The previous article explains how to use memory_profiler to evaluate the memory consumption of a Python program. Here I will explain how to plot that consumption using matplotlib.
This article is based on the article Memory plots with memory_profiler (broken link).
A small modification will be made to the program that calculates the inverse matrix:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#Importar de memory_profiler a memory_usage
from memory_profiler import memory_usage
#Se importa numpy como np
import numpy as np
#Se crea la funcion que calcula la matriz inversa
def Inversa(n):
return np.matrix(np.random.rand(n,n)).I
#rando = np.random.rand(n, n)
#a = np.matrix(rando)
#inversa = a.I
return inversa
if __name__ == '__main__':
#Se define una lista de tamaños de la matriz
tamagno = 2 ** np.arange(0, 12)
#Se calcula la memoria usada de la funcion Inversa pasando el ultimo tamaño de la lista generada
mem_usage = memory_usage((Inversa,(tamagno[-1],)),interval=.01)
#Se imprime la lista que contiene la información de la memoria usada
print mem_usage
#Se importa pylab
import pylab as pl
#Se genera la gráfica
pl.plot(np.arange(len(mem_usage)) * .01, mem_usage, label='Matriz Inversa')
pl.xlabel('Tiempo (seg)')
pl.ylabel('Consumo de memoria (MB)')
pl.show()
The result of the execution is the following:
[11.39453125, 13.39453125, 15.45703125, 17.51953125, 19.83984375, 21.90234375, 23.96484375, 26.02734375, 28.34765625, 30.41015625, 32.73046875, 34.79296875, 36.85546875, 38.91796875, 41.23828125, 43.30078125, 53.80859375, 64.37890625, 74.94921875, 48.70703125, 60.30859375, 71.91015625, 77.8046875, 81.15625, 85.0234375, 88.890625, 92.5, 96.109375, 99.71875, 103.5859375, 107.453125, 111.04296875, 114.39453125, 118.00390625, 121.61328125, 125.22265625, 128.83203125, 132.44140625, 134.76171875, 138.11328125, 139.6328125, 139.6328125, 140.5703125, 140.5703125, 140.5703125, 140.5703125, 140.5703125, 140.5703125, 140.5703125, 140.4609375, 140.4609375, 140.4609375, 139.953125, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.20703125, 140.20703125, 139.953125, 140.20703125, 140.20703125, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.4609375, 140.6640625, 140.6640625, 140.6640625, 140.05078125, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.625, 140.625, 140.625, 140.625, 140.05078125, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.05078125, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.05078125, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.55859375, 140.359375, 140.61328125, 140.61328125, 140.61328125, 140.61328125, 140.61328125, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.7265625, 140.75, 140.75, 140.75, 140.75, 140.75, 140.75, 140.75, 140.75, 140.75, 140.75, 140.75, 140.75, 141.26171875, 141.26171875, 141.26171875, 141.26171875, 140.75, 140.75, 141.26171875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 144.8671875, 142.80859375, 140.75, 140.75, 140.75, 141.26171875, 141.26171875, 141.26171875, 141.26171875, 140.8359375, 149.08203125, 149.08203125, 153.4609375, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 157.328125, 149.08203125, 149.08203125, 140.8359375, 140.8359375, 141.34765625, 141.34765625, 141.34765625, 141.34765625, 141.34765625, 142.89453125, 142.89453125, 143.6640625, 144.953125, 144.953125, 144.953125, 144.953125, 144.953125, 144.953125, 144.953125, 144.953125, 144.953125, 144.953125, 144.953125, 142.89453125, 142.89453125, 142.89453125, 142.89453125, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 140.8359375, 141.34375, 141.34375, 141.34375, 140.83984375, 140.83984375, 140.83984375, 142.8984375, 143.41015625, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 144.95703125, 142.8984375, 142.8984375, 140.84375, 140.84375, 140.84375, 141.35546875, 141.35546875, 141.35546875, 141.35546875, 140.84375, 144.96484375, 148.83203125, 156.3046875, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 157.078125, 149.08984375, 149.08984375, 140.84375, 140.84375, 141.35546875, 141.35546875, 141.35546875, 141.35546875, 140.84375, 142.90234375, 142.90234375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 144.9609375, 142.90234375, 142.90234375, 140.84375, 140.84375, 140.84375, 141.35546875, 141.35546875, 141.35546875, 141.35546875, 140.84375, 140.84375, 141.765625, 150.015625, 160.84375, 161.35546875, 76.8515625, 12.84375]The plot that is generated is the following:
The plot shows that memory consumption rises quickly, in less than 1 second, to approximately 140MB, followed by a stabilization with small increases and decreases around a constant value until a drop to 12MB occurs 5 seconds into the execution.
If you want to see more examples of how to plot memory consumption, you can check the article this one is based on.
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