diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 49d755ae1954..1be58211442d 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -6234,26 +6234,34 @@ def _normalize_input(inp, ename='input'):
# adopted from adjust_x/ylim part of the bar method
if orientation == 'horizontal':
- xmin0 = max(_saved_bounds[0]*0.9, minimum)
- xmax = self.dataLim.intervalx[1]
- for m in n:
- if np.sum(m) > 0: # make sure there are counts
- xmin = np.amin(m[m != 0])
- # filter out the 0 height bins
- xmin = max(xmin*0.9, minimum) if not input_empty else minimum
- xmin = min(xmin0, xmin)
- self.dataLim.intervalx = (xmin, xmax)
+ if normed:
+ self.set_autoscalex_on(True)
+ else:
+ xmin0 = max(_saved_bounds[0]*0.9, minimum)
+ xmax = self.dataLim.intervalx[1]
+ for m in n:
+ if np.sum(m) > 0: # make sure there are counts
+ xmin = np.amin(m[m != 0])
+ # filter out the 0 height bins
+ xmin = max(xmin*0.9,
+ minimum) if not input_empty else minimum
+ xmin = min(xmin0, xmin)
+ self.dataLim.intervalx = (xmin, xmax)
elif orientation == 'vertical':
- ymin0 = max(_saved_bounds[1]*0.9, minimum)
- ymax = self.dataLim.intervaly[1]
-
- for m in n:
- if np.sum(m) > 0: # make sure there are counts
- ymin = np.amin(m[m != 0])
- # filter out the 0 height bins
- ymin = max(ymin*0.9, minimum) if not input_empty else minimum
- ymin = min(ymin0, ymin)
- self.dataLim.intervaly = (ymin, ymax)
+ # If norm, autoscale axis
+ if normed:
+ self.set_autoscaley_on(True)
+ else:
+ ymin0 = max(_saved_bounds[1]*0.9, minimum)
+ ymax = self.dataLim.intervaly[1]
+ for m in n:
+ if np.sum(m) > 0: # make sure there are counts
+ ymin = np.amin(m[m != 0])
+ # filter out the 0 height bins
+ ymin = max(ymin*0.9,
+ minimum) if not input_empty else minimum
+ ymin = min(ymin0, ymin)
+ self.dataLim.intervaly = (ymin, ymax)
if label is None:
labels = [None]
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