Thanks Jeff,
I'll study your code and will see if I can adapt it for my use.
Great tool also,
Roger
--
On Fri, Dec 12, 2008 at 3:20 PM, Jeff Whitaker <jsw...@fastmail.fm> wrote:
> 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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