matplotlib-users Mailing List for matplotlib

On 2012年07月25日 12:45 PM, Scott Henderson wrote:
> Thanks for the response. To be more specific, if I run the modified
> script in an ipython --pylab terminal:
>
> In [1]: %run lasso_demo.py
>
> In [2]:
>
> The function returns, and the lasso manager is garbage collected as
> you say. In other words, the plot shows up, but I can't draw a lasso
> and select points. If I run the script in ipython without the pylab
> flag it works!
>
> Why does enabling --pylab have this effect? Since I use pylab for
> day-to-day computing I'd like to incorporate some of these widgit
> tools in interactive sessions, but maybe that's not possible?
It is entirely possible.
--pylab mode turns mpl interactive mode on, so that the call to show() 
does not block. But because you have that call inside your function, if 
show() doesn't block, you immediately return from the function, and as 
Tony noted, your widget gets garbage-collected.
Try this: keep references to your figure and LassoManager in a list, 
dictionary, class, or whatever you like, and return that from your run() 
function. When you call run, assign its output to a name, so there is a 
reference to it, and indirectly to the LassoManager.
Eric
>
> Thanks,
> Scott
>
>
>
> On Jul 25, 2012, at 5:10 PM, Tony Yu wrote:
>
>> def run(points):
>> data = [Datum(*xy) for xy in points]
>> fig = figure()
>> ax = fig.add_subplot(111, xlim=(0,1), ylim=(0,1), autoscale_on=False)
>> lman = LassoManager(ax, data)
>> show()
>>
>> if __name__ == '__main__':
>> points = rand(100, 2)
>> run(points)
>
>
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Thanks for the response. To be more specific, if I run the modified script in an ipython --pylab terminal:
In [1]: %run lasso_demo.py
In [2]:
The function returns, and the lasso manager is garbage collected as you say. In other words, the plot shows up, but I can't draw a lasso and select points. If I run the script in ipython without the pylab flag it works!
Why does enabling --pylab have this effect? Since I use pylab for day-to-day computing I'd like to incorporate some of these widgit tools in interactive sessions, but maybe that's not possible? 
Thanks, 
Scott
On Jul 25, 2012, at 5:10 PM, Tony Yu wrote:
> def run(points):
> data = [Datum(*xy) for xy in points]
> fig = figure()
> ax = fig.add_subplot(111, xlim=(0,1), ylim=(0,1), autoscale_on=False)
> lman = LassoManager(ax, data)
> show()
> 
> if __name__ == '__main__':
> points = rand(100, 2)
> run(points)

On Wed, Jul 25, 2012 at 5:11 PM, Scott Henderson <st...@co...> wrote:
> Hello, I'm trying to incorporate some matplotlib widgets into my scripts
> that generate plots. I'm unsure why, but if I wrap the figure creation
> lines in lasso_demo.py with a function the demo does not work. Why?
>
> #Original:
> #------------
> if __name__ == '__main__':
> data = [Datum(*xy) for xy in randn(100,2)]
> fig = figure()
> ax = fig.add_subplot(111, xlim=(0,1), ylim=(0,1), autoscale_on=False)
> lman = LassoManager(ax, data)
> show()
>
> #Change:
> #------------
> def run(points):
> data = [Datum(*xy) for xy in points]
> fig = figure()
> ax = fig.add_subplot(111, xlim=(0,1), ylim=(0,1), autoscale_on=False)
> lman = LassoManager(ax, data)
> show()
>
> if __name__ == '__main__':
> points = rand(100, 2)
> run(points)
>
>
> --
> ---------------
> Scott T. Henderson
> http://www.geo.cornell.edu/eas/gstudent/sth54/contact.html
>
Your changes work fine on my system.
Are you sure the example you wrote above is exactly the same as what you're
running? For example, if I move the `show` out of the function and back
into '__main__', then the lasso stops working (because the lasso manager
gets garbage collected). Otherwise, I can't reproduce the issue. Actually,
what did you mean by "does not work" (e.g., no lasso, error,)?
-Tony

Hello, I'm trying to incorporate some matplotlib widgets into my scripts 
that generate plots. I'm unsure why, but if I wrap the figure creation 
lines in lasso_demo.py with a function the demo does not work. Why?
#Original:
#------------
if __name__ == '__main__':
 data = [Datum(*xy) for xy in randn(100,2)]
 fig = figure()
 ax = fig.add_subplot(111, xlim=(0,1), ylim=(0,1), autoscale_on=False)
 lman = LassoManager(ax, data)
 show()
#Change:
#------------
def run(points):
 data = [Datum(*xy) for xy in points]
 fig = figure()
 ax = fig.add_subplot(111, xlim=(0,1), ylim=(0,1), autoscale_on=False)
 lman = LassoManager(ax, data)
 show()
if __name__ == '__main__':
 points = rand(100, 2)
 run(points)
-- 
---------------
Scott T. Henderson
http://www.geo.cornell.edu/eas/gstudent/sth54/contact.html

On Wednesday, July 25, 2012, Rutger Kassies wrote:
> Dear list,
>
> I am trying to plot an image using imshow for which i want to have full
> control of the interpolation taking place. I would rather not have any
> interpolation at all, but if the input image is very small, it may be
> interpolated (nearest neighbor) with an integer factor of my choice (2x,3x
> etc.).
>
> To achieve this i decided that the first and most simple case would be to
> output exactly the image which i load by using plt.imread(). If this
> succeeds i would expand it by adding a buffer around the image to allow
> some annotation like for example a title and a colorbar.
>
> Here is my initial try:
>
> import matplotlib.pyplot as plt
>
> img = plt.imread('D:\\input_image.png')
>
> # get x and y dimension of the image
> xpixels = img.shape[1]
> ypixels = img.shape[0]
>
> # calculate the dimensions in inch for a given dpi
> zoomfactor = 1
> dpi = 72
> xinch = ( xpixels * zoomfactor) / dpi
> yinch = (ypixels * zoomfactor) / dpi
>
> # create the figure
> fig = plt.figure(figsize=(xinch,yinch))
>
> # stretch the axes to the full extent of the figure
> ax = plt.axes([0.0, 0.0, 1.0, 1.0])
>
> ax.imshow(img, interpolation='nearest')
>
> # hide axis stuff
> ax.axes.get_yaxis().set_visible(False)
> ax.axes.get_xaxis().set_visible(False)
>
> # remove the border of the axes
> plt.setp(ax, frame_on=False)
>
> # save the figure with the given dpi
> plt.savefig('D:\\output_image.png', dpi=dpi)
>
>
> The resulting output image is equal in dimensions to the input image, so
> far so good. However, when i overlay them in a graphics program, i can
> clearly see that some nearest neighbor interpolation has taken place. It
> appears as if the top-right corner of the image is plotted 1 or 2 pixels
> beyond the edge of the figure. Does anyone have any idea how to get this
> right?
>
> Thanks in advance!
>
>
> Regards,
> Rutger
>
>
If you are using v1.1.0 or later, you can set interpolation to "none". Note
the difference between providing the string "none", which means to do no
interpolation at all, while the python None means to do the default.
Ben Root

Dear list,
I am trying to plot an image using imshow for which i want to have full
control of the interpolation taking place. I would rather not have any
interpolation at all, but if the input image is very small, it may be
interpolated (nearest neighbor) with an integer factor of my choice (2x,3x
etc.).
To achieve this i decided that the first and most simple case would be to
output exactly the image which i load by using plt.imread(). If this
succeeds i would expand it by adding a buffer around the image to allow
some annotation like for example a title and a colorbar.
Here is my initial try:
import matplotlib.pyplot as plt
img = plt.imread('D:\\input_image.png')
# get x and y dimension of the image
xpixels = img.shape[1]
ypixels = img.shape[0]
# calculate the dimensions in inch for a given dpi
zoomfactor = 1
dpi = 72
xinch = ( xpixels * zoomfactor) / dpi
yinch = (ypixels * zoomfactor) / dpi
# create the figure
fig = plt.figure(figsize=(xinch,yinch))
# stretch the axes to the full extent of the figure
ax = plt.axes([0.0, 0.0, 1.0, 1.0])
ax.imshow(img, interpolation='nearest')
# hide axis stuff
ax.axes.get_yaxis().set_visible(False)
ax.axes.get_xaxis().set_visible(False)
# remove the border of the axes
plt.setp(ax, frame_on=False)
# save the figure with the given dpi
plt.savefig('D:\\output_image.png', dpi=dpi)
The resulting output image is equal in dimensions to the input image, so
far so good. However, when i overlay them in a graphics program, i can
clearly see that some nearest neighbor interpolation has taken place. It
appears as if the top-right corner of the image is plotted 1 or 2 pixels
beyond the edge of the figure. Does anyone have any idea how to get this
right?
Thanks in advance!
Regards,
Rutger

On 7/24/12 8:04 PM, Vladan Divljak wrote:
> Hi,
>
> I'm a physics student, and got interested in retrieving meteorological 
> data (through Internet)
> I spent good couple of days to just make a picture of file formats 
> providing this data, sources and tools for reading it, and then give 
> up discouridged by ECMWF and finally EUMETSAT portals. I have a 
> picture of total mess unlike my expectations, like why I "must" 
> strictly study Fortran when data is so divergent?
>
> Then I thought to make it easier by using some of available java 
> frontends which were offered on sites I already visited. First I tried 
> three different versions of OPeNDAP Data Connector, but it couldn't 
> run on my Windows PC. Then I found IDV from Unidata, and after 
> spending some time with it I think it has very good scheme for 
> application workflow, while it has a learning curve and it's most 
> useful for US residents, while I live somewhere in Europe.
>
> I discovered also Basemap, and it's excellent package. If data is 
> provided, it offers interface and utilities to whole Python in the 
> same way. That is why I joined this mailing list and thought to ask 
> for similar experiences, potentially advice on available tools for 
> retrieving meteorological data and data itself.
> For example, I can retrieve data from GFS/NOAA (which is BTW great, as 
> only light in whole this experiance), and would appreciate similar 
> maybe localized meso model data, and current satellite or radar data, 
> or appropriate tools. I'm thinking I must have missed something, as 
> this shouldn't be so difficult task, or?
Vladan: You can access current NOAA forecasts (atmosphere and ocean) 
via opendap from http://nomads.ncep.noaa.gov. The netcdf4-python 
package (http://netcdf4-python.googlecode.com) can connect to these 
remote datasets via opendap, in fact many of the Basemap examples use 
this (see fcstmaps.py and plotsst.py in the examples directory).
You might also be interested in the data here
http://motherlode.ucar.edu/thredds/catalog.html
and here
http://www.esrl.noaa.gov/psd/data/gridded/
-Jeff

On Tuesday, July 24, 2012, Vladan Divljak wrote:
> On Wed, Jul 25, 2012 at 4:22 AM, Eric Firing wrote:
>
>>
>> It sounds like maybe you missed http://pydap.org.
>>
>
> I have it :)
> I installed it yesterday.
> On a first sight, it seemed to me like a tool for users interested in
> making local dap server (I have even considered that, until I run to IDV),
> but maybe I didn't got it's potential.
>
> I'll check it out again, thanks
>
For archived data, NCDC is king in my book. Especially when it comes to US
NEXRAD radar data. They offer both GUI and CLI tools for data
visualization and conversion. Usually, I Jude it to convert my radar data
into netcdf format.
By the way, there are three major binary data formats to know in the metro
world: grib, netcdf and hdf.
Happy data mining!
Ben Root

On Wed, Jul 25, 2012 at 4:51 AM, Jeff Whitaker <js...@fa...> wrote:
> Vladan: You can access current NOAA forecasts (atmosphere and ocean) via
> opendap from http://nomads.ncep.noaa.gov. The netcdf4-python package (
> http://netcdf4-python.**googlecode.com<http://netcdf4-python.googlecode.com>)
> can connect to these remote datasets via opendap, in fact many of the
> Basemap examples use this (see fcstmaps.py and plotsst.py in the examples
> directory).
>
> You might also be interested in the data here
>
> http://motherlode.ucar.edu/**thredds/catalog.html<http://motherlode.ucar.edu/thredds/catalog.html>
>
> and here
>
> http://www.esrl.noaa.gov/psd/**data/gridded/<http://www.esrl.noaa.gov/psd/data/gridded/>
>
>
Thanks Jeff,
and all you guys. I'm glad I asked this question, as I got great responses
in just half an hour, compared to mostly useless days searching for same
Best wishes

On Wed, Jul 25, 2012 at 4:38 AM, Patrick Marsh wrote:
> I'm finishing up my PhD in Meteorology. Although I don't use it explicitly
> use pydap for my research, I do use it to download meteorological data from
> various dap servers.
>
OK, thanks
I'm just browsing datasets at opendap wiki, which I somehow missed the
other day, probably because of lots of informations about formats, sources
...
I guess I had to ask, to get to second level. Now it seems like
straitforward: parse thredds catalogs and opendap datasets, and
additionally search for same, then dive ;)
Thanks for your pointers in right direction
Cheers :)

On Wed, Jul 25, 2012 at 4:22 AM, Eric Firing wrote:
>
> It sounds like maybe you missed http://pydap.org.
>
I have it :)
I installed it yesterday.
On a first sight, it seemed to me like a tool for users interested in
making local dap server (I have even considered that, until I run to IDV),
but maybe I didn't got it's potential.
I'll check it out again, thanks

On 2012年07月24日 4:04 PM, Vladan Divljak wrote:
> Hi,
>
> I'm a physics student, and got interested in retrieving meteorological
> data (through Internet)
> I spent good couple of days to just make a picture of file formats
> providing this data, sources and tools for reading it, and then give up
> discouridged by ECMWF and finally EUMETSAT portals. I have a picture of
> total mess unlike my expectations, like why I "must" strictly study
> Fortran when data is so divergent?
>
> Then I thought to make it easier by using some of available java
> frontends which were offered on sites I already visited. First I tried
> three different versions of OPeNDAP Data Connector, but it couldn't run
> on my Windows PC. Then I found IDV from Unidata, and after spending some
> time with it I think it has very good scheme for application workflow,
> while it has a learning curve and it's most useful for US residents,
> while I live somewhere in Europe.
>
> I discovered also Basemap, and it's excellent package. If data is
> provided, it offers interface and utilities to whole Python in the same
> way. That is why I joined this mailing list and thought to ask for
> similar experiences, potentially advice on available tools for
> retrieving meteorological data and data itself.
> For example, I can retrieve data from GFS/NOAA (which is BTW great, as
> only light in whole this experiance), and would appreciate similar maybe
> localized meso model data, and current satellite or radar data, or
> appropriate tools. I'm thinking I must have missed something, as this
> shouldn't be so difficult task, or?
>
It sounds like maybe you missed http://pydap.org.
Eric

Hi,
I'm a physics student, and got interested in retrieving meteorological data
(through Internet)
I spent good couple of days to just make a picture of file formats
providing this data, sources and tools for reading it, and then give up
discouridged by ECMWF and finally EUMETSAT portals. I have a picture of
total mess unlike my expectations, like why I "must" strictly study Fortran
when data is so divergent?
Then I thought to make it easier by using some of available java frontends
which were offered on sites I already visited. First I tried three
different versions of OPeNDAP Data Connector, but it couldn't run on my
Windows PC. Then I found IDV from Unidata, and after spending some time
with it I think it has very good scheme for application workflow, while it
has a learning curve and it's most useful for US residents, while I live
somewhere in Europe.
I discovered also Basemap, and it's excellent package. If data is provided,
it offers interface and utilities to whole Python in the same way. That is
why I joined this mailing list and thought to ask for similar experiences,
potentially advice on available tools for retrieving meteorological data
and data itself.
For example, I can retrieve data from GFS/NOAA (which is BTW great, as only
light in whole this experiance), and would appreciate similar maybe
localized meso model data, and current satellite or radar data, or
appropriate tools. I'm thinking I must have missed something, as this
shouldn't be so difficult task, or?