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# Import packages |
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import pandas as pd |
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import numpy as np |
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import csv |
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import matplotlib.pyplot as plt |
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from dir_walker import walker |
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import os |
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import warnings |
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#-----------------------------------MODULE CHECKS------------------------------ |
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# Check for modules, try to exit gracefully if not found |
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import sys |
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import imp |
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try: |
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imp.find_module('numpy') |
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foundnp = True |
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except ImportError: |
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foundnp = False |
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try: |
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imp.find_module('matplotlib') |
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foundplot = True |
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except ImportError: |
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foundplot = False |
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try: |
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imp.find_module('pandas') |
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foundpd = True |
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except ImportError: |
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foundplot = False |
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if not foundnp: |
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print("Numpy is required. Exiting") |
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sys.exit() |
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if not foundplot: |
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print("Matplotlib is required. Exiting") |
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sys.exit() |
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if not foundpd: |
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print("Pandas is required. Exiting") |
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sys.exit() |
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#------------------------------------------------------------------------------- |
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class cv: |
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_colors = {'red': 'r', 'green': 'g', 'blue': 'b', 'yellow': 'y', 'cyan': 'c', 'magenta': 'm', 'black': 'k', 'white': 'w'} |
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_linestyles = {' ': ' ', '-': 'solid', '--': 'dashed', '-.': 'dash_dot', ':': 'dotted'} |
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_markers = {' ': ' ', '.': '.', '_' : '_', 'o': 'o', '*': '*', '+': '+', 'x': 'x', 'square': 's', 'triangle': '^', 'diamond': 'd'} |
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# Find relevant files in folder |
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path = os.getcwd() |
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extension = ['csv','txt','dat'] # Should include all the possible ones |
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os.chdir(path) |
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result = [i for i in glob.glob('*.{}'.format(extension))] |
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print("Plotting the following:") |
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print(result) |
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#Make x-axis |
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t = np.linspace(325, 1100, 776) |
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def det(a, b): |
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return a[0] * b[1] - a[1] * b[0] |
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#set initial values (for later use) |
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k=0 |
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totalexport=[] |
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#iterate through CSVs |
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while k < len(csvresult): |
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horiz=.04 |
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exportlist=[] |
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path = os.getcwd() |
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path = path + "/" + csvresult[k] |
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cv=pd.read_csv(path, delimiter=",", error_bad_lines=False) |
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#Extract Data from Current Column |
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currentwhole=cv.values[:,1] |
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72
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ind=np.where(currentwhole == ' Current/A') |
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startdata=ind[0][0] |
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startdata=startdata + 1 |
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currentdatastr=cv.values[startdata:,1] |
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#Extract Data from Potential Column |
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potentialdatastr=cv.values[startdata:,0] |
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#Convert all elements from strings to floats so they can be math. manipulated |
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currentdataOG = [ float(x) for x in currentdatastr ] |
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potentialdata = [ float(x) for x in potentialdatastr ] |
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#Multiply current data to fit axis properly |
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currentdata = [ i * (100000) for i in currentdataOG ] |
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#assign attributes to plot |
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colour = 'b' |
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plotlabel = csvresult[k] |
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plotlabel = plotlabel[:-4] |
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totalexport.append(plotlabel) |
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#plot graph |
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plt.plot(potentialdata, currentdata, colour, label= plotlabel) |
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plt.draw() |
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count=1 |
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while count <= 2: |
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xline1=[] #x values of line 1 to fill in as code proceeds |
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yline1=[] |
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xline2=[] |
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yline2=[] |
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#Point Clicks and Intersections |
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View Code Duplication |
if count==1: |
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print('>> Please choose two points for first line (onset of oxidation)') |
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line1 = plt.ginput(2) # it will wait for two clicks |
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line1=np.array(line1) |
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x1=line1[0,0] |
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xline1.append(x1) |
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y1=line1[0,1] |
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yline1.append(y1) |
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print('>> Please choose two points for first line (onset of oxidation)') |
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line2 = plt.ginput(2) |
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line2=np.array(line2) |
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x2=line2[0,0] |
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xline2.append(x2) |
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y2=line2[0,1] |
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yline2.append(y2) |
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View Code Duplication |
if count==2: |
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print('>> Please choose two points for first line (onset of reduction)') |
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line1 = plt.ginput(2) # it will wait for two clicks |
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line1=np.array(line1) |
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x1=line1[0,0] |
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xline1.append(x1) |
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y1=line1[0,1] |
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yline1.append(y1) |
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print('>> Please choose two points for first line (onset of reduction)') |
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line2 = plt.ginput(2) |
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line2=np.array(line2) |
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x2=line2[0,0] |
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xline2.append(x2) |
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y2=line2[0,1] |
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yline2.append(y2) |
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#Find intersection |
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xdiff = (line1[0][0] - line1[1][0], line2[0][0] - line2[1][0]) |
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ydiff = (line1[0][1] - line1[1][1], line2[0][1] - line2[1][1]) |
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div = det(xdiff, ydiff) |
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#if div == 0: |
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#raise Exception('lines do not intersect') |
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d = (det(*line1), det(*line2)) |
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#calculate onset |
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x = det(d, xdiff) / div |
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xline1.append(x) |
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xline2.append(x) |
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#make point of intersection |
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y = det(d, ydiff) / div |
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yline1.append(y) |
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yline2.append(y) |
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148
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plt.plot(xline1,yline1,'r') |
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plt.plot(xline2,yline2,'r') |
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150
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plt.draw() |
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152
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exportlist.append(x) |
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#calculate homo/lumo |
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holu = x - 4.8 |
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exportlist.append(holu) |
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#round these values |
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x = round(x,2) |
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158
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holu = round(holu,2) |
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159
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#make these strings instead of integers |
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x = str(x) |
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holu = str(holu) |
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#print message on command prompt |
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if count==1: |
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print('------------') |
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print("Oxidation onset:") |
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print(x + " V") |
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print('------------') |
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if count==2: |
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print('------------') |
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print("Reduction onset:") |
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print(x + " V") |
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print('------------') |
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count=count+1 |
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175
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totalexport.append(exportlist) |
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#write texts to put on chart |
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ox=exportlist[0] |
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ox=round(ox,3) |
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179
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ox=str(ox) |
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180
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homo=exportlist[1] |
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181
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homo=round(homo,3) |
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182
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homo=str(homo) |
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183
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red=exportlist[2] |
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184
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red=round(red,3) |
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185
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red=str(red) |
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186
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lumo=exportlist[3] |
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187
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lumo=round(lumo,3) |
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188
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lumo=str(lumo) |
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189
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oxtxt = 'OX' '$_{onset}$' + ' = ' + ox + "V" |
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redtxt = 'RED' '$_{onset}$' + ' = ' + red + "V" |
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homotxt = 'HOMO = ' + homo + "eV" |
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lumotxt = 'LUMO = ' + lumo + "eV" |
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#change initial value and make first text label |
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plt.gca().set_position((.1, .28, .8, .65)) # to make a bit of room for extra text |
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vert = 0.14 |
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plt.figtext(horiz,vert,plotlabel,style='italic') |
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197
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#plt.figure().add_subplot(111).plot(range(10), range(10)) |
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txtlist = [oxtxt,redtxt,homotxt, lumotxt] |
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199
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i=0 |
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200
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#iterate through text list to make text boxes of values |
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while i < len(txtlist): |
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if i == 2: |
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horiz=horiz+0.25 |
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204
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vert=vert+0.1 |
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205
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vert=vert-0.05 |
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206
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curr=txtlist[i] |
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207
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plt.figtext(horiz,vert,curr) |
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208
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i=i+1 |
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209
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continue |
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#----------------------------------EXCEL EXPORT--------------------------------- |
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212
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# Create a Pandas dataframe title for data. |
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df0 = pd.DataFrame({'Data': ['Onset of Oxidation (V)','HOMO (eV)','Onset of Reduction (V)','LUMO (eV)']}) |
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214
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215
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# Create a Pandas Excel writer using XlsxWriter as the engine. |
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216
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folder = 'Processed CV Data' |
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217
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if not os.path.exists(folder): |
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os.makedirs(folder) |
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219
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220
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calcname=csvresult[k] |
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221
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calcname=calcname + '-calcs.xlsx' |
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222
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writer = pd.ExcelWriter(os.path.join(folder,calcname), engine='xlsxwriter') |
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223
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224
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#Loop through data from total export list |
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225
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j=0 |
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226
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index=1 |
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227
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while j < len(totalexport): |
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228
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plotlabel=totalexport[j] |
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229
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data=totalexport[j+1] |
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230
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df = pd.DataFrame({plotlabel: data}) |
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231
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df.to_excel(writer, sheet_name='Sheet1', startcol=index, index=False) |
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232
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j=j+2 |
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233
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index=index+1 |
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234
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continue |
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235
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236
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# Convert the dataframe to an XlsxWriter Excel object. |
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237
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df0.to_excel(writer, sheet_name='Sheet1', startrow=1, header = False, index=False) |
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238
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239
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# Get the xlsxwriter workbook and worksheet objects. |
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240
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workbook = writer.book |
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241
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worksheet = writer.sheets['Sheet1'] |
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242
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243
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# Set the column width and format. |
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244
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worksheet.set_column('A:A', 20) |
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245
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worksheet.set_column('B:B', 15) |
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246
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worksheet.set_column('C:C', 15) |
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247
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worksheet.set_column('D:D', 15) |
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248
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249
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# Close the Pandas Excel writer and output the Excel file. |
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250
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writer.save() |
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251
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msg= csvresult[k][:-4] + ' CV Calculations exported --------->' |
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252
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print(msg) |
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253
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254
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#---------> DATA TO CREATE GRAPH IN EXCEL |
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255
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256
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# Create a Pandas Excel writer using XlsxWriter as the engine. |
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257
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dataname=csvresult[k] |
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258
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dataname=dataname + '-dataset.xlsx' |
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259
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writer = pd.ExcelWriter(os.path.join(folder,dataname), engine='xlsxwriter') |
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260
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261
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#Create wavelength column |
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262
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potentialdatastr=[float(x) for x in potentialdatastr] |
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263
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df = pd.DataFrame({'Potential/ V': potentialdatastr}) |
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264
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df.to_excel(writer, sheet_name='Sheet1', startcol=0, startrow=0, index=False) |
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265
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df1 = pd.DataFrame({'Current/ A': currentdatastr}) |
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266
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df1.to_excel(writer, sheet_name='Sheet1', startcol=1, startrow=0, index=False) |
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267
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i=0 |
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268
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currentdatastr=[float(x) for x in currentdatastr] |
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269
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microcurrentdatastr=[x*1000000 for x in currentdatastr] |
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270
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df2 = pd.DataFrame({'Current/ microA': microcurrentdatastr}) |
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271
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df2.to_excel(writer, sheet_name='Sheet1', startcol=2, startrow=0, index=False) |
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272
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273
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# Get the xlsxwriter workbook and worksheet objects. |
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274
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workbook = writer.book |
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275
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worksheet = writer.sheets['Sheet1'] |
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276
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277
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# Set the column width and format. |
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278
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worksheet.set_column('A:A', 11) |
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279
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worksheet.set_column('B:B', 11) |
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280
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worksheet.set_column('C:C', 11) |
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281
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282
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# Close the Pandas Excel writer and output the Excel file. |
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283
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writer.save() |
|
284
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msg= csvresult[k][:-4] + ' CV Dataset exported --------->' |
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|
|
285
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print(msg) |
|
286
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|
287
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|
|
#------------------------------------------------------------------------------- |
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288
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289
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#Bold axis numbers and change font sizes |
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290
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|
|
ax=plt.gca() |
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|
291
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for tick in ax.xaxis.get_major_ticks(): |
|
292
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tick.label1.set_fontsize(9) |
|
293
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|
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tick.label1.set_fontweight('bold') |
|
294
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|
|
for tick in ax.yaxis.get_major_ticks(): |
|
295
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|
|
tick.label1.set_fontsize(9) |
|
296
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|
|
tick.label1.set_fontweight('bold') |
|
297
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|
|
#Labels |
|
298
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|
|
plt.xlabel('Potential (V) / V',weight='bold') |
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|
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|
|
299
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|
|
plt.ylabel('Current (I) / x10$^{-1}$${\mu}A$',weight='bold') |
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|
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|
|
|
300
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|
|
plt.draw() |
|
301
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|
#Graph Finished Message |
|
302
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|
|
sep=" " |
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|
303
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|
name = os.getlogin() |
|
304
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|
|
name = name.split(sep, 1)[0] |
|
305
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|
|
msg = 'Hey ' + name + ', ' + csvresult[k][:-4] + "'s graph has finished processing." |
|
306
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|
|
picname= csvresult[k][:-4] + '.png' |
|
|
|
|
|
|
307
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|
|
print(msg) |
|
308
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|
|
plt.savefig(os.path.join(folder,picname), bbox_inches='tight') |
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|
|
|
|
|
309
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|
|
k=k+1 |
|
|
|
|
|
|
310
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|
|
#case where if you reach your last CSV, then break the loop |
|
311
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|
|
plt.show() |
|
312
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|
|
if k == len(csvresult): |
|
313
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|
|
plt.title('Cyclic Voltammetry',weight='bold') |
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|
|
|
|
|
314
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|
|
plt.legend(loc='best') |
|
315
|
|
|
break |
|
316
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|
|
continue |
|
317
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|
sabiharustam /
voltcycle
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The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods:
If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions.