Timench is a small framework for measure execution time of one function, multiple functions and some code with context
Timench has three internal dicts:
- Timench.funcs = {case_name: func} - for storage funcs for measurement by case name
- Timench.times = {case_name: [time1, time2, ]} - for storage measurement time (as list) by case name
- Timench.reports = {case_name: 'report'} for storage for measurement reports (as string) by case name
For example let's try to measure run time of time.sleep() function
tmnch = Timench()
case_name = 'sleep_1sec'
tmnch.add_func(case_name, time.sleep) # Add function with case name to benchmark listNow we can set args for time.sleep(*args) and run benchmark
repeats = 10 # Count of repeats
sleep_time = 1.0 # args of time.sleep(sleep_time)
tmnch.run(case_name, repeats, sleep_time) # Run benchmark
print(tmnch.get_report(case_name)) # Print benchmark reportand write report to txt-file
tmnch.write_reports('example_1_report.txt')My favorite way of Timench usage is compare execution time of different code with same features. Here is basic setup of example:
import time
from timench import Timench
tmnch = Timench()
repeats = 10
env_args = { # dict structure: {case_name: [args, kwargs] of function func(*args, **kwargs), }
'sleep_1s': [[1.0, ], None],
'sleep_2s': [[2.0, ], None],
'sleep_3s': [[3.0, ], None]
}
for case_name in env_args: # # Add functions to benchmark list
tmnch.add_func(case_name, time.sleep)Run all benchmarks:
tmnch.multiple_run(repeats, env_args) # Run multiple benchmarksOutput reports to terminal and txt-file:
for case_name in env_args:
print(tmnch.get_report(case_name)) # Print to terminal all reports
tmnch.write_reports('example_2_report.txt') # Write all reports to txt-fileSometimes we need to know execution times of code without function creation and benchmark setup.
Let's look to simple example:
import time
from timench import Timench
with Timench(): # Just wrap your code with Timench
time.sleep(1.0)import time
from timench import Timench
tmnch = Timench()
case_name = 'sleep_1sec' # Set run case name
repeats = 10 # Count of repeats
sleep_time = 1.0 # args of time.sleep(sleep_time)
tmnch.add_func(case_name, time.sleep) # Add function to benchmark list
tmnch.run(case_name, repeats, sleep_time) # Run benchmark
print(tmnch.get_report(case_name)) # Print benchmark report
tmnch.write_reports('example_1_report.txt') # Write all reports to txt-fileOutput example:
Case: sleep_1sec
---
Function: sleep
Total time = 10.0128 sec
Best loop time = 1.00024 sec
Average loop time = 1.00128 sec
Repeats = 10
import time
from timench import Timench
tmnch = Timench()
repeats = 10
env_args = { # dict structure: {case_name: [args, kwargs] of function func(*args, **kwargs), }
'sleep_1s': [[1.0, ], None],
'sleep_2s': [[2.0, ], None],
'sleep_3s': [[3.0, ], None]
}
for case_name in env_args: # # Add functions to benchmark list
tmnch.add_func(case_name, time.sleep)
tmnch.multiple_run(repeats, env_args) # Run multiple benchmarks
for case_name in env_args:
print(tmnch.get_report(case_name)) # Print to terminal all reports
tmnch.write_reports('example_2_report.txt') # Write all reports to txt-fileOutput example:
Case: sleep_1s
---
Function: sleep
Total time = 10.1691 sec
Best loop time = 1.00017 sec
Average loop time = 1.01691 sec
Repeats = 10
Case: sleep_2s
---
Function: sleep
Total time = 20.0776 sec
Best loop time = 2.00185 sec
Average loop time = 2.00776 sec
Repeats = 10
Case: sleep_3s
---
Function: sleep
Total time = 30.0205 sec
Best loop time = 3.00012 sec
Average loop time = 3.00205 sec
Repeats = 10
import time
from timench import Timench
with Timench(): # Run time measurement for some code
time.sleep(1.0)
time.sleep(2.0)
time.sleep(3.0)Output example:
Run time = 6.00682 sec