Hi all, thanks for your time.
Paul, yes, it should be 'omg', I did some copy-and-paste error while
writing the email and test the code at the same time.
Alex, yes, that's the problem that i wasn't sure how to do. But thanks,
I'll try to figure it out how and try in a bit.
Cheers,
Postgraduate St
Fadzil,
Questions like this that involve code are great to ask on
stackoverflow, tagging with "netcdf", "matplotlib" and "python".
-Rich
On Wed, Mar 5, 2014 at 6:57 AM, Fadzil Mnor wrote:
> Hi all, thanks for your time.
> Paul, yes, it should be 'omg', I did some copy-and-paste error while wri
The issue I see is one of ensuring that your inputs into contourf() are of
the expected shapes. If you want a generalized script, then you will need
to do various steps that can prepare 3d data into a 2d by assuming a
particular level. The fun part of all of this is that different data
sources orga
Hi all,
Signel, yeah, I should have post it on stackoverflow. Will do that next
time.
Benjamin, I've been using GrADS for the last 3 years, and just recently
using python for more functionality (which came with a price,of
course-learning curve).
Seems like I 've found the solution. I should have d
Hi,
I am making a stacked histogram where one must enter the desired colors
together in a list/array when the histogram is called. For certain objects
in my code, it's helpful to assign a color to them, so that they are
immediately identified across various plots. Therefore, I essentially want
t
Well, the hack wasn't as messy as I thought. Still feel like there's a
better way...
def show_colors_default():
fig, axfoo = plt.subplots()
clist = []
c = axfoo._get_lines.color_cycle.next()
# Iterate until duplicate is found
while c not in clist:
clist.append(c)
On 05.03.2014 20:56, Adam Hughes wrote:> Hi,
>
> I am making a stacked histogram where one must enter the desired colors
> together in a list/array when the histogram is called. For certain
> objects in my code, it's helpful to assign a color to them, so that they
> are immediately identified acro
Thanks Andreas. That is correct; however, I'd rather not make this change
global. I only want a subset of my plots to have this behavior. I feel
like changing the rcparams would change this globally and probably confuse
users who don't know this is being called.
I should have realized this befo
Hi
I am trying to merge a heat map(matplotlib) with a tree(.png), but the .png
does not plot as needed or for that matter cannot be seen easily. Here is
my code:
### CODE
cmap = mpl.cm.hot
if outlier==0:
threshold=1
else:
threshold=outlier-0.01
fig = plt.figure(figs
Olga Botnivik is doing some work with these types of figures in her fork of
the seaborn project.
Example here: http://nbviewer.ipython.org/gist/olgabot/8341784
Link to the PR in github: https://github.com/mwaskom/seaborn/pull/73
Those might be a good place to start.
On Wed, Mar 5, 2014 at 12:19
Thank you for replying!!
The tree I generate is not based on clustering of matrices but rather on
sequence data and hence the tree generation is different in this case. I am
still looking into the seaborn project if it can lead me anywhere.
Thanks
Asma
On Wed, Mar 5, 2014 at 3:34 PM, Paul Hobso
Hi Asma,
Le 05/03/2014 21:19, Asma Riyaz a écrit :
I am trying to merge a heat map(matplotlib) with a tree(.png), but the
.png does not plot as needed or for that matter cannot be seen easily.
Here is my code:
### CODE
[]
img = ht_ax.imshow(data, cmap=cmap,
interpolation='none',vm
Sorry I somehow forgot to paste the imshow call to plot the .png
image(pasted the entire code again), I am trying the different
interpolation parameters in the mean time as suggested by Pierre
data=np.array(full_len)
cmap = mpl.cm.hot
if outlier==0:
threshold=1
else:
On 2014/03/05 11:37 AM, Asma Riyaz wrote:
> Sorry I somehow forgot to paste the imshow call to plot the .png
> image(pasted the entire code again), I am trying the different
> interpolation parameters in the mean time as suggested by Pierre
>
> data=np.array(full_len)
> cmap = mpl.cm.hot
>
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