Develop Reference

Speed up order_by and pagination in Django

Currently I have this result:
That's not bad (I guess), but I'm wondering if I can speed things up a little bit.
I've looked at penultimate query and don't really know how to speed it up, I guess I should get rid off join, but don't know how:
I'm already using prefetch_related in my viewset, my viewset is:
class GameViewSet(viewsets.ModelViewSet):
queryset = Game.objects.prefetch_related(
"timestamp",
"fighters",
"score",
"coefs",
"rounds",
"rounds_view",
"rounds_view_f",
"finishes",
"rounds_time",
"round_time",
"time_coef",
"totals",
).all()
serializer_class = GameSerializer
permission_classes = [AllowAny]
pagination_class = StandardResultsSetPagination
#silk_profile(name="Get Games")
def list(self, request):
qs = self.get_queryset().order_by("-timestamp__ts")
page = self.paginate_queryset(qs)
if page is not None:
serializer = GameSerializer(page, many=True)
return self.get_paginated_response(serializer.data)
serializer = self.get_serializer(qs, many=True)
return Response(serializer.data)
Join is happening because I'm ordering on a related field?
My models looks like:
class Game(models.Model):
id = models.AutoField(primary_key=True)
...
class Timestamp(models.Model):
id = models.AutoField(primary_key=True)
game = models.ForeignKey(Game, related_name="timestamp", on_delete=models.CASCADE)
ts = models.DateTimeField(db_index=True)
time_of_day = models.TimeField()
And my serializers:
class TimestampSerializer(serializers.Serializer):
ts = serializers.DateTimeField(read_only=True)
time_of_day = serializers.TimeField(read_only=True)
class GameSerializer(serializers.Serializer):
id = serializers.IntegerField(read_only=True)
timestamp = TimestampSerializer(many=True)
fighters = FighterSerializer(many=True)
score = ScoreSerializer(many=True)
coefs = CoefsSerializer(many=True)
rounds = RoundsSerializer(many=True)
rounds_view = RoundsViewSerializer(many=True)
rounds_view_f = RoundsViewFinishSerializer(many=True)
finishes = FinishesSerializer(many=True)
rounds_time = RoundTimesSerializer(many=True)
round_time = RoundTimeSerializer(many=True)
time_coef = TimeCoefsSerializer(many=True)
totals = TotalsSerializer(many=True)
Also results of profling:
166039 function calls (159016 primitive calls) in 3.226 seconds
Ordered by: internal time
List reduced from 677 to 100 due to restriction <100>
ncalls tottime percall cumtime percall filename:lineno(function)
20959/20958 0.206 0.000 0.283 0.000 {built-in method builtins.isinstance}
2700 0.123 0.000 0.359 0.000 fields.py:62(is_simple_callable)
390/30 0.113 0.000 1.211 0.040 serializers.py:493(to_representation)
8943/8473 0.098 0.000 0.307 0.000 {built-in method builtins.getattr}
2700 0.096 0.000 0.650 0.000 fields.py:85(get_attribute)
14 0.068 0.005 0.130 0.009 traceback.py:388(format)
7653 0.065 0.000 0.065 0.000 {method 'append' of 'list' objects}
28 0.064 0.002 0.072 0.003 {method 'execute' of 'psycopg2.extensions.cursor' objects}
390 0.062 0.000 0.153 0.000 base.py:406(__init__)
3090 0.060 0.000 0.257 0.000 serializers.py:359(_readable_fields)
3090 0.059 0.000 0.093 0.000 _collections_abc.py:760(__iter__)
6390 0.055 0.000 0.055 0.000 {built-in method builtins.hasattr}
1440 0.054 0.000 0.112 0.000 related.py:652(get_instance_value_for_fields)
2749 0.052 0.000 0.078 0.000 abc.py:96(__instancecheck__)
388 0.052 0.000 0.107 0.000 query.py:303(clone)
2700 0.049 0.000 0.072 0.000 inspect.py:158(isfunction)
2700 0.048 0.000 0.699 0.000 fields.py:451(get_attribute)
2702 0.048 0.000 0.071 0.000 inspect.py:80(ismethod)
2701 0.047 0.000 0.070 0.000 inspect.py:285(isbuiltin)
14 0.047 0.003 0.189 0.014 traceback.py:321(extract)
3786/3426 0.043 0.000 0.123 0.000 {built-in method builtins.setattr}
4445/247 0.038 0.000 1.936 0.008 {built-in method builtins.len}
360 0.035 0.000 0.374 0.001 related_descriptors.py:575(_apply_rel_filters)
2247 0.034 0.000 0.088 0.000 traceback.py:285(line)
3203 0.029 0.000 0.029 0.000 {method 'copy' of 'dict' objects}
12 0.028 0.002 1.836 0.153 query.py:1831(prefetch_one_level)
2749 0.026 0.000 0.026 0.000 {built-in method _abc._abc_instancecheck}
2700 0.024 0.000 0.024 0.000 serializer_helpers.py:154(__getitem__)
2780 0.024 0.000 0.024 0.000 {method 'get' of 'dict' objects}
360 0.023 0.000 0.069 0.000 related_lookups.py:26(get_normalized_value)
720 0.022 0.000 0.458 0.001 related_descriptors.py:615(get_queryset)
744 0.022 0.000 0.087 0.000 related_descriptors.py:523(__get__)
360 0.022 0.000 0.057 0.000 related_descriptors.py:203(__set__)
470 0.022 0.000 0.081 0.000 local.py:46(_get_context_id)
749 0.020 0.000 0.048 0.000 linecache.py:15(getline)
720 0.019 0.000 0.031 0.000 lookups.py:252(resolve_expression_parameter)
361/1 0.018 0.000 1.211 1.211 serializers.py:655(to_representation)
296/14 0.018 0.000 0.084 0.006 copy.py:128(deepcopy)
470 0.018 0.000 0.106 0.000 local.py:82(_get_storage)
732 0.017 0.000 0.043 0.000 related_descriptors.py:560(__init__)
720 0.017 0.000 0.040 0.000 related_descriptors.py:76(__set__)
1185/1151 0.017 0.000 0.032 0.000 {method 'join' of 'str' objects}
1501 0.017 0.000 0.017 0.000 {method 'format' of 'str' objects}
732 0.016 0.000 0.026 0.000 manager.py:26(__init__)
372 0.016 0.000 0.414 0.001 query.py:951(_filter_or_exclude)
14 0.016 0.001 0.028 0.002 traceback.py:369(from_list)
759 0.015 0.000 0.040 0.000 query.py:178(__init__)
387 0.015 0.000 0.143 0.000 query.py:1308(_clone)
732 0.015 0.000 0.022 0.000 manager.py:20(__new__)
1710 0.015 0.000 0.015 0.000 {built-in method __new__ of type object at 0x7fa87d9ad940}
720 0.015 0.000 0.034 0.000 __init__.py:1818(get_prep_value)
470 0.014 0.000 0.033 0.000 sync.py:469(get_current_task)
390 0.014 0.000 0.174 0.000 base.py:507(from_db)
1365 0.014 0.000 0.014 0.000 {method 'update' of 'dict' objects}
749 0.013 0.000 0.022 0.000 linecache.py:37(getlines)
744 0.013 0.000 0.044 0.000 lookups.py:266()
12 0.013 0.001 0.054 0.004 lookups.py:230(get_prep_lookup)
749 0.013 0.000 0.020 0.000 linecache.py:147(lazycache)
720 0.013 0.000 0.066 0.000 related.py:646(get_local_related_value)
720 0.013 0.000 0.071 0.000 related.py:649(get_foreign_related_value)
720 0.013 0.000 0.019 0.000 __init__.py:824(get_prep_value)
1506 0.013 0.000 0.013 0.000 {method 'strip' of 'str' objects}
372/12 0.013 0.000 0.026 0.002 query.py:1088(resolve_lookup_value)
638 0.013 0.000 0.018 0.000 threading.py:1306(current_thread)
732 0.013 0.000 0.021 0.000 reverse_related.py:200(get_cache_name)
720 0.013 0.000 0.018 0.000 base.py:573(_get_pk_val)
360 0.012 0.000 0.021 0.000 __init__.py:543(__hash__)
470 0.011 0.000 0.117 0.000 local.py:101(__getattr__)
28 0.011 0.000 0.089 0.003 compiler.py:199(get_select)
385 0.011 0.000 0.857 0.002 query.py:265(__iter__)
320 0.011 0.000 0.019 0.000 __init__.py:515(__eq__)
387 0.011 0.000 0.120 0.000 query.py:354(chain)
780 0.011 0.000 0.021 0.000 dispatcher.py:159(send)
360 0.010 0.000 0.031 0.000 related.py:976(get_prep_value)
387 0.010 0.000 0.157 0.000 query.py:1296(_chain)
372 0.010 0.000 0.014 0.000 query.py:151(__init__)
403 0.010 0.000 0.914 0.002 query.py:45(__iter__)
1204 0.010 0.000 0.010 0.000 {built-in method builtins.iter}
360 0.010 0.000 0.016 0.000 :1017(_handle_fromlist)
372 0.010 0.000 0.434 0.001 query.py:935(filter)
360 0.010 0.000 0.023 0.000 query.py:1124(check_query_object_type)
360 0.009 0.000 0.017 0.000 mixins.py:21(is_cached)
1152 0.009 0.000 0.009 0.000 query.py:194(query)
104 0.009 0.000 0.017 0.000 fields.py:323(__init__)
732 0.009 0.000 0.009 0.000 manager.py:120(_set_creation_counter)
470 0.009 0.000 0.013 0.000 :389(parent)
470 0.009 0.000 0.014 0.000 tasks.py:34(current_task)
459 0.009 0.000 0.013 0.000 deconstruct.py:14(__new__)
870 0.009 0.000 0.009 0.000 fields.py:810(to_representation)
322 0.009 0.000 0.019 0.000 linecache.py:53(checkcache)
318/266 0.009 0.000 0.145 0.001 compiler.py:434(compile)
763 0.008 0.000 0.008 0.000 traceback.py:292(walk_stack)
494 0.008 0.000 0.014 0.000 compiler.py:417(quote_name_unless_alias)
396 0.008 0.000 0.019 0.000 related.py:710(get_path_info)
374 0.008 0.000 0.011 0.000 utils.py:237(_route_db)
796 0.008 0.000 0.008 0.000 tree.py:21(__init__)
322 0.008 0.000 0.008 0.000 {built-in method posix.stat}
413/365 0.008 0.000 1.938 0.005 query.py:1322(_fetch_all)
12 0.008 0.001 1.244 0.104 related_descriptors.py:622(get_prefetch_queryset)
732 0.008 0.000 0.008 0.000 reverse_related.py:180(get_accessor_name)
And visual representation:
So, my questions is, how can I speed it up?

On the database side, you could set up a Materialized View for your query and trigger an update anytime a new timestamp is added (or whatever is happening in your application that requires a refresh). That way the results are pre-calculated for your lookup. However, I suppose there could be edge cases where when you update and lookup at the same time, you end up with a pre-updated result? It'd be a trade off and only you know whether that'd be worth it...
In any case, I did not come up with that, check out e.g. https://hashrocket.com/blog/posts/materialized-view-strategies-using-postgresql

CPU bound code twice as fast on laptop as desktop

I have a single-threaded Python 3 program that's CPU bound, the only IO is printing a couple of lines to output (no reading/writing of files).
On my desktop machine (AMD Ryzen 1700x 3.8 GHz, 16GB 3000 MHz DDR4) it performs (consistently) at 3400 episodes/second where a run takes around 60 seconds.
On my laptop (Intel i7-6600U 2.8 GHz, 16GB 2000 MHz DDR3) the performance is doubled at 7000 episodes/second, and a run coming in at just under 30 seconds.
Both machines run the same operating systems (Fedora 26) and the same python version (not built from source).
What's more, when profiling, there's a line showing
10.999 tottime, 28.814 cumtime for arrayprint.py:557(fillFormat)
but only when the code is run on the desktop. On the laptop, the particular function does not appear at all (and none of the arrayprint functions use more than 1 second tottime).
Not only is it strange that the performance differs between the machines, but no arrays or lists are ever printed to the screen, converted to strings, or saved to files during the execution of the program.
Here's the full profile for the desktop:
54499635 function calls (53787999 primitive calls) in 58.746 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
533727 0.359 0.000 0.514 0.000 <frozen importlib._bootstrap>:402(parent)
533727 0.469 0.000 0.697 0.000 <frozen importlib._bootstrap>:989(_handle_fromlist)
1 0.000 0.000 58.746 58.746 <string>:1(<module>)
4 0.000 0.000 0.000 0.000 __init__.py:120(getLevelName)
567524 0.237 0.000 0.727 0.000 __init__.py:1284(debug)
2 0.000 0.000 0.000 0.000 __init__.py:1296(info)
2 0.000 0.000 0.000 0.000 __init__.py:1308(warning)
2 0.000 0.000 0.000 0.000 __init__.py:1320(warn)
4 0.000 0.000 0.000 0.000 __init__.py:1374(findCaller)
4 0.000 0.000 0.000 0.000 __init__.py:1404(makeRecord)
4 0.000 0.000 0.000 0.000 __init__.py:1419(_log)
4 0.000 0.000 0.000 0.000 __init__.py:1444(handle)
4 0.000 0.000 0.000 0.000 __init__.py:1498(callHandlers)
567528 0.175 0.000 0.175 0.000 __init__.py:1528(getEffectiveLevel)
567528 0.315 0.000 0.490 0.000 __init__.py:1542(isEnabledFor)
4 0.000 0.000 0.000 0.000 __init__.py:157(<lambda>)
4 0.000 0.000 0.000 0.000 __init__.py:251(__init__)
4 0.000 0.000 0.000 0.000 __init__.py:329(getMessage)
4 0.000 0.000 0.000 0.000 __init__.py:387(usesTime)
4 0.000 0.000 0.000 0.000 __init__.py:390(format)
4 0.000 0.000 0.000 0.000 __init__.py:540(usesTime)
4 0.000 0.000 0.000 0.000 __init__.py:546(formatMessage)
4 0.000 0.000 0.000 0.000 __init__.py:562(format)
8 0.000 0.000 0.000 0.000 __init__.py:703(filter)
8 0.000 0.000 0.000 0.000 __init__.py:807(acquire)
8 0.000 0.000 0.000 0.000 __init__.py:814(release)
4 0.000 0.000 0.000 0.000 __init__.py:827(format)
4 0.000 0.000 0.000 0.000 __init__.py:850(handle)
4 0.000 0.000 0.000 0.000 __init__.py:969(flush)
4 0.000 0.000 0.000 0.000 __init__.py:980(emit)
289159 0.101 0.000 1.491 0.000 _methods.py:31(_sum)
533727 0.175 0.000 1.613 0.000 _methods.py:37(_any)
177909 0.862 0.000 33.737 0.000 arrayprint.py:237(_get_formatdict)
177909 0.370 0.000 34.214 0.000 arrayprint.py:273(_get_format_function)
177909 0.686 0.000 39.971 0.000 arrayprint.py:315(_array2string)
533727/177909 0.674 0.000 40.351 0.000 arrayprint.py:340(array2string)
1224652 0.960 0.000 1.554 0.000 arrayprint.py:467(_extendLine)
177909 1.671 0.000 4.320 0.000 arrayprint.py:475(_formatArray)
533727 0.682 0.000 29.496 0.000 arrayprint.py:543(__init__)
533727 10.999 0.000 28.814 0.000 arrayprint.py:557(fillFormat)
355336 1.600 0.000 5.432 0.000 arrayprint.py:589(<listcomp>)
2416068 2.677 0.000 3.832 0.000 arrayprint.py:642(_digits)
177909 0.720 0.000 2.378 0.000 arrayprint.py:652(__init__)
1224652 1.057 0.000 1.057 0.000 arrayprint.py:665(__call__)
533727 0.147 0.000 0.147 0.000 arrayprint.py:674(__init__)
177909 0.227 0.000 0.319 0.000 arrayprint.py:702(__init__)
177909 0.415 0.000 17.986 0.000 arrayprint.py:713(__init__)
177909 0.166 0.000 0.166 0.000 arrayprint.py:730(__init__)
177909 0.046 0.000 0.046 0.000 arrayprint.py:751(__init__)
1 0.000 0.000 0.000 0.000 enum.py:265(__call__)
1 0.000 0.000 0.000 0.000 enum.py:515(__new__)
1 0.000 0.000 0.000 0.000 enum.py:544(_missing_)
177909 0.206 0.000 0.206 0.000 enum.py:552(__str__)
177909 0.269 0.000 0.475 0.000 enum.py:564(__format__)
755408 0.248 0.000 0.366 0.000 enum.py:579(__hash__)
200000 0.037 0.000 0.037 0.000 enum.py:592(name)
27976 0.005 0.000 0.005 0.000 enum.py:597(value)
200524 0.443 0.000 0.641 0.000 eventgen.py:115(_push)
200001 0.492 0.000 0.885 0.000 eventgen.py:122(pop)
200000 0.892 0.000 1.036 0.000 eventgen.py:137(ce_str)
13988 0.017 0.000 0.034 0.000 eventgen.py:15(__lt__)
99676 0.168 0.000 0.911 0.000 eventgen.py:44(event_new)
79335 0.096 0.000 0.520 0.000 eventgen.py:52(event_end)
11689 0.078 0.000 0.261 0.000 eventgen.py:61(event_new_handoff)
9824 0.014 0.000 0.098 0.000 eventgen.py:90(event_end_handoff)
77441 0.295 0.000 0.380 0.000 eventgen.py:94(reassign)
177909 0.177 0.000 0.555 0.000 fromnumeric.py:1364(ravel)
200001 0.093 0.000 0.464 0.000 fromnumeric.py:1471(nonzero)
289159 0.542 0.000 2.148 0.000 fromnumeric.py:1710(sum)
533727 0.637 0.000 2.956 0.000 fromnumeric.py:1866(any)
200001 0.120 0.000 0.372 0.000 fromnumeric.py:55(_wrapfunc)
4 0.000 0.000 0.000 0.000 genericpath.py:117(_splitext)
49 0.000 0.000 0.001 0.000 grid.py:172(neighbors1)
49 0.001 0.000 0.001 0.000 grid.py:195(neighbors2)
533727/177909 0.311 0.000 40.472 0.000 numeric.py:1927(array_str)
1067454 1.724 0.000 4.091 0.000 numeric.py:2692(seterr)
1067454 1.466 0.000 1.603 0.000 numeric.py:2792(geterr)
533727 0.299 0.000 0.422 0.000 numeric.py:3085(__init__)
533727 0.411 0.000 2.588 0.000 numeric.py:3089(__enter__)
533727 0.461 0.000 2.374 0.000 numeric.py:3094(__exit__)
177909 0.064 0.000 0.151 0.000 numeric.py:463(asarray)
711636 0.223 0.000 0.503 0.000 numeric.py:534(asanyarray)
4 0.000 0.000 0.000 0.000 posixpath.py:119(splitext)
4 0.000 0.000 0.000 0.000 posixpath.py:142(basename)
4 0.000 0.000 0.000 0.000 posixpath.py:39(_get_sep)
6 0.000 0.000 0.000 0.000 posixpath.py:50(normcase)
4 0.000 0.000 0.000 0.000 process.py:137(name)
4 0.000 0.000 0.000 0.000 process.py:35(current_process)
1 0.000 0.000 0.000 0.000 signal.py:25(_int_to_enum)
2 0.000 0.000 0.000 0.000 signal.py:35(_enum_to_int)
1 0.000 0.000 0.000 0.000 signal.py:45(signal)
99627 0.062 0.000 0.062 0.000 stats.py:38(new)
20292 0.028 0.000 0.039 0.000 stats.py:42(new_rej)
88750 0.047 0.000 0.047 0.000 stats.py:48(end)
11623 0.005 0.000 0.005 0.000 stats.py:51(hoff_new)
1799 0.001 0.000 0.002 0.000 stats.py:54(hoff_rej)
22091 0.012 0.000 0.012 0.000 stats.py:58(rej)
200000 0.234 0.000 1.513 0.000 stats.py:64(iter)
1 0.000 0.000 0.000 0.000 stats.py:69(n_iter)
1 0.000 0.000 0.000 0.000 stats.py:86(endsim)
1 0.000 0.000 0.001 0.001 strats.py:189(get_init_action)
200000 1.070 0.000 49.964 0.000 strats.py:193(get_action)
177909 1.348 0.000 1.937 0.000 strats.py:220(execute_action)
200001 4.572 0.000 47.626 0.000 strats.py:243(optimal_ch)
89158 0.071 0.000 0.958 0.000 strats.py:299(reward)
89158 0.018 0.000 0.018 0.000 strats.py:308(discount)
1242355 0.944 0.000 0.944 0.000 strats.py:333(get_qval)
89158 0.160 0.000 0.160 0.000 strats.py:336(update_qval)
1 0.000 0.000 58.746 58.746 strats.py:40(init_sim)
1 1.271 1.271 58.745 58.745 strats.py:49(_simulate)
4 0.000 0.000 0.000 0.000 threading.py:1076(name)
4 0.000 0.000 0.000 0.000 threading.py:1230(current_thread)
227976 0.120 0.000 0.162 0.000 types.py:135(__get__)
177909 0.079 0.000 0.079 0.000 {built-in method _functools.reduce}
200001 0.192 0.000 0.222 0.000 {built-in method _heapq.heappop}
200524 0.084 0.000 0.088 0.000 {built-in method _heapq.heappush}
310143 0.064 0.000 0.064 0.000 {built-in method _operator.gt}
843054 0.152 0.000 0.152 0.000 {built-in method _operator.lt}
1 0.000 0.000 0.000 0.000 {built-in method _signal.signal}
8 0.000 0.000 0.000 0.000 {built-in method _thread.get_ident}
2 0.000 0.000 0.000 0.000 {built-in method _warnings.warn}
1 0.000 0.000 58.746 58.746 {built-in method builtins.exec}
200001 0.056 0.000 0.056 0.000 {built-in method builtins.getattr}
1067468 0.228 0.000 0.228 0.000 {built-in method builtins.hasattr}
755408 0.118 0.000 0.118 0.000 {built-in method builtins.hash}
467082 0.164 0.000 0.164 0.000 {built-in method builtins.isinstance}
533727 0.107 0.000 0.107 0.000 {built-in method builtins.issubclass}
10361766 1.076 0.000 1.076 0.000 {built-in method builtins.len}
533441 0.304 0.000 0.304 0.000 {built-in method builtins.max}
533923 0.198 0.000 0.198 0.000 {built-in method builtins.min}
889545 0.368 0.000 0.368 0.000 {built-in method numpy.core.multiarray.array}
111251 0.101 0.000 0.101 0.000 {built-in method numpy.core.multiarray.where}
2134908 0.377 0.000 0.377 0.000 {built-in method numpy.core.umath.geterrobj}
1067454 0.524 0.000 0.524 0.000 {built-in method numpy.core.umath.seterrobj}
14 0.000 0.000 0.000 0.000 {built-in method posix.fspath}
4 0.000 0.000 0.000 0.000 {built-in method posix.getpid}
4 0.000 0.000 0.000 0.000 {built-in method sys._getframe}
5 0.000 0.000 0.000 0.000 {built-in method time.time}
8 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.RLock' objects}
533727 0.299 0.000 1.912 0.000 {method 'any' of 'numpy.ndarray' objects}
875 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
16544 0.119 0.000 0.119 0.000 {method 'choice' of 'mtrand.RandomState' objects}
533727 0.872 0.000 0.872 0.000 {method 'compress' of 'numpy.ndarray' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
188835 0.641 0.000 0.641 0.000 {method 'exponential' of 'mtrand.RandomState' objects}
4 0.000 0.000 0.000 0.000 {method 'find' of 'str' objects}
4 0.000 0.000 0.000 0.000 {method 'flush' of '_io.TextIOWrapper' objects}
8 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}
355818 0.069 0.000 0.069 0.000 {method 'item' of 'numpy.ndarray' objects}
200001 0.196 0.000 0.196 0.000 {method 'nonzero' of 'numpy.ndarray' objects}
533727 0.123 0.000 0.123 0.000 {method 'pop' of 'dict' objects}
11689 0.053 0.000 0.053 0.000 {method 'randint' of 'mtrand.RandomState' objects}
168494 0.128 0.000 0.128 0.000 {method 'random_sample' of 'mtrand.RandomState' objects}
177909 0.232 0.000 0.232 0.000 {method 'ravel' of 'numpy.ndarray' objects}
1889376 5.023 0.000 5.023 0.000 {method 'reduce' of 'numpy.ufunc' objects}
8 0.000 0.000 0.000 0.000 {method 'release' of '_thread.RLock' objects}
12 0.000 0.000 0.000 0.000 {method 'rfind' of 'str' objects}
533727 0.155 0.000 0.155 0.000 {method 'rpartition' of 'str' objects}
4865786 1.100 0.000 1.100 0.000 {method 'rstrip' of 'str' objects}
8 0.000 0.000 0.000 0.000 {method 'write' of '_io.TextIOWrapper' objects}
And here's for the laptop:
27738517 function calls (26673571 primitive calls) in 28.612 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 28.612 28.612 <string>:1(<module>)
4 0.000 0.000 0.000 0.000 __init__.py:120(getLevelName)
566894 0.244 0.000 0.720 0.000 __init__.py:1284(debug)
2 0.000 0.000 0.000 0.000 __init__.py:1296(info)
2 0.000 0.000 0.000 0.000 __init__.py:1308(warning)
2 0.000 0.000 0.000 0.000 __init__.py:1320(warn)
4 0.000 0.000 0.000 0.000 __init__.py:1374(findCaller)
4 0.000 0.000 0.000 0.000 __init__.py:1404(makeRecord)
4 0.000 0.000 0.000 0.000 __init__.py:1419(_log)
4 0.000 0.000 0.000 0.000 __init__.py:1444(handle)
4 0.000 0.000 0.000 0.000 __init__.py:1498(callHandlers)
566898 0.166 0.000 0.166 0.000 __init__.py:1528(getEffectiveLevel)
566898 0.309 0.000 0.476 0.000 __init__.py:1542(isEnabledFor)
4 0.000 0.000 0.000 0.000 __init__.py:157(<lambda>)
4 0.000 0.000 0.000 0.000 __init__.py:251(__init__)
4 0.000 0.000 0.000 0.000 __init__.py:329(getMessage)
4 0.000 0.000 0.000 0.000 __init__.py:387(usesTime)
4 0.000 0.000 0.000 0.000 __init__.py:390(format)
4 0.000 0.000 0.000 0.000 __init__.py:540(usesTime)
4 0.000 0.000 0.000 0.000 __init__.py:546(formatMessage)
4 0.000 0.000 0.000 0.000 __init__.py:562(format)
8 0.000 0.000 0.000 0.000 __init__.py:703(filter)
8 0.000 0.000 0.000 0.000 __init__.py:807(acquire)
8 0.000 0.000 0.000 0.000 __init__.py:814(release)
4 0.000 0.000 0.000 0.000 __init__.py:827(format)
4 0.000 0.000 0.000 0.000 __init__.py:850(handle)
4 0.000 0.000 0.000 0.000 __init__.py:969(flush)
4 0.000 0.000 0.000 0.000 __init__.py:980(emit)
288946 0.112 0.000 1.643 0.000 _methods.py:31(_sum)
177491 0.330 0.000 0.330 0.000 arrayprint.py:256(_get_formatdict)
177491 0.169 0.000 3.542 0.000 arrayprint.py:259(<lambda>)
177491 0.465 0.000 4.419 0.000 arrayprint.py:299(_get_format_function)
177491 0.623 0.000 9.729 0.000 arrayprint.py:343(_array2string)
532473/177491 0.987 0.000 10.679 0.000 arrayprint.py:381(wrapper)
532473/177491 0.721 0.000 10.150 0.000 arrayprint.py:399(array2string)
1225350 0.971 0.000 1.470 0.000 arrayprint.py:527(_extendLine)
177491 1.458 0.000 3.920 0.000 arrayprint.py:535(_formatArray)
177491 0.768 0.000 3.373 0.000 arrayprint.py:712(__init__)
1225350 0.960 0.000 0.960 0.000 arrayprint.py:725(__call__)
1 0.000 0.000 0.000 0.000 enum.py:265(__call__)
1 0.000 0.000 0.000 0.000 enum.py:515(__new__)
1 0.000 0.000 0.000 0.000 enum.py:544(_missing_)
177491 0.209 0.000 0.209 0.000 enum.py:552(__str__)
177491 0.316 0.000 0.525 0.000 enum.py:564(__format__)
755255 0.238 0.000 0.352 0.000 enum.py:579(__hash__)
200000 0.039 0.000 0.039 0.000 enum.py:592(name)
28626 0.005 0.000 0.005 0.000 enum.py:597(value)
200505 0.443 0.000 0.643 0.000 eventgen.py:115(_push)
200001 0.474 0.000 0.863 0.000 eventgen.py:122(pop)
200000 0.834 0.000 0.983 0.000 eventgen.py:137(ce_str)
14313 0.017 0.000 0.035 0.000 eventgen.py:15(__lt__)
99673 0.186 0.000 0.939 0.000 eventgen.py:44(event_new)
78949 0.094 0.000 0.500 0.000 eventgen.py:52(event_end)
11887 0.078 0.000 0.261 0.000 eventgen.py:61(event_new_handoff)
9996 0.017 0.000 0.103 0.000 eventgen.py:90(event_end_handoff)
77374 0.284 0.000 0.364 0.000 eventgen.py:94(reassign)
177491 0.195 0.000 0.595 0.000 fromnumeric.py:1380(ravel)
200001 0.098 0.000 0.490 0.000 fromnumeric.py:1487(nonzero)
288946 0.590 0.000 2.352 0.000 fromnumeric.py:1730(sum)
200001 0.130 0.000 0.392 0.000 fromnumeric.py:55(_wrapfunc)
4 0.000 0.000 0.000 0.000 genericpath.py:117(_splitext)
49 0.000 0.000 0.001 0.000 grid.py:172(neighbors1)
49 0.001 0.000 0.001 0.000 grid.py:195(neighbors2)
532473/177491 0.365 0.000 10.826 0.000 numeric.py:1905(array_str)
177491 0.062 0.000 0.151 0.000 numeric.py:463(asarray)
177491 0.051 0.000 0.104 0.000 numeric.py:534(asanyarray)
4 0.000 0.000 0.000 0.000 posixpath.py:119(splitext)
4 0.000 0.000 0.000 0.000 posixpath.py:142(basename)
4 0.000 0.000 0.000 0.000 posixpath.py:39(_get_sep)
6 0.000 0.000 0.000 0.000 posixpath.py:50(normcase)
4 0.000 0.000 0.000 0.000 process.py:137(name)
4 0.000 0.000 0.000 0.000 process.py:35(current_process)
1 0.000 0.000 0.000 0.000 signal.py:25(_int_to_enum)
2 0.000 0.000 0.000 0.000 signal.py:35(_enum_to_int)
1 0.000 0.000 0.000 0.000 signal.py:45(signal)
99624 0.066 0.000 0.066 0.000 stats.py:38(new)
20675 0.028 0.000 0.040 0.000 stats.py:42(new_rej)
88545 0.045 0.000 0.045 0.000 stats.py:48(end)
11831 0.006 0.000 0.006 0.000 stats.py:51(hoff_new)
1835 0.001 0.000 0.002 0.000 stats.py:54(hoff_rej)
22510 0.013 0.000 0.013 0.000 stats.py:58(rej)
200000 0.261 0.000 1.490 0.000 stats.py:64(iter)
1 0.000 0.000 0.000 0.000 stats.py:69(n_iter)
1 0.000 0.000 0.000 0.000 stats.py:86(endsim)
1 0.000 0.000 0.000 0.000 strats.py:189(get_init_action)
200000 1.234 0.000 19.760 0.000 strats.py:193(get_action)
177490 1.294 0.000 1.860 0.000 strats.py:220(execute_action)
200001 3.897 0.000 17.128 0.000 strats.py:243(optimal_ch)
88945 0.074 0.000 1.112 0.000 strats.py:299(reward)
88945 0.017 0.000 0.017 0.000 strats.py:308(discount)
1241938 0.681 0.000 0.681 0.000 strats.py:333(get_qval)
88945 0.167 0.000 0.167 0.000 strats.py:336(update_qval)
1 0.000 0.000 28.612 28.612 strats.py:40(init_sim)
1 1.383 1.383 28.611 28.611 strats.py:49(_simulate)
4 0.000 0.000 0.000 0.000 threading.py:1076(name)
4 0.000 0.000 0.000 0.000 threading.py:1230(current_thread)
228626 0.122 0.000 0.166 0.000 types.py:135(__get__)
177491 0.075 0.000 0.075 0.000 {built-in method _functools.reduce}
200001 0.203 0.000 0.234 0.000 {built-in method _heapq.heappop}
200505 0.079 0.000 0.083 0.000 {built-in method _heapq.heappush}
320262 0.068 0.000 0.068 0.000 {built-in method _operator.gt}
832731 0.136 0.000 0.136 0.000 {built-in method _operator.lt}
1 0.000 0.000 0.000 0.000 {built-in method _signal.signal}
532481 0.090 0.000 0.090 0.000 {built-in method _thread.get_ident}
2 0.000 0.000 0.000 0.000 {built-in method _warnings.warn}
1 0.000 0.000 28.612 28.612 {built-in method builtins.exec}
200001 0.066 0.000 0.066 0.000 {built-in method builtins.getattr}
14 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}
755255 0.113 0.000 0.113 0.000 {built-in method builtins.hash}
532473 0.092 0.000 0.092 0.000 {built-in method builtins.id}
466451 0.166 0.000 0.166 0.000 {built-in method builtins.isinstance}
532473 0.083 0.000 0.083 0.000 {built-in method builtins.issubclass}
3750044 0.325 0.000 0.325 0.000 {built-in method builtins.len}
177687 0.091 0.000 0.091 0.000 {built-in method builtins.max}
196 0.000 0.000 0.000 0.000 {built-in method builtins.min}
354982 0.142 0.000 0.142 0.000 {built-in method numpy.core.multiarray.array}
111456 0.095 0.000 0.095 0.000 {built-in method numpy.core.multiarray.where}
14 0.000 0.000 0.000 0.000 {built-in method posix.fspath}
4 0.000 0.000 0.000 0.000 {built-in method posix.getpid}
4 0.000 0.000 0.000 0.000 {built-in method sys._getframe}
5 0.000 0.000 0.000 0.000 {built-in method time.time}
8 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.RLock' objects}
532473 0.089 0.000 0.089 0.000 {method 'add' of 'set' objects}
875 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
16345 0.110 0.000 0.110 0.000 {method 'choice' of 'mtrand.RandomState' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
532473 0.097 0.000 0.097 0.000 {method 'discard' of 'set' objects}
188618 0.633 0.000 0.633 0.000 {method 'exponential' of 'mtrand.RandomState' objects}
4 0.000 0.000 0.000 0.000 {method 'find' of 'str' objects}
4 0.000 0.000 0.000 0.000 {method 'flush' of '_io.TextIOWrapper' objects}
8 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}
354982 0.066 0.000 0.066 0.000 {method 'item' of 'numpy.ndarray' objects}
200001 0.196 0.000 0.196 0.000 {method 'nonzero' of 'numpy.ndarray' objects}
11887 0.052 0.000 0.052 0.000 {method 'randint' of 'mtrand.RandomState' objects}
167895 0.157 0.000 0.157 0.000 {method 'random_sample' of 'mtrand.RandomState' objects}
177491 0.251 0.000 0.251 0.000 {method 'ravel' of 'numpy.ndarray' objects}
643928 2.511 0.000 2.511 0.000 {method 'reduce' of 'numpy.ufunc' objects}
8 0.000 0.000 0.000 0.000 {method 'release' of '_thread.RLock' objects}
12 0.000 0.000 0.000 0.000 {method 'rfind' of 'str' objects}
2451118 0.328 0.000 0.328 0.000 {method 'rstrip' of 'str' objects}
8 0.000 0.000 0.000 0.000 {method 'write' of '_io.TextIOWrapper' objects}

numpy was installed through pip on the laptop, and through the Fedora repositories on the desktop. Removing the package and installing it through pip removed arrayprint (fillFormat) from the profiling results and the runtime is now very much the same (which is still a bit weird). It's also strange that the other arrayprint functions are still being called, with 10 seconds of cumulative time.

Improve performance of MongoDB client (sockets)

I am using Python 2.7 (Anaconda distribution) on Windows 8.1 Pro.
I have a database of articles with their respective topics.
I am building an application which queries textual phrases in my database and associates article topics to each queried phrase. The topics are assigned based on the relevance of the phrase for the article.
The bottleneck seems to be Python socket communication with the localhost.
Here are my cProfile outputs:
topics_fit (PhraseVectorizer_1_1.py:668)
function called 1 times
1930698 function calls (1929630 primitive calls) in 148.209 seconds
Ordered by: cumulative time, internal time, call count
List reduced from 286 to 40 due to restriction <40>
ncalls tottime percall cumtime percall filename:lineno(function)
1 1.224 1.224 148.209 148.209 PhraseVectorizer_1_1.py:668(topics_fit)
206272 0.193 0.000 146.780 0.001 cursor.py:1041(next)
601 0.189 0.000 146.455 0.244 cursor.py:944(_refresh)
534 0.030 0.000 146.263 0.274 cursor.py:796(__send_message)
534 0.009 0.000 141.532 0.265 mongo_client.py:725(_send_message_with_response)
534 0.002 0.000 141.484 0.265 mongo_client.py:768(_reset_on_error)
534 0.019 0.000 141.482 0.265 server.py:69(send_message_with_response)
534 0.002 0.000 141.364 0.265 pool.py:225(receive_message)
535 0.083 0.000 141.362 0.264 network.py:106(receive_message)
1070 1.202 0.001 141.278 0.132 network.py:127(_receive_data_on_socket)
3340 140.074 0.042 140.074 0.042 {method 'recv' of '_socket.socket' objects}
535 0.778 0.001 4.700 0.009 helpers.py:88(_unpack_response)
535 3.828 0.007 3.920 0.007 {bson._cbson.decode_all}
67 0.099 0.001 0.196 0.003 {method 'sort' of 'list' objects}
206187 0.096 0.000 0.096 0.000 PhraseVectorizer_1_1.py:705(<lambda>)
206187 0.096 0.000 0.096 0.000 database.py:339(_fix_outgoing)
206187 0.074 0.000 0.092 0.000 objectid.py:68(__init__)
1068 0.005 0.000 0.054 0.000 server.py:135(get_socket)
1068/534 0.010 0.000 0.041 0.000 contextlib.py:21(__exit__)
1068 0.004 0.000 0.041 0.000 pool.py:501(get_socket)
534 0.003 0.000 0.028 0.000 pool.py:208(send_message)
534 0.009 0.000 0.026 0.000 pool.py:573(return_socket)
567 0.001 0.000 0.026 0.000 socket.py:227(meth)
535 0.024 0.000 0.024 0.000 {method 'sendall' of '_socket.socket' objects}
534 0.003 0.000 0.023 0.000 topology.py:134(select_server)
206806 0.020 0.000 0.020 0.000 collection.py:249(database)
418997 0.019 0.000 0.019 0.000 {len}
449 0.001 0.000 0.018 0.000 topology.py:143(select_server_by_address)
534 0.005 0.000 0.018 0.000 topology.py:82(select_servers)
1068/534 0.001 0.000 0.018 0.000 contextlib.py:15(__enter__)
534 0.002 0.000 0.013 0.000 thread_util.py:83(release)
207307 0.010 0.000 0.011 0.000 {isinstance}
534 0.005 0.000 0.011 0.000 pool.py:538(_get_socket_no_auth)
534 0.004 0.000 0.011 0.000 thread_util.py:63(release)
534 0.001 0.000 0.011 0.000 mongo_client.py:673(_get_topology)
535 0.003 0.000 0.010 0.000 topology.py:57(open)
206187 0.008 0.000 0.008 0.000 {method 'popleft' of 'collections.deque' objects}
535 0.002 0.000 0.007 0.000 topology.py:327(_apply_selector)
536 0.003 0.000 0.007 0.000 topology.py:286(_ensure_opened)
1071 0.004 0.000 0.007 0.000 periodic_executor.py:50(open)
In particular: {method 'recv' of '_socket.socket' objects} seems to cause trouble.
According to suggestions found in What can I do to improve socket performance in Python 3?, I tried gevent.
I added this snippet at the beginning of my script (before importing anything):
from gevent import monkey
monkey.patch_all()
This resulted in even slower performance...
*** PROFILER RESULTS ***
topics_fit (PhraseVectorizer_1_1.py:671)
function called 1 times
1956879 function calls (1951292 primitive calls) in 158.260 seconds
Ordered by: cumulative time, internal time, call count
List reduced from 427 to 40 due to restriction <40>
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 158.170 158.170 hub.py:358(run)
1 0.000 0.000 158.170 158.170 {method 'run' of 'gevent.core.loop' objects}
2/1 1.286 0.643 158.166 158.166 PhraseVectorizer_1_1.py:671(topics_fit)
206272 0.198 0.000 156.670 0.001 cursor.py:1041(next)
601 0.192 0.000 156.203 0.260 cursor.py:944(_refresh)
534 0.029 0.000 156.008 0.292 cursor.py:796(__send_message)
534 0.012 0.000 150.514 0.282 mongo_client.py:725(_send_message_with_response)
534 0.002 0.000 150.439 0.282 mongo_client.py:768(_reset_on_error)
534 0.017 0.000 150.437 0.282 server.py:69(send_message_with_response)
551/535 0.002 0.000 150.316 0.281 pool.py:225(receive_message)
552/536 0.079 0.000 150.314 0.280 network.py:106(receive_message)
1104/1072 0.815 0.001 150.234 0.140 network.py:127(_receive_data_on_socket)
2427/2395 0.019 0.000 149.418 0.062 socket.py:381(recv)
608/592 0.003 0.000 48.541 0.082 socket.py:284(_wait)
552 0.885 0.002 5.464 0.010 helpers.py:88(_unpack_response)
552 4.475 0.008 4.577 0.008 {bson._cbson.decode_all}
3033 2.021 0.001 2.021 0.001 {method 'recv' of '_socket.socket' objects}
7/4 0.000 0.000 0.221 0.055 hub.py:189(_import)
4 0.127 0.032 0.221 0.055 {__import__}
67 0.104 0.002 0.202 0.003 {method 'sort' of 'list' objects}
536/535 0.003 0.000 0.142 0.000 topology.py:57(open)
537/536 0.002 0.000 0.139 0.000 topology.py:286(_ensure_opened)
1072/1071 0.003 0.000 0.138 0.000 periodic_executor.py:50(open)
537/536 0.001 0.000 0.136 0.000 server.py:33(open)
537/536 0.001 0.000 0.135 0.000 monitor.py:69(open)
20/19 0.000 0.000 0.132 0.007 topology.py:342(_update_servers)
4 0.000 0.000 0.131 0.033 hub.py:418(_get_resolver)
1 0.000 0.000 0.122 0.122 resolver_thread.py:13(__init__)
1 0.000 0.000 0.122 0.122 hub.py:433(_get_threadpool)
206187 0.081 0.000 0.101 0.000 objectid.py:68(__init__)
206187 0.100 0.000 0.100 0.000 database.py:339(_fix_outgoing)
206187 0.098 0.000 0.098 0.000 PhraseVectorizer_1_1.py:708(<lambda>)
1 0.073 0.073 0.093 0.093 threadpool.py:2(<module>)
2037 0.003 0.000 0.092 0.000 hub.py:159(get_hub)
2 0.000 0.000 0.090 0.045 thread.py:39(start_new_thread)
2 0.000 0.000 0.090 0.045 greenlet.py:195(spawn)
2 0.000 0.000 0.090 0.045 greenlet.py:74(__init__)
1 0.000 0.000 0.090 0.090 hub.py:259(__init__)
1102 0.004 0.000 0.078 0.000 pool.py:501(get_socket)
1068 0.005 0.000 0.074 0.000 server.py:135(get_socket)
This performance is somewhat unacceptable for my application - I would like it to be much faster (this is timed and profiled for a subset of ~20 documents, and I need to process few tens of thousands).
Any ideas on how to speed it up?
Much appreciated.
Edit:
Code snippet that I profiled:
# also tried monkey patching all here, see profiler
from pymongo import MongoClient
def topics_fit(self):
client = MongoClient()
# tried motor for multithreading - also slow
#client = motor.motor_tornado.MotorClient()
# initialize DB cursors
db_wiki = client.wiki
# initialize topic feature dictionary
self.topics = OrderedDict()
self.topic_mapping = OrderedDict()
vocabulary_keys = self.vocabulary.keys()
num_categories = 0
for phrase in vocabulary_keys:
phrase_tokens = phrase.split()
if len(phrase_tokens) > 1:
# query for current phrase
AND_phrase = "\"" + phrase + "\""
cursor = db_wiki.categories.find({ "$text" : { "$search": AND_phrase } },{ "score": { "$meta": "textScore" } })
cursor = list(cursor)
if cursor:
cursor.sort(key=lambda k: k["score"], reverse = True)
added_categories = cursor[0]["category_ids"]
for added_category in added_categories:
if not (added_category in self.topics):
self.topics[added_category] = num_categories
if not (self.vocabulary[phrase] in self.topic_mapping):
self.topic_mapping[self.vocabulary[phrase]] = [num_categories, ]
else:
self.topic_mapping[self.vocabulary[phrase]].append(num_categories)
num_categories+=1
else:
if not (self.vocabulary[phrase] in self.topic_mapping):
self.topic_mapping[self.vocabulary[phrase]] = [self.topics[added_category], ]
else:
self.topic_mapping[self.vocabulary[phrase]].append(self.topics[added_category])
Edit 2: output of index_information():
{u'_id_':
{u'ns': u'wiki.categories', u'key': [(u'_id', 1)], u'v': 1},
u'article_title_text_article_body_text_category_names_text': {u'default_language': u'english', u'weights': SON([(u'article_body', 1), (u'article_title', 1), (u'category_names', 1)]), u'key': [(u'_fts', u'text'), (u'_ftsx', 1)], u'v': 1, u'language_override': u'language', u'ns': u'wiki.categories', u'textIndexVersion': 2}}

Optimizing point - circle distance method

I'm implementing a RANSAC algorithm for circle detection in images. I profiled the execution and I get:
13699392 function calls in 799.981 seconds
Random listing order was used
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.000 0.000 {time.time}
579810 0.564 0.000 0.564 0.000 {getattr}
289905 2.343 0.000 8.661 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/blas.py:226(_get_funcs)
579810 0.124 0.000 0.124 0.000 {method 'get' of 'dict' objects}
289905 0.645 0.000 2.676 0.000 {map}
2954 0.005 0.000 0.005 0.000 {method 'transpose' of 'numpy.ndarray' objects}
2954 0.023 0.000 0.464 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/shape_base.py:179(vstack)
2954 2.373 0.001 2.373 0.001 {method 'read' of 'cv2.VideoCapture' objects}
579810 0.966 0.000 2.031 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/function_base.py:550(asarray_chkfinite)
289905 10.164 0.000 24.316 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/basic.py:456(lstsq)
2954 1.090 0.000 1.090 0.000 {normalize}
1455433 3.827 0.000 3.827 0.000 {numpy.core.multiarray.array}
579810 2.899 0.000 3.148 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numerictypes.py:949(_can_coerce_all)
1 0.000 0.000 0.000 0.000 {numpy.core.multiarray.empty}
2954 32.544 0.011 795.875 0.269 git/tra-python-processer/tra/ransac.py:31(image_search)
289905 0.714 0.000 38.644 0.000 git/tra-python-processer/tra/features.py:44(__init__)
289905 2.157 0.000 2.157 0.000 {method 'randint' of 'mtrand.RandomState' objects}
1 0.005 0.005 0.005 0.005 {VideoCapture}
289905 1.026 0.000 1.026 0.000 {method 'astype' of 'numpy.generic' objects}
2954 0.006 0.000 0.010 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/fromnumeric.py:495(transpose)
289905 11.303 0.000 37.930 0.000 git/tra-python-processer/tra/features.py:48(__gen)
3496584 0.343 0.000 0.343 0.000 {len}
2954 0.344 0.000 0.344 0.000 {numpy.core.multiarray.concatenate}
2954 3.214 0.001 3.214 0.001 {numpy.core.multiarray.where}
869715 0.575 0.000 0.575 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2514(size)
869715 0.778 0.000 2.278 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numeric.py:394(asarray)
289905 716.946 0.002 716.946 0.002 git/tra-python-processer/tra/features.py:89(points_distance)
5908 0.015 0.000 0.031 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numeric.py:464(asanyarray)
289905 0.275 0.000 0.275 0.000 {isinstance}
289905 0.342 0.000 9.003 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/lapack.py:255(get_lapack_funcs)
5908 0.058 0.000 0.097 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/shape_base.py:60(atleast_2d)
295813 0.089 0.000 0.089 0.000 {method 'append' of 'list' objects}
289905 0.645 0.000 3.793 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numerictypes.py:970(find_common_type)
2954 0.221 0.000 0.221 0.000 {threshold}
1 0.000 0.000 0.000 0.000 {method 'get' of 'cv2.VideoCapture' objects}
1 0.000 0.000 0.000 0.000 git/tra-python-processer/tra/ransac.py:24(__init__)
2954 0.009 0.000 0.009 0.000 {numpy.core.multiarray.zeros}
579810 0.143 0.000 0.143 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/misc.py:126(_datacopied)
1 0.201 0.201 799.981 799.981 git/tra-python-processer/tra/ransac.py:122(video_processing)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
2954 1.528 0.001 1.528 0.001 {cvtColor}
289905 1.280 0.000 5.346 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/blas.py:182(find_best_blas_type)
289905 0.198 0.000 0.198 0.000 {method 'index' of 'list' objects}
It's first time I use profiler, however for what I can understand the function that is most heavy is features.py:89(points_distance) that comes out to be a very easy implementation:
def points_distance(self,points):
d = n.abs(\
n.sqrt(\
n.power(self.xc - points[:,0],2) + n.power(self.yc - points[:,1],2)
)\
- self.radius
)
return d
Any suggestions? Maybe cython?

Use scipy.spatial.distance.cdist for the distance calculation in points_distance.
First, optimize your code in pure Python and numpy. Then if necessary port the critical parts to Cython. Since a number of functions are called repeatedly a few ~100000 times, you should get some speed up from Cython for those parts. Unless, of course, the computational bottleneck is in the distance calculation, which will then limit the overall execution time.
By the way, you should sort your profiler results by tottime so they are easier to read.

Tests are 4 times slower under PyPy

I am running tests for my project using nose2:
#!/bin/sh
nose2 --config=tests/nose2.cfg "$#"
Under CPython tests run 4 times faster than under PyPy:
Python 2.7.8 (default, Oct 20 2014, 15:05:19)
[GCC 4.9.1] on linux2
...
Ran 58 tests in 25.369s
Python 2.7.9 (2.5.1+dfsg-1~ppa1+ubuntu14.04, Mar 27 2015, 19:19:42)
[PyPy 2.5.1 with GCC 4.8.2] on linux2
...
Ran 58 tests in 100.854s
What could be the cause?
Is there a way to tweak PyPy configuration using environment variables or or a configuration file on some standard path? Because in my case I am running nose bootstrap script and I cannot control command line options for PyPy.
Here is one specific test:
1272695 function calls (1261234 primitive calls) in 1.165 seconds
Ordered by: cumulative time, internal time, call count
List reduced from 1224 to 40 due to restriction <40>
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 1.171 1.171 test_progress.py:37(test_progress)
15 0.000 0.000 1.169 0.078 __init__.py:52(api_request)
15 0.000 0.000 1.160 0.077 __init__.py:46(request)
15 0.000 0.000 1.152 0.077 test.py:695(open)
15 0.000 0.000 1.150 0.077 test.py:655(run_wsgi_app)
15 0.000 0.000 1.144 0.076 test.py:828(run_wsgi_app)
15 0.000 0.000 1.144 0.076 application.py:101(__call__)
15 0.000 0.000 1.138 0.076 sessions.py:329(__call__)
15 0.000 0.000 1.071 0.071 course_object.py:14(__call__)
15 0.000 0.000 1.005 0.067 user_auth.py:7(__call__)
15 0.000 0.000 0.938 0.063 application.py:27(application)
15 0.000 0.000 0.938 0.063 application.py:81(wsgi_app)
15 0.000 0.000 0.876 0.058 ember_backend.py:188(__call__)
15 0.000 0.000 0.875 0.058 ember_backend.py:233(handle_request)
176 0.002 0.000 0.738 0.004 __init__.py:42(instrumented_method)
176 0.003 0.000 0.623 0.004 __init__.py:58(get_stack)
176 0.000 0.000 0.619 0.004 inspect.py:1053(stack)
176 0.010 0.000 0.619 0.004 inspect.py:1026(getouterframes)
294 0.001 0.000 0.614 0.002 cursor.py:1072(next)
248 0.001 0.000 0.612 0.002 cursor.py:998(_refresh)
144 0.002 0.000 0.608 0.004 cursor.py:912(__send_message)
7248 0.041 0.000 0.607 0.000 inspect.py:988(getframeinfo)
8 0.000 0.000 0.544 0.068 test_progress.py:31(get_progress_data)
4 0.000 0.000 0.529 0.132 test_progress.py:25(finish_item)
249 0.001 0.000 0.511 0.002 base.py:1131(next)
7 0.000 0.000 0.449 0.064 ember_backend.py:240(_handle_request)
8 0.000 0.000 0.420 0.053 ember_backend.py:307(_handle_request)
8 0.000 0.000 0.420 0.053 user_state.py:13(list)
8 0.001 0.000 0.407 0.051 user_progress.py:28(update_progress)
4 0.000 0.000 0.397 0.099 entity.py:253(post)
7248 0.051 0.000 0.362 0.000 inspect.py:518(findsource)
14532 0.083 0.000 0.332 0.000 inspect.py:440(getsourcefile)
92/63 0.000 0.000 0.308 0.005 objects.py:22(__get__)
61 0.001 0.000 0.304 0.005 base.py:168(get)
139 0.000 0.000 0.250 0.002 queryset.py:65(_iter_results)
51 0.001 0.000 0.249 0.005 queryset.py:83(_populate_cache)
29 0.000 0.000 0.220 0.008 __init__.py:81(save)
29 0.001 0.000 0.219 0.008 document.py:181(save)
21780 0.051 0.000 0.140 0.000 inspect.py:398(getfile)
32 0.002 0.000 0.139 0.004 {pymongo._cmessage._do_batched_write_command}
And the with PyPy:
6037905 function calls (6012014 primitive calls) in 7.475 seconds
Ordered by: cumulative time, internal time, call count
List reduced from 1354 to 40 due to restriction <40>
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 7.960 7.960 test_progress.py:37(test_progress)
15 0.000 0.000 7.948 0.530 __init__.py:52(api_request)
15 0.000 0.000 7.873 0.525 __init__.py:46(request)
15 0.000 0.000 7.860 0.524 test.py:695(open)
15 0.000 0.000 7.856 0.524 test.py:655(run_wsgi_app)
15 0.000 0.000 7.845 0.523 test.py:828(run_wsgi_app)
15 0.000 0.000 7.844 0.523 application.py:101(__call__)
15 0.000 0.000 7.827 0.522 sessions.py:329(__call__)
15 0.000 0.000 7.205 0.480 course_object.py:14(__call__)
176 0.004 0.000 6.605 0.038 __init__.py:42(instrumented_method)
15 0.000 0.000 6.591 0.439 user_auth.py:7(__call__)
176 0.008 0.000 6.314 0.036 __init__.py:58(get_stack)
176 0.001 0.000 6.305 0.036 inspect.py:1063(stack)
176 0.027 0.000 6.304 0.036 inspect.py:1036(getouterframes)
7839 0.081 0.000 6.274 0.001 inspect.py:998(getframeinfo)
15 0.000 0.000 5.983 0.399 application.py:27(application)
15 0.001 0.000 5.983 0.399 application.py:81(wsgi_app)
15 0.000 0.000 5.901 0.393 ember_backend.py:188(__call__)
15 0.000 0.000 5.899 0.393 ember_backend.py:233(handle_request)
15714/15713 0.189 0.000 5.828 0.000 inspect.py:441(getsourcefile)
294 0.002 0.000 5.473 0.019 cursor.py:1072(next)
248 0.002 0.000 5.470 0.022 cursor.py:998(_refresh)
144 0.004 0.000 5.445 0.038 cursor.py:912(__send_message)
8367 2.133 0.000 5.342 0.001 inspect.py:473(getmodule)
249 0.002 0.000 4.316 0.017 base.py:1131(next)
8 0.000 0.000 3.966 0.496 test_progress.py:31(get_progress_data)
4 0.000 0.000 3.209 0.802 test_progress.py:25(finish_item)
7839 0.098 0.000 3.185 0.000 inspect.py:519(findsource)
7 0.000 0.000 2.944 0.421 ember_backend.py:240(_handle_request)
8 0.000 0.000 2.898 0.362 ember_backend.py:307(_handle_request)
8 0.000 0.000 2.898 0.362 user_state.py:13(list)
8 0.001 0.000 2.820 0.352 user_progress.py:28(update_progress)
61 0.001 0.000 2.546 0.042 base.py:168(get)
4 0.000 0.000 2.534 0.633 entity.py:253(post)
850362/849305 2.315 0.000 2.344 0.000 {hasattr}
92/63 0.001 0.000 2.004 0.032 objects.py:22(__get__)
127 0.001 0.000 1.915 0.015 queryset.py:65(_iter_results)
51 0.001 0.000 1.914 0.038 queryset.py:83(_populate_cache)
29 0.000 0.000 1.607 0.055 __init__.py:81(save)
29 0.001 0.000 1.605 0.055 document.py:181(save)