Python Slots Speed

Posted on 4/7/2022 admin

PyAudioAnalysis is the Python library used for audio processing. It performs various audio features like classification, extraction, segmentation, etc. PyAudioAnalysis is also efficient in classifying unknown sounds and extracting audio. This special tool of Python also helps to detect audio chunks and remove unnecessary slots from heavy.
Speed is, in fact, a very important property in data structures. Why does it take much less time to use NumPy operations over vanilla python? Let’s have a look at a few examples.

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Python Lab: Wheel of Fortune

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In this lab, you will write a program that plays the game “Wheel of Fortune”. First, your program asks the user to enter an integer number larger than zero (as your spin of the wheel). You need to check whether the input is legitimate. Your wheel has 10 slots (spaces). So your program will generate the awards on the wheel by generating a sequence of 10 random numbers (one for each slot), each number is between 0 and 1000, where the minimum unit is 100. You need to print the sequence and tell how much the player wins based on the spin.
Here’s an example of a run of the program. The output from the program is in blue, and the input (what you type) is in black.
Welcome to Wheel of Fortune!
There are 10 slots in your wheel.
Spin your wheel by entering an integer number: hello
This is not an integer greater than 0.
please enter an integer: 0
This is not an integer greater than 0.
please enter an integer: 22
The wheel is
[100, 800, 700, 200, 400, 400, 600, 700, 0, 0]
You won $700 in the Wheel of Fortune!
press enter to exit:
At the beginning of the spin, the player is at the first slot (the slot with 100). So a spin of 10 will return to the first slot and a spin of 22 will land in the-700-dollar slot.
1. Coding the assignment
Open the python IDLE GUI. Similar to the last lab, you will be using input function to get the user input, and printing out the final “Press enter to exist:” message. There is more than one way to do this lab, so we will not give step-by-step instructions. Some hints and requirements are:
· Your program should first import the random module. Right after that, you must put in this line of code (in red):
random.seed(1)
· You must check the input is actually an integer greater than 0. It cannot be 0 or any character. You can do this by using a while loop to keep asking the user until he/she enters the correct input.
· You may use the randrange() function in the random module to generate an integer. How do you use randrange()? You do the following:
random.randrange(11)
This will randomly generate an integer number less than 11. You can multiple this number by 100 to generate the award on each slot on the wheel.
· To generate 10 awards, one for each slot, you may use a for loop. Inside the for loop, you should call randrange to generate a random number and add that to your sequence.
· You need to use remainder to decide which award the player should get. For example, if the player’s number is 11, it means one round and 1 more slot. So the award is the second number in your sequence.

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· You need to write comments for your code (line by line).

When running a complex Python program that takes quite a long time to execute, you might want to improve its execution time. But how?

First of all, you need the tools to detect the bottlenecks of your code, i.e. which parts take longer to execute. This way, you can concentrate in speeding these parts first.

And also, you should also control the memory and CPU usage, as it can point you towards new portions of code that could be improved.

Therefore, in this post I’ll comment on 7 different Python tools that give you some insight about the execution time of your functions and the Memory and CPU usage.

1. Use a decorator to time your functions

The simpler way to time a function is to define a decorator that measures the elapsed time in running the function, and prints the result:

</div><table><tbody><tr><td><div><div>2</div><div>4</div><div>6</div><div>8</div><div>10</div><div>12</div><div>14</div></div></td><td><div><div><span>from </span><span>functools </span><span>import </span><span>wraps</span></div><div><span>@</span><span>wraps</span><span>(</span><span>function</span><span>)</span></div><div><span>t0</span><span>=</span><span>time</span><span>.</span><span>time</span><span>(</span><span>)</span></div><div><span>t1</span><span>=</span><span>time</span><span>.</span><span>time</span><span>(</span><span>)</span></div><div><span>(</span><span>function</span><span>.</span><span>func_name</span><span>,</span><span>str</span><span>(</span><span>t1</span><span>-</span><span>t0</span><span>)</span><span>)</span></div><div><span>return</span><span>result</span></div></div></td></tr></tbody></table><p>Then, you have to add this decorator before the function you want to measure, like</p><table><tbody><tr><td><div>2</div></td><td><div><span>def </span><span>myfunction</span><span>(</span><span>.</span><span>.</span><span>.</span><span>)</span><span>:</span></div></td></tr></tbody></table><p>For example, let’s measure how long it takes to sort an array of 2000000 random numbers:</p><div><textarea wrap='soft' readonly='>@fn_timer def random_sort(n): return sorted([random.random() for i in range(n)]) if __name__ '__main__': random_sort(2000000)

def random_sort(n):

if__name__'__main__':

If you run your script, you should see something like

</div><table><tbody><tr><td><div><span>$</span><span>python</span><span>-</span><span>m</span><span>timeit</span><span>-</span><span>n</span><span>4</span><span>-</span><span>r</span><span>5</span><span>-</span><span>s</span><span>'import timing_functions'</span><span>'timing_functions.random_sort(2000000)'</span></div></td></tr></tbody></table><p>where <span>timing_functions</span> is the name of your script.</p><p>At the end of the output, you should see something like:</p><p>indicating that of 4 times running the test (<span>-n 4</span>), and averaging 5 repetitions on each test (<span>-r 5</span>), the best test result was of 2.08 seconds.</p><p>If you don’t specify the number of tests or repetitions, it defaults to 10 loops and 5 repetitions.</p><h2>3. Using the time Unix command</h2><p>However, both the decorator and the timeit module are based on Python. This is why the unix time utility may be useful, as it is an external Python measure.</p><p>To run the time utility type:</p><p>which gives the output:</p><div><textarea wrap='soft' readonly='>Total time running random_sort: 1.3931210041 seconds real 1.49 user 1.40 sys 0.08

2 4	Total time running random_sort:1.3931210041seconds user1.40

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The first line comes from the decorator we defined, and the other three:

Real indicates the total time spent executing the script.
User indicates the amount of time the CPU spent executing the script
Sys indicates the amount of time spent in kernel-level functions.

Note: as defined in wikipedia, the kernel is a computer program that manages input/output requests from software, and translates them into data processing instructions for the central processing unit (CPU) and other electronic components of a computer.

Therefore, the difference between the real time and the sum of user+sys may indicate the time spent waiting for input/output or that the system is busy running other external tasks.

4. Using the cProfile module

If you want to know how much time is spent on each function and method, and how many times each of them is called, you can use the cProfile module:

</div><table><tbody><tr><td><div><div>2</div><div>4</div><div>6</div><div>8</div></div></td><td><div><div><span>def </span><span>random_sort2</span><span>(</span><span>n</span><span>)</span><span>:</span></div><div><span>l</span><span>.</span><span>sort</span><span>(</span><span>)</span></div><div><span>random_sort2</span><span>(</span><span>2000000</span><span>)</span></div></div></td></tr></tbody></table><p>Finally, you can obtain a line by line description of the <span>random_sort2</span> function by typing:</p><p>where the <span>-l</span> flag indicates line-by-line and the <span>-v</span> flag indicates verbose output. With this method, we see that the array construction takes about 44% of the computation time, whereas the <span>sort()</span> method takes the remaining 56%.</p><p>You will also see that due to the time measurements, the script might take longer to execute.</p><h2>6. Use the memory_profiler module</h2><p>The memory_profiler module is used to measure memory usage in your code, on a line-by-line basis. However, it can make your code to run much more slower.</p><p>Install it with</p><img src='https://cdn.shoplightspeed.com/shops/624828/files/19702561/1600x2048x2/joy-susan-mini-crossbody-wristlet-clutch.jpg' alt='Speed' title='Speed' /><p>Also, it is recommended to install the psutil package, so that the <span>memory_profile</span> runs faster:</p><h3 id='python-slots-speed-game'>Python Slots Speed Game</h3><p>In a similar way as the <span>line_profiler</span>, use the <span>@profile</span> decorator to mark which functions to track. Next, type:</p><p>yes, the previous script takes longer than the 1 or 2 seconds that took before. And if you didn’t install the <span>psutil</span> package, maybe you’re still waiting for the results!</p><p>Looking at the output, note that the memory usage is expressed in terms of MiB, which stand for mebibyte (1MiB = 1.05MB).</p><h2>7. Using the guppy package</h2><p>Finally, with this package you’ll be able to track how many objects of each type (str, tuple, dict, etc) are created at each stage in your code.</p><p>Install it with</p><p>Next, add it in your code as:</p><div><textarea wrap='soft' readonly='>from guppy import hpy def random_sort3(n): hp = hpy() print 'Heap at the beginning of the functionn', hp.heap() l = [random.random() for i in range(n)] l.sort() print 'Heap at the end of the functionn', hp.heap() return l if __name__ '__main__': random_sort3(2000000)

def random_sort3(n):

print'Heap at the beginning of the functionn',hp.heap()

l.sort()

print'Heap at the end of the functionn',hp.heap()

if__name__'__main__':

And run your code with:

You’ll see something like the following output:

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By placing the heap at different places in your code, you can study the object creation and deletion in the script flow.

If you want to learn more about speeding your Python code, I recommend you the book High Performance Python: Practical Performant Programming for Humans, september 2014.

Hope it was useful! 🙂

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