@@ -1038,6 +1038,12 @@ def __call__(self, value, clip=None):
10381038class LogNorm (Normalize ):
10391039 """Normalize a given value to the 0-1 range on a log scale."""
10401040
1041+ def _check_vmin_vmax (self ):
1042+ if self .vmin > self .vmax :
1043+ raise ValueError ("minvalue must be less than or equal to maxvalue" )
1044+ elif self .vmin <= 0 :
1045+ raise ValueError ("minvalue must be positive" )
1046+
10411047 def __call__ (self , value , clip = None ):
10421048 if clip is None :
10431049 clip = self .clip
@@ -1047,12 +1053,9 @@ def __call__(self, value, clip=None):
10471053 result = np .ma .masked_less_equal (result , 0 , copy = False )
10481054
10491055 self .autoscale_None (result )
1056+ self ._check_vmin_vmax ()
10501057 vmin , vmax = self .vmin , self .vmax
1051- if vmin > vmax :
1052- raise ValueError ("minvalue must be less than or equal to maxvalue" )
1053- elif vmin <= 0 :
1054- raise ValueError ("values must all be positive" )
1055- elif vmin == vmax :
1058+ if vmin == vmax :
10561059 result .fill (0 )
10571060 else :
10581061 if clip :
@@ -1078,6 +1081,7 @@ def __call__(self, value, clip=None):
10781081 def inverse (self , value ):
10791082 if not self .scaled ():
10801083 raise ValueError ("Not invertible until scaled" )
1084+ self ._check_vmin_vmax ()
10811085 vmin , vmax = self .vmin , self .vmax
10821086
10831087 if np .iterable (value ):
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