Working on the calibration of the XANES-data, subtracting background and defining post-edge
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halvorhv
parent
7c95135c33
commit
b0130d49b8
1 changed files with 118 additions and 74 deletions
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@ -1,6 +1,8 @@
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import pandas as pd
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import pandas as pd
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import numpy as np
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import numpy as np
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import os
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import os
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import matplotlib.pyplot as plt
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import beamtime.auxillary as aux
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def rbkerbest():
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def rbkerbest():
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print("ROSENBORG!<3")
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print("ROSENBORG!<3")
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@ -13,82 +15,124 @@ def rbkerbest():
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##Better to make a new function that loops through the files, and performing the split_xanes_scan on
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##Better to make a new function that loops through the files, and performing the split_xanes_scan on
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def split_xanes_scan(root, destination=None, replace=False):
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def pre_edge_subtraction(df,filenames, options={}):
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#root is the path to the beamtime-folder
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#destination should be the path to the processed data
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#insert a for-loop to go through all the folders.dat-files in the folder root\xanes\raw
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required_options = ['edge', 'print']
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default_options = {
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'edge' : 'Mn',
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'print': False
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}
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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with open(filename, 'r') as f:
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#Defining the end of the pre-edge-region for Mn/Ni, thus start of the edge
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lines = f.readlines()
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if str(options['edge']) == 'Mn':
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edge_start = 6.45
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datas = []
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if str(options['edge']) == 'Ni':
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data = []
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edge_start = 8.3
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headers = []
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header = ''
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start = False
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for line in lines:
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if line[0:2] == "#L":
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start = True
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header = line[2:].split()
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continue
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elif line[0:2] == "#C":
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start = False
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if data:
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datas.append(data)
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data = []
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if header:
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headers.append(header)
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header = ''
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#making a function to check the difference between values in the list and the defined start of the edge (where background regression will stop):
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absolute_difference_function = lambda list_value : abs(list_value - edge_start)
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if start == False:
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#finding the energy data point value that is closest to what I defined as the end of the background
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continue
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edge_start_value = min(df["ZapEnergy"], key=absolute_difference_function)
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else:
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#Finding what the index of the edge shift end point is
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data.append(line.split())
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start_index=df[df["ZapEnergy"]==edge_start_value].index.values[0]
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#Defining x-range for linear background fit, ending at the edge start index
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df_start=df[0:start_index]
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#Making a new dataframe, with only the ZapEnergies as the first column
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df_background = pd.DataFrame(df["ZapEnergy"])
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for files in filenames:
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#Fitting linear function to the pre-edge
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d = np.polyfit(df_start["ZapEnergy"],df_start[files],1)
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function_pre = np.poly1d(d)
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#making a list, y_pre,so the background will be applied to all ZapEnergy-values
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y_pre=function_pre(df["ZapEnergy"])
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#adding a new column in df_background with the y-values of the background
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df_background.insert(1,files,y_pre)
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#Plotting the calculated pre-edge background with the region used for the regression
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### FOR FIGURING OUT WHERE IT GOES WRONG/WHICH FILES IS CORRUPT
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#ax = df.plot(x = "ZapEnergy",y=files)
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if options['print'] == True:
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#Plotting an example of the edge_start region and the fitted background that will later be subtracted
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ax = df.plot(x = "ZapEnergy",y=filenames[0]) #defining x and y
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plt.axvline(x = edge_start_value)
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fig = plt.figure(figsize=(15,15))
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df_background.plot(x="ZapEnergy", y=filenames[0],color="Red",ax=ax)
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###################### Subtracting the pre edge from xmap_roi00 ################
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#making a new dataframe to insert the background subtracted intensities
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df_new = pd.DataFrame(df["ZapEnergy"])
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#inserting the pre_edge-background subtracted original xmap_roi00 data
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for files in filenames:
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newintensity_calc=df[files]-df_background[files]
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df_new.insert(1,files,newintensity_calc)
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if options['print'] == True:
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#Plotting original data (black) and background subtracted data (red)
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ax = df.plot(x = "ZapEnergy",y=filenames[0], color="Black")
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plt.axvline(x = edge_start_value)
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fig = plt.figure(figsize=(15,15))
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df_new.plot(x="ZapEnergy", y=filenames[0],color="Red",ax=ax)
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return df_new
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def post_edge_normalization(df,df_new,filenames, options={}):
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required_options = ['edge', 'print']
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default_options = {
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'edge' : 'Mn',
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'print': False
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}
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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#Defining the end of the pre-edge-region for Mn/Ni, thus start of the edge
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if str(options['edge']) == 'Mn':
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edge_stop = 6.565
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if str(options['edge']) == 'Ni':
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edge_stop = 8.361
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absolute_difference_function = lambda list_value : abs(list_value - edge_stop)
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edge_stop_value = min(df_new["ZapEnergy"], key=absolute_difference_function)
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end_index=df_new[df_new["ZapEnergy"]==edge_stop_value].index.values[0]
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#Defining x-range for linear fit
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df_fix=df_new
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df_fix.dropna(inplace=True) #Removing all indexes without any value, as some of the data sets misses the few last data points and fucks up the fit
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df_end=df_fix[end_index:] #The region of interest for the post edge
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#print(df_end)
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#Fitting linear function to the pre-edge using the background corrected intensities to make the post edge fit
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df_postedge = pd.DataFrame(df["ZapEnergy"])
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function_post_list=[]
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for files in filenames:
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d = np.polyfit(df_end["ZapEnergy"],df_end[files],1)
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function_post = np.poly1d(d)
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y_post=function_post(df["ZapEnergy"])
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function_post_list.append(function_post)
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df_postedge.insert(1,files,y_post) #adding a new column with the y-values of the fitted post edge
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#print(filenames[0])
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#print(df_postedge)
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#Plotting the background subtracted signal with the post-edge regression line and the start point for the linear regression line
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if options['print'] == True:
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ax = df_new.plot(x = "ZapEnergy",y=filenames) #defining x and y
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plt.axvline(x = edge_stop_value)
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fig = plt.figure(figsize=(15,15))
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df_postedge.plot(x="ZapEnergy", y=filenames,color="Green",ax=ax, legend=False)
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#print(function_post_list)
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#print(function_post)
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ax = df_new.plot(x = "ZapEnergy",y=filenames, legend=False) #defining x and y
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df_postedge.plot(x="ZapEnergy", y=filenames,color="Green",ax=ax, legend=False)
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plt.axvline(x = edge_stop_value)
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edges = {'Mn': [6.0, 6.1, 6.2, 6.3, 6.4, 6.5], 'Fe': [6.8, 6.9, 7.0, 7.1, 7.2], 'Co': [7.6, 7.7, 7.8, 7.9], 'Ni': [8.1, 8.2, 8.3, 8.4, 8.5]}
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edge_count = {'Mn': 0, 'Fe': 0, 'Co': 0, 'Ni': 0}
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for ind, data in enumerate(datas):
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df = pd.DataFrame(data)
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df.columns = headers[ind]
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edge_start = np.round((float(df["ZapEnergy"].min())), 1)
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for edge, energies in edges.items():
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if edge_start in energies:
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edge_actual = edge
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edge_count[edge] += 1
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filename = filename.split('/')[-1]
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count = str(edge_count[edge_actual]).zfill(4)
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# Save
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if destination:
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cwd = os.getcwd()
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if not os.path.isdir(destination):
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os.mkdir(destination)
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os.chdir(destination)
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df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
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os.chdir(cwd)
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else:
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df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
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