diff --git a/nafuma/xanes/calib.py b/nafuma/xanes/calib.py
index a45457b..2b12c78 100644
--- a/nafuma/xanes/calib.py
+++ b/nafuma/xanes/calib.py
@@ -1,3 +1,5 @@
+from logging import raiseExceptions
+from jinja2 import TemplateRuntimeError
 import pandas as pd
 import numpy as np
 import os
@@ -41,9 +43,9 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
 
     # FIXME Add log-file
 
-    required_options = ['edge_start', 'log', 'logfile', 'save_plots', 'save_folder']
+    required_options = ['pre_edge_start', 'log', 'logfile', 'save_plots', 'save_folder']
     default_options = {
-        'edge_start': None,
+        'pre_edge_start': None,
         'log': False,
         'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_pre_edge_fit.log',
         'save_plots': False,
@@ -60,18 +62,13 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
     # FIXME Implement with finding accurate edge position
     # FIXME Allow specification of start of pre-edge area
     # Find the cutoff point at which the edge starts - everything to the LEFT of this point will be used in the pre edge function fit
-    if not options['edge_start']:
-        pre_edge_limit_offsets = {
-            'Mn': 0.03,
-            'Fe': 0.03,
-            'Co': 0.03,
-            'Ni': 0.03        
-        }
+    if not options['pre_edge_start']:
+        pre_edge_limit_offset = 0.03
 
         data['edge'] = find_element(data)
 
         edge_position = estimate_edge_position(data, options, index=0)
-        pre_edge_limit = edge_position - pre_edge_limit_offsets[data['edge']]
+        pre_edge_limit = edge_position - pre_edge_limit_offset
 
     # FIXME There should be an option to specify the interval in which to fit the background - now it is taking everything to the left of edge_start parameter, but if there are some artifacts in this area, it should be possible to
     # limit the interval
@@ -81,6 +78,8 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
     # Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data
     pre_edge_fit_data = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
 
+    data['pre_edge_params'] = {}
+
     for i, filename in enumerate(data['path']):
         if options['log']:
             aux.write_log(message=f'Fitting background on {os.path.basename(filename)} ({i+1} / {len(data["path"])})', options=options)
@@ -88,6 +87,8 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
         #Fitting linear function to the background
         params = np.polyfit(pre_edge_data["ZapEnergy"],pre_edge_data[filename],1)
         fit_function = np.poly1d(params)
+
+        data['pre_edge_params'][filename] = params
         
         #making a list, y_pre,so the background will be applied to all ZapEnergy-values
         background=fit_function(pre_edge_fit_data["ZapEnergy"])
@@ -131,7 +132,7 @@ def pre_edge_subtraction(data: dict, options={}):
     required_options = ['log', 'logfile', 'save_plots', 'save_folder']
     default_options = {
         'log': False,
-        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S.log")}_pre_edge_subtraction.log',
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_pre_edge_subtraction.log',
         'save_plots': False,
         'save_folder': './'
     }
@@ -165,11 +166,167 @@ def pre_edge_subtraction(data: dict, options={}):
     return xanes_data_bkgd_subtracted
 
 
+
+
+
+def post_edge_fit(data: dict, options={}):
+    #FIXME should be called "fitting post edge" (normalization is not done here, need edge shift position)
+    required_options = ['log', 'logfile', 'post_edge_interval']
+    default_options = {
+        'log': False,
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_post_edge_fit.log',
+        'post_edge_interval': [None, None],
+    }
+    options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
+
+
+    if not options['post_edge_interval'][0]:
+        post_edge_limit_offset = 0.03
+
+        data['edge'] = find_element(data)
+
+        edge_position = estimate_edge_position(data, options, index=0)
+        options['post_edge_interval'][0] = edge_position + post_edge_limit_offset
+
+
+    if not options['post_edge_interval'][1]:
+        options['post_edge_interval'][1] = data['xanes_data_original']['ZapEnergy'].max()
+
+
+    post_edge_data = data['xanes_data_original'].loc[(data['xanes_data_original']["ZapEnergy"] > options['post_edge_interval'][0]) & (data['xanes_data_original']["ZapEnergy"] < options['post_edge_interval'][1])]
+    post_edge_data.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
+
+    # Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data
+    post_edge_fit_data = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
+
+    data['post_edge_params'] = {}
+    
+    for i, filename in enumerate(data['path']):
+        if options['log']:
+            aux.write_log(message=f'Fitting post edge on {os.path.basename(filename)} ({i+1} / {len(data["path"])})', options=options)
+
+        #Fitting linear function to the background
+        params = np.polyfit(post_edge_data["ZapEnergy"], post_edge_data[filename], 2)
+        fit_function = np.poly1d(params)
+
+        data['post_edge_params'][filename] = params
+        
+        #making a list, y_pre,so the background will be applied to all ZapEnergy-values
+        background=fit_function(post_edge_fit_data["ZapEnergy"])
+            
+        #adding a new column in df_background with the y-values of the background
+        post_edge_fit_data.insert(1,filename,background) 
+        
+        if options['save_plots']:
+            if not os.path.isdir(options['save_folder']):
+                os.makedirs(options['save_folder'])
+
+            dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_post_edge_fit.png'
+
+            fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,5))
+            data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax1)
+            post_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax1)
+            ax1.axvline(x = max(post_edge_data['ZapEnergy']), ls='--')
+            ax1.set_title(f'{os.path.basename(filename)} - Full view', size=20)
+
+            data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax2)
+            post_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax2)
+            ax2.axvline(x = max(post_edge_data['ZapEnergy']), ls='--')
+            ax2.set_xlim([min(post_edge_data['ZapEnergy']), max(post_edge_data['ZapEnergy'])])
+            ax2.set_ylim([min(post_edge_data[filename]), max(post_edge_data[filename])])
+            ax2.set_title(f'{os.path.basename(filename)} - Fit region', size=20)
+
+
+            plt.savefig(dst, transparent=False)
+            plt.close()
+
+
+    return post_edge_fit_data
+
+def smoothing(data: dict, options={}):
+
+    # FIXME Add logging
+    # FIXME Add saving of files
+
+    required_options = ['log', 'logfile', 'window_length','polyorder', 'save_default']
+    default_options = {
+        'log': False,
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_smoothing.log',
+        'save_plots': False,
+        'save_folder': './',
+        'window_length': 3,
+        'polyorder': 2,
+        'save_default': False
+    }
+    options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
+
+    df_smooth = pd.DataFrame(data['xanes_data']['ZapEnergy'])
+
+    if options['save_default']:
+        df_smooth_default = pd.DataFrame(data['xanes_data']['ZapEnergy'])
+
+    # FIXME Add other types of filters
+    # FIXME Instead of assigning values directly to the data dictionary, these should be made into an own DataFrame that you can decide later what to do with - these variables should
+    # then be returned
+    for filename in data['path']:
+        df_smooth.insert(1, filename, savgol_filter(data['xanes_data'][filename], options['window_length'], options['polyorder']))
+        
+        if options['save_default']:
+            df_smooth_default.insert(1, filename, savgol_filter(data['xanes_data'][filename], default_options['window_length'], default_options['polyorder']))
+        
+
+        if options['save_plots']:
+            if not os.path.isdir(options['save_folder']):
+                os.makedirs(options['save_folder'])
+
+            dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_smooth.png'
+
+            edge_pos = estimate_edge_position(data=data, options=options)
+            intensity_midpoint = df_smooth[filename].iloc[np.where(df_smooth['ZapEnergy'] == find_nearest(df_smooth['ZapEnergy'], edge_pos))].values[0]
+    
+            if options['save_default']:
+                fig, (ax1, ax2) = plt.subplots(1,2,figsize=(20,5))
+                data['xanes_data'].loc[(data['xanes_data']['ZapEnergy'] > edge_pos-0.0015) & (data['xanes_data']['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='black', ax=ax1, kind='scatter')
+                df_smooth.loc[(df_smooth['ZapEnergy'] > edge_pos-0.0015) & (df_smooth['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='red', ax=ax1)
+                ax1.set_title(f'{os.path.basename(filename)} - Smooth', size=20)
+
+                data['xanes_data'].loc[(data['xanes_data']['ZapEnergy'] > edge_pos-0.0015) & (data['xanes_data']['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='black', ax=ax2,  kind='scatter')
+                df_smooth_default.loc[(df_smooth_default['ZapEnergy'] > edge_pos-0.0015) & (df_smooth_default['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='red', ax=ax2)
+                ax2.set_title(f'{os.path.basename(filename)} - Smooth (default values)', size=20)
+
+            elif not options['save_default']:
+                fig, ax = plt.subplots(figsize=(10,5))
+                data['xanes_data'].loc[(data['xanes_data']['ZapEnergy'] > edge_pos-0.0015) & (data['xanes_data']['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='black', ax=ax,  kind='scatter')
+                df_smooth.loc[(df_smooth['ZapEnergy'] > edge_pos-0.0015) & (df_smooth['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='red', ax=ax)
+                ax.set_xlim([edge_pos-0.0015, edge_pos+0.0015])
+                ax.set_ylim([intensity_midpoint*0.9, intensity_midpoint*1.1])
+                
+                ax.set_title(f'{os.path.basename(filename)} - Smooth', size=20)
+
+
+            plt.savefig(dst, transparent=False)
+            plt.close()
+    
+    if not options['save_default']:
+        df_smooth_default = None
+    
+    return df_smooth, df_smooth_default
+
+
+
+def find_nearest(array, value):
+    #function to find the value closes to "value" in an "array"
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return array[idx]
+
+
 def estimate_edge_position(data: dict, options={}, index=0):
     #a dataset is differentiated to find a first estimate of the edge shift to use as starting point. 
-    required_options = ['print','periods']
+    required_options = ['log','logfile', 'periods']
     default_options = {
-        'print': False,
+        'log': False,
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_edge_position_estimation.log',
         'periods': 2, #Periods needs to be an even number for the shifting of values to work properly
     }
     options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
@@ -190,256 +347,224 @@ def estimate_edge_position(data: dict, options={}, index=0):
 
     return estimated_edge_shift
 
-
-def post_edge_fit(path, options={}):
-    #FIXME should be called "fitting post edge" (normalization is not done here, need edge shift position)
-    required_options = ['print']
+def determine_edge_position(data: dict, options={}):
+    
+    required_options = ['save_values', 'log', 'logfile', 'save_plots', 'save_folder', 'periods', 'diff', 'double_diff', 'fit_region']
     default_options = {
-        'print': False
+        'save_values': True,
+        'log': False,
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_determine_edge_position.log',
+        'save_plots': False,
+        'save_folder': './',
+        'periods': 2, #Periods needs to be an even number for the shifting of values to work properly,
+        'diff': True,
+        'double_diff': False,
+        'fit_region': 0.0005
+    
     }
+
     options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
-    
-    df_bkgd_sub,filenames,edge = pre_edge_subtraction(path, options=options)
-    #Defining the end of the pre-edge-region for Mn/Ni, thus start of the edge
-    #FIXME Use rought edge shift estimate, add X eV as first guess, have an option to adjust this value with widget
-    if edge == 'Mn':
-        edge_stop = 6.565
-    if edge == 'Ni':
-        edge_stop = 8.361
-
-    df_end= df_bkgd_sub.loc[df_bkgd_sub["ZapEnergy"] > edge_stop] # new dataframe only containing the post edge, where a regression line will be calculated in the for-loop below
-    df_end.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
-    df_postedge = pd.DataFrame(df_bkgd_sub["ZapEnergy"]) #making a new dataframe 
-
-    function_post_list=[]
-    for files in filenames: 
-        d = np.polyfit(df_end["ZapEnergy"],df_end[files],1)
-        function_post = np.poly1d(d)
-        y_post=function_post(df_bkgd_sub["ZapEnergy"]) 
-        function_post_list.append(function_post)
-        df_postedge.insert(1,files,y_post) #adding a new column with the y-values of the fitted post edge
-
-    #Plotting the background subtracted signal with the post-edge regression line and the start point for the linear regression line
-    if options['print'] == True:
-        ax = df_bkgd_sub.plot(x = "ZapEnergy",y=filenames) #defining x and y
-        plt.axvline(x = min(df_end["ZapEnergy"])) 
-        fig = plt.figure(figsize=(15,15))
-        df_postedge.plot(x="ZapEnergy", y=filenames,color="Green",ax=ax, legend=False)  
-        ax = df_bkgd_sub.plot(x = "ZapEnergy",y=filenames, legend=False) #defining x and y
-        df_postedge.plot(x="ZapEnergy", y=filenames,color="Green",ax=ax, legend=False)  
-        plt.axvline(x = min(df_end["ZapEnergy"])) 
-
-    return df_bkgd_sub, df_postedge, filenames, edge
-
-def smoothing(path, options={}):
-    required_options = ['print','window_length','polyorder']
-    default_options = {
-        'print': False,
-        'window_length': 3,
-        'polyorder': 2
-    }
-    options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
-
-    df_bkgd_sub, df_postedge, filenames, edge = post_edge_fit(path,options=options)
-    #================= SMOOTHING
-    df_smooth = pd.DataFrame(df_bkgd_sub["ZapEnergy"])
-    df_default = pd.DataFrame(df_bkgd_sub["ZapEnergy"])
-    #df_smooth[filenames] = df_bkgd_sub.iloc[:,2].rolling(window=rolling_av).mean()
-    #df_smooth[filenames] = df_smooth[filenames].shift(-int((rolling_av)/2))
-    for filename in filenames:
-        x_smooth=savgol_filter(df_bkgd_sub[filename], options['window_length'],options['polyorder'])
-        df_smooth[filename] = x_smooth
-        x_default=savgol_filter(df_bkgd_sub[filename],default_options['window_length'],default_options['polyorder'])
-        df_default[filename] = x_default
-    
-    
-        
-    #printing the smoothed curves vs data
-    if options['print'] == True:
-
-        ## ================================================
-        #df_diff = pd.DataFrame(df_smooth["ZapEnergy"])
-        #df_diff_estimated_max = df_diff[filenames].dropna().max()
-
-    
-        #estimated_edge_shift=df_diff.loc[df_diff[filenames] == df_diff_max,'ZapEnergy'].values[0]
-        # ==========================================
-        
-
-        fig, (ax1,ax2) = plt.subplots(1,2,figsize=(15,5))
-        x_range_zoom=[6.54,6.55] #make into widget
-        y_range_zoom=[20000,80000] #make into widget
-
-        df_bkgd_sub.plot.scatter(x = "ZapEnergy",y=filenames, ax=ax1, color="Red") 
-        df_smooth.plot(x = "ZapEnergy",y=filenames, ax=ax1, color="Blue")
-        ax1.set_xlim(x_range_zoom)
-        ax1.set_ylim(y_range_zoom)
-        ax1.set_title("Smoothed curve (blue) vs data (red) used for further analysis")
-
-        df_bkgd_sub.plot.scatter(x = "ZapEnergy",y=filenames, ax=ax2, color="Red") 
-        df_default.plot(x = "ZapEnergy",y=filenames, ax=ax2, color="Green") 
-        ax2.set_xlim(x_range_zoom)
-        ax2.set_ylim(y_range_zoom)
-        ax2.set_title("Smoothed curve (green) vs data (red) using default window_length and polyorder")
-    
-    return df_smooth, filenames
-
-
-
-def find_nearest(array, value):
-    #function to find the value closes to "value" in an "array"
-    array = np.asarray(array)
-    idx = (np.abs(array - value)).argmin()
-    return array[idx]
-
-def finding_e0(path, options={}):
-    required_options = ['print','periods']
-    default_options = {
-        'print': False,
-        'periods': 2, #Periods needs to be an even number for the shifting of values to work properly
-    }
-    options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
-
-    df_smooth, filenames = smoothing(path, options=options) #This way the smoothing is printed as long as the "finding e0" is printed.
 
     if options['periods'] % 2 == 1:
-        print("NB!!!!!!!!!!!!!!!!! Periods needs to be an even number for the shifting of values to work properly")
-    ###df_diff = pd.DataFrame(df_smooth["ZapEnergy"]) #
-    if len(filenames) == 1:
-        filenames=filenames[0]
-    else:
-       print("MORE THAN ONE FILE --> generalize")
-    
-    #####
-    estimated_edge_shift, df_diff, df_diff_max = estimate_edge_position(df_smooth, filenames,options=options)
-    print(estimated_edge_shift)
-    ####
-    ###df_diff[filenames]=df_smooth[filenames].diff(periods=options['periods']) #
-    df_doublediff=pd.DataFrame(df_smooth["ZapEnergy"])
-    df_doublediff[filenames]=df_diff[filenames].diff(periods=options['periods'])
-    
-    if options['print'] == True:
-        fig, (ax1,ax2) = plt.subplots(1,2,figsize=(15,5))
+        raise Exception("NB! Periods needs to be an even number for the shifting of values to work properly")
 
-        df_diff.plot(x = "ZapEnergy",y=filenames, ax=ax1) #defining x and y
-        df_doublediff.plot(x = "ZapEnergy",y=filenames,ax=ax2) #defining x and y
-
-    #shifting column values up so that average differential fits right between the points used in the calculation
-    #df_diff[filenames]=df_diff[filenames].shift(-int(options['periods']/2)) #
-    df_doublediff[filenames]=df_doublediff[filenames].shift(-int(options['periods']))
-    
-    #finding maximum value to maneuver to the correct part of the data set
-    #df_diff_max = df_diff[filenames].dropna().max()
-
-    
-    estimated_edge_shift=df_diff.loc[df_diff[filenames] == df_diff_max,'ZapEnergy'].values[0]
-
-    fit_region = 0.0004
-    df_diff_edge=df_diff.loc[(df_diff["ZapEnergy"] < estimated_edge_shift+fit_region)]# and (df_diff["ZapEnergy"] > estimated_edge_shift-0.05)]
-    df_diff_edge=df_diff_edge.loc[(df_diff["ZapEnergy"] > estimated_edge_shift-fit_region)]
-    
-    
-    
-    
-    df_doublediff_edge=df_doublediff.loc[(df_doublediff["ZapEnergy"] < estimated_edge_shift+fit_region)]# and (df_diff["ZapEnergy"] > estimated_edge_shift-0.05)]
-    df_doublediff_edge=df_doublediff_edge.loc[(df_doublediff["ZapEnergy"] > estimated_edge_shift-fit_region)]
-    #df_diff_edge=df_diff.loc[(df_diff["ZapEnergy"] > estimated_edge_shift-0.15) and (df_diff["ZapEnergy"] < estimated_edge_shift+0.15)]
-
-    #df_diff_edge=df_diff.loc[df_diff["ZapEnergy"] > estimated_edge_shift-0.15]
-    #print(df_diff_edge)
-    if options['print'] == True:
-        fig, (ax3,ax4) = plt.subplots(1,2,figsize=(15,5))
-
-        df_diff_edge.plot(x = "ZapEnergy",y=filenames,ax=ax3) #defining x and y
-        ax3.set_title("Zoomed into edge region (derivative))")
-        ax3.axvline(x = estimated_edge_shift)
-
-        df_doublediff_edge.plot(x = "ZapEnergy",y=filenames,ax=ax4,kind="scatter") #defining x and y
-        ax4.set_title("Zoomed into edge region (double derivative)")
-        ax4.axvline(x = estimated_edge_shift)
-        ax4.axhline(0)
-
-
-
-        #ax1.set_xlim([estimated_edge_shift-fit_region,estimated_edge_shift+fit_region])
-        #ax1.set_title("not sure what this is tbh")
-  
-        #ax2.set_xlim([estimated_edge_shift-fit_region,estimated_edge_shift+fit_region])
-        #ax2.set_title("not sure what this is either tbh")
-
-    #==============
-    #df_smooth=df_smooth2
-    #=================
-
-
-
-
-    #========================== fitting first differential ==========
-    df_diff = df_diff[df_diff[filenames].notna()]
-
-    #fitting a function to the chosen interval
-    d = np.polyfit(df_diff_edge["ZapEnergy"],df_diff_edge[filenames],2)
-    function_diff = np.poly1d(d)
-
-    x_diff=np.linspace(df_diff_edge["ZapEnergy"].iloc[0],df_diff_edge["ZapEnergy"].iloc[-1],num=1000)
-    y_diff=function_diff(x_diff)
-    #print(df_diff_edge["ZapEnergy"].iloc[-1])
-    if options['print'] == True:
-        ax3.plot(x_diff,y_diff,color='Green')
-
-    #y_diff_max=np.amax(y_diff,0)
-    y_diff_max_index = np.where(y_diff == np.amax(y_diff))
-    #print(y_diff_max_index[0])
-    edge_shift_diff=float(x_diff[y_diff_max_index])
-    print("Edge shift estimated by the differential maximum is "+str(round(edge_shift_diff,5)))
-    if options['print'] == True:
-        ax3.axvline(x=edge_shift_diff,color="green")
-    #print(df_doublediff_edge["ZapEnergy"].iloc[0])
-    #ax4.plot(x_doublediff,y_doublediff,color='Green'))
-
-
-    #fitting double differentiate 
-    df_doublediff = df_doublediff[df_doublediff[filenames].notna()]
-    d = np.polyfit(df_doublediff_edge["ZapEnergy"],df_doublediff_edge[filenames],2)
-    function_doublediff = np.poly1d(d)
-
-    x_doublediff=np.linspace(df_doublediff_edge["ZapEnergy"].iloc[0],df_doublediff_edge["ZapEnergy"].iloc[-1],num=10000)
-    y_doublediff=function_doublediff(x_doublediff)
-
-    if options['print'] == True:
-        ax4.plot(x_doublediff,y_doublediff,color='Green')
-
-    y_doublediff_zero=find_nearest(y_doublediff,0)
-    y_doublediff_zero_index = np.where(y_doublediff == y_doublediff_zero)
-    
-    edge_shift_doublediff=float(x_doublediff[y_doublediff_zero_index])
    
-    print("Edge shift estimated by the double differential zero-point is "+str(round(edge_shift_doublediff,5)))
-    if options['print'] == True:
-        ax4.axvline(x=edge_shift_doublediff,color="green")
+    #####
 
-    return df_smooth, filenames, edge_shift_diff
+    if options['diff']:
+        df_diff = pd.DataFrame(data['xanes_data']['ZapEnergy'])
+    if options['double_diff']:
+        df_double_diff = pd.DataFrame(data['xanes_data']['ZapEnergy'])
+    if options['save_values']:
+        data['e0'] = {}
 
-def normalization(data,options={}):
-    required_options = ['print']
+
+    for i, filename in enumerate(data['path']):
+        estimated_edge_pos = estimate_edge_position(data, options=options, index=i)
+
+        
+        #========================== fitting first differential ==========
+
+        if options['diff']:
+            df_diff[filename] = data['xanes_data'][filename].diff(periods=options['periods'])
+            df_diff[filename]=df_diff[filename].shift(-int(options['periods']/2))
+
+            df_diff_edge = df_diff.loc[(df_diff["ZapEnergy"] < estimated_edge_pos+options['fit_region']) & ((df_diff["ZapEnergy"] > estimated_edge_pos-options['fit_region']))]
+    
+            
+            # Fitting a function to the chosen interval
+            params = np.polyfit(df_diff_edge["ZapEnergy"], df_diff_edge[filename], 2)
+            diff_function = np.poly1d(params)
+
+            x_diff=np.linspace(df_diff_edge["ZapEnergy"].iloc[0],df_diff_edge["ZapEnergy"].iloc[-1],num=10000)
+            y_diff=diff_function(x_diff)
+
+            df_diff_fit_function = pd.DataFrame(x_diff)
+            df_diff_fit_function['y_diff'] = y_diff
+            df_diff_fit_function.columns = ['x_diff', 'y_diff']
+            
+            # Picks out the x-value where the y-value is at a maximum
+            edge_pos_diff=x_diff[np.where(y_diff == np.amax(y_diff))][0]
+            
+            if options['log']:
+                aux.write_log(message=f"Edge position estimated by the differential maximum is: {str(round(edge_pos_diff,5))}", options=options)
+            
+            if options['save_values']:
+                data['e0'][filename] = edge_pos_diff
+
+
+        if options['double_diff']:
+            df_double_diff[filename] = data['xanes_data'][filename].diff(periods=options['periods']).diff(periods=options['periods'])
+            df_double_diff[filename]=df_double_diff[filename].shift(-int(options['periods']))
+            
+            # Pick out region of interest
+            df_double_diff_edge = df_double_diff.loc[(df_double_diff["ZapEnergy"] < estimated_edge_pos+options['fit_region']) & ((df_double_diff["ZapEnergy"] > estimated_edge_pos-options['fit_region']))]
+
+            # Fitting a function to the chosen interval
+            params = np.polyfit(df_double_diff_edge["ZapEnergy"], df_double_diff_edge[filename], 2)
+            double_diff_function = np.poly1d(params)
+
+            x_double_diff=np.linspace(df_double_diff_edge["ZapEnergy"].iloc[0], df_double_diff_edge["ZapEnergy"].iloc[-1],num=10000)
+            y_double_diff=double_diff_function(x_double_diff)
+
+            df_double_diff_fit_function = pd.DataFrame(x_double_diff)
+            df_double_diff_fit_function['y_diff'] = y_double_diff
+            df_double_diff_fit_function.columns = ['x_diff', 'y_diff']
+
+
+            # Picks out the x-value where the y-value is closest to 0
+            edge_pos_double_diff=x_double_diff[np.where(y_double_diff == find_nearest(y_double_diff,0))][0]
+        
+            if options['log']:
+                aux.write_log(message=f"Edge shift estimated by the double differential zero-point is {str(round(edge_pos_double_diff,5))}", options=options)
+
+            if options['save_plots']:
+
+                if options['diff'] and options['double_diff']:
+                    
+                    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2, figsize=(20,20))
+                    df_diff.plot(x='ZapEnergy', y=filename, ax=ax1, kind='scatter')
+                    df_diff_fit_function.plot(x='x_diff', y='y_diff', ax=ax1)
+                    ax1.set_xlim([edge_pos_diff-0.0015, edge_pos_diff+0.0015])
+                    ax1.axvline(x=edge_pos_diff-options['fit_region'], ls='--', c='black')
+                    ax1.axvline(x=edge_pos_diff, ls='--', c='green')
+                    ax1.axvline(x=edge_pos_diff+options['fit_region'], ls='--', c='black')
+                    ax1.set_title('Fit region of differentiated data')
+
+                    df_diff_edge.plot(x='ZapEnergy', y=filename, ax=ax2, kind='scatter')
+                    df_diff_fit_function.plot(x='x_diff', y='y_diff', ax=ax2)
+                    ax2.axvline(x=edge_pos_diff, ls='--', c='green')
+                    ax2.axvline(x=estimated_edge_pos, ls='--', c='red')
+                    ax2.set_title('Fit of differentiated data')
+
+
+                    df_double_diff.plot(x='ZapEnergy', y=filename, ax=ax3, kind='scatter')
+                    df_double_diff_fit_function.plot(x='x_diff', y='y_diff', ax=ax3)
+                    ax3.set_xlim([edge_pos_double_diff-0.0015, edge_pos_double_diff+0.0015])
+                    ax3.axvline(x=edge_pos_double_diff-options['fit_region'], ls='--', c='black')
+                    ax3.axvline(x=edge_pos_double_diff, ls='--', c='green')
+                    ax3.axvline(x=edge_pos_double_diff+options['fit_region'], ls='--', c='black')
+
+                    df_double_diff_edge.plot(x='ZapEnergy', y=filename, ax=ax4, kind='scatter')
+                    df_double_diff_fit_function.plot(x='x_diff', y='y_diff', ax=ax4)
+                    ax4.axvline(x=edge_pos_double_diff, ls='--', c='green')
+                    ax4.axvline(x=estimated_edge_pos, ls='--', c='red')
+
+                    
+
+
+                elif options['diff']:
+                    fig, (ax1, ax2) = plt.subplots(ncols=2,nrows=1, figsize=(20, 10))
+                    df_diff.plot(x='ZapEnergy', y=filename, ax=ax1, kind='scatter')
+                    ax1.set_xlim([edge_pos_diff-0.5, edge_pos_diff+0.5])
+                    ax1.axvline(x=edge_pos_diff-options['fit_region'], ls='--', c='black')
+                    ax1.axvline(x=edge_pos_diff, ls='--', c='green')
+                    ax1.axvline(x=edge_pos_diff+options['fit_region'], ls='--', c='black')
+
+                    df_diff_edge.plot(x='ZapEnergy', y=filename, ax=ax2)
+                    ax2.axvline(x=edge_pos_diff, ls='--', c='green')
+                    ax2.axvline(x=estimated_edge_pos, ls='--', c='red')
+
+                
+                elif options['double_diff']:
+                    fig, (ax1, ax2) = plt.subplots(ncols=2,nrows=1, figsize=(20, 10))
+                    df_double_diff.plot(x='ZapEnergy', y=filename, ax=ax1, kind='scatter')
+                    ax1.set_xlim([edge_pos_double_diff-0.5, edge_pos_double_diff+0.5])
+                    ax1.axvline(x=edge_pos_double_diff-options['fit_region'], ls='--', c='black')
+                    ax1.axvline(x=edge_pos_double_diff, ls='--', c='green')
+                    ax1.axvline(x=edge_pos_double_diff+options['fit_region'], ls='--', c='black')
+
+                    df_double_diff_edge.plot(x='ZapEnergy', y=filename, ax=ax2)
+                    ax2.axvline(x=edge_pos_double_diff, ls='--', c='green')
+                    ax2.axvline(x=estimated_edge_pos, ls='--', c='red')
+
+
+    if not options['diff']:
+        edge_pos_diff = None
+    if not options['double_diff']:
+        edge_pos_double_diff = None
+
+    return edge_pos_diff, edge_pos_double_diff
+
+def normalise(data: dict, options={}):
+    required_options = ['log', 'logfile', 'save_values']
     default_options = {
-        'print': False,
+        'log': False,
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_normalisation.log',
+        'save_values': True
     }
     options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
 
-    #Finding the normalization constant µ_0(E_0), by subtracting the value of the pre-edge-line from the value of the post-edge line at e0
-    normalization_constant=post_edge_fit_function(e0) - pre_edge_fit_function(e0)
-    
-    #subtracting background (as in pre_edge_subtraction)
+    normalised_df = pd.DataFrame(data['xanes_data']['ZapEnergy'])
+    data['normalisation_constants'] = {}
 
-    #dividing the background-subtracted data with the normalization constant
+    #Finding the normalisation constant µ_0(E_0), by subtracting the value of the pre-edge-line from the value of the post-edge line at e0
+    for filename in data['path']:
+        e0_ind = data['post_edge_fit_data'].loc[data['post_edge_fit_data']['ZapEnergy'] == find_nearest(data['post_edge_fit_data']['ZapEnergy'], data['e0'][filename])].index.values[0]
+        #norm = data['post_edge_fit_data'][filename].iloc[find_nearest(data['post_edge_fit_data'][filename], data['e0'][filename])]
+        normalisation_constant = data['post_edge_fit_data'][filename].iloc[e0_ind] - data['pre_edge_fit_data'][filename].iloc[e0_ind]
+        normalised_df.insert(1, filename, data['xanes_data'][filename] / normalisation_constant)
+
+
+        # Normalise the pre-edge and post-edge fit function data
+        data['pre_edge_fit_data'][filename] = data['pre_edge_fit_data'][filename] / normalisation_constant
+        data['post_edge_fit_data'][filename] = data['post_edge_fit_data'][filename] / normalisation_constant
+
+        data['normalisation_constants'][filename] = normalisation_constant
+
+    if options['save_values']:
+        data['xanes_data'] = normalised_df
+
+
+    return normalised_df
 
     
-def flattening(data,options={}):
+def flatten(data:dict, options={}):
     #only picking out zapenergy-values higher than edge position (edge pos and below remains untouched)
-    df_e0_and_above=df.loc[df['ZapEnergy'] > edge_shift_diff]
 
-    flattened_data = post_edge_fit_function(df_e0_and_above['ZapEnergy']) - pre_edge_fit_function(df_e0_and_above['ZapEnergy'])
+    required_options = ['log', 'logfile', 'save_values']
+    default_options = {
+        'log': False,
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_flattening.log',
+        'save_values': True
+    }
+    options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
+
+
+    flattened_df = pd.DataFrame(data['xanes_data']['ZapEnergy'])
+
+    for filename in data['path']:
+        fit_function_diff = -data['post_edge_fit_data'][filename] + data['pre_edge_params'][filename][0]
+        fit_function_diff.loc[flattened_df['ZapEnergy'] <= data['e0'][filename]] = 0
+
+        flattened_df[filename] = data['xanes_data'][filename] - fit_function_diff
+
+
+    if options['save_values']:
+        data['xanes_data'] = flattened_df
+    
+
+    return flattened_df, fit_function_diff
 
     #make a new dataframe with flattened values
 
diff --git a/nafuma/xanes/io.py b/nafuma/xanes/io.py
index 816b8f5..f623a38 100644
--- a/nafuma/xanes/io.py
+++ b/nafuma/xanes/io.py
@@ -2,94 +2,170 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import os
 import numpy as np
-import nafuma.auxillary as aux 
+import nafuma.auxillary as aux
+from nafuma.xanes.calib import find_element
+from datetime import datetime
 
 
-def split_xanes_scan(root, destination=None, replace=False):
-    #root is the path to the beamtime-folder
-    #destination should be the path to the processed data
-    
-    #insert a for-loop to go through all the folders.dat-files in the folder root\xanes\raw
-
-    # FIXME Only adding this variable to pass the Linting-tests - will refactor this later
-    filename = 'dummy'
-    
-    with open(filename, 'r') as f:
-        lines = f.readlines()
-        
-    datas = []
-    data = []
-    headers = []
-    header = ''
-    start = False
-    
-    for line in lines:
-        if line[0:2] == "#L":
-            start = True
-            header = line[2:].split()
-            continue
-            
-        elif line[0:2] == "#C":
-            start = False
-            
-            if data:
-                datas.append(data)
-                data = []
-                
-            if header:
-                headers.append(header)
-                header = ''
-                
-                
-
-        if start == False:
-            continue
-            
-        else:
-            data.append(line.split())
-            
-            
-            
-            
-    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]}
-    edge_count = {'Mn': 0, 'Fe': 0, 'Co': 0, 'Ni': 0}
+def split_scan_data(data: dict, options={}) -> list:
+    ''' Splits a XANES-file from BM31 into different files depending on the edge. Has the option to add intensities of all scans of same edge into the same file. 
+    As of now only picks out xmap_rois (fluoresence mode) and for Mn, Fe, Co and Ni K-edges.'''
     
 
-    for ind, data in enumerate(datas):
-        df = pd.DataFrame(data)
-        df.columns = headers[ind]
+    required_options = ['log', 'logfile', 'save', 'save_folder', 'replace', 'add_rois']
 
-        edge_start = np.round((float(df["ZapEnergy"].min())), 1)
+    default_options = {
+        'log': False,
+        'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_split_edges.log',
+        'save': False, # whether to save the files or not
+        'save_folder': '.', # root folder of where to save the files
+        'replace': False, # whether to replace the files if they already exist
+        'add_rois': False # Whether to add the rois of individual scans of the same edge together
+    }
 
-        for edge, energies in edges.items():
-            if edge_start in energies:
-                edge_actual = edge
-                edge_count[edge] += 1
+    options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
 
+    if not isinstance(data['path'], list):
+        data['path'] = [data['path']]
+
+    all_scans = []
+
+    if options['log']:
+        aux.write_log(message='Starting file splitting...', options=options)
+    
+    for filename in data['path']:
+
+        if options['log']:
+            aux.write_log(message=f'Reading {filename}...', options=options)
+
+        with open(filename, 'r') as f:
+            lines = f.readlines()
+            
+        scan_datas, scan_data = [], []
+        headers, header = [], ''
+        read_data = False
         
-        
-        filename = filename.split('/')[-1]
-        count = str(edge_count[edge_actual]).zfill(4)
+        for line in lines:
+            # Header line starts with #L - reads headers, and toggles data read-in on
+            if line[0:2] == "#L":
+                header, read_data = line[2:].split(), True
 
-        
-        # Save 
-        if destination:
-            cwd = os.getcwd()
+                if options['log']:
+                    aux.write_log(message='... Found scan data. Starting read-in...', options=options)
+                continue
 
-            if not os.path.isdir(destination):
-                os.mkdir(destination)
+            # First line after data started with #C - stops data read-in
+            elif line[0:2] == "#C":
+                read_data = False
                 
-            os.chdir(destination)
+                if scan_data:
+                    scan_datas.append(scan_data); scan_data = []
+                    
+                if header:
+                    headers.append(header); header = ''
+                    
+            # Ignore line if read-in not toggled       
+            if read_data == False:
+                continue
+            
+            # Read in data if it is
+            else:
+                scan_data.append(line.split())
+                
+                
+        edges = {'Mn': [], 'Fe': [], 'Co': [], 'Ni': []}
+        
 
-            df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
+        for i, scan_data in enumerate(scan_datas):
+            
+            xanes_df = pd.DataFrame(scan_data).apply(pd.to_numeric)
+            xanes_df.columns = headers[i]
+            edge = find_element({'xanes_data_original': xanes_df})
 
-            os.chdir(cwd)
-
-        else:
-            df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
+            if options['log']:
+                aux.write_log(message=f'... Starting data clean-up ({edge}-edge)... ({i+1}/{len(scan_datas)})', options=options)
 
 
+            if not ('xmap_roi00' in headers[i]) and (not 'xmap_roi01' in headers[i]):
+                if options['log']:
+                    aux.write_log(message='... ... Did not find fluoresence data. Skipping...', options=options)
 
+                continue 
+
+            
+            
+            edges[edge].append(xanes_df)
+            
+        
+        if options['add']:
+
+            if options['log']:
+                aux.write_log(message=f'... Addition of rois enabled. Starting addition...', options=options)
+   
+            added_edges = {'Mn': [], 'Fe': [], 'Co': [], 'Ni': []}
+            for edge, scans in edges.items():
+
+                if options['log']:
+                    aux.write_log(message=f'... ... Adding rois of the {edge}-edge...', options=options)
+                
+                if scans:
+                    xanes_df = scans[0]
+
+                    for i, scan in enumerate(scans):
+                        if i > 0:
+
+                            if options['log']:
+                                aux.write_log(message=f'... ... ... Adding {i+1}/{len(scans)}', options=options)
+
+                            if 'xmap_roi00' in xanes_df.columns:
+                                xanes_df['xmap_roi00'] += scan['xmap_roi00']
+                            if 'xmap_roi01' in xanes_df.columns:
+                                xanes_df['xmap_roi01'] += scan['xmap_roi01']
+
+                    added_edges[edge].append(xanes_df)
+
+            edges = added_edges
+            
+        if options['save']:
+
+            if options['log']:
+                aux.write_log(message=f'... Saving data to {options["save_folder"]}', options=options)
+
+            if not os.path.isdir(options['save_folder']):
+                if options['log']:
+                    aux.write_log(message=f'... ... {options["save_folder"]} does not exist. Creating folder.', options=options)
+
+                os.makedirs(options['save_folder'])
+
+
+            filename = os.path.basename(filename).split('.')[0]
+
+            for edge, scans in edges.items():
+                for i, scan in enumerate(scans):
+                    count = '' if options['add'] else '_'+str(i).zfill(4)
+                    path = os.path.join(options['save_folder'], f'{filename}_{edge}{count}.dat')
+                    
+                    if not os.path.isfile(path):
+                        scan.to_csv(path)
+                        if options['log']:
+                            aux.write_log(message=f'... ... Scan saved to {path}', options=options)
+                    
+                    elif options['replace'] and os.path.isfile(path):
+                        scan.to_csv(path)
+                        if options['log']:
+                            aux.write_log(message=f'... ... File already exists. Overwriting to {path}', options=options)
+
+                    elif not options['replace'] and os.path.isfile(path):
+                        if options['log']:
+                            aux.write_log(message=f'... ... File already exists. Skipping...', options=options)
+
+        all_scans.append(edges)
+
+    if options['log']:
+        aux.write_log(message=f'All done!', options=options)
+
+
+    return all_scans
 
 
 
@@ -98,9 +174,9 @@ def read_data(data: dict, options={}) -> pd.DataFrame:
 
     # FIXME Handle the case when dataseries are not the same size
 
-    required_options = []
+    required_options = ['adjust']
     default_options = {
-
+        'adjust': 0
     }
 
     options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
@@ -109,6 +185,7 @@ def read_data(data: dict, options={}) -> pd.DataFrame:
 
     # Initialise DataFrame with only ZapEnergy-column
     xanes_data = pd.read_csv(data['path'][0])[['ZapEnergy']]
+    xanes_data['ZapEnergy'] += options['adjust']
 
     if not isinstance(data['path'], list):
         data['path'] = [data['path']]
@@ -117,6 +194,7 @@ def read_data(data: dict, options={}) -> pd.DataFrame:
         columns.append(filename)
 
         scan_data = pd.read_csv(filename)
+
         scan_data = scan_data[[determine_active_roi(scan_data)]]
         xanes_data = pd.concat([xanes_data, scan_data], axis=1)
 
@@ -130,6 +208,7 @@ def read_data(data: dict, options={}) -> pd.DataFrame:
 
 
 
+
 def determine_active_roi(scan_data):
 
     # FIXME For Co-edge, this gave a wrong scan