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Adjusting post edge processing

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This commit is contained in:
halvorhv 2022-04-07 11:34:44 +02:00
parent 872ae759b2
commit 135d577b4b
1 changed files with 43 additions and 25 deletions
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68
beamtime/xanes/calib.py
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@ -44,7 +44,7 @@ def pre_edge_subtraction(path, options={}):
#Defining the end of the region used to define the background, thus start of the edge
#implement widget
if edge == 'Mn':
edge_start = 6.45
edge_start = 6.42
if edge == 'Ni':
edge_start = 8.3
@ -73,30 +73,44 @@ def pre_edge_subtraction(path, options={}):
#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) = plt.subplots(1,2,figsize=(15,5))
df.plot(x = "ZapEnergy",y=filenames[0],ax=ax1) #defining x and y
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[0],color="Red",ax=ax1)
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 pre_edge-background subtracted original xmap_roi00 data
#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[0], color="Black", ax=ax2, legend=False)
plt.axvline(x = max(df_start["ZapEnergy"]))
df_bkgd_sub.plot(x="ZapEnergy", y=filenames[0],color="Red",ax=ax2, legend=False)
ax2.set_title('Data and background-subtracted data')
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
return df_bkgd_sub,filenames,edge
def post_edge_normalization(df,df_backg_sub,filenames, options={}):
def post_edge_normalization(path, options={}):
required_options = ['print']
default_options = {
@ -104,7 +118,7 @@ def post_edge_normalization(df,df_backg_sub,filenames, options={}):
}
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
edge=finding_edge(df)
df_bkgd_sub,filenames,edge = pre_edge_subtraction(path)
#Defining the end of the pre-edge-region for Mn/Ni, thus start of the edge
#Implement widget
if edge == 'Mn':
@ -112,22 +126,26 @@ def post_edge_normalization(df,df_backg_sub,filenames, options={}):
if edge == 'Ni':
edge_stop = 8.361
absolute_difference_function = lambda list_value : abs(list_value - edge_stop)
edge_stop_value = min(df_backg_sub["ZapEnergy"], key=absolute_difference_function)
end_index=df_backg_sub[df_backg_sub["ZapEnergy"]==edge_stop_value].index.values[0]
#=============================================================
#absolute_difference_function = lambda list_value : abs(list_value - edge_stop)
#edge_stop_value = min(df_bkgd_sub["ZapEnergy"], key=absolute_difference_function)
#end_index=df_bkgd_sub[df_bkgd_sub["ZapEnergy"]==edge_stop_value].index.values[0]
#Defining x-range for linear fit
df_fix=df_backg_sub
df_fix.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_end=df_fix[end_index:] #The region of interest for the post edge
#print(df_end)
#df_fix=df_bkgd_sub
#df_fix.dropna(inplace=True)
#df_end=df_fix[end_index:] #The region of interest for the post edge
#Fitting linear function to the pre-edge using the background corrected intensities to make the post edge fit
df_postedge = pd.DataFrame(df["ZapEnergy"])
#===============================================================
df_end= df_bkgd_sub.loc[df_bkgd_sub["ZapEnergy"] > edge_stop] # new dataframe only containing the post edge -> to be fitted
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
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["ZapEnergy"])
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
@ -135,13 +153,13 @@ def post_edge_normalization(df,df_backg_sub,filenames, options={}):
#print(df_postedge)
#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_backg_sub.plot(x = "ZapEnergy",y=filenames) #defining x and y
plt.axvline(x = edge_stop_value)
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)
#print(function_post_list)
#print(function_post)
ax = df_backg_sub.plot(x = "ZapEnergy",y=filenames, legend=False) #defining x and y
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 = edge_stop_value)
plt.axvline(x = min(df_end["ZapEnergy"]))