My current plot takes data to construct a 2d histogram. In gnuplot i would
accomplish this by using splot, dgrid3d, and pulling in a 'matrix' data
file. The code below has produced nearly what I need. However, the axes
limits are set based on the indices of the incoming data (i.e. the number of
rows or columns in my data matrix) instead of something meaningful. For
example, my x-axis is set from 0 to 2000, but I'd like it to span from -1 to
+1 because my x-data is a cosine function...
I would imagine that some type of axis-scaling function would take a
function to scale the labels... something akin to:
scale_axis(lambda x: x*.001-1.0, ax)
would do the trick of scaling my (0, 2000) data to the (-1.0, 1.0) extents.
Is there any such functionality to actually scale the values of the axis
tick-labels?
<code snippet>
1 import sys
2 from scipy import *
3 import scipy.io.array_import
4
5 # plotting libs
6 import matplotlib.pyplot as plt
7 from pylab import *
8
9
10
11 file = scipy.io.array_import.read_array(sys.argv[1])
12 data = []
13
14 for i in range(len(file[0])):
15 data.append(file[:,i])
16
17 # create a figure
18 fig = figure(1)
19
20 cmBase = cm.jet # choose a colormap to use for the plot
21
22 # This processes the data and adds the colored 2D histogram to the
figure
23 im = imshow(data, interpolation='bilinear')
24 # A little magic to create a colorbar - legend for the plot
25 plt.colorbar(im)
26
27 # Turn on the X-Y grid
28 grid(True)
29
30 # Pass go - collect $200
31 draw()
32 show()
</code snippet>
···
--
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use "extent" argument.
http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.imshow
-JJ
···
On Fri, Oct 2, 2009 at 7:21 PM, shamster <eric.shamay@...287...> wrote:
My current plot takes data to construct a 2d histogram. In gnuplot i would
accomplish this by using splot, dgrid3d, and pulling in a 'matrix' data
file. The code below has produced nearly what I need. However, the axes
limits are set based on the indices of the incoming data (i.e. the number of
rows or columns in my data matrix) instead of something meaningful. For
example, my x-axis is set from 0 to 2000, but I'd like it to span from -1 to
+1 because my x-data is a cosine function...
I would imagine that some type of axis-scaling function would take a
function to scale the labels... something akin to:
scale_axis(lambda x: x*.001-1.0, ax)
would do the trick of scaling my (0, 2000) data to the (-1.0, 1.0) extents.
Is there any such functionality to actually scale the values of the axis
tick-labels?
<code snippet>
1 import sys
2 from scipy import *
3 import scipy.io.array_import
4
5 # plotting libs
6 import matplotlib.pyplot as plt
7 from pylab import *
8
9
10
11 file = scipy.io.array_import.read_array(sys.argv[1])
12 data =
13
14 for i in range(len(file[0])):
15 data.append(file[:,i])
16
17 # create a figure
18 fig = figure(1)
19
20 cmBase = cm.jet # choose a colormap to use for the plot
21
22 # This processes the data and adds the colored 2D histogram to the
figure
23 im = imshow(data, interpolation='bilinear')
24 # A little magic to create a colorbar - legend for the plot
25 plt.colorbar(im)
26
27 # Turn on the X-Y grid
28 grid(True)
29
30 # Pass go - collect $200
31 draw()
32 show()
</code snippet>
--
View this message in context: http://www.nabble.com/Scaling-the-axis-values-from-list-indices-to-meaningful-values-tp25724127p25724127.html
Sent from the matplotlib - users mailing list archive at Nabble.com.
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