Fix normalisation issues
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327cef5b51
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6eb45772d1
2 changed files with 6 additions and 4 deletions
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@ -206,7 +206,7 @@ def pre_edge_subtraction(data: dict, options={}):
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'show_plots': False,
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'save_plots': False,
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'save_folder': './',
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'pre_edge_subtraction_store_data': False
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'pre_edge_subtraction_store_data': True
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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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@ -893,8 +893,10 @@ def normalise(data: dict, options={}):
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#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
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for filename in data['path']:
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e0_ind = data['post_edge_fit_data'].loc[data['post_edge_fit_data']['ZapEnergy'] == find_nearest(data['post_edge_fit_data']['ZapEnergy'], data['e0_diff'][filename])].index.values[0]
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#norm = data['post_edge_fit_data'][filename].iloc[find_nearest(data['post_edge_fit_data'][filename], data['e0'][filename])]
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normalisation_constant = data['post_edge_fit_data'][filename].iloc[e0_ind] #- data['pre_edge_fit_data'][filename].iloc[e0_ind]
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normalisation_constant = data['post_edge_fit_data'][filename].iloc[e0_ind] - data['pre_edge_fit_data'][filename].iloc[e0_ind]
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print(normalisation_constant)
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normalised_df.insert(1, filename, data['xanes_data'][filename] / normalisation_constant)
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@ -967,7 +969,7 @@ def flatten(data:dict, options={}):
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for filename in data['path']:
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# Subtract 1 from the _normalised_ post edge fit function
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fit_function_diff = data['post_edge_fit_data_norm'][filename] - 1
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fit_function_diff = data['post_edge_fit_data_norm'][filename] - 1 - data['pre_edge_fit_data_norm'][filename]
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# Set all values from edge position and downwards to 0 so that only data above the edge position will be adjusted
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fit_function_diff.loc[flattened_df['ZapEnergy'] <= data['e0_diff'][filename]] = 0
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@ -57,7 +57,7 @@ def split_scan_data(data: dict, options={}) -> list:
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continue
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# First line after data started with #C - stops data read-in
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elif line[0:2] == "#C":
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elif line[0:2] == "#C" or line[0:2] == '#S':
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read_data = False
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if scan_data:
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