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Refactor pre edge subtraction

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rasmusvt 2022-06-16 15:42:50 +02:00
parent e0b71a85b7
commit bac137042e
1 changed files with 27 additions and 93 deletions
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120
nafuma/xanes/calib.py
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@ -41,12 +41,12 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
# FIXME Add log-file # FIXME Add log-file
required_options = ['edge_start', 'log', 'troubleshoot'] required_options = ['edge_start', 'log', 'logfile', 'save_plots', 'save_folder']
default_options = { default_options = {
'edge_start': None, 'edge_start': None,
'log': False, 'log': False,
'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S.log")}_pre_edge_fit.log', 'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S.log")}_pre_edge_fit.log',
'save_fit': False, 'save_plots': False,
'save_folder': './' 'save_folder': './'
} }
@ -72,10 +72,10 @@ 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 # 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 # 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) # 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'].loc[data['xanes_data']["ZapEnergy"] < edge_start] pre_edge_data = data['xanes_data_original'].loc[data['xanes_data_original']["ZapEnergy"] < edge_start]
# Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data # 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']["ZapEnergy"]) pre_edge_fit_data = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
for i, filename in enumerate(data['path']): for i, filename in enumerate(data['path']):
if options['log']: if options['log']:
@ -91,11 +91,11 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
#adding a new column in df_background with the y-values of the background #adding a new column in df_background with the y-values of the background
pre_edge_fit_data.insert(1,filename,background) pre_edge_fit_data.insert(1,filename,background)
if options['save_fit']: if options['save_plots']:
if not os.path.isdir(options['save_folder']): if not os.path.isdir(options['save_folder']):
os.makedirs(options['save_folder']) os.makedirs(options['save_folder'])
dst = os.path.join(options['save_folder'], filename) + '.png' dst = os.path.join(options['save_folder'], filename) + '_pre_edge_fit.png'
fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,5)) fig, (ax1, ax2) = plt.subplots(1,2,figsize=(10,5))
data['xanes_data'].plot(x='ZapEnergy', y=filename, color='black', ax=ax1) data['xanes_data'].plot(x='ZapEnergy', y=filename, color='black', ax=ax1)
@ -124,107 +124,41 @@ def pre_edge_fit(data: dict, options={}) -> pd.DataFrame:
def pre_edge_subtraction(data: dict, options={}): def pre_edge_subtraction(data: dict, options={}):
required_options = ['log', 'logfile'] required_options = ['log', 'logfile', 'save_plots', 'save_folder']
default_options = { default_options = {
'log': False, '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.log")}_pre_edge_subtraction.log',
'save_plots': False,
'save_folder': './'
} }
if options['log']:
aux.write_log(message='Starting pre edge subtraction', options=options)
xanes_data_bkgd_subtracted = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
for i, filename in enumerate(data['path']):
if options['log']:
aux.write_log(message=f'Subtracting background on {filename} ({i} / {len(data["path"])}', options=options)
xanes_data_bkgd_subtracted.insert(1, filename, data['xanes_data'][filename] - data['pre_edge_fit_data'][filename])
def pre_edge_subtraction_legacy(data: dict, options={}): if options['save_plots']:
#FIXME add log-file instead of the troubleshoot-option if not os.path.isdir(options['save_folder']):
required_options = ['print','troubleshoot'] os.makedirs(options['save_folder'])
default_options = {
'print': False,
'troubleshoot': False
}
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
filenames = xas.io.get_filenames(path) dst = os.path.join(options['save_folder'], filename) + '_pre_edge_subtraction.png'
df= xas.io.put_in_dataframe(path)
edge=find_element(df)
#Defining the end of the region used to define the background, thus start of the edge fig, ax = plt.subplots(1,2,figsize=(10,5))
data['xanes_data'].plot(x='ZapEnergy', y=filename, color='black', ax=ax)
xanes_data_bkgd_subtracted.plot(x='ZapEnergy', y=filename, color='red', ax=ax)
ax.set_title(f'{os.path.basename(filename)} - After subtraction', size=20)
#######================================================================================================================================================ plt.savefig(dst)
#FIXME Trying to implement automatical region determination based on an estimate of the edge shift plt.close()
#print(df)
#estimated_edge_shift, df_diff, df_diff_max = find_pos_maxdiff(df, filenames,options=options)
#print(estimated_edge_shift) return xanes_data_bkgd_subtracted
#estimated_edge_shift
###=========================================================================================================================================================================
#implement widget
if edge == 'Mn':
edge_start = 6.42
#edge_start = estimated_edge_shift
if edge == 'Ni':
edge_start = 8.3
#making a dataframe only containing the rows that are included in the background subtraction (points lower than where the edge start is defined)
df_start=df.loc[df["ZapEnergy"] < edge_start]
#Making a new dataframe, with only the ZapEnergies as the first column -> will be filled to include the background data
df_bkgd = pd.DataFrame(df["ZapEnergy"])
for files in filenames:
#Fitting linear function to the background
d = np.polyfit(df_start["ZapEnergy"],df_start[files],1)
function_bkgd = np.poly1d(d)
#making a list, y_pre,so the background will be applied to all ZapEnergy-values
y_bkgd=function_bkgd(df["ZapEnergy"])
#adding a new column in df_background with the y-values of the background
df_bkgd.insert(1,files,y_bkgd)
if options['troubleshoot'] == True:
### FOR FIGURING OUT WHERE IT GOES WRONG/WHICH FILE IS CORRUPT
ax = df.plot(x = "ZapEnergy",y=files)
#Plotting the calculated pre-edge background with the region used for the regression
if options['print'] == True:
#Plotting an example of the edge_start region and the fitted background that will later be subtracted
fig, (ax1,ax2,ax3) = plt.subplots(1,3,figsize=(15,5))
df.plot(x="ZapEnergy", y=filenames,color="Black",ax=ax1)
df_bkgd.plot(x="ZapEnergy", y=filenames,color="Red",ax=ax1)
plt.axvline(x = max(df_start["ZapEnergy"]))
#fig = plt.figure(figsize=(15,15))
df_bkgd.plot(x="ZapEnergy", y=filenames,color="Red",ax=ax2)
ax1.set_title('Data and fitted background')
#Zooming into bacground region to confirm fit and limits looks reasonable
df.plot(x = "ZapEnergy",y=filenames,ax=ax2) #defining x and y)
ax2.set_xlim([min(df_start["ZapEnergy"]),max(df_start["ZapEnergy"])+0.01])
#finding maximum and minimum values in the backgrounds
min_values=[]
max_values=[]
for file in filenames:
min_values.append(min(df_start[file]))
max_values.append(max(df_start[file]))
ax2.set_ylim([min(min_values),max(max_values)])
plt.axvline(x = max(df_start["ZapEnergy"]))
#ax2.set_xlim([25, 50])
###################### Subtracting the pre edge from xmap_roi00 ################
#making a new dataframe to insert the background subtracted intensities
df_bkgd_sub = pd.DataFrame(df["ZapEnergy"])
#inserting the background subtracted original xmap_roi00 data
for files in filenames:
newintensity_calc=df[files]-df_bkgd[files]
df_bkgd_sub.insert(1,files,newintensity_calc)
if options['print'] == True:
df.plot(x = "ZapEnergy",y=filenames, color="Black", ax=ax3, legend=False)
#plt.axvline(x = max(df_start["ZapEnergy"]))
df_bkgd_sub.plot(x="ZapEnergy", y=filenames,color="Red",ax=ax3, legend=False)
ax3.set_title('Data and background-subtracted data')
return df_bkgd_sub,filenames,edge
def post_edge_fit(path, options={}): def post_edge_fit(path, options={}):
#FIXME should be called "fitting post edge" (normalization is not done here, need edge shift position) #FIXME should be called "fitting post edge" (normalization is not done here, need edge shift position)