8000 ENH: improve contourf color autoscaling · matplotlib/matplotlib@e67f71e · GitHub
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ENH: improve contourf color autoscaling
Contour level auto-selection: clip unused Locator output: Locator.tick_values() returns levels beyond the data limits. In the case of LogLocator with a small data range, the overrun can be large because it is expanding to the decade points. In addition, no account was being taken of the "extend" kwarg. With this changeset, the outermost levels will be the miminum required to include the data interval in the default case, and will be reduced when "extend" is used so that some of the data range will fall in the extended sections. This is expected to be rare, however; normally the "extend" kwarg would be used only when levels are explicitly set, not auto-selected with a Locator. The contour_hatching test results were modified. The difference was reduced by putting the hatches in the same order as they were in the reference figure, but the images still don't match because the gray shades have changed, as expected. (Note that in the original and new reference figures the pdf and svg backends handle the alpha differently than the agg backend. This is unrelated to the PR.) For logscale contour colors, interpolate in screen space by taking the geometric mean of adjacent levels. The test_contour.py::test_contourf_log_extension code and image have been modified for this change in colors, and to make the behavior more clear in the case where levels are not specified. Add a note to next_api_changes. Treat contour levels (almost) the same as contourf levels Update additional test images affected by the contour color changes. mplot3d: don't assume every contour level has a line.
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‎doc/api/next_api_changes/2018-06-09-EF-contour_levels.rst

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@@ -0,0 +1,16 @@
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Contour color autoscaling improvements
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--------------------------------------
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Selection of contour levels is now the same for contour and
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contourf; previously, for contour, levels outside the data range were
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deleted. (Exception: if no contour levels are found within the
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data range, the `levels` attribute is replaced with a list holding
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only the minimum of the data range.)
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When contour is called with levels specified as a target number rather
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than a list, and the 'extend' kwarg is used, the levels are now chosen
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such that some data typically will fall in the extended range.
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When contour is called with a `LogNorm` or a `LogLocator`, it will now
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select colors using the geometric mean rather than the arithmetic mean
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of the contour levels.

‎lib/matplotlib/contour.py

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Original file line numberDiff line numberDiff line change
@@ -1174,22 +1174,45 @@ def _autolev(self, N):
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"""
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Select contour levels to span the data.
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The target number of levels, *N*, is used only when the
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scale is not log and default locator is used.
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We need two more levels for filled contours than for
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line contours, because for the latter we need to specify
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the lower and upper boundary of each range. For example,
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a single contour boundary, say at z = 0, requires only
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one contour line, but two filled regions, and therefore
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three levels to provide boundaries for both regions.
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"""
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self._auto = True
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if self.locator is None:
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if self.logscale:
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self.locator = ticker.LogLocator()
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else:
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self.locator = ticker.MaxNLocator(N + 1, min_n_ticks=1)
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lev = self.locator.tick_values(self.zmin, self.zmax)
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self._auto = True
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return lev
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try:
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if self.locator._symmetric:
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return lev
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except AttributeError:
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pass
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# Trim excess levels the locator may have supplied.
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under = np.nonzero(lev < self.zmin)[0]
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i0 = under[-1] if len(under) else 0
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over = np.nonzero(lev > self.zmax)[0]
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i1 = over[0] + 1 if len(over) else len(lev)
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if self.extend in ('min', 'both'):
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i0 += 1
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if self.extend in ('max', 'both'):
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i1 -= 1
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if i1 - i0 < 3:
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i0, i1 = 0, len(lev)
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< F438 code>1215+
return lev[i0:i1]
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def _contour_level_args(self, z, args):
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"""
@@ -1220,8 +1243,8 @@ def _contour_level_args(self, z, args):
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if not self.filled:
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inside = (self.levels > self.zmin) & (self.levels < self.zmax)
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self.levels = self.levels[inside]
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if len(self.levels) == 0:
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levels_in = self.levels[inside]
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if len(levels_in) == 0:
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self.levels = [self.zmin]
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warnings.warn("No contour levels were found"
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" within the data range.")
@@ -1246,27 +1269,28 @@ def _process_levels(self):
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# (Colorbar needs this even for line contours.)
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self._levels = list(self.levels)
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if self.logscale:
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lower, upper = 1e-250, 1e250
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else:
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lower, upper = -1e250, 1e250
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if self.extend in ('both', 'min'):
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self._levels.insert(0, min(self.levels[0], self.zmin) - 1)
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self._levels.insert(0, lower)
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if self.extend in ('both', 'max'):
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self._levels.append(max(self.levels[-1], self.zmax) + 1)
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self._levels.append(upper)
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self._levels = np.asarray(self._levels)
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if not self.filled:
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self.layers = self.levels
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return
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# layer values are mid-way between levels
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self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
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# ...except that extended layers must be outside the
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# normed range:
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if self.extend in ('both', 'min'):
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if self.logscale:
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self.layers[0] = 1e-150
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else:
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self.layers[0] = -1e150
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if self.extend in ('both', 'max'):
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self.layers[-1] = 1e150
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# Layer values are mid-way between levels in screen space.
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if self.logscale:
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# Avoid overflow by taking sqrt before multiplying.
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self.layers = (np.sqrt(self._levels[:-1])
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* np.sqrt(self._levels[1:]))
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else:
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self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
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def _process_colors(self):
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"""

‎lib/matplotlib/tests/baseline_images/test_axes/contour_colorbar.pdf

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‎lib/matplotlib/tests/baseline_images/test_axes/contour_colorbar.png

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