min_location_count applies before knn

tramway/helper/tessellation.py

@@ -572,6 +572,10 @@ def cell_plot(cells, xy_layer=None, output_file=None, fig_format=None, \
 			labels = ()
 		elif not isinstance(labels, (tuple, list)):
 			labels = (labels, )
+		if isinstance(input_file, (tuple, list)):
+			if input_file[1:]:
+				warn('can only process a single file', RuntimeWarning)
+			input_file = input_file[0]
 		try:
 			analyses = load_rwa(input_file)
 			if labels:
@@ -580,8 +584,6 @@ def cell_plot(cells, xy_layer=None, output_file=None, fig_format=None, \
 			if e.args and 'analyses' not in e.args[0]:
 				raise
 			# legacy code
-			if isinstance(input_file, list):
-				input_file = input_file[0]
 			imt_path = input_file
 			if imt_path is None:
 				raise ValueError('undefined input file')
@@ -685,7 +687,7 @@ def cell_plot(cells, xy_layer=None, output_file=None, fig_format=None, \
 				plot_points(cells, min_count=min_location_count, **locations)
 		if aspect is not None:
 			fig.gca().set_aspect(aspect)
-		if xy_layer == 'voronoi' or voronoi:
+		if xy_layer != 'delaunay' and voronoi:
 			if not isinstance(voronoi, dict):
 				voronoi = {}
 			plot_voronoi(cells, **voronoi)

tramway/tessellation/base.py

@@ -719,6 +719,13 @@ def cell_index(self, points, format=None, select=None, knn=None,
 						x = points[cell]
 					if not filter(self, c, x):
 						K[cell] = -1
+			# min_location_count:
+			# set K[i] = -1 for all point i in cells that are too small
+			if min_location_count:
+				excluded_cells = positive_count < min_location_count
+				if np.any(excluded_cells):
+					for c in nonempty[excluded_cells]:
+						K[K == c] = -1
 			# max_nn:
 			# set K[i] = -1 for all point i in cells that are too large
 			if max_nn:
@@ -751,18 +758,10 @@ def cell_index(self, points, format=None, select=None, knn=None,
 					Ic = np.logical_not(point_in_small_cells)
 					Jc = K[Ic]
 					Ic, = Ic.nonzero()
-					if max_nn:
-						Ic = Ic[0 <= Jc]
-						Jc = Jc[0 <= Jc]
+					Ic = Ic[0 <= Jc]
+					Jc = Jc[0 <= Jc]
 					#
 					K = (np.concatenate((I, Ic)), np.concatenate((J, Jc)))
-			# min_location_count:
-			# set K[i] = -1 for all point i in cells that are too small
-			elif min_location_count:
-				excluded_cells = positive_count < min_location_count
-				if np.any(excluded_cells):
-					for c in nonempty[excluded_cells]:
-						K[K == c] = -1
 		else:
 			K = np.argmin(D, axis=1) # cell indices
 		point_count = points.shape[0]