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Functions.py
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Functions.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jul 12 09:50:00 2019
This module contains all the functions used in the modules IV.py, CV.py, IVmultiple.py, CVmultiple.py, Vbd.py.
@author: Sneha Ramshanker
"""
"""
Importing packages
"""
import Constants as con
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import csv
from scipy import stats
from scipy.optimize import fsolve
import seaborn as sns
from scipy.interpolate import UnivariateSpline
from openpyxl import load_workbook
"""
Functions
"""
def bvol(df, x, y, thresh = 0.9995):
"""
Parameters:
df: Pandas dataframe
x: String - name of x column in df (voltage)
y: String - name of y column in df (capacitance)
Optional Parameters:
thresh: Float - r squared threshold value
Returns:
bvol: Float - Breakdown Voltage
Note:
This function determines the breakdown by calculation the point where the relationship between the first and second derivative becomes extremely linear.
"""
frst_der = manual_der(df[x], df[y])
scnd_der = manual_der(df[x], frst_der)
r_value = 1
N = 2
while round(r_value,4) >= thresh: #Threshold for linearity
X = np.log(frst_der[-N:])
Y = np.log(scnd_der[-N:])
slope, intercept, r_value, p_value, std_err = stats.linregress(X,Y)
N = N+1
voltage = df[x].as_matrix()
bvol = voltage[-N+1]
return bvol
def calculate_width(C, A):
"""
Parameters:
C: Float - aapacitance
A: Float - area of the detector
Returns:
integer - width of capacitor
"""
#C = eps * A / d
return (0 if (C == 0) else ((con.eps_si * con.eps0 * A) / C))
def cleanupCV(df, x, y):
"""
Parameters:
df: Pandas dataframe
x: String - name of x column in df (voltage)
y: String - name of y column in df (capacitance)
Returns:
df: Pandas dataframe
Note:
The data is cleaned up so that it can be stored nicely in a dataframe. The cleanup proceduce is specific to the format of the textfile the CV data is stored in.
"""
df = df.iloc[2:]
df.loc[x] = pd.to_numeric(df.loc[:,x])
df[x] = abs(df[x])
return df
def cleanupIV(df, x, y):
"""
Parameters:
df: Pandas dataframe
x: String - name of x column in df (voltage)
y: String - name of y column in df (capacitance)
Returns:
dfclean: Pandas dataframe
Note:
The data is cleaned up so that it can be stored nicely in a dataframe. The cleanup proceduce is specific to the format of the textfile the IV data is stored in.
"""
df = df.iloc[2:]
df[x] = df[x].convert_objects(convert_numeric=True)
df[x] = abs(df[x])
df["Outlier"] = ""
dfclean = (df.dropna(subset = [x, y]))
return dfclean
def dataplot(df, x, y,image_path, log, xlabel, ylabel, iden = "None", gaindeplvol = 0, totdeplvol = 0, gainwidth = 0, bvol = 0):
"""
Parameters:
df: Pandas Dataframe
x: String - name of x column in df
y: String - name of y column in df
image_path: String - path where the image will be saved
log: String ("log" or "ylog" or "nolog") - should the x axis or y axis be log scale? "log" makes xaxis log scaled and "ylog" makes yaxis log scaled.
xlabel: String - label of xaxis
ylabel: String - label of yaxis
Optional Parameters:
iden: String - identification tag for plotting additional elements ["DPWcut", "invCV", "IV"]
gaindeplvol: Float - value of depletion voltage of gain layer
totdeplvol: Float - value of total depletion voltage
gainwidth: Float - value of peak of gain layer (measured in microns)
bvol: Float - value of breakdown voltage
Returns:
No returns but saves the plot
Note:
The following function produces and saves a x-y plot from data in a dataframe .
Given additional optional parameters, the function can produce more complex plots.
"""
plt.figure(figsize=(12,8))
plt.plot(df[x], df[y], '-')
if log == "log":
print("Log is working")
plt.xscale('log')
if log == "ylog":
plt.yscale('log')
if iden == "DPWcut":
plt.xlim(0,5)
if iden == "invCV":
plt.axvline(gaindeplvol, linestyle = '--', color = 'forestgreen', label = 'Gain Depl Vol: '+ str(round(gaindeplvol,1)))
plt.axvline(totdeplvol, linestyle = '--', color = 'darkorchid', label = 'Total Depl Vol: '+ str(round(totdeplvol,1)))
plt.legend()
if iden == "IV":
plt.axvline(bvol, linestyle = '--', color = 'k', alpha = 0.5, label = 'Breakdown V: '+ str(round(bvol,1)) + " V")
plt.legend()
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.savefig(image_path)
plt.clf()
plt.close()
def dataplotDPW(df, x, y,image_path, log, xlabel, ylabel, gainwidth, gainpeak, FWHM = [0, 0, 0]):
"""
Parameters:
df: Pandas dataframe
x: String - name of x column in df
y: String - name of y column in df
image_path: String - path where the image will be saved
xlabel: String - label of xaxis
ylabel: String - label of yaxis
gainwidth: Float - width value of peak of gain layer (measured in microns)
gainpeak: Float - Doping profile value of the peak of gain layer (measured in (cm^-3) )
Optional Parameters:
FWHM: list [fwhm, x, g] where:
fwhm: float - full width half maximum
x: numpy array - x values of plot
g: numpy array - gaussian fit generated using astropy
Returns:
No returns but saves plot
Note:
This function is specific for plotting Doping Profile vs Width
"""
plt.figure(figsize=(12,8))
plt.plot(df[x], df[y], '-')
if log == "log":
print("Log is working")
plt.xscale('log')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.vlines(df[x].max(), df[y].min(), df[y].max() + 0.2*(df[y].max()), 'darkorchid', linestyles = "dashed", label = "Total Depth: "+ str(round(df[x].max(),2)) + r"$ \mu m $")
plt.vlines(gainwidth, df[y].min(), df[y].max() + 0.2*(df[y].max()), 'forestgreen', linestyles = "dashed", label = "Peak Depth: "+ str(round(gainwidth,2)) + r"$\mu m$")
plt.hlines(gainpeak, 0, gainwidth, 'deepskyblue', linestyles = "dashed", label = "Peak Value: "+ str('%.2g' % gainpeak) + r"$ cm^{-3}$")
if FWHM != [0, 0, 0]:
plt.plot([], [], ' ', label="Gain width (FWHM): "+ str(round(FWHM[0], 2))+ r"$ \mu m $")
plt.plot(FWHM[1], FWHM[2](FWHM[1]),'--y', label='Gaussian Fit')
plt.legend(loc = 'upper center')
plt.savefig(image_path)
plt.clf()
plt.close()
def dataplotIV(df,xd,yd,hued, impathIV, bvol):
"""
Parameters:
df: Pandas Dataframe
xd: String - name of x column in df
yd: String - name of y column in df
hued: String - name of hue column in df (Seaborn functionality for lmplot)
impathIV: String - path where image will be saved
bvol: Float - Breakdown Voltage
Return:
No returns but image is saved
Note:
This is an outdated function that plots a visualiztion of how breakdown voltage is determined by plotting two linear fits of the data points and determining the point of intersection.
"""
g = sns.lmplot(x = xd, y = yd, hue = hued, data = df, aspect = 1.5, legend = False )
plt.axvline(bvol, linestyle = '--', color = 'k', label = 'Breakdown Voltage: '+ str(round(bvol,1)))
#g.set( yscale="log")
#g.subplots(figsize=(20,15))
g.set(ylim=(0, 5*10**-9))
plt.legend(fontsize = 'large')
g.set_axis_labels('Voltage (V)', 'Current (A)')
g.savefig(impathIV)
def FWHM(arr_x,arr_y):
"""
Parameters:
arr_x: Numpy Array - x array of gaussian fit
arr_y: Numpy Array - y array of gaussian fit
Return:
FWHM: float - full width half maximum
Note:
This function returns the full width half maximum of a Gaussian Fit
"""
difference = max(arr_y) - min(arr_y)
HM = difference / 2
pos_extremum = arr_y.argmax() # or in your case: arr_y.argmin()
nearest_above = (np.abs(arr_y[pos_extremum:-1] - HM)).argmin()
nearest_below = (np.abs(arr_y[0:pos_extremum] - HM)).argmin()
FWHM = (np.mean(arr_x[nearest_above + pos_extremum]) -
np.mean(arr_x[nearest_below]))
return FWHM
def findIntersection(s1,in1,s2, in2, estimate = 0):
"""
Parameters:
s1: Float - slope of first line
s2: Float - slope of second line
in1: Float - y intercept of first line
in2: Float - y intercept of second line
Optional Parameters
estimate: Float - estimate of the point of intersection (improves accuracy of the result)
Return
float - x value of the point of intersection
"""
return fsolve(lambda x : (s1*x+in1) - (s2*x+in2),estimate)
def gen_der(spline):
"""
Parameters:
spline: UnivariateSpline
Return:
UnivariateSpline
Note:
The function calculates the derivative of an inputted function
"""
return spline.derivative()
def gen_spline(x,y):
"""
Parameters:
x: list or array
y: list or array
Return:
spline
Note:
function produces a spline given x and y arrays
"""
return UnivariateSpline(x, y, k=3, s=0)
def get_doping_profile(CV,area):
"""
Parameters:
CV: Pandas Dataframe - 2 column dataframe with column names {'voltage', 'capacitance'}
area: Float
Return:
df: Pandas Dataframe
Note:
The following function used the CV curve to obtain the doping profile
"""
width, profile = [], []
for n in range(2,CV.shape[0]):
slope = lin_reg(CV.iloc[n-1:n+1,:],'voltage','capacitance')[0]*10**12
C = CV.iloc[n,1]*10**12
constant = con.eps_si*con.q*con.eps0*area**2
if slope == 0:
N = 0
else:
N = abs((1/slope)*(C**3/(constant)))*10**-24
w = calculate_width(CV['capacitance'][n], area)*10**4
width.append(w) #Reporting in um
profile.append(N) #Reporting in cm^-3
df = pd.DataFrame(columns=['width', 'profile'])
df['width'] = width
df['profile'] = profile
return df
def isoutlier(df, a, p, column, start = 0):
"""
Parameters:
df: Pandas Dataframe
a: Integer - number of standard deviations to determine outlier
p: Float - fraction of data to be used to determine mean and standard deviation
column: String - name of the column for the current
Optional Parameters:
start: Integer - index the scanning for outliers begins at
Return:
df: Pandas Dataframe
Note:
This function is used to fit two lines to IV curves by classifying each point as outliers or not i.e. in the Baseline region or the Breakdown region
"""
#a sets the number of standard deviations to determine outlier
#N is the number of points over which mean and stdev should be determined
#Picking a small subset of datapoints
#N = round(p * df.shape[0])
df_sub = df.sort_values(column).loc[strt_idx:end_idx]
df = df.loc[turn_on_curve:]
mean = df_sub[column].mean()
std = df_sub[column].std()
imin = df.index[0]
imax = df.index[-1]
#Determining if the points are outliers
for i in range (imin, imax):
x = df.loc[i,column]
#((mean-a*std) <= x)
if (mean-a*std) <= x and x<= (mean + a*std):
df.loc[i, "Outlier"] = 'Baseline Region'
else:
df.loc[i,"Outlier"] = 'Breakdown Region'
df['Outlier'].replace('', np.nan, inplace=True)
df['Outlier'].dropna()
return df
def isoutlierCV (df, a, p, column, direction):
"""
Parameters:
df: Pandas dataframe
a: Integer - number of standard deviations to determine outlier
p: Float - fraction of data to be used to determine mean and standard deviation
column: String - name of the column for the current
direction: {'lr', 'rl'} - Direction to scan for outliers (left to right or right to left)
Return:
df: Pandas Dataframe
Note:
This function is used to classify points and outliers or not in order to fit two lines to the data and find the point of intersection.
"""
N = round(p * df.shape[0])
if direction == 'lr':
df_sub = df.sort_values(column, ascending = True).head(N)
elif direction == 'rl':
df_sub = df.sort_values(column, ascending = False).head(N)
df = df.sort_index(axis = 0)
else:
print("Error")
mean = df_sub[column].mean()
std = df_sub[column].std()
imin = df.index[0]
imax = df.index[-1]
for i in range (imin, imax):
x = df.loc[i,column]
if (mean-a*std) <= x and x<= (mean + a*std):
df.loc[i, "Outlier"] = 'y'
else:
df.loc[i,"Outlier"] = 'n'
return df
def lin_reg(df,x,y):
"""
Parameters:
df: Pandas Dataframe
x: String - name of x column in df
y: String - name of y column in df
Return:
[slope, intercept]: List
slope: float
intercept: float
Note:
This function performs linear regression on a dataframe
"""
Y = df[y].as_matrix()
X = df[x].as_matrix()
slope, intercept, r_value, p_value, std_err = stats.linregress(X,Y)
return [slope, intercept]
def manual_der (X, Y):
"""
Parameters:
X: List/Array
Y: List/Array
Return
der: List/Array - derivative dY/dX
Note:
This function performs manual differentiation to calculate dY/dX
"""
der = []
for n in range(0, len(X)):
#slope, intercept, r_value, p_value, std_err = stats.linregress(X[n:n+1],Y[n:n+1])
if n == 0:
slope, intercept, r_value, p_value, std_err = stats.linregress(X[n:n+1],Y[n:n+1])
else:
slope, intercept, r_value, p_value, std_err = stats.linregress(X[n-1:n+1],Y[n-1:n+1])
der.append(slope)
return der
def nextopen(value, column, sheet):
"""
Parameters:
value: String/Integer/Float - Value to be inputted in the excel sheet
column: String - Column name on excel sheet. Example : 'A'
sheet: String - Sheet name on excel. Example: "Sheet1"
Returns:
None
Note:
This function inputs a given value in the next open cell in a specified column of an excel sheet
"""
cnum = ord(column)-64
if sheet.cell(row=sheet.max_row, column= cnum).value == None:
sheet[column + str(sheet.max_row)] = value
else:
sheet[column + str(sheet.max_row+1)] = value
def reg_intersect (df1, df2, x, y, estimate = 0 ):
"""
Parameters:
df1: Pandas Dataframe
df2: Pandas Dataframe
x: String - name of x column for df1 and df2 (should be the same)
y: String - name of y column for df1 and df2 (should be the same)
Optional Parameters:
estimate: estimated value for the point of intersection
Return:
float - x value of the point of intersection
Note:
This function determines the point of intersection of two lines described by two dataframes
"""
[s1,i1] = lin_reg(df1,x, y)
[s2,i2] = lin_reg(df2,x, y)
#return [s1, i1, s2, i2]
return findIntersection(s1, i1, s2, i2, estimate)
def scatterplot(df, x, y):
"""
Parameters:
df: Pandas Dataframe
x: String - name of x column in df
y: String - name of y column in df
Returns:
None
Note:
The function plots a scatter plot given a dataframe and the x and y column names
"""
plt.plot(df[x],df[y],'o')
plt.xlabel(x)
plt.ylabel(y)
def simpletextread(file, delim): #FOR DEBUGGING
"""
Parameters:
file: String - path of file
delim: String - delimiter used to separate columns. Example '\t'
Return:
data: Pandas Dataframe
Note:
This function converts a file with a specified delimiter to a pandas dataframe. It is really useful for debugging
"""
data = pd.read_csv(file, delimiter = delim)
return data
def splitdata(df):
"""
Parameters:
df: Pandas Dataframe
Return:
Concatenated Pandas Dataframe
return[0] = df_out: Pandas Dataframe
return[1] = df_nout: Pandas Dataframe
Note:
This function splits a dataframe into two depending on whether it is a outlier or not {Outlier: 'y', Not Outlier: 'n'}
"""
df_out = df.loc[df['Outlier'] =='y']
df_nout = df.loc[df['Outlier'] =='n']
return(df_out, df_nout)
def storedataCV(file):
"""
Parameters:
df: Pandas Dataframe
Return:
df: Pandas Dataframe
Note:
This function stores CV data into a dataframe
"""
with open(file, 'r') as f_in:
lists = [row for row in csv.reader(f_in, delimiter=',')]
# write a list of lists to a csv file
with open("Output.csv", 'w') as f_out:
writer = csv.writer(f_out)
writer.writerows(lists[3:])
df = pd.read_csv('Output.csv') #Store data into dataframe
return df
def storedataIV(file):
"""
Parameters:
df: Pandas Dataframe
Return:
df: Pandas Dataframe
Note:
This function stores IV data into a dataframe
"""
df = pd.read_csv(file, skiprows = 1) #Store data into dataframe
return df
def setPlotStyle():
"""
Parameters:
None
Return:
None
Note:
This function sets are the styllistic parameters for plots
"""
plt.style.use('ggplot')
plt.rcParams['lines.linewidth'] = 2.15
plt.rcParams['lines.markeredgewidth'] = 0.0
plt.rcParams['font.size'] = 14
plt.rcParams['axes.labelsize'] = 18
plt.rcParams['axes.labelweight'] = 'bold'
plt.rcParams['axes.facecolor'] = 'white'
plt.rcParams['grid.color'] = 'black'
plt.rcParams['grid.linestyle'] = '--'
plt.rcParams['grid.linewidth'] = '0.25'
plt.rcParams['grid.alpha'] = '0.25'
plt.rcParams['xtick.labelsize'] = 16
plt.rcParams['ytick.labelsize'] = 16
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['legend.frameon'] = False
plt.rcParams['figure.titlesize'] = 'large'
plt.rcParams['figure.titleweight'] = 'bold'
plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['axes.edgecolor'] = 'black'
plt.rcParams['patch.edgecolor'] = 'none'
plt.rcParams.update({'figure.max_open_warning': 0})
plt.rcParams['legend.fontsize'] = 20
def tablebvol(dest,bvol, column, sheet):
"""
Parameters:
dest: String - path of excel file
bvol: Float - Breakdown voltage value
column: String - Name of excel column to store data
sheet: String - Name of excel sheet to store data
Return:
None
Note:
This function stores breakdown voltage in specified location in an excel sheet
"""
#Open an xlsx for reading
wb = load_workbook(filename = dest)
#Setting working sheet
ws = wb.get_sheet_by_name(sheet)
#Storing in next open cell in the column
nextopen(bvol,column,ws)
#ws['B' + str(3)] = 2
wb.save(dest)
#Outdated Functions
def calcbvol(df,x, y):
"""
Parameters:
df: Pandas dataframe
x: String - name of x column in df (Voltage)
y: String - name of y column in df (Current)
Returns:
bvol: integer - Breakdown Voltage
Note:
This function does not calculate breakdown voltage well
"""
x = df[x].as_matrix()
y = (df[y].as_matrix())
frst_der = gen_der(gen_spline(x,y))
scnd_der = gen_der(gen_der(gen_spline(x,y)))
thrd_der = gen_der(scnd_der)
df2 = pd.DataFrame({'x':x, 'y':thrd_der(x)})
dupli = df2[df2['y'].duplicated() == True]
i = -1
while dupli['y'].iloc[i] == dupli['y'].iloc[i-1]:
index = (df2.shape[0]-1)+(i-1)
bvol = (df2['x'].iloc[index])
i = i-1
if i < -10:
break
return bvol