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Add masks to pre and post edge fitting

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rasmusvt 2022-06-27 13:34:18 +02:00
parent 8e0d8f4861
commit 537c7b3c5a
1 changed files with 40 additions and 7 deletions
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45
nafuma/xanes/calib.py
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@ -46,14 +46,16 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
# FIXME Add log-file
required_options = ['pre_edge_limit', 'log', 'logfile', 'show_plots', 'save_plots', 'save_folder', 'interactive']
required_options = ['pre_edge_limit', 'masks', 'log', 'logfile', 'show_plots', 'save_plots', 'save_folder', 'ylim', 'interactive']
default_options = {
'pre_edge_limit': [None, None],
'masks': [],
'log': False,
'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_pre_edge_fit.log',
'show_plots': False,
'save_plots': False,
'save_folder': './',
'ylim': [None, None],
'interactive': False
}
@ -68,6 +70,7 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
if not options['pre_edge_limit'][0]:
options['pre_edge_limit'][0] = data['xanes_data_original']['ZapEnergy'].min()
if not options['pre_edge_limit'][1]:
pre_edge_limit_offset = 0.03
@ -89,7 +92,12 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
# 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
# limit the interval
# Making a dataframe only containing the rows that are included in the background subtraction (points lower than where the edge start is defined)
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])]
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])].copy()
for mask in options['masks']:
pre_edge_data.loc[(pre_edge_data['ZapEnergy'] > mask[0]) & (pre_edge_data['ZapEnergy'] < mask[1])] = np.nan
pre_edge_data = pre_edge_data.dropna()
# Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data
pre_edge_fit_data = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
@ -120,6 +128,14 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
ax1.axvline(x = min(pre_edge_data['ZapEnergy']), ls='--')
ax1.set_title(f'{os.path.basename(filename)} - Full view', size=20)
if options['ylim'][0] != None:
ax1.set_ylim(bottom=options['ylim'][0])
if options['ylim'][1]:
ax1.set_ylim(top=options['ylim'][1])
for mask in options['masks']:
ax1.fill_between(x=mask, y1=0, y2=data['xanes_data_original'][filename].max()*2, alpha=0.2, color='black')
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='--')
@ -204,11 +220,13 @@ def pre_edge_subtraction(data: dict, options={}):
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 = ['log', 'logfile', 'post_edge_limit', 'interactive']
required_options = ['log', 'logfile', 'masks', 'post_edge_limit', 'interactive', 'show_plots', 'save_plots', 'save_folder']
default_options = {
'log': False,
'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_post_edge_fit.log',
'post_edge_limit': [None, None],
'masks': [],
'polyorder': 2,
'interactive': False,
'show_plots': False,
'save_plots': False,
@ -239,7 +257,11 @@ def post_edge_fit(data: dict, options={}):
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])]
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])].copy()
for mask in options['masks']:
post_edge_data.loc[(post_edge_data['ZapEnergy'] > mask[0]) & (post_edge_data['ZapEnergy'] < mask[1])] = np.nan
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
# Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data
@ -249,12 +271,15 @@ def post_edge_fit(data: dict, options={}):
for i, filename in enumerate(data['path']):
if options['log']:
aux.write_log(message=f'Fitting post edge on {os.path.basename(filename)} ({i+1} / {len(data["path"])})', options=options)
aux.write_log(message=f'Fitting post edge on {os.path.basename(filename)} ({i+1} / {len(data["path"])}) with polynomial order {options["polyorder"]}', options=options)
#Fitting linear function to the background
params = np.polyfit(post_edge_data["ZapEnergy"], post_edge_data[filename], 2)
params = np.polyfit(post_edge_data["ZapEnergy"], post_edge_data[filename], options['polyorder'])
fit_function = np.poly1d(params)
if options['log']:
aux.write_log(message=f'Post edge fitted with parameters: {params}')
data['post_edge_params'][filename] = params
#making a list, y_pre,so the background will be applied to all ZapEnergy-values
@ -273,6 +298,14 @@ def post_edge_fit(data: dict, options={}):
ax1.axvline(x = min(post_edge_data['ZapEnergy']), ls='--')
ax1.set_title(f'{os.path.basename(filename)} - Full view', size=20)
for mask in options['masks']:
ax1.fill_between(x=mask, y1=0, y2=data['xanes_data_original'][filename].max()*2, alpha=0.2, color='black')
if options['ylim'][0] != None:
ax1.set_ylim(bottom=options['ylim'][0])
if options['ylim'][1] != None:
ax1.set_ylim(top=options['ylim'][1])
data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax2)
post_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax2)
ax2.axvline(x = max(post_edge_data['ZapEnergy']), ls='--')