Add the tutorial on batch

.gitignore

 .DS_Store
-Example/.DS_Store
+howto_parameters/.DS_Store
+howto_parameters/.ipynb_checkpoints/

README.md

-TutorialGenX.ipynb is a small tutorial on how to fill in the different parameters in GenX (https://aglavic.github.io/genx/index.html)
+# Tutorials GenX
+
+Here you will find small tutorials on how to use GenX (https://aglavic.github.io/genx/index.html).
+
+## howto_batch
+The notebook ```batch_genx.ipynb``` details the different steps on how to use GenX with command lines.
+The aim is to batch fits for XRR without the GUI. To do so we edit the .hgx files, which are just hdf5 files.
+
+The script ```batch_genx.ipy``` is a condensed script without all the details from the notebook.
+
+## howto_parameters
+Explains what are the different parameters in the GUI.

howto_batch/batch_genx.ipy

+import numpy as np
+import h5py
+import os as os
+
+scan0 = np.genfromtxt('data/scan0.dat')
+scan1 = np.genfromtxt('data/scan1.dat')
+scan2 = np.genfromtxt('data/scan2.dat')
+
+scans = [scan0, scan1, scan2]
+
+# We change the upper bound of the electron density for each scan
+max_rho = [0.04, 0.05, 0.045]
+
+is_first_loop = True
+
+for i, scan in enumerate(scans):
+    
+    f = h5py.File("data/model_for_fit.hgx", "a")
+    
+    ########################################################
+    # Load the scan data into the model
+    ########################################################
+    f['current/data/datasets/0/x'][...] = scan[:,0]
+    f['current/data/datasets/0/y'][...] = scan[:,1]
+    f['current/data/datasets/0/error'][...] = scan[:,2]
+    
+    #########################################################
+    # Change upper bound for electron density
+    #########################################################
+    # Index of columns (always the same)
+    # 0: Parameter (string)
+    # 1: Value (float)
+    # 2: Fit (bool)
+    # 3: Min (float)
+    # 4: Max (float)
+    # 5: Error (float)
+
+    # Extract the parameter names
+    col0 = [val.decode("UTF-8") for val in f['current/parameters/data col 0'].value]
+    
+    # Find where the param is
+    arg_param = col0.index("Sub.setDens")
+    # Change its max value (needs to extract and update the whole array)
+    # 'Max' value is in data col 4 (see list above)
+    temp = f['current/parameters/data col 4'].value
+    temp[arg_param] = max_rho[i]
+    f['current/parameters/data col 4'][...] = temp
+    
+    f.close()
+    
+    #########################################################
+    # Do the fit
+    #########################################################
+    print(20*'#')
+    print('Fit %s'%str(i))
+    result_file = 'results_C/result'+str(i)+'.hgx'
+    os.system('genx --run --mgen=100 data/model_for_fit.hgx '+result_file)
+    
+    #########################################################   
+    # Extract and save the fit
+    #########################################################
+    
+    f = h5py.File("results_C/result"+str(i)+".hgx", "r")
+
+    # Extract and save the curves
+    x = f['current/data/datasets/0/x'].value
+    y = f['current/data/datasets/0/y'].value
+    y_sim = f['current/data/datasets/0/y_sim'].value
+    error = f['current/data/datasets/0/error'].value
+ 
+    np.savetxt('results_C/fit'+str(i)+'.dat', np.transpose([x, y, y_sim, error]),
+               header='#x\t#y\t#y_fit\t#error' )
+
+    # Extract and save the parameters
+    col0 = [val.decode("UTF-8") for val in f['current/parameters/data col 0'].value]
+    col1 = [val for val in f['current/parameters/data col 1'].value]
+    col2 = [val for val in f['current/parameters/data col 2'].value]
+    col3 = [val for val in f['current/parameters/data col 3'].value]
+    col4 = [val for val in f['current/parameters/data col 4'].value]
+    col5 = [val for val in f['current/parameters/data col 5'].value]
+    col_labels = [val.decode("UTF-8") for val in f['current/parameters/data_labels'].value]
+    f.close()
+
+    # Reorganise as a table and print
+    tab0 = np.stack((col0, col1, col2, col3, col4, col5), axis = 1)
+    tab = np.vstack([col_labels, tab0])
+    print('Fit results')
+    print(10*'-')
+    for line in tab:
+        if line[0] != '':
+            try:
+                print('%s\t%g'%(line[0], float(line[1])))
+            except:
+                print('%s\t%s'%(line[0], line[1]))
+                
+    print('')
+    
+    # Save
+    if is_first_loop:
+        # Write the header
+        
+        # Get rid of the empty lines in the parameter list
+        pos_arg = np.array([True if val != '' else False for val in col0])
+        
+        # Mask the arrays to keep only the non-empty lines
+        col0 = np.array(col0)[pos_arg]
+        
+        with open('results_C/summary.dat', 'w') as file:
+            file.write('#index_fit\t')
+            for item in col0:
+                file.write('#%s\t'%item)
+            file.write('\n')
+        
+        
+        is_first_loop = False
+    
+    # Mask the arrays to keep only the non-empty lines
+    col1 = np.array(col1)[pos_arg]
+    with open('results_C/summary.dat', 'a') as file:
+        file.write('%s\t'%str(i))
+        for item in col1:
+            file.write('%g\t'%item)
+        file.write('\n')
+    
+    
+       
+    
+    
+    
+    
+    
+    
+    
\ No newline at end of file

howto_batch/data/scan0.dat

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howto_batch/data/scan1.dat

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howto_batch/data/scan2.dat

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howto_batch/results_B/fit0.dat

+# #x	#y	#y_fit	#error
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howto_batch/results_B/fit1.dat

+# #x	#y	#y_fit	#error
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howto_batch/results_B/fit2.dat

+# #x	#y	#y_fit	#error
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