CAMS

h1. Code performance analysis

h2. Execution time

h3. Unscientific way → time module: One sample

Do not forget to configure your logger to the DEBUG level

<pre><code class="python">

from functools import wraps

from time import time

def timing(f):

    @wraps(f)

    def wrap(*args, **kw):

        ts = time()

        result = f(*args, **kw)

        te = time()

        _logger.debug('func:%r took: %2.4fs args:[%r, %r] ', f.__name__, te-ts, args, kw)

        return result

    return wrap

@timing

def my_func(a,b,c):

     d = []

     return a + b + c

</code></pre>

h3. Scientific way → timeit module

It runs a function multiple times to statisitically determine the execution time

h2. Memory

To analyze the dynamic usage of memory within a function use the <code class="python">memory_profiler</code> python package. It will give you a line-by-line report of memory usage.

<pre><code class="python">

from memory_profiler import profile

@profile

def my_func(a,b,c):

     d = [b + (c * i) for i in range(len(a))]

     return d

</code></pre>

<code class="python"></code>