Need some help adjusting placement of colorbar in basemap-derived graphic

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

···

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)

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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--
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Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@...259...
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Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg

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 <jswhit@…146…> 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 = (0.5np.pi-deltanp.indices((nlats,nlons))[0,:,:])

lons = (delta*np.indices((nlats,nlons))[1,:,:])

wave = 0.75*(np.sin(2.lats)**8np.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(lons180./np.pi, lats180./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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Matplotlib-users mailing list

Matplotlib-users@lists.sourceforge.net

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Jeffrey S. Whitaker Phone : (303)497-6313

Meteorologist FAX : (303)497-6449

NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@…259…

325 Broadway Office : Skaggs Research Cntr 1D-113

Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg

Hi Jeff,

Thanks very much for this bit of code that you sent me. I got some graph paper out and plotted all of the axes in your code and it finally all made sense.

Roger

···

On Fri, Dec 12, 2008 at 3:50 PM, Roger André <randre@…287…> wrote:

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 <jswhit@…146…> 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 = (0.5np.pi-deltanp.indices((nlats,nlons))[0,:,:])

lons = (delta*np.indices((nlats,nlons))[1,:,:])

wave = 0.75*(np.sin(2.lats)**8np.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(lons180./np.pi, lats180./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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Matplotlib-users mailing list

Matplotlib-users@lists.sourceforge.net

https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Jeffrey S. Whitaker Phone : (303)497-6313

Meteorologist FAX : (303)497-6449

NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@…259…

325 Broadway Office : Skaggs Research Cntr 1D-113

Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg