
>>
>>
>>
>> ------------------------------------------------------------------------------
>> Let Crystal Reports handle the reporting - Free Crystal Reports 
>> 2008 30-Day trial. Simplify your report design, integration and 
>> deployment - and focus on what you do best, core application 
>> coding. Discover what's new with
>> Crystal Reports now. http://p.sf.net/sfu/bobj-july
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>>
>
> -- 
> Michael Droettboom
> Science Software Branch
> Operations and Engineering Division
> Space Telescope Science Institute
> Operated by AURA for NASA
>

It looks like you are storing your source data in a python list. Matplotlib runs much faster if you store your data using a numpy array instead. 
I'm no expert, but it certianly sped up my graph drawing.
-Tom
Message: 5
Date: 2009年11月11日 08:53:58 -0600
From: Mike Anderson <mba...@wi...>
Subject: [Matplotlib-users] matplotlib slow compared to gnuplot?
To: mat...@li...
Message-ID: <AE4...@wi...>
Content-Type: text/plain; charset=us-ascii; format=flowed; delsp=yes
Hi all,
Previously I was a user of gnuplot but have been giving matplotlib a 
try. One thing I've run in to right away is that matplotlib appears 
to be significantly slower.
A script to produce a dozen plots was taking me ~1 second with 
gnuplot, and now takes me ~18 seconds with matplotlib.
I'm curious if anyone knows how to speed things up. To figure out 
what is taking most of the time, I've used cProfile and pstats and 
below is the top 15 functions taking the most time.
 (note: "plotStackedJobsVsTime" is my function that uses matplotlib.)
My script, for the curious, is at
 http://www.hep.wisc.edu/cms/comp/routerqMonitor/prodJobMonitorPlots_matplotlib.py
and produces these plots:
 http://www.hep.wisc.edu/cms/comp/routerqMonitor/index.html
Any hints at what I can do to speed up my script? Or is it out of my 
hands because it's all in matplotlib?
Thanks for any help,
Mike

Which older revision (or release) worked as you expected?
Mike
Tony S Yu wrote:
> Hi,
>
> Awhile back, Jae-Joon helped me transform collection sizes from points 
> to data values by overriding the `get_transform` method of a 
> RegularPolyCollection (see example code below).
>
> When I tried the code today, the collection didn't appear on the plot. 
> Removing the get_transform method below brings the collection back, 
> but of course, the sizes are not what I want.
>
> Some simple testing suggests that changing the transform returned by 
> `get_transform` changes the offsets of the collection items. In other 
> words, the transform originally (i.e. in some older revision) modified 
> only the scale of collection items, but now appears to modify both the 
> scale and offset.
>
> Is there a way to modify only the scale of collection items?
>
> Thanks,
> -Tony
>
> BTW, I'm on svn r7950.
>
> #-----------------
> # The squares will not show up with the code below
> # Comment out the get_transform method to see squares
> # Change scale_x and scale_y to values close to 1 to see how they 
> change both scale and offset
>
> class SquareCollection(collections.RegularPolyCollection):
> """Return a collection of squares."""
>
> def __init__(self, **kwargs):
> super(SquareCollection, self).__init__(4, rotation=np.pi/4., 
> **kwargs)
>
> def get_transform(self):
> """Return transform scaling circle areas to data space."""
> ax = self.axes
> pts2pixels = 72.0 / ax.figure.dpi
> scale_x = pts2pixels * ax.bbox.width / ax.viewLim.width
> scale_y = pts2pixels * ax.bbox.height / ax.viewLim.height
> return transforms.Affine2D().scale(scale_x, scale_y)
>
> ax = plt.gca()
> circle_areas = np.pi * np.ones(4)
> xy = [(0, 0), (0, 1), (1, 0), (1, 1)]
> squares = SquareCollection(sizes=circle_areas,
> offsets=xy, transOffset=ax.transData,
> facecolor='black')
> ax.add_collection(squares, autolim=True)
> plt.axis([-0.5, 1.5, -0.5, 1.5])
> plt.show()
>
>
>
> ------------------------------------------------------------------------------
> Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day 
> trial. Simplify your report design, integration and deployment - and focus on 
> what you do best, core application coding. Discover what's new with
> Crystal Reports now. http://p.sf.net/sfu/bobj-july
> _______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
> 
-- 
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA

Hi,
Awhile back, Jae-Joon helped me transform collection sizes from points 
to data values by overriding the `get_transform` method of a 
RegularPolyCollection (see example code below).
When I tried the code today, the collection didn't appear on the plot. 
Removing the get_transform method below brings the collection back, 
but of course, the sizes are not what I want.
Some simple testing suggests that changing the transform returned by 
`get_transform` changes the offsets of the collection items. In other 
words, the transform originally (i.e. in some older revision) modified 
only the scale of collection items, but now appears to modify both the 
scale and offset.
Is there a way to modify only the scale of collection items?
Thanks,
-Tony
BTW, I'm on svn r7950.
#-----------------
# The squares will not show up with the code below
# Comment out the get_transform method to see squares
# Change scale_x and scale_y to values close to 1 to see how they 
change both scale and offset
class SquareCollection(collections.RegularPolyCollection):
 """Return a collection of squares."""
 def __init__(self, **kwargs):
 super(SquareCollection, self).__init__(4, rotation=np.pi/4., 
**kwargs)
 def get_transform(self):
 """Return transform scaling circle areas to data space."""
 ax = self.axes
 pts2pixels = 72.0 / ax.figure.dpi
 scale_x = pts2pixels * ax.bbox.width / ax.viewLim.width
 scale_y = pts2pixels * ax.bbox.height / ax.viewLim.height
 return transforms.Affine2D().scale(scale_x, scale_y)
ax = plt.gca()
circle_areas = np.pi * np.ones(4)
xy = [(0, 0), (0, 1), (1, 0), (1, 1)]
squares = SquareCollection(sizes=circle_areas,
 offsets=xy, transOffset=ax.transData,
 facecolor='black')
ax.add_collection(squares, autolim=True)
plt.axis([-0.5, 1.5, -0.5, 1.5])
plt.show()

On Wed, Nov 11, 2009 at 11:53 AM, per freem <per...@gm...> wrote:
> hi all,
>
> i've been using genfromtxt to parse tab separated files for plotting
> purposes in matplotlib. the problem is that genfromtxt seems to give
> only two ways to access the contents of the file: one is by column,
> where you can use:
>
> d = genfromtxt(...)
>
> and then do d['header_name1'] to access the column named by
> 'header_name1', d['header_name2'] to access the column named by
> 'header_name2', etc. Or it will allow you to traverse the file line
> by line, and then access each header by number, i.e.
>
> for line in d:
> field1 = d[0]
> field2 = d[1]
> # etc.
>
> the problem is that the second method relies on knowing the order of
> the fields rather than just their name, and the first method does not
> allow line by line iteration.
> ideally what i would like is to be able to traverse each line of the
> parsed file, and then refer to each of its fields by header name, so
> that if the column order in the file changes my program will be
> unaffected:
>
> for line in d:
> field1 = ['header_name1']
> field2 = ['header_name2']
>
> is there a way to do this using standard matplotlib/numpy/scipy
> utilities? i could write my own code to do this but it seems like
> something somebody probably already thought of a good representation
> for and has implemented a more optimized version than i could write on
> my own. does such a thing exist?
>
> thanks very much
I have a constructor class to read space-delimited ASCII files.
class NasaFile(object):
 def __init__(self, filename):
 ...
 # Reading data
 _data = np.loadtxt(filename, dtype='float', skiprows=self.NLHEAD).T
 # Read using data['Time'] syntax
 self.data = dict(zip(self.VDESC, _data))
 ...
There is a meta-header in this type of data and NLHEAD is the variable
telling me how many lines to skip to reach the actual data. VDESC
tells me what each columns are (starting with Time variable and many
other different measurement results.)
There is not any column dependence in this case, and generically read
any length specifically formatted data. For instance:
from nasafile import NasaFile
c = NasaFile("mydata")
c.data['Time'] gets me the whole Time column as an ndarray . Why do
you think dictionaries are not sufficient for your case? I was using
locals() to create automatic names but that was not a very wise
approach.
> _______________________________________________
> NumPy-Discussion mailing list
> Num...@sc...
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
-- 
Gökhan

> ideally what i would like is to be able to traverse each line of the
> parsed file, and then refer to each of its fields by header name, so
> that if the column order in the file changes my program will be
> unaffected:
What you want is a DictReader.
For a quick example of me using that,
----------
aReader = csv.DictReader(open(inputFilename, 'r'), delimiter='\t')
yValues = {} # Will be a dictionary of lists (these are lists of y- 
values)
xValues = [] # List of x values (timestamps)
for row in aReader:
	xValues.append(datetime.datetime(*(time.strptime(row 
['TimeStamp'],timeStampFormat)[0:6])))
	for field in sorted(row,reverse=True): # Read from last 
to first in field list
		if field=='TimeStamp': continue # Column of x-data
		if not field in yValues.keys():
			yValues[field] = [nt(row[field])] # Start list of the values 
in this column
		else:
			yValues[field].append(int(row[field])) # Add to list of values in 
this column
----------
Mike

hi all,
i've been using genfromtxt to parse tab separated files for plotting
purposes in matplotlib. the problem is that genfromtxt seems to give
only two ways to access the contents of the file: one is by column,
where you can use:
d = genfromtxt(...)
and then do d['header_name1'] to access the column named by
'header_name1', d['header_name2'] to access the column named by
'header_name2', etc. Or it will allow you to traverse the file line
by line, and then access each header by number, i.e.
for line in d:
 field1 = d[0]
 field2 = d[1]
 # etc.
the problem is that the second method relies on knowing the order of
the fields rather than just their name, and the first method does not
allow line by line iteration.
ideally what i would like is to be able to traverse each line of the
parsed file, and then refer to each of its fields by header name, so
that if the column order in the file changes my program will be
unaffected:
for line in d:
 field1 = ['header_name1']
 field2 = ['header_name2']
is there a way to do this using standard matplotlib/numpy/scipy
utilities? i could write my own code to do this but it seems like
something somebody probably already thought of a good representation
for and has implemented a more optimized version than i could write on
my own. does such a thing exist?
thanks very much

Hi,
I'm running matplotlib v 0.99.0.
I can create a 3D scatterplot, like this:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter( ... )
so far so good.
Now, say I want to add a 2D contourplot (a "slice" of the 3D scatter 
contoured).
Is there some way how I can specify along which coordinates the 
contourplot is plotted, or is it always plotted on the (x,y) plane?
How I have tried this is by
from matplotlib.mlab import griddata
fi = griddata( ... )
ax.contourf(x,z,fi, ... )
# even though I specify z, it plots the contourf along the y axis.
Thank you all for any pointers,
Cheers,
Claus

Hi,
Is there a way to somehow squeeze two datasets with two different color
maps into one pcolormesh? Say one dataset occupies one triangle (e. g.
numpy.triu(dataset1, 1)) and the second dataset a different area (e. g.
numpy.tril(dataset2, -1)) and then paste the two datasets in one
pcolormesh (e.g. pylab.pcolormesh(numpy.triu(dataset1, 1) +
numpy.tril(dataset2, -1)) ). This will obviously share one colormap. But
does anybody know a trick to use two different ones?
TIA
Christian
PS Forgot to thank Eric Firing for his tip last time I asked. Thank you
Eric: You solved my problem!

It's actually just a nice part of the python language, setattr(a,
> 'foo', 'bar') is the same code as a.foo = bar. When working with
> NetCDF, the only time I've needed to use setarr (or getattr) is when
> the name of the attribute I want isn't a valid python identifier (like
> if an attribute has a - in it, e.g. 'unit-type').
>
>
Slick! Another great reason for python. Thanks for the rapid response.

On Wed, Nov 11, 2009 at 9:26 AM, John <was...@gm...> wrote:
> No problem.. just wanted to make sure it would work and is 'pythonic'.
>
> I guess the biggest concern is that for others using the netcdf file, say
> from matlab or somewhere else, will be able to access the attributes.
>
> It seemed so easy to do it this way, I was curious why the tutorials suggest
> using setattr(), and I wonder if perhaps what I'm setting is not 'global' or
> perhaps wouldn't match some ISO or otherwise NetCDF standard??
It's actually just a nice part of the python language, setattr(a,
'foo', 'bar') is the same code as a.foo = bar. When working with
NetCDF, the only time I've needed to use setarr (or getattr) is when
the name of the attribute I want isn't a valid python identifier (like
if an attribute has a - in it, e.g. 'unit-type').
Ryan
-- 
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma

No problem.. just wanted to make sure it would work and is 'pythonic'.
I guess the biggest concern is that for others using the netcdf file, say
from matlab or somewhere else, will be able to access the attributes.
It seemed so easy to do it this way, I was curious why the tutorials suggest
using setattr(), and I wonder if perhaps what I'm setting is not 'global' or
perhaps wouldn't match some ISO or otherwise NetCDF standard??
Thanks!
On Wed, Nov 11, 2009 at 4:03 PM, Ryan May <rm...@gm...> wrote:
> On Wed, Nov 11, 2009 at 7:31 AM, John [H2O] <was...@gm...> wrote:
> >
> > Hello, I'm trying to write a function to create a 'vanilla' NetCDF file
> to
> > which I can add data.
> >
> > So far I have created the following, which is designed to set up a netcdf
> > file for the addition of global or at least lat/lon datasets. My question
> > has to do with attribute setting. Is it valid to do what I'm doing below
> > "nco.attribute = attributevalue" or do you have to use the setattr
> function
> > as outlined in some tutorials?
> >
> > Thanks!
> >
> > def default_netcdf(nco_filename,
> > lon0=-179.5,lat0=-89.5,
> > nx=720,ny=360,
> > dx=0.5,dy=0.5):
> > """ add default attributes and dimensions to the nc file """
> > nco = NetCDFFile(nco_filename,'w')
> > nco.author = "Some One"
> > nco.createdate = dt.datetime.now().ctime()
> > nco.contact = "som...@py..."
> > nco.Conventions = "CF-1.4"
> >
> > nco.createDimension('lon',nx)
> > nco.createDimension('lat',ny)
> > nco.createVariable('lat','d',('lat',))
> > nco.createVariable('lon','d',('lon',))
> > lon = np.arange(lon0,lon0+(nx*dx),dx)
> > lat = np.arange(lat0,lat0+(ny*dy),dy)
> > nco.variables['lat'][:] = lat
> > nco.variables['lon'][:] = lon
> > nco.createVariable('data','d',('lon','lat'))
> >
> > return nco
>
> That should work fine for settings attributes. Are you having a
> problem? If so, can you paste the actual traceback?
>
> Ryan
>
> --
> Ryan May
> Graduate Research Assistant
> School of Meteorology
> University of Oklahoma
>
-- 
Configuration
``````````````````````````
Plone 2.5.3-final,
CMF-1.6.4,
Zope (Zope 2.9.7-final, python 2.4.4, linux2),
Five 1.4.1,
Python 2.4.4 (#1, Jul 3 2007, 22:58:17) [GCC 4.1.1 20070105 (Red Hat
4.1.1-51)],
PIL 1.1.6
Mailman 2.1.9
Postfix 2.4.5
Procmail v3.22 2001年09月10日

On Wed, Nov 11, 2009 at 7:31 AM, John [H2O] <was...@gm...> wrote:
>
> Hello, I'm trying to write a function to create a 'vanilla' NetCDF file to
> which I can add data.
>
> So far I have created the following, which is designed to set up a netcdf
> file for the addition of global or at least lat/lon datasets. My question
> has to do with attribute setting. Is it valid to do what I'm doing below
> "nco.attribute = attributevalue" or do you have to use the setattr function
> as outlined in some tutorials?
>
> Thanks!
>
> def default_netcdf(nco_filename,
>          lon0=-179.5,lat0=-89.5,
>          nx=720,ny=360,
>          dx=0.5,dy=0.5):
>  """ add default attributes and dimensions to the nc file """
>  nco = NetCDFFile(nco_filename,'w')
>  nco.author = "Some One"
>  nco.createdate = dt.datetime.now().ctime()
>  nco.contact = "som...@py..."
>  nco.Conventions = "CF-1.4"
>
>  nco.createDimension('lon',nx)
>  nco.createDimension('lat',ny)
>  nco.createVariable('lat','d',('lat',))
>  nco.createVariable('lon','d',('lon',))
>  lon = np.arange(lon0,lon0+(nx*dx),dx)
>  lat = np.arange(lat0,lat0+(ny*dy),dy)
>  nco.variables['lat'][:] = lat
>  nco.variables['lon'][:] = lon
>  nco.createVariable('data','d',('lon','lat'))
>
>  return nco
That should work fine for settings attributes. Are you having a
problem? If so, can you paste the actual traceback?
Ryan
-- 
Ryan May
Graduate Research Assistant
School of Meteorology
University of Oklahoma

Hi all,
Previously I was a user of gnuplot but have been giving matplotlib a 
try. One thing I've run in to right away is that matplotlib appears 
to be significantly slower.
A script to produce a dozen plots was taking me ~1 second with 
gnuplot, and now takes me ~18 seconds with matplotlib.
I'm curious if anyone knows how to speed things up. To figure out 
what is taking most of the time, I've used cProfile and pstats and 
below is the top 15 functions taking the most time.
 (note: "plotStackedJobsVsTime" is my function that uses matplotlib.)
----------------------------------------
Wed Nov 11 08:43:52 2009 fooprof
 3069840 function calls (3009711 primitive calls) in 17.658 
CPU seconds
 Ordered by: cumulative time
 List reduced from 873 to 15 due to restriction <15>
 ncalls tottime percall cumtime percall filename:lineno(function)
 1 0.000 0.000 17.658 17.658 <string>:1(<module>)
 1 0.007 0.007 17.658 17.658 /cms/cmsprod/bin/ 
prodJobMonitorPlots_matplotlib.py:151(main)
 13 0.620 0.048 17.647 1.357 /cms/cmsprod/bin/ 
prodJobMonitorPlots_matplotlib.py:54(plotStackedJobsVsTime)
 13 0.000 0.000 10.087 0.776 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/figure.py:959 
(savefig)
 13 0.028 0.002 10.086 0.776 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/ 
backend_bases.py:1372(print_figure)
 13 0.028 0.002 9.847 0.757 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/backends/ 
backend_agg.py:357(print_png)
 13 0.000 0.000 9.243 0.711 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/backends/ 
backend_agg.py:307(draw)
 3206/13 0.040 0.000 9.222 0.709 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/artist.py:44 
(draw_wrapper)
 13 0.001 0.000 9.222 0.709 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/figure.py:729 
(draw)
 13 0.005 0.000 9.200 0.708 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/axes.py:1632 
(draw)
 26 0.029 0.001 5.624 0.216 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/axis.py:727 
(draw)
1533/1325 0.078 0.000 3.663 0.003 /afs/hep.wisc.edu/cms/sw/ 
python/x86_64/2.5.4/lib/python2.5/site-packages/matplotlib-0.99.1.1_r0- 
py2.5-linux-x86_64.egg/matplotlib/axis.py:61(__init__)
 811 0.035 0.000 3.057 0.004 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/axis.py:181 
(draw)
 13 0.001 0.000 2.619 0.201 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/legend.py:342 
(draw)
 1065/961 0.012 0.000 2.543 0.003 /afs/hep.wisc.edu/cms/ 
sw/python/x86_64/2.5.4/lib/python2.5/site-packages/ 
matplotlib-0.99.1.1_r0-py2.5-linux-x86_64.egg/matplotlib/axis.py:1207 
(_get_tick)
----------------------------------------
My script, for the curious, is at
 http://www.hep.wisc.edu/cms/comp/routerqMonitor/prodJobMonitorPlots_matplotlib.py
and produces these plots:
 http://www.hep.wisc.edu/cms/comp/routerqMonitor/index.html
Any hints at what I can do to speed up my script? Or is it out of my 
hands because it's all in matplotlib?
Thanks for any help,
Mike

Hello, I'm trying to write a function to create a 'vanilla' NetCDF file to
which I can add data.
So far I have created the following, which is designed to set up a netcdf
file for the addition of global or at least lat/lon datasets. My question
has to do with attribute setting. Is it valid to do what I'm doing below
"nco.attribute = attributevalue" or do you have to use the setattr function
as outlined in some tutorials?
Thanks!
def default_netcdf(nco_filename,
 lon0=-179.5,lat0=-89.5,
 nx=720,ny=360,
 dx=0.5,dy=0.5):
 """ add default attributes and dimensions to the nc file """
 nco = NetCDFFile(nco_filename,'w')
 nco.author = "Some One"
 nco.createdate = dt.datetime.now().ctime()
 nco.contact = "som...@py..."
 nco.Conventions = "CF-1.4"
 
 nco.createDimension('lon',nx)
 nco.createDimension('lat',ny)
 nco.createVariable('lat','d',('lat',))
 nco.createVariable('lon','d',('lon',))
 lon = np.arange(lon0,lon0+(nx*dx),dx)
 lat = np.arange(lat0,lat0+(ny*dy),dy)
 nco.variables['lat'][:] = lat
 nco.variables['lon'][:] = lon
 nco.createVariable('data','d',('lon','lat'))
 
 return nco
-- 
View this message in context: http://old.nabble.com/function-to-create-netcdf-file-tp26301238p26301238.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

On 2009年11月10日 13:13 -0500, "George Bonev"
<geo...@gm...> wrote:
 Hello,
 I am plotting a fig in Basemap, but I don't need the extra
 while space on the sides of the figure when I use savfig(). I
 tried axes([0,0,1,1]) but that just gave me a blank white
 screen with some ticks on the sides. Is there another way to
 do it with Basemap? Please help!
 Thank you,
 George B.
George: If you want to preserve the aspect ratio of the map, you
need to calculate it ahead of time and make sure that you create
a figure with the same aspect ratio and define an axes with no
white space. For example, for a global cylindrical equidistant
projection, the aspect ration should be 2:1, so you would create
a figure like this
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(10,5))
ax = plt.axes([0,0,1,1])
m = Basemap(projection='cyl',lon_0=180)
m.drawcoastlines()
m.fillcontinents()
m.drawparallels(np.arange(-60,61,30))
m.drawmeridians(np.arange(60,301,60))
plt.savefig('nowhite.png')
plt.show()
Note that you need to create the axes instance before the Basemap
instance.
If you don't care about the aspect ratio of the map being
correct, you can set fix_aspect='False', create a map of any size
you like, and use an axes with no white space. For example,
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(8,8))
ax = plt.axes([0,0,1,1])
m = Basemap(projection='cyl',lon_0=180,fix_aspect=False)
m.drawcoastlines()
m.fillcontinents()
m.drawparallels(np.arange(-60,61,30))
m.drawmeridians(np.arange(60,301,60))
plt.savefig('nowhite2.png')
plt.show()
If you don't set fix_aspect=False in this example, you'll get a
map with a 2:1 aspect ratio and lots of white space on the top
and bottom of the plot.
-Jeff
------
Jeffrey S. Whitaker
Phone: (303)497-6313
FAX: (303)497-6449
NOAA/OAR/CDC R/CDC1 
325 Broadway, Boulder, CO, USA 80305-3328 
http://www.cdc.noaa.gov/people/jeffrey.s.whitaker