matplotlib-users Mailing List for matplotlib (Page 9)

On Fri, Jul 1, 2011 at 11:14 AM, Hackett, John (Norcross, GA)
<Joh...@un...> wrote:
> After some experimentation (and judicious peeking at the source code), I
> think I’ve got the hang of writing custom functions to pass into these
> modules – basically, anything that accepts a list of values sliced from a
> single column on the structured array and returns a single list seems to
> work well. In functional programming terms, rec_summarize appears similar to
> “map”, rec_groupby appears similar to “reduce”.
>
>
>
> Now – what if I want to derive a calculation from multiple statistics in the
> original dataset – eg. create a new column on the array which is derived
> from 2 (or up to n) other fields in a custom function which I pass into the
> process?
>
>
>
> For example, conditional counts/summaries (count transactions and sum the
> sales on all orders that weighed > 5K lbs).
>
>
>
> Is there a way to do this within numpy or mlab without going all the way out
> to python and creating a list comprehension?

There are a couple of ways with the existing functions.

One is to use a logical mask::

   mask = r.weight>5
   rg = mlab.rec_groupby(r[mask], groupby, stats)

You could also create a new categorical variable with one or more
values and attach it to your record array and then use rec_groupby::

  heavy = np.where(r.weight>5, 1, 0)

and add that to your record array

  r = mlab.rec_append_fields(r, ['heavy'], [heavy])

and then do a rec_group_by using 'heavy' as your group by attribute.

Brian Schwartz has a preliminary implementation of rec_query which
allows you to make a SQL query on a record array by converting it to a
sqllite table, running the sql query, and returning the results as a
new record array, which would solve your problem more cleanly and
generically.  The code needs a little more polishing, but perhaps
Brian you can send over what you have in case John wants to take a
look.

JDH

Good morning -

 

Got a question for a mlab module guru.

 

After some experimentation (and judicious peeking at the source code), I
think I've got the hang of writing custom functions to pass into these
modules - basically, anything that accepts a list of values sliced from
a single column on the structured array and returns a single list seems
to work well. In functional programming terms, rec_summarize appears
similar to "map", rec_groupby appears similar to "reduce".

 

Now - what if I want to derive a calculation from multiple statistics in
the original dataset - eg. create a new column on the array which is
derived from 2 (or up to n) other fields in a custom function which I
pass into the process? 

 

For example, conditional counts/summaries (count transactions and sum
the sales on all orders that weighed > 5K lbs).

 

Is there a way to do this within numpy or mlab without going all the way
out to python and creating a list comprehension?

 

Thanks.

 

John

 

 

hi all,

i'm trying to get the bounding box of a map plotted with basemap to place a
colorbar. in this reduced example from the example directory of basemap, the
colorbar is set to the hight of the axes of the plot. 

from mpl_toolkits.basemap import Basemap, shiftgrid
import numpy as np
import matplotlib.pyplot as plt

topoin = np.loadtxt('etopo20data.gz')
lons = np.loadtxt('etopo20lons.gz')
lats = np.loadtxt('etopo20lats.gz')

topoin,lons = shiftgrid(180.,topoin,lons,start=False)

m = Basemap(llcrnrlon=-57,llcrnrlat=37,urcrnrlon=-3,urcrnrlat=60,
            resolution='i',projection='tmerc',lon_0=-41,lat_0=45)
# transform to nx x ny regularly spaced native projection grid
nx = int((m.xmax-m.xmin)/40000.)+1; ny = int((m.ymax-m.ymin)/40000.)+1
topodat,x,y = m.transform_scalar(topoin,lons,lats,nx,ny,returnxy=True)
# create the figure.
fig=plt.figure(figsize=(8,8))
# add an axes, leaving room for colorbar on the right.
ax = fig.add_axes([0.1,0.1,0.7,0.7])
# associate this axes with the Basemap instance.
m.ax = ax
# plot image over map with imshow.
im = m.imshow(topodat,plt.cm.jet)
# setup colorbar axes instance.
pos = ax.get_position()
l, b, w, h = pos.bounds
cax = plt.axes([l+w+0.075, b, 0.05, h])
plt.colorbar(im,cax=cax) # draw colorbar
# plot blue dot on boulder, colorado and label it as such.
plt.show()

what i'm interested in is the bounding box of m, the real plotting area, so
i can scale the colorbar axes to that height or width.

is there a way to get this?

thanks in advance

mario
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Hi,

thanks for the very quick response & fix. We were surprised, too, that we hadn't found more about this problem. We were put on the right track by this related report: http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=395867 - Thanks also for the link to the other report. I'll see if I can come up with a nice example and submit it there.

Cheers
HB

> On 06/30/2011 01:10 PM, Michael Droettboom wrote:
> > I'm surprised this bug (which really lies in Tkinter) isn't more widely
> > known -- searching the Python bug tracker revealed nothing.  It would be
> > great to follow-up there (with a standalone Tkinter-crashing example) if
> > you're so inclined.
> I did find this bug, which seems to be related.
> 
> http://bugs.python.org/issue10647
> 
> Mike