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Keras model library mixed,
some wrong paths fixed
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@fipeop
fipeop authored May 24, 2019
1 parent 073db31 commit e7de04b
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278 changes: 139 additions & 139 deletions autoXRD.py
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"""
AUTO-XRD
filneame: autoXRD.py version: 1.0
Series of functions for XRD spectra pre-processing, normalization
and data augmentation
@authors: Felipe Oviedo and Danny Zekun Ren
MIT Photovoltaics Laboratory / Singapore and MIT Alliance for Research and Tehcnology
All code is under Apache 2.0 license, please cite any use of the code as explained
in the README.rst file, in the GitHub repository.
"""

#################################################################
#Libraries and dependencies
################################################################

import pandas as pd
import numpy as np
from scipy.signal import savgol_filter
from scipy.signal import find_peaks_cwt

#################################################################
# Functions
#################################################################

# Data normalization from 0 to 1 for double column dataframe, returns single column array
def normdata(data):

(len1,w1) = np.shape(data)
ndata = np.zeros([len1,w1//2])
for i in range(w1//2):
ndata[:,i]=(data[:,2*i+1]-min(data[:,2*i+1]))/(max(data[:,2*i+1])-min(data[:,2*i+1]))
return ndata

# Data normalization from 0 to 1 for single column dataframe
def normdatasingle(data):

(len1,w1) = np.shape(data)
ndata = np.zeros([len1,w1])
for i in range(w1):
ndata[:,i]=(data[:,i]-min(data[:,i]))/(max(data[:,i])-min(data[:,i]))
return ndata

# Data augmendatation for simulated XRD patterns
def augdata(data,num,par1,minn,maxn):

(len1,w1) = np.shape(data)
augd =np.zeros([len1,num])
naugd=np.zeros([len1,num])
newaugd=np.zeros([len1,num])
crop_augd = np.zeros([maxn-minn,num])
pard = []

for i in range(num):
rnd = np.random.randint(0,w1)
# create the first filter for peak elimination
dumb= np.repeat(np.random.choice([0,1,1],300),len1//300)
dumb1= np.append(dumb,np.zeros([len1-len(dumb),]))
# create the second filter for peak scaling
dumbrnd= np.repeat(np.random.rand(100,),len1//100)
dumbrnd1=np.append(dumbrnd,np.zeros([len1-len(dumbrnd),]))
#peak eleminsation and scaling
augd[:,i] = np.multiply((data[:,rnd]),dumbrnd1)
augd[:,i] = np.multiply(augd[:,i],dumb1)
#nomrlization
naugd[:,i] = (augd[:,i]-min(augd[:,i]))/(max(augd[:,i])-min(augd[:,i])+1e-9)
pard.append (par1[2*rnd])
#adding shift
cut = np.random.randint(-20*1,20)
#XRD spectrum shift to left
if cut>=0:
newaugd[:,i] = np.append(naugd[cut:,i],np.zeros([cut,]))
#XRD spectrum shift to right
else:
newaugd[:,i] = np.append(naugd[0:len1+cut,i],np.zeros([cut*-1,]))

crop_augd[:,i] = newaugd[minn:maxn,i]
#
return newaugd, pard,crop_augd
# Data augmendatation for experimental XRD pattern
def exp_augdata(data,num,label):

(len1,w1) = np.shape(data)
augd =np.zeros([len1,num])
naugd=np.zeros([len1,num])
newaugd=np.zeros([len1,num])
par=np.zeros([num,])

for i in range(num):
rnd = np.random.randint(0,w1)

# create the first filter for peak elimination
dumb= np.repeat(np.random.choice([0,1,1],300),len1//300)
dumb1= np.append(dumb,np.zeros([len1-len(dumb),]))
# create the second filter for peak scaling
dumbrnd= np.repeat(np.random.rand(200,),len1//200)
dumbrnd1=np.append(dumbrnd,np.zeros([len1-len(dumbrnd),]))
#peak eleminsation and scaling
augd[:,i] = np.multiply((data[:,rnd]),dumbrnd1)
augd[:,i] = np.multiply(augd[:,i],dumb1)
#normalization
naugd[:,i] = (augd[:,i]-min(augd[:,i]))/(max(augd[:,i])-min(augd[:,i])+1e-9)
par[i,] =label[rnd,]
#adding shift
cut = np.random.randint(-20*1,20)
#XRD spectrum shift to left
if cut>=0:
newaugd[:,i] = np.append(naugd[cut:,i],np.zeros([cut,]))
#XRD spectrum shift to right
else:
newaugd[:,i] = np.append(naugd[0:len1+cut,i],np.zeros([cut*-1,]))

return newaugd, par

# Extracting experimental data
def exp_data_processing (data,minn,maxn,window):

(len1,w1) = np.shape(data)
nexp1 =np.zeros([maxn-minn,w1])
for i in range(w1):
#savgol_filter to smooth the data
new1 = savgol_filter(data[minn:maxn,i], 31, 3)
#peak finding
zf= find_peaks_cwt(new1, np.arange(10,15), noise_perc=0.01)
#background substraction
for j in range(len(zf)-1):
zf_start= np.maximum(0,zf[j+1]-window//2)
zf_end = np.minimum(zf[j+1]+window//2,maxn)
peak = new1[zf_start:zf_end]

##abritaryly remove 1/4 data
npeak = np.maximum(0,peak-max(np.partition(peak,window//5 )[0:window//5]))
nexp1[zf_start:zf_end,i]= npeak
return nexp1

"""
AUTO-XRD
filneame: autoXRD.py version: 1.0
Series of functions for XRD spectra pre-processing, normalization
and data augmentation
@authors: Felipe Oviedo and Danny Zekun Ren
MIT Photovoltaics Laboratory / Singapore and MIT Alliance for Research and Tehcnology
All code is under Apache 2.0 license, please cite any use of the code as explained
in the README.rst file, in the GitHub repository.
"""

#################################################################
#Libraries and dependencies
################################################################

import pandas as pd
import numpy as np
from scipy.signal import savgol_filter
from scipy.signal import find_peaks_cwt

#################################################################
# Functions
#################################################################

# Data normalization from 0 to 1 for double column dataframe, returns single column array
def normdata(data):

(len1,w1) = np.shape(data)
ndata = np.zeros([len1,w1//2])
for i in range(w1//2):
ndata[:,i]=(data[:,2*i+1]-min(data[:,2*i+1]))/(max(data[:,2*i+1])-min(data[:,2*i+1]))
return ndata

# Data normalization from 0 to 1 for single column dataframe
def normdatasingle(data):

(len1,w1) = np.shape(data)
ndata = np.zeros([len1,w1])
for i in range(w1):
ndata[:,i]=(data[:,i]-min(data[:,i]))/(max(data[:,i])-min(data[:,i]))
return ndata

# Data augmendatation for simulated XRD patterns
def augdata(data,num,par1,minn,maxn):

(len1,w1) = np.shape(data)
augd =np.zeros([len1,num])
naugd=np.zeros([len1,num])
newaugd=np.zeros([len1,num])
crop_augd = np.zeros([maxn-minn,num])
pard = []

for i in range(num):
rnd = np.random.randint(0,w1)
# create the first filter for peak elimination
dumb= np.repeat(np.random.choice([0,1,1],300),len1//300)
dumb1= np.append(dumb,np.zeros([len1-len(dumb),]))
# create the second filter for peak scaling
dumbrnd= np.repeat(np.random.rand(100,),len1//100)
dumbrnd1=np.append(dumbrnd,np.zeros([len1-len(dumbrnd),]))
#peak eleminsation and scaling
augd[:,i] = np.multiply((data[:,rnd]),dumbrnd1)
augd[:,i] = np.multiply(augd[:,i],dumb1)
#nomrlization
naugd[:,i] = (augd[:,i]-min(augd[:,i]))/(max(augd[:,i])-min(augd[:,i])+1e-9)
pard.append (par1[2*rnd])
#adding shift
cut = np.random.randint(-20*1,20)
#XRD spectrum shift to left
if cut>=0:
newaugd[:,i] = np.append(naugd[cut:,i],np.zeros([cut,]))
#XRD spectrum shift to right
else:
newaugd[:,i] = np.append(naugd[0:len1+cut,i],np.zeros([cut*-1,]))

crop_augd[:,i] = newaugd[minn:maxn,i]
#
return newaugd, pard,crop_augd
# Data augmendatation for experimental XRD pattern
def exp_augdata(data,num,label):

(len1,w1) = np.shape(data)
augd =np.zeros([len1,num])
naugd=np.zeros([len1,num])
newaugd=np.zeros([len1,num])
par=np.zeros([num,])

for i in range(num):
rnd = np.random.randint(0,w1)

# create the first filter for peak elimination
dumb= np.repeat(np.random.choice([0,1,1],300),len1//300)
dumb1= np.append(dumb,np.zeros([len1-len(dumb),]))
# create the second filter for peak scaling
dumbrnd= np.repeat(np.random.rand(200,),len1//200)
dumbrnd1=np.append(dumbrnd,np.zeros([len1-len(dumbrnd),]))
#peak eleminsation and scaling
augd[:,i] = np.multiply((data[:,rnd]),dumbrnd1)
augd[:,i] = np.multiply(augd[:,i],dumb1)
#normalization
naugd[:,i] = (augd[:,i]-min(augd[:,i]))/(max(augd[:,i])-min(augd[:,i])+1e-9)
par[i,] =label[rnd,]
#adding shift
cut = np.random.randint(-20*1,20)
#XRD spectrum shift to left
if cut>=0:
newaugd[:,i] = np.append(naugd[cut:,i],np.zeros([cut,]))
#XRD spectrum shift to right
else:
newaugd[:,i] = np.append(naugd[0:len1+cut,i],np.zeros([cut*-1,]))

return newaugd, par

# Extracting experimental data
def exp_data_processing (data,minn,maxn,window):

(len1,w1) = np.shape(data)
nexp1 =np.zeros([maxn-minn,w1])
for i in range(w1):
#savgol_filter to smooth the data
new1 = savgol_filter(data[minn:maxn,i], 31, 3)
#peak finding
zf= find_peaks_cwt(new1, np.arange(10,15), noise_perc=0.01)
#background substraction
for j in range(len(zf)-1):
zf_start= np.maximum(0,zf[j+1]-window//2)
zf_end = np.minimum(zf[j+1]+window//2,maxn)
peak = new1[zf_start:zf_end]

##abritaryly remove 1/4 data
npeak = np.maximum(0,peak-max(np.partition(peak,window//5 )[0:window//5]))
nexp1[zf_start:zf_end,i]= npeak
return nexp1

##################################################################
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