[Matplotlib-users] Custom data transformations with log ticks

[Shareef Dabdoub]

Hello All,

I have data that I need to apply log-based transformations to followed
by plotting. Ideally I would like the plot to display the nicely
formatted log-scale major/minor ticks as when setting xscale('log').
Of course, doing that applies the default log transformation to
already log-transformed data.

I played around with the matplotlib.ticker classes, but the LogLocator
only seems to work properly when applied to a plot with axes set to
log scale or plotted with something like semilog (as in
http://old.nabble.com/ticks---labels-td18931344.html).

An example:

import matplotlib.pyplot as plt
import matplotlib.ticker as mt
import numpy as np

x = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600,
700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000,
10000]
xlog = np.log10(x) #or some other more complex transform
y = [1 for i in x]

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(xlog,y, '.', c='blue', ms=2)
ax.xaxis.set_major_locator(mt.LogLocator())
plt.show()

Resulting in: Loading Image...
As opposed to what I would like: Loading Image... (plotting
x instead of xlog, and ax.set_xscale('log'))

I'm probably missing something simple, but I haven't been able to
figure it out as of yet.

Thanks in advance for any suggestions/help.
Shareef

[Michael Droettboom]

The problem is that the LogLocator expects the data to be
non-log-transformed, since it is providing the log transform itself. It
actually is doing the right thing since the data passed to plot ranges
from 1 to 4.

As a workaround, you can manually set the ticks with set_xticks,
set_xmajorticklabels and set_xminorticklabels -- you will need to set
the tick positions and their display separately since you are trying to
print values that are not the actual values of the data you're passing in.

If you want your graph to be interactive, however, you will probably
want to look into writing a custom scale. See this document:

http://matplotlib.sourceforge.net/devel/add_new_projection.html

and this example:


http://matplotlib.sourceforge.net/examples/api/custom_scale_example.html?highlight=custom_scale_example

Mike

Post by Shareef Dabdoub
Hello All,
I have data that I need to apply log-based transformations to followed
by plotting. Ideally I would like the plot to display the nicely
formatted log-scale major/minor ticks as when setting xscale('log').
Of course, doing that applies the default log transformation to
already log-transformed data.
I played around with the matplotlib.ticker classes, but the LogLocator
only seems to work properly when applied to a plot with axes set to
log scale or plotted with something like semilog (as in
http://old.nabble.com/ticks---labels-td18931344.html).
import matplotlib.pyplot as plt
import matplotlib.ticker as mt
import numpy as np
x = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600,
700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000,
10000]
xlog = np.log10(x) #or some other more complex transform
y = [1 for i in x]
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(xlog,y, '.', c='blue', ms=2)
ax.xaxis.set_major_locator(mt.LogLocator())
plt.show()
Resulting in: http://imgur.com/MtsLE.png
As opposed to what I would like: http://imgur.com/QELVD.png (plotting
x instead of xlog, and ax.set_xscale('log'))
I'm probably missing something simple, but I haven't been able to
figure it out as of yet.
Thanks in advance for any suggestions/help.
Shareef
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