@@ -558,17 +558,18 @@ def annotate(self, *args, **kwargs):
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"figure points", "figure pixels", "figure fraction", "axes
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points", .... See `matplotlib.text.Annotation` for more details.
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- textcoords : string, optional, default: None
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+ textcoords : string, optional
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string that indicates what type of coordinates `text` is. Examples:
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"figure points", "figure pixels", "figure fraction", "axes
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points", .... See `matplotlib.text.Annotation` for more details.
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+ Default is None.
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- arrowprops : `matplotlib.lines.Line2D` properties, optional, default: None
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+ arrowprops : `matplotlib.lines.Line2D` properties, optional
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Dictionnary of line properties for the arrow that connects the
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annotation to the point. If the dictionnary has a key
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`arrowstyle`, a `FancyArrowPatch` instance is created and drawn.
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See `matplotlib.text.Annotation` for more details on valid
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- options.
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+ options. Default is None.
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Returns
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-------
@@ -3643,7 +3644,7 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
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# Transform accum if needed
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if bins == 'log' :
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accum = np .log10 (accum + 1 )
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- elif bins != None :
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+ elif bins is not None :
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if not iterable (bins ):
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minimum , maximum = min (accum ), max (accum )
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bins -= 1 # one less edge than bins
@@ -5119,7 +5120,8 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
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Returns
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-------
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- tuple : (n, bins, patches) or ([n0, n1, ...], bins, [patches0, patches1,...])
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+ tuple : ``(n, bins, patches)`` or \
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+ ``([n0, n1, ...], bins, [patches0, patches1,...])``
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Other Parameters
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----------------
@@ -5246,7 +5248,7 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
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# this will automatically overwrite bins,
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# so that each histogram uses the same bins
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m , bins = np .histogram (x [i ], bins , weights = w [i ], ** hist_kwargs )
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- m = m .astype (float ) # causes problems later if it's an int
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+ m = m .astype (float ) # causes problems later if it's an int
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if mlast is None :
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mlast = np .zeros (len (bins )- 1 , m .dtype )
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if normed and not stacked :
@@ -5417,15 +5419,15 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
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xmin0 = max (_saved_bounds [0 ]* 0.9 , minimum )
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xmax = self .dataLim .intervalx [1 ]
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for m in n :
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- xmin = np .amin (m [m != 0 ]) # filter out the 0 height bins
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+ xmin = np .amin (m [m != 0 ]) # filter out the 0 height bins
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xmin = max (xmin * 0.9 , minimum )
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xmin = min (xmin0 , xmin )
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self .dataLim .intervalx = (xmin , xmax )
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elif orientation == 'vertical' :
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ymin0 = max (_saved_bounds [1 ]* 0.9 , minimum )
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ymax = self .dataLim .intervaly [1 ]
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for m in n :
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- ymin = np .amin (m [m != 0 ]) # filter out the 0 height bins
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+ ymin = np .amin (m [m != 0 ]) # filter out the 0 height bins
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ymin = max (ymin * 0.9 , minimum )
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ymin = min (ymin0 , ymin )
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self .dataLim .intervaly = (ymin , ymax )
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