A Python module for measuring the execution time of code
Project description
ExecTiming
An advanced timer for Python that makes it easy to determine execution times.
Python has a builtin package named timeit
which provides a way to
"Measure execution time of small code snippets." It can be great for quick
tests, but lacks more expansive features like curve fitting, statistical information,
and the ability to use in existing projects. ExecTiming seeks to change that by
including most of the features of timeit
and adding many more like decorators,
argument calling and replacement, bestfitcurve determination, and inproject use.
Features
StaticTimer
which provides the ability to time functions via a decorator, strings, or just seeing the amount of time elapsed between a call to.start_elapsed()
andelapsed()
Timer
which gives a way to log measured times and then display statistical data, perform curvefitting for functions with parameters as the independent variables and the measured time as the dependent variable, and all the features the StaticTimer has. All output can be redirected by changing
output_stream
, which can be any filelike object or anything with a.write()
method.  A wrapper for
logging.info
andlogging.debug
is included to redirect output to those sources  Measured times can be displayed in seconds
s
, millisecondsms
, microsecondsus
, or nanosecondsns
.  The same block can be executed multiple times to get a more accurate reading.
This is done by setting
iterations_per_run
. After that many iterations, the elapsed time is measured.  Multiple runs can be carried out and averaged to remove outlying results.
Wiki
Glossary
Installation
pip install exectiming
For full functionality:
scipy
 for bestfitcurvescikitlearn
 for bestfitcurvenumpy
 for bestfitcurvematplotlib
 for plotting
However, basic functionality will still exist even if those dependencies aren't found
Static decorate
from exectiming.exectiming import StaticTimer from random import randint @StaticTimer.decorate(runs=5, average_runs=False, call_callable_args=True, log_arguments=True) def factorial(n): if n == 1: return 1 else: return n * factorial.__wrapped__(n1) factorial(lambda: randint(3, 40)) # 0.01663 ms  factorial(33) ... [runs= 1, iterations= 1]  Run 1 # 0.00544 ms  factorial(19) ... [runs= 1, iterations= 1]  Run 2 # 0.00736 ms  factorial(28) ... [runs= 1, iterations= 1]  Run 3 # 0.00448 ms  factorial(17) ... [runs= 1, iterations= 1]  Run 4 # 0.01087 ms  factorial(38) ... [runs= 1, iterations= 1]  Run 5
 Functions, even recursive ones, can be wrapped so that any time they are called they get timed
 You can replace arguments with functions which can be automatically called and the argument replaced
to allow testing like shown above,
call_callable_args=True
 Arguments show up in the output to provide more information,
log_arguments=True
Static time_it
from exectiming.exectiming import StaticTimer StaticTimer.time_it("pow(2, 64)", runs=10, iterations_per_run=10000) # 107.10668 ms  pow(2, 64) ... [runs= 10, iterations=10000] StaticTimer.time_it("2**64", runs=10, iterations_per_run=10000) # 68.75266 ms  2**64 ... [runs= 10, iterations=10000] StaticTimer.time_it("1<<64", runs=10, iterations_per_run=10000) # 65.53690 ms  1<<64 ... [runs= 10, iterations=10000]
 Strings can be timed
 Multiple runs can be measured then averaged together to get a more accurate result
 Anything needed names can be passed in by setting
globals=
,locals=
, orsetup=
. The first two must be maps of names to objects. The second is a string that is executed once because the stringblock
is timed.
time_it
can be used to rewrite the decorator above example like so:
StaticTimer.time_it(factorial, lambda: randint(3, 40), call_callable_args=True, average_runs=False, runs=5, log_arguments=True)
Assume
from exectiming.exectiming import Timer timer = Timer()
Transformers, statistics, and best fit
@timer.decorate(runs=5, log_arguments=True, call_callable_args=True) def bubble_sort(array): while True: switched = False for i in range(0, len(array)1): if array[i] > array[i+1]: array[i], array[i+1] = array[i+1], array[i] switched = True if not switched: break return array bubble_sort(lambda: [randint(0, 1000) for _ in range(randint(100, 5000))]) timer.output(split_index="bubble_sort", transformers={0: len}) # bubble_sort: # 1333.19493 ms  bubble_sort(2141) ... [runs= 1, iterations= 1] # 1413.75243 ms  bubble_sort(2546) ... [runs= 1, iterations= 1] # 4247.70385 ms  bubble_sort(4530) ... [runs= 1, iterations= 1] # 34.01533 ms  bubble_sort(421) ... [runs= 1, iterations= 1] # 675.07202 ms  bubble_sort(1752) ... [runs= 1, iterations= 1] timer.statistics() # bubble_sort[runs=5, total=7703.73856 ms]: # Min  Max  Average = 34.01533  4247.70385  1540.74771 ms # Standard Deviation = 1442.66797 ms # Variance = 2081.29087 ms print(timer.best_fit_curve(transformers=len)) # ('Polynomial', {'a': 2.8042911992314363e07, 'b': 9.670141667209306e05, 'c': 0.024305228337961525})
Timer
stores the output until requested Function parameters can be transformed in the output. In the above example,
transformers={0: len}
indicates that the positional argument at index0
should have its value in the output replaced by the result of calling the function with that parameter. The output otherwise would have been something likebubble_sort([1, 2, 3, 4, 5, ...])
transformers=len
is also valid as all of the arguments can be transformed withlen
, so there is no need to specify which index/key the function should be used for. Basic statistics can be displayed
 A bestfitcurve can be determined. To use this, all logged function parameters must be integers. In this case, one was an a list, so it was transformed so that the analysis was done on the length of the list, instead of the list itself.
 The resulting best fit curve is, if
x=len(list)
,y = ax^2 + bx + c
. We can extrapolate execution time using this curve to determine how long it would take to sort a list of lengthx=10000
, which would be0.0000002804*10000^2  0.0000967*10000 + 0.0243
. That is27.10 s
or27100 ms
Plotting factorial
@timer.decorate(runs=100, iterations_per_run=10, call_callable_args=True, log_arguments=True) def factorial(n): if n == 1: return 1 else: return n * factorial.__wrapped__(n1) factorial(lambda: randint(1, 100)) timer.plot(plot_curve=True, time_unit=timer.US, equation_rounding=5)
.plot()
provides a quick way to plot the measured times against an argument that is the independent variable The best fit curve and equation can be automatically determined and added
by setting
plot_curve=True
Plotting bubble_sort
# using bubble_sort from above, just with runs=10 bubble_sort(lambda: [randint(0, 100) for _ in range(randint(100, 2500))]) curve = timer.best_fit_curve(transformers=len) timer.plot(transformer=len, plot_curve=True, curve=curve, x_label="List Length")
 The curve can be determined beforehand and then passed into
plot()
plot()
needstransformer=len
because the independent and dependent variables must be integers, solen
is used to make it it one
Plotting binary_search
@timer.decorate(runs=100, iterations_per_run=5, log_arguments=True, call_callable_args=True) def binary_search(sorted_array, element): lower, upper = 0, len(sorted_array) middle = upper // 2 while middle >= lower and middle != upper: if element == sorted_array[middle]: return middle elif element > sorted_array[middle]: lower = middle + 1 # lower must be beyond middle because the middle wasn't right else: upper = middle  1 # upper must be lower than the middle because the middle wasn't right middle = (upper + lower) // 2 return None # couldn't find it binary_search(lambda: [i for i in range(randint(0, 10000))], lambda: randint(0, 10000)) timer.plot(plot_curve=True, curve=timer.best_fit_curve(exclude={1}, transformers=len), key=0, transformer=len, time_unit=timer.US, x_label="List Length", equation_rounding=4, title="Binary Search  Random Size, Random Element")
binary_search()
takes two arguments, sobest_fit_curve
is set to ignore the second one, at index 1, and to transform the argument at index 0 usinglen
 Once the curve is determined, the split must be plotted. Again, there are
two arguments, so
key=0
says to use the first as the independent variable andtransformer=len
will transform the list into an integer  Additionally, the title and xaxis labels are specified and rounding set lower
 The equation displayed on the plot uses the timescale specified by
time_unit
Plotting multiple splits
from exectiming.exectiming import Timer from random import randint timer = Timer() def bubble_sort(array): while True: switched = False for i in range(0, len(array)1): if array[i] > array[i+1]: array[i], array[i+1] = array[i+1], array[i] switched = True if not switched: break return array def selection_sort(array): for i in range(len(array)  1): # find min min_i = i for j in range(i+1, len(array)): if array[j] < array[min_i]: min_i = j # swap array[i], array[min_i] = array[min_i], array[i] return array timer.time_it(bubble_sort, lambda: [randint(0, 1000) for _ in range(randint(100, 3000))], call_callable_args=True, log_arguments=True, runs=10) timer.time_it(selection_sort, lambda: [randint(0, 1000) for _ in range(randint(100, 3000))], call_callable_args=True, log_arguments=True, runs=10) timer.time_it(sorted, lambda: [randint(0, 1000) for _ in range(randint(100, 3000))], call_callable_args=True, log_arguments=True, runs=10) bubble_sort_curve = timer.best_fit_curve("bubble_sort", transformers=len) selection_sort_curve = timer.best_fit_curve("selection_sort", transformers=len) sorted_curve = timer.best_fit_curve("sorted", transformers=len) timer.plot("bubble_sort", plot_curve=True, curve=bubble_sort_curve, multiple=True, transformer=len) timer.plot("selection_sort", plot_curve=True, curve=selection_sort_curve, multiple=True, transformer=len) timer.plot("sorted", plot_curve=True, curve=sorted_curve, title="Sorting Algorithms", x_label="List Length", transformer=len)
 Multiple splits and curves can be plotted by setting
multiple=True
for all but the last call to.plot()
. title
,x_label
, andy_label
of the last call will be used
TODO
 Add parameter checking to
.plot()
and.best_fit_curve()
to make sure arguments are integers to avoid difficult to decipher errors  Change
.best_fit_curve()
to allowtransformers
to be a callable  Change
.output()
to not requiresplit_index
iftransformers={0:len}
. Allowtransformers
to be just a function, if there is only one argument, or a map or a map of a map.  Change
.sort_runs()
to reflect that values don't have to be integers, they just have to be comparable. If they aren't, then a transformer is needed. This change is mainly cosmetic. (BJ  nothing actually needed changed)  Add
.predict(params, arguments)
toTimer
. Should basically be a passthrough call to.calculate_point()
on the correct bestfitcurve  Collapse
exclude_args
andexclude_kwargs
down into justexclude
. The difference between positional and keyword arguments can be determined as int vs. str.  Change how coefficients are returned for
BestFitLinear
, maybe use x_{index/key}  Add context manager
 Make scipy, numpy, and scikitlearn optional, just prohibit
best_fit_curve
if they aren't there  Add graphing feature with matplotlib, Linear will only be graphed if there is a single argument
 Add the ability to sort runs so they are display in some sort of order. Maybe allow sorting by time or by an argument
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