matplotlib-users Mailing List for matplotlib

On 06/08/2011 01:28 AM, Daniel Mader wrote:
> Dear Eric,
>
> thanks again for your comment, I am aware that the script contained
> both the individual colorbars and the common one. My comment in the
> code was because the placement on the figure is somewhat cramped:
That's what I suspected, so the main point of the modified version was 
using subplots_adjust to give more room at the bottom, and then shifting 
the bottom colorbar so that it was less cramped. Granted, this approach 
takes some fiddling; but if you are using subplots and care about 
appearance, then sooner or later you will probably benefit from 
subplots_adjust, a function/method which is probably not as well-known 
as it deserves to be.
Eric
>
> ## doesn't really work :/ ## in what way?
> cax = fig.add_axes([0.25, 0.06, 0.5, 0.02])
> fig.colorbar(im2, cax, orientation='horizontal')
>
> Ideally, I'd need to create a new subfig 313 with a much reduced height.
>
> Either way, you helped me a lot!
>
>
> 2011年6月7日 Eric Firing<ef...@ha...>:
>> On 06/07/2011 01:37 AM, Daniel Mader wrote:
>>>
>>> Hi Eric,
>>>
>>>
>>> 2011年6月6日 Eric Firing<ef...@ha...>:
>>>>
>>>> It's not quite clear to me yet, but I assume you want to use a call to
>>>> imshow with a different data set in the second subplot, but have the
>>>> color scale and colorbar be identical to those in the first subplot. Is
>>>> that correct? If so, all you need to do is use the same norm for both
>>>> calls to imshow--that is, define a norm, set the limits you want on it,
>>>> and supply it as a kwarg.
>>>
>>> thanks a lot, you helped me to work around my problem, see code below :)
>>>
>>>> Also, for this sort of comparison, sometimes it is more efficient to use
>>>> a single colorbar for multiple panels, as in this example:
>>>>
>>>>
>>>> http://matplotlib.sourceforge.net/examples/pylab_examples/multi_image.html
>>>
>>> Very nice example! It's a little too complex for me, though, with all
>>> the calculations for the axes layout -- I prefer subplots :) However,
>>> I think I have found a nice compromise:
>>
>> Attached is a slight modification, much simpler than the example above, but
>> retaining the single colorbar. Alternatively, if you stick with the
>> colorbar for each panel (which is sometimes clearer), it illustrates a
>> slightly clearer way of handling the cmap and norm, explicitly using the
>> same instance of each for both images.
>>
>> Eric
>>
>>>
>>> import pylab
>>> import matplotlib as mpl
>>>
>>> pylab.close('all')
>>>
>>> dat = pylab.array([[1,2,3,4],[5,6,7,8]])
>>> datT = dat/2
>>>
>>> fig = pylab.figure()
>>>
>>> ax1 = fig.add_subplot(211)
>>> ax1.set_title('raw data')
>>> im1 = ax1.imshow(dat, interpolation='nearest',
>>> cmap=mpl.cm.get_cmap('rainbow', 20))
>>> fig.colorbar(im1)
>>>
>>> ax2 = fig.add_subplot(212)
>>> ax2.set_title('leveled')
>>> im2 = ax2.imshow(datT, interpolation='nearest',
>>> cmap=mpl.cm.get_cmap('rainbow', 20))
>>> ## apply norm:
>>> norm = mpl.colors.Normalize(vmin=dat.min(), vmax=dat.max())
>>> im2.set_norm(norm)
>>> fig.colorbar(im2)
>>>
>>> ## doesn't really work :/
>>> cax = fig.add_axes([0.25, 0.04, 0.5, 0.02])
>>> fig.colorbar(im2, cax, orientation='horizontal')
>>>
>>> pylab.show()
>>>
>>> Thanks a lot,
>>> best regards,
>>>
>>> Daniel
>
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On Tue, Jun 7, 2011 at 5:16 PM, Shankararaman Ramakrishnan <
Sha...@tr...> wrote:
> Hi,
>
>
>
> I use matplotlib as my python graphics library. I happen to see the
> following problems with the plot() function and would appreciate any help to
> resolve these problems!
>
>
>
> 1. Using the default line style under plot() results in the function
> dropping data points from the plot. Plotting the same data with a ‘dot’
> linestyle shows the missing data points.
>
> 2. Any large outliers in a data set are generally excluded from the
> resulting plot.
>
>
>
> The original plot generated using the default linestyle does not show the
> large outliers in the dataset. Further comparing against the second plot
> shows missing data points which were not connected by the default linestyle.
> Several missing data points are not necessarily large outliers. My initial
> guess for missing large outliers was that the function was aliasing down
> large values.
>
>
>
> Missing nominal data points makes me wonder if the plot function internally
> generates a "best-fit" or least squares plot of the data points? This
> inconsistent plot occurs only with large data sets. The two plots were
> generated with over 100000 (hundred thousand) data points.
>
>
>
> Would be happy to send you the original data if that may help troubleshoot
> this problem.
>
In all liklihood, you are using an older version of matplotlib which had a
bug in the path.simplify logic that caused some points to be dropped. The
solution is to either upgrade to the latest matplotlib (1.0.1) or turn off
path simplification by setting 'path.simplify : False' in your matplotlibrc
http://matplotlib.sourceforge.net/users/customizing.html
You can find out what version of matplotlib you are running by doing
In [127]: import matplotlib
In [128]: matplotlib.__version__
Out[128]: '1.0.1'
JDH

Dear Eric,
thanks again for your comment, I am aware that the script contained
both the individual colorbars and the common one. My comment in the
code was because the placement on the figure is somewhat cramped:
## doesn't really work :/ ## in what way?
cax = fig.add_axes([0.25, 0.06, 0.5, 0.02])
fig.colorbar(im2, cax, orientation='horizontal')
Ideally, I'd need to create a new subfig 313 with a much reduced height.
Either way, you helped me a lot!
2011年6月7日 Eric Firing <ef...@ha...>:
> On 06/07/2011 01:37 AM, Daniel Mader wrote:
>>
>> Hi Eric,
>>
>>
>> 2011年6月6日 Eric Firing<ef...@ha...>:
>>>
>>> It's not quite clear to me yet, but I assume you want to use a call to
>>> imshow with a different data set in the second subplot, but have the
>>> color scale and colorbar be identical to those in the first subplot. Is
>>> that correct? If so, all you need to do is use the same norm for both
>>> calls to imshow--that is, define a norm, set the limits you want on it,
>>> and supply it as a kwarg.
>>
>> thanks a lot, you helped me to work around my problem, see code below :)
>>
>>> Also, for this sort of comparison, sometimes it is more efficient to use
>>> a single colorbar for multiple panels, as in this example:
>>>
>>>
>>> http://matplotlib.sourceforge.net/examples/pylab_examples/multi_image.html
>>
>> Very nice example! It's a little too complex for me, though, with all
>> the calculations for the axes layout -- I prefer subplots :) However,
>> I think I have found a nice compromise:
>
> Attached is a slight modification, much simpler than the example above, but
> retaining the single colorbar. Alternatively, if you stick with the
> colorbar for each panel (which is sometimes clearer), it illustrates a
> slightly clearer way of handling the cmap and norm, explicitly using the
> same instance of each for both images.
>
> Eric
>
>>
>> import pylab
>> import matplotlib as mpl
>>
>> pylab.close('all')
>>
>> dat = pylab.array([[1,2,3,4],[5,6,7,8]])
>> datT = dat/2
>>
>> fig = pylab.figure()
>>
>> ax1 = fig.add_subplot(211)
>> ax1.set_title('raw data')
>> im1 = ax1.imshow(dat, interpolation='nearest',
>> cmap=mpl.cm.get_cmap('rainbow', 20))
>> fig.colorbar(im1)
>>
>> ax2 = fig.add_subplot(212)
>> ax2.set_title('leveled')
>> im2 = ax2.imshow(datT, interpolation='nearest',
>> cmap=mpl.cm.get_cmap('rainbow', 20))
>> ## apply norm:
>> norm = mpl.colors.Normalize(vmin=dat.min(), vmax=dat.max())
>> im2.set_norm(norm)
>> fig.colorbar(im2)
>>
>> ## doesn't really work :/
>> cax = fig.add_axes([0.25, 0.04, 0.5, 0.02])
>> fig.colorbar(im2, cax, orientation='horizontal')
>>
>> pylab.show()
>>
>> Thanks a lot,
>> best regards,
>>
>> Daniel

Hello,
I've got following function describing any kind of animal dispersal kernel:
def pdf(x,s1,s2):
 return (p/(math.sqrt(2*math.pi*s1**2))*numpy.exp(-((x-0)**(2)/(2*s1**(2)))))+((1-p)/(s2*math.sqrt(2*math.pi))*numpy.exp(-((x-0)**(2)/(2*s2**(2)))))
On the other hand I've got data from literature with which I want to fit the function so that I get s1, s2 and x. 
Ususally the data in the literature are as follows:
Example 1: 50% of the animals are between -270m and +270m and 90% are between -500m and + 500m
Example 2: 84% is between - 5000 m and +5000m, and 73% are between -1000m and +1000m
So far as I understand an integration of the function is needed to solve for s1 and s2 as all the literature data give percentage (area under the curve) Can that be used to fit the curve or can that create ranges for s1 and s2.
/Johannes
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efiring wrote:
> 
> My github branch now includes a changeset with augmented docstrings for 
> show and draw.
> 
Your new docstrings are a great improvement. They answer many of the
questions I have been having for years.
As for where the interactive mode/non-interactive mode discussion should be,
I think that while the mere existence of this discussion is an improvement. 
I vote for moving it to a more prominent place than the FAQ, as it is about
an important, practical subject that users should be aware of quite early
when they learn Matplotlib.
Anyway, many thanks to you and Ben for devoting time to this.
:-)
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