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Refactor post_edge_fit

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rasmusvt 2022-06-17 16:59:37 +02:00
parent d497217ae3
commit 9c6a7d5991
1 changed files with 61 additions and 39 deletions
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100
nafuma/xanes/calib.py
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@ -41,9 +41,9 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
# FIXME Add log-file
required_options = ['edge_start', 'log', 'logfile', 'save_plots', 'save_folder']
required_options = ['pre_edge_start', 'log', 'logfile', 'save_plots', 'save_folder']
default_options = {
'edge_start': None,
'pre_edge_start': None,
'log': False,
'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_pre_edge_fit.log',
'save_plots': False,
@ -60,18 +60,13 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
# FIXME Implement with finding accurate edge position
# FIXME Allow specification of start of pre-edge area
# 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
if not options['edge_start']:
pre_edge_limit_offsets = {
'Mn': 0.03,
'Fe': 0.03,
'Co': 0.03,
'Ni': 0.03
}
if not options['pre_edge_start']:
pre_edge_limit_offset = 0.03
data['edge'] = find_element(data)
edge_position = estimate_edge_position(data, options, index=0)
pre_edge_limit = edge_position - pre_edge_limit_offsets[data['edge']]
pre_edge_limit = edge_position - pre_edge_limit_offset
# 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
@ -169,6 +164,7 @@ 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']
default_options = {
'print': False,
'periods': 2, #Periods needs to be an even number for the shifting of values to work properly
}
@ -191,45 +187,71 @@ def estimate_edge_position(data: dict, options={}, index=0):
return estimated_edge_shift
def post_edge_fit(path, 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 = ['print']
required_options = ['post_edge_start', 'print']
default_options = {
'post_edge_start': None,
'print': False
}
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']:
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
post_edge_data = data['xanes_data_original'].loc[data['xanes_data_original']["ZapEnergy"] > post_edge_limit]
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
post_edge_fit_data = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
df_bkgd_sub,filenames,edge = pre_edge_subtraction(path, options=options)
#Defining the end of the pre-edge-region for Mn/Ni, thus start of the edge
#FIXME Use rought edge shift estimate, add X eV as first guess, have an option to adjust this value with widget
if edge == 'Mn':
edge_stop = 6.565
if edge == 'Ni':
edge_stop = 8.361
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)
df_end= df_bkgd_sub.loc[df_bkgd_sub["ZapEnergy"] > edge_stop] # new dataframe only containing the post edge, where a regression line will be calculated in the for-loop below
df_end.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
df_postedge = pd.DataFrame(df_bkgd_sub["ZapEnergy"]) #making a new dataframe
#Fitting linear function to the background
params = np.polyfit(post_edge_data["ZapEnergy"], post_edge_data[filename], 2)
fit_function = np.poly1d(params)
#making a list, y_pre,so the background will be applied to all ZapEnergy-values
background=fit_function(post_edge_fit_data["ZapEnergy"])
#adding a new column in df_background with the y-values of the background
post_edge_fit_data.insert(1,filename,background)
if options['save_plots']:
if not os.path.isdir(options['save_folder']):
os.makedirs(options['save_folder'])
function_post_list=[]
for files in filenames:
d = np.polyfit(df_end["ZapEnergy"],df_end[files],1)
function_post = np.poly1d(d)
y_post=function_post(df_bkgd_sub["ZapEnergy"])
function_post_list.append(function_post)
df_postedge.insert(1,files,y_post) #adding a new column with the y-values of the fitted post edge
dst = os.path.join(options['save_folder'], os.path.basename(filename)) + '_post_edge_fit.png'
#Plotting the background subtracted signal with the post-edge regression line and the start point for the linear regression line
if options['print'] == True:
ax = df_bkgd_sub.plot(x = "ZapEnergy",y=filenames) #defining x and y
plt.axvline(x = min(df_end["ZapEnergy"]))
fig = plt.figure(figsize=(15,15))
df_postedge.plot(x="ZapEnergy", y=filenames,color="Green",ax=ax, legend=False)
ax = df_bkgd_sub.plot(x = "ZapEnergy",y=filenames, legend=False) #defining x and y
df_postedge.plot(x="ZapEnergy", y=filenames,color="Green",ax=ax, legend=False)
plt.axvline(x = min(df_end["ZapEnergy"]))
fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,5))
data['xanes_data_original'].plot(x='ZapEnergy', y=filename, color='black', ax=ax1)
post_edge_fit_data.plot(x='ZapEnergy', y=filename, color='red', ax=ax1)
ax1.axvline(x = max(post_edge_data['ZapEnergy']), ls='--')
ax1.set_title(f'{os.path.basename(filename)} - Full view', size=20)
return df_bkgd_sub, df_postedge, filenames, edge
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='--')
ax2.set_xlim([min(post_edge_data['ZapEnergy']), max(post_edge_data['ZapEnergy'])])
ax2.set_ylim([min(post_edge_data[filename]), max(post_edge_data[filename])])
ax2.set_title(f'{os.path.basename(filename)} - Fit region', size=20)
plt.savefig(dst, transparent=False)
plt.close()
return post_edge_fit_data
def smoothing(path, options={}):
required_options = ['print','window_length','polyorder']