Start clean-up of smoothing
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rasmusvt
parent
054311ca10
commit
1cf949e36b
1 changed files with 59 additions and 38 deletions
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@ -126,7 +126,7 @@ 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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'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_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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@ -162,10 +162,10 @@ def pre_edge_subtraction(data: dict, options={}):
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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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required_options = ['log','logfile', 'periods']
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default_options = {
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'print': False,
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'log': False,
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'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_edge_position_estimation.log',
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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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@ -189,25 +189,29 @@ def estimate_edge_position(data: dict, options={}, index=0):
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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 = ['post_edge_start', 'print']
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required_options = ['log', 'logfile', 'post_edge_interval']
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default_options = {
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'post_edge_start': None,
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'print': False
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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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}
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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#FIXME Allow min and max limits
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if not options['post_edge_start']:
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if not options['post_edge_interval'][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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post_edge_limit = edge_position + post_edge_limit_offset
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options['post_edge_interval'][0] = edge_position + post_edge_limit_offset
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post_edge_data = data['xanes_data_original'].loc[data['xanes_data_original']["ZapEnergy"] > post_edge_limit]
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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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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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# Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data
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@ -253,40 +257,31 @@ def post_edge_fit(data: dict, options={}):
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return post_edge_fit_data
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def smoothing(path, options={}):
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required_options = ['print','window_length','polyorder']
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def smoothing(data: dict, options={}):
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# FIXME Add logging
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# FIXME Add saving of files
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required_options = ['log', 'logfile', 'window_length','polyorder']
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default_options = {
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'print': False,
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'log': False,
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'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_smoothing.log',
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'save_plots': False,
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'save_folder': './',
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'window_length': 3,
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'polyorder': 2
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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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df_bkgd_sub, df_postedge, filenames, edge = post_edge_fit(path,options=options)
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#================= SMOOTHING
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df_smooth = pd.DataFrame(df_bkgd_sub["ZapEnergy"])
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df_default = pd.DataFrame(df_bkgd_sub["ZapEnergy"])
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#df_smooth[filenames] = df_bkgd_sub.iloc[:,2].rolling(window=rolling_av).mean()
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#df_smooth[filenames] = df_smooth[filenames].shift(-int((rolling_av)/2))
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for filename in filenames:
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x_smooth=savgol_filter(df_bkgd_sub[filename], options['window_length'],options['polyorder'])
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df_smooth[filename] = x_smooth
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x_default=savgol_filter(df_bkgd_sub[filename],default_options['window_length'],default_options['polyorder'])
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df_default[filename] = x_default
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# FIXME Add other types of filters
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for filename in data['path']:
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xanes_smooth = savgol_filter(data['xanes_data'][filename], options['window_length'], options['polyorder'])
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default_smooth = savgol_filter(data['xanes_data'][filename], default_options['window_length'], default_options['polyorder'])
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#printing the smoothed curves vs data
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if options['print'] == True:
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## ================================================
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#df_diff = pd.DataFrame(df_smooth["ZapEnergy"])
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#df_diff_estimated_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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# ==========================================
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if options['save_folder'] == True:
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fig, (ax1,ax2) = plt.subplots(1,2,figsize=(15,5))
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x_range_zoom=[6.54,6.55] #make into widget
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@ -303,8 +298,34 @@ def smoothing(path, options={}):
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ax2.set_xlim(x_range_zoom)
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ax2.set_ylim(y_range_zoom)
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ax2.set_title("Smoothed curve (green) vs data (red) using default window_length and polyorder")
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# FIXME Clear up these two plotting functions
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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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return df_smooth, filenames
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return xanes_smooth, default_smooth
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