Merge branch 'release-1.2.0'

FlowCal/__init__.py

@@ -6,12 +6,12 @@
 # Versions should comply with PEP440.  For a discussion on single-sourcing
 # the version across setup.py and the project code, see
 # https://packaging.python.org/en/latest/single_source_version.html
-__version__ = '1.1.4'
+__version__ = '1.2.0'
 
-import io
-import excel_ui
-import gate
-import transform
-import mef
-import plot
-import stats
+from . import io
+from . import excel_ui
+from . import gate
+from . import transform
+from . import mef
+from . import plot
+from . import stats

FlowCal/gate.py

@@ -21,7 +21,7 @@
 
 import numpy as np
 import scipy.ndimage.filters
-import matplotlib._cntr         # matplotlib contour, implemented in C
+import skimage.measure
 import collections
 
 ###
@@ -506,32 +506,32 @@ def density2d(data,
     gated_data = data[mask]
 
     if full_output:
-        # Use matplotlib contour plotter (implemented in C) to generate contour(s)
-        # at the probability associated with the last accepted bin.
+        # Use scikit-image to find the contour of the gated region
+        #
+        # To find the contour of the gated region, values in the 2D probability
+        # mass function ``D`` are used to trace contours at the level of the
+        # probability associated with the last accepted bin, ``vD[sidx[Nidx]]``.
+
+        # find_contours() specifies contours as collections of row and column
+        # indices into the density matrix. The row or column index may be
+        # interpolated (i.e. non-integer) for greater precision.
+        contours_ij = skimage.measure.find_contours(D, vD[sidx[Nidx]])
+
+        # Map contours from indices into density matrix to histogram x and y
+        # coordinate spaces (assume values in the density matrix are associated
+        # with histogram bin centers).
         xc = (xe[:-1] + xe[1:]) / 2.0   # x-axis bin centers
         yc = (ye[:-1] + ye[1:]) / 2.0   # y-axis bin centers
-        x,y = np.meshgrid(xc, yc, indexing='ij')
-        mpl_cntr = matplotlib._cntr.Cntr(x,y,D)
-        tr = mpl_cntr.trace(vD[sidx[Nidx]])
-
-        # trace returns a list of arrays which contain vertices and path codes
-        # used in matplotlib Path objects (see http://stackoverflow.com/a/18309914
-        # and the documentation for matplotlib.path.Path for more details). I'm
-        # just going to make sure the path codes aren't unfamiliar and then extract
-        # all of the vertices and pack them into a list of 2D contours.
-        cntr = []
-        num_cntrs = len(tr)/2
-        for idx in xrange(num_cntrs):
-            vertices = tr[idx]
-            codes = tr[num_cntrs+idx]
-
-            # I am only expecting codes 1 and 2 ('MOVETO' and 'LINETO' codes)
-            if not np.all((codes==1)|(codes==2)):
-                raise Exception('Contour error: unrecognized path code')
-
-            cntr.append(vertices)
+
+        contours = [np.array([np.interp(contour_ij[:,0],
+                                        np.arange(len(xc)),
+                                        xc),
+                              np.interp(contour_ij[:,1],
+                                        np.arange(len(yc)),
+                                        yc)]).T
+                    for contour_ij in contours_ij]
 
         return Density2dGateOutput(
-            gated_data=gated_data, mask=mask, contour=cntr)
+            gated_data=gated_data, mask=mask, contour=contours)
     else:
         return gated_data