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Attempt to get flattening and normalisation to behave properly

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This commit is contained in:
rasmusvt 2022-06-23 15:32:29 +02:00
parent ec1fba1c82
commit 2b14a64c4b
1 changed files with 36 additions and 9 deletions
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45
nafuma/xanes/calib.py
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@ -78,6 +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)
@ -86,6 +88,8 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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"])
@ -195,6 +199,8 @@ def post_edge_fit(data: dict, options={}):
# 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)
@ -203,6 +209,8 @@ def post_edge_fit(data: dict, options={}):
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"])
@ -341,8 +349,9 @@ def estimate_edge_position(data: dict, options={}, index=0):
def determine_edge_position(data: dict, options={}):
required_options = ['log', 'logfile', 'save_plots', 'save_folder', 'periods', 'diff', 'double_diff', 'fit_region']
required_options = ['save_values', 'log', 'logfile', 'save_plots', 'save_folder', 'periods', 'diff', 'double_diff', 'fit_region']
default_options = {
'save_values': True,
'log': False,
'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_determine_edge_position.log',
'save_plots': False,
@ -366,6 +375,9 @@ def determine_edge_position(data: dict, options={}):
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'] = {}
for i, filename in enumerate(data['path']):
estimated_edge_pos = estimate_edge_position(data, options=options, index=i)
@ -395,7 +407,10 @@ def determine_edge_position(data: dict, options={}):
edge_pos_diff=x_diff[np.where(y_diff == np.amax(y_diff))][0]
if options['log']:
aux.write_log(message=f"Edge shift estimated by the differential maximum is: {str(round(edge_pos_diff,5))}", options=options)
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']:
@ -501,12 +516,21 @@ def normalise(data: dict, options={}):
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
normalised_df = pd.DataFrame(data['xanes_data']['ZapEnergy'])
data['normalisation_constants'] = {}
#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]]
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)
normalised_df.insert(1, filename, data['xanes_data'] / 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
@ -527,17 +551,20 @@ def flatten(data:dict, options={}):
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] -
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
flattened_data = post_edge_fit_function(df_e0_and_above['ZapEnergy']) - pre_edge_fit_function(df_e0_and_above['ZapEnergy'])
return flattened_df, fit_function_diff
#make a new dataframe with flattened values