Diffusion computation

In [1]:

    

'''
author: Alvason Zhenhua Li
date:   03/19/2015
'''

%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

import alva_machinery_diffusion as alva

AlvaFontSize = 23
AlvaFigSize = (16, 7)
numberingFig = 0


    

In [2]:

    

'''Logistic randomness --- Logistic distribution'''
totalPoint_Input = int(300 + 1)
gInput = np.arange(totalPoint_Input)
meanL = totalPoint_Input/2

randomSeed_normal = np.random.standard_normal(totalPoint_Input)
randomSeed = np.random.logistic(0, 3, totalPoint_Input)

totalLevel = int(totalPoint_Input/1)
category = alva.AlvaLevel(randomSeed, totalLevel, False)
gLevel = category[0]
numberLevel = category[1]
print category[2].shape

# calculating the mean
sumL = 0
for i in range(totalPoint_Input):
    sumL = sumL + randomSeed[i]
current_mean = sumL/(totalPoint_Input)
print ('current mean', current_mean)

totalLevel = int(totalPoint_Input/1)
category_normal = alva.AlvaLevel(randomSeed_normal, totalLevel, False)
gLevel_normal = category_normal[0]
numberLevel_normal = category_normal[1]

category = alva.AlvaLevel(randomSeed, totalLevel, False)
gLevel = category[0]
numberLevel = category[1]
print category[2].shape

numberingFig = numberingFig + 1
figure = plt.figure(numberingFig, figsize = AlvaFigSize)
plot1 = figure.add_subplot(1, 2, 1)
plot1.plot(gInput, randomSeed, color = 'gray', marker = 'o', label = 'data')
plot1.plot(gInput, alva.AlvaMinMax(randomSeed), color = 'red', marker = 'o', label = 'minMaxListing')
plot1.plot(gInput, alva.AlvaMinMax(randomSeed_normal), label = 'minMax_normal')
if totalPoint_Input < 100:
    plot1.set_xticks(gInput, minor = True) 
    plot1.set_yticks(randomSeed, minor = True)
    plot1.grid(True, which = 'minor')
else:
    plot1.grid(True, which = 'major')
plt.title(r'$ Random \ output \ (total-input = %i,\ mean = %f) $'%(totalPoint_Input, meanL)
          , fontsize = AlvaFontSize)
plt.xlabel(r'$ input-time $', fontsize = AlvaFontSize)
plt.ylabel(r'$ output $', fontsize = AlvaFontSize)
plt.legend(loc = (0, -0.2))

plot2 = figure.add_subplot(1, 2, 2)
plot2.plot(numberLevel_normal, gLevel_normal,  label = 'category_normal')
plot2.plot(numberLevel, gLevel, color = 'red', marker = 'o', label = 'category')
if totalPoint_Input < 100:
    plot2.set_xticks(numberLevel, minor = True) 
    plot2.set_yticks(gLevel, minor = True)
    plot2.grid(True, which = 'minor')
else:
    plot2.grid(True, which = 'major')
plt.title(r'$ Logistic \ distribution\ (data = %i,\ level = %i) $'%(totalPoint_Input, totalLevel)
          , fontsize = AlvaFontSize)
plt.xlabel(r'$ Number/level $', fontsize = AlvaFontSize)
plt.ylabel(r'$ Output-level $', fontsize = AlvaFontSize)
plt.legend(loc = (0, -0.2))

figure.tight_layout()
plt.show()


    

    




(301, 2)
('current mean', 0.032736073553725556)
(301, 2)






    

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