#!/usr/bin/env python

# coding: utf-8

# ## RRAM Stanford model data extraction

# In[103]:

#get_ipython().magic(u'reset')

# In[104]:

import numpy as np

import matplotlib.pyplot as plt

import xlrd

from scipy import stats

import scipy.stats as st

import probscale

plt.rcParams.update({'font.size':20})

# In[105]:

#.xlsx file which contains the extracted data from the paper

loc=('./Default Dataset.xlsx')

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wb = xlrd.open_workbook(loc)

sheet = wb.sheet_by_index(0)

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n=sheet.nrows

# ### Resistance Values

# In[108]:

R=np.zeros(n)

for i_rows in range(n):

R[i_rows]=float(sheet.cell_value(i_rows,0))

# In[109]:

R

# ### $Z_{score}$ values

# In[110]:

Zscore=np.zeros(n)

for i_rows in range(n):

Zscore[i_rows]=float(sheet.cell_value(i_rows,1))

# In[111]:

Zscore

# ### CDF is calculated from $Z_{Score}$

# In[112]:

CDF=st.norm.cdf(Zscore)

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CDF

# ### Plot of CDF vs R with y axis as linear

# In[129]:

plt.rc('font', family='serif')

fig = plt.figure(figsize=(10,8))

ax = fig.add_subplot(1, 1, 1)

line, = ax.plot(R/1000,CDF*100,'v ')

ax.set_xscale('log')

plt.xlim([1,1e4])

plt.ylim([0,100])

plt.yticks([0,25,50,75,100])

plt.xlabel('R(in KOhm)')

plt.ylabel('CDF(%)')

plt.title('Extracted data-linear y scale')

plt.grid()

plt.show()

# ### Plot of CDF vs R in probplot

# In[130]:

fig = plt.figure(figsize=(10,8))

ax3=fig.add_subplot(1, 1, 1)

common_opts = dict(

)

fig = probscale.probplot(R/1000, ax=ax3, plottype='prob',

problabel='Cumulative Probabilities(%)', **common_opts)

ax3.set_xlim(left=2, right=1e4)

ax3.set_ylim(bottom=0.13, top=99.87)

ax3.grid()

plt.yticks([0.5,2,10,30,50,70,90,98,99.5])

plt.tight_layout()

plt.title('Extracted data-Probability y scale')

plt.show()

# ### Fitting Polynomial Equation

# In[116]:

poly_order=7 # Fitting Polynomial order

z = np.polyfit(R/1000, CDF, poly_order)

# In[117]:

R/1000

# In[118]:

z

# In[119]:

p = np.poly1d(z)

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print(np.poly1d(p))

# In[121]:

Rfit=np.linspace(12,105,101)

# In[122]:

CDFfit=p(Rfit)

# In[123]:

CDFfit

# In[136]:

fig = plt.figure(figsize=(10,8))

ax = fig.add_subplot(1, 1, 1)

line, = ax.plot(R/1000,CDF*100,'v ',label='data')

line, = ax.plot(Rfit,CDFfit*100,label='fit')

ax.set_xscale('log')

plt.xlim([1,1e4])

plt.ylim([0,100])

plt.xlabel('R(in KOhm)')

plt.ylabel('CDF(%)')

plt.grid()

plt.title('Original and Fitted plot')

ax.legend()

plt.show()

# ### PDF calculation by differentiating the CDF polynomial fit function

# ### Original Polynomial = $\sum\limits_{i=0}^n z[n-i]x^i$

# ### Differentiation = $\sum\limits_{i=1}^n iz[n-i] x^{i-1}$

# In[125]:

def PDF_fit(z,x,n):

return Val

# In[126]:

PDF_fit_value=np.zeros(len(Rfit))

for i in range(len(Rfit)):

PDF_fit_value[i]=PDF_fit(z,Rfit[i],poly_order)

PDF_fit_value

# In[131]:

fig = plt.figure(figsize=(10,8))

ax = fig.add_subplot(1, 1, 1)

line, = ax.plot(Rfit,PDF_fit_value,'*-')

plt.xlabel('R(in KOhm)')

plt.ylabel('PDF')

plt.grid()

plt.title('Probability plot')

plt.show()

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