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Runtime and number of calls for designated functions, methods, or pieces of code. Optionally output nice report.

Project description

Measures how often designated functions, methods, or pieces of code are executed and what their runtime is. Optionally prints a nice report to the screen, although the raw data is available for further processing as well.

Outline

  1. Mark parts of the code that should be monitored with the provided context manager or decorators.
  2. Tell your program to output the report or provide you the data when it’s done.
  3. Run your program.
  4. ?????
  5. Profit.

Basic Usage

How to designate code that should be monitored.

A free-standing piece of code.

from aeon import timer

with timer('my measurement'):
    # do stuff here...

# to assign the measurement to a specific group
with timer('my measurement', 'general frobnication'):
    # do stuff here

A function.

from aeon import timer

@timer
def my_function():
    pass

A method.

from aeon import timer

class Foo(object):
    @timer.method
    def bar(self):
        pass

How to see the report.

from aeon import timer

print timer.report()
print timer  # equivalent

Further features

You can instantiate your own timer if you want to, in case you want to use several in parallel.

from aeon import Timer

my_timer= Timer()

with my_timer('my_measurement'):
    pass

# or
with my_timer('my_measurement', 'my_group'):
    pass

@my_timer
def foo():
    pass

class Foo(object):
    @my_timer.method
    def bar(self):
        pass

The timer object can be queried for specific measurements or the data with which it generates the report.

Also, nothing prevents you from using the Measurement class on its own:

from aeon import Measurement

m = Measurement()
for i in xrange(100):
    m.start()
    # stuff happens here
    m.stop()

assert m.calls == 100
print m.total_runtime, m.time_per_call

Installation

Installation is easy as:

$ sudo pip install aeon

Rationale

The code has originally been used in a computational physics project where the typical runtime distribution is very dependent on the problem at hand. It has proven itself useful for giving a feel for where time is spent during computation and quickly showing when parts of code went on a riot. In fact, in that project, it is enabled in production since the overhead is low.

What sets it apart is the possibility to monitor only specific parts of the code and optionally have these parts logically grouped (by default, it will use the class or module names).

There are better alternatives for proper benchmarking, like cProfile.

Project details


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