Bug in contour x/y values filtering in mpl_toolkits/basemap/__init__.py ?

mpl_toolkits/basemap/init.py の contour(self, x, y, data, ...) のフィルタリングロジックから始め、入力を matplotlib.tri triangulation traceback と比較します。提案されている座標境界によるフィルタリングを確認し、利用可能であれば既存の contour または contourf テストを実行します。完了の条件は、contour が報告された非構造化データを、空の座標配列を triangulation に渡すことなく処理できることです。

I have been trying to plot unstructured data with contour and contourf, using the tri=True parameter. It works fine with contourf, but I get a huge traceback when using contour on the same data.

[...]
  File "/home/share/unix_files/cdat/versions/cdat_install_uv-2.1.0_x86_64_gcc4_13/lib/python2.7/site-packages/matplotlib-1.4.0-py2.7-linux-x86_64.egg/matplotlib/tri/triangulation.py", line 55, in __init__
    self.triangles, self._neighbors = _qhull.delaunay(x, y)
ValueError: x and y arrays must have a length of at least 3

After spending some time in the python debugger, I have found out that indeed the x and y coordinate arrays passed to the triangulation function had a zero size (and therefore less than 3 elements) because they were empty! More digging helped me find a very suspicious way of filtering the x and y values in the contour(self,x,y,data,args,*kwargs) function

                # for unstructured grids, toss out points outside
                # projection limb (don't use those points in triangulation).
[...]
                mask = np.logical_or(x<self.xmin,y<self.xmin) +\
                       np.logical_or(x>self.xmax,y>self.xmax)
                x = np.compress(mask,x)
                y = np.compress(mask,y)

Why would y be compared to the values of xmin and xmax instead of ymin and ymax, and is the logical combination ok???

I think we want to keep the values where: xmin<=x<=xmax AND ymin<=y<=ymax. And the mask has to be True where we want to keep the value, in np.compress! This is a bit misleading because it works in the opposite way that masks work in np.ma ...

The contour function works fine if I replace the mask definition above with

                mask = np.logical_and(np.logical_and(x>=self.xmin, x<=self.xmax),
                                      np.logical_and(y>=self.ymin, y<=self.ymax))

Can somebody review this? And it may be wise to use a slightly less misleading name for the mask variable. Maybe replace mask with select_xy_ok?