Re: [Matplotlib-users] Altering Basemap Colobar and Label positioning

[ChaoYue <cha...@gm...>]

Hi Andruska,

The Basemap.colorbar has a "size" keyword to allow you have the shrink-like
function to adjust the size of the colorbar.
Otherwise you can creat an axes on the exact position you want to hold the
colorbar, like below I have prepared an example for you:

arr = np.arange(100).reshape(10,10)
fig,ax = plt.subplots(1,1)
cs = ax.imshow(arr)
ax.set_position([0.2, 0.3, 0.6, 0.6])
axt = fig.add_axes([0.4,0.2,0.4,0.05])
cbar = plt.colorbar(cs,cax=axt,orientation='horizontal')
fig.text(0.25,0.22,'I am label',va='center',size=13)
draw()

I think it's hard to use the colorbar.set_label put the label directly on
the left of your colorbar, I rather suggest you to use fig.text to
position exactly a text for your label.

At the beginning of matplotlib you might feel confused, but after investing
a significant amount of time you feel it extremely flexible, and going to
like it :)

Cheers,

Chao



On Mon, Jun 16, 2014 at 6:32 PM, Andruska, Michael [via matplotlib] <
ml-...@n5...> wrote:

>  Hi all,
>
>
>
> I am having great difficulty understanding how to change the size of my
> basemap colorbar, altering its position and moving the text label all at
> the same time. I would like to:
>
> 1.       Shrink the size of the colorbar (there doesn’t seem to be a
> shrink property in the basemap.colorbar() method (only plt.colorbar() or
> fig.colorbar())
>
> 2.       Move the bar so it is not centered but instead so its right edge
> is aligned vertically with the right end of the basemap.
>
> 3.       Move the colorbar W/m^2 text label so it is not below the
> colorbar but is instead directly to its left.
>
>
>
> I looked up several other responses online that mentioned doing things
> such as adding a second axes, or using the shrink command from
> plt.colorbar(), and changing some other properties such as padding, but in
> the end, most of these alterations seem to introduce another problem when I
> try them. Even after viewing their documentation, I still do not fully
> understand their proper usage. Also, I tried a few properties listed in the
> matplotlib documentation such as anchor and panchor in my the
> fig.colorbar() method in attempt to move the bar around but when I tried to
> run it, the keyword was not recognized by the interpreter and produced an
> error (it seems strange that some of the keywords listed in the docs aren’t
> being recognized; and I’m pretty sure I have the most current matplotlib
> version too). You can see some of the commented commands I tried in the
> code below (not all at once, of course, but just in various conjunctions
> with one another). Here is an example of my code and an attached example of
> what the plot currently looks like after running said code. Any helpful
> advice would be greatly appreciated. So confused right now and I feel like
> I’ve read the docs over and over to little avail (P.S. Getting down to the
> nitty gritty of working with matplotlib objects and understanding its inner
> workings to customize my plots better is really confusing, even with the
> docs, (sigh)):
>
>
>
> swi = swi.reshape(1059, 1799)
>
> lat = lat.reshape(1059, 1799)
>
> lon = lon.reshape(1059, 1799)
>
>
>
> def plot_conus():
>
> m = mpl_toolkits.basemap.Basemap(
>
> llcrnrlon=-135.0,
>
> llcrnrlat=19.0,
>
> urcrnrlon=-60.0,
>
> urcrnrlat=54.0,
>
> projection='mill',
>
> resolution='c')
>
> m.drawcoastlines()
>
> m.drawcountries()
>
> m.drawstates()
>
> # draw parallels
>
> parallels = np.arange(0.,90,10.)
>
> m.drawparallels(parallels,labels=[1,0,0,0],fontsize=10)
>
> # draw meridians
>
> meridians = np.arange(180.,360.,10.)
>
> m.drawmeridians(meridians,labels=[0,0,0,1],fontsize=10)
>
> return m
>
>
>
> # find hex color values at http://www.colorpicker.com
>
> swi_colors = [
>
> #"#f800fd", # light purple
>
> #"#9854c6", # dark purple
>
> "#04e9e7",
>
> "#019ff4",
>
> "#0300f4",
>
> "#02fd02",
>
> "#01c501",
>
> "#008e00",
>
> "#fdf802",
>
> "#e5bc00",
>
> "#fd9500",
>
> "#fd0000",
>
> "#d40000",
>
> "#bc0000",
>
> "#A10505" # brick
>
> ]
>
>
>
> swi_colormap = matplotlib.colors.ListedColormap(swi_colors)
>
>
>
> m = plot_conus()
>
>
>
> levels = []
>
> for i in range(13):
>
> levels.append(i*90.0)
>
>
>
> # create black and white cross at observatory location on map
>
> site_lon = -87.99495
>
> site_lat = 41.70121
>
> x_site, y_site = m(site_lon, site_lat)
>
> m.plot(x_site, y_site, 'w+', markersize=30, markeredgewidth=8) # white
> cross
>
> m.plot(x_site, y_site, 'k+', markersize=25, markeredgewidth=3) # black
> cross
>
>
>
> norm = matplotlib.colors.BoundaryNorm(levels, 13)
>
> cax = m.pcolormesh(lon, lat, swi, latlon=True, norm=norm,
>
> cmap=swi_colormap)
>
>
>
> #cbar = m.colorbar(cax)
>
> fig = plt.gcf()
>
> #ax = plt.gca()
>
> #cbar = fig.colorbar(cax, orientation='horizontal', shrink=0.75)
>
> #cbaxes = fig.add_axes([0.8, 0.1, 0.03, 0.8])
>
> #cb = fig.colorbar(cax)
>
> cbar = m.colorbar(cax, location='bottom', pad='6%')
>
> cbar.set_label('$W/m^2$', fontsize=18)
>
>
>
> plt.title('NOAA LAPS GHI, RT ' + modelrun_time_label + ', VT ' +
> fcst_time_label)
>
> plt.show()
>
>
>
>
>
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Chao YUE
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