profiling: fix priority distribution and add new ones

--HG--
extra : convert_revision : svn%3Ab85faa78-f9eb-468e-a121-7cced6da292c%401001

scrapy/trunk/profiling/priorityqueue/run.py

@@ -8,7 +8,7 @@ from pq_classes import *
 
 TESTID = os.getpid()
 
-TESTCASES = [
+TESTCASES = (
        ("heapq", PriorityQueue1),
        #("heapq+int", PriorityQueue1b),
        ("dict+deque", PriorityQueue2),
@@ -18,7 +18,7 @@ TESTCASES = [
        #('list+deque', PriorityQueue5),
        ('list+deque+cache', PriorityQueue5b),
        #('list+deque+cache+islice', PriorityQueue5c),
-       ]
+       )
 
 
 stmt_fmt = """
@@ -44,20 +44,37 @@ with open('%(samplefile)s') as samples:
         randomprio.append(prio)
 """
 
-# def gen_random_priority(priorities):
-#     return int(random.random() * priorities) - (priorities / 2)
+
+def _distribution(priorities, distribution):
+    half = priorities // 2
+    prio = -priorities
+    while not (-half <= prio <= half):
+        prio = round(distribution())
+    return min(max(prio, -half), half)
+
+def normal_priority(priorities):
+    sigma = priorities / 4.0
+    dist = lambda: random.normalvariate(mu=0, sigma=sigma)
+    return _distribution(priorities, dist)
+
+def gauss_priority(priorities):
+    sigma = priorities / 4.0
+    dist = lambda: random.gauss(mu=0, sigma=sigma)
+    return _distribution(priorities, dist)
+
+def triangular_priority(priorities):
+    half = priorities // 2
+    return random.triangular(-half-1, half+1, 0)
 
 def uniform_priority(priorities):
     return int(random.random() * priorities) - (priorities / 2)
 
-def normal_priority(priorities):
-    prio = int(random.normalvariate(priorities/2, priorities/4)) - (priorities / 2)
-    return min(max(prio, -priorities/2), priorities/2)
-
 
 PRIORITY_DISTRIBUTIONS = {
         'uniform': uniform_priority,
         'normal': normal_priority,
+        'gauss': gauss_priority,
+        'triangular': triangular_priority,
         }
 
 
@@ -71,7 +88,7 @@ def gen_samples(count, priorities, priority_distribution=uniform_priority):
     return fn
 
 def runtests(pushpops=50*1000, times=30, priorities=1, samplefile=None, priority_distribution=uniform_priority):
-    samplefile = samplefile or gen_samples(pushpops, priorities)
+    samplefile = samplefile or gen_samples(pushpops, priorities, priority_distribution)
 
     print "\n== With %s priorities (%s) ==\n" % (priorities, samplefile)
     print "pushpops = %s, times = %s" % (pushpops, times)