Correction import rtree

Author: ymeneroux

tracklib/algo/stochastics.py

@@ -320,7 +320,6 @@ def noise(
 	:param force: force definite-positive matrix with removal of negative eigen values
     :param control: control points (list of coords) for conditional simulations 
     :param N: number of tracks to generate (returns track collection if N > 1)"""
-
     if n == 1:
         return __noise(track, sigma, kernel, distribution, mode, force, cycle, control, direction)
     else:
@@ -386,7 +385,7 @@ def __noise(
         # Zero-amplitude case
         if (sigma[ik] == 0):
             continue
-
+       
         # Building covariance matrix
         SIGMA_S  = makeCovarianceMatrixFromKernel(kernel[ik], noised_track, force=force, mode=mode, control=control)
         SIGMA_S += np.identity(SIGMA_S.shape[0]) * 1e-12
@@ -410,7 +409,7 @@ def __noise(
             Yy = np.matmul(L22, __randomSampler(N, distribution)) + np.matmul(L12, np.linalg.solve(L11, Yc))
         if (direction in [MODE_DIRECTION_Z, MODE_DIRECTION_YZ, MODE_DIRECTION_XZ, MODE_DIRECTION_XYZ, MODE_DIRECTION_ORTHO]):
             Yz = np.matmul(L22, __randomSampler(N, distribution)) + np.matmul(L12, np.linalg.solve(L11, Zc))
-        
+      
         # Building noised track
         if (direction == MODE_DIRECTION_ORTHO):
             for i in range(N-1):
@@ -422,12 +421,18 @@ def __noise(
         else:
             for i in range(N):
                 noised_track.getObs(i).position.translate(Yx[i], Yy[i], Yz[i])
-           
+         
         if mode == 'circular':
             noised_track.loop()
 
+        # Cast to real float    
+        for p in noised_track:
+            p.position.E = p.position.E.real
+            p.position.N = p.position.N.real
+            p.position.U = p.position.U.real
+           
     noised_track.removeAnalyticalFeature("abs_curv")
-          
+     
     return noised_track
 
 def randomizer(input, f, sigma=[7], kernel=[GaussianKernel(650)], N=10):

tracklib/core/utils.py

@@ -62,7 +62,12 @@
 import itertools
 import matplotlib.pyplot as plt
 
-from rtree import index
+try:
+    from rtree import index
+except ImportError:
+    print ('Code running in a no rtree environment')
+
+
 
 try:
     import shapely
@@ -630,7 +635,8 @@ def co_median(tarray):
 # covariogram of standard deviation h
 # ---------------------------------------------------------------------------------
 def conflateOnNetwork(geom, network, threshold=1e300, h=30, verbose=True):
-	
+
+	from tracklib.core import ENUCoords
 	from tracklib.core import TrackCollection
 	from tracklib.algo.interpolation import conflate
 	from tracklib.algo.comparison import compare, MODE_COMPARISON_POINTWISE
@@ -646,13 +652,14 @@ def conflateOnNetwork(geom, network, threshold=1e300, h=30, verbose=True):
 
 	for segment in geom:
 		edge = network.getEdge(segment.tid)
+		
 		p1 = edge.source.coord
 		p2 = edge.target.coord
 
 		h11 = p1.distance2DTo(segment[ 0].position); h12 = p2.distance2DTo(segment[-1].position); h1 = (h11**2+h12**2)**0.5/1.414
 		h21 = p1.distance2DTo(segment[-1].position); h22 = p2.distance2DTo(segment[ 0].position); h2 = (h21**2+h22**2)**0.5/1.141
 		HMIN = min(h1, h2)
-	
+
 		if (h2 < h1):
 			ptemp = p1; p1 = p2; p2 = ptemp
 

tracklib/util/centerline.py

@@ -207,7 +207,7 @@ def run(self):
 
         for key in self.dct_polygons.keys():
             poly_geom = self.dct_polygons[key]
-            centerlineObj = CenterlineCenterline(poly_geom, self.dist, self.clean_dist)
+            centerlineObj = Centerline(poly_geom, self.dist, self.clean_dist)
 
             self.dct_centerlines[key] = centerlineObj.createCenterline()