fix hist limit issue for step* for both linear and log scale

lib/matplotlib/axes.py

@@ -7920,7 +7920,7 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
                 _barfunc = self.barh
             else:  # orientation == 'vertical'
                 _barfunc = self.bar
-
+            
             for m, c in zip(n, color):
                 patch = _barfunc(bins[:-1]+boffset, m, width, bottom,
                                   align='center', log=log,
@@ -7938,15 +7938,37 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
 
             x[0::2], x[1::2] = bins, bins
 
-            minimum = min(bins)
+            minimum = np.min(n)
 
             if align == 'left' or align == 'center':
                 x -= 0.5*(bins[1]-bins[0])
             elif align == 'right':
                 x += 0.5*(bins[1]-bins[0])
 
             if log:
-                y[0],y[-1] = minimum, minimum
+                #in the case of log scale there are three cases 
+                #to consider for the y axis limit
+                #1) has zero or negative bin but other bins may be 0<n<1
+                #   due to weighting or normalization. We want 
+                #   (positive minimum)*0.1 to be the minimum
+                #2) has zero/negative bin but the rest are greater than 1.
+                #   this one we want minimum at 0.1 (normal cases)
+                #3) all zero/negative bin .... no idea how to display
+                #   this anyway so put minimum at 0.1
+                ndata = np.array(n)
+                if np.any(ndata<=0):
+                    warnings.warn(
+                        'Histogram bins with zero or negative count '+
+                        'are not displayed in log scale.')
+
+                if np.any(ndata>0):
+                    if np.all(ndata[ndata!=0]>=1):
+                        minimum = 0.1 # case 2)
+                    else:
+                        minimum = np.min(ndata[ndata>0])*0.1 #case 1)
+                else: 
+                    minimum = 0.1 #case 3)
+
                 if orientation == 'horizontal':
                     self.set_xscale('log')
                 else:  # orientation == 'vertical'
@@ -7957,7 +7979,11 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
             for m, c in zip(n, color):
                 y[1:-1:2], y[2::2] = m, m
                 if log:
-                    y[y<minimum]=minimum
+                    tiniest = np.finfo(np.float).tiny
+                    #make sure they don't show up in display
+                    y[y<minimum]= tiniest
+                    #making sure it is filled all the way down the axis
+                    y[0],y[-1] = tiniest, tiniest
                 if orientation == 'horizontal':
                     x,y = y,x
 
@@ -7970,22 +7996,14 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,
 
             # adopted from adjust_x/ylim part of the bar method
             if orientation == 'horizontal':
-                xmin0 = max(_saved_bounds[0]*0.9, minimum)
+                xmin = minimum
                 xmax = self.dataLim.intervalx[1]
-                for m in n:
-                    xmin = np.amin(m[m!=0]) # filter out the 0 height bins
-                xmin = max(xmin*0.9, minimum)
-                xmin = min(xmin0, xmin)
                 self.dataLim.intervalx = (xmin, xmax)
             elif orientation == 'vertical':
-                ymin0 = max(_saved_bounds[1]*0.9, minimum)
+                ymin = minimum
                 ymax = self.dataLim.intervaly[1]
-                for m in n:
-                    ymin = np.amin(m[m!=0]) # filter out the 0 height bins
-                ymin = max(ymin*0.9, minimum)
-                ymin = min(ymin0, ymin)
                 self.dataLim.intervaly = (ymin, ymax)
-
+                
         if label is None:
             labels = [None]
         elif is_string_like(label):