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How do I time python code, similar to unix time command? [duplicate]

I am working on a Python script that is going to be run in the command line. The idea is to get a command from the user, run it and then provide the wall-clock time and the CPU time of the command provided by the user. See code below.
#!/usr/bin/env python
import os
import sys
def execut_cmd(cmd_line):
utime = os.system('time '+cmd_line)
# Here I would like to store the wall-clock time in the Python variable
# utime.
cputime = os.system('time '+cmd_line)
# Here the CPU time in the cputime variable. utime and cputime are going to
# be used later in my Python script. In addition, I would like to silence the
# output of time in the screen.
execut_cmd(sys.argv[1])
print ('Your command wall-clock time is '+utime)
print ('Your command cpu time is '+ cputime)
How can I accomplish this? Also, if there is a better method than using 'time' I am open to try it.

From Python Documentation for wall time:
... On Windows, time.clock() has microsecond granularity, but time.time()’s granularity is 1/60th of a second. On Unix, time.clock() has 1/100th of a second granularity, and time.time() is much more precise. On either platform, default_timer() measures wall clock time, not the CPU time. This means that other processes running on the same computer may interfere with the timing.
For wall time you can use timeit.default_timer() which gets the timer with best granularity described above.
From Python 3.3 and above you can use time.process_time() or time.process_time_ns() . Below is the documentation entry for process_time method:
Return the value (in fractional seconds) of the sum of the system and user CPU time of the current process. It does not include time elapsed during sleep. It is process-wide by definition. The reference point of the returned value is undefined, so that only the difference between the results of consecutive calls is valid.

To provide the current wall time, time.time() can be used to get the epoch time.
To provide the elapsed wall time, time.perf_counter() can be used at the start and end of the operation with the difference in results reflecting the elapsed time. The results cannot be used to give an absolute time as the reference point is undefined. As mentioned in other answers, you can use timeit.default_time() but this will always return time.perf_counter() as of python 3.3
To provide the elapsed CPU time, time.process_time() can be used in a similar manner to time.perf_counter(). This will provide the sum of the system and user CPU time.

With the little time I have spent using the timing functions on Linux systems. I have observed that
timeit.default_timer() and time.perf_counter() numerically gives the same result.
Also, when measuring the duration of a time interval, timeit.default_timer(), time.perf_counter() and time.time() all virtually gives the same result. So this means that any of these functions can be used to measure the elapsed time or wall time for any process.
I think I should also mention that the difference between time.time() and others is that it gives the current time in seconds from epoch which is from 1 January 1970 12:00AM
time.clock() and time.process_time() also gives the same numerical value
time.process_time() is most suitable for measuring the cpu time since time.clock() is already deprecated in python 3

Python CPU Clock. time.clock() vs time.perf_counter() vs time.process_time()

I wanted to measure how long sections of my code run. I needed this to be deterministic so that I get the same duration every time (seconds/milliseconds/etc) regardless of what is happening in the background. So i wanted to measure CPU time using time.clock() on unix.
time.clock() has since been decrecated and its suggested to switch to either perf_counter or process_time. I was looking at the python doc and found the following.
time.perf_counter() → float Return the value (in fractional seconds)
of a performance counter, i.e. a clock with the highest available
resolution to measure a short duration. It does include time elapsed
during sleep and is system-wide. The reference point of the returned
value is undefined, so that only the difference between the results of
consecutive calls is valid.
time.process_time() → float Return the value (in fractional seconds)
of the sum of the system and user CPU time of the current process. It
does not include time elapsed during sleep. It is process-wide by
definition. The reference point of the returned value is undefined, so
that only the difference between the results of consecutive calls is
valid.
Since I am not using any form of sleep in my code, the primary difference seems to be 'process-wide' and 'system-wide'. Could sometime elaborate the difference?
Secondly, is this the correct way to do this?

Both perf_counter and timeit will give you the time that your block of code tested had taken to perform.
time.process_time() it does not and it calculates what the CPU have taken, which is not necessarily the same as the function or block of code.
I found this thread on Github, seems that the question is quite advanced and may be completely different depending on the OS or program to be benchmark-ed.
Something that time.process_time() is not counting is the Parent Multi-thread:
"One consequence of using time.process_time is that the time spent in child processes of the benchmark is not included. Multithreaded benchmarks also return the total CPU time counting all CPUs."
perf_counter
from time import perf_counter
start = perf_counter()
for _ in range(10000):
x = "-".join(str(n) for n in range(100))
end = perf_counter()
print('Perf Counter= ', end-start)
# Perf Counter= 0.23170840000000004
timeit
import timeit
print(timeit.timeit('"-".join(str(n) for n in range(100))', number=10000))
# 0.20687929999999993

Timing Code Execution Time

So, I am interested in timing some of the code I am setting up. Borrowing a timer function from the 4th edition of Learning Python, I tried:
import time
reps = 100
repslist = range(reps)
def timer(func):
start = time.clock()
for i in repslist:
ret = func()
elasped = time.clock()-start
return elapsed
Then, I paste in whatever I want to time, and put:
print(timer(func)) #replace func with the function you want to time
When I run it on my code, I do get an answer, but it's nonsense. Suspecting something was wrong, I put a time.sleep(0.1) call in my code, and got a result of 0.8231
Does anybody know why this might be the case or how to fix it? I suspect that the time.clock() call might be at fault.

According to the help docs for clock:
Return the CPU time or real time since the start of the process or since the first call to clock(). This has as much precision as the system records.
The second call to clock already returns the elapsed time between it and the first clock call. You don't need to manually subtract start.
Change
elasped = time.clock()-start
to
elasped = time.clock()

If you want to timer a function perhaps give decorators a try(documentation here):
import time
def timeit(f):
def timed(*args, **kw):
ts = time.time()
result = f(*args, **kw)
te = time.time()
print 'func:%r args:[%r, %r] took: %2.4f sec' % \
(f.__name__, args, kw, te-ts)
return result
return timed
Then when you write a function you just use the decorator, here:
#timeit
def my_example_function():
for i in range(10000):
print "x"
This will print out the time the function took to execute:
func:'my_example_function' args:[(), {}] took: 0.4220 sec

After fixing the typo in the first intended use of elapsed, your code works fine with either time.clock or time.time (or Py3's time.monotonic for that matter) on my Linux system.
The difference would be in the (OS specific) behavior for clock; on most UNIX-like OSes it will return the processor time used by the program since it launched (so time spent blocked, on I/O, locks, page faults, etc. wouldn't count), while on Windows it's a wall clock timer (so time spent blocked would count) that counts seconds since first call.
The UNIX-like version of time.clock is also fairly unreliable if used in a long running program when clock_t is only 32 bits; the value it returns will wrap roughly every 72 minutes of processor time.
Of course, time.time isn't perfect either; it follows the system clock, so an NTP time update (or any other change to the system clock) occurring between calls will give erroneous results (on Python 3.3+, you'd use time.monotonic to avoid this problem). It's also not guaranteed to have granularity finer than 1 second, so if your function doesn't take an awfully long time to run, on a system with low res time.time you won't get particularly useful results.
Really, you should be looking at the Python batteries designed for this (that also handle issues like garbage collection overhead and the like). The timeit module already has a function that does what you want, but handles all the edge cases and issues I mentioned. For example, to time some global function named foo for 100 reps, you'd just do:
import timeit
def foo():
...
print(timeit.timeit('foo()', 'from __main__ import foo', number=100))
It fixes most of the issues I mention by selecting the best timing function for the OS you're on (and also fixes other sources of jitter, e.g. cyclic garbage collection, which is disabled during the test and reenabled at the end).
Even if you don't want to use that for some reason, if you're using Python 3.3 or higher, take a look at the replacements for time.clock, e.g. time.perf_counter (includes time spent sleeping) or time.process_time (includes only CPU time), both of which are portable, reliable, fast, and high resolution for better accuracy.

The time.sleep() will terminate for any signal. read about it here ...
http://www.tutorialspoint.com/python/time_sleep.htm

How to: Execution time of module in Python with HIGH precision [duplicate]

Which is better to use for timing in Python? time.clock() or time.time()? Which one provides more accuracy?
for example:
start = time.clock()
... do something
elapsed = (time.clock() - start)
vs.
start = time.time()
... do something
elapsed = (time.time() - start)

As of 3.3, time.clock() is deprecated, and it's suggested to use time.process_time() or time.perf_counter() instead.
Previously in 2.7, according to the time module docs:
time.clock()
On Unix, return the current processor time as a floating point number
expressed in seconds. The precision, and in fact the very definition
of the meaning of “processor time”, depends on that of the C function
of the same name, but in any case, this is the function to use for
benchmarking Python or timing algorithms.
On Windows, this function returns wall-clock seconds elapsed since the
first call to this function, as a floating point number, based on the
Win32 function QueryPerformanceCounter(). The resolution is typically
better than one microsecond.
Additionally, there is the timeit module for benchmarking code snippets.

The short answer is: most of the time time.clock() will be better.
However, if you're timing some hardware (for example some algorithm you put in the GPU), then time.clock() will get rid of this time and time.time() is the only solution left.
Note: whatever the method used, the timing will depend on factors you cannot control (when will the process switch, how often, ...), this is worse with time.time() but exists also with time.clock(), so you should never run one timing test only, but always run a series of test and look at mean/variance of the times.

Others have answered re: time.time() vs. time.clock().
However, if you're timing the execution of a block of code for benchmarking/profiling purposes, you should take a look at the timeit module.

One thing to keep in mind:
Changing the system time affects time.time() but not time.clock().
I needed to control some automatic tests executions. If one step of the test case took more than a given amount of time, that TC was aborted to go on with the next one.
But sometimes a step needed to change the system time (to check the scheduler module of the application under test), so after setting the system time a few hours in the future, the TC timeout expired and the test case was aborted. I had to switch from time.time() to time.clock() to handle this properly.

clock() -> floating point number
Return the CPU time or real time since the start of the process or since
the first call to clock(). This has as much precision as the system
records.
time() -> floating point number
Return the current time in seconds since the Epoch.
Fractions of a second may be present if the system clock provides them.
Usually time() is more precise, because operating systems do not store the process running time with the precision they store the system time (ie, actual time)

Depends on what you care about. If you mean WALL TIME (as in, the time on the clock on your wall), time.clock() provides NO accuracy because it may manage CPU time.

time() has better precision than clock() on Linux. clock() only has precision less than 10 ms. While time() gives prefect precision.
My test is on CentOS 6.4， python 2.6
using time():
1 requests, response time: 14.1749382019 ms
2 requests, response time: 8.01301002502 ms
3 requests, response time: 8.01491737366 ms
4 requests, response time: 8.41021537781 ms
5 requests, response time: 8.38804244995 ms
using clock():
1 requests, response time: 10.0 ms
2 requests, response time: 0.0 ms
3 requests, response time: 0.0 ms
4 requests, response time: 10.0 ms
5 requests, response time: 0.0 ms
6 requests, response time: 0.0 ms
7 requests, response time: 0.0 ms
8 requests, response time: 0.0 ms

As others have noted time.clock() is deprecated in favour of time.perf_counter() or time.process_time(), but Python 3.7 introduces nanosecond resolution timing with time.perf_counter_ns(), time.process_time_ns(), and time.time_ns(), along with 3 other functions.
These 6 new nansecond resolution functions are detailed in PEP 564:
time.clock_gettime_ns(clock_id)
time.clock_settime_ns(clock_id, time:int)
time.monotonic_ns()
time.perf_counter_ns()
time.process_time_ns()
time.time_ns()
These functions are similar to the version without the _ns suffix, but
return a number of nanoseconds as a Python int.
As others have also noted, use the timeit module to time functions and small code snippets.

The difference is very platform-specific.
clock() is very different on Windows than on Linux, for example.
For the sort of examples you describe, you probably want the "timeit" module instead.

I use this code to compare 2 methods .My OS is windows 8 , processor core i5 , RAM 4GB
import time
def t_time():
start=time.time()
time.sleep(0.1)
return (time.time()-start)
def t_clock():
start=time.clock()
time.sleep(0.1)
return (time.clock()-start)
counter_time=0
counter_clock=0
for i in range(1,100):
counter_time += t_time()
for i in range(1,100):
counter_clock += t_clock()
print "time() =",counter_time/100
print "clock() =",counter_clock/100
output:
time() = 0.0993799996376
clock() = 0.0993572257367

time.clock() was removed in Python 3.8 because it had platform-dependent behavior:
On Unix, return the current processor time as a floating point number expressed in seconds.
On Windows, this function returns wall-clock seconds elapsed since the first call to this function, as a floating point number
print(time.clock()); time.sleep(10); print(time.clock())
# Linux : 0.0382 0.0384 # see Processor Time
# Windows: 26.1224 36.1566 # see Wall-Clock Time
So which function to pick instead?
Processor Time: This is how long this specific process spends actively being executed on the CPU. Sleep, waiting for a web request, or time when only other processes are executed will not contribute to this.
Use time.process_time()
Wall-Clock Time: This refers to how much time has passed "on a clock hanging on the wall", i.e. outside real time.
Use time.perf_counter()
time.time() also measures wall-clock time but can be reset, so you could go back in time
time.monotonic() cannot be reset (monotonic = only goes forward) but has lower precision than time.perf_counter()

On Unix time.clock() measures the amount of CPU time that has been used by the current process, so it's no good for measuring elapsed time from some point in the past. On Windows it will measure wall-clock seconds elapsed since the first call to the function. On either system time.time() will return seconds passed since the epoch.
If you're writing code that's meant only for Windows, either will work (though you'll use the two differently - no subtraction is necessary for time.clock()). If this is going to run on a Unix system or you want code that is guaranteed to be portable, you will want to use time.time().

Short answer: use time.clock() for timing in Python.
On *nix systems, clock() returns the processor time as a floating point number, expressed in seconds. On Windows, it returns the seconds elapsed since the first call to this function, as a floating point number.
time() returns the the seconds since the epoch, in UTC, as a floating point number. There is no guarantee that you will get a better precision that 1 second (even though time() returns a floating point number). Also note that if the system clock has been set back between two calls to this function, the second function call will return a lower value.

To the best of my understanding, time.clock() has as much precision as your system will allow it.

Right answer : They're both the same length of a fraction.
But which faster if subject is time ?
A little test case :
import timeit
import time
clock_list = []
time_list = []
test1 = """
def test(v=time.clock()):
s = time.clock() - v
"""
test2 = """
def test(v=time.time()):
s = time.time() - v
"""
def test_it(Range) :
for i in range(Range) :
clk = timeit.timeit(test1, number=10000)
clock_list.append(clk)
tml = timeit.timeit(test2, number=10000)
time_list.append(tml)
test_it(100)
print "Clock Min: %f Max: %f Average: %f" %(min(clock_list), max(clock_list), sum(clock_list)/float(len(clock_list)))
print "Time Min: %f Max: %f Average: %f" %(min(time_list), max(time_list), sum(time_list)/float(len(time_list)))
I am not work an Swiss labs but I've tested..
Based of this question : time.clock() is better than time.time()
Edit : time.clock() is internal counter so can't use outside, got limitations max 32BIT FLOAT, can't continued counting if not store first/last values. Can't merge another one counter...

Comparing test result between Ubuntu Linux and Windows 7.
On Ubuntu
>>> start = time.time(); time.sleep(0.5); (time.time() - start)
0.5005500316619873
On Windows 7
>>> start = time.time(); time.sleep(0.5); (time.time() - start)
0.5

Run a python function every second

What I want is to be able to run a function every second, irrelevant of how long the function takes (it should always be under a second). I've considered a number of options but not sure which is best.
If I just use the delay function it isn't going to take into account the time the function takes to run.
If I time the function and then subtract that from a second and make up the rest in the delay it's not going to take into account the time calculations.
I tried using threading.timer (I'm not sure about the ins and outs of how this works) but it did seem to be slower than the 1s.
Here's the code I tried for testing threading.timer:
def update(i):
sys.stdout.write(str(i)+'\r')
sys.stdout.flush()
print i
i += 1
threading.Timer(1, update, [i]).start()
Is there a way to do this irrelevant of the length of the time the function takes?

This will do it, and its accuracy won't drift with time.
import time
start_time = time.time()
interval = 1
for i in range(20):
time.sleep(start_time + i*interval - time.time())
f()

The approach using a threading.Timer (see code below) should in fact not be used, as a new thread is launched at every interval and this loop can never be stopped cleanly.
# as seen here: https://stackoverflow.com/a/3393759/1025391
def update(i):
threading.Timer(1, update, [i+1]).start()
# business logic here
If you want a background loop it is better to launch a new thread that runs a loop as described in the other answer. Which is able to receive a stop signal, s.t. you can join() the thread eventually.
This related answer seems to be a great starting point to implement this.

if f() always takes less than a second then to run it on a one second boundary (without a drift):
import time
while True:
time.sleep(1 - time.monotonic() % 1)
f()
The idea is from #Dave Rove's answer to a similar question.
To understand how it works, consider an example:
time.monotonic() returns 13.7 and time.sleep(0.3) is called
f() is called around (±some error) 14 seconds (since time.monotonic() epoch)
f() is run and it takes 0.1 (< 1) seconds
time.monotonic() returns around 14.1 seconds and time.sleep(0.9) is called
Step 2. is repeated around 15 seconds (since time.monotonic() epoch)
f() is run and it takes 0.3 (< 1) seconds (note: the value is different from Step 2.)
time.monotonic() returns around 15.3 and time.sleep(0.7) is called
f() is called around 16 seconds and the loop is repeated.
At each step f() is called on a one second boundary (according to time.monotonic() timer). The errors do not accumulate. There is no drift.
See also: How to run a function periodically in python (using tkinter).

How about this: After each run, sleep for (1.0 - launch interval) seconds. You can change the terminate condition by changing while True:. Although if the your function takes more than 1 second to run, this will go wrong.
from time import time, sleep
while True:
startTime = time()
yourFunction()
endTime = time()-startTime
sleep(1.0-endTime)

Threading may be a good choice. The basic concept is as follows.
import threading
def looper():
# i as interval in seconds
threading.Timer(i, looper).start()
# put your action here
foo()
#to start
looper()

I would like to recommend the following code. You can replace the True with any condition if you want.
while True:
time.sleep(1) #sleep for 1 second
func() #function you want to trigger
Tell me if it works.