nanowhale
GitHub
commit
d497217ae3
3 changed files with 265 additions and 161 deletions
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@ -1,5 +1,6 @@
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import json
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import numpy as np
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import os
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def update_options(options, required_options, default_options):
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''' Takes a dictionary of options along with a list of required options and dictionary of default options, and sets all keyval-pairs of options that is not already defined to the default values'''
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@ -53,3 +54,35 @@ def floor(a, roundto=1):
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a = np.floor(a*fac) / fac
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return a
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def write_log(message, options={}):
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from datetime import datetime
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required_options = ['logfile']
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default_options = {
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'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S.log")}'
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}
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options = update_options(options=options, required_options=required_options, default_options=default_options)
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now = datetime.now().strftime('%Y/%m/%d %H:%M:%S')
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message = f'[{now}] {message} \n'
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with open(options['logfile'], 'a') as f:
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f.write(message)
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#Function that "collects" all the files in a folder, only accepting .dat-files from xanes-measurements
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def get_filenames(path, ext, filter=''):
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''' Collects all filenames from specified path with a specificed extension
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Input:
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path: path to find all filenames (relative or absolute)
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ext: extension (including ".")'''
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filenames = [os.path.join(path, filename) for filename in os.listdir(path) if os.path.isfile(os.path.join(path, filename)) and filename.endswith(ext) and filter in filename]
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return filenames
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@ -6,111 +6,190 @@ import nafuma.auxillary as aux
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import nafuma.xanes as xas
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import nafuma.xanes.io as io
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from scipy.signal import savgol_filter
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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 finding_edge(df):
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#FIXME add Fe and Co
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if 5.9 < df["ZapEnergy"][0] < 6.5:
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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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'Mn': [5.9, 6.5],
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'Fe': [7.0, 7.2],
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'Co': [7.6, 7.8],
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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[index]) & (data['xanes_data_original']["ZapEnergy"].iloc[index] < element_energy_intervals['Mn'][1]):
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edge = 'Mn'
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return(edge)
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if 8.0 < df["ZapEnergy"][0] < 8.6:
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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[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[index]) & (data['xanes_data_original']["ZapEnergy"].iloc[index] < element_energy_intervals['Ni'][1]):
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edge = 'Ni'
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return(edge)
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def pre_edge_subtraction(path, options={}):
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#FIXME add log-file instead of the troubleshoot-option
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required_options = ['print','troubleshoot']
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def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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# FIXME Add log-file
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required_options = ['edge_start', 'log', 'logfile', 'save_plots', 'save_folder']
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default_options = {
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'edge_start': None,
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'log': False,
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'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_pre_edge_fit.log',
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'save_plots': False,
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'save_folder': './'
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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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if options['log']:
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aux.write_log(message='Starting pre edge fit', options=options)
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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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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_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"] < 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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for i, filename in enumerate(data['path']):
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if options['log']:
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aux.write_log(message=f'Fitting background on {os.path.basename(filename)} ({i+1} / {len(data["path"])})', options=options)
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#Fitting linear function to the background
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params = np.polyfit(pre_edge_data["ZapEnergy"],pre_edge_data[filename],1)
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fit_function = np.poly1d(params)
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#making a list, y_pre,so the background will be applied to all ZapEnergy-values
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background=fit_function(pre_edge_fit_data["ZapEnergy"])
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#adding a new column in df_background with the y-values of the background
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pre_edge_fit_data.insert(1,filename,background)
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if options['save_plots']:
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if not os.path.isdir(options['save_folder']):
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os.makedirs(options['save_folder'])
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dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_pre_edge_fit.png'
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fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,5))
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data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax1)
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pre_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax1)
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ax1.axvline(x = max(pre_edge_data['ZapEnergy']), ls='--')
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ax1.set_title(f'{os.path.basename(filename)} - Full view', size=20)
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data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax2)
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pre_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax2)
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ax2.axvline(x = max(pre_edge_data['ZapEnergy']), ls='--')
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ax2.set_xlim([min(pre_edge_data['ZapEnergy']), max(pre_edge_data['ZapEnergy'])])
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ax2.set_ylim([min(pre_edge_data[filename]), max(pre_edge_data[filename])])
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ax2.set_title(f'{os.path.basename(filename)} - Fit region', size=20)
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plt.savefig(dst, transparent=False)
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plt.close()
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if options['log']:
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aux.write_log(message=f'Pre edge fitting done.', options=options)
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return pre_edge_fit_data
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def pre_edge_subtraction(data: dict, options={}):
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required_options = ['log', 'logfile', 'save_plots', 'save_folder']
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default_options = {
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'log': False,
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'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S.log")}_pre_edge_subtraction.log',
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'save_plots': False,
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'save_folder': './'
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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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if options['log']:
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aux.write_log(message='Starting pre edge subtraction', options=options)
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xanes_data_bkgd_subtracted = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
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for i, filename in enumerate(data['path']):
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if options['log']:
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aux.write_log(message=f'Subtracting background on {filename} ({i} / {len(data["path"])}', options=options)
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xanes_data_bkgd_subtracted.insert(1, filename, data['xanes_data_original'][filename] - data['pre_edge_fit_data'][filename])
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if options['save_plots']:
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if not os.path.isdir(options['save_folder']):
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os.makedirs(options['save_folder'])
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dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_pre_edge_subtraction.png'
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fig, ax = plt.subplots(figsize=(10,5))
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data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax)
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xanes_data_bkgd_subtracted.plot(x='ZapEnergy', y=filename, color='red', ax=ax)
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ax.set_title(f'{os.path.basename(filename)} - After subtraction', size=20)
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plt.savefig(dst)
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plt.close()
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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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'troubleshoot': 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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filenames = xas.io.get_filenames(path)
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df= xas.io.put_in_dataframe(path)
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edge=finding_edge(df)
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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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#Defining the end of the region used to define the background, thus start of the edge
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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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#######================================================================================================================================================
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#FIXME Trying to implement automatical region determination based on an estimate of the edge shift
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#print(df)
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#estimated_edge_shift, df_diff, df_diff_max = find_pos_maxdiff(df, filenames,options=options)
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# FIXME Add logging option to see the result
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#print(estimated_edge_shift)
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#estimated_edge_shift
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###=========================================================================================================================================================================
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#implement widget
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if edge == 'Mn':
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edge_start = 6.42
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#edge_start = estimated_edge_shift
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if edge == 'Ni':
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edge_start = 8.3
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if options['log']:
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aux.write_log(message=f'Estimated edge shift for determination of pre-edge area is: {estimated_edge_shift} keV', options=options)
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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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df_start=df.loc[df["ZapEnergy"] < edge_start]
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return estimated_edge_shift
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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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df_bkgd = pd.DataFrame(df["ZapEnergy"])
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for files in filenames:
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#Fitting linear function to the background
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d = np.polyfit(df_start["ZapEnergy"],df_start[files],1)
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function_bkgd = np.poly1d(d)
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#making a list, y_pre,so the background will be applied to all ZapEnergy-values
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y_bkgd=function_bkgd(df["ZapEnergy"])
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#adding a new column in df_background with the y-values of the background
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df_bkgd.insert(1,files,y_bkgd)
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if options['troubleshoot'] == True:
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### FOR FIGURING OUT WHERE IT GOES WRONG/WHICH FILE IS CORRUPT
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ax = df.plot(x = "ZapEnergy",y=files)
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#Plotting the calculated pre-edge background with the region used for the regression
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if options['print'] == True:
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#Plotting an example of the edge_start region and the fitted background that will later be subtracted
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fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5))
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df.plot(x="ZapEnergy", y=filenames,color="Black",ax=ax1)
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df_bkgd.plot(x="ZapEnergy", y=filenames,color="Red",ax=ax1)
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plt.axvline(x = max(df_start["ZapEnergy"]))
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#fig = plt.figure(figsize=(15,15))
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df_bkgd.plot(x="ZapEnergy", y=filenames,color="Red",ax=ax2)
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ax1.set_title('Data and fitted background')
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#Zooming into bacground region to confirm fit and limits looks reasonable
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df.plot(x = "ZapEnergy",y=filenames,ax=ax2) #defining x and y)
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ax2.set_xlim([min(df_start["ZapEnergy"]),max(df_start["ZapEnergy"])+0.01])
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#finding maximum and minimum values in the backgrounds
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min_values=[]
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max_values=[]
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for file in filenames:
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min_values.append(min(df_start[file]))
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max_values.append(max(df_start[file]))
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ax2.set_ylim([min(min_values),max(max_values)])
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plt.axvline(x = max(df_start["ZapEnergy"]))
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#ax2.set_xlim([25, 50])
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###################### Subtracting the pre edge from xmap_roi00 ################
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#making a new dataframe to insert the background subtracted intensities
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df_bkgd_sub = pd.DataFrame(df["ZapEnergy"])
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#inserting the background subtracted original xmap_roi00 data
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for files in filenames:
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newintensity_calc=df[files]-df_bkgd[files]
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df_bkgd_sub.insert(1,files,newintensity_calc)
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if options['print'] == True:
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df.plot(x = "ZapEnergy",y=filenames, color="Black", ax=ax3, legend=False)
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#plt.axvline(x = max(df_start["ZapEnergy"]))
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df_bkgd_sub.plot(x="ZapEnergy", y=filenames,color="Red",ax=ax3, legend=False)
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ax3.set_title('Data and background-subtracted data')
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return df_bkgd_sub,filenames,edge
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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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@ -205,25 +284,7 @@ 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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@ -250,7 +311,7 @@ def finding_e0(path, options={}):
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print("MORE THAN ONE FILE --> generalize")
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#####
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estimated_edge_shift, df_diff, df_diff_max = find_pos_maxdiff(df_smooth, filenames,options=options)
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estimated_edge_shift, df_diff, df_diff_max = estimate_edge_position(df_smooth, filenames,options=options)
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print(estimated_edge_shift)
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####
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###df_diff[filenames]=df_smooth[filenames].diff(periods=options['periods']) #
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@ -2,6 +2,8 @@ import pandas as pd
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import matplotlib.pyplot as plt
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import os
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import numpy as np
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import nafuma.auxillary as aux
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def split_xanes_scan(root, destination=None, replace=False):
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#root is the path to the beamtime-folder
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@ -9,6 +11,9 @@ def split_xanes_scan(root, destination=None, replace=False):
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#insert a for-loop to go through all the folders.dat-files in the folder root\xanes\raw
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# FIXME Only adding this variable to pass the Linting-tests - will refactor this later
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filename = 'dummy'
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with open(filename, 'r') as f:
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lines = f.readlines()
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@ -84,65 +89,70 @@ def split_xanes_scan(root, destination=None, replace=False):
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df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
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#Function that "collects" all the files in a folder, only accepting .dat-files from xanes-measurements
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def get_filenames(path):
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cwd = os.getcwd()
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# Change into path provided
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os.chdir(path)
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filenames = [os.path.join(path, filename) for filename in os.listdir() if os.path.isfile(filename) and filename[-4:] == '.dat'] #changed
|
||||
|
||||
|
||||
|
||||
# Change directory back to where you ran the script from
|
||||
os.chdir(cwd)
|
||||
|
||||
return filenames
|
||||
def read_data(data: dict, options={}) -> pd.DataFrame:
|
||||
|
||||
def put_in_dataframe(path):
|
||||
filenames = get_filenames(path)
|
||||
|
||||
#making the column names to be used in the dataframe, making sure the first column is the ZapEnergy
|
||||
column_names = ["ZapEnergy"]
|
||||
# FIXME Handle the case when dataseries are not the same size
|
||||
|
||||
for i in range(len(filenames)):
|
||||
column_names.append(filenames[i])
|
||||
required_options = []
|
||||
default_options = {
|
||||
|
||||
#Taking the first file in the folder and extracting ZapEnergies and intensity from that (only need the intensity from the rest)
|
||||
first = pd.read_csv(filenames[0], skiprows=0)
|
||||
}
|
||||
|
||||
#Making a data frame with the correct columns, and will fill inn data afterwards
|
||||
df = pd.DataFrame(columns = column_names)
|
||||
#First putting in the 2theta-values
|
||||
df["ZapEnergy"]=first["ZapEnergy"]
|
||||
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
|
||||
|
||||
#filling in the intensities from all files into the corresponding column in the dataframe
|
||||
for i in range(len(filenames)):
|
||||
df2 = pd.read_csv(filenames[i])
|
||||
df2 = df2.drop(['Mon','Det1','Det2','Det3','Det4','Det5', 'Det6','Ion1'], axis=1) #, axis=1)
|
||||
df2 = df2.drop(['MonEx','Ion2','Htime','MusstEnc1','MusstEnc3','MusstEnc4', 'TwoTheta', 'ZCryo'], axis=1)
|
||||
df2 = df2.drop(['ZBlower1', 'ZBlower2', 'ZSrcur'], axis=1)#, axis=19) #removing the sigma at this point
|
||||
columns = ['ZapEnergy']
|
||||
|
||||
# Initialise DataFrame with only ZapEnergy-column
|
||||
xanes_data = pd.read_csv(data['path'][0])[['ZapEnergy']]
|
||||
|
||||
if not isinstance(data['path'], list):
|
||||
data['path'] = [data['path']]
|
||||
|
||||
for filename in data['path']:
|
||||
columns.append(filename)
|
||||
|
||||
scan_data = pd.read_csv(filename)
|
||||
scan_data = scan_data[[determine_active_roi(scan_data)]]
|
||||
xanes_data = pd.concat([xanes_data, scan_data], axis=1)
|
||||
|
||||
|
||||
xanes_data.columns = columns
|
||||
|
||||
|
||||
return xanes_data
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def determine_active_roi(scan_data):
|
||||
|
||||
# FIXME For Co-edge, this gave a wrong scan
|
||||
|
||||
############## THIS PART PICKS OUT WHICH ROI IS OF INTEREST, BUT MUST BE FIXED IF LOOKING AT THREE EDGES (roi00,roi01,roi02) #####################
|
||||
if 'xmap_roi01' in df2.columns:
|
||||
#Trying to pick the roi with the highest difference between maximum and minimum intensity --> biggest edge shift
|
||||
if max(df2["xmap_roi00"])-min(df2["xmap_roi00"])>max(df2["xmap_roi01"])-min(df2["xmap_roi01"]):
|
||||
df[filenames[i]]=df2["xmap_roi00"] #forMn
|
||||
# if max(scan_data["xmap_roi00"])-min(scan_data["xmap_roi00"])>max(scan_data["xmap_roi01"])-min(scan_data["xmap_roi01"]):
|
||||
# active_roi = 'xmap_roi00'
|
||||
# else:
|
||||
# active_roi = 'xmap_roi01'
|
||||
|
||||
if (scan_data['xmap_roi00'].iloc[0:100].mean() < scan_data['xmap_roi00'].iloc[-100:].mean()) and (scan_data['xmap_roi01'].iloc[0:100].mean() < scan_data['xmap_roi01'].iloc[-100:].mean()):
|
||||
if (scan_data['xmap_roi00'].max()-scan_data['xmap_roi00'].min()) > (scan_data['xmap_roi01'].max() - scan_data['xmap_roi01'].min()):
|
||||
active_roi = 'xmap_roi00'
|
||||
else:
|
||||
df[filenames[i]]=df2["xmap_roi01"] #forNi
|
||||
active_roi = 'xmap_roi01'
|
||||
|
||||
elif scan_data['xmap_roi00'].iloc[0:100].mean() < scan_data['xmap_roi00'].iloc[-100:].mean():
|
||||
active_roi = 'xmap_roi00'
|
||||
|
||||
elif scan_data['xmap_roi01'].iloc[0:100].mean() < scan_data['xmap_roi01'].iloc[-100:].mean():
|
||||
active_roi = 'xmap_roi01'
|
||||
|
||||
else:
|
||||
df[filenames[i]]=df2["xmap_roi00"]
|
||||
###############################################################################################
|
||||
active_roi = None
|
||||
|
||||
i=i+1
|
||||
|
||||
|
||||
#print(df)
|
||||
#If I want to make a csv-file of the raw data. Decided that was not necessary:
|
||||
#df.to_csv('static-Mn-edge.csv') #writing it to a csv, first row is datapoint (index), second column is 2theta, and from there the scans starts
|
||||
|
||||
|
||||
return df
|
||||
return active_roi
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue