python - How do I profile my time spent in library functions? -


when try profile class, unable break down time spent past each of own methods , functions.

for example, using cprofile, able following:

import numpy np import cprofile  class test_class:     def __init__(self, length, width):         self.length = length         self.width = width         self.populate()         return none      def populate(self):         self.array = np.random.randint(0, 3, (self.length, self.width))         return none      def add(self, additional_array):         self.array = np.add(self.array, additional_array)         return none   def test_function():     x, y = 3000, 2000     test_array = np.random.randint(0, 2, (x, y))     model_one = test_class(x, y)     model_one.add(test_array)  cprofile.run("test_function()")

i given analysis:

         9 function calls in 0.214 seconds     ordered by: standard name     ncalls  tottime  percall  cumtime  percall filename:lineno(function)         1    0.002    0.002    0.214    0.214 <string>:1(<module>)         1    0.000    0.000    0.119    0.119 temp.py:11(populate)         1    0.020    0.020    0.020    0.020 temp.py:15(add)         1    0.002    0.002    0.212    0.212 temp.py:24(test_function)         1    0.000    0.000    0.119    0.119 temp.py:5(__init__)         1    0.000    0.000    0.214    0.214 {built-in method exec}         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.profiler' objects}         2    0.190    0.095    0.190    0.095 {method 'randint' of 'mtrand.randomstate' objects}

alternatively, using profilehooks able following:

@profile(immediate=true) def test_function():     x, y = 3000, 2000     test_array = np.random.randint(0, 2, (x, y))     model_one = test_class(x, y)     model_one.add(test_array)  test_function()

and given analysis:

*** profiler results *** test_function (c:/users/mack/desktop/temp2.py:19) function called 1 times           7 function calls in 0.205 seconds     ordered by: cumulative time, internal time, call count     ncalls  tottime  percall  cumtime  percall filename:lineno(function)         1    0.000    0.000    0.205    0.205 temp.py:19(test_function)         2    0.185    0.093    0.185    0.093 {method 'randint' of 'mtrand.randomstate' objects}         1    0.000    0.000    0.113    0.113 temp.py:5(__init__)         1    0.000    0.000    0.113    0.113 temp.py:11(populate)         1    0.020    0.020    0.020    0.020 temp.py:15(add)         1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.profiler' objects}         0    0.000             0.000          profile:0(profiler)

neither technique show how time spent in library functions. how can analysis shows me how time spent, instance, in numpy.add()?


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