less_simple_linear_interpolation can be replaced by np.interp.

anntzer · anntzer · commit adb2f8aa2b25 · 2017-12-01T11:23:33.000-08:00
Also, in contour (the only place that used it), -1 (instead of nan) is
a perfectly reasonable marker for out of bounds which avoids having to
dance with the errstate.
diff --git a/doc/api/api_changes/2017-11-31-AL.rst b/doc/api/api_changes/2017-11-31-AL.rst
@@ -0,0 +1,5 @@
+Deprecations
+````````````
+
+The now unused ``mlab.less_simple_linear_interpolation`` function is
+deprecated.
diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
@@ -417,63 +417,42 @@ def calc_label_rot_and_inline(self, slc, ind, lw, lc=None, spacing=5):
         else:
             dp = np.zeros_like(xi)
 
-        ll = mlab.less_simple_linear_interpolation(pl, slc, dp + xi,
-                                                   extrap=True)
-
-        # get vector in pixel space coordinates from one point to other
-        dd = np.diff(ll, axis=0).ravel()
-
-        # Get angle of vector - must be calculated in pixel space for
-        # text rotation to work correctly
-        if np.all(dd == 0):  # Must deal with case of zero length label
-            rotation = 0.0
-        else:
-            rotation = np.rad2deg(np.arctan2(dd[1], dd[0]))
+        # Get angle of vector between the two ends of the label - must be
+        # calculated in pixel space for text rotation to work correctly.
+        (dx,), (dy,) = (np.diff(np.interp(dp + xi, pl, slc_col))
+                        for slc_col in slc.T)
+        rotation = np.rad2deg(np.arctan2(dy, dx))
 
         if self.rightside_up:
             # Fix angle so text is never upside-down
-            if rotation > 90:
-                rotation = rotation - 180.0
-            if rotation < -90:
-                rotation = 180.0 + rotation
+            rotation = (rotation + 90) % 180 - 90
 
         # Break contour if desired
         nlc = []
         if len(lc):
             # Expand range by spacing
             xi = dp + xi + np.array([-spacing, spacing])
 
-            # Get indices near points of interest
-            I = mlab.less_simple_linear_interpolation(
-                pl, np.arange(len(pl)), xi, extrap=False)
-
-            # If those indices aren't beyond contour edge, find x,y
-            if (not np.isnan(I[0])) and int(I[0]) != I[0]:
-                xy1 = mlab.less_simple_linear_interpolation(
-                    pl, lc, [xi[0]])
-
-            if (not np.isnan(I[1])) and int(I[1]) != I[1]:
-                xy2 = mlab.less_simple_linear_interpolation(
-                    pl, lc, [xi[1]])
-
-            # Round to integer values but keep as float
-            # To allow check against nan below
-            # Ignore nans here to avoid throwing an error on Appveyor build
-            # (can possibly be removed when build uses numpy 1.13)
-            with np.errstate(invalid='ignore'):
-                I = [np.floor(I[0]), np.ceil(I[1])]
+            # Get (integer) indices near points of interest; use -1 as marker
+            # for out of bounds.
+            I = np.interp(xi, pl, np.arange(len(pl)), left=-1, right=-1)
+            I = [np.floor(I[0]).astype(int), np.ceil(I[1]).astype(int)]
+            if I[0] != -1:
+                xy1 = [np.interp(xi[0], pl, lc_col) for lc_col in lc.T]
+            if I[1] != -1:
+                xy2 = [np.interp(xi[1], pl, lc_col) for lc_col in lc.T]
 
             # Actually break contours
             if closed:
                 # This will remove contour if shorter than label
-                if np.all(~np.isnan(I)):
-                    nlc.append(np.r_[xy2, lc[int(I[1]):int(I[0]) + 1], xy1])
+                if np.all(I != -1):
+                    nlc.append(np.row_stack([xy2, lc[I[1]:I[0]+1], xy1]))
             else:
                 # These will remove pieces of contour if they have length zero
-                if not np.isnan(I[0]):
-                    nlc.append(np.r_[lc[:int(I[0]) + 1], xy1])
-                if not np.isnan(I[1]):
-                    nlc.append(np.r_[xy2, lc[int(I[1]):]])
+                if I[0] != -1:
+                    nlc.append(np.row_stack([lc[:I[0]+1], xy1]))
+                if I[1] != -1:
+                    nlc.append(np.row_stack([xy2, lc[I[1]:]]))
 
             # The current implementation removes contours completely
             # covered by labels.  Uncomment line below to keep
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -3404,6 +3404,7 @@ def griddata(x, y, z, xi, yi, interp='nn'):
 ##################################################
 # Linear interpolation algorithms
 ##################################################
+@cbook.deprecated("2.2", alternative="np.interp")
 def less_simple_linear_interpolation(x, y, xi, extrap=False):
     """
     This function provides simple (but somewhat less so than
diff --git a/lib/matplotlib/tests/test_patheffects.py b/lib/matplotlib/tests/test_patheffects.py
@@ -122,7 +122,7 @@ def test_SimplePatchShadow_offset():
     assert pe._offset == (4, 5)
 
 
-@image_comparison(baseline_images=['collection'], tol=0.015)
+@image_comparison(baseline_images=['collection'], tol=0.02)
 def test_collection():
     x, y = np.meshgrid(np.linspace(0, 10, 150), np.linspace(-5, 5, 100))
     data = np.sin(x) + np.cos(y)
