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Refactor normalisation and flattening functions

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rasmusvt 2022-06-23 11:46:06 +02:00
parent 4d501adb72
commit ec1fba1c82
1 changed files with 36 additions and 9 deletions
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45
nafuma/xanes/calib.py
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@ -491,25 +491,52 @@ def determine_edge_position(data: dict, options={}):
return edge_pos_diff, edge_pos_double_diff
def normalization(data,options={}):
required_options = ['print']
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'])
#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']:
normalisation_constant = data['post_edge_fit_function'][filename].loc[data['post_edge_fit_function']['ZapEnergy'] == data['e0'][filename]] - data['pre_edge_fit_function'].loc[data['pre_edge_fit_function']['ZapEnergy'] == data['e0'][filename]]
normalised_df.insert(1, filename, data['xanes_data'] / 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)
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)
df_e0_and_above=df.loc[df['ZapEnergy'] > edge_shift_diff]
flattened_df = pd.DataFrame(data['xanes_data']['ZapEnergy'])
for filename in data['path']:
above_e0 = data['xanes_data'][filename].loc(data['xanes_data']['ZapEnergy'] > data['e0'][filename])
flattened_data = data['post_edge_fit_function'][filename] -
flattened_data = post_edge_fit_function(df_e0_and_above['ZapEnergy']) - pre_edge_fit_function(df_e0_and_above['ZapEnergy'])
#make a new dataframe with flattened values