@@ -5420,9 +5420,9 @@ def hist(self, x, bins=10, normed=False, cumulative=False,
54205420 bottom = None , histtype = 'bar' , align = 'edge' ,
54215421 orientation = 'vertical' , width = None , log = False , ** kwargs ):
54225422 """
5423- HIST(x, bins=10, normed=False, bottom=None, histtype='bar' ,
5424- align='edge', orientation='vertical ', width=None ,
5425- log=False, **kwargs)
5423+ HIST(x, bins=10, normed=False, cumulative=False ,
5424+ bottom=None, histtype='bar ', align='edge' ,
5425+ orientation='vertical', width=None, log=False, **kwargs)
54265426
54275427 Compute the histogram of x. bins is either an integer number of
54285428 bins or a sequence giving the bins. x are the data to be binned.
@@ -5437,13 +5437,13 @@ def hist(self, x, bins=10, normed=False, cumulative=False,
54375437
54385438 # trapezoidal integration of the probability density function
54395439 pdf, bins, patches = ax.hist(...)
5440- print np.trapz (pdf, bins)
5440+ print np.sum (pdf * np.diff( bins) )
54415441
5442- If cumulative is True then histogram is computed where each bin
5442+ If cumulative is True then a histogram is computed where each bin
54435443 gives the counts in that bin plus all bins for smaller values.
54445444 The last bins gives the total number of datapoints. If normed is
54455445 also True then the histogram is normalized such that the last bin
5446- equals one (assuming equally spaced bins) .
5446+ equals one.
54475447
54485448 histtype = 'bar' | 'step'. The type of histogram to draw.
54495449 'bar' is a traditional bar-type histogram, 'step' generates
@@ -5469,10 +5469,10 @@ def hist(self, x, bins=10, normed=False, cumulative=False,
54695469 normed = bool (normed ), new = True )
54705470
54715471 if cumulative :
5472- n = n .cumsum ()
54735472 if normed :
5474- # normalize to 1
5475- n *= (bins [1 ]- bins [0 ])
5473+ n = (n * np .diff (bins )).cumsum ()
5474+ else :
5475+ n = n .cumsum ()
54765476
54775477 if histtype == 'bar' :
54785478 if width is None :
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