TCombobox1['values'] = photos Frame3 = Frame(root) Frame3.place(relx=0.02, rely=0.33, relheight=0.63, relwidth=0.46) Frame3.configure(relief=GROOVE) Frame3.configure(borderwidth="2") Frame3.configure(relief=GROOVE) Frame3.configure(width=285) Label4 = Label(Frame3) Label4.place(relx=0.04, rely=-0.04, height=21, width=44) Label4.configure(text='''Follow''') Radiobutton1 = Radiobutton(Frame3) Radiobutton1.place(relx=0.07, rely=0.08, relheight=0.09, relwidth=0.38) Radiobutton1.configure(text='''Single Hashtag''') Radiobutton1.configure(variable=radio) Radiobutton1.configure(value="1") Radiobutton1.invoke() TCombobox3 = Combobox(Frame3) TCombobox3.place(relx=0.11, rely=0.19, relheight=0.08, relwidth=0.6) TCombobox3.configure(textvariable=combobox2) TCombobox3.configure(width=173) TCombobox3.configure(takefocus="") TCombobox3['values'] = hashtags Radiobutton2 = Radiobutton(Frame3) Radiobutton2.place(relx=0.07, rely=0.3, relheight=0.09, relwidth=0.44) Radiobutton2.configure(text='''Multiple Hashtags''') Radiobutton2.configure(value="2")
class AnalyzeSPC:
def __init__( self, master ):
#Set Master TK Window from intiation (root)
self.master = master
#Name the Window
master.title("Analyze Rennishaw SPC files")
#Style a general outside padding of 10px
self.mf = Frame( master, padding = "10" )
#Initiate Menubar
self.menubar = Menu( self.master )
#File Menu
self.filemenu = Menu( self.menubar, tearoff = 0 )
self.filemenu.add_command( label = "Exit", command = self.master.quit, accelerator = "Alt+F4" )
self.menubar.add_cascade( label = "File", menu = self.filemenu )
#Help Menu
self.helpmenu = Menu( self.menubar, tearoff = 0 )
self.helpmenu.add_command( label = "How to use", command = lambda: self.output_message("How to use this application", "Base file names must follow a specific format such that each measured grain is indicated, along with how many measurements are made on that grain.\n\n\tFor Example:\n\n\t\tSomeFileName_GrainNumber_MeasurementNumberOnGrain.spc\n\n\t\t5 measurements on a single grain would look like this:\n\t\t\tSomeFileName_1_1.spc\n\t\t\tSomeFileName_1_2.spc\n\t\t\tSomeFileName_1_3.spc\n\t\t\tSomeFileName_1_4.spc\n\t\t\tSomeFileName_1_5.spc\n\n\t\t2 measurements each made on 3 grains would look like this:\n\t\t\tSomeFileName_1_1.spc\n\t\t\tSomeFileName_1_2.spc\n\t\t\tSomeFileName_2_1.spc\n\t\t\tSomeFileName_2_2.spc\n\t\t\tSomeFileName_3_1.spc\n\t\t\tSomeFileName_3_2.spc\n\n\tThis format is important for the code to differentiate which items should be compared for Averaging and SEM purposes.", "Close", None))
self.menubar.add_cascade( label = "Help", menu = self.helpmenu )
self.master.config( menu = self.helpmenu )
#Initiate Grid format for TKinter
self.mf.grid(column = 0, row = 0, sticky = ( N, W, E, S ) )
self.mf.columnconfigure( 0, weight = 1 )
self.mf.rowconfigure( 0, weight = 1 )
#Build Input variables
self.message = "Select folder containing *.SPC files to be Analyzed"
self.label_text = StringVar()
self.folder = StringVar()
self.output_fmt = StringVar( value = "csv")
self.output_folder = StringVar()
self.output_plots = IntVar( value = 1 )
self.output_fmt_bool = IntVar( value = 0 )
self.progress_var = DoubleVar( )
#Set Initial Output Information for User
self.label_text.set( self.message )
#Label User Choices
self.label = Label( self.mf, textvariable = self.label_text )
self.folder_label = Label( self.mf, text = "Input Folder" )
self.output_fmt_label = Label( self.mf, text = "Output Format" )
self.output_fold_label = Label( self.mf, text = "Output Folder" )
#Build Output Selection Choices for User
self.fmt_txt = Radiobutton( self.mf, text = "TXT", variable = self.output_fmt, value = 'txt', state = DISABLED )
self.fmt_csv = Radiobutton( self.mf, text = "CSV", variable = self.output_fmt, value = 'csv', state = DISABLED )
self.fmt_plot = Checkbutton( self.mf, text = "Plot Data", variable = self.output_plots, onvalue = 1, offvalue = 0 )
self.fmt_bool = Checkbutton( self.mf, text = "Output Raw Data", variable = self.output_fmt_bool, onvalue = 1, offvalue = 0, command = self.output_enable)
#Progressbar
self.p_bar = Progressbar( self.mf, length = 300, mode = 'determinate', variable = self.progress_var, maximum = 100. )
#Build Directory Input/Output for User
self.folder_entry = Entry( self.mf, textvariable = self.folder )
self.folder_output = Entry( self.mf, textvariable = self.output_folder )
#Build Buttons for User
self.sel_folder = Button( self.mf, text = "Browse", command = self.ask_dir )
self.sel_out_folder = Button( self.mf, text = "Browse", command = self.ask_out_dir )
self.analyze_btn = Button( self.mf, text = "Analyze", command = self.convert )
#Build GUI Layout
self.label.grid( row = 0, column = 0, columnspan = 4, sticky = W + E) #Label
self.folder_label.grid( row = 1, column = 0, sticky = E ) #Input Folder Directory Label
self.folder_entry.grid( row = 1, column = 1, sticky = W + E, columnspan = 2 ) #Input Folder Input
self.sel_folder.grid( row = 1, column = 3, sticky = W ) #Input Folder Directory Button
self.fmt_bool.grid( row = 2, column = 0, sticky = W, columnspan = 2 ) #Boolean that allows raw data output
self.output_fmt_label.grid( row = 3, column = 0, sticky = E ) #Output Format type (TXT, CSV Radio buttons Label)
self.fmt_txt.grid( row = 3, column = 1, sticky = W ) #Output Format type TXT button
self.fmt_csv.grid( row = 3, column = 2, sticky = W ) #Output Format type CSV button
self.fmt_plot.grid( row = 3, column = 3, sticky = W ) #Output Plots
self.output_fold_label.grid( row = 4, column = 0, sticky = E ) #Output Folder Directory Label
self.folder_output.grid( row = 4, column = 1, sticky = W + E, columnspan = 2 ) #Output Folder Input
self.sel_out_folder.grid( row = 4, column = 3, sticky = W ) #Output Folder Directory Button
self.analyze_btn.grid( row = 5, column = 1, columnspan = 2, sticky = W + E ) #Analyze Files Button
self.p_bar.grid( row = 6, column = 0, columnspan = 4, sticky = E )
#Add 5px padding to all elements
for child in self.mf.winfo_children():
child.grid_configure( padx = 5, pady = 5 )
def output_enable(self):
if not self.output_fmt_bool.get():
self.fmt_txt.configure(state = DISABLED)
self.fmt_csv.configure(state = DISABLED)
else:
self.fmt_txt.configure(state = NORMAL)
self.fmt_csv.configure(state = NORMAL)
def convert(self):
#Get Folder names
self.fol_val = str( self.folder.get() )
self.fol_out_val = str( self.output_folder.get() )
ffn_out = os.path.abspath(self.fol_out_val)
#Get output format (txt or csv)
self.fmt_val = str( self.output_fmt.get() )
#Get whether to plot or not
self.fmt_plot_val = bool( self.output_plots.get() )
#Inform user of starting conversions
print( "Analyzing {} with {} ext".format(self.fol_val, self.fmt_val) )
#Figure output file extension and delimiter
if self.fmt_val == 'txt':
exten = '.txt'
delim = '\t'
else:
#Default is CSV
exten = '.csv'
delim = ','
#Initiate grain and measurement indices
self.grain = 0
self.measurement = 0
#Build list for files
flist = []
#Get file directory from folder value
ffn = os.path.abspath( self.fol_val )
#Check for output folder location. If none, default to Input folder
out_str = self.fol_out_val.replace(" ","")
#If No output folder selected
if out_str == "":
#Default to input folder
self.fol_out_val = self.fol_val
self.output_folder.set( value = self.fol_out_val )
#Find how many grain files exist in folder
self.grain_count = 0
for f in os.listdir( ffn ):
#Check extension
if f.lower().endswith('spc'):
#Split file name by underscores
f_split = f.split("_")
#Ensure correct format for tracking
if len(f_split) >= 2:
#Look at second to last in split ( SomeFileName_1_1.spc )
if int( f_split[-2] ) > self.grain_count:
self.grain_count = int( f_split[-2] )
#Consider grain count
if self.grain_count == 0:
self.output_message("Input Folder Empty", "Your input folder is either empty or the spc files are not configured correctly.", "Close", None)
#Cycle through each grain count and append files
for i in range(self.grain_count):
#Build Measurements list
mlist = []
#Append sorted files to the filelist
for f in os.listdir( ffn ):
#Check extension
if f.lower().endswith('spc'):
fl = f
#Split file name to find current
f_split = fl.split("_")
#Ensure correct format for tracking
if len(f_split) >= 2:
#Check if current file is the current grain
if int( f_split[-2] ) == i+1:
#print( f )
#Add to measurement list
mlist.append( os.path.join( ffn , f) )
#Append grain i+1 files
flist.append( mlist )
###### STOPPED HERE! FILES SORTED BY GRAIN. START ANALYZING ######
#Check if any output is selected. If not, make a silly message
output_data = bool( self.output_fmt_bool.get() )
output_plot = bool( self.output_plots.get() )
if (not output_data) and (not output_plot):
self.output_message( "What are you doing?", "... You have to select some output method...\n\nI mean, I could analyze this for you but you won't see it.", "Oh yeah, duh!", None )
#Else, continue analyzing
else:
prog_incr = 100./len( flist )
#Look at grain i
#Loop through i grains
for i in range( len( flist ) ): #3
tot_grain_data_list = [] #All unanalyzed grain data
ave_grain_data_list = [] #Averaged data per grain
sem_grain_data_list = [] #SEM data per grain
#Loop through j measurements
for j in range( len( flist[i] ) ): #5
#Open file into array
f = spc.File( flist[i][j] )
xy_data = f.data_list() #xy data for grain i, measurement j
#Append collected data to the grain_data_list for later processing
tot_grain_data_list.append( xy_data )
#print( len(tot_grain_data_list) ) #total measurements
#print( len(tot_grain_data_list[0]) ) #x and y
#Average arrays (function? OUT = Single array)
normalized_to = 1.0
ave_grain_data_list.append( self.grain_average( tot_grain_data_list, normalized_to ) )
#Find SEM in arrays (function? OUT = Single array of +/-)
sem_grain_data_list.append( self.SEM_calc( tot_grain_data_list, ave_grain_data_list[0] ) )
#print( sem_grain_data_list[0][0][:5] )
#if i==0:
# print( sem_grain_data_list[0][0][:10] )
#If plot, save plot data as PNG
if output_plot:
#Plot normalized and averaged values
figsize = ( 16, 9 )
plt.figure( figsize = figsize, dpi = 600 )
plt.plot( ave_grain_data_list[0][0], ave_grain_data_list[0][1], color = '#000099', lw = 0.5 )
#Calc total SEM Values
sem_above = []
sem_below = []
for k in range( len( sem_grain_data_list[0][0] ) ):
sem_above.append( ave_grain_data_list[0][1][k] + sem_grain_data_list[0][1][k] )
sem_below.append( ave_grain_data_list[0][1][k] + sem_grain_data_list[0][0][k] )
#Plot +/- SEM
plt.fill_between( ave_grain_data_list[0][0], sem_below, sem_above, facecolor = '#9999FF', interpolate = True )
plt.xlabel( 'cm^-1' )
plt.ylabel( 'Intensity (Normalized to {})'.format( normalized_to ) )
plt.ylim( 0., normalized_to*1.1 )
plt.xticks( np.arange( min( ave_grain_data_list[0][0] ), max( ave_grain_data_list[0][0] )+1, 500.) )
plt.yticks( np.arange( 0., normalized_to*1.1, step = 0.2*normalized_to ) )
data_line = mpatches.Patch(color='#000099', label='Averaged Data' )
sem_colors = mpatches.Patch(color='#9999FF', label='SEM Range')
plt.legend(handles=[data_line, sem_colors])
plt.title( "Averaged Data in Grain {}".format( ( i+1 ) ) )
plt.grid(True)
plt.savefig( os.path.join(ffn_out, "Grain {} - Averaged_RAMAN.png".format( (i+1) )) )
#If output type enabled, output data type
if output_data:
#Open File
fle = open( os.path.join(ffn_out, "Grain {} - Averaged_RAMAN{}".format( (i+1), exten )) , 'w' )
#Create Header
header = "x" + delim + "y-Ave" + delim + "y-SEM\n"
fle.write( header )
#Cycle through 'x' values for SEM and
for l in range( len( sem_grain_data_list[0][0] ) ):
line = str( ave_grain_data_list[0][0][l] )
line += delim + str( ave_grain_data_list[0][1][l] )
line += delim + str( sem_grain_data_list[0][1][l] )
line += "\n"
fle.write( line )
#Close File
fle.close()
#Update Progress bar
self.progress_var.set( prog_incr*(i+1) )
self.mf.update_idletasks()
self.progress_var.set( 0. )
self.mf.update_idletasks()
'''
#Process SPC files
for fpath in flist:
#Check if file ends with 'spc'
if fpath.lower().endswith('spc'):
#Get file name - the extension
foutp = fpath[:-4] + exten
try:
print(fpath, end=' ')
f = spc.File(fpath)
f.write_file(foutp, delimiter=delim)
print('Converted')
except:
print('Error processing %s' % fpath)
else:
print('%s not spc file, skipping' % fpath)
'''
#Removes stray data points that aren't real measurements
def sanitize_list(self, m_data, slope_threshold = 50. ):
#Check data for large unwanted slopes
for i in range( len( m_data[1] ) ):
#If on first point, set the previous point
if not i == 0:
slope = abs( (m_data[1][i] - m_data[1][i-1] )/( m_data[0][i-1] - m_data[0][i] ) )
#If the slope is too large, remove the point
if slope >= slope_threshold:
#If not the 2nd to last point
if not i == ( len( m_data[1] ) - 2 ):
#Remove the large point and replace with an average
point_ave = ( m_data[1][i-1] + m_data[1][i+1] )/2.
m_data[1][i] = point_ave
#Return the resultant list
return m_data
#Normalize given data to given max height
def measurement_normalized( self, m_data, max_h, m_count):
#Remove nasty stray data points
clean_list = self.sanitize_list( m_data )
#Find maximum value in list
max_val = 0.
#Cycle through 'y' values
for i in range( len( clean_list[1] ) ):
if clean_list[1][i] > max_val:
max_val = clean_list[1][i]
#Calculate Normalizing value
norm_val = max_h/( max_val*m_count )
#Normalize system to max_h/m_count
for i in range( len( clean_list[1] ) ):
clean_list[1][i] *= norm_val
#Return Normalized data
return clean_list
#Average given data
def grain_average( self, g_data, n_to ):
#g_data = [
# [ [x,x,x,x], [y,y,y,y] ],
# [ [x,x,x,x], [y,y,y,y] ],
# [ [x,x,x,x], [y,y,y,y] ],
# ... ]
ave_grain_list = []
#Cycle through each measurement on this grain
for i in range( len( g_data ) ): #5
#m_list = [ [x,x,x,x], [y,y,y,y] ]
m_list = g_data[i]
norm_m_list = self.measurement_normalized( m_list, n_to, len( g_data ) ) #( [[x],[y]], 1.0, 5 )
#If first loop
if i == 0:
ave_grain_list.append( norm_m_list )
#Else, add the list y-elements
else:
for j in range( len( ave_grain_list[0][1] ) ):
ave_grain_list[0][1][j] += norm_m_list[1][j]
#Return Final List
return ave_grain_list[0]
#Standard Error of the Mean Calculation
def SEM_calc(self, g_data, ave_g_data):
p_sem = []
m_sem = []
sem_l = []
#Cycle through each 'x' value
for i in range( len( ave_g_data[0] ) ):
#SEM at point i
sem = 0.
#Cycle through each 'y' value at the given 'x'
count = len( g_data ) #5
for j in range( count ):
sem += math.pow( ( ave_g_data[1][i] - g_data[j][1][i] ) , 2. )
sem = math.sqrt( sem )
sem /= (count - 1.)*count
p_sem.append( sem )
m_sem.append( -1.*sem )
sem_l.append( m_sem )
sem_l.append( p_sem )
return sem_l
#Ask for directory. (Used in init)
def ask_dir(self):
self.folder.set( filedialog.askdirectory() )
def ask_out_dir(self):
self.output_folder.set( filedialog.askdirectory() )
def output_message(self, title, message, button_txt, event):
#Build Toplevel window and set its title
self.sel_top = Toplevel(master = None, padx = 10, pady = 10)
self.sel_top.title( title )
self.sel_msg_txt = message
#Show Message in window
self.sel_msg = Message( self.sel_top, text = self.sel_msg_txt )
self.sel_msg.pack()
#Add button to window
self.sel_button = Button( self.sel_top, text = button_txt, command = self.sel_top.destroy )
self.sel_button.pack()