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SF.net SVN: matplotlib:[8218] trunk/matplotlib/lib/matplotlib/axes.py
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From: <ef...@us...> - 2010-04-02 21:29:45
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Revision: 8218
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=8218&view=rev
Author: efiring
Date: 2010-04-02 21:29:38 +0000 (Fri, 02 Apr 2010)
Log Message:
-----------
Axes.hist: style changes, bug fixes
Modified Paths:
--------------
trunk/matplotlib/lib/matplotlib/axes.py
Modified: trunk/matplotlib/lib/matplotlib/axes.py
===================================================================
--- trunk/matplotlib/lib/matplotlib/axes.py 2010-04-02 18:47:00 UTC (rev 8217)
+++ trunk/matplotlib/lib/matplotlib/axes.py 2010-04-02 21:29:38 UTC (rev 8218)
@@ -5311,7 +5311,7 @@
s = np.ma.ravel(s) # This doesn't have to match x, y in size.
- c_is_stringy = is_string_like(c) or cbook.is_sequence_of_strings(c)
+ c_is_stringy = is_string_like(c) or is_sequence_of_strings(c)
if not c_is_stringy:
c = np.asanyarray(c)
if c.size == x.size:
@@ -6989,6 +6989,7 @@
def hist(self, x, bins=10, range=None, normed=False, weights=None,
cumulative=False, bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False,
+ color=None,
**kwargs):
"""
call signature::
@@ -7002,16 +7003,21 @@
[*patches0*, *patches1*,...]) if the input contains multiple
data.
+ Multiple data can be provided via *x* as a list of datasets
+ of potentially different length ([*x0*, *x1*, ...]), or as
+ a 2-D ndarray in which each column is a dataset. Note that
+ the ndarray form is transposed relative to the list form.
+
+ Masked arrays are not supported at present.
+
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,
- 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.
+ bins. If *bins* is an integer, *bins* + 1 bin edges
+ will be returned, consistent with :func:`numpy.histogram`
+ for numpy version >= 1.3, and with the *new* = True argument
+ in earlier versions.
Unequally spaced bins are supported if *bins* is a sequence.
*range*:
@@ -7090,6 +7096,9 @@
be filtered out and only the non-empty (*n*, *bins*,
*patches*) will be returned.
+ *color*:
+
+
kwargs are used to update the properties of the hist
:class:`~matplotlib.patches.Rectangle` instances:
@@ -7117,65 +7126,74 @@
# NOTE: the range keyword overwrites the built-in func range !!!
# needs to be fixed in numpy !!!
+ # Validate string inputs here so we don't have to clutter
+ # subsequent code.
+ if histtype not in ['bar', 'barstacked', 'step', 'stepfilled']:
+ raise ValueError("histtype %s is not recognized" % histtype)
+
+ if align not in ['left', 'mid', 'right']:
+ raise ValueError("align kwarg %s is not recognized" % align)
+
+ if orientation not in [ 'horizontal', 'vertical']:
+ raise ValueError(
+ "orientation kwarg %s is not recognized" % orientation)
+
+
if kwargs.get('width') is not None:
raise DeprecationWarning(
'hist now uses the rwidth to give relative width '
'and not absolute width')
- try:
- # 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]:
- warnings.warn('2D hist should be nsamples x nvariables; '
+ if isinstance(x, np.ndarray):
+ # TODO: support masked arrays;
+ # Why is the copy needed?
+ x = np.array(x, copy=True, subok=False)
+ if x.ndim == 2:
+ x = x.T
+ elif x.ndim == 1:
+ x.shape = (1, x.shape[0])
+ else:
+ raise ValueError("x must be 1D or 2D")
+ if x.shape[1] < x.shape[0]:
+ warnings.warn('2D hist input should be nsamples x nvariables; '
'this looks transposed')
- except ValueError:
+ else:
# multiple hist with data of different length
- if iterable(x[0]) and not is_string_like(x[0]):
- tx = []
- for i in xrange(len(x)):
- tx.append( np.array(x[i]) )
- x = tx
- else:
- raise ValueError, 'Can not use provided data to create a histogram'
+ x = [np.array(xi) for xi in x]
+ nx = len(x) # number of datasets
+
if weights is not None:
- try:
- w = np.transpose(np.array(weights))
- if len(w.shape)==1:
+ if isinstance(w, np.ndarray):
+ w = np.array(weights)
+ if w.ndim == 2:
+ w = w.T
+ elif w.ndim == 1:
w.shape = (1, w.shape[0])
- except:
- if iterable(weights[0]) and not is_string_like(weights[0]):
- tw = []
- for i in xrange(len(weights)):
- tw.append( np.array(weights[i]) )
- w = tw
else:
- raise ValueError, 'Can not use provided weights to create a hist'
+ raise ValueError("weights must be 1D or 2D")
+ else:
+ w = [np.array(wi) for wi in weights]
- if len(x) != len(w):
- raise ValueError, 'weights should have the same shape as x'
- for i in xrange(len(x)):
- if len(x[i]) != len(w[i]):
- raise ValueError, 'weights should have the same shape as x'
+ if len(w) != nx:
+ raise ValueError('weights should have the same shape as x')
+ for i in xrange(nx):
+ if len(w[i]) != len(x[i]):
+ raise ValueError(
+ 'weights should have the same shape as x')
else:
- w = [None]*len(x)
+ w = [None]*nx
# Check whether bins or range are given explicitly. In that
# case do not autoscale axes.
binsgiven = (cbook.iterable(bins) or range != None)
- # check the version of the numpy
+ hist_kwargs = dict(range=range, normed=bool(normed))
if np.__version__ < "1.3": # version 1.1 and 1.2
- hist_kwargs = dict(range=range,
- normed=bool(normed), new=True)
- else: # version 1.3 and later, drop new=True
- hist_kwargs = dict(range=range,
- normed=bool(normed))
+ hist_kwargs['new'] = True
n = []
- for i in xrange(len(x)):
+ for i in xrange(nx):
# this will automatically overwrite bins,
# so that each histogram uses the same bins
m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
@@ -7195,60 +7213,50 @@
if histtype.startswith('bar'):
totwidth = np.diff(bins)
- stacked = False
- if rwidth is not None: dr = min(1., max(0., rwidth))
- elif len(n)>1: dr = 0.8
- else: dr = 1.0
+ if rwidth is not None:
+ dr = min(1.0, max(0.0, rwidth))
+ elif len(n)>1:
+ dr = 0.8
+ else:
+ dr = 1.0
if histtype=='bar':
- width = dr*totwidth/len(n)
+ width = dr*totwidth/nx
dw = width
- if len(n)>1:
- boffset = -0.5*dr*totwidth*(1.-1./len(n))
+ if nx > 1:
+ boffset = -0.5*dr*totwidth*(1.0-1.0/nx)
else:
boffset = 0.0
+ stacked = False
elif histtype=='barstacked':
width = dr*totwidth
boffset, dw = 0.0, 0.0
-
stacked = True
- else:
- raise ValueError, 'invalid histtype: %s' % histtype
if align == 'mid' or align == 'edge':
boffset += 0.5*totwidth
elif align == 'right':
boffset += totwidth
- elif align != 'left' and align != 'center':
- raise ValueError, 'invalid align: %s' % align
if orientation == 'horizontal':
- for m in n:
- color = self._get_lines.color_cycle.next()
- patch = self.barh(bins[:-1]+boffset, m, height=width,
- left=bottom, align='center', log=log,
- color=color)
- patches.append(patch)
- if stacked:
- if bottom is None: bottom = 0.0
- bottom += m
- boffset += dw
- elif orientation == 'vertical':
- for m in n:
- color = self._get_lines.color_cycle.next()
- patch = self.bar(bins[:-1]+boffset, m, width=width,
- bottom=bottom, align='center', log=log,
- color=color)
- patches.append(patch)
- if stacked:
- if bottom is None: bottom = 0.0
- bottom += m
- boffset += dw
- else:
- raise ValueError, 'invalid orientation: %s' % orientation
+ _barfunc = self.barh
+ else: # orientation == 'vertical'
+ _barfunc = self.bar
+ for m in n:
+ color = self._get_lines.color_cycle.next()
+ patch = _barfunc(bins[:-1]+boffset, m, width, bottom,
+ align='center', log=log,
+ color=color)
+ patches.append(patch)
+ if stacked:
+ if bottom is None:
+ bottom = 0.0
+ bottom += m
+ boffset += dw
+
elif histtype.startswith('step'):
x = np.zeros( 2*len(bins), np.float )
y = np.zeros( 2*len(bins), np.float )
@@ -7259,21 +7267,15 @@
x -= 0.5*(bins[1]-bins[0])
elif align == 'right':
x += 0.5*(bins[1]-bins[0])
- elif align != 'mid' and align != 'edge':
- raise ValueError, 'invalid align: %s' % align
if log:
y[0],y[-1] = 1e-100, 1e-100
if orientation == 'horizontal':
self.set_xscale('log')
- elif orientation == 'vertical':
+ else: # orientation == 'vertical'
self.set_yscale('log')
- fill = False
- if histtype == 'stepfilled':
- fill = True
- elif histtype != 'step':
- raise ValueError, 'invalid histtype: %s' % histtype
+ fill = (histtype == 'stepfilled')
for m in n:
y[1:-1:2], y[2::2] = m, m
@@ -7281,8 +7283,6 @@
y[y<1e-100]=1e-100
if orientation == 'horizontal':
x,y = y,x
- elif orientation != 'vertical':
- raise ValueError, 'invalid orientation: %s' % orientation
color = self._get_lines.color_cycle.next()
if fill:
@@ -7307,17 +7307,17 @@
self.dataLim.intervaly = (ymin, ymax)
self.autoscale_view()
- else:
- raise ValueError, 'invalid histtype: %s' % histtype
+ label = kwargs.pop('label', '_nolegend_')
- label = kwargs.pop('label', '')
-
if is_string_like(label):
- labels = [label] + ['_nolegend_']*(len(patches)-1)
- elif is_sequence_of_strings:
- labels = list(label) + ['_nolegend_']*(len(patches)-1)
+ labels = [label]
+ elif is_sequence_of_strings(label):
+ labels = list(label)
else:
- raise ValueError, 'invalid label: must be string or sequence of strings'
+ raise ValueError(
+ 'invalid label: must be string or sequence of strings')
+ if len(labels) < nx:
+ labels += ['_nolegend_'] * (nx - len(labels))
for (patch, lbl) in zip(patches, labels):
for p in patch:
@@ -7329,14 +7329,16 @@
self.set_autoscale_on(False)
if orientation == 'vertical':
self.autoscale_view(scalex=False, scaley=True)
- XL = self.xaxis.get_major_locator().view_limits(bins[0], bins[-1])
+ XL = self.xaxis.get_major_locator().view_limits(
+ bins[0], bins[-1])
self.set_xbound(XL)
else:
self.autoscale_view(scalex=True, scaley=False)
- YL = self.yaxis.get_major_locator().view_limits(bins[0], bins[-1])
+ YL = self.yaxis.get_major_locator().view_limits(
+ bins[0], bins[-1])
self.set_ybound(YL)
- if len(n)==1:
+ if nx == 1:
return n[0], bins, cbook.silent_list('Patch', patches[0])
else:
return n, bins, cbook.silent_list('Lists of Patches', patches)
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