Python Elapsed Time Functions
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x = np.arange(1,10002,500)
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x = np.arange(1,1000002,50000)
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For more complex projects, a profiler such as '''profile''' or '''cProfile''' maybe a better solution to avoid the extra timer code and for a more comprehensive analysis.
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Sometimes a certain section of code is a majority of the run-time so the code may be ported to a compiled language such as C++ or Fortran that is then called from Python, especially if improving a particular section can greatly reduce the total time. That one section may be converted to a langauge that is built for speed and then called by Python. The following tutorial shows to how to implement a simple timer on sections of code. For more complex projects, a profiler such as '''profile''' or '''cProfile''' maybe a better solution to avoid the extra timer code and for a more comprehensive analysis.
to:
Sometimes a certain section of code is a majority of the run-time so the code may be ported to a compiled language such as C++ or Fortran that is then called from Python, especially if improving a particular section can greatly reduce the total time. That one section may be converted to a langauge that is built for speed and then called by Python. The following tutorial shows to how to implement a simple timer on sections of code.
%width=550px%Attach:python_timers.png
For more complex projects, a profiler such as '''profile''' or '''cProfile''' maybe a better solution to avoid the extra timer code and for a more comprehensive analysis.
%width=550px%Attach:python_timers.png
For more complex projects, a profiler such as '''profile''' or '''cProfile''' maybe a better solution to avoid the extra timer code and for a more comprehensive analysis.
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(:title Python Elapsed Time Functions:)
(:keywords time, python, timeit, default_timer, timer, cpu_time:)
(:description Python has a number of timing functions to show you how much time has elapsed between the start and end of a process.:)
Code in Python is not typically built for speed but it is improved with compilers such as Nuitka, PyPy, and Cython. It often helps to know how long certain sections of code require to run so that those sections can be optimized, parallelized, or compiled.
(:source lang=python:)
import time
start = time.time()
# some code to time
end = time.time()
elapsed = end-start
(:sourceend:)
Sometimes a certain section of code is a majority of the run-time so the code may be ported to a compiled language such as C++ or Fortran that is then called from Python, especially if improving a particular section can greatly reduce the total time. That one section may be converted to a langauge that is built for speed and then called by Python. The following tutorial shows to how to implement a simple timer on sections of code. For more complex projects, a profiler such as '''profile''' or '''cProfile''' maybe a better solution to avoid the extra timer code and for a more comprehensive analysis.
(:html:)
<iframe width="560" height="315" src="//www.youtube.com/embed/tnoAro7whYw?rel=0" frameborder="0" allowfullscreen></iframe>
(:htmlend:)
(:toggle hide timing button show="Show Source":)
(:div id=timing:)
(:source lang=python:)
import time
import timeit
import datetime
def concat1(n):
start = time.time()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = time.time()
return end-start
def concat2(n):
start = timeit.default_timer()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = timeit.default_timer()
return end-start
def concat3(n):
start = datetime.datetime.now()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = datetime.datetime.now()
return (end-start).total_seconds()
def concat4(n):
start = time.process_time()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = time.process_time()
return end-start
print(timeit.timeit('"-".join(str(i) for i in range(100000))',number=5))
print(concat1(100000))
print(concat2(100000))
print(concat3(100000))
print(concat4(100000))
import numpy as np
x = np.arange(1,10002,500)
y1 = np.empty(len(x))
y2 = np.empty(len(x))
y3 = np.empty(len(x))
y4 = np.empty(len(x))
for j,xj in enumerate(x):
y1[j] = concat1(xj)
y2[j] = concat2(xj)
y3[j] = concat3(xj)
y4[j] = concat4(xj)
print(j,xj,y1[j],y2[j],y3[j])
import matplotlib.pyplot as plt
plt.plot(x,y1,label='time.time()')
plt.plot(x,y2,label='timeit.default_timer()')
plt.plot(x,y3,label='datetime.datetime.now()')
plt.plot(x,y4,label='time.process_time()')
plt.legend()
plt.show()
(:sourceend:)
(:divend:)
----
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<div id="disqus_thread"></div>
<script type="text/javascript">
/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
var disqus_shortname = 'apmonitor'; // required: replace example with your forum shortname
/* * * DON'T EDIT BELOW THIS LINE * * */
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var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
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(:htmlend:)
(:keywords time, python, timeit, default_timer, timer, cpu_time:)
(:description Python has a number of timing functions to show you how much time has elapsed between the start and end of a process.:)
Code in Python is not typically built for speed but it is improved with compilers such as Nuitka, PyPy, and Cython. It often helps to know how long certain sections of code require to run so that those sections can be optimized, parallelized, or compiled.
(:source lang=python:)
import time
start = time.time()
# some code to time
end = time.time()
elapsed = end-start
(:sourceend:)
Sometimes a certain section of code is a majority of the run-time so the code may be ported to a compiled language such as C++ or Fortran that is then called from Python, especially if improving a particular section can greatly reduce the total time. That one section may be converted to a langauge that is built for speed and then called by Python. The following tutorial shows to how to implement a simple timer on sections of code. For more complex projects, a profiler such as '''profile''' or '''cProfile''' maybe a better solution to avoid the extra timer code and for a more comprehensive analysis.
(:html:)
<iframe width="560" height="315" src="//www.youtube.com/embed/tnoAro7whYw?rel=0" frameborder="0" allowfullscreen></iframe>
(:htmlend:)
(:toggle hide timing button show="Show Source":)
(:div id=timing:)
(:source lang=python:)
import time
import timeit
import datetime
def concat1(n):
start = time.time()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = time.time()
return end-start
def concat2(n):
start = timeit.default_timer()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = timeit.default_timer()
return end-start
def concat3(n):
start = datetime.datetime.now()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = datetime.datetime.now()
return (end-start).total_seconds()
def concat4(n):
start = time.process_time()
time.sleep(0.02)
s = "-".join(str(i) for i in range(n))
end = time.process_time()
return end-start
print(timeit.timeit('"-".join(str(i) for i in range(100000))',number=5))
print(concat1(100000))
print(concat2(100000))
print(concat3(100000))
print(concat4(100000))
import numpy as np
x = np.arange(1,10002,500)
y1 = np.empty(len(x))
y2 = np.empty(len(x))
y3 = np.empty(len(x))
y4 = np.empty(len(x))
for j,xj in enumerate(x):
y1[j] = concat1(xj)
y2[j] = concat2(xj)
y3[j] = concat3(xj)
y4[j] = concat4(xj)
print(j,xj,y1[j],y2[j],y3[j])
import matplotlib.pyplot as plt
plt.plot(x,y1,label='time.time()')
plt.plot(x,y2,label='timeit.default_timer()')
plt.plot(x,y3,label='datetime.datetime.now()')
plt.plot(x,y4,label='time.process_time()')
plt.legend()
plt.show()
(:sourceend:)
(:divend:)
----
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<script type="text/javascript">
/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
var disqus_shortname = 'apmonitor'; // required: replace example with your forum shortname
/* * * DON'T EDIT BELOW THIS LINE * * */
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var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
dsq.src = 'https://' + disqus_shortname + '.disqus.com/embed.js';
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