ADd interactive mode for pre and post edge fitting
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rasmusvt
parent
931b3e42ae
commit
8e0d8f4861
1 changed files with 96 additions and 33 deletions
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@ -5,10 +5,13 @@ import numpy as np
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import os
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import matplotlib.pyplot as plt
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import nafuma.auxillary as aux
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import nafuma.plotting as btp
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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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import ipywidgets as widgets
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from IPython.display import display
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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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@ -43,13 +46,15 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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# FIXME Add log-file
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required_options = ['pre_edge_start', 'log', 'logfile', 'save_plots', 'save_folder']
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required_options = ['pre_edge_limit', 'log', 'logfile', 'show_plots', 'save_plots', 'save_folder', 'interactive']
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default_options = {
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'pre_edge_start': None,
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'pre_edge_limit': [None, 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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'show_plots': False,
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'save_plots': False,
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'save_folder': './'
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'save_folder': './',
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'interactive': False
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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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@ -57,23 +62,34 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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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['pre_edge_start']:
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if not options['pre_edge_limit'][0]:
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options['pre_edge_limit'][0] = data['xanes_data_original']['ZapEnergy'].min()
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if not options['pre_edge_limit'][1]:
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pre_edge_limit_offset = 0.03
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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_offset
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options['pre_edge_limit'][1] = edge_position - pre_edge_limit_offset
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# Start inteactive session with ipywidgets. Disables options['interactive'] in order for the interactive loop to not start another interactive session
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if options['interactive']:
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options['interactive'] = False
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options['interactive_session_active'] = True
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options['show_plots'] = True
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pre_edge_fit_interactive(data=data, options=options)
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return
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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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pre_edge_data = data['xanes_data_original'].loc[(data['xanes_data_original']["ZapEnergy"] > options['pre_edge_limit'][0]) & (data['xanes_data_original']["ZapEnergy"] < options['pre_edge_limit'][1])]
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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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@ -96,16 +112,12 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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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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if options['show_plots'] or options['save_plots']:
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fig, (ax1, ax2) = plt.subplots(1,2,figsize=(20,10))
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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.axvline(x = min(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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@ -115,9 +127,15 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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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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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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plt.savefig(dst, transparent=False)
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plt.close()
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dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_pre_edge_fit.png'
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plt.savefig(dst, transparent=False)
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if not options['show_plots']:
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plt.close()
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if options['log']:
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@ -127,6 +145,21 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
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def pre_edge_fit_interactive(data: dict, options: dict) -> None:
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w = widgets.interactive(
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btp.ipywidgets_update, func=widgets.fixed(pre_edge_fit), data=widgets.fixed(data), options=widgets.fixed(options),
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pre_edge_limit=widgets.FloatRangeSlider(value=[options['pre_edge_limit'][0], options['pre_edge_limit'][1]], min=data['xanes_data_original']['ZapEnergy'].min(), max=data['xanes_data_original']['ZapEnergy'].max(), step=0.001)
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)
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options['widget'] = w
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display(w)
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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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@ -171,30 +204,43 @@ def pre_edge_subtraction(data: dict, options={}):
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def post_edge_fit(data: dict, 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 = ['log', 'logfile', 'post_edge_interval']
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required_options = ['log', 'logfile', 'post_edge_limit', 'interactive']
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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")}_post_edge_fit.log',
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'post_edge_interval': [None, None],
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'post_edge_limit': [None, None],
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'interactive': False,
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'show_plots': False,
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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 not options['post_edge_interval'][0]:
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if not options['post_edge_limit'][0]:
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post_edge_limit_offset = 0.03
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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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options['post_edge_interval'][0] = edge_position + post_edge_limit_offset
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options['post_edge_limit'][0] = edge_position + post_edge_limit_offset
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if not options['post_edge_interval'][1]:
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options['post_edge_interval'][1] = data['xanes_data_original']['ZapEnergy'].max()
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if not options['post_edge_limit'][1]:
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options['post_edge_limit'][1] = data['xanes_data_original']['ZapEnergy'].max()
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# Start inteactive session with ipywidgets. Disables options['interactive'] in order for the interactive loop to not start another interactive session
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if options['interactive']:
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options['interactive'] = False
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options['interactive_session_active'] = True
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options['show_plots'] = True
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post_edge_fit_interactive(data=data, options=options)
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return
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post_edge_data = data['xanes_data_original'].loc[(data['xanes_data_original']["ZapEnergy"] > options['post_edge_interval'][0]) & (data['xanes_data_original']["ZapEnergy"] < options['post_edge_interval'][1])]
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post_edge_data.dropna(inplace=True) #Removing all indexes without any value, as some of the data sets misses the few last data points and fucks up the fit
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post_edge_data = data['xanes_data_original'].loc[(data['xanes_data_original']["ZapEnergy"] > options['post_edge_limit'][0]) & (data['xanes_data_original']["ZapEnergy"] < options['post_edge_limit'][1])]
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post_edge_data = post_edge_data.dropna() #Removing all indexes without any value, as some of the data sets misses the few last data points and fucks up the fit
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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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post_edge_fit_data = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
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@ -217,16 +263,14 @@ def post_edge_fit(data: dict, options={}):
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#adding a new column in df_background with the y-values of the background
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post_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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if options['save_plots'] or options['show_plots']:
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dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_post_edge_fit.png'
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fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,5))
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fig, (ax1, ax2) = plt.subplots(1,2,figsize=(20,10))
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data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax1)
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post_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax1)
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ax1.axvline(x = max(post_edge_data['ZapEnergy']), ls='--')
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ax1.axvline(x = min(post_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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@ -236,13 +280,32 @@ def post_edge_fit(data: dict, options={}):
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ax2.set_ylim([min(post_edge_data[filename]), max(post_edge_data[filename])])
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ax2.set_title(f'{os.path.basename(filename)} - Fit region', size=20)
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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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plt.savefig(dst, transparent=False)
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plt.close()
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dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_post_edge_fit.png'
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plt.savefig(dst, transparent=False)
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if not options['show_plots']:
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plt.close()
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return post_edge_fit_data
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def post_edge_fit_interactive(data: dict, options: dict) -> None:
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w = widgets.interactive(
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btp.ipywidgets_update, func=widgets.fixed(post_edge_fit), data=widgets.fixed(data), options=widgets.fixed(options),
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post_edge_limit=widgets.FloatRangeSlider(value=[options['post_edge_limit'][0], options['post_edge_limit'][1]], min=data['xanes_data_original']['ZapEnergy'].min(), max=data['xanes_data_original']['ZapEnergy'].max(), step=0.001)
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)
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options['widget'] = w
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display(w)
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def smoothing(data: dict, options={}):
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# FIXME Add logging
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@ -295,7 +358,7 @@ def smoothing(data: dict, options={}):
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ax2.set_title(f'{os.path.basename(filename)} - Smooth (default values)', size=20)
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elif not options['save_default']:
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fig, ax = plt.subplots(figsize=(10,5))
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fig, ax = plt.subplots(figsize=(20,10))
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data['xanes_data'].loc[(data['xanes_data']['ZapEnergy'] > edge_pos-0.0015) & (data['xanes_data']['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='black', ax=ax, kind='scatter')
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df_smooth.loc[(df_smooth['ZapEnergy'] > edge_pos-0.0015) & (df_smooth['ZapEnergy'] < edge_pos+0.0015)].plot(x='ZapEnergy', y=filename, color='red', ax=ax)
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ax.set_xlim([edge_pos-0.0015, edge_pos+0.0015])
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