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Replaced time.clock() by time.process_time() #5

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Replaced time.clock() by time.process_time() #5

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8 changes: 4 additions & 4 deletions pyrtree/bench/bench_libspatial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,20 +15,20 @@
if __name__ == "__main__":
G = RectangleGen()
idx = Rtree() # this is a libspatialindex one.
start = time.clock()
interval_start = time.clock()
start = time.process_time()
interval_start = time.process_time()
for v in range(ITER):
if 0 == (v % INTERVAL):
# interval time taken, total time taken, # rects, cur max depth
t = time.clock()
t = time.process_time()

dt = t - interval_start
print("%d,%s,%f" % (v, "itime_t", dt))
print("%d,%s,%f" % (v, "avg_insert_t", (dt/float(INTERVAL))))
#print("%d,%s,%d" % (v, "max_depth", rt.node.max_depth()))
#print("%d,%s,%d" % (v, "mean_depth", rt.node.mean_depth()))

interval_start = time.clock()
interval_start = time.process_time()
rect = G.rect(0.000001)
idx.add(v,rect.coords())

8 changes: 4 additions & 4 deletions pyrtree/bench/bench_rtree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,12 +25,12 @@
gc.disable() # FFFFUUUUUUUUUUU
G = RectangleGen()
rt = RTree()
start = time.clock()
interval_start = time.clock()
start = time.process_time()
interval_start = time.process_time()
for v in range(ITER):
if 0 == (v % INTERVAL):
# interval time taken, total time taken, # rects, cur max depth
t = time.clock()
t = time.process_time()

dt = t - interval_start
print("%d,%s,%f" % (v, "itime_t", dt))
Expand All @@ -44,7 +44,7 @@
#print("%d,%s,%d" % (v, "max_depth", rt.node.max_depth()))
#print("%d,%s,%d" % (v, "mean_depth", rt.node.mean_depth()))

interval_start = time.clock()
interval_start = time.process_time()
o = TstO(G.rect(0.000001))
rt.insert(v,o.rect)

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8 changes: 4 additions & 4 deletions pyrtree/rtree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,7 +238,7 @@ def _balance(self):
return


t = time.clock()
t = time.process_time()

cur_score = -10

Expand All @@ -253,7 +253,7 @@ def _balance(self):

self._set_children(nodes)

dur = (time.clock() - t)
dur = (time.process_time() - t)
c = float(self.root.stats["overflow_f"])
oa = self.root.stats["avg_overflow_t_f"]
self.root.stats["avg_overflow_t_f"] = (dur / (c + 1.0)) + (c * oa / (c + 1.0))
Expand Down Expand Up @@ -370,7 +370,7 @@ def closest(centroids, node):


def k_means_cluster(root, k, nodes):
t = time.clock()
t = time.process_time()
if len(nodes) <= k: return [ [n] for n in nodes ]

ns = list(nodes)
Expand Down Expand Up @@ -409,7 +409,7 @@ def k_means_cluster(root, k, nodes):
new_cluster_centers = [ center_of_gravity(c) for c in clusters ]
if new_cluster_centers == cluster_centers :
root.stats["avg_kmeans_iter_f"] = float(root.stats["sum_kmeans_iter_f"] / root.stats["count_kmeans_iter_f"])
root.stats["longest_kmeans"] = max(root.stats["longest_kmeans"], (time.clock() - t))
root.stats["longest_kmeans"] = max(root.stats["longest_kmeans"], (time.process_time() - t))
return clusters
else: cluster_centers = new_cluster_centers

Expand Down