Please note that the example actually call ax.step directly instead of using the pylab interface; I guess this has to be added (boilerplate.py?)
Manuel Eric Firing wrote:
Manuel Metz wrote:May I ask again: Is there any interest in a step-plotting function?Yes, so thanks for taking the initiative and for being persistent.Please add a docstring and a simple demo suitable for the examples subdirectory. I will commit the patch, or some modification of it.If so, who will commit the patch? Do I have to add more myself (documentation for sure needs to be added, what else ?)I think that if you change the npy.asarray to npyma.array, and similarly for the zeros(), that will provide basic masked array support. Please look at masked_demo.py for an example of the use of masked arrays. (It is very artificial, of course. A typical use case for masked arrays is when you have a data stream with some bad points that you want to edit out, but you want to keep the array dimensions unchanged. In the case of a line plot or step plot, you want the line to break at the missing point to show that a point has been removed.)Manuel Manuel Metz wrote:Hi,okay, I have added a keyword 'where' as suggested. I also now changed the way the incoming data is converted. I took this from the axes.pie() function. I don't know much about the unit types yet :-(Concerning masked arrays: Do I have to consider something special there?EricManuel Ted Drain wrote:At 10:36 AM 8/14/2007, Eric Firing wrote:Agreed - but the way the patch was written, I don't think it will support anything but float (especially not the unit types).Ted Drain wrote:Manuel,We do plots like this all the time. One thing we've found that's nice to have is a keyword that controls when the increase in y happens. We use a step style keyword that can be 'pre' (go up then right), 'post' (go right then up), and 'mid' (right 0.5, up, right 0.5).Good idea.Regarding your patch, you might want to check other areas in MPL for data processing examples. I could be wrong but I'm not sure you can assume that incoming data is a float. Some of the unit conversion examples or the line collection code might have better examples.Incoming data can be any numeric type, but it ends up getting converted to the default float type (not float32) internally.Whenever possible, it is good to support masked array input.EricTed At 07:59 AM 8/14/2007, Manuel Metz wrote:------------------------------------------------------------------------ -------------------------------------------------------------------------Hi,I have created a patch against latest svn that adds a function step to the axes class to plot step-functions ;-) It's really simple but nice ... Any interest in adding this?Manuel Index: axes.py =================================================================== --- axes.py (revision 3709) +++ axes.py (working copy) @@ -4995,6 +4995,18 @@steps=[1, 2, 5, 10],integer=True)) return im + + def step(self, x, y, *args, **kwargs): + x2 = npy.zeros((2*len(x)), npy.float32) + y2 = npy.zeros((2*len(x)), npy.float32) + + x2[0::2] = x + x2[1::2] = x + + y2[1::2] = y + y2[2::2] = y[:-1] + + self.plot(x2, y2, *args, **kwargs) class SubplotBase: """-------------------------------------------------------------------------This SF.net email is sponsored by: Splunk Inc. Still grepping through log files to find problems? 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Index: axes.py
===================================================================
--- axes.py (revision 3720)
+++ axes.py (working copy)
@@ -4987,7 +4987,54 @@
steps=[1, 2, 5, 10],
integer=True))
return im
+
+ def step(self, x, y, *args, **kwargs):
+ '''
+ STEP(x, y, *args, **kwargs)
+
+ Make a step plot. The args and keyword args to step are the same
+ as the args to plot. See help plot for more info.
+
+ Additional keyword args for step:
+
+ * where: can be 'pre', 'post' or 'mid'. Determines where the
+ step occurs.
+ '''
+
+ where = kwargs.pop('where', 'pre')
+
+ if not iterable(x): x = npy.ma.array([x]).astype(npy.float32)
+ else: x = npy.ma.array(x).astype(npy.float32)
+
+ if not iterable(y): x = npy.ma.array([y]).astype(npy.float32)
+ else: y = npy.ma.array(y).astype(npy.float32)
+
+ if where=='pre':
+ x2 = npy.ma.zeros((2*len(x)-1,), npy.float32)
+ y2 = npy.ma.zeros((2*len(y)-1,), npy.float32)
+
+ x2[0::2], x2[1::2] = x, x[:-1]
+ y2[0::2], y2[1:-1:2] = y, y[1:]
+ elif where=='post':
+ x2 = npy.ma.zeros((2*len(x)-1,), npy.float32)
+ y2 = npy.ma.zeros((2*len(y)-1,), npy.float32)
+
+ x2[::2], x2[1:-1:2] = x, x[1:]
+ y2[0::2], y2[1::2] = y, y[:-1]
+
+ elif where=='mid':
+ x2 = npy.ma.zeros((2*len(x),), npy.float32)
+ y2 = npy.ma.zeros((2*len(y),), npy.float32)
+
+ x2[1:-1:2] = 0.5*(x[:-1]+x[1:])
+ x2[2::2] = 0.5*(x[:-1]+x[1:])
+ x2[0], x2[-1] = x[0], x[-1]
+
+ y2[0::2], y2[1::2] = y, y
+
+ return self.plot(x2, y2, *args, **kwargs)
+
class SubplotBase:
"""
Emulate matlab's(TM) subplot command, creating axes with
import matplotlib.numerix as npy from pylab import * x = npy.arange(1.,10.) y = arange(1.,10.) x[4] += 0.4 fig = figure() ax = fig.gca() ax.step(x,y, where='post') y += 1. ax.step(x,y,where='pre') y += 1.5 ax.step(x,y,where='mid') xlim(0,10) ylim(-1,13) show()
<<inline: stepplot.png>>
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