@@ -6397,7 +6397,7 @@ def _normalize_input(inp, ename='input'):
63976397 xvals .append (x .copy ())
63986398 yvals .append (y .copy ())
63996399
6400- #stepfill is closed, step is not
6400+ # stepfill is closed, step is not
64016401 split = - 1 if fill else 2 * len (bins )
64026402 # add patches in reverse order so that when stacking,
64036403 # items lower in the stack are plottted on top of
@@ -6419,9 +6419,13 @@ def _normalize_input(inp, ename='input'):
64196419 xmin0 = max (_saved_bounds [0 ]* 0.9 , minimum )
64206420 xmax = self .dataLim .intervalx [1 ]
64216421 for m in n :
6422- if np . sum ( m ) > 0 : # make sure there are counts
6423- xmin = np .amin ( m [ m != 0 ])
6422+ # make sure there are counts
6423+ if np .sum ( m ) > 0 :
64246424 # filter out the 0 height bins
6425+ xmin = np .amin (m [m != 0 ])
6426+ # If no counts, set min to zero
6427+ else :
6428+ xmin = 0.0
64256429 xmin = max (xmin * 0.9 , minimum ) if not input_empty else minimum
64266430 xmin = min (xmin0 , xmin )
64276431 self .dataLim .intervalx = (xmin , xmax )
@@ -6430,9 +6434,13 @@ def _normalize_input(inp, ename='input'):
64306434 ymax = self .dataLim .intervaly [1 ]
64316435
64326436 for m in n :
6433- if np . sum ( m ) > 0 : # make sure there are counts
6434- ymin = np .amin ( m [ m != 0 ])
6437+ # make sure there are counts
6438+ if np .sum ( m ) > 0 :
64356439 # filter out the 0 height bins
6440+ ymin = np .amin (m [m != 0 ])
6441+ # If no counts, set min to zero
6442+ else :
6443+ ymin = 0.0
64366444 ymin = max (ymin * 0.9 , minimum ) if not input_empty else minimum
64376445 ymin = min (ymin0 , ymin )
64386446 self .dataLim .intervaly = (ymin , ymax )
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