From e67c7fc42bd0502c70eb508e452c2fcec182861c Mon Sep 17 00:00:00 2001
From: Antony Lee <anntzer.lee@gmail.com>
Date: Wed, 30 Dec 2015 08:17:10 -0800
Subject: [PATCH] More precise choice of axes limits.

plt.plot([-.1, .2]) used to pick (in round numbers mode) [-.1, .25] as
ylims due to floating point inaccuracies; change it to pick [-.1, .2]
(up to floating point inaccuracies).

Support for the (unused and deprecated-in-comment) "trim" keyword
argument has been dropped as the way ticks are picked has changed.

Many test images have changed!

Implementation notes:
- A bug in numpy's implementation of divmod (numpy/numpy#6127) is worked
around.
- The implementation of scale_range has also been cleaned.

See #5767, #5738.
---
 .../test_axes/autoscale_tiny_range.pdf        |  Bin 2399 -> 2332 bytes
 .../test_axes/autoscale_tiny_range.png        |  Bin 13238 -> 13402 bytes
 .../test_axes/autoscale_tiny_range.svg        |  436 +-
 .../test_axes/bar_tick_label_single.png       |  Bin 8828 -> 8724 bytes
 .../test_axes/errorbar_mixed.pdf              |  Bin 20168 -> 20173 bytes
 .../test_axes/errorbar_mixed.svg              | 1012 ++---
 .../test_axes/formatter_large_small.pdf       |  Bin 7331 -> 7304 bytes
 .../test_axes/formatter_large_small.png       |  Bin 23432 -> 23340 bytes
 .../test_axes/formatter_large_small.svg       |  234 +-
 .../test_axes/twin_spines_on_top.png          |  Bin 8044 -> 5800 bytes
 .../bbox_inches_tight_raster.pdf              |  Bin 6569 -> 7455 bytes
 .../bbox_inches_tight_raster.png              |  Bin 11458 -> 11651 bytes
 .../bbox_inches_tight_raster.svg              |   72 +-
 lib/matplotlib/tests/test_axes.py             |    5 +-
 lib/matplotlib/ticker.py                      |   81 +-
 .../baseline_images/test_mplot3d/quiver3d.pdf |  Bin 18559 -> 18589 bytes
 .../baseline_images/test_mplot3d/quiver3d.png |  Bin 104331 -> 104755 bytes
 .../baseline_images/test_mplot3d/quiver3d.svg | 3493 ++++++++---------
 .../test_mplot3d/quiver3d_masked.pdf          |  Bin 10938 -> 10939 bytes
 .../test_mplot3d/quiver3d_masked.png          |  Bin 79952 -> 80392 bytes
 .../test_mplot3d/quiver3d_masked.svg          | 1755 ++++-----
 .../test_mplot3d/quiver3d_pivot_middle.png    |  Bin 104684 -> 104735 bytes
 .../test_mplot3d/quiver3d_pivot_tail.png      |  Bin 104295 -> 104951 bytes
 23 files changed, 3495 insertions(+), 3593 deletions(-)

diff --git a/lib/matplotlib/tests/baseline_images/test_axes/autoscale_tiny_range.pdf b/lib/matplotlib/tests/baseline_images/test_axes/autoscale_tiny_range.pdf
index 815604f0f66a3a9de65af20e72572ac9f731bfd9..42652378a5d02d0ed8ffcd9ff682e0f2de0ca0aa 100644
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/autoscale_tiny_range.png b/lib/matplotlib/tests/baseline_images/test_axes/autoscale_tiny_range.png
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diff --git a/lib/matplotlib/tests/baseline_images/test_axes/formatter_large_small.png b/lib/matplotlib/tests/baseline_images/test_axes/formatter_large_small.png
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diff --git a/lib/matplotlib/tests/baseline_images/test_bbox_tight/bbox_inches_tight_raster.pdf b/lib/matplotlib/tests/baseline_images/test_bbox_tight/bbox_inches_tight_raster.pdf
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+++ b/lib/matplotlib/tests/baseline_images/test_bbox_tight/bbox_inches_tight_raster.svg
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" 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@@ -138,92 +138,80 @@ L 0 4
       <defs>
        <path d="M 0 0 
 L 4 0 
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+" id="m60f474c7a2" style="stroke:#000000;stroke-width:0.5;"/>
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       <defs>
        <path d="M 0 0 
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-" id="m7838ae9005" style="stroke:#000000;stroke-width:0.500000;"/>
+" id="m833b6c161b" style="stroke:#000000;stroke-width:0.5;"/>
       </defs>
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diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index df0e48e0cef0..83dacc71d9ca 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -22,7 +22,7 @@
 from matplotlib.testing.decorators import image_comparison, cleanup
 import matplotlib.pyplot as plt
 import matplotlib.markers as mmarkers
-from numpy.testing import assert_array_equal
+from numpy.testing import assert_allclose, assert_array_equal
 import warnings
 from matplotlib.cbook import IgnoredKeywordWarning
 
@@ -3715,8 +3715,7 @@ def test_vline_limit():
     ax.axvline(0.5)
     ax.plot([-0.1, 0, 0.2, 0.1])
     (ymin, ymax) = ax.get_ylim()
-    assert ymin == -0.1
-    assert ymax == 0.25
+    assert_allclose(ax.get_ylim(), (-.1, .2))
 
 
 @cleanup
diff --git a/lib/matplotlib/ticker.py b/lib/matplotlib/ticker.py
index 7455d03365a2..231a5f3193a0 100644
--- a/lib/matplotlib/ticker.py
+++ b/lib/matplotlib/ticker.py
@@ -161,6 +161,7 @@
 from matplotlib import rcParams
 from matplotlib import cbook
 from matplotlib import transforms as mtransforms
+from matplotlib.cbook import mplDeprecation
 
 import warnings
 
@@ -168,6 +169,15 @@
     long = int
 
 
+# Work around numpy/numpy#6127.
+def _divmod(x, y):
+    if isinstance(x, np.generic):
+        x = x.item()
+    if isinstance(y, np.generic):
+        y = y.item()
+    return six.moves.builtins.divmod(x, y)
+
+
 def _mathdefault(s):
     """
     For backward compatibility, in classic mode we display
@@ -1221,7 +1231,7 @@ def view_limits(self, vmin, vmax):
             vmax += 1
 
         if rcParams['axes.autolimit_mode'] == 'round_numbers':
-            exponent, remainder = divmod(math.log10(vmax - vmin), 1)
+            exponent, remainder = _divmod(math.log10(vmax - vmin), 1)
             if remainder < 0.5:
                 exponent -= 1
             scale = 10 ** (-exponent)
@@ -1247,14 +1257,14 @@ def __init__(self, base):
 
     def lt(self, x):
         'return the largest multiple of base < x'
-        d, m = divmod(x, self._base)
+        d, m = _divmod(x, self._base)
         if closeto(m, 0) and not closeto(m / self._base, 1):
             return (d - 1) * self._base
         return d * self._base
 
     def le(self, x):
         'return the largest multiple of base <= x'
-        d, m = divmod(x, self._base)
+        d, m = _divmod(x, self._base)
         if closeto(m / self._base, 1):  # was closeto(m, self._base)
             #looks like floating point error
             return (d + 1) * self._base
@@ -1262,7 +1272,7 @@ def le(self, x):
 
     def gt(self, x):
         'return the smallest multiple of base > x'
-        d, m = divmod(x, self._base)
+        d, m = _divmod(x, self._base)
         if closeto(m / self._base, 1):
             #looks like floating point error
             return (d + 2) * self._base
@@ -1270,7 +1280,7 @@ def gt(self, x):
 
     def ge(self, x):
         'return the smallest multiple of base >= x'
-        d, m = divmod(x, self._base)
+        d, m = _divmod(x, self._base)
         if closeto(m, 0) and not closeto(m / self._base, 1):
             return d * self._base
         return (d + 1) * self._base
@@ -1326,23 +1336,13 @@ def view_limits(self, dmin, dmax):
 
 
 def scale_range(vmin, vmax, n=1, threshold=100):
-    dv = abs(vmax - vmin)
-    if dv == 0:     # maxabsv == 0 is a special case of this.
-        return 1.0, 0.0
-        # Note: this should never occur because
-        # vmin, vmax should have been checked by nonsingular(),
-        # and spread apart if necessary.
-    meanv = 0.5 * (vmax + vmin)
+    dv = abs(vmax - vmin)  # > 0 as nonsingular is called before.
+    meanv = (vmax + vmin) / 2
     if abs(meanv) / dv < threshold:
         offset = 0
-    elif meanv > 0:
-        ex = divmod(math.log10(meanv), 1)[0]
-        offset = 10 ** ex
     else:
-        ex = divmod(math.log10(-meanv), 1)[0]
-        offset = -10 ** ex
-    ex = divmod(math.log10(dv / n), 1)[0]
-    scale = 10 ** ex
+        offset = math.copysign(10 ** (math.log10(abs(meanv)) // 1), meanv)
+    scale = 10 ** (math.log10(dv / n) // 1)
     return scale, offset
 
 
@@ -1352,7 +1352,6 @@ class MaxNLocator(Locator):
     """
     default_params = dict(nbins=10,
                           steps=None,
-                          trim=True,
                           integer=False,
                           symmetric=False,
                           prune=None)
@@ -1388,9 +1387,6 @@ def __init__(self, *args, **kwargs):
             will be removed.  If prune==None, no ticks will be removed.
 
         """
-        # I left "trim" out; it defaults to True, and it is not
-        # clear that there is any use case for False, so we may
-        # want to remove that kwarg.  EF 2010/04/18
         if args:
             kwargs['nbins'] = args[0]
             if len(args) > 1:
@@ -1406,7 +1402,9 @@ def set_params(self, **kwargs):
             if self._nbins != 'auto':
                 self._nbins = int(self._nbins)
         if 'trim' in kwargs:
-            self._trim = kwargs['trim']
+            warnings.warn(
+                "The 'trim' keyword has no effect since version 2.0.",
+                mplDeprecation)
         if 'integer' in kwargs:
             self._integer = kwargs['integer']
         if 'symmetric' in kwargs:
@@ -1429,9 +1427,9 @@ def set_params(self, **kwargs):
         if 'integer' in kwargs:
             self._integer = kwargs['integer']
         if self._integer:
-            self._steps = [n for n in self._steps if divmod(n, 1)[1] < 0.001]
+            self._steps = [n for n in self._steps if _divmod(n, 1)[1] < 0.001]
 
-    def bin_boundaries(self, vmin, vmax):
+    def _raw_ticks(self, vmin, vmax):
         nbins = self._nbins
         if nbins == 'auto':
             nbins = max(min(self.axis.get_tick_space(), 9), 1)
@@ -1449,23 +1447,30 @@ def bin_boundaries(self, vmin, vmax):
             if step < scaled_raw_step:
                 continue
             step *= scale
-            best_vmin = step * divmod(vmin, step)[0]
+            best_vmin = vmin // step * step
             best_vmax = best_vmin + step * nbins
-            if (best_vmax >= vmax):
+            if best_vmax >= vmax:
                 break
-        if self._trim:
-            extra_bins = int(divmod((best_vmax - vmax), step)[0])
-            nbins -= extra_bins
-        return (np.arange(nbins + 1) * step + best_vmin + offset)
+
+        # More than nbins may be required, e.g. vmin, vmax = -4.1, 4.1 gives
+        # nbins=9 but 10 bins are actually required after rounding.  So we just
+        # create the bins that span the range we need instead.
+        low = round(Base(step).le(vmin - best_vmin) / step)
+        high = round(Base(step).ge(vmax - best_vmin) / step)
+        return np.arange(low, high + 1) * step + best_vmin + offset
+
+    @cbook.deprecated("2.0")
+    def bin_boundaries(self, vmin, vmax):
+        return self._raw_ticks(vmin, vmax)
 
     def __call__(self):
         vmin, vmax = self.axis.get_view_interval()
         return self.tick_values(vmin, vmax)
 
     def tick_values(self, vmin, vmax):
-        vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander=1e-13,
-                                                         tiny=1e-14)
-        locs = self.bin_boundaries(vmin, vmax)
+        vmin, vmax = mtransforms.nonsingular(
+            vmin, vmax, expander=1e-13, tiny=1e-14)
+        locs = self._raw_ticks(vmin, vmax)
         prune = self._prune
         if prune == 'lower':
             locs = locs[1:]
@@ -1482,11 +1487,11 @@ def view_limits(self, dmin, dmax):
                 dmin = -maxabs
                 dmax = maxabs
 
-        dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander=1e-12,
-                                                        tiny=1.e-13)
+        dmin, dmax = mtransforms.nonsingular(
+            dmin, dmax, expander=1e-12, tiny=1e-13)
 
         if rcParams['axes.autolimit_mode'] == 'round_numbers':
-            return np.take(self.bin_boundaries(dmin, dmax), [0, -1])
+            return self._raw_ticks(dmin, dmax)[[0, -1]]
         else:
             return dmin, dmax
 
diff --git a/lib/mpl_toolkits/tests/baseline_images/test_mplot3d/quiver3d.pdf b/lib/mpl_toolkits/tests/baseline_images/test_mplot3d/quiver3d.pdf
index 58524d9e9019d47186bad518dfabbf19220c8c35..5594461185a7b61473cb5854fee92372a49213cd 100644
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diff --git a/lib/mpl_toolkits/tests/baseline_images/test_mplot3d/quiver3d_pivot_tail.png b/lib/mpl_toolkits/tests/baseline_images/test_mplot3d/quiver3d_pivot_tail.png
index 7ab9fcc5dfd2056d57228a5e4547bc5f74fb0e7b..1352d5bf8d5a997a96b1b565c976b711cad4a18b 100644
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