Refactor estimation of edge position and automatise pre edge limit setting
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rasmusvt
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cb2c7532e6
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ba349a5892
1 changed files with 40 additions and 30 deletions
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@ -12,7 +12,7 @@ from datetime import datetime
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##Better to make a new function that loops through the files, and performing the split_xanes_scan on
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#Trying to make a function that can decide which edge it is based on the first ZapEnergy-value
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def find_element(data: dict) -> str:
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def find_element(data: dict, index=0) -> str:
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''' Takes the data dictionary and determines based on the start value of the ZapEnergy-column which element the edge is from.'''
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element_energy_intervals = {
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@ -22,13 +22,13 @@ def find_element(data: dict) -> str:
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'Ni': [8.0, 8.6]
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}
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if (element_energy_intervals['Mn'][0] < data['xanes_data_original']["ZapEnergy"].iloc[0]) & (data['xanes_data_original']["ZapEnergy"].iloc[0] < element_energy_intervals['Mn'][1]):
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if (element_energy_intervals['Mn'][0] < data['xanes_data_original']["ZapEnergy"].iloc[index]) & (data['xanes_data_original']["ZapEnergy"].iloc[index] < element_energy_intervals['Mn'][1]):
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edge = 'Mn'
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elif (element_energy_intervals['Fe'][0] < data['xanes_data_original']["ZapEnergy"].iloc[0]) & (data['xanes_data_original']["ZapEnergy"].iloc[0] < element_energy_intervals['Fe'][1]):
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elif (element_energy_intervals['Fe'][0] < data['xanes_data_original']["ZapEnergy"].iloc[index]) & (data['xanes_data_original']["ZapEnergy"].iloc[index] < element_energy_intervals['Fe'][1]):
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edge = 'Fe'
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elif (element_energy_intervals['Co'][0] < data['xanes_data_original']["ZapEnergy"].iloc[0]) & (data['xanes_data_original']["ZapEnergy"].iloc[0] < element_energy_intervals['Co'][1]):
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elif (element_energy_intervals['Co'][0] < data['xanes_data_original']["ZapEnergy"].iloc[index]) & (data['xanes_data_original']["ZapEnergy"].iloc[index] < element_energy_intervals['Co'][1]):
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edge = 'Co'
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elif (element_energy_intervals['Ni'][0] < data['xanes_data_original']["ZapEnergy"].iloc[0]) & (data['xanes_data_original']["ZapEnergy"].iloc[0] < element_energy_intervals['Ni'][1]):
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elif (element_energy_intervals['Ni'][0] < data['xanes_data_original']["ZapEnergy"].iloc[index]) & (data['xanes_data_original']["ZapEnergy"].iloc[index] < element_energy_intervals['Ni'][1]):
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edge = 'Ni'
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@ -58,22 +58,25 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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# FIXME Implement with finding accurate edge position
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# FIXME Allow specification of start of pre-edge area
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# Find the cutoff point at which the edge starts - everything to the LEFT of this point will be used in the pre edge function fit
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if not options['edge_start']:
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edge_starts = {
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'Mn': 6.42,
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'Fe': 7.09,
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'Co': 7.705,
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'Ni': 8.3
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pre_edge_limit_offsets = {
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'Mn': 0.03,
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'Fe': 0.03,
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'Co': 0.03,
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'Ni': 0.03
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}
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data['edge'] = find_element(data)
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edge_start = edge_starts[data['edge']]
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edge_position = estimate_edge_position(data, options, index=0)
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pre_edge_limit = edge_position - pre_edge_limit_offsets[data['edge']]
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# FIXME There should be an option to specify the interval in which to fit the background - now it is taking everything to the left of edge_start parameter, but if there are some artifacts in this area, it should be possible to
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# limit the interval
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# Making a dataframe only containing the rows that are included in the background subtraction (points lower than where the edge start is defined)
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pre_edge_data = data['xanes_data_original'].loc[data['xanes_data_original']["ZapEnergy"] < edge_start]
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pre_edge_data = data['xanes_data_original'].loc[data['xanes_data_original']["ZapEnergy"] < pre_edge_limit]
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# Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data
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pre_edge_fit_data = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
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@ -161,6 +164,31 @@ def pre_edge_subtraction(data: dict, options={}):
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return xanes_data_bkgd_subtracted
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def estimate_edge_position(data: dict, options={}, index=0):
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#a dataset is differentiated to find a first estimate of the edge shift to use as starting point.
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required_options = ['print','periods']
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default_options = {
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'print': False,
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'periods': 2, #Periods needs to be an even number for the shifting of values to work properly
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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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#making new dataframe to keep the differentiated data
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df_diff = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
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df_diff[data['path'][index]]=data['xanes_data_original'][data['path'][index]].diff(periods=options['periods'])
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#shifting column values up so that average differential fits right between the points used in the calculation
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df_diff[data['path'][index]]=df_diff[data['path'][index]].shift(-int(options['periods']/2))
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df_diff_max = df_diff[data['path'][index]].dropna().max()
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estimated_edge_shift =df_diff.loc[df_diff[data['path'][index]] == df_diff_max,'ZapEnergy'].values[0]
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# FIXME Add logging option to see the result
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print(estimated_edge_shift)
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return estimated_edge_shift
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def post_edge_fit(path, options={}):
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#FIXME should be called "fitting post edge" (normalization is not done here, need edge shift position)
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required_options = ['print']
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@ -254,25 +282,7 @@ def smoothing(path, options={}):
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return df_smooth, filenames
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def find_pos_maxdiff(df, filenames,options={}):
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#a dataset is differentiated to find a first estimate of the edge shift to use as starting point.
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required_options = ['print','periods']
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default_options = {
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'print': False,
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'periods': 2, #Periods needs to be an even number for the shifting of values to work properly
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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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#making new dataframe to keep the differentiated data
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df_diff = pd.DataFrame(df["ZapEnergy"])
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df_diff[filenames]=df[filenames].diff(periods=options['periods'])
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#shifting column values up so that average differential fits right between the points used in the calculation
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df_diff[filenames]=df_diff[filenames].shift(-int(options['periods']/2))
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df_diff_max = df_diff[filenames].dropna().max()
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estimated_edge_shift =df_diff.loc[df_diff[filenames] == df_diff_max,'ZapEnergy'].values[0]
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return estimated_edge_shift, df_diff, df_diff_max
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def find_nearest(array, value):
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#function to find the value closes to "value" in an "array"
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