diff --git a/beamtime/xanes/io.py b/beamtime/xanes/io.py
index a818d86..2ed6d29 100644
--- a/beamtime/xanes/io.py
+++ b/beamtime/xanes/io.py
@@ -1,2 +1,148 @@
-#hello
-#yeah
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+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+
+
+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
+    
+    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}
+    
+
+    for ind, data in enumerate(datas):
+        df = pd.DataFrame(data)
+        df.columns = headers[ind]
+
+        edge_start = np.round((float(df["ZapEnergy"].min())), 1)
+
+        for edge, energies in edges.items():
+            if edge_start in energies:
+                edge_actual = edge
+                edge_count[edge] += 1
+
+        
+        
+        filename = filename.split('/')[-1]
+        count = str(edge_count[edge_actual]).zfill(4)
+
+        
+        # Save 
+        if destination:
+            cwd = os.getcwd()
+
+            if not os.path.isdir(destination):
+                os.mkdir(destination)
+                
+            os.chdir(destination)
+
+            df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
+
+            os.chdir(cwd)
+
+        else:
+            df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
+
+
+#Function that "collects" all the files in a folder, only accepting .dat-files from xanes-measurements
+def get_filenames(path):
+    
+    
+    cwd = os.getcwd()
+    
+    # Change into path provided
+    os.chdir(path)
+    
+    filenames = [os.path.join(path, filename) for filename in os.listdir() if os.path.isfile(filename) and filename[-4:] == '.dat'] #changed
+    
+    
+    
+    # Change directory back to where you ran the script from
+    os.chdir(cwd)
+    
+    return filenames
+
+def put_in_dataframe(path):
+    filenames = get_filenames(path) 
+
+    #making the column names to be used in the dataframe, making sure the first column is the ZapEnergy
+    column_names = ["ZapEnergy"]
+
+    for i in range(len(filenames)):
+        column_names.append(filenames[i])
+
+    #Taking the first file in the folder and extracting ZapEnergies and intensity from that (only need the intensity from the rest)
+    first = pd.read_csv(filenames[0], skiprows=0)
+
+    #Making a data frame with the correct columns, and will fill inn data afterwards
+    df = pd.DataFrame(columns = column_names)
+    #First putting in the 2theta-values
+    df["ZapEnergy"]=first["ZapEnergy"]
+
+    #filling in the intensities from all files into the corresponding column in the dataframe
+    for i in range(len(filenames)):
+        df2 = pd.read_csv(filenames[i])
+        df2 = df2.drop(['Mon','Det1','Det2','Det3','Det4','Det5', 'Det6','Ion1'], axis=1) #, axis=1)
+        df2 = df2.drop(['MonEx','Ion2','Htime','MusstEnc1','MusstEnc3','MusstEnc4', 'TwoTheta', 'ZCryo'], axis=1)
+        df2 = df2.drop(['ZBlower1', 'ZBlower2', 'ZSrcur'], axis=1)#, axis=19) #removing the sigma at this point
+        
+    ##############     THIS PART PICKS OUT WHICH ROI IS OF INTEREST, BUT MUST BE FIXED IF LOOKING AT THREE EDGES (roi00,roi01,roi02)    #####################
+        if 'xmap_roi01' in df2.columns: 
+            #Trying to pick the roi with the highest difference between maximum and minimum intensity --> biggest edge shift
+            if max(df2["xmap_roi00"])-min(df2["xmap_roi00"])>max(df2["xmap_roi01"])-min(df2["xmap_roi01"]):
+                df[filenames[i]]=df2["xmap_roi00"] #forMn
+            else: 
+                df[filenames[i]]=df2["xmap_roi01"] #forNi
+        else:
+            df[filenames[i]]=df2["xmap_roi00"]
+    ###############################################################################################
+
+        i=i+1
+
+
+    #print(df)
+    #If I want to make a csv-file of the raw data. Decided that was not necessary:
+    #df.to_csv('static-Mn-edge.csv') #writing it to a csv, first row is datapoint (index), second column is 2theta, and from there the scans starts
+
+
+    return df
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