Revision: 6239
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=6239&view=rev
Author: mmetz_bn
Date: 2008-10-17 14:27:28 +0000 (Fri, 17 Oct 2008)
Log Message:
-----------
minor hist and hist docs updates
Modified Paths:
--------------
trunk/matplotlib/lib/matplotlib/axes.py
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2008-10-17 14:27:26 UTC (rev
6238)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2008-10-17 14:27:28 UTC (rev
6239)
@@ -6183,9 +6183,10 @@
Keyword arguments:
*bins*:
- either an integer number of bins or a sequence giving the
- bins. *x* are the data to be binned. *x* can be an array or a
- 2D array with multiple data in its columns. Note, if *bins*
+ Either an integer number of bins or a sequence giving the
+ bins. *x* are the data to be binned. *x* can be an array,
+ a 2D array with multiple data in its columns, or a list of
+ arrays with data of different length. Note, if *bins*
is an integer input argument=numbins, *bins* + 1 bin edges
will be returned, compatible with the semantics of
:func:`numpy.histogram` with the *new* = True argument.
@@ -6211,7 +6212,7 @@
gives the counts in that bin plus all bins for smaller values.
The last bin gives the total number of datapoints. If *normed*
is also *True* then the histogram is normalized such that the
- last bin equals one. If *cumulative* evaluates to less than 0
+ last bin equals 1. If *cumulative* evaluates to less than 0
(e.g. -1), the direction of accumulation is reversed. In this
case, if *normed* is also *True*, then the histogram is normalized
such that the first bin equals 1.
@@ -6219,13 +6220,14 @@
*histtype*: [ 'bar' | 'barstacked' | 'step' | 'stepfilled' ]
The type of histogram to draw.
- - 'bar' is a traditional bar-type histogram
+ - 'bar' is a traditional bar-type histogram. If multiple data
+ are given the bars are aranged side by side.
- 'barstacked' is a bar-type histogram where multiple
- data are stacked on top of each other.
+ data are stacked on top of each other.
- 'step' generates a lineplot that is by default
- unfilled
+ unfilled.
- 'stepfilled' generates a lineplot that is by default
filled.
@@ -6233,9 +6235,9 @@
*align*: ['left' | 'mid' | 'right' ]
Controls how the histogram is plotted.
- - 'left': bars are centered on the left bin edges
+ - 'left': bars are centered on the left bin edges.
- - 'mid': bars are centered between the bin edges
+ - 'mid': bars are centered between the bin edges.
- 'right': bars are centered on the right bin edges.
@@ -6245,9 +6247,9 @@
the left edges.
*rwidth*:
- the relative width of the bars as a fraction of the bin
+ The relative width of the bars as a fraction of the bin
width. If *None*, automatically compute the width. Ignored
- if *histtype* = 'step'.
+ if *histtype* = 'step' or 'stepfilled'.
*log*:
If *True*, the histogram axis will be set to a log scale.
@@ -6280,7 +6282,8 @@
'hist now uses the rwidth to give relative width and not
absolute width')
try:
- x = np.transpose(np.asarray(x).copy())
+ # make sure a copy is created: don't use asarray
+ x = np.transpose(np.array(x))
if len(x.shape)==1:
x.shape = (1,x.shape[0])
elif len(x.shape)==2 and x.shape[1]<x.shape[0]:
@@ -6290,7 +6293,7 @@
if iterable(x[0]) and not is_string_like(x[0]):
tx = []
for i in xrange(len(x)):
- tx.append( np.asarray(x[i]).copy() )
+ tx.append( np.array(x[i]) )
x = tx
n = []
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