Roger André wrote:
> Hi All,
>
> I'm very new to Matplotlib and am having some trouble getting a
> colorbar to be positioned and sized I want it to. A big part of the
> problem is that I have adapted several examples from the Cookbook and
> Gallery, just to see if I could roughly approximate what I want to
> see, and now am having trouble integrating the different pieces.
> Specifically, I can't seem to resolve when to use add_subplot vs.
> add_axes. Below are 2 examples of code. The first one shows correct
> layout of a data figure and a separate colorbar below it. The
> colorbar is the correct size, and is located in the right spot. The
> second example has the correct data mapped in it using the basemap
> module, but I cannot get the colorbar to move up closer to the figure,
> or to shrink it. Could someone advise me on this? I've looked at the
> "Artist tutorial", and although it is very well written, I'm still not
> sure how to get this done .
>
> Thanks in advance,
>
> Roger
Roger: I usually explicity two separate axes instances, one for the
plot and one for the colorbar. The location of the axes always takes a
bit of tweaking to get right. Here's an example:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
# create figure instance.
fig = plt.figure(figsize=(8,8))
# create an axes instance, leaving room for colorbars on right and bottom.
ax = fig.add_axes([0.05,0.15,0.8,0.8]) # color on bottom
# set up orthographic map projection with
# perspective of satellite looking down at 50N, 100W.
# use low resolution coastlines.
map = Basemap(projection='ortho',lat_0=50,lon_0=-100,resolution='c')
# draw coastlines, country boundaries, fill continents.
map.drawcoastlines()
# draw the edge of the map projection region (the projection limb)
map.drawmapboundary()
# draw lat/lon grid lines every 30 degrees.
map.drawmeridians(np.arange(0,360,30))
map.drawparallels(np.arange(-90,90,30))
# make up some data on a regular lat/lon grid.
nlats = 73; nlons = 145; delta = 2.*np.pi/(nlons-1)
lats = (0.5*np.pi-delta*np.indices((nlats,nlons))[0,:,:])
lons = (delta*np.indices((nlats,nlons))[1,:,:])
wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
# compute native map projection coordinates of lat/lon grid.
x, y = map(lons*180./np.pi, lats*180./np.pi)
# contour data over the map.
cs = map.contourf(x,y,wave+mean,15)
# get axes bounds.
pos = ax.get_position()
l, b, w, h = pos.bounds
# create axes instance for colorbar on right.
cax = plt.axes([l+w+0.03, b, 0.04, h])
# draw colorbar on right.
plt.colorbar(cax=cax,orientation='vertical')
# create axes instance for colorbar on bottom.
cax = plt.axes([l, b-0.07, w, 0.04])
# draw colorbar on bottom.
plt.colorbar(cax=cax,orientation='horizontal')
plt.show()
HTH,
-Jeff
> -------------
>
> Example 1:
>
> #! /usr/bin/python
>
> import matplotlib.pyplot as plt
> import numpy as np
> from numpy.random import randn
> from matplotlib import mpl
>
> # Make plot with horizontal colorbar
>
> fig = plt.figure(figsize=(7,8))
> ax = fig.add_subplot(111)
>
> # 'add_axes' for color bar
> ax1 = fig.add_axes([0.25, .07, 0.5, 0.03]) # [x_loc, y_loc, x_size,
> y_size]
>
> data = np.clip(randn(250, 250), -1, 1) # DATA FOR SQUARE FIG
> ax.imshow(data, interpolation='nearest') # DRAW DATA IN SQUARE FIG
>
> ax.set_title('Monthly PCP percentiles for 9-2008')
>
> ########### Colorbar Settings ########
> cmap = mpl.cm.cool
> norm = mpl.colors.Normalize(vmin=0.0, vmax=1.0)
> cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm,
> orientation='horizontal')
> cb1.set_label('percentile')
> ################################
>
> plt.show()
> fig.savefig('test.png')
>
> --------------------------------------------------------------------------------------------
> Example 2:
>
> #! /usr/bin/python
>
> """taken from geos_demo_2.py"""
>
> from PIL import Image
> from mpl_toolkits.basemap import Basemap
> import numpy as np
> import matplotlib
> from matplotlib import mpl
> import matplotlib.pyplot as plt
> from matplotlib.image import pil_to_array
>
> plot_name = 'hydro_demo.png'
> overlay_color = 'black'
>
> # read in jpeg image to rgb array
> pilImage = Image.open('wms_mapser.png')
>
> #data = asarray(pilImage)
> data = pil_to_array(pilImage)
> data = data[:, :, :] # get data from RGB channels of image
>
> # define data region and projection parameters
> ll_lon = -125
> ll_lat = 39
> ur_lon = -108
> ur_lat = 54
> lon_0 = 0
>
>
> fig = plt.figure(figsize=(7,8))
> #ax = fig.add_axes((0.1,0.1,0.8,0.8))
> ax = fig.add_axes((.1,0.1,0.8,0.8))
>
> # create Basemap instance for cylindrical equidistant projection,
> htdro image domain
> m = Basemap(projection='cyl', lon_0=lon_0, llcrnrlon=ll_lon,
> llcrnrlat=ll_lat, urcrnrlon=ur_lon,
> urcrnrlat=ur_lat, suppress_ticks=False)
>
> # add data
> cmap = mpl.cm.cool
> m.imshow(data, cmap, interpolation=None)
> plt.clim(0, 1)
>
> # add a colobar
> plt.colorbar(orientation='horizontal')
>
> # add timestamp and save
> fig = plt.gcf()
>
> # ADD FIGURE TEXT
> fig.text(x=0.5, y=0.1,
> s='percentile',
> fontsize=10,
> )
>
> fig.set_size_inches((7,7))
>
> # ADD A FIGURE TITLE
> plt.title('Monthly PCP percentiles for 9-2008',y=1.05,fontsize=12)
>
> plt.show()
> #fig.savefig(plot_name)
> #print 'Plot saved to %s' % (plot_name)
> ------------------------------------------------------------------------
>
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--
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : jeffrey.s.whita...@noaa.gov
325 Broadway Office : Skaggs Research Cntr 1D-113
Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg
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