MAINT: Response to review comments to #6251

lib/matplotlib/tests/test_ticker.py

@@ -367,9 +367,9 @@ def test_formatstrformatter():
     nose.tools.assert_equal('00002', tmp_form(2))
 
 
-def _percent_format_helper(mx, decimals, symbol, x, d, expected):
-    form = mticker.PercentFormatter(mx, decimals, symbol)
-    nose.tools.assert_equal(form.format_pct(x, d), expected)
+def _percent_format_helper(max, decimals, symbol, x, range, expected):
+    formatter = mticker.PercentFormatter(max, decimals, symbol)
+    nose.tools.assert_equal(formatter.format_pct(x, range), expected)
 
 
 def test_percentformatter():
@@ -395,8 +395,8 @@ def test_percentformatter():
         (75, 3, '', 50, 100, '66.667'),
         (42, None, '^^Foobar$$', 21, 12, '50.0^^Foobar$$'),
     )
-    for mx, decimals, symbol, x, d, expected in test_cases:
-        yield _percent_format_helper, mx, decimals, symbol, x, d, expected
+    for max, decimals, symbol, x, range, expected in test_cases:
+        yield _percent_format_helper, max, decimals, symbol, x, range, expected
 
 
 if __name__ == '__main__':

lib/matplotlib/ticker.py

@@ -1012,46 +1012,47 @@ def __call__(self, x, pos=None):
         Formats the tick as a percentage with the appropriate scaling.
         """
         xmin, xmax = self.axis.get_view_interval()
-        d = abs(xmax - xmin)
+        range = abs(xmax - xmin)
 
-        return self.fix_minus(self.format_pct(x, d))
+        return self.fix_minus(self.format_pct(x, range))
 
-    def format_pct(self, x, d):
+    def format_pct(self, x, range):
         """
         Formats the number as a percentage number with the correct
         number of decimals and adds the percent symbol, if any.
 
         If `self.decimals` is `None`, the number of digits after the
-        decimal point is set based on the width of the domain `d` as
-        follows:
-
-        +-------+----------+------------------------+
-        |   d   | decimals |          sample        |
-        +-------+----------+------------------------+
-        | >50   |   0      | ``x = 34.5`` => 34%    |
-        +-------+----------+------------------------+
-        | >5    |   1      | ``x = 34.5`` => 34.5%  |
-        +-------+----------+------------------------+
-        | >0.5  |   2      | ``x = 34.5`` => 34.50% |
-        +-------+----------+------------------------+
-        |  ...  |   ...    |          ...           |
-        +-------+----------+------------------------+
-
-        This method will not be very good for tiny ranges or extremely
-        large ranges. It assumes that the values on the chart are
-        percentages displayed on a reasonable scale.
+        decimal point is set based on the displayed `range` of the axis
+        as follows:
+
+        +--------+----------+------------------------+
+        | domain | decimals |          sample        |
+        +--------+----------+------------------------+
+        | >50    |    0     | ``x = 34.5`` => 35%    |
+        +--------+----------+------------------------+
+        | >5     |    1     | ``x = 34.5`` => 34.5%  |
+        +--------+----------+------------------------+
+        | >0.5   |    2     | ``x = 34.5`` => 34.50% |
+        +--------+----------+------------------------+
+        |   ...  |   ...    |          ...           |
+        +--------+----------+------------------------+
+
+        This method will not be very good for tiny axis ranges or
+        extremely large ones. It assumes that the values on the chart
+        are percentages displayed on a reasonable scale.
         """
         x = self.convert_to_pct(x)
         if self.decimals is None:
-            d = self.convert_to_pct(d)  # d is a difference, so this works
-            if d <= 0:
+            # conversion works because range is a difference
+            scaled_range = self.convert_to_pct(range)
+            if scaled_range <= 0:
                 decimals = 0
             else:
                 # Luckily Python's built-in `ceil` rounds to +inf, not away
                 # from zero. This is very important since the equation for
-                # `decimals` starts out as `d > 0.5 * 10**(2 - decimals)`
-                # and ends up with `decimals > 2 - log10(2 * d)`.
-                decimals = math.ceil(2.0 - math.log10(2.0 * d))
+                # `decimals` starts out as `scaled_range > 0.5 * 10**(2 - decimals)`
+                # and ends up with `decimals > 2 - log10(2 * scaled_range)`.
+                decimals = math.ceil(2.0 - math.log10(2.0 * scaled_ranged))
                 if decimals > 5:
                     decimals = 5
                 elif decimals < 0: