diff --git a/nafuma/xanes/calib.py b/nafuma/xanes/calib.py index 10afda9..4692446 100644 --- a/nafuma/xanes/calib.py +++ b/nafuma/xanes/calib.py @@ -126,7 +126,7 @@ def pre_edge_subtraction(data: dict, options={}): required_options = ['log', 'logfile', 'save_plots', 'save_folder'] default_options = { 'log': False, - 'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S.log")}_pre_edge_subtraction.log', + 'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_pre_edge_subtraction.log', 'save_plots': False, 'save_folder': './' } @@ -162,10 +162,10 @@ def pre_edge_subtraction(data: dict, options={}): def estimate_edge_position(data: dict, options={}, index=0): #a dataset is differentiated to find a first estimate of the edge shift to use as starting point. - required_options = ['print','periods'] + required_options = ['log','logfile', 'periods'] default_options = { - - 'print': False, + 'log': False, + 'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_edge_position_estimation.log', 'periods': 2, #Periods needs to be an even number for the shifting of values to work properly } options = aux.update_options(options=options, required_options=required_options, default_options=default_options) @@ -189,25 +189,29 @@ def estimate_edge_position(data: dict, options={}, index=0): def post_edge_fit(data: dict, options={}): #FIXME should be called "fitting post edge" (normalization is not done here, need edge shift position) - required_options = ['post_edge_start', 'print'] + required_options = ['log', 'logfile', 'post_edge_interval'] default_options = { - 'post_edge_start': None, - 'print': False + 'log': False, + 'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_post_edge_fit.log', + 'post_edge_interval': [None, None], } options = aux.update_options(options=options, required_options=required_options, default_options=default_options) - #FIXME Allow min and max limits - if not options['post_edge_start']: + if not options['post_edge_interval'][0]: post_edge_limit_offset = 0.03 data['edge'] = find_element(data) edge_position = estimate_edge_position(data, options, index=0) - post_edge_limit = edge_position + post_edge_limit_offset + options['post_edge_interval'][0] = edge_position + post_edge_limit_offset - post_edge_data = data['xanes_data_original'].loc[data['xanes_data_original']["ZapEnergy"] > post_edge_limit] + if not options['post_edge_interval'][1]: + options['post_edge_interval'][1] = data['xanes_data_original']['ZapEnergy'].max() + + + 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])] 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 # Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data @@ -253,40 +257,31 @@ def post_edge_fit(data: dict, options={}): return post_edge_fit_data -def smoothing(path, options={}): - required_options = ['print','window_length','polyorder'] +def smoothing(data: dict, options={}): + + # FIXME Add logging + # FIXME Add saving of files + + required_options = ['log', 'logfile', 'window_length','polyorder'] default_options = { - 'print': False, + 'log': False, + 'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_smoothing.log', + 'save_plots': False, + 'save_folder': './', 'window_length': 3, 'polyorder': 2 } options = aux.update_options(options=options, required_options=required_options, default_options=default_options) - df_bkgd_sub, df_postedge, filenames, edge = post_edge_fit(path,options=options) - #================= SMOOTHING - df_smooth = pd.DataFrame(df_bkgd_sub["ZapEnergy"]) - df_default = pd.DataFrame(df_bkgd_sub["ZapEnergy"]) - #df_smooth[filenames] = df_bkgd_sub.iloc[:,2].rolling(window=rolling_av).mean() - #df_smooth[filenames] = df_smooth[filenames].shift(-int((rolling_av)/2)) - for filename in filenames: - x_smooth=savgol_filter(df_bkgd_sub[filename], options['window_length'],options['polyorder']) - df_smooth[filename] = x_smooth - x_default=savgol_filter(df_bkgd_sub[filename],default_options['window_length'],default_options['polyorder']) - df_default[filename] = x_default - - + + # FIXME Add other types of filters + for filename in data['path']: + xanes_smooth = savgol_filter(data['xanes_data'][filename], options['window_length'], options['polyorder']) + default_smooth = savgol_filter(data['xanes_data'][filename], default_options['window_length'], default_options['polyorder']) + #printing the smoothed curves vs data - if options['print'] == True: - - ## ================================================ - #df_diff = pd.DataFrame(df_smooth["ZapEnergy"]) - #df_diff_estimated_max = df_diff[filenames].dropna().max() - - - #estimated_edge_shift=df_diff.loc[df_diff[filenames] == df_diff_max,'ZapEnergy'].values[0] - # ========================================== - + if options['save_folder'] == True: fig, (ax1,ax2) = plt.subplots(1,2,figsize=(15,5)) x_range_zoom=[6.54,6.55] #make into widget @@ -303,8 +298,34 @@ def smoothing(path, options={}): ax2.set_xlim(x_range_zoom) ax2.set_ylim(y_range_zoom) ax2.set_title("Smoothed curve (green) vs data (red) using default window_length and polyorder") + + + # FIXME Clear up these two plotting functions + + if options['save_plots']: + if not os.path.isdir(options['save_folder']): + os.makedirs(options['save_folder']) + + dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_pre_edge_fit.png' + + fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,5)) + data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax1) + pre_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax1) + ax1.axvline(x = max(pre_edge_data['ZapEnergy']), ls='--') + ax1.set_title(f'{os.path.basename(filename)} - Full view', size=20) + + data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax2) + pre_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax2) + ax2.axvline(x = max(pre_edge_data['ZapEnergy']), ls='--') + ax2.set_xlim([min(pre_edge_data['ZapEnergy']), max(pre_edge_data['ZapEnergy'])]) + ax2.set_ylim([min(pre_edge_data[filename]), max(pre_edge_data[filename])]) + ax2.set_title(f'{os.path.basename(filename)} - Fit region', size=20) + + + plt.savefig(dst, transparent=False) + plt.close() - return df_smooth, filenames + return xanes_smooth, default_smooth